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data processing</title> <meta name="description" content="Search results for: real time data processing"> <meta name="keywords" content="real time data processing"> <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 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action="https://publications.waset.org/abstracts/search"> <div 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="real time data processing"> <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> 40592</div> </div> </div> </div> <h1 class="mt-3 mb-3 text-center" style="font-size:1.6rem;">Search results for: real time data processing</h1> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">40592</span> Robust Barcode Detection with Synthetic-to-Real Data Augmentation</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Xiaoyan%20Dai">Xiaoyan Dai</a>, <a href="https://publications.waset.org/abstracts/search?q=Hsieh%20Yisan"> Hsieh Yisan</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Barcode processing of captured images is a huge challenge, as different shooting conditions can result in different barcode appearances. This paper proposes a deep learning-based barcode detection using synthetic-to-real data augmentation. We first augment barcodes themselves; we then augment images containing the barcodes to generate a large variety of data that is close to the actual shooting environments. Comparisons with previous works and evaluations with our original data show that this approach achieves state-of-the-art performance in various real images. In addition, the system uses hybrid resolution for barcode “scan” and is applicable to real-time applications. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=barcode%20detection" title="barcode detection">barcode detection</a>, <a href="https://publications.waset.org/abstracts/search?q=data%20augmentation" title=" data augmentation"> data augmentation</a>, <a href="https://publications.waset.org/abstracts/search?q=deep%20learning" title=" deep learning"> deep learning</a>, <a href="https://publications.waset.org/abstracts/search?q=image-based%20processing" title=" image-based processing"> image-based processing</a> </p> <a href="https://publications.waset.org/abstracts/153243/robust-barcode-detection-with-synthetic-to-real-data-augmentation" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/153243.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">169</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">40591</span> Design and Development of a Platform for Analyzing Spatio-Temporal Data from Wireless Sensor Networks</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Walid%20Fantazi">Walid Fantazi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The development of sensor technology (such as microelectromechanical systems (MEMS), wireless communications, embedded systems, distributed processing and wireless sensor applications) has contributed to a broad range of WSN applications which are capable of collecting a large amount of spatiotemporal data in real time. These systems require real-time data processing to manage storage in real time and query the data they process. In order to cover these needs, we propose in this paper a Snapshot spatiotemporal data model based on object-oriented concepts. This model allows saving storing and reducing data redundancy which makes it easier to execute spatiotemporal queries and save analyzes time. Further, to ensure the robustness of the system as well as the elimination of congestion from the main access memory we propose a spatiotemporal indexing technique in RAM called Captree *. As a result, we offer an RIA (Rich Internet Application) -based SOA application architecture which allows the remote monitoring and control. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=WSN" title="WSN">WSN</a>, <a href="https://publications.waset.org/abstracts/search?q=indexing%20data" title=" indexing data"> indexing data</a>, <a href="https://publications.waset.org/abstracts/search?q=SOA" title=" SOA"> SOA</a>, <a href="https://publications.waset.org/abstracts/search?q=RIA" title=" RIA"> RIA</a>, <a href="https://publications.waset.org/abstracts/search?q=geographic%20information%20system" title=" geographic information system "> geographic information system </a> </p> <a href="https://publications.waset.org/abstracts/88946/design-and-development-of-a-platform-for-analyzing-spatio-temporal-data-from-wireless-sensor-networks" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/88946.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">254</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">40590</span> Real-Time Visualization Using GPU-Accelerated Filtering of LiDAR Data</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Sa%C5%A1o%20Pe%C4%8Dnik">Sašo Pečnik</a>, <a href="https://publications.waset.org/abstracts/search?q=Borut%20%C5%BDalik"> Borut Žalik</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper presents a real-time visualization technique and filtering of classified LiDAR point clouds. The visualization is capable of displaying filtered information organized in layers by the classification attribute saved within LiDAR data sets. We explain the used data structure and data management, which enables real-time presentation of layered LiDAR data. Real-time visualization is achieved with LOD optimization based on the distance from the observer without loss of quality. The filtering process is done in two steps and is entirely executed on the GPU and implemented using programmable shaders. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=filtering" title="filtering">filtering</a>, <a href="https://publications.waset.org/abstracts/search?q=graphics" title=" graphics"> graphics</a>, <a href="https://publications.waset.org/abstracts/search?q=level-of-details" title=" level-of-details"> level-of-details</a>, <a href="https://publications.waset.org/abstracts/search?q=LiDAR" title=" LiDAR"> LiDAR</a>, <a href="https://publications.waset.org/abstracts/search?q=real-time%20visualization" title=" real-time visualization"> real-time visualization</a> </p> <a href="https://publications.waset.org/abstracts/16857/real-time-visualization-using-gpu-accelerated-filtering-of-lidar-data" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/16857.