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signals</title> <meta name="description" content="Search results for: system signals"> <meta name="keywords" content="system signals"> <meta name="viewport" content="width=device-width, initial-scale=1, minimum-scale=1, maximum-scale=1, user-scalable=no"> <meta charset="utf-8"> <link href="https://cdn.waset.org/favicon.ico" type="image/x-icon" rel="shortcut icon"> <link href="https://cdn.waset.org/static/plugins/bootstrap-4.2.1/css/bootstrap.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/plugins/fontawesome/css/all.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/css/site.css?v=150220211555" rel="stylesheet"> </head> <body> <header> <div class="container"> <nav class="navbar navbar-expand-lg navbar-light"> <a class="navbar-brand" href="https://waset.org"> <img src="https://cdn.waset.org/static/images/wasetc.png" alt="Open Science Research Excellence" title="Open Science Research Excellence" /> </a> <button class="d-block d-lg-none navbar-toggler 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id="custom-search-input"> <div class="input-group"> <i class="fas fa-search"></i> <input type="text" class="search-query" name="q" placeholder="Author, Title, Abstract, Keywords" value="system signals"> <input type="submit" class="btn_search" value="Search"> </div> </div> </form> </div> </div> <div class="row mt-3"> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Commenced</strong> in January 2007</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Frequency:</strong> Monthly</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Edition:</strong> International</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Paper Count:</strong> 18171</div> </div> </div> </div> <h1 class="mt-3 mb-3 text-center" style="font-size:1.6rem;">Search results for: system signals</h1> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">18171</span> System for Electromyography Signal Emulation Through the Use of Embedded Systems</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Valentina%20Narvaez%20Gaitan">Valentina Narvaez Gaitan</a>, <a href="https://publications.waset.org/abstracts/search?q=Laura%20Valentina%20Rodriguez%20Leguizamon"> Laura Valentina Rodriguez Leguizamon</a>, <a href="https://publications.waset.org/abstracts/search?q=Ruben%20Dario%20Hernandez%20B."> Ruben Dario Hernandez B.</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This work describes a physiological signal emulation system that uses electromyography (EMG) signals obtained from muscle sensors in the first instance. These signals are used to extract their characteristics to model and emulate specific arm movements. The main objective of this effort is to develop a new biomedical software system capable of generating physiological signals through the use of embedded systems by establishing the characteristics of the acquired signals. The acquisition system used was Biosignals, which contains two EMG electrodes used to acquire signals from the forearm muscles placed on the extensor and flexor muscles. Processing algorithms were implemented to classify the signals generated by the arm muscles when performing specific movements such as wrist flexion extension, palmar grip, and wrist pronation-supination. Matlab software was used to condition and preprocess the signals for subsequent classification. Subsequently, the mathematical modeling of each signal is performed to be generated by the embedded system, with a validation of the accuracy of the obtained signal using the percentage of cross-correlation, obtaining a precision of 96%. The equations are then discretized to be emulated in the embedded system, obtaining a system capable of generating physiological signals according to the characteristics of medical analysis. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=classification" title="classification">classification</a>, <a href="https://publications.waset.org/abstracts/search?q=electromyography" title=" electromyography"> electromyography</a>, <a href="https://publications.waset.org/abstracts/search?q=embedded%20system" title=" embedded system"> embedded system</a>, <a href="https://publications.waset.org/abstracts/search?q=emulation" title=" emulation"> emulation</a>, <a href="https://publications.waset.org/abstracts/search?q=physiological%20signals" title=" physiological signals"> physiological signals</a> </p> <a href="https://publications.waset.org/abstracts/165468/system-for-electromyography-signal-emulation-through-the-use-of-embedded-systems" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/165468.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">111</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">18170</span> Low Cost Real Time Robust Identification of Impulsive Signals</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=R.%20Biondi">R. Biondi</a>, <a href="https://publications.waset.org/abstracts/search?q=G.%20Dys"> G. Dys</a>, <a href="https://publications.waset.org/abstracts/search?q=G.%20Ferone"> G. Ferone</a>, <a href="https://publications.waset.org/abstracts/search?q=T.%20Renard"> T. Renard</a>, <a href="https://publications.waset.org/abstracts/search?q=M.%20Zysman"> M. Zysman</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper describes an automated implementable system for impulsive signals detection and recognition. The system uses a Digital Signal Processing device for the detection and identification process. Here the system analyses the signals in real time in order to produce a particular response if needed. The system analyses the signals in real time in order to produce a specific output if needed. Detection is achieved through normalizing the inputs and comparing the read signals to a dynamic threshold and thus avoiding detections linked to loud or fluctuating environing noise. Identification is done through neuronal network algorithms. As a setup our system can receive signals to “learn” certain patterns. Through “learning” the system can recognize signals faster, inducing flexibility to new patterns similar to those known. Sound is captured through a simple jack input, and could be changed for an enhanced recording surface such as a wide-area recorder. Furthermore a communication module can be added to the apparatus to send alerts to another interface if needed. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=sound%20detection" title="sound detection">sound detection</a>, <a href="https://publications.waset.org/abstracts/search?q=impulsive%20signal" title=" impulsive signal"> impulsive signal</a>, <a href="https://publications.waset.org/abstracts/search?q=background%20noise" title=" background noise"> background noise</a>, <a href="https://publications.waset.org/abstracts/search?q=neural%20network" title=" neural network"> neural network</a> </p> <a href="https://publications.waset.org/abstracts/14114/low-cost-real-time-robust-identification-of-impulsive-signals" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/14114.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">319</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">18169</span> Identification of the Relationship Between Signals in Continuous Monitoring of Production Systems</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Maciej%20Zar%C4%99ba">Maciej Zaręba</a>, <a href="https://publications.waset.org/abstracts/search?q=S%C5%82awomir%20Lasota"> Sławomir Lasota</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Understanding the dependencies between the input signal, that controls the production system and signals, that capture its output, is of a great importance in intelligent systems. The method for identification of the relationship between signals in continuous monitoring of production systems is described in the paper. The method discovers the correlation between changes in the states derived from input signals and resulting changes in the states of output signals of the production system. The method is able to handle system inertia, which determines the time shift of the relationship between the input and output. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=manufacturing%20operation%20management" title="manufacturing operation management">manufacturing operation management</a>, <a href="https://publications.waset.org/abstracts/search?q=signal%20relationship" title=" signal relationship"> signal relationship</a>, <a href="https://publications.waset.org/abstracts/search?q=continuous%20monitoring" title=" continuous monitoring"> continuous monitoring</a>, <a href="https://publications.waset.org/abstracts/search?q=production%20systems" title=" production systems"> production systems</a> </p> <a href="https://publications.waset.org/abstracts/155368/identification-of-the-relationship-between-signals-in-continuous-monitoring-of-production-systems" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/155368.