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Ph.D<br /><div class="js-profile-less-about u-linkUnstyled u-tcGrayDarker u-textDecorationUnderline u-displayNone">less</div></div></div><div class="suggested-academics-container"><div class="suggested-academics--header"><p class="ds2-5-body-md-bold">Related Authors</p></div><ul class="suggested-user-card-list"><div class="suggested-user-card"><div class="suggested-user-card__avatar social-profile-avatar-container"><a href="https://ukim.academia.edu/DejanGjorgjevikj"><img class="profile-avatar u-positionAbsolute" alt="Dejan Gjorgjevikj" border="0" onerror="if (this.src != '//a.academia-assets.com/images/s200_no_pic.png') this.src = '//a.academia-assets.com/images/s200_no_pic.png';" width="200" height="200" src="https://0.academia-photos.com/53004/83742/91624/s200_dejan.gjorgjevikj.jpg" /></a></div><div class="suggested-user-card__user-info"><a class="suggested-user-card__user-info__header ds2-5-body-sm-bold ds2-5-body-link" href="https://ukim.academia.edu/DejanGjorgjevikj">Dejan 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class="profile--tab_heading_container js-section-heading" data-section="Papers" id="Papers"><h3 class="profile--tab_heading_container">Papers by Dr.Viswanatha V .</h3></div><div class="js-work-strip profile--work_container" data-work-id="106298009"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/106298009/An_Intelligent_Camera_Based_Eye_Controlled_Wheelchair_System_Haar_Cascade_and_Gaze_Estimation_Algorithms"><img alt="Research paper thumbnail of An Intelligent Camera Based Eye Controlled Wheelchair System: Haar Cascade and Gaze Estimation Algorithms" class="work-thumbnail" src="https://attachments.academia-assets.com/105533266/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/106298009/An_Intelligent_Camera_Based_Eye_Controlled_Wheelchair_System_Haar_Cascade_and_Gaze_Estimation_Algorithms">An Intelligent Camera Based Eye Controlled Wheelchair System: Haar Cascade and Gaze Estimation Algorithms</a></div><div class="wp-workCard_item wp-workCard--coauthors"><span>by </span><span><a class="" data-click-track="profile-work-strip-authors" href="https://acharaya.academia.edu/ViswanathaV">Dr.Viswanatha V .</a>, <a class="" data-click-track="profile-work-strip-authors" href="https://independent.academia.edu/LokeshwarGowkanapalli">Lokeshwar Gowkanapalli</a>, and <a class="" data-click-track="profile-work-strip-authors" href="https://independent.academia.edu/TharunReddy146">Tharun Reddy</a></span></div><div class="wp-workCard_item"><span>2023 International Conference on Applied Intelligence and Sustainable Computing (ICAISC)</span><span>, 2023</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">This article proposes a system that aids people with disabilities. An Electric Eye Controlled Whe...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">This article proposes a system that aids people with disabilities. An Electric Eye Controlled Wheelchair System is built to help disabled people. With the designed system, disabled people can move effortlessly without support from others. The system uses image acquisition wherein the image of the eye is processed to find out the gaze direction of the eye using Haar cascade and gaze estimation algorithms and hence wheelchair moves according to the direction of eyeball movement. The gaze estimation algorithm is so precise and one single algorithm does the job of what two algorithms (Canny Edge detection, Hough Transform) are supposed to do and to execute the same task. With this technique, a disabled person can steer their wheelchair with their eye movement. The webcam is placed in Infront of the person which captures the live movements, and an image processing technique is used to track the position of the pupil in both eyes with the help of a raspberry pi processor. The image processing technique used here is Gaze tracking which uses Open CV. The gaze tracking tracks pupil movement and depicts its coordinates. According to pupil motion, the motor driver will be instructed to go forward, left, and right. A blink instruction is used to stop the wheelchair when the person blinks. Additionally, a front-mounted ultrasonic sensor that can detect obstructions and automatically halt wheelchair movement is mounted for safety reasons. The system is monitored by a Raspberry Pi device, which lowers the cost.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="f12c53ed9fc6a0816845022c28c1e6fb" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":105533266,"asset_id":106298009,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/105533266/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="106298009"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="106298009"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 106298009; 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="106297921"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/106297921/Implementation_of_Smart_Security_System_in_Agriculture_elds_Using_Embedded_Machine_Learning"><img alt="Research paper thumbnail of Implementation of Smart Security System in Agriculture elds Using Embedded Machine Learning" class="work-thumbnail" src="https://attachments.academia-assets.com/105533207/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/106297921/Implementation_of_Smart_Security_System_in_Agriculture_elds_Using_Embedded_Machine_Learning">Implementation of Smart Security System in Agriculture elds Using Embedded Machine Learning</a></div><div class="wp-workCard_item"><span>2023 International Conference on Applied Intelligence and Sustainable Computing (ICAISC)</span><span>, 2023</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Tiny Machine Learning (TinyML), a branch of machine learning that focuses on the effectiveness of...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Tiny Machine Learning (TinyML), a branch of<br />machine learning that focuses on the effectiveness of machine<br />learning on extremely constrained edge machines, is flourishing.<br />Deep learning techniques are being used more frequently lately<br />in a variety of data-intensive and time-sensitive Internet of<br />Things (IoT) apps. Because MCUs lack resources like RAM,<br />deploying new methods like Deep Neural Networks (DNN)<br />models on them has proven challenging. However, recent<br />developments in the TinyML space promise to create a<br />completely new class of peripheral apps. By eliminating the need<br />for the cloud's omnipresent computing support, which uses<br />power and presents risks to data security and privacy, TinyML<br />paves the way for the development of original apps and services.<br />Traditional machine learning needs a lot of processing capacity<br />to predict a scenario. This computational capacity will be moved<br />from high-end systems to low-end devices thanks to the TinyML<br />method for machine learning on small devices. To keep the<br />precision of the learning models, enable resource-efficient small<br />edge devices to manage the training and deployment process,<br />maximize computing capacity, and enhance dependability are<br />some of the challenges presented by this change. Here in this<br />paper, we propose a efficient method to detect animals near<br />farmland for security purposes using TinyML and compared<br />with many algorithms and their effectiveness.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="c46c114736913a10ab3f3f701f96805d" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":105533207,"asset_id":106297921,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/105533207/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="106297921"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="106297921"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 106297921; 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="105353908"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/105353908/Tuberculosis_Prediction_using_KNN_Algorithm"><img alt="Research paper thumbnail of Tuberculosis Prediction using KNN Algorithm" class="work-thumbnail" src="https://attachments.academia-assets.com/104830461/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/105353908/Tuberculosis_Prediction_using_KNN_Algorithm">Tuberculosis Prediction using KNN Algorithm</a></div><div class="wp-workCard_item"><span>International Journal of Engineering and Management Research</span><span>, 2023</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">In this paper, a machine-learning model is used to develop a model that is used for tuberculosis ...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">In this paper, a machine-learning model is used to<br />develop a model that is used for tuberculosis prediction.<br />Tuberculosis is known to be one of the top reasons for death<br />from an infectious agent that affects the lungs and<br />continues to threaten the human population on a wider<br />basis. According to WHO, tuberculosis is a serious threat to<br />the human population after HIV/AIDS. It is estimated by<br />the World Health Organization (WHO) that 1/3rd of the<br />the global population is infected with TB and seven to<br />eight million new cases of TB occur each year across the<br />globe Because the disease is difficult to differentiate<br />between the common cold, it takes a long time to decide the<br />patient is affected by the disease. So we use the detection of<br />tuberculosis by utilizing the K-NN algorithm method for<br />classification and HOG as feature extraction. K-NN<br />abbreviated as K-Nearest Neighbour is one of the simplest<br />Machine Learning algorithms based on the Supervised<br />Learning technique.<br />The data provided K-NN model should be labeled<br />one. Then these datasets are given to a training model<br />where the training process of the model is being undergone.<br />Once the training is completed, the next step is to predict<br />the output. For this process, we have to provide new data<br />that may or may not belong to the dataset, so that the<br />model can predict the output of it. If the prediction is<br />wrong, again the training is done until we get the actual<br />output matching with the desired output given by the<br />designer for verification purposes. This is the basic working<br />process under the K-NN algorithm. The data that is used<br />for this separation is a Tuberculosis dataset that contains<br />various information about the different symptoms that are<br />helpful in detecting tuberculosis effectively. Here it is used<br />in the early detection of tuberculosis which helps save<br />millions of people which might otherwise lead to death<br />because of lack of detection. ML model helps to improve<br />the efficiency in detecting by considering various<br />symptoms. ML models are more accurate at differentiating<br />even the slightest difference that deviates from the data<br />that was used to train the model. Unlike the manpower we<br />fail to detect the slightest as we notice the symptoms only<br />after they become more severe. The accuracy of this model<br />was found to be 98%. The following model uses a dataset<br />consisting of data that contrasts between males and females<br />and the various symptoms are shown in them. It also<br />contrasts the severity of these two.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="ec1cce7a06df8e32d99ec92c2a6be1d9" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":104830461,"asset_id":105353908,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/104830461/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="105353908"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="105353908"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 105353908; 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="95244107"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/95244107/Handwritten_Digit_Recognition_Using_CNN"><img alt="Research paper thumbnail of Handwritten Digit Recognition Using CNN" class="work-thumbnail" src="https://attachments.academia-assets.com/97479131/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/95244107/Handwritten_Digit_Recognition_Using_CNN">Handwritten Digit Recognition Using CNN</a></div><div class="wp-workCard_item"><span>International Journal of innovative research in computer and Communication Engineering </span><span>, 2023</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">In this paper, we are going to see how we can train a neural network model to recognize a handwri...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">In this paper, we are going to see how we can train a neural network model to recognize a handwritten digit which is given as an input to the model. The algorithm used to realize it is Convolution Neural Network (CNN).It is a network architecture for deep learning. It learns from the data which isthrough the images. It finds patterns in images and recognizes objects and categories. The CNN has several layers which takes the input, analyzes the input, and producesoutput. It's very much used in Deep Learning and very efficient in the modern world filled with AI. It's a part of ANN that has been the superior algorithm incomputer vision tasks. It has achieved top-level performances in various fields like medical research, AI, etc. CNN, which is used for processing data, is a type ofdeep learning model that has grid patternsi.e.