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">308</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">40589</span> Agile Real-Time Field Programmable Gate Array-Based Image Processing System for Drone Imagery in Digital Agriculture</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Sabiha%20Shahid%20Antora">Sabiha Shahid Antora</a>, <a href="https://publications.waset.org/abstracts/search?q=Young%20Ki%20Chang"> Young Ki Chang</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Along with various farm management technologies, imagery is an important tool that facilitates crop assessment, monitoring, and management. As a consequence, drone imaging technology is playing a vital role to capture the state of the entire field for yield mapping, crop scouting, weed detection, and so on. Although it is essential to inspect the cultivable lands in real-time for making rapid decisions regarding field variable inputs to combat stresses and diseases, drone imagery is still evolving in this area of interest. Cost margin and post-processing complexions of the image stream are the main challenges of imaging technology. Therefore, this proposed project involves the cost-effective field programmable gate array (FPGA) based image processing device that would process the image stream in real-time as well as providing the processed output to support on-the-spot decisions in the crop field. As a result, the real-time FPGA-based image processing system would reduce operating costs while minimizing a few intermediate steps to deliver scalable field decisions. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=real-time" title="real-time">real-time</a>, <a href="https://publications.waset.org/abstracts/search?q=FPGA" title=" FPGA"> FPGA</a>, <a href="https://publications.waset.org/abstracts/search?q=drone%20imagery" title=" drone imagery"> drone imagery</a>, <a href="https://publications.waset.org/abstracts/search?q=image%20processing" title=" image processing"> image processing</a>, <a href="https://publications.waset.org/abstracts/search?q=crop%20monitoring" title=" crop monitoring"> crop monitoring</a> </p> <a href="https://publications.waset.org/abstracts/132611/agile-real-time-field-programmable-gate-array-based-image-processing-system-for-drone-imagery-in-digital-agriculture" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/132611.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">113</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">40588</span> Data-Mining Approach to Analyzing Industrial Process Information for Real-Time Monitoring</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Seung-Lock%20Seo">Seung-Lock Seo</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This work presents a data-mining empirical monitoring scheme for industrial processes with partially unbalanced data. Measurement data of good operations are relatively easy to gather, but in unusual special events or faults it is generally difficult to collect process information or almost impossible to analyze some noisy data of industrial processes. At this time some noise filtering techniques can be used to enhance process monitoring performance in a real-time basis. In addition, pre-processing of raw process data is helpful to eliminate unwanted variation of industrial process data. In this work, the performance of various monitoring schemes was tested and demonstrated for discrete batch process data. It showed that the monitoring performance was improved significantly in terms of monitoring success rate of given process faults. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=data%20mining" title="data mining">data mining</a>, <a href="https://publications.waset.org/abstracts/search?q=process%20data" title=" process data"> process data</a>, <a href="https://publications.waset.org/abstracts/search?q=monitoring" title=" monitoring"> monitoring</a>, <a href="https://publications.waset.org/abstracts/search?q=safety" title=" safety"> safety</a>, <a href="https://publications.waset.org/abstracts/search?q=industrial%20processes" title=" industrial processes"> industrial processes</a> </p> <a href="https://publications.waset.org/abstracts/3929/data-mining-approach-to-analyzing-industrial-process-information-for-real-time-monitoring" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/3929.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">401</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">40587</span> One Step Further: Pull-Process-Push Data Processing</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Romeo%20Botes">Romeo Botes</a>, <a href="https://publications.waset.org/abstracts/search?q=Imelda%20Smit"> Imelda Smit</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In today’s modern age of technology vast amounts of data needs to be processed in real-time to keep users satisfied. This data comes from various sources and in many formats, including electronic and mobile devices such as GPRS modems and GPS devices. They make use of different protocols including TCP, UDP, and HTTP/s for data communication to web servers and eventually to users. The data obtained from these devices may provide valuable information to users, but are mostly in an unreadable format which needs to be processed to provide information and business intelligence. This data is not always current, it is mostly historical data. The data is not subject to implementation of consistency and redundancy measures as most other data usually is. Most important to the users is that the data are to be pre-processed in a readable format when it is entered into the