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">92</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">18168</span> Early Warning Signals: Role and Status of Risk Management in Small and Medium Enterprises</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Alexander%20Kel%C3%AD%C5%A1ek">Alexander Kelíšek</a>, <a href="https://publications.waset.org/abstracts/search?q=Denisa%20Janasov%C3%A1"> Denisa Janasová</a>, <a href="https://publications.waset.org/abstracts/search?q=Veronika%20Mita%C5%A1ov%C3%A1"> Veronika Mitašová</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Weak signals using is often associated with early warning. It is possible to find a link between early warning, respectively early problems detection and risk management. The idea of early warning is very important in the context of crisis management because of the risk prevention possibility. Weak signals are likened to risk symptoms. Nowadays, their usefulness as a tool of proactive problems solving is emphasized. Based on it, it is possible to use weak signals not only in strategic planning, project management, or early warning system, but also as a subsidiary element in risk management. The main question is how to effectively integrate weak signals into risk management. The main aim of the paper is to point out the possibilities of weak signals using in small and medium enterprises risk management. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=early%20warning%20system" title="early warning system">early warning system</a>, <a href="https://publications.waset.org/abstracts/search?q=weak%20signals" title=" weak signals"> weak signals</a>, <a href="https://publications.waset.org/abstracts/search?q=risk%20management" title=" risk management"> risk management</a>, <a href="https://publications.waset.org/abstracts/search?q=small%20and%20medium%20enterprises%20%28SMEs%29" title=" small and medium enterprises (SMEs)"> small and medium enterprises (SMEs)</a> </p> <a href="https://publications.waset.org/abstracts/59504/early-warning-signals-role-and-status-of-risk-management-in-small-and-medium-enterprises" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/59504.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">427</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">18167</span> A Method for Quantitative Assessment of the Dependencies between Input Signals and Output Indicators in Production Systems</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Maciej%20Zar%C4%99ba">Maciej Zaręba</a>, <a href="https://publications.waset.org/abstracts/search?q=S%C5%82awomir%20Lasota"> Sławomir Lasota</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Knowing the degree of dependencies between the sets of input signals and selected sets of indicators that measure a production system's effectiveness is of great importance in the industry. This paper introduces the SELM method that enables the selection of sets of input signals, which affects the most the selected subset of indicators that measures the effectiveness of a production system. For defined set of output indicators, the method quantifies the impact of input signals that are gathered in the continuous monitoring production system. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=manufacturing%20operation%20management" title="manufacturing operation management">manufacturing operation management</a>, <a href="https://publications.waset.org/abstracts/search?q=signal%20relationship" title=" signal relationship"> signal relationship</a>, <a href="https://publications.waset.org/abstracts/search?q=continuous%20monitoring" title=" continuous monitoring"> continuous monitoring</a>, <a href="https://publications.waset.org/abstracts/search?q=production%20systems" title=" production systems"> production systems</a> </p> <a href="https://publications.waset.org/abstracts/155375/a-method-for-quantitative-assessment-of-the-dependencies-between-input-signals-and-output-indicators-in-production-systems" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/155375.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">119</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">18166</span> A Hybrid Expert System for Generating Stock Trading Signals</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Hosein%20Hamisheh%20Bahar">Hosein Hamisheh Bahar</a>, <a href="https://publications.waset.org/abstracts/search?q=Mohammad%20Hossein%20Fazel%20Zarandi"> Mohammad Hossein Fazel Zarandi</a>, <a href="https://publications.waset.org/abstracts/search?q=Akbar%20Esfahanipour"> Akbar Esfahanipour</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, a hybrid expert system is developed by using fuzzy genetic network programming with reinforcement learning (GNP-RL). In this system, the frame-based structure of the system uses the trading rules extracted by GNP. These rules are extracted by using technical indices of the stock prices in the training time period. For developing this system, we applied fuzzy node transition and decision making in both processing and judgment nodes of GNP-RL. Consequently, using these method not only did increase the accuracy of node transition and decision making in GNP's nodes, but also extended the GNP's binary signals to ternary trading signals. In the other words, in our proposed Fuzzy GNP-RL model, a No Trade signal is added to conventional Buy or Sell signals. Finally, the obtained rules are used in a frame-based system implemented in Kappa-PC software. This developed trading system has been used to generate trading signals for ten companies listed in Tehran Stock Exchange (TSE). The simulation results in the testing time period shows that the developed system has more favorable performance in comparison with the Buy and Hold strategy. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=fuzzy%20genetic%20network%20programming" title="fuzzy genetic network programming">fuzzy genetic network programming</a>, <a href="https://publications.waset.org/abstracts/search?q=hybrid%20expert%20system" title=" hybrid expert system"> hybrid expert system</a>, <a href="https://publications.waset.org/abstracts/search?q=technical%20trading%20signal" title=" technical trading signal"> technical trading signal</a>, <a href="https://publications.waset.org/abstracts/search?q=Tehran%20stock%20exchange" title=" Tehran stock exchange"> Tehran stock exchange</a> </p> <a href="https://publications.waset.org/abstracts/49708/a-hybrid-expert-system-for-generating-stock-trading-signals" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/49708.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">332</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">18165</span> Remote Wireless Patient Monitoring System</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Sagar%20R.%20Patil">Sagar R. Patil</a>, <a href="https://publications.waset.org/abstracts/search?q=Dinesh%20R.%20Gawade"> Dinesh R. Gawade</a>, <a href="https://publications.waset.org/abstracts/search?q=Sudhir%20N.%20Divekar"> Sudhir N. Divekar </a> </p> <p class="card-text"><strong>Abstract:</strong></p> One of the medical devices we found when we visit a hospital care unit such device is ‘patient monitoring system’. This device (patient monitoring system) informs doctors and nurses about the patient’s physiological signals. However, this device (patient monitoring system) does not have a remote monitoring capability, which is necessitates constant onsite attendance by support personnel (doctors and nurses). Thus, we have developed a Remote Wireless Patient Monitoring System using some biomedical sensors and Android OS, which is a portable patient monitoring. This device(Remote Wireless Patient Monitoring System) monitors the biomedical signals of patients in real time and sends them to remote stations (doctors and nurse’s android Smartphone and web) for display and with alerts when necessary. Wireless Patient Monitoring System different from conventional device (Patient Monitoring system) in two aspects: First its wireless communication capability allows physiological signals to be monitored remotely and second, it is portable so patients can move while there biomedical signals are being monitor. Wireless Patient Monitoring is also notable because of its implementation. We are integrated four sensors such as pulse oximeter (SPO2), thermometer, respiration, blood pressure (BP), heart rate and electrocardiogram (ECG) in this device (Wireless Patient Monitoring System) and Monitoring and communication applications are implemented on the Android OS using threads, which facilitate the stable and timely manipulation of signals and the appropriate sharing of resources. The biomedical data will be display on android smart phone as well as on web Using web server and database system we can share these physiological signals with remote place medical personnel’s or with any where in the world medical personnel’s. We verified that the multitasking implementation used in the system was suitable for patient monitoring and for other Healthcare applications. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=patient%20monitoring" title="patient monitoring">patient monitoring</a>, <a href="https://publications.waset.org/abstracts/search?q=wireless%20patient%20monitoring" title=" wireless patient monitoring"> wireless patient monitoring</a>, <a href="https://publications.waset.org/abstracts/search?q=bio-medical%20signals" title=" bio-medical signals"> bio-medical signals</a>, <a href="https://publications.waset.org/abstracts/search?q=physiological%20signals" title=" physiological signals"> physiological signals</a>, <a href="https://publications.waset.org/abstracts/search?q=embedded%20system" title=" embedded system"> embedded system</a>, <a href="https://publications.waset.org/abstracts/search?q=Android%20OS" title=" Android OS"> Android OS</a>, <a href="https://publications.waset.org/abstracts/search?q=healthcare" title=" healthcare"> healthcare</a>, <a href="https://publications.waset.org/abstracts/search?q=pulse%20oximeter%20%28SPO2%29" title=" pulse oximeter (SPO2)"> pulse oximeter (SPO2)</a>, <a href="https://publications.waset.org/abstracts/search?q=thermometer" title=" thermometer"> thermometer</a>, <a href="https://publications.waset.org/abstracts/search?q=respiration" title=" respiration"> respiration</a>, <a href="https://publications.waset.org/abstracts/search?q=blood%20pressure%20%28BP%29" title=" blood pressure (BP)"> blood pressure (BP)</a>, <a href="https://publications.waset.org/abstracts/search?q=heart%20rate" title=" heart rate"> heart rate</a>, <a href="https://publications.waset.org/abstracts/search?q=electrocardiogram%20%28ECG%29" title=" electrocardiogram (ECG)"> electrocardiogram (ECG)</a> </p> <a href="https://publications.waset.org/abstracts/26470/remote-wireless-patient-monitoring-system" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/26470.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">571</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">18164</span> A Fast GPS Satellites Signals Detection Algorithm Based on Simplified Fast Fourier Transform</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Beldjilali%20Bilal">Beldjilali Bilal</a>, <a href="https://publications.waset.org/abstracts/search?q=Benadda%20Belkacem"> Benadda Belkacem</a>, <a href="https://publications.waset.org/abstracts/search?q=Kahlouche%20Salem"> Kahlouche Salem</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Due to the Doppler effect caused by the high velocity of satellite and in some case receivers, the frequency of the Global Positioning System (GPS) signals are transformed into a new ones. Several acquisition algorithms frequency of the Global Positioning System (GPS) signals are transformed can be used to estimate the new frequency and phase shifts values. Numerous algorithms are based on the frequencies domain calculation. Our developed algorithm is a new approach dedicated to the Global Positioning System signal acquisition based on the fast Fourier transform. Our proposed new algorithm is easier to implement and has fast execution time compared with elder ones. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=global%20positioning%20system" title="global positioning system">global positioning system</a>, <a href="https://publications.waset.org/abstracts/search?q=acquisition" title=" acquisition"> acquisition</a>, <a href="https://publications.waset.org/abstracts/search?q=FFT" title=" FFT"> FFT</a>, <a href="https://publications.waset.org/abstracts/search?q=GPS%2FL1" title=" GPS/L1"> GPS/L1</a>, <a href="https://publications.waset.org/abstracts/search?q=software%20receiver" title=" software receiver"> software receiver</a>, <a href="https://publications.waset.org/abstracts/search?q=weak%20signal" title=" weak signal"> weak signal</a> </p> <a href="https://publications.waset.org/abstracts/84390/a-fast-gps-satellites-signals-detection-algorithm-based-on-simplified-fast-fourier-transform" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/84390.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">251</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">18163</span> Development of Scratching Monitoring System Based on Mathematical Model of Unconstrained Bed Sensing Method</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Takuya%20Sumi">Takuya Sumi</a>, <a href="https://publications.waset.org/abstracts/search?q=Syoko%20Nukaya"> Syoko Nukaya</a>, <a href="https://publications.waset.org/abstracts/search?q=Takashi%20Kaburagi"> Takashi Kaburagi</a>, <a href="https://publications.waset.org/abstracts/search?q=Hiroshi%20Tanaka"> Hiroshi Tanaka</a>, <a href="https://publications.waset.org/abstracts/search?q=Kajiro%20Watanabe"> Kajiro Watanabe</a>, <a href="https://publications.waset.org/abstracts/search?q=Yosuke%20Kurihara"> Yosuke Kurihara</a> </p> <p class="card-text"><strong>Abstract:</strong></p> We propose an unconstrained measurement system for scratching motion based on mathematical model of unconstrained bed sensing method which could measure the bed vibrations due to the motion of the person on the bed. In this paper, we construct mathematical model of the unconstrained bed monitoring system, and we apply the unconstrained bed sensing method to the system for detecting scratching motion. The proposed sensors are placed under the three bed feet. When the person is lying on the bed, the output signals from the sensors are proportional to the magnitude of the vibration due to the scratching motion. Hence, we could detect the subject’s scratching motion from the output signals from ceramic sensors. We evaluated two scratching motions using the proposed system in the validity experiment as follows: First experiment is the subject’s scratching the right side cheek with his right hand, and; second experiment is the subject’s scratching the shin with another foot. As the results of the experiment, we recognized the scratching signals that enable the determination when the scratching occurred. Furthermore, the difference among the amplitudes of the output signals enabled us to estimate where the subject scratched. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=unconstrained%20bed%20sensing%20method" title="unconstrained bed sensing method">unconstrained bed sensing method</a>, <a href="https://publications.waset.org/abstracts/search?q=scratching" title=" scratching"> scratching</a>, <a href="https://publications.waset.org/abstracts/search?q=body%20movement" title=" body movement"> body movement</a>, <a href="https://publications.waset.org/abstracts/search?q=itchy" title=" itchy"> itchy</a>, <a href="https://publications.waset.org/abstracts/search?q=piezoceramics" title=" piezoceramics"> piezoceramics</a> </p> <a href="https://publications.waset.org/abstracts/1382/development-of-scratching-monitoring-system-based-on-mathematical-model-of-unconstrained-bed-sensing-method" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/1382.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">411</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">18162</span> Monitoring of Belt-Drive Defects Using the Vibration Signals and Simulation Models</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=A.%20Nabhan">A. Nabhan</a>, <a href="https://publications.waset.org/abstracts/search?q=Mohamed%20R.%20El-Sharkawy"> Mohamed R. El-Sharkawy</a>, <a href="https://publications.waset.org/abstracts/search?q=A.%20Rashed"> A. Rashed </a> </p> <p class="card-text"><strong>Abstract:</strong></p> The main aim of this paper is to dedicate the belt drive system faults like cogs missing, misalignment and belt worm using vibration analysis technique. Experimentally, the belt drive test-rig is equipped to measure vibrations signals under different operating conditions. Finite element 3D model of belt drive system is created and vibration response analyzed using commercial finite element software ABAQUS/CAE.  Root mean square (RMS) and Crest Factor will serve as indicators of average amplitude of envelope analysis signals. The vibration signals pattern obtained from the simulation model and experimental data have the same characteristics. It can be concluded that each case of the RMS is more effective in detecting the defect for acceleration response. While Crest Factor parameter has a response with the displacement and velocity of vibration signals. Also it can be noticed that the model has difficulty in completing the solution when the misalignment angle is higher than 1 degree. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=simulation%20model" title="simulation model">simulation model</a>, <a href="https://publications.waset.org/abstracts/search?q=misalignment" title=" misalignment"> misalignment</a>, <a href="https://publications.waset.org/abstracts/search?q=cogs%20missing" title=" cogs missing"> cogs missing</a>, <a href="https://publications.waset.org/abstracts/search?q=vibration%20analysis" title=" vibration analysis"> vibration analysis</a> </p> <a href="https://publications.waset.org/abstracts/98593/monitoring-of-belt-drive-defects-using-the-vibration-signals-and-simulation-models" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/98593.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">284</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">18161</span> Signals Monitored During Anaesthesia</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Launcelot%20McGrath">Launcelot McGrath</a> </p> <p class="card-text"><strong>Abstract:</strong></p> A comprehensive understanding of physiological data is a vital aid to the anaesthesiologist in monitoring and maintaining the well-being of a patient undergoing surgery. Bio signal analysis is one of the most important topics that researchers have tried to develop over the last century to understand numerous human diseases. Understanding which biological signals are most important during anaesthesia is critically important. It is important that the anaesthesiologist understand both the signals themselves and the limitations introduced by the processes of acquisition. In this article, we provide an overview of different types of biological signals as well as the mechanisms applied to acquire them. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=biological%20signals" title="biological signals">biological signals</a>, <a href="https://publications.waset.org/abstracts/search?q=signal%20acquisition" title=" signal acquisition"> signal acquisition</a>, <a href="https://publications.waset.org/abstracts/search?q=anaesthesiology" title=" anaesthesiology"> anaesthesiology</a>, <a href="https://publications.waset.org/abstracts/search?q=patient%20monitoring" title=" patient monitoring"> patient monitoring</a> </p> <a href="https://publications.waset.org/abstracts/158784/signals-monitored-during-anaesthesia" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/158784.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">138</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">18160</span> Combined Odd Pair Autoregressive Coefficients for Epileptic EEG Signals Classification by Radial Basis Function Neural Network</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Boukari%20Nassim">Boukari Nassim</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper describes the use of odd pair autoregressive coefficients (Yule _Walker and Burg) for the feature extraction of electroencephalogram (EEG) signals. In the classification: the radial basis function neural network neural network (RBFNN) is employed. The RBFNN is described by his architecture and his characteristics: as the RBF is defined by the spread which is modified for improving the results of the classification. Five types of EEG signals are defined for this work: Set A, Set B for normal signals, Set C, Set D for interictal signals, set E for ictal signal (we can found that in Bonn university). In outputs, two classes are given (AC, AD, AE, BC, BD, BE, CE, DE), the best accuracy is calculated at 99% for the combined odd pair autoregressive coefficients. Our method is very effective for the diagnosis of epileptic EEG signals. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=epilepsy" title="epilepsy">epilepsy</a>, <a href="https://publications.waset.org/abstracts/search?q=EEG%20signals%20classification" title=" EEG signals classification"> EEG signals classification</a>, <a href="https://publications.waset.org/abstracts/search?q=combined%20odd%20pair%20autoregressive%20coefficients" title=" combined odd pair autoregressive coefficients"> combined odd pair autoregressive coefficients</a>, <a href="https://publications.waset.org/abstracts/search?q=radial%20basis%20function%20neural%20network" title=" radial basis function neural network"> radial basis function neural network</a> </p> <a href="https://publications.waset.org/abstracts/47454/combined-odd-pair-autoregressive-coefficients-for-epileptic-eeg-signals-classification-by-radial-basis-function-neural-network" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/47454.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">346</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">18159</span> Road Vehicle Recognition Using Magnetic Sensing Feature Extraction and Classification </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Xiao%20Chen">Xiao Chen</a>, <a href="https://publications.waset.org/abstracts/search?q=Xiaoying%20Kong"> Xiaoying Kong</a>, <a href="https://publications.waset.org/abstracts/search?q=Min%20Xu"> Min Xu</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper presents a road vehicle detection approach for the intelligent transportation system. This approach mainly uses low-cost magnetic sensor and associated data collection system to collect magnetic signals. This system can measure the magnetic field changing, and it also can detect and count vehicles. We extend Mel Frequency Cepstral Coefficients to analyze vehicle magnetic signals. Vehicle type features are extracted using representation of cepstrum, frame energy, and gap cepstrum of magnetic signals. We design a 2-dimensional map algorithm using Vector Quantization to classify vehicle magnetic features to four typical types of vehicles in Australian suburbs: sedan, VAN, truck, and bus. Experiments results show that our approach achieves a high level of accuracy for vehicle detection and classification. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=vehicle%20classification" title="vehicle classification">vehicle classification</a>, <a href="https://publications.waset.org/abstracts/search?q=signal%20processing" title=" signal processing"> signal processing</a>, <a href="https://publications.waset.org/abstracts/search?q=road%20traffic%20model" title=" road traffic model"> road traffic model</a>, <a href="https://publications.waset.org/abstracts/search?q=magnetic%20sensing" title=" magnetic sensing"> magnetic sensing</a> </p> <a href="https://publications.waset.org/abstracts/86644/road-vehicle-recognition-using-magnetic-sensing-feature-extraction-and-classification" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/86644.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">320</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">18158</span> Field-Programmable Gate Array Based Tester for Protective Relay </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=H.%20Bentarzi">H. Bentarzi</a>, <a href="https://publications.waset.org/abstracts/search?q=A.%20Zitouni"> A. Zitouni</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The reliability of the power grid depends on the successful operation of thousands of protective relays. The failure of one relay to operate as intended may lead the entire power grid to blackout. In fact, major power system failures during transient disturbances may be caused by unnecessary protective relay tripping rather than by the failure of a relay to operate. Adequate relay testing provides a first defense against false trips of the relay and hence improves power grid stability and prevents catastrophic bulk power system failures. The goal of this research project is to design and enhance the relay tester using a technology such as Field Programmable Gate Array (FPGA) card NI 7851. A PC based tester framework has been developed using Simulink power system model for generating signals under different conditions (faults or transient disturbances) and LabVIEW for developing the graphical user interface and configuring the FPGA. Besides, the interface system has been developed for outputting and amplifying the signals without distortion. These signals should be like the generated ones by the real power system and large enough for testing the relay’s functionality. The signals generated that have been displayed on the scope are satisfactory. Furthermore, the proposed testing system can be used for improving the performance of protective relay. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=amplifier%20class%20D" title="amplifier class D">amplifier class D</a>, <a href="https://publications.waset.org/abstracts/search?q=field-programmable%20gate%20array%20%28FPGA%29" title=" field-programmable gate array (FPGA)"> field-programmable gate array (FPGA)</a>, <a href="https://publications.waset.org/abstracts/search?q=protective%20relay" title=" protective relay"> protective relay</a>, <a href="https://publications.waset.org/abstracts/search?q=tester" title=" tester"> tester</a> </p> <a href="https://publications.waset.org/abstracts/74115/field-programmable-gate-array-based-tester-for-protective-relay" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/74115.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">216</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">18157</span> Theoretical BER Analyzing of MPSK Signals Based on the Signal Space</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Jing%20Qing-feng">Jing Qing-feng</a>, <a href="https://publications.waset.org/abstracts/search?q=Liu%20Danmei"> Liu Danmei</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Based on the optimum detection, signal projection and Maximum A Posteriori (MAP) rule, Proakis has deduced the theoretical BER equation of Gray coded MPSK signals. Proakis analyzed the BER theoretical equations mainly based on the projection of signals, which is difficult to be understood. This article solve the same problem based on the signal space, which explains the vectors relations among the sending signals, received signals and noises. The more explicit and easy-deduced process is illustrated in this article based on the signal space, which can illustrated the relations among the signals and noises clearly. This kind of deduction has a univocal geometry meaning. It can explain the correlation between the production and calculation of BER in vector level. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=MPSK" title="MPSK">MPSK</a>, <a href="https://publications.waset.org/abstracts/search?q=MAP" title=" MAP"> MAP</a>, <a href="https://publications.waset.org/abstracts/search?q=signal%20space" title=" signal space"> signal space</a>, <a href="https://publications.waset.org/abstracts/search?q=BER" title="BER">BER</a> </p> <a href="https://publications.waset.org/abstracts/45896/theoretical-ber-analyzing-of-mpsk-signals-based-on-the-signal-space" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/45896.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">346</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">18156</span> Signals Monitored during Anaesthesia</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Launcelot.McGrath">Launcelot.McGrath</a> </p> <p class="card-text"><strong>Abstract:</strong></p> A comprehensive understanding of physiological data is a vital aid to the anaesthesiologist in monitoring and maintaining the well-being of a patient undergoing surgery. Biosignal analysis is one of the most important topics that researchers have tried to develop over the last century to understand numerous human diseases. Understanding which biological signals are most important during anaesthesia is critically important. It is important that the anaesthesiologist understand both the signals themselves and the limitations introduced by the processes of acquisition. In this article, we provide an overview of different types of biological signals as well as the mechanisms applied to acquire them. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=general%20biosignals" title="general biosignals">general biosignals</a>, <a href="https://publications.waset.org/abstracts/search?q=anaesthesia" title=" anaesthesia"> anaesthesia</a>, <a href="https://publications.waset.org/abstracts/search?q=biological" title=" biological"> biological</a>, <a href="https://publications.waset.org/abstracts/search?q=electroencephalogram" title=" electroencephalogram"> electroencephalogram</a> </p> <a href="https://publications.waset.org/abstracts/158537/signals-monitored-during-anaesthesia" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/158537.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">146</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">18155</span> Signals Monitored During Anaesthesia</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Launcelot%20McGrath">Launcelot McGrath</a>, <a href="https://publications.waset.org/abstracts/search?q=Xiaoxiao%20Liu"> Xiaoxiao Liu</a>, <a href="https://publications.waset.org/abstracts/search?q=Colin%20Flanagan"> Colin Flanagan</a> </p> <p class="card-text"><strong>Abstract:</strong></p> It is widely recognised that a comprehensive understanding of physiological data is a vital aid to the anaesthesiologist in monitoring and maintaining the well-being of a patient undergoing surgery. Bio signal analysis is one of the most important topics that researchers have tried to develop over the last century to understand numerous human diseases. There are tremendous biological signals during anaesthesia, and not all of them are important, which to choose to observe is a significant decision. It is important that the anaesthesiologist understand both the signals themselves, and the limitations introduced by the processes of acquisition. In this article, we provide an all-sided overview of different types of biological signals as well as the mechanisms applied to acquire them. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=general%20biosignals" title="general biosignals">general biosignals</a>, <a href="https://publications.waset.org/abstracts/search?q=anaesthesia" title=" anaesthesia"> anaesthesia</a>, <a href="https://publications.waset.org/abstracts/search?q=biological" title=" biological"> biological</a>, <a href="https://publications.waset.org/abstracts/search?q=electroencephalogram" title=" electroencephalogram"> electroencephalogram</a> </p> <a href="https://publications.waset.org/abstracts/157332/signals-monitored-during-anaesthesia" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/157332.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">105</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">18154</span> Motion Detection Method for Clutter Rejection in the Bio-Radar Signal Processing</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Carolina%20Gouveia">Carolina Gouveia</a>, <a href="https://publications.waset.org/abstracts/search?q=Jos%C3%A9%20Vieira"> José Vieira</a>, <a href="https://publications.waset.org/abstracts/search?q=Pedro%20Pinho"> Pedro Pinho</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The cardiopulmonary signal monitoring, without the usage of contact electrodes or any type of in-body sensors, has several applications such as sleeping monitoring and continuous monitoring of vital signals in bedridden patients. This system has also applications in the vehicular environment to monitor the driver, in order to avoid any possible accident in case of cardiac failure. Thus, the bio-radar system proposed in this paper, can measure vital signals accurately by using the Doppler effect principle that relates the received signal properties with the distance change between the radar antennas and the person’s chest-wall. Once the bio-radar aim is to monitor subjects in real-time and during long periods of time, it is impossible to guarantee the patient immobilization, hence their random motion will interfere in the acquired signals. In this paper, a mathematical model of the bio-radar is presented, as well as its simulation in MATLAB. The used algorithm for breath rate extraction is explained and a method for DC offsets removal based in a motion detection system is proposed. Furthermore, experimental tests were conducted with a view to prove that the unavoidable random motion can be used to estimate the DC offsets accurately and thus remove them successfully. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=bio-signals" title="bio-signals">bio-signals</a>, <a href="https://publications.waset.org/abstracts/search?q=DC%20component" title=" DC component"> DC component</a>, <a href="https://publications.waset.org/abstracts/search?q=Doppler%20effect" title=" Doppler effect"> Doppler effect</a>, <a href="https://publications.waset.org/abstracts/search?q=ellipse%20fitting" title=" ellipse fitting"> ellipse fitting</a>, <a href="https://publications.waset.org/abstracts/search?q=radar" title=" radar"> radar</a>, <a href="https://publications.waset.org/abstracts/search?q=SDR" title=" SDR"> SDR</a> </p> <a href="https://publications.waset.org/abstracts/95280/motion-detection-method-for-clutter-rejection-in-the-bio-radar-signal-processing" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/95280.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">140</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">18153</span> Low Cost Surface Electromyographic Signal Amplifier Based on Arduino Microcontroller</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Igor%20Luiz%20Bernardes%20de%20Moura">Igor Luiz Bernardes de Moura</a>, <a href="https://publications.waset.org/abstracts/search?q=Luan%20Carlos%20de%20Sena%20Monteiro%20Ozelim"> Luan Carlos de Sena Monteiro Ozelim</a>, <a href="https://publications.waset.org/abstracts/search?q=Fabiano%20Araujo%20Soares"> Fabiano Araujo Soares</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The development of a low cost acquisition system of S-EMG signals which are reliable, comfortable for the user and with high mobility shows to be a relevant proposition in modern biomedical engineering scenario. In the study, the sampling capacity of the Arduino microcontroller Atmel Atmega328 with an A/D converter with 10-bit resolution and its reconstructing capability of a signal of surface electromyography are analyzed. An electronic circuit to capture the signal through two differential channels was designed, signals from Biceps Brachialis of a healthy man of 21 years was acquired to test the system prototype. ARV, MDF, MNF and RMS estimators were used to compare de acquired signals with physiological values. The Arduino was configured with a sampling frequency of 1.5 kHz for each channel, and the tests with the circuit designed offered a SNR of 20.57dB. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=electromyography" title="electromyography">electromyography</a>, <a href="https://publications.waset.org/abstracts/search?q=Arduino" title=" Arduino"> Arduino</a>, <a href="https://publications.waset.org/abstracts/search?q=low-cost" title=" low-cost"> low-cost</a>, <a href="https://publications.waset.org/abstracts/search?q=atmel%20atmega328%20microcontroller" title=" atmel atmega328 microcontroller"> atmel atmega328 microcontroller</a> </p> <a href="https://publications.waset.org/abstracts/5919/low-cost-surface-electromyographic-signal-amplifier-based-on-arduino-microcontroller" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/5919.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">366</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">18152</span> Electroencephalogram Signals Controlling a Parallax Boe-Bot Robot </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Nema%20M.%20Salem">Nema M. Salem</a>, <a href="https://publications.waset.org/abstracts/search?q=Hanan%20A.