-images. It is a construct that has three kinds of layers namely convolution, pooling, and fully connected layersrespectively.The first few layers do the feature extraction, whereas the next layer maps the extracted features into final output. The convolution layer plays an importantrole in CNN. It is composed of a stack of mathematical operations such as convolution. The pixel values are stored in a twodimensional array in the digital images and small grid of parameters called kernel is applied at each image position. This makes the CNN highly efficient for image processing. The layers perform convolutionand subsampling one after another. Output of one layer is input of the next layer. The output of the final layer is our predicted value. Extracted features can progressivelybecome more complex, as one layer feeds its output to the next layer.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="08d7a32bb730fad902c97ca1ba97efa1" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":97479131,"asset_id":95244107,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/97479131/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="95244107"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="95244107"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 95244107; 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="93304608"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/93304608/Custom_Hardware_and_Software_Integration_Bluetooth_Based_Wireless_Thermal_Printer_for_Restaurant_and_Hospital_Management"><img alt="Research paper thumbnail of Custom Hardware and Software Integration: Bluetooth Based Wireless Thermal Printer for Restaurant and Hospital Management" class="work-thumbnail" src="https://attachments.academia-assets.com/96078927/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/93304608/Custom_Hardware_and_Software_Integration_Bluetooth_Based_Wireless_Thermal_Printer_for_Restaurant_and_Hospital_Management">Custom Hardware and Software Integration: Bluetooth Based Wireless Thermal Printer for Restaurant and Hospital Management</a></div><div class="wp-workCard_item wp-workCard--coauthors"><span>by </span><span><a class="" data-click-track="profile-work-strip-authors" href="https://acharaya.academia.edu/ViswanathaV">Dr.Viswanatha V .</a> and <a class="" data-click-track="profile-work-strip-authors" href="https://independent.academia.edu/SrinivasaMurthy64">Srinivasa Murthy</a></span></div><div class="wp-workCard_item"><span>IEEE</span><span>, 2022</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">In this paper, the communication between Arduino Mega microcontroller and mini thermal printer is...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">In this paper, the communication between Arduino Mega microcontroller and mini thermal printer is implemented by hardware and software integration. The microcontroller is programmed to transfer the data from microcontroller to printer using a serial communication protocol such as universal asynchronous receiver and transmitter (UART) via Serial communication devices such as MAX232 converter, DB9 connector which is nothing but RS232 serial port, and universal serial bus (USB) communication port. Data sent by the microcontroller is made to print from the printer and it is shown in the result section. Digital pins '3' and '4' on the microcontroller are used as virtual Rx and Tx serial lines while leaving the main serial port open for debugging purposes. Interfacing between UART of microcontroller, MAX232, serial port, and the USB port of printer are well established with the complete schematic diagram. The mini thermal printer prints whatever is sent by the microcontroller. A simple code similar to the one used for the serial monitor works for the printer. The baud rate needs to be set at 9600 for the microcontroller to communicate with the printer. The system is designed to print data wirelessly using Bluetooth HC-05 for restaurant and hospital management applications. The system has high adaptability. It can be used in many situations not only for restaurant and hospital management.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="2cb7d38632d03208b2f6c99e1b41a1a8" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":96078927,"asset_id":93304608,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/96078927/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="93304608"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="93304608"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 93304608; 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="93304388"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/93304388/Implementation_of_IoT_in_Agriculture_A_Scientific_Approach_for_Smart_Irrigation"><img alt="Research paper thumbnail of Implementation of IoT in Agriculture: A Scientific Approach for Smart Irrigation" class="work-thumbnail" src="https://attachments.academia-assets.com/96078755/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/93304388/Implementation_of_IoT_in_Agriculture_A_Scientific_Approach_for_Smart_Irrigation">Implementation of IoT in Agriculture: A Scientific Approach for Smart Irrigation</a></div><div class="wp-workCard_item"><span>IEEE</span><span>, 2022</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Digital technologies empower the transformation into data-driven, intelligent, agile, and autonom...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Digital technologies empower the transformation into data-driven, intelligent, agile, and autonomous farm operations and are typically considered a key to addressing the grand challenges in agriculture. To avoid unscientific water supply for plantation as well as to save the water and also yield the better crop, therefore, to increase production efficiency out of smart irrigation and to send the status of irrigation at standard environmental conditions, The Internet of Things (IoT) based prototype is designed and implemented. The prototype automatically turns ON /OFF the motor pump based on the moisture level of the soil by taking the temperature and humidity of the environment near the plantation into consideration (In India, the standard parameters for watering the vegetable plantation are Humidity>60%, Temperature < 25掳C and Humidity<40%). The prototype is designed with an ESP32S microcontroller with DHT 11 and a moisture sensor. Arduino IDE development tool is used for programming ESP32S using embedded C programming language. The prototype is configured, programmed, and connected to the Arduino IoT cloud. The data of temperature, humidity, and moisture are received via message queuing telemetry transport (MQTT) protocol on IoT cloud through public IP therefore the data can be accessed worldwide. The authorized person can access the data and control the motor pump from anywhere across the world. The test data obtained out of the prototype over the cloud and at the system are presented in the result section.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="172cc5f0e85f11bce4b09f93889c0491" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":96078755,"asset_id":93304388,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/96078755/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="93304388"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="93304388"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 93304388; 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="88763357"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/88763357/IoT_Based_Smart_Mirror_Using_Raspberry_Pi_4_and_YOLO_Algorithm_A_Novel_Framework_for_Interactive_Display"><img alt="Research paper thumbnail of IoT Based Smart Mirror Using Raspberry Pi 4 and YOLO Algorithm: A Novel Framework for Interactive Display" class="work-thumbnail" src="https://attachments.academia-assets.com/94017551/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/88763357/IoT_Based_Smart_Mirror_Using_Raspberry_Pi_4_and_YOLO_Algorithm_A_Novel_Framework_for_Interactive_Display">IoT Based Smart Mirror Using Raspberry Pi 4 and YOLO Algorithm: A Novel Framework for Interactive Display</a></div><div class="wp-workCard_item"><span>INDIAN JOURNAL OF SCIENCE AND TECHNOLOGY</span><span>, 2022</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Objectives: The primary goal of this device is to support the user in a variety of ways, and by e...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Objectives: The primary goal of this device is to support the user in a variety<br />of ways, and by enabling communication between the user and the gadget,<br />we can demonstrate the user鈥檚 control over the location where the device<br />will be Built and set up the home environment for the human detection<br />using You Only Look Once (YOLO) algorithm. Methods: The technology is<br />made to show the current news, weather, and temperature on the mirror.<br />The technology is primarily intended to be used as an intrusion detection<br />system and for human monitoring. YOLO algorithm is used to find the object<br />in the given image. The suggested design is envisioned as a collection of<br />components that can be utilized to provide security as well as simply display<br />information on a screen. The system is created utilizing Python programming<br />and hardware components such as Raspberry Pi 3 model, a microphone, a<br />touch screen, a mobile device, a camera, and passive infrared (PIR) sensors.<br />Findings: This study provides thorough information regarding the operation<br />of a smart mirror, which enables us to perform all necessary actions or tasks<br />that we perform regularly and to complete them in a predetermined amount<br />of time. Since the research is primarily intended for intrusion detection, we can<br />also get the most out of it by using it in a specific manner. We would use it to<br />monitor children鈥檚 whereabouts. Novelty: This technology has a high economic<br />value because it has so many uses and tends to help users with daily tasks.<br />And because it is created using machine learning language and has room for<br />artificial intelligence, even the user who uses it can add some of his remainders<br />to the device, and it can also alert a user in the designated remainder time<br />so that person won鈥檛 miss their important meeting. As a result, this device is<br />more valuable, useful, and collectively beneficial. This technology will produce<br />a finished product with the highest level of accuracy and precision.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="d4f41767402879e356b62a196ce1c088" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":94017551,"asset_id":88763357,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/94017551/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="88763357"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="88763357"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 88763357; 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="88763240"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/88763240/Advanced_Driving_Assistance_System_for_Cars_Using_Raspberry_Pi"><img alt="Research paper thumbnail of Advanced Driving Assistance System for Cars Using Raspberry Pi" class="work-thumbnail" src="https://attachments.academia-assets.com/94018201/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/88763240/Advanced_Driving_Assistance_System_for_Cars_Using_Raspberry_Pi">Advanced Driving Assistance System for Cars Using Raspberry Pi</a></div><div class="wp-workCard_item"><span>Indian Journal of Science and Technology</span><span>, 2022</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Objectives: Hardware implementation of advanced driving assistance system which can be able to id...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Objectives: Hardware implementation of advanced driving assistance system<br />which can be able to identify i). Lane detection and assist system. ii). Blind<br />spot detection and warning system (BSDW). iii). Forward collision and warning<br />system (FCWS). iv). Pedestrian detection system. The primary goal of the<br />developed system is to identify the above features in order to prevent accidents<br />on the road and ensure pedestrian safety. Methods: The suggested method<br />uses a canny edges detection algorithm is used to detect road edges. The<br />input to this system is images captured by the camera with the help of the<br />Open CV library a python image processing algorithm is created that tracks<br />the lane. Histogram of Orientation (HOG) using the sliding window method<br />is used for pedestrian detection. The control unit for the proposed system is<br />Raspberry Pi module 3B, JSN-SR04T ultrasonic sensor and HC-SR04 ultrasonic<br />sensor has been used for (BSDW) system and (FCWS) respectively. Findings:<br />Results demonstrate that the suggested technique can accurately recognize<br />both straight and curved lanes using an edge detection algorithm, and is also able<br />to identify vehicles in the Blindspot area. Novelty: This technology has a high<br />demand in the automotive industry and the system can be implemented in<br />all future cars which can able to reduce accident rates.