database. To accomplish this, programmers build processing programs and scripts to decode and process the information stored in databases. Programmers make use of various techniques in such programs to accomplish this, but sometimes neglect the effect some of these techniques may have on database performance. One of the techniques generally used,is to pull data from the database server, process it and push it back to the database server in one single step. Since the processing of the data usually takes some time, it keeps the database busy and locked for the period of time that the processing takes place. Because of this, it decreases the overall performance of the database server and therefore the system’s performance. This paper follows on a paper discussing the performance increase that may be achieved by utilizing array lists along with a pull-process-push data processing technique split in three steps. The purpose of this paper is to expand the number of clients when comparing the two techniques to establish the impact it may have on performance of the CPU storage and processing time. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=performance%20measures" title="performance measures">performance measures</a>, <a href="https://publications.waset.org/abstracts/search?q=algorithm%20techniques" title=" algorithm techniques"> algorithm techniques</a>, <a href="https://publications.waset.org/abstracts/search?q=data%20processing" title=" data processing"> data processing</a>, <a href="https://publications.waset.org/abstracts/search?q=push%20data" title=" push data"> push data</a>, <a href="https://publications.waset.org/abstracts/search?q=process%20data" title=" process data"> process data</a>, <a href="https://publications.waset.org/abstracts/search?q=array%20list" title=" array list"> array list</a> </p> <a href="https://publications.waset.org/abstracts/27763/one-step-further-pull-process-push-data-processing" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/27763.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">244</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">40586</span> Adaptive Data Approximations Codec (ADAC) for AI/ML-based Cyber-Physical Systems</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Yong-Kyu%20Jung">Yong-Kyu Jung</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The fast growth in information technology has led to de-mands to access/process data. CPSs heavily depend on the time of hardware/software operations and communication over the network (i.e., real-time/parallel operations in CPSs (e.g., autonomous vehicles). Since data processing is an im-portant means to overcome the issue confronting data management, reducing the gap between the technological-growth and the data-complexity and channel-bandwidth. An adaptive perpetual data approximation method is intro-duced to manage the actual entropy of the digital spectrum. An ADAC implemented as an accelerator and/or apps for servers/smart-connected devices adaptively rescales digital contents (avg.62.8%), data processing/access time/energy, encryption/decryption overheads in AI/ML applications (facial ID/recognition). <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=adaptive%20codec" title="adaptive codec">adaptive codec</a>, <a href="https://publications.waset.org/abstracts/search?q=AI" title=" AI"> AI</a>, <a href="https://publications.waset.org/abstracts/search?q=ML" title=" ML"> ML</a>, <a href="https://publications.waset.org/abstracts/search?q=HPC" title=" HPC"> HPC</a>, <a href="https://publications.waset.org/abstracts/search?q=cyber-physical" title=" cyber-physical"> cyber-physical</a>, <a href="https://publications.waset.org/abstracts/search?q=cybersecurity" title=" cybersecurity"> cybersecurity</a> </p> <a href="https://publications.waset.org/abstracts/160223/adaptive-data-approximations-codec-adac-for-aiml-based-cyber-physical-systems" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/160223.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">78</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">40585</span> Real-Time Data Stream Partitioning over a Sliding Window in Real-Time Spatial Big Data</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Sana%20Hamdi">Sana Hamdi</a>, <a href="https://publications.waset.org/abstracts/search?q=Emna%20Bouazizi"> Emna Bouazizi</a>, <a href="https://publications.waset.org/abstracts/search?q=Sami%20Faiz"> Sami Faiz</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In recent years, real-time spatial applications, like location-aware services and traffic monitoring, have become more and more important. Such applications result dynamic environments where data as well as queries are continuously moving. As a result, there is a tremendous amount of real-time spatial data generated every day. The growth of the data volume seems to outspeed the advance of our computing infrastructure. For instance, in real-time spatial Big Data, users expect to receive the results of each query within a short time period without holding in account the load of the system. But with a huge amount of real-time spatial data generated, the system performance degrades rapidly especially in overload situations. To solve this problem, we propose the use of data partitioning as an optimization technique. Traditional horizontal and vertical partitioning can increase the performance of the system and simplify data management. But they remain insufficient for real-time spatial Big data; they can’t deal with real-time and stream queries efficiently. Thus, in this paper, we propose a novel data partitioning approach for real-time spatial Big data named VPA-RTSBD (Vertical Partitioning Approach for Real-Time Spatial Big data). This contribution is an implementation of the Matching algorithm for traditional vertical partitioning. We find, firstly, the optimal attribute sequence by the use of Matching algorithm. Then, we propose a new cost model used for database partitioning, for keeping the data amount of each partition more balanced limit and for providing a parallel execution guarantees for the most frequent queries. VPA-RTSBD aims to obtain a real-time partitioning scheme and deals with stream data. It improves the performance of query execution by maximizing the degree of parallel execution. This affects QoS (Quality Of Service) improvement in real-time spatial Big Data especially with a huge volume of stream data. The performance of our contribution is evaluated via simulation experiments. The results show that the proposed algorithm is both efficient and scalable, and that it outperforms comparable algorithms. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=real-time%20spatial%20big%20data" title="real-time spatial big data">real-time spatial big data</a>, <a href="https://publications.waset.org/abstracts/search?q=quality%20of%20service" title=" quality of service"> quality of service</a>, <a href="https://publications.waset.org/abstracts/search?q=vertical%20partitioning" title=" vertical partitioning"> vertical partitioning</a>, <a href="https://publications.waset.org/abstracts/search?q=horizontal%20partitioning" title=" horizontal partitioning"> horizontal partitioning</a>, <a href="https://publications.waset.org/abstracts/search?q=matching%20algorithm" title=" matching algorithm"> matching algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=hamming%20distance" title=" hamming distance"> hamming distance</a>, <a href="https://publications.waset.org/abstracts/search?q=stream%20query" title=" stream query"> stream query</a> </p> <a href="https://publications.waset.org/abstracts/93904/real-time-data-stream-partitioning-over-a-sliding-window-in-real-time-spatial-big-data" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/93904.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">157</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">40584</span> Futuristic Black Box Design Considerations and Global Networking for Real Time Monitoring of Flight Performance Parameters</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=K.%20Parandhama%20Gowd">K. Parandhama Gowd</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The aim of this research paper is to conceptualize, discuss, analyze and propose alternate design methodologies for futuristic Black Box for flight safety. The proposal also includes global networking concepts for real time surveillance and monitoring of flight performance parameters including GPS parameters. It is expected that this proposal will serve as a failsafe real time diagnostic tool for accident investigation and location of debris in real time. In this paper, an attempt is made to improve the existing methods of flight data recording techniques and improve upon design considerations for futuristic FDR to overcome the trauma of not able to locate the block box. Since modern day communications and information technologies with large bandwidth are available coupled with faster computer processing techniques, the attempt made in this paper to develop a failsafe recording technique is feasible. Further data fusion/data warehousing technologies are available for exploitation. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=flight%20data%20recorder%20%28FDR%29" title="flight data recorder (FDR)">flight data recorder (FDR)</a>, <a href="https://publications.waset.org/abstracts/search?q=black%20box" title=" black box"> black box</a>, <a href="https://publications.waset.org/abstracts/search?q=diagnostic%20tool" title=" diagnostic tool"> diagnostic tool</a>, <a href="https://publications.waset.org/abstracts/search?q=global%20networking" title=" global networking"> global networking</a>, <a href="https://publications.waset.org/abstracts/search?q=cockpit%20voice%20and%20data%20recorder%20%28CVDR%29" title=" cockpit voice and data recorder (CVDR)"> cockpit voice and data recorder (CVDR)</a>, <a href="https://publications.waset.org/abstracts/search?q=air%20traffic%20control%20%28ATC%29" title=" air traffic control (ATC)"> air traffic control (ATC)</a>, <a href="https://publications.waset.org/abstracts/search?q=air%20traffic" title=" air traffic"> air traffic</a>, <a href="https://publications.waset.org/abstracts/search?q=telemetry" title=" telemetry"> telemetry</a>, <a href="https://publications.waset.org/abstracts/search?q=tracking%20and%20control%20centers%20ATTTCC%29" title=" tracking and control centers ATTTCC)"> tracking and control centers ATTTCC)</a> </p> <a href="https://publications.waset.org/abstracts/34485/futuristic-black-box-design-considerations-and-global-networking-for-real-time-monitoring-of-flight-performance-parameters" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/34485.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">572</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">40583</span> Using Deep Learning Real-Time Object Detection Convolution Neural Networks for Fast Fruit Recognition in the Tree</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=K.%20Bresilla">K. Bresilla</a>, <a href="https://publications.waset.org/abstracts/search?q=L.%20Manfrini"> L. Manfrini</a>, <a href="https://publications.waset.org/abstracts/search?q=B.%20Morandi"> B. Morandi</a>, <a href="https://publications.waset.org/abstracts/search?q=A.%20Boini"> A. Boini</a>, <a href="https://publications.waset.org/abstracts/search?q=G.%20Perulli"> G. Perulli</a>, <a href="https://publications.waset.org/abstracts/search?q=L.%20C.