%20Altukhaifi"> Hanan A. Altukhaifi</a>, <a href="https://publications.waset.org/abstracts/search?q=Amal%20Mukhtar"> Amal Mukhtar</a>, <a href="https://publications.waset.org/abstracts/search?q=Reemaz%20K.%20Hetaimish"> Reemaz K. Hetaimish</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Recently, BCI field of research has gained a lot of interest. Apart from motor neuroprosthetics, many studies showed the possibility of controlling a virtual environment of a videogame using the acquired electroencephalogram signals (EEG) from the gamer. In addition, another study had successfully moved a farm tractor using the human’s EEG signals. This article utilizes the use of EEG signals, as a source of technology, in controlling a Parallax Boe-Bot robot. The commercial Emotive Epoc headset has been used in acquiring the EEG signals from rested subjects. Because the human's visual cortex can successfully differentiate between different colors, the red and green colors are used as visual stimuli for generating EEG signals using the Epoc. Arduino and Labview are used to translate the virtually pressed keys into instructions controlling the motion and rotation of the robot. Optimistic results have been achieved except for minor delay and accuracy in the robot’s response. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=BCI" title="BCI">BCI</a>, <a href="https://publications.waset.org/abstracts/search?q=Emotiv%20Epoc%20headset" title=" Emotiv Epoc headset"> Emotiv Epoc headset</a>, <a href="https://publications.waset.org/abstracts/search?q=EEG" title=" EEG"> EEG</a>, <a href="https://publications.waset.org/abstracts/search?q=Labview" title=" Labview"> Labview</a>, <a href="https://publications.waset.org/abstracts/search?q=Arduino%20applications" title=" Arduino applications"> Arduino applications</a>, <a href="https://publications.waset.org/abstracts/search?q=robot" title=" robot"> robot</a> </p> <a href="https://publications.waset.org/abstracts/19505/electroencephalogram-signals-controlling-a-parallax-boe-bot-robot" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/19505.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">522</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">18151</span> The Analysis of Brain Response to Auditory Stimuli through EEG Signals’ Non-Linear Analysis</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=H.%20Namazi">H. Namazi</a>, <a href="https://publications.waset.org/abstracts/search?q=H.%20T.%20N.%20Kuan"> H. T. N. Kuan</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Brain activity can be measured by acquiring and analyzing EEG signals from an individual. In fact, the human brain response to external and internal stimuli is mapped in his EEG signals. During years some methods such as Fourier transform, wavelet transform, empirical mode decomposition, etc. have been used to analyze the EEG signals in order to find the effect of stimuli, especially external stimuli. But each of these methods has some weak points in analysis of EEG signals. For instance, Fourier transform and wavelet transform methods are linear signal analysis methods which are not good to be used for analysis of EEG signals as nonlinear signals. In this research we analyze the brain response to auditory stimuli by extracting information in the form of various measures from EEG signals using a software developed by our research group. The used measures are Jeﬀrey’s measure, Fractal dimension and Hurst exponent. The results of these analyses are useful not only for fundamental understanding of brain response to auditory stimuli but provide us with very good recommendations for clinical purposes. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=auditory%20stimuli" title="auditory stimuli">auditory stimuli</a>, <a href="https://publications.waset.org/abstracts/search?q=brain%20response" title=" brain response"> brain response</a>, <a href="https://publications.waset.org/abstracts/search?q=EEG%20signal" title=" EEG signal"> EEG signal</a>, <a href="https://publications.waset.org/abstracts/search?q=fractal%20dimension" title=" fractal dimension"> fractal dimension</a>, <a href="https://publications.waset.org/abstracts/search?q=hurst%20exponent" title=" hurst exponent"> hurst exponent</a>, <a href="https://publications.waset.org/abstracts/search?q=Je%EF%AC%80rey%E2%80%99s%20measure" title=" Jeﬀrey’s measure"> Jeﬀrey’s measure</a> </p> <a href="https://publications.waset.org/abstracts/18990/the-analysis-of-brain-response-to-auditory-stimuli-through-eeg-signals-non-linear-analysis" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/18990.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">534</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">18150</span> Wireless Based System for Continuous Electrocardiography Monitoring during Surgery</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=K.%20Bensafia">K. Bensafia</a>, <a href="https://publications.waset.org/abstracts/search?q=A.%20Mansour"> A. Mansour</a>, <a href="https://publications.waset.org/abstracts/search?q=G.%20Le%20Maillot"> G. Le Maillot</a>, <a href="https://publications.waset.org/abstracts/search?q=B.%20Clement"> B. Clement</a>, <a href="https://publications.waset.org/abstracts/search?q=O.%20Reynet"> O. Reynet</a>, <a href="https://publications.waset.org/abstracts/search?q=P.%20Ari%C3%A8s"> P. Ariès</a>, <a href="https://publications.waset.org/abstracts/search?q=S.%20Haddab"> S. Haddab</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper presents a system designed for wireless acquisition, the recording of electrocardiogram (ECG) signals and the monitoring of the heart’s health during surgery. This wireless recording system allows us to visualize and monitor the state of the heart’s health during a surgery, even if the patient is moved from the operating theater to post anesthesia care unit. The acquired signal is transmitted via a Bluetooth unit to a PC where the data are displayed, stored and processed. To test the reliability of our system, a comparison between ECG signals processed by a conventional ECG monitoring system (Datex-Ohmeda) and by our wireless system is made. The comparison is based on the shape of the ECG signal, the duration of the QRS complex, the P and T waves, as well as the position of the ST segments with respect to the isoelectric line. The proposed system is presented and discussed. The results have confirmed that the use of Bluetooth during surgery does not affect the devices used and vice versa. Pre- and post-processing steps are briefly discussed. Experimental results are also provided. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=electrocardiography" title="electrocardiography">electrocardiography</a>, <a href="https://publications.waset.org/abstracts/search?q=monitoring" title=" monitoring"> monitoring</a>, <a href="https://publications.waset.org/abstracts/search?q=surgery" title=" surgery"> surgery</a>, <a href="https://publications.waset.org/abstracts/search?q=wireless%20system" title=" wireless system"> wireless system</a> </p> <a href="https://publications.waset.org/abstracts/79677/wireless-based-system-for-continuous-electrocardiography-monitoring-during-surgery" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/79677.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">370</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">18149</span> Analysis of Brain Signals Using Neural Networks Optimized by Co-Evolution Algorithms</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Zahra%20Abdolkarimi">Zahra Abdolkarimi</a>, <a href="https://publications.waset.org/abstracts/search?q=Naser%20Zourikalatehsamad"> Naser Zourikalatehsamad</a>, <a href="https://publications.waset.org/abstracts/search?q="></a> </p> <p class="card-text"><strong>Abstract:</strong></p> Up to 40 years ago, after recognition of epilepsy, it was generally believed that these attacks occurred randomly and suddenly. However, thanks to the advance of mathematics and engineering, such attacks can be predicted within a few minutes or hours. In this way, various algorithms for long-term prediction of the time and frequency of the first attack are presented. In this paper, by considering the nonlinear nature of brain signals and dynamic recorded brain signals, ANFIS model is presented to predict the brain signals, since according to physiologic structure of the onset of attacks, more complex neural structures can better model the signal during attacks. Contribution of this work is the co-evolution algorithm for optimization of ANFIS network parameters. Our objective is to predict brain signals based on time series obtained from brain signals of the people suffering from epilepsy using ANFIS. Results reveal that compared to other methods, this method has less sensitivity to uncertainties such as presence of noise and interruption in recorded signals of the brain as well as more accuracy. Long-term prediction capacity of the model illustrates the usage of planted systems for warning medication and preventing brain signals. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=co-evolution%20algorithms" title="co-evolution algorithms">co-evolution algorithms</a>, <a href="https://publications.waset.org/abstracts/search?q=brain%20signals" title=" brain signals"> brain signals</a>, <a href="https://publications.waset.org/abstracts/search?q=time%20series" title=" time series"> time series</a>, <a href="https://publications.waset.org/abstracts/search?q=neural%20networks" title=" neural networks"> neural networks</a>, <a href="https://publications.waset.org/abstracts/search?q=ANFIS%20model" title=" ANFIS model"> ANFIS model</a>, <a href="https://publications.waset.org/abstracts/search?q=physiologic%20structure" title=" physiologic structure"> physiologic structure</a>, <a href="https://publications.waset.org/abstracts/search?q=time%20prediction" title=" time prediction"> time prediction</a>, <a href="https://publications.waset.org/abstracts/search?q=epilepsy%20suffering" title=" epilepsy suffering"> epilepsy suffering</a>, <a href="https://publications.waset.org/abstracts/search?q=illustrates%20model" title=" illustrates model "> illustrates model </a> </p> <a href="https://publications.waset.org/abstracts/44734/analysis-of-brain-signals-using-neural-networks-optimized-by-co-evolution-algorithms" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/44734.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">282</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">18148</span> Roasting Degree of Cocoa Beans by Artificial Neural Network (ANN) Based Electronic Nose System and Gas Chromatography (GC)</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Juzhong%20Tan">Juzhong Tan</a>, <a href="https://publications.waset.org/abstracts/search?q=William%20Kerr"> William Kerr</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Roasting is one critical procedure in chocolate processing, where special favors are developed, moisture content is decreased, and better processing properties are developed. Therefore, determination of roasting degree of cocoa bean is important for chocolate manufacturers to ensure the quality of chocolate products, and it also decides the commercial value of cocoa beans collected from cocoa farmers. The roasting degree of cocoa beans currently relies on human specialists, who sometimes are biased, and chemical analysis, which take long time and are inaccessible to many manufacturers and farmers. In this study, a self-made electronic nose system consists of gas sensors (TGS 800 and 2000 series) was used to detecting the gas generated by cocoa beans with a different roasting degree (0min, 20min, 30min, and 40min) and the signals collected by gas sensors were used to train a three-layers ANN. Chemical analysis of the graded beans was operated by traditional GC-MS system and the contents of volatile chemical compounds were used to train another ANN as a reference to electronic nosed signals trained ANN. Both trained ANN were used to predict cocoa beans with a different roasting degree for validation. The best accuracy of grading achieved by electronic nose signals trained ANN (using signals from TGS 813 826 820 880 830 2620 2602 2610) turned out to be 96.7%, however, the GC trained ANN got the accuracy of 83.8%. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=artificial%20neutron%20network" title="artificial neutron network">artificial neutron network</a>, <a href="https://publications.waset.org/abstracts/search?q=cocoa%20bean" title=" cocoa bean"> cocoa bean</a>, <a href="https://publications.waset.org/abstracts/search?q=electronic%20nose" title=" electronic nose"> electronic nose</a>, <a href="https://publications.waset.org/abstracts/search?q=roasting" title=" roasting"> roasting</a> </p> <a href="https://publications.waset.org/abstracts/60042/roasting-degree-of-cocoa-beans-by-artificial-neural-network-ann-based-electronic-nose-system-and-gas-chromatography-gc" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/60042.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">234</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">18147</span> Application of the Bionic Wavelet Transform and Psycho-Acoustic Model for Speech Compression </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Chafik%20Barnoussi">Chafik Barnoussi</a>, <a href="https://publications.waset.org/abstracts/search?q=Mourad%20Talbi"> Mourad Talbi</a>, <a href="https://publications.waset.org/abstracts/search?q=Adnane%20Cherif"> Adnane Cherif</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper we propose a new speech compression system based on the application of the Bionic Wavelet Transform (BWT) combined with the psychoacoustic model. This compression system is a modified version of the compression system using a MDCT (Modified Discrete Cosine Transform) filter banks of 32 filters each and the psychoacoustic model. This modification consists in replacing the banks of the MDCT filter banks by the bionic wavelet coefficients which are obtained from the application of the BWT to the speech signal to be compressed. These two methods are evaluated and compared with each other by computing bits before and bits after compression. They are tested on different speech signals and the obtained simulation results show that the proposed technique outperforms the second technique and this in term of compressed file size. In term of SNR, PSNR and NRMSE, the outputs speech signals of the proposed compression system are with acceptable quality. In term of PESQ and speech signal intelligibility, the proposed speech compression technique permits to obtain reconstructed speech signals with good quality. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=speech%20compression" title="speech compression">speech compression</a>, <a href="https://publications.waset.org/abstracts/search?q=bionic%20wavelet%20transform" title=" bionic wavelet transform"> bionic wavelet transform</a>, <a href="https://publications.waset.org/abstracts/search?q=filterbanks" title=" filterbanks"> filterbanks</a>, <a href="https://publications.waset.org/abstracts/search?q=psychoacoustic%20model" title=" psychoacoustic model"> psychoacoustic model</a> </p> <a href="https://publications.waset.org/abstracts/1921/application-of-the-bionic-wavelet-transform-and-psycho-acoustic-model-for-speech-compression" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/1921.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">384</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">18146</span> Imaging Based On Bi-Static SAR Using GPS L5 Signal</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Tahir%20Saleem">Tahir Saleem</a>, <a href="https://publications.waset.org/abstracts/search?q=Mohammad%20Usman"> Mohammad Usman</a>, <a href="https://publications.waset.org/abstracts/search?q=Nadeem%20Khan"> Nadeem Khan</a> </p> <p class="card-text"><strong>Abstract:</strong></p> GPS signals are used for navigation and positioning purposes by a diverse set of users. However, this project intends to utilize the reflected GPS L5 signals for location of target in a region of interest by generating an image that highlights the positions of targets in the area of interest. The principle of bi-static radar is used to detect the targets or any movement or changes. The idea is confirmed by the results obtained during MATLAB simulations. A matched filter based technique is employed in the signal processing to improve the system resolution. The simulation is carried out under different conditions with moving receiver and targets. Noise and attenuation is also induced and atmospheric conditions that affect the direct and reflected GPS signals have been simulated to generate a more practical scenario. A realistic GPS L5 signal has been simulated, the simulation results verify that the detection and imaging of targets is possible by employing reflected GPS using L5 signals and matched filter processing technique with acceptable spatial resolution. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=GPS" title="GPS">GPS</a>, <a href="https://publications.waset.org/abstracts/search?q=L5%20Signal" title=" L5 Signal"> L5 Signal</a>, <a href="https://publications.waset.org/abstracts/search?q=SAR" title=" SAR"> SAR</a>, <a href="https://publications.waset.org/abstracts/search?q=spatial%20resolution" title=" spatial resolution"> spatial resolution</a> </p> <a href="https://publications.waset.org/abstracts/23371/imaging-based-on-bi-static-sar-using-gps-l5-signal" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/23371.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">534</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">18145</span> The Non-Linear Analysis of Brain Response to Visual Stimuli</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=H.%20Namazi">H. Namazi</a>, <a href="https://publications.waset.org/abstracts/search?q=H.%20T.%20N.