<br />Keywords: Adaptive Cruise Control; Blind Spot Detection; Autonomous<br />Driving Assistance system; Lane Detection System; Forward Collision;<br />Pedestrian detection; OpenCV</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="129f9e4ce1f2870fdadb7a85a5c844e0" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":94018201,"asset_id":88763240,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/94018201/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="88763240"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="88763240"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 88763240; 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="86972974"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/86972974/In_Cabin_Radar_Monitoring_System_Detection_and_Localization_of_People_Inside_Vehicle_using_Vital_Sign_Sensing_Algorithm"><img alt="Research paper thumbnail of In-Cabin Radar Monitoring System: Detection and Localization of People Inside Vehicle using Vital Sign Sensing Algorithm" class="work-thumbnail" src="https://attachments.academia-assets.com/94018508/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/86972974/In_Cabin_Radar_Monitoring_System_Detection_and_Localization_of_People_Inside_Vehicle_using_Vital_Sign_Sensing_Algorithm">In-Cabin Radar Monitoring System: Detection and Localization of People Inside Vehicle using Vital Sign Sensing Algorithm</a></div><div class="wp-workCard_item wp-workCard--coauthors"><span>by </span><span><a class="" data-click-track="profile-work-strip-authors" href="https://acharaya.academia.edu/ViswanathaV">Dr.Viswanatha V .</a> and <a class="" data-click-track="profile-work-strip-authors" href="https://independent.academia.edu/PannirSelvamElamvazhuthi">Pannir Selvam Elamvazhuthi</a></span></div><div class="wp-workCard_item"><span> International Journal on Recent and Innovation Trends in Computing and Communication</span><span>, 2022</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Radars are used in automobiles for various functionalities, starting from the obstacle alarm duri...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Radars are used in automobiles for various functionalities, starting from the obstacle alarm during vehicle reversing to advanced functionalities like autonomous driving. A practical method for monitoring people inside a vehicle for various applications (surveillance, safety, etc.) could be built using Radar. This paper presents the embedded implementation of a vital sign sensing algorithm using the radar signal processing (RSP) technique. MEX (MATLAB executable) interface is performed with the embedded C code of the vital sign sensing algorithm generated for validating the results with the RSP technique. Finally, Unit testing is performed on the developed embedded C code of the vital sign sensing algorithm to remove the dead codes and to verify whether all branches and statements in a developed algorithm are working accordingly. The embedded C code results were found to be matching precisely with the RSP technique. With the help of obtained results, we can differentiate between an adult and a baby inside a vehicle.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="c71f1eebe7d6fd42b11f82a00bc32da3" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":94018508,"asset_id":86972974,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/94018508/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="86972974"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="86972974"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 86972974; 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="83069966"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/83069966/Real_Time_Object_Detection_System_with_YOLO_and_CNN_Models_A_Review"><img alt="Research paper thumbnail of Real Time Object Detection System with YOLO and CNN Models: A Review" class="work-thumbnail" src="https://attachments.academia-assets.com/88548882/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/83069966/Real_Time_Object_Detection_System_with_YOLO_and_CNN_Models_A_Review">Real Time Object Detection System with YOLO and CNN Models: A Review</a></div><div class="wp-workCard_item"><span>JOURNAL OF XI'AN UNIVERSITY OF ARCHITECTURE & TECHNOLOGY</span><span>, 2022</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">The field of artificial intelligence is built on object detection techniques. YOU ONLY LOOK ONCE ...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">The field of artificial intelligence is built on object detection techniques. YOU ONLY LOOK ONCE (YOLO) algorithm and it's more evolved versions are briefly described in this research survey. This survey is all about YOLO and convolution neural networks (CNN) in the direction of real time object detection. YOLO does generalized object representation more effectively without precision losses than other object detection models. CNN architecture models have the ability to eliminate highlights and identify objects in any given image. When implemented appropriately, CNN models can address issues like deformity diagnosis, creating educational or instructive application, etc. This article reached at number of observations and perspective findings through the analysis. Also it provides support for the focused visual information and feature extraction in the financial and other industries, highlights the method of target detection and feature selection, and briefly describe the development process of yolo algorithm.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="d7fe8bec87efdee1beabc1224de79374" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":88548882,"asset_id":83069966,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/88548882/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="83069966"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="83069966"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 83069966; 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="83028258"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/83028258/IMPLEMENTATION_OF_TINY_MACHINE_LEARNING_MODELS_ON_ARDUINO_33_BLE_FOR_GESTURE_AND_SPEECH_RECOGNITION"><img alt="Research paper thumbnail of IMPLEMENTATION OF TINY MACHINE LEARNING MODELS ON ARDUINO 33 -BLE FOR GESTURE AND SPEECH RECOGNITION" class="work-thumbnail" src="https://attachments.academia-assets.com/88524045/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/83028258/IMPLEMENTATION_OF_TINY_MACHINE_LEARNING_MODELS_ON_ARDUINO_33_BLE_FOR_GESTURE_AND_SPEECH_RECOGNITION">IMPLEMENTATION OF TINY MACHINE LEARNING MODELS ON ARDUINO 33 -BLE FOR GESTURE AND SPEECH RECOGNITION</a></div><div class="wp-workCard_item"><span>JOURNAL OF XI'AN UNIVERSITY OF ARCHITECTURE & TECHNOLOGY</span><span>, 2022</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">In this article gesture recognition and speech recognition applications are implemented on embedd...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">In this article gesture recognition and speech recognition applications are implemented on embedded systems with Tiny Machine Learning (TinyML).The main benefit of using TinyML is its portability. It can be run on cheap microcontrollers with tiny batteries and low power consumption, and it can easily integrate machine learning with virtually anything. It also has the benefit of increased security due to local nature of computing. The benefit of using Arduino Nano 33 BLE sense is that it has a set of sensors embedded on the top, which gives us a lot of options to try ideas without having to generate the circuit to such sensors in prototyping board. It features 3-axis accelerometer, 3axis gyroscope and 3-axis magnetometer. The gesture recognition, provides an innovative approach nonverbal communication. It has wide applications in human-computer interaction and sign language. Here in the implementation of hand gesture recognition, TinyML model is trained and deployed from EdgeImpulse framework for hand gesture recognition and based on the hand movements, Arduino Nano 33 BLE device having 6-axis IMU can find out the direction of movement of hand. The Speech is a mode of communication. Speech recognition is a way by which the statements or commands of human speech is understood by the computer which reacts accordingly. The main aim of speech recognition is to achieve communication between man and machine. Here in the implementation of speech recognition, TinyML model is trained and deployed from EdgeImpulse framework for speech recognition and based on the keyword pronounced by human, Arduino Nano 33 BLE device having built-in microphone can make an RGB LED glow like red, green or blue based on keyword pronounced. The results of each application are obtained and listed in the results section and given the analysis upon the results.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="8787f7f70bdc0f1da9785f4e4ebc6b1f" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":88524045,"asset_id":83028258,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/88524045/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="83028258"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="83028258"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 83028258; 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="77660136"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/77660136/Bidirectional_DC_DC_converter_circuits_and_smart_control_algorithms_a_review_"><img alt="Research paper thumbnail of Bidirectional DC-DC converter circuits and smart control algorithms a review ." class="work-thumbnail" src="https://attachments.academia-assets.com/84969920/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/77660136/Bidirectional_DC_DC_converter_circuits_and_smart_control_algorithms_a_review_">Bidirectional DC-DC converter circuits and smart control algorithms a review .</a></div><div class="wp-workCard_item wp-workCard--coauthors"><span>by </span><span><a class="" data-click-track="profile-work-strip-authors" href="https://acharaya.academia.edu/ViswanathaV">Dr.Viswanatha V .</a> and <a class="" data-click-track="profile-work-strip-authors" href="https://independent.academia.edu/ReddyRVSiva">R V Siva Reddy</a></span></div><div class="wp-workCard_item"><span>Journal of Electrical Systems and Information Technology</span><span>, 2022</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">The entire article has been dedicated to cover the current state of the art in bidirectional DC-D...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">The entire article has been dedicated to cover the current state of the art in bidirectional DC-DC converter topologies and its smart control algorithms, identified the research gaps and concluded with the motivation for taking up the work. It covers the literature survey of bidirectional buck鈥揵oost DC-DC converters, and control schemes are carried out on two aspects, one is on topology perspective and another one is on control schemes. Different topologies with and without transformers of bidirectional DC-DC converters are discussed. Non-isolated converters establish the DC path between input and output sides while transformer-based converters cancel the DC path in between input and output sides since it introduces AC line between two DC lines just like in flyback converter. The transformer-less converter is preferred when there is no much protection needed for load from high voltage levels, also these converters are used in high-power applications. The bidirectional DC-DC converter can switch the power between two DC sources and the load. To do so, it has to use proper control schemes and control algorithms. It can store the excess energy in batteries or in super capacitors. In contrast, isolated topologies contain transformers in their circuits. Due to this, it offers advantages like safeguarding sensitive loads from high power which is at input side. In addition to it, multiple input and output ports can be established. With the isolation in DC-DC converters, input and output sections are separated from electrical stand point of view. With isolation, both input and output sections will not be having common ground point. The DC path is removed with isolation due to usage of transformer in DC-DC converters. In contrast to its features, it is capable to be used in low-power applications since transformer is switching at high frequency, the size of the coil reduces and hence it can handle limited rate of current. The bidirectional DC-DC converters are categorized based on isolation property so-called isolated bidirectional converters. Features and applications of each topology are presented. Comparative analysis w.r.t research gaps between all the topologies is presented. Also the scope of control schemes with artificial intelligence is discussed. Pros and cons of each control scheme, i.e. research gaps in control schemes and impact of control scheme for bidirectional DC-DC converters, are also presented.