%20Grappadelli"> L. C. Grappadelli</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Image/video processing for fruit in the tree using hard-coded feature extraction algorithms have shown high accuracy during recent years. While accurate, these approaches even with high-end hardware are computationally intensive and too slow for real-time systems. This paper details the use of deep convolution neural networks (CNNs), specifically an algorithm (YOLO - You Only Look Once) with 24+2 convolution layers. Using deep-learning techniques eliminated the need for hard-code specific features for specific fruit shapes, color and/or other attributes. This CNN is trained on more than 5000 images of apple and pear fruits on 960 cores GPU (Graphical Processing Unit). Testing set showed an accuracy of 90%. After this, trained data were transferred to an embedded device (Raspberry Pi gen.3) with camera for more portability. Based on correlation between number of visible fruits or detected fruits on one frame and the real number of fruits on one tree, a model was created to accommodate this error rate. Speed of processing and detection of the whole platform was higher than 40 frames per second. This speed is fast enough for any grasping/harvesting robotic arm or other real-time applications. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=artificial%20intelligence" title="artificial intelligence">artificial intelligence</a>, <a href="https://publications.waset.org/abstracts/search?q=computer%20vision" title=" computer vision"> computer vision</a>, <a href="https://publications.waset.org/abstracts/search?q=deep%20learning" title=" deep learning"> deep learning</a>, <a href="https://publications.waset.org/abstracts/search?q=fruit%20recognition" title=" fruit recognition"> fruit recognition</a>, <a href="https://publications.waset.org/abstracts/search?q=harvesting%20robot" title=" harvesting robot"> harvesting robot</a>, <a href="https://publications.waset.org/abstracts/search?q=precision%20agriculture" title=" precision agriculture"> precision agriculture</a> </p> <a href="https://publications.waset.org/abstracts/79886/using-deep-learning-real-time-object-detection-convolution-neural-networks-for-fast-fruit-recognition-in-the-tree" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/79886.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">420</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">40582</span> Algorithm for Automatic Real-Time Electrooculographic Artifact Correction</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Norman%20Sinnigen">Norman Sinnigen</a>, <a href="https://publications.waset.org/abstracts/search?q=Igor%20Izyurov"> Igor Izyurov</a>, <a href="https://publications.waset.org/abstracts/search?q=Marina%20Krylova"> Marina Krylova</a>, <a href="https://publications.waset.org/abstracts/search?q=Hamidreza%20Jamalabadi"> Hamidreza Jamalabadi</a>, <a href="https://publications.waset.org/abstracts/search?q=Sarah%20Alizadeh"> Sarah Alizadeh</a>, <a href="https://publications.waset.org/abstracts/search?q=Martin%20Walter"> Martin Walter</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Background: EEG is a non-invasive brain activity recording technique with a high temporal resolution that allows the use of real-time applications, such as neurofeedback. However, EEG data are susceptible to electrooculographic (EOG) and electromyography (EMG) artifacts (i.e., jaw clenching, teeth squeezing and forehead movements). Due to their non-stationary nature, these artifacts greatly obscure the information and power spectrum of EEG signals. Many EEG artifact correction methods are too time-consuming when applied to low-density EEG and have been focusing on offline processing or handling one single type of EEG artifact. A software-only real-time method for correcting multiple types of EEG artifacts of high-density EEG remains a significant challenge. Methods: We demonstrate an improved approach for automatic real-time EEG artifact correction of EOG and EMG artifacts. The method was tested on three healthy subjects using 64 EEG channels (Brain Products GmbH) and a sampling rate of 1,000 Hz. Captured EEG signals were imported in MATLAB with the lab streaming layer interface allowing buffering of EEG data. EMG artifacts were detected by channel variance and adaptive thresholding and corrected by using channel interpolation. Real-time independent component analysis (ICA) was applied for correcting EOG artifacts. Results: Our results demonstrate that the algorithm effectively reduces EMG artifacts, such as jaw clenching, teeth squeezing and forehead movements, and EOG artifacts (horizontal and vertical eye movements) of high-density EEG while preserving brain neuronal activity information. The average computation time of EOG and EMG artifact correction for 80 s (80,000 data points) 64-channel data is 300 – 700 ms depending on the convergence of ICA and the type and intensity of the artifact. Conclusion: An automatic EEG artifact correction algorithm based on channel variance, adaptive thresholding, and ICA improves high-density EEG recordings contaminated with EOG and EMG artifacts in real-time. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=EEG" title="EEG">EEG</a>, <a href="https://publications.waset.org/abstracts/search?q=muscle%20artifacts" title=" muscle artifacts"> muscle artifacts</a>, <a href="https://publications.waset.org/abstracts/search?q=ocular%20artifacts" title=" ocular artifacts"> ocular artifacts</a>, <a href="https://publications.waset.org/abstracts/search?q=real-time%20artifact%20correction" title=" real-time artifact correction"> real-time artifact correction</a>, <a href="https://publications.waset.org/abstracts/search?q=real-time%20ICA" title=" real-time ICA"> real-time ICA</a> </p> <a href="https://publications.waset.org/abstracts/102844/algorithm-for-automatic-real-time-electrooculographic-artifact-correction" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/102844.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">180</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">40581</span> Classification of Generative Adversarial Network Generated Multivariate Time Series Data Featuring Transformer-Based Deep Learning Architecture</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Thrivikraman%20Aswathi">Thrivikraman Aswathi</a>, <a href="https://publications.waset.org/abstracts/search?q=S.