%20Kuan"> H. T. N. Kuan</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Brain activity can be measured by acquiring and analyzing EEG signals from an individual. In fact, the human brain response to external and internal stimuli is mapped in his EEG signals. During years some methods such as Fourier transform, wavelet transform, empirical mode decomposition, etc. have been used to analyze the EEG signals in order to find the effect of stimuli, especially external stimuli. But each of these methods has some weak points in analysis of EEG signals. For instance, Fourier transform and wavelet transform methods are linear signal analysis methods which are not good to be used for analysis of EEG signals as nonlinear signals. In this research we analyze the brain response to visual stimuli by extracting information in the form of various measures from EEG signals using a software developed by our research group. The used measures are Jeﬀrey’s measure, Fractal dimension and Hurst exponent. The results of these analyses are useful not only for fundamental understanding of brain response to visual stimuli but provide us with very good recommendations for clinical purposes. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=visual%20stimuli" title="visual stimuli">visual stimuli</a>, <a href="https://publications.waset.org/abstracts/search?q=brain%20response" title=" brain response"> brain response</a>, <a href="https://publications.waset.org/abstracts/search?q=EEG%20signal" title=" EEG signal"> EEG signal</a>, <a href="https://publications.waset.org/abstracts/search?q=fractal%20dimension" title=" fractal dimension"> fractal dimension</a>, <a href="https://publications.waset.org/abstracts/search?q=hurst%20exponent" title=" hurst exponent"> hurst exponent</a>, <a href="https://publications.waset.org/abstracts/search?q=Je%EF%AC%80rey%E2%80%99s%20measure" title=" Jeﬀrey’s measure"> Jeﬀrey’s measure</a> </p> <a href="https://publications.waset.org/abstracts/19758/the-non-linear-analysis-of-brain-response-to-visual-stimuli" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/19758.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">561</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">18144</span> Experimental Investigations to Measure Surface Fatigue Wear in Journal Bearing by Using Vibration Signal Analysis</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Amarnath%20M.">Amarnath M.</a>, <a href="https://publications.waset.org/abstracts/search?q=Ramachandra%20C.%20G."> Ramachandra C. G.</a>, <a href="https://publications.waset.org/abstracts/search?q=H.%20Chelladurai"> H. Chelladurai</a>, <a href="https://publications.waset.org/abstracts/search?q=P..Sateesh%20Kumar"> P..Sateesh Kumar</a>, <a href="https://publications.waset.org/abstracts/search?q=K.%20Santhosh%20Kumar"> K. Santhosh Kumar</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Journal bearings are extensively used sliding contact machine elements to support radial/axial loaded rotors used in various applications viz. automobile crankshaft, turbine propeller shaft, rope conveyer, heavy duty electric motors. The primary reasons for the failures of these bearings include unstable lubricant film, oil degradation, misalignment, etc. This paper describes the results of experimental investigations carried out to detect surface fatigue wear developed on load bearing the contact surfaces of journal bearing. The test bearing was subjected to fatigue load cycles over a period of 600 hours. The vibration signals were acquired from the journal bearing at regular intervals of 100 hrs. These signals were post-processed by using the vibration analysis technique to obtain diagnostic information of wear propagated in the journal-bearing system. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=fatigue" title="fatigue">fatigue</a>, <a href="https://publications.waset.org/abstracts/search?q=journal%20bearing" title=" journal bearing"> journal bearing</a>, <a href="https://publications.waset.org/abstracts/search?q=sound%20signals" title=" sound signals"> sound signals</a>, <a href="https://publications.waset.org/abstracts/search?q=vibration%20signals" title=" vibration signals"> vibration signals</a>, <a href="https://publications.waset.org/abstracts/search?q=wear" title=" wear"> wear</a> </p> <a href="https://publications.waset.org/abstracts/180321/experimental-investigations-to-measure-surface-fatigue-wear-in-journal-bearing-by-using-vibration-signal-analysis" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/180321.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">81</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">18143</span> Signals Affecting Crowdfunding Success for Australian Social Enterprises</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mai%20Yen%20Nhi%20Doan">Mai Yen Nhi Doan</a>, <a href="https://publications.waset.org/abstracts/search?q=Viet%20Le"> Viet Le</a>, <a href="https://publications.waset.org/abstracts/search?q=Chamindika%20Weerakoon"> Chamindika Weerakoon</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Social enterprises have emerged as sustainable organisations that deliver social achievement along with long-term financial advancement. However, recorded financial barriers have urged social enterprises to divert to other financing methods due to the misaligned ideology with traditional financing capitalists, in which crowdfunding can be a promising alternative. Previous studies in crowdfunding have inadequately addressed crowdfunding for social enterprises, with conflicting results due to the unsuitable analysis of signals in isolation rather than in combinations, using the data from platforms that do not support social enterprises. Extending the signalling theory, this study suggests that crowdfunding success results from the collaboration between costly and costless signals. The proposed conceptual framework enlightens the interaction between costly signals as “organisational information”, “social entrepreneur’s credibility,” and “third-party endorsement” and costless signals as various sub-signals under the “campaign preparedness” signal to achieve crowdfunding success. Using Qualitative Comparative Analysis, this study examined 45 crowdfunding campaigns run by Australian social enterprises on StartSomeGood and Chuffed. The analysis found that different combinations of costly and costless signals can lead to crowdfunding success, allowing social enterprises to adopt suitable combinations of signals to their context. Costless signal – campaign preparedness is fundamental for success, though different costless sub-signals under campaign preparedness can interact with different costly signals for the desired outcome. Third-party endorsement signal was found to be the necessary signal for crowdfunding success for Australian social enterprises. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=crowdfunding" title="crowdfunding">crowdfunding</a>, <a href="https://publications.waset.org/abstracts/search?q=qualitative%20comparative%20analysis%20%28QCA%29" title=" qualitative comparative analysis (QCA)"> qualitative comparative analysis (QCA)</a>, <a href="https://publications.waset.org/abstracts/search?q=signalling%20theory" title=" signalling theory"> signalling theory</a>, <a href="https://publications.waset.org/abstracts/search?q=social%20enterprises" title=" social enterprises"> social enterprises</a> </p> <a href="https://publications.waset.org/abstracts/165858/signals-affecting-crowdfunding-success-for-australian-social-enterprises" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/165858.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">103</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">18142</span> Denoising of Magnetotelluric Signals by Filtering </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Rodrigo%20Montufar-Chaveznava">Rodrigo Montufar-Chaveznava</a>, <a href="https://publications.waset.org/abstracts/search?q=Fernando%20Brambila-Paz"> Fernando Brambila-Paz</a>, <a href="https://publications.waset.org/abstracts/search?q=Ivette%20Caldelas"> Ivette Caldelas</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, we present the advances corresponding to the denoising processing of magnetotelluric signals using several filters. In particular, we use the most common spatial domain filters such as median and mean, but we are also using the Fourier and wavelet transform for frequency domain filtering. We employ three datasets obtained at the different sampling rate (128, 4096 and 8192 bps) and evaluate the mean square error, signal-to-noise relation, and peak signal-to-noise relation to compare the kernels and determine the most suitable for each case. The magnetotelluric signals correspond to earth exploration when water is searched. The object is to find a denoising strategy different to the one included in the commercial equipment that is employed in this task. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=denoising" title="denoising">denoising</a>, <a href="https://publications.waset.org/abstracts/search?q=filtering" title=" filtering"> filtering</a>, <a href="https://publications.waset.org/abstracts/search?q=magnetotelluric%20signals" title=" magnetotelluric signals"> magnetotelluric signals</a>, <a href="https://publications.waset.org/abstracts/search?q=wavelet%20transform" title=" wavelet transform"> wavelet transform</a> </p> <a href="https://publications.waset.org/abstracts/91383/denoising-of-magnetotelluric-signals-by-filtering" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/91383.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right 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