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="ee40db5890158223dd0c9715c3371a43" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":84969920,"asset_id":77660136,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/84969920/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="77660136"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="77660136"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 77660136; 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="43516603"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/43516603/Closed_Loop_Control_of_Bidirectional_Buck_Boost_Converter_for_Battery_Management_in_Automotive_Systems"><img alt="Research paper thumbnail of Closed Loop Control of Bidirectional Buck-Boost Converter for Battery Management in Automotive Systems" class="work-thumbnail" src="https://attachments.academia-assets.com/63827405/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/43516603/Closed_Loop_Control_of_Bidirectional_Buck_Boost_Converter_for_Battery_Management_in_Automotive_Systems">Closed Loop Control of Bidirectional Buck-Boost Converter for Battery Management in Automotive Systems</a></div><div class="wp-workCard_item"><span>International Journal of Advanced Science and Technology</span><span>, 2020</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">A new control logic is constructed for bidirectional buck-boost converter (BBC) through mathemati...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">A new control logic is constructed for bidirectional buck-boost converter (BBC) through mathematical modeling and implemented in Simulink. The BBC bridges 12 V and 24 V in dual battery automotive systems to meet the power load requirements of advanced automotive electronics. From mathematical modeling, gains of proportional, integral, derivative with filter (PIDN) control law are obtained. Mode selection circuit is developed for Buck and Boost mode control. For each mode of operation, separate PIDN control law and pulse width modulation (PWM) system is developed in control topology under voltage feedback control method. Auto mode transition is realized based on DC bus voltage. Load and line regulations in both buck and boost mode of BBC are realized based on load voltage. The constructed control logic offers accurate bidirectional voltage regulation which ensures precise power transfer in both the directions. Battery charging and discharging characteristics are realized. PIDN control law is compared with proportional, integral, derivative (PID) control law.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="eb8a466e5d534eaf5d7bb3f540744392" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":63827405,"asset_id":43516603,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/63827405/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="43516603"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="43516603"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 43516603; 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="42925717"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/42925717/Characterization_of_analog_and_digital_control_loops_for_bidirectional_buck_boost_converter_using_PID_PIDN_algorithms"><img alt="Research paper thumbnail of Characterization of聽analog and聽digital control loops for聽bidirectional buck鈥揵oost converter using PID/PIDN algorithms" class="work-thumbnail" src="https://attachments.academia-assets.com/63177523/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/42925717/Characterization_of_analog_and_digital_control_loops_for_bidirectional_buck_boost_converter_using_PID_PIDN_algorithms">Characterization of聽analog and聽digital control loops for聽bidirectional buck鈥揵oost converter using PID/PIDN algorithms</a></div><div class="wp-workCard_item"><span>Journal of Electrical systems and information technology ". </span><span>, 2020</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">This article presents the characterization of analog and digital control loops using PID/ PIDN co...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">This article presents the characterization of analog and digital control loops using PID/<br />PIDN control algorithms for bidirectional buck鈥揵oost converter (BBC). Control loops <br />of BBC are designed and implemented in MATLAB code using transfer functions in <br />time domain with unit step response and in frequency domain with bode plots and <br />pole-zero plots. These transfer functions are obtained by average large signal modeling <br />of BBC. Actions of analog and digital control loops are characterized in order to ensure <br />stability and dynamic response of BBC which is a bottleneck in renewable energy <br />applications. Improvement in dynamic response and stability of BBC with PIDN control <br />algorithm is demonstrated using bode plots, pole-zero plots, and step response. Con-<br />trol loop gain due to transfer functions of power stage and controllers is demonstrated, <br />and it is found stable in both analog and digital control loops. PIDN compensator is <br />proposed to maintain a healthy balance between the stability and transient behavior <br />since both are indirectly proportional. BBC is modeled using average large signal mod-<br />eling technique, simulated using MATLAB tool, and analysis of dynamic and stability <br />response is done through unit step input, bode plot, and pole-zero plot. Hardware is <br />designed and implemented using TMS320F28335 controller.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="1367c9ea81c3e7893cbfb074aeee2b56" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":63177523,"asset_id":42925717,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/63177523/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="42925717"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="42925717"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 42925717; 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="41651485"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/41651485/Microcontroller_based_bidirectional_buck_boost_converter_for_photo_voltaic_power_plant"><img alt="Research paper thumbnail of Microcontroller based bidirectional buck鈥揵oost converter for photo-voltaic power plant" class="work-thumbnail" src="https://attachments.academia-assets.com/61804180/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/41651485/Microcontroller_based_bidirectional_buck_boost_converter_for_photo_voltaic_power_plant">Microcontroller based bidirectional buck鈥揵oost converter for photo-voltaic power plant</a></div><div class="wp-workCard_item"><span>Journal of Electrical Systems and Information Technology</span><span>, 2018</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">A common configuration for a stand-alone PV power system may consist of three converters: a buck ...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">A common configuration for a stand-alone PV power system may consist of three converters: a buck converter for the PV panel<br />to charge the battery, a boost converter for the battery to discharge to the load and one for the load voltage regulation. Such a system<br />requires a coordinated control scheme for three converters which can be complicated. A simple structure for a stand-alone PV plant<br />consists of a PV array, a battery unit, and its associated bidirectional converter which is a combination of a buck and boost converter.<br />When controlled properly the system can provide uninterrupted power to the load, despite the intermittent availability of sunlight. In<br />this paper complete design of the converter is carried out and the simulation has been performed using Psim. From the simulation,<br />the graphs are presented to show the converter working in buck mode and boost mode. Controller is designed to take care of mode<br />transition, buck to boost and boost to buck mode automatically based on source voltage. Hardware implementation has been done<br />using microcontroller (8051).</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="bf9f4bc5913417205704f6c78cfb1dfa" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":61804180,"asset_id":41651485,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/61804180/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="41651485"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="41651485"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 41651485; 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="profile--tab_heading_container js-section-heading" data-section="Books" id="Books"><h3 class="profile--tab_heading_container">Books by Dr.Viswanatha V .</h3></div><div class="js-work-strip profile--work_container" data-work-id="113665217"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" rel="nofollow" href="https://www.academia.edu/113665217/Intelligent_Camera_Based_Eye_Controlled_Wheelchair_System_Raspberry_Pi_and_Advanced_Algorithms"><img alt="Research paper thumbnail of Intelligent Camera-Based Eye-Controlled Wheelchair System: Raspberry Pi and Advanced Algorithms" class="work-thumbnail" src="https://attachments.academia-assets.com/110567294/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" rel="nofollow" href="https://www.academia.edu/113665217/Intelligent_Camera_Based_Eye_Controlled_Wheelchair_System_Raspberry_Pi_and_Advanced_Algorithms">Intelligent Camera-Based Eye-Controlled Wheelchair System: Raspberry Pi and Advanced Algorithms</a></div><div class="wp-workCard_item"><span> Advances in Communication and Applications</span><span>, 2024</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">People with tetraplegia are unable to move any body parts below the neck. The eye-controlled whee...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">People with tetraplegia are unable to move any body parts below the neck. The eye-controlled wheelchair system uses a revolutionary technique. This system's major goal is to make it unnecessary for impaired people to need help. With this technique, a disabled person can steer their wheelchair by their eye movement. The webcam is placed in front of the person, and image processing technique is used to track the position of the pupil in the left or right eye. According to pupil motions, the motor driver will be instructed to go forward, left and right. Additionally, a front-mounted ultrasonic sensor that can detect obstructions and automatically halt wheelchair movement is mounted for safety reasons. The system is monitored by a Raspberry Pi device, which lowers the cost.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="1055d3930f2e0e9b886e633b34358c50" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":110567294,"asset_id":113665217,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/110567294/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="113665217"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="113665217"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 113665217; 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="113664919"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" rel="nofollow" href="https://www.academia.edu/113664919/TinyML_Based_Human_and_Animal_Movement_Detection_in_Agriculture_Fields_in_India"><img alt="Research paper thumbnail of TinyML-Based Human and Animal Movement Detection in Agriculture Fields in India" class="work-thumbnail" src="https://attachments.academia-assets.com/110567032/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" rel="nofollow" href="https://www.academia.edu/113664919/TinyML_Based_Human_and_Animal_Movement_Detection_in_Agriculture_Fields_in_India">TinyML-Based Human and Animal Movement Detection in Agriculture Fields in India</a></div><div class="wp-workCard_item"><span> Advances in Communication and Applications</span><span>, 2024</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Tiny machine learning (TinyML) is blooming in ML field that deals with the performance of machine...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Tiny machine learning (TinyML) is blooming in ML field that deals with the performance of machine learning on extremely limited edge devices. Recently, a variety of data-intensive and time-sensitive Internet of Things (IoT) applications are using deep learning algorithms more often. As a result, various fresh strategies, such as deep neural networks (DNN) model deployment on MCUs, have become challenging task as they lack resources like memory. But recent advancement in the field of TinyML promises to open up a brand-new category of edge applications. In this paper, animal detection in farmlands is addressed using TinyML models deployed on SparkFun Edge device which supports high-resolution tiny camera attached to the device board itself in view of protecting farmlands which are widely being developed in India from animal attacks since TinyML provides the path for the creation of unique apps and services that do not require the cloud's ubiquitous computing support, which consumes power and poses dangers to data security and privacy. Also the models are tested on Google Colab. The results are obtained and discussed in result section.