%20Advaith"> S. Advaith</a> </p> <p class="card-text"><strong>Abstract:</strong></p> As there can be cases where the use of real data is somehow limited, such as when it is hard to get access to a large volume of real data, we need to go for synthetic data generation. This produces high-quality synthetic data while maintaining the statistical properties of a specific dataset. In the present work, a generative adversarial network (GAN) is trained to produce multivariate time series (MTS) data since the MTS is now being gathered more often in various real-world systems. Furthermore, the GAN-generated MTS data is fed into a transformer-based deep learning architecture that carries out the data categorization into predefined classes. Further, the model is evaluated across various distinct domains by generating corresponding MTS data. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=GAN" title="GAN">GAN</a>, <a href="https://publications.waset.org/abstracts/search?q=transformer" title=" transformer"> transformer</a>, <a href="https://publications.waset.org/abstracts/search?q=classification" title=" classification"> classification</a>, <a href="https://publications.waset.org/abstracts/search?q=multivariate%20time%20series" title=" multivariate time series"> multivariate time series</a> </p> <a href="https://publications.waset.org/abstracts/157460/classification-of-generative-adversarial-network-generated-multivariate-time-series-data-featuring-transformer-based-deep-learning-architecture" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/157460.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">130</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">40580</span> A Real Time Monitoring System of the Supply Chain Conditions, Products and Means of Transport</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Dimitris%20E.%20Kontaxis">Dimitris E. Kontaxis</a>, <a href="https://publications.waset.org/abstracts/search?q=George%20Litainas"> George Litainas</a>, <a href="https://publications.waset.org/abstracts/search?q=Dimitris%20P.%20Ptochos"> Dimitris P. Ptochos</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Real-time monitoring of the supply chain conditions and procedures is a critical element for the optimal coordination and safety of the deliveries, as well as for the minimization of the delivery time and cost. Real-time monitoring requires IoT data streams, which are related to the conditions of the products and the means of transport (e.g., location, temperature/humidity conditions, kinematic state, ambient light conditions, etc.). These streams are generated by battery-based IoT tracking devices, equipped with appropriate sensors, and are transmitted to a cloud-based back-end system. Proper handling and processing of the IoT data streams, using predictive and artificial intelligence algorithms, can provide significant and useful results, which can be exploited by the supply chain stakeholders in order to enhance their financial benefits, as well as the efficiency, security, transparency, coordination, and sustainability of the supply chain procedures. The technology, the features, and the characteristics of a complete, proprietary system, including hardware, firmware, and software tools -developed in the context of a co-funded R&D programme- are addressed and presented in this paper. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=IoT%20embedded%20electronics" title="IoT embedded electronics">IoT embedded electronics</a>, <a href="https://publications.waset.org/abstracts/search?q=real-time%20monitoring" title=" real-time monitoring"> real-time monitoring</a>, <a href="https://publications.waset.org/abstracts/search?q=tracking%20device" title=" tracking device"> tracking device</a>, <a href="https://publications.waset.org/abstracts/search?q=sensor%20platform" title=" sensor platform"> sensor platform</a> </p> <a href="https://publications.waset.org/abstracts/138517/a-real-time-monitoring-system-of-the-supply-chain-conditions-products-and-means-of-transport" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/138517.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">177</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">40579</span> IoT Based Information Processing and Computing</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mannan%20Ahmad%20Rasheed">Mannan Ahmad Rasheed</a>, <a href="https://publications.waset.org/abstracts/search?q=Sawera%20Kanwal"> Sawera Kanwal</a>, <a href="https://publications.waset.org/abstracts/search?q=Mansoor%20Ahmad%20Rasheed"> Mansoor Ahmad Rasheed</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The Internet of Things (IoT) has revolutionized the way we collect and process information, making it possible to gather data from a wide range of connected devices and sensors. This has led to the development of IoT-based information processing and computing systems that are capable of handling large amounts of data in real time. This paper provides a comprehensive overview of the current state of IoT-based information processing and computing, as well as the key challenges and gaps that need to be addressed. This paper discusses the potential benefits of IoT-based information processing and computing, such as improved efficiency, enhanced decision-making, and cost savings. Despite the numerous benefits of IoT-based information processing and computing, several challenges need to be addressed to realize the full potential of these systems. These challenges include security and privacy concerns, interoperability issues, scalability and reliability of IoT devices, and the need for standardization and regulation of IoT technologies. Moreover, this paper identifies several gaps in the current research related to IoT-based information processing and computing. One major gap is the lack of a comprehensive framework for designing and implementing IoT-based information processing and computing systems. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=IoT" title="IoT">IoT</a>, <a href="https://publications.waset.org/abstracts/search?q=computing" title=" computing"> computing</a>, <a href="https://publications.waset.org/abstracts/search?q=information%20processing" title=" information processing"> information processing</a>, <a href="https://publications.waset.org/abstracts/search?q=Iot%20computing" title=" Iot computing"> Iot computing</a> </p> <a href="https://publications.waset.org/abstracts/165683/iot-based-information-processing-and-computing" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/165683.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">188</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">40578</span> LiDAR Based Real Time Multiple Vehicle Detection and Tracking</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Zhongzhen%20Luo">Zhongzhen Luo</a>, <a href="https://publications.waset.org/abstracts/search?q=Saeid%20Habibi"> Saeid Habibi</a>, <a href="https://publications.waset.org/abstracts/search?q=Martin%20v.%20Mohrenschildt"> Martin v. Mohrenschildt</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Self-driving vehicle require a high level of situational awareness in order to maneuver safely when driving in real world condition. This paper presents a LiDAR based real time perception system that is able to process sensor raw data for multiple target detection and tracking in dynamic environment. The proposed algorithm is nonparametric and deterministic that is no assumptions and priori knowledge are needed from the input data and no initializations are required. Additionally, the proposed method is working on the three-dimensional data directly generated by LiDAR while not scarifying the rich information contained in the domain of 3D. Moreover, a fast and efficient for real time clustering algorithm is applied based on a radially bounded nearest neighbor (RBNN). Hungarian algorithm procedure and adaptive Kalman filtering are used for data association and tracking algorithm. The proposed algorithm is able to run in real time with average run time of 70ms per frame. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=lidar" title="lidar">lidar</a>, <a href="https://publications.waset.org/abstracts/search?q=segmentation" title=" segmentation"> segmentation</a>, <a href="https://publications.waset.org/abstracts/search?q=clustering" title=" clustering"> clustering</a>, <a href="https://publications.waset.org/abstracts/search?q=tracking" title=" tracking"> tracking</a> </p> <a href="https://publications.waset.org/abstracts/43729/lidar-based-real-time-multiple-vehicle-detection-and-tracking" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/43729.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">423</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">40577</span> Generating Real-Time Visual Summaries from Located Sensor-Based Data with Chorems </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Z.%20Bouattou">Z. Bouattou</a>, <a href="https://publications.waset.org/abstracts/search?q=R.%20Laurini"> R. Laurini</a>, <a href="https://publications.waset.org/abstracts/search?q=H.%20Belbachir"> H. Belbachir</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper describes a new approach for the automatic generation of the visual summaries dealing with cartographic visualization methods and sensors real time data modeling. Hence, the concept of chorems seems an interesting candidate to visualize real time geographic database summaries. Chorems have been defined by Roger Brunet (1980) as schematized visual representations of territories. However, the time information is not yet handled in existing chorematic map approaches, issue has been discussed in this paper. Our approach is based on spatial analysis by interpolating the values recorded at the same time, by sensors available, so we have a number of distributed observations on study areas and used spatial interpolation methods to find the concentration fields, from these fields and by using some spatial data mining procedures on the fly, it is possible to extract important patterns as geographic rules. Then, those patterns are visualized as chorems. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=geovisualization" title="geovisualization">geovisualization</a>, <a href="https://publications.waset.org/abstracts/search?q=spatial%20analytics" title=" spatial analytics"> spatial analytics</a>, <a href="https://publications.waset.org/abstracts/search?q=real-time" title=" real-time"> real-time</a>, <a href="https://publications.waset.org/abstracts/search?q=geographic%20data%20streams" title=" geographic data streams"> geographic data streams</a>, <a href="https://publications.waset.org/abstracts/search?q=sensors" title=" sensors"> sensors</a>, <a href="https://publications.waset.org/abstracts/search?q=chorems" title=" chorems"> chorems</a> </p> <a href="https://publications.waset.org/abstracts/30697/generating-real-time-visual-summaries-from-located-sensor-based-data-with-chorems" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/30697.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">401</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">40576</span> FLIME - Fast Low Light Image Enhancement for Real-Time Video</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Vinay%20P.">Vinay P.</a>, <a href="https://publications.waset.org/abstracts/search?q=Srinivas%20K.%20S."> Srinivas K. S.