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="e8bf3f0f5791a4e354f84575947f4b1c" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":110567032,"asset_id":113664919,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/110567032/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="113664919"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="113664919"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 113664919; 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> </div><div class="profile--tab_content_container js-tab-pane tab-pane" data-section-id="7205923" id="papers"><div class="js-work-strip profile--work_container" data-work-id="106298009"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/106298009/An_Intelligent_Camera_Based_Eye_Controlled_Wheelchair_System_Haar_Cascade_and_Gaze_Estimation_Algorithms"><img alt="Research paper thumbnail of An Intelligent Camera Based Eye Controlled Wheelchair System: Haar Cascade and Gaze Estimation Algorithms" class="work-thumbnail" src="https://attachments.academia-assets.com/105533266/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/106298009/An_Intelligent_Camera_Based_Eye_Controlled_Wheelchair_System_Haar_Cascade_and_Gaze_Estimation_Algorithms">An Intelligent Camera Based Eye Controlled Wheelchair System: Haar Cascade and Gaze Estimation Algorithms</a></div><div class="wp-workCard_item wp-workCard--coauthors"><span>by </span><span><a class="" data-click-track="profile-work-strip-authors" href="https://acharaya.academia.edu/ViswanathaV">Dr.Viswanatha V .</a>, <a class="" data-click-track="profile-work-strip-authors" href="https://independent.academia.edu/LokeshwarGowkanapalli">Lokeshwar Gowkanapalli</a>, and <a class="" data-click-track="profile-work-strip-authors" href="https://independent.academia.edu/TharunReddy146">Tharun Reddy</a></span></div><div class="wp-workCard_item"><span>2023 International Conference on Applied Intelligence and Sustainable Computing (ICAISC)</span><span>, 2023</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">This article proposes a system that aids people with disabilities. An Electric Eye Controlled Whe...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">This article proposes a system that aids people with disabilities. An Electric Eye Controlled Wheelchair System is built to help disabled people. With the designed system, disabled people can move effortlessly without support from others. The system uses image acquisition wherein the image of the eye is processed to find out the gaze direction of the eye using Haar cascade and gaze estimation algorithms and hence wheelchair moves according to the direction of eyeball movement. The gaze estimation algorithm is so precise and one single algorithm does the job of what two algorithms (Canny Edge detection, Hough Transform) are supposed to do and to execute the same task. With this technique, a disabled person can steer their wheelchair with their eye movement. The webcam is placed in Infront of the person which captures the live movements, and an image processing technique is used to track the position of the pupil in both eyes with the help of a raspberry pi processor. The image processing technique used here is Gaze tracking which uses Open CV. The gaze tracking tracks pupil movement and depicts its coordinates. According to pupil motion, the motor driver will be instructed to go forward, left, and right. A blink instruction is used to stop the wheelchair when the person blinks. Additionally, a front-mounted ultrasonic sensor that can detect obstructions and automatically halt wheelchair movement is mounted for safety reasons. The system is monitored by a Raspberry Pi device, which lowers the cost.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="f12c53ed9fc6a0816845022c28c1e6fb" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":105533266,"asset_id":106298009,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/105533266/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="106298009"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="106298009"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 106298009; 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="106297921"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/106297921/Implementation_of_Smart_Security_System_in_Agriculture_elds_Using_Embedded_Machine_Learning"><img alt="Research paper thumbnail of Implementation of Smart Security System in Agriculture elds Using Embedded Machine Learning" class="work-thumbnail" src="https://attachments.academia-assets.com/105533207/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/106297921/Implementation_of_Smart_Security_System_in_Agriculture_elds_Using_Embedded_Machine_Learning">Implementation of Smart Security System in Agriculture elds Using Embedded Machine Learning</a></div><div class="wp-workCard_item"><span>2023 International Conference on Applied Intelligence and Sustainable Computing (ICAISC)</span><span>, 2023</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Tiny Machine Learning (TinyML), a branch of machine learning that focuses on the effectiveness of...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Tiny Machine Learning (TinyML), a branch of<br />machine learning that focuses on the effectiveness of machine<br />learning on extremely constrained edge machines, is flourishing.<br />Deep learning techniques are being used more frequently lately<br />in a variety of data-intensive and time-sensitive Internet of<br />Things (IoT) apps. Because MCUs lack resources like RAM,<br />deploying new methods like Deep Neural Networks (DNN)<br />models on them has proven challenging. However, recent<br />developments in the TinyML space promise to create a<br />completely new class of peripheral apps. By eliminating the need<br />for the cloud's omnipresent computing support, which uses<br />power and presents risks to data security and privacy, TinyML<br />paves the way for the development of original apps and services.<br />Traditional machine learning needs a lot of processing capacity<br />to predict a scenario. This computational capacity will be moved<br />from high-end systems to low-end devices thanks to the TinyML<br />method for machine learning on small devices. To keep the<br />precision of the learning models, enable resource-efficient small<br />edge devices to manage the training and deployment process,<br />maximize computing capacity, and enhance dependability are<br />some of the challenges presented by this change. Here in this<br />paper, we propose a efficient method to detect animals near<br />farmland for security purposes using TinyML and compared<br />with many algorithms and their effectiveness.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="c46c114736913a10ab3f3f701f96805d" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":105533207,"asset_id":106297921,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/105533207/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="106297921"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="106297921"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 106297921; 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="105353908"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/105353908/Tuberculosis_Prediction_using_KNN_Algorithm"><img alt="Research paper thumbnail of Tuberculosis Prediction using KNN Algorithm" class="work-thumbnail" src="https://attachments.academia-assets.com/104830461/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/105353908/Tuberculosis_Prediction_using_KNN_Algorithm">Tuberculosis Prediction using KNN Algorithm</a></div><div class="wp-workCard_item"><span>International Journal of Engineering and Management Research</span><span>, 2023</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">In this paper, a machine-learning model is used to develop a model that is used for tuberculosis ...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">In this paper, a machine-learning model is used to<br />develop a model that is used for tuberculosis prediction.<br />Tuberculosis is known to be one of the top reasons for death<br />from an infectious agent that affects the lungs and<br />continues to threaten the human population on a wider<br />basis. According to WHO, tuberculosis is a serious threat to<br />the human population after HIV/AIDS. It is estimated by<br />the World Health Organization (WHO) that 1/3rd of the<br />the global population is infected with TB and seven to<br />eight million new cases of TB occur each year across the<br />globe Because the disease is difficult to differentiate<br />between the common cold, it takes a long time to decide the<br />patient is affected by the disease. So we use the detection of<br />tuberculosis by utilizing the K-NN algorithm method for<br />classification and HOG as feature extraction. K-NN<br />abbreviated as K-Nearest Neighbour is one of the simplest<br />Machine Learning algorithms based on the Supervised<br />Learning technique.<br />The data provided K-NN model should be labeled<br />one. Then these datasets are given to a training model<br />where the training process of the model is being undergone.<br />Once the training is completed, the next step is to predict<br />the output. For this process, we have to provide new data<br />that may or may not belong to the dataset, so that the<br />model can predict the output of it. If the prediction is<br />wrong, again the training is done until we get the actual<br />output matching with the desired output given by the<br />designer for verification purposes. This is the basic working<br />process under the K-NN algorithm. The data that is used<br />for this separation is a Tuberculosis dataset that contains<br />various information about the different symptoms that are<br />helpful in detecting tuberculosis effectively. Here it is used<br />in the early detection of tuberculosis which helps save<br />millions of people which might otherwise lead to death<br />because of lack of detection. ML model helps to improve<br />the efficiency in detecting by considering various<br />symptoms. ML models are more accurate at differentiating<br />even the slightest difference that deviates from the data<br />that was used to train the model. Unlike the manpower we<br />fail to detect the slightest as we notice the symptoms only<br />after they become more severe. The accuracy of this model<br />was found to be 98%. The following model uses a dataset<br />consisting of data that contrasts between males and females<br />and the various symptoms are shown in them. It also<br />contrasts the severity of these two.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="ec1cce7a06df8e32d99ec92c2a6be1d9" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":104830461,"asset_id":105353908,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/104830461/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="105353908"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="105353908"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 105353908; 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="95244107"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/95244107/Handwritten_Digit_Recognition_Using_CNN"><img alt="Research paper thumbnail of Handwritten Digit Recognition Using CNN" class="work-thumbnail" src="https://attachments.academia-assets.com/97479131/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/95244107/Handwritten_Digit_Recognition_Using_CNN">Handwritten Digit Recognition Using CNN</a></div><div class="wp-workCard_item"><span>International Journal of innovative research in computer and Communication Engineering </span><span>, 2023</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">In this paper, we are going to see how we can train a neural network model to recognize a handwri...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">In this paper, we are going to see how we can train a neural network model to recognize a handwritten digit which is given as an input to the model. The algorithm used to realize it is Convolution Neural Network (CNN).It is a network architecture for deep learning. It learns from the data which isthrough the images. It finds patterns in images and recognizes objects and categories. The CNN has several layers which takes the input, analyzes the input, and producesoutput. It's very much used in Deep Learning and very efficient in the modern world filled with AI. It's a part of ANN that has been the superior algorithm incomputer vision tasks. It has achieved top-level performances in various fields like medical research, AI, etc. CNN, which is used for processing data, is a type ofdeep learning model that has grid patternsi.e.-images. It is a construct that has three kinds of layers namely convolution, pooling, and fully connected layersrespectively.The first few layers do the feature extraction, whereas the next layer maps the extracted features into final output. The convolution layer plays an importantrole in CNN. It is composed of a stack of mathematical operations such as convolution. The pixel values are stored in a twodimensional array in the digital images and small grid of parameters called kernel is applied at each image position. This makes the CNN highly efficient for image processing. The layers perform convolutionand subsampling one after another. Output of one layer is input of the next layer. The output of the final layer is our predicted value. Extracted features can progressivelybecome more complex, as one layer feeds its output to the next layer.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="08d7a32bb730fad902c97ca1ba97efa1" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":97479131,"asset_id":95244107,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/97479131/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="95244107"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="95244107"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 95244107; 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="93304608"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/93304608/Custom_Hardware_and_Software_Integration_Bluetooth_Based_Wireless_Thermal_Printer_for_Restaurant_and_Hospital_Management"><img alt="Research paper thumbnail of Custom Hardware and Software Integration: Bluetooth Based Wireless Thermal Printer for Restaurant and Hospital Management" class="work-thumbnail" src="https://attachments.academia-assets.com/96078927/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/93304608/Custom_Hardware_and_Software_Integration_Bluetooth_Based_Wireless_Thermal_Printer_for_Restaurant_and_Hospital_Management">Custom Hardware and Software Integration: Bluetooth Based Wireless Thermal Printer for Restaurant and Hospital Management</a></div><div class="wp-workCard_item wp-workCard--coauthors"><span>by </span><span><a class="" data-click-track="profile-work-strip-authors" href="https://acharaya.academia.edu/ViswanathaV">Dr.Viswanatha V .</a> and <a class="" data-click-track="profile-work-strip-authors" href="https://independent.academia.edu/SrinivasaMurthy64">Srinivasa Murthy</a></span></div><div class="wp-workCard_item"><span>IEEE</span><span>, 2022</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">In this paper, the communication between Arduino Mega microcontroller and mini thermal printer is...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">In this paper, the communication between Arduino Mega microcontroller and mini thermal printer is implemented by hardware and software integration. The microcontroller is programmed to transfer the data from microcontroller to printer using a serial communication protocol such as universal asynchronous receiver and transmitter (UART) via Serial communication devices such as MAX232 converter, DB9 connector which is nothing but RS232 serial port, and universal serial bus (USB) communication port. Data sent by the microcontroller is made to print from the printer and it is shown in the result section. Digital pins '3' and '4' on the microcontroller are used as virtual Rx and Tx serial lines while leaving the main serial port open for debugging purposes. Interfacing between UART of microcontroller, MAX232, serial port, and the USB port of printer are well established with the complete schematic diagram. The mini thermal printer prints whatever is sent by the microcontroller. A simple code similar to the one used for the serial monitor works for the printer. The baud rate needs to be set at 9600 for the microcontroller to communicate with the printer. The system is designed to print data wirelessly using Bluetooth HC-05 for restaurant and hospital management applications. The system has high adaptability. It can be used in many situations not only for restaurant and hospital management.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="2cb7d38632d03208b2f6c99e1b41a1a8" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":96078927,"asset_id":93304608,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/96078927/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="93304608"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="93304608"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 93304608; 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="93304388"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/93304388/Implementation_of_IoT_in_Agriculture_A_Scientific_Approach_for_Smart_Irrigation"><img alt="Research paper thumbnail of Implementation of IoT in Agriculture: A Scientific Approach for Smart Irrigation" class="work-thumbnail" src="https://attachments.academia-assets.com/96078755/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/93304388/Implementation_of_IoT_in_Agriculture_A_Scientific_Approach_for_Smart_Irrigation">Implementation of IoT in Agriculture: A Scientific Approach for Smart Irrigation</a></div><div class="wp-workCard_item"><span>IEEE</span><span>, 2022</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Digital technologies empower the transformation into data-driven, intelligent, agile, and autonom...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Digital technologies empower the transformation into data-driven, intelligent, agile, and autonomous farm operations and are typically considered a key to addressing the grand challenges in agriculture. To avoid unscientific water supply for plantation as well as to save the water and also yield the better crop, therefore, to increase production efficiency out of smart irrigation and to send the status of irrigation at standard environmental conditions, The Internet of Things (IoT) based prototype is designed and implemented. The prototype automatically turns ON /OFF the motor pump based on the moisture level of the soil by taking the temperature and humidity of the environment near the plantation into consideration (In India, the standard parameters for watering the vegetable plantation are Humidity>60%, Temperature < 25掳C and Humidity<40%). The prototype is designed with an ESP32S microcontroller with DHT 11 and a moisture sensor. Arduino IDE development tool is used for programming ESP32S using embedded C programming language. The prototype is configured, programmed, and connected to the Arduino IoT cloud. The data of temperature, humidity, and moisture are received via message queuing telemetry transport (MQTT) protocol on IoT cloud through public IP therefore the data can be accessed worldwide. The authorized person can access the data and control the motor pump from anywhere across the world. The test data obtained out of the prototype over the cloud and at the system are presented in the result section.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="172cc5f0e85f11bce4b09f93889c0491" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":96078755,"asset_id":93304388,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/96078755/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="93304388"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="93304388"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 93304388; 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="88763357"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/88763357/IoT_Based_Smart_Mirror_Using_Raspberry_Pi_4_and_YOLO_Algorithm_A_Novel_Framework_for_Interactive_Display"><img alt="Research paper thumbnail of IoT Based Smart Mirror Using Raspberry Pi 4 and YOLO Algorithm: A Novel Framework for Interactive Display" class="work-thumbnail" src="https://attachments.academia-assets.com/94017551/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/88763357/IoT_Based_Smart_Mirror_Using_Raspberry_Pi_4_and_YOLO_Algorithm_A_Novel_Framework_for_Interactive_Display">IoT Based Smart Mirror Using Raspberry Pi 4 and YOLO Algorithm: A Novel Framework for Interactive Display</a></div><div class="wp-workCard_item"><span>INDIAN JOURNAL OF SCIENCE AND TECHNOLOGY</span><span>, 2022</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Objectives: The primary goal of this device is to support the user in a variety of ways, and by e...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Objectives: The primary goal of this device is to support the user in a variety<br />of ways, and by enabling communication between the user and the gadget,<br />we can demonstrate the user鈥檚 control over the location where the device<br />will be Built and set up the home environment for the human detection<br />using You Only Look Once (YOLO) algorithm. Methods: The technology is<br />made to show the current news, weather, and temperature on the mirror.<br />The technology is primarily intended to be used as an intrusion detection<br />system and for human monitoring. YOLO algorithm is used to find the object<br />in the given image. The suggested design is envisioned as a collection of<br />components that can be utilized to provide security as well as simply display<br />information on a screen. The system is created utilizing Python programming<br />and hardware components such as Raspberry Pi 3 model, a microphone, a<br />touch screen, a mobile device, a camera, and passive infrared (PIR) sensors.<br />Findings: This study provides thorough information regarding the operation<br />of a smart mirror, which enables us to perform all necessary actions or tasks<br />that we perform regularly and to complete them in a predetermined amount<br />of time. Since the research is primarily intended for intrusion detection, we can<br />also get the most out of it by using it in a specific manner. We would use it to<br />monitor children鈥檚 whereabouts. Novelty: This technology has a high economic<br />value because it has so many uses and tends to help users with daily tasks.<br />And because it is created using machine learning language and has room for<br />artificial intelligence, even the user who uses it can add some of his remainders<br />to the device, and it can also alert a user in the designated remainder time<br />so that person won鈥檛 miss their important meeting. As a result, this device is<br />more valuable, useful, and collectively beneficial. This technology will produce<br />a finished product with the highest level of accuracy and precision.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="d4f41767402879e356b62a196ce1c088" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":94017551,"asset_id":88763357,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/94017551/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="88763357"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="88763357"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 88763357; 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="88763240"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/88763240/Advanced_Driving_Assistance_System_for_Cars_Using_Raspberry_Pi"><img alt="Research paper thumbnail of Advanced Driving Assistance System for Cars Using Raspberry Pi" class="work-thumbnail" src="https://attachments.academia-assets.com/94018201/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/88763240/Advanced_Driving_Assistance_System_for_Cars_Using_Raspberry_Pi">Advanced Driving Assistance System for Cars Using Raspberry Pi</a></div><div class="wp-workCard_item"><span>Indian Journal of Science and Technology</span><span>, 2022</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Objectives: Hardware implementation of advanced driving assistance system which can be able to id...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Objectives: Hardware implementation of advanced driving assistance system<br />which can be able to identify i). Lane detection and assist system. ii). Blind<br />spot detection and warning system (BSDW). iii). Forward collision and warning<br />system (FCWS). iv). Pedestrian detection system. The primary goal of the<br />developed system is to identify the above features in order to prevent accidents<br />on the road and ensure pedestrian safety. Methods: The suggested method<br />uses a canny edges detection algorithm is used to detect road edges. The<br />input to this system is images captured by the camera with the help of the<br />Open CV library a python image processing algorithm is created that tracks<br />the lane. Histogram of Orientation (HOG) using the sliding window method<br />is used for pedestrian detection. The control unit for the proposed system is<br />Raspberry Pi module 3B, JSN-SR04T ultrasonic sensor and HC-SR04 ultrasonic<br />sensor has been used for (BSDW) system and (FCWS) respectively. Findings:<br />Results demonstrate that the suggested technique can accurately recognize<br />both straight and curved lanes using an edge detection algorithm, and is also able<br />to identify vehicles in the Blindspot area. Novelty: This technology has a high<br />demand in the automotive industry and the system can be implemented in<br />all future cars which can able to reduce accident rates.<br />Keywords: Adaptive Cruise Control; Blind Spot Detection; Autonomous<br />Driving Assistance system; Lane Detection System; Forward Collision;<br />Pedestrian detection; OpenCV</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="129f9e4ce1f2870fdadb7a85a5c844e0" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":94018201,"asset_id":88763240,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/94018201/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="88763240"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="88763240"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 88763240; 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="86972974"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/86972974/In_Cabin_Radar_Monitoring_System_Detection_and_Localization_of_People_Inside_Vehicle_using_Vital_Sign_Sensing_Algorithm"><img alt="Research paper thumbnail of In-Cabin Radar Monitoring System: Detection and Localization of People Inside Vehicle using Vital Sign Sensing Algorithm" class="work-thumbnail" src="https://attachments.academia-assets.com/94018508/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/86972974/In_Cabin_Radar_Monitoring_System_Detection_and_Localization_of_People_Inside_Vehicle_using_Vital_Sign_Sensing_Algorithm">In-Cabin Radar Monitoring System: Detection and Localization of People Inside Vehicle using Vital Sign Sensing Algorithm</a></div><div class="wp-workCard_item wp-workCard--coauthors"><span>by </span><span><a class="" data-click-track="profile-work-strip-authors" href="https://acharaya.academia.edu/ViswanathaV">Dr.Viswanatha V .</a> and <a class="" data-click-track="profile-work-strip-authors" href="https://independent.academia.edu/PannirSelvamElamvazhuthi">Pannir Selvam Elamvazhuthi</a></span></div><div class="wp-workCard_item"><span> International Journal on Recent and Innovation Trends in Computing and Communication</span><span>, 2022</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Radars are used in automobiles for various functionalities, starting from the obstacle alarm duri...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Radars are used in automobiles for various functionalities, starting from the obstacle alarm during vehicle reversing to advanced functionalities like autonomous driving. A practical method for monitoring people inside a vehicle for various applications (surveillance, safety, etc.) could be built using Radar. This paper presents the embedded implementation of a vital sign sensing algorithm using the radar signal processing (RSP) technique. MEX (MATLAB executable) interface is performed with the embedded C code of the vital sign sensing algorithm generated for validating the results with the RSP technique. Finally, Unit testing is performed on the developed embedded C code of the vital sign sensing algorithm to remove the dead codes and to verify whether all branches and statements in a developed algorithm are working accordingly. The embedded C code results were found to be matching precisely with the RSP technique. With the help of obtained results, we can differentiate between an adult and a baby inside a vehicle.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="c71f1eebe7d6fd42b11f82a00bc32da3" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":94018508,"asset_id":86972974,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/94018508/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="86972974"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="86972974"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 86972974; 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="83069966"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/83069966/Real_Time_Object_Detection_System_with_YOLO_and_CNN_Models_A_Review"><img alt="Research paper thumbnail of Real Time Object Detection System with YOLO and CNN Models: A Review" class="work-thumbnail" src="https://attachments.academia-assets.com/88548882/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/83069966/Real_Time_Object_Detection_System_with_YOLO_and_CNN_Models_A_Review">Real Time Object Detection System with YOLO and CNN Models: A Review</a></div><div class="wp-workCard_item"><span>JOURNAL OF XI'AN UNIVERSITY OF ARCHITECTURE & TECHNOLOGY</span><span>, 2022</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">The field of artificial intelligence is built on object detection techniques. YOU ONLY LOOK ONCE ...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">The field of artificial intelligence is built on object detection techniques. YOU ONLY LOOK ONCE (YOLO) algorithm and it's more evolved versions are briefly described in this research survey. This survey is all about YOLO and convolution neural networks (CNN) in the direction of real time object detection. YOLO does generalized object representation more effectively without precision losses than other object detection models. CNN architecture models have the ability to eliminate highlights and identify objects in any given image. When implemented appropriately, CNN models can address issues like deformity diagnosis, creating educational or instructive application, etc. This article reached at number of observations and perspective findings through the analysis. Also it provides support for the focused visual information and feature extraction in the financial and other industries, highlights the method of target detection and feature selection, and briefly describe the development process of yolo algorithm.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="d7fe8bec87efdee1beabc1224de79374" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":88548882,"asset_id":83069966,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/88548882/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="83069966"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="83069966"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 83069966; 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="83028258"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/83028258/IMPLEMENTATION_OF_TINY_MACHINE_LEARNING_MODELS_ON_ARDUINO_33_BLE_FOR_GESTURE_AND_SPEECH_RECOGNITION"><img alt="Research paper thumbnail of IMPLEMENTATION OF TINY MACHINE LEARNING MODELS ON ARDUINO 33 -BLE FOR GESTURE AND SPEECH RECOGNITION" class="work-thumbnail" src="https://attachments.academia-assets.com/88524045/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/83028258/IMPLEMENTATION_OF_TINY_MACHINE_LEARNING_MODELS_ON_ARDUINO_33_BLE_FOR_GESTURE_AND_SPEECH_RECOGNITION">IMPLEMENTATION OF TINY MACHINE LEARNING MODELS ON ARDUINO 33 -BLE FOR GESTURE AND SPEECH RECOGNITION</a></div><div class="wp-workCard_item"><span>JOURNAL OF XI'AN UNIVERSITY OF ARCHITECTURE & TECHNOLOGY</span><span>, 2022</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">In this article gesture recognition and speech recognition applications are implemented on embedd...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">In this article gesture recognition and speech recognition applications are implemented on embedded systems with Tiny Machine Learning (TinyML).The main benefit of using TinyML is its portability. It can be run on cheap microcontrollers with tiny batteries and low power consumption, and it can easily integrate machine learning with virtually anything. It also has the benefit of increased security due to local nature of computing. The benefit of using Arduino Nano 33 BLE sense is that it has a set of sensors embedded on the top, which gives us a lot of options to try ideas without having to generate the circuit to such sensors in prototyping board. It features 3-axis accelerometer, 3axis gyroscope and 3-axis magnetometer. The gesture recognition, provides an innovative approach nonverbal communication. It has wide applications in human-computer interaction and sign language. Here in the implementation of hand gesture recognition, TinyML model is trained and deployed from EdgeImpulse framework for hand gesture recognition and based on the hand movements, Arduino Nano 33 BLE device having 6-axis IMU can find out the direction of movement of hand. The Speech is a mode of communication. Speech recognition is a way by which the statements or commands of human speech is understood by the computer which reacts accordingly. The main aim of speech recognition is to achieve communication between man and machine. Here in the implementation of speech recognition, TinyML model is trained and deployed from EdgeImpulse framework for speech recognition and based on the keyword pronounced by human, Arduino Nano 33 BLE device having built-in microphone can make an RGB LED glow like red, green or blue based on keyword pronounced. The results of each application are obtained and listed in the results section and given the analysis upon the results.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="8787f7f70bdc0f1da9785f4e4ebc6b1f" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":88524045,"asset_id":83028258,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/88524045/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="83028258"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="83028258"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 83028258; 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="77660136"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/77660136/Bidirectional_DC_DC_converter_circuits_and_smart_control_algorithms_a_review_"><img alt="Research paper thumbnail of Bidirectional DC-DC converter circuits and smart control algorithms a review ." class="work-thumbnail" src="https://attachments.academia-assets.com/84969920/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/77660136/Bidirectional_DC_DC_converter_circuits_and_smart_control_algorithms_a_review_">Bidirectional DC-DC converter circuits and smart control algorithms a review .</a></div><div class="wp-workCard_item wp-workCard--coauthors"><span>by </span><span><a class="" data-click-track="profile-work-strip-authors" href="https://acharaya.academia.edu/ViswanathaV">Dr.Viswanatha V .</a> and <a class="" data-click-track="profile-work-strip-authors" href="https://independent.academia.edu/ReddyRVSiva">R V Siva Reddy</a></span></div><div class="wp-workCard_item"><span>Journal of Electrical Systems and Information Technology</span><span>, 2022</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">The entire article has been dedicated to cover the current state of the art in bidirectional DC-D...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">The entire article has been dedicated to cover the current state of the art in bidirectional DC-DC converter topologies and its smart control algorithms, identified the research gaps and concluded with the motivation for taking up the work. It covers the literature survey of bidirectional buck鈥揵oost DC-DC converters, and control schemes are carried out on two aspects, one is on topology perspective and another one is on control schemes. Different topologies with and without transformers of bidirectional DC-DC converters are discussed. Non-isolated converters establish the DC path between input and output sides while transformer-based converters cancel the DC path in between input and output sides since it introduces AC line between two DC lines just like in flyback converter. The transformer-less converter is preferred when there is no much protection needed for load from high voltage levels, also these converters are used in high-power applications. The bidirectional DC-DC converter can switch the power between two DC sources and the load. To do so, it has to use proper control schemes and control algorithms. It can store the excess energy in batteries or in super capacitors. In contrast, isolated topologies contain transformers in their circuits. Due to this, it offers advantages like safeguarding sensitive loads from high power which is at input side. In addition to it, multiple input and output ports can be established. With the isolation in DC-DC converters, input and output sections are separated from electrical stand point of view. With isolation, both input and output sections will not be having common ground point. The DC path is removed with isolation due to usage of transformer in DC-DC converters. In contrast to its features, it is capable to be used in low-power applications since transformer is switching at high frequency, the size of the coil reduces and hence it can handle limited rate of current. The bidirectional DC-DC converters are categorized based on isolation property so-called isolated bidirectional converters. Features and applications of each topology are presented. Comparative analysis w.r.t research gaps between all the topologies is presented. Also the scope of control schemes with artificial intelligence is discussed. Pros and cons of each control scheme, i.e. research gaps in control schemes and impact of control scheme for bidirectional DC-DC converters, are also presented.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="ee40db5890158223dd0c9715c3371a43" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":84969920,"asset_id":77660136,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/84969920/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="77660136"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="77660136"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 77660136; 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="43516603"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/43516603/Closed_Loop_Control_of_Bidirectional_Buck_Boost_Converter_for_Battery_Management_in_Automotive_Systems"><img alt="Research paper thumbnail of Closed Loop Control of Bidirectional Buck-Boost Converter for Battery Management in Automotive Systems" class="work-thumbnail" src="https://attachments.academia-assets.com/63827405/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/43516603/Closed_Loop_Control_of_Bidirectional_Buck_Boost_Converter_for_Battery_Management_in_Automotive_Systems">Closed Loop Control of Bidirectional Buck-Boost Converter for Battery Management in Automotive Systems</a></div><div class="wp-workCard_item"><span>International Journal of Advanced Science and Technology</span><span>, 2020</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">A new control logic is constructed for bidirectional buck-boost converter (BBC) through mathemati...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">A new control logic is constructed for bidirectional buck-boost converter (BBC) through mathematical modeling and implemented in Simulink. The BBC bridges 12 V and 24 V in dual battery automotive systems to meet the power load requirements of advanced automotive electronics. From mathematical modeling, gains of proportional, integral, derivative with filter (PIDN) control law are obtained. Mode selection circuit is developed for Buck and Boost mode control. For each mode of operation, separate PIDN control law and pulse width modulation (PWM) system is developed in control topology under voltage feedback control method. Auto mode transition is realized based on DC bus voltage. Load and line regulations in both buck and boost mode of BBC are realized based on load voltage. The constructed control logic offers accurate bidirectional voltage regulation which ensures precise power transfer in both the directions. Battery charging and discharging characteristics are realized. PIDN control law is compared with proportional, integral, derivative (PID) control law.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="eb8a466e5d534eaf5d7bb3f540744392" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":63827405,"asset_id":43516603,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/63827405/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="43516603"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="43516603"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 43516603; 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="42925717"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/42925717/Characterization_of_analog_and_digital_control_loops_for_bidirectional_buck_boost_converter_using_PID_PIDN_algorithms"><img alt="Research paper thumbnail of Characterization of聽analog and聽digital control loops for聽bidirectional buck鈥揵oost converter using PID/PIDN algorithms" class="work-thumbnail" src="https://attachments.academia-assets.com/63177523/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/42925717/Characterization_of_analog_and_digital_control_loops_for_bidirectional_buck_boost_converter_using_PID_PIDN_algorithms">Characterization of聽analog and聽digital control loops for聽bidirectional buck鈥揵oost converter using PID/PIDN algorithms</a></div><div class="wp-workCard_item"><span>Journal of Electrical systems and information technology ". </span><span>, 2020</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">This article presents the characterization of analog and digital control loops using PID/ PIDN co...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">This article presents the characterization of analog and digital control loops using PID/<br />PIDN control algorithms for bidirectional buck鈥揵oost converter (BBC). Control loops <br />of BBC are designed and implemented in MATLAB code using transfer functions in <br />time domain with unit step response and in frequency domain with bode plots and <br />pole-zero plots. These transfer functions are obtained by average large signal modeling <br />of BBC. Actions of analog and digital control loops are characterized in order to ensure <br />stability and dynamic response of BBC which is a bottleneck in renewable energy <br />applications. Improvement in dynamic response and stability of BBC with PIDN control <br />algorithm is demonstrated using bode plots, pole-zero plots, and step response. Con-<br />trol loop gain due to transfer functions of power stage and controllers is demonstrated, <br />and it is found stable in both analog and digital control loops. PIDN compensator is <br />proposed to maintain a healthy balance between the stability and transient behavior <br />since both are indirectly proportional. BBC is modeled using average large signal mod-<br />eling technique, simulated using MATLAB tool, and analysis of dynamic and stability <br />response is done through unit step input, bode plot, and pole-zero plot. Hardware is <br />designed and implemented using TMS320F28335 controller.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="1367c9ea81c3e7893cbfb074aeee2b56" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":63177523,"asset_id":42925717,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/63177523/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="42925717"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="42925717"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 42925717; 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="41651485"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/41651485/Microcontroller_based_bidirectional_buck_boost_converter_for_photo_voltaic_power_plant"><img alt="Research paper thumbnail of Microcontroller based bidirectional buck鈥揵oost converter for photo-voltaic power plant" class="work-thumbnail" src="https://attachments.academia-assets.com/61804180/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/41651485/Microcontroller_based_bidirectional_buck_boost_converter_for_photo_voltaic_power_plant">Microcontroller based bidirectional buck鈥揵oost converter for photo-voltaic power plant</a></div><div class="wp-workCard_item"><span>Journal of Electrical Systems and Information Technology</span><span>, 2018</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">A common configuration for a stand-alone PV power system may consist of three converters: a buck ...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">A common configuration for a stand-alone PV power system may consist of three converters: a buck converter for the PV panel<br />to charge the battery, a boost converter for the battery to discharge to the load and one for the load voltage regulation. Such a system<br />requires a coordinated control scheme for three converters which can be complicated. A simple structure for a stand-alone PV plant<br />consists of a PV array, a battery unit, and its associated bidirectional converter which is a combination of a buck and boost converter.<br />When controlled properly the system can provide uninterrupted power to the load, despite the intermittent availability of sunlight. In<br />this paper complete design of the converter is carried out and the simulation has been performed using Psim. From the simulation,<br />the graphs are presented to show the converter working in buck mode and boost mode. Controller is designed to take care of mode<br />transition, buck to boost and boost to buck mode automatically based on source voltage. Hardware implementation has been done<br />using microcontroller (8051).</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="bf9f4bc5913417205704f6c78cfb1dfa" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":61804180,"asset_id":41651485,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/61804180/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="41651485"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="41651485"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 41651485; 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> </div><div class="profile--tab_content_container js-tab-pane tab-pane" data-section-id="19122233" id="books"><div class="js-work-strip profile--work_container" data-work-id="113665217"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" rel="nofollow" href="https://www.academia.edu/113665217/Intelligent_Camera_Based_Eye_Controlled_Wheelchair_System_Raspberry_Pi_and_Advanced_Algorithms"><img alt="Research paper thumbnail of Intelligent Camera-Based Eye-Controlled Wheelchair System: Raspberry Pi and Advanced Algorithms" class="work-thumbnail" src="https://attachments.academia-assets.com/110567294/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" rel="nofollow" href="https://www.academia.edu/113665217/Intelligent_Camera_Based_Eye_Controlled_Wheelchair_System_Raspberry_Pi_and_Advanced_Algorithms">Intelligent Camera-Based Eye-Controlled Wheelchair System: Raspberry Pi and Advanced Algorithms</a></div><div class="wp-workCard_item"><span> Advances in Communication and Applications</span><span>, 2024</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">People with tetraplegia are unable to move any body parts below the neck. The eye-controlled whee...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">People with tetraplegia are unable to move any body parts below the neck. The eye-controlled wheelchair system uses a revolutionary technique. This system's major goal is to make it unnecessary for impaired people to need help. With this technique, a disabled person can steer their wheelchair by their eye movement. The webcam is placed in front of the person, and image processing technique is used to track the position of the pupil in the left or right eye. According to pupil motions, the motor driver will be instructed to go forward, left and right. Additionally, a front-mounted ultrasonic sensor that can detect obstructions and automatically halt wheelchair movement is mounted for safety reasons. The system is monitored by a Raspberry Pi device, which lowers the cost.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="1055d3930f2e0e9b886e633b34358c50" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":110567294,"asset_id":113665217,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/110567294/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="113665217"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="113665217"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 113665217; 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="113664919"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" rel="nofollow" href="https://www.academia.edu/113664919/TinyML_Based_Human_and_Animal_Movement_Detection_in_Agriculture_Fields_in_India"><img alt="Research paper thumbnail of TinyML-Based Human and Animal Movement Detection in Agriculture Fields in India" class="work-thumbnail" src="https://attachments.academia-assets.com/110567032/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" rel="nofollow" href="https://www.academia.edu/113664919/TinyML_Based_Human_and_Animal_Movement_Detection_in_Agriculture_Fields_in_India">TinyML-Based Human and Animal Movement Detection in Agriculture Fields in India</a></div><div class="wp-workCard_item"><span> Advances in Communication and Applications</span><span>, 2024</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Tiny machine learning (TinyML) is blooming in ML field that deals with the performance of machine...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Tiny machine learning (TinyML) is blooming in ML field that deals with the performance of machine learning on extremely limited edge devices. Recently, a variety of data-intensive and time-sensitive Internet of Things (IoT) applications are using deep learning algorithms more often. As a result, various fresh strategies, such as deep neural networks (DNN) model deployment on MCUs, have become challenging task as they lack resources like memory. But recent advancement in the field of TinyML promises to open up a brand-new category of edge applications. In this paper, animal detection in farmlands is addressed using TinyML models deployed on SparkFun Edge device which supports high-resolution tiny camera attached to the device board itself in view of protecting farmlands which are widely being developed in India from animal attacks since TinyML provides the path for the creation of unique apps and services that do not require the cloud's ubiquitous computing support, which consumes power and poses dangers to data security and privacy. Also the models are tested on Google Colab. 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