</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Low Light Image Enhancement is of utmost impor- tance in computer vision based tasks. Applications include vision systems for autonomous driving, night vision devices for defence systems, low light object detection tasks. Many of the existing deep learning methods are resource intensive during the inference step and take considerable time for processing. The algorithm should take considerably less than 41 milliseconds in order to process a real-time video feed with 24 frames per second and should be even less for a video with 30 or 60 frames per second. The paper presents a fast and efficient solution which has two main advantages, it has the potential to be used for a real-time video feed, and it can be used in low compute environments because of the lightweight nature. The proposed solution is a pipeline of three steps, the first one is the use of a simple function to map input RGB values to output RGB values, the second is to balance the colors and the final step is to adjust the contrast of the image. Hence a custom dataset is carefully prepared using images taken in low and bright lighting conditions. The preparation of the dataset, the proposed model, the processing time are discussed in detail and the quality of the enhanced images using different methods is shown. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=low%20light%20image%20enhancement" title="low light image enhancement">low light image enhancement</a>, <a href="https://publications.waset.org/abstracts/search?q=real-time%20video" title=" real-time video"> real-time video</a>, <a href="https://publications.waset.org/abstracts/search?q=computer%20vision" title=" computer vision"> computer vision</a>, <a href="https://publications.waset.org/abstracts/search?q=machine%20learning" title=" machine learning"> machine learning</a> </p> <a href="https://publications.waset.org/abstracts/144526/flime-fast-low-light-image-enhancement-for-real-time-video" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/144526.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">206</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">40575</span> A Real-time Classification of Lying Bodies for Care Application of Elderly Patients</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=E.%20Vazquez-Santacruz">E. Vazquez-Santacruz</a>, <a href="https://publications.waset.org/abstracts/search?q=M.%20Gamboa-Zuniga"> M. Gamboa-Zuniga</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, we show a methodology for bodies classification in lying state using HOG descriptors and pressures sensors positioned in a matrix form (14 x 32 sensors) on the surface where bodies lie down. it will be done in real time. Our system is embedded in a care robot that can assist the elderly patient and medical staff around to get a better quality of life in and out of hospitals. Due to current technology a limited number of sensors is used, wich results in low-resolution data array, that will be used as image of 14 x 32 pixels. Our work considers the problem of human posture classification with few information (sensors), applying digital process to expand the original data of the sensors and so get more significant data for the classification, however, this is done with low-cost algorithms to ensure the real-time execution. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=real-time%20classification" title="real-time classification">real-time classification</a>, <a href="https://publications.waset.org/abstracts/search?q=sensors" title=" sensors"> sensors</a>, <a href="https://publications.waset.org/abstracts/search?q=robots" title=" robots"> robots</a>, <a href="https://publications.waset.org/abstracts/search?q=health%20care" title=" health care"> health care</a>, <a href="https://publications.waset.org/abstracts/search?q=elderly%20patients" title=" elderly patients"> elderly patients</a>, <a href="https://publications.waset.org/abstracts/search?q=artificial%20intelligence" title=" artificial intelligence "> artificial intelligence </a> </p> <a href="https://publications.waset.org/abstracts/24235/a-real-time-classification-of-lying-bodies-for-care-application-of-elderly-patients" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/24235.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">866</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">40574</span> A Proposal for U-City (Smart City) Service Method Using Real-Time Digital Map</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=SangWon%20Han">SangWon Han</a>, <a href="https://publications.waset.org/abstracts/search?q=MuWook%20Pyeon"> MuWook Pyeon</a>, <a href="https://publications.waset.org/abstracts/search?q=Sujung%20Moon"> Sujung Moon</a>, <a href="https://publications.waset.org/abstracts/search?q=DaeKyo%20Seo"> DaeKyo Seo</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Recently, technologies based on three-dimensional (3D) space information are being developed and quality of life is improving as a result. Research on real-time digital map (RDM) is being conducted now to provide 3D space information. RDM is a service that creates and supplies 3D space information in real time based on location/shape detection. Research subjects on RDM include the construction of 3D space information with matching image data, complementing the weaknesses of image acquisition using multi-source data, and data collection methods using big data. Using RDM will be effective for space analysis using 3D space information in a U-City and for other space information utilization technologies. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=RDM" title="RDM">RDM</a>, <a href="https://publications.waset.org/abstracts/search?q=multi-source%20data" title=" multi-source data"> multi-source data</a>, <a href="https://publications.waset.org/abstracts/search?q=big%20data" title=" big data"> big data</a>, <a href="https://publications.waset.org/abstracts/search?q=U-City" title=" U-City"> U-City</a> </p> <a href="https://publications.waset.org/abstracts/60903/a-proposal-for-u-city-smart-city-service-method-using-real-time-digital-map" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/60903.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">433</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">40573</span> Instant Location Detection of Objects Moving at High Speed in C-OTDR Monitoring Systems</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Andrey%20V.%20Timofeev">Andrey V. Timofeev</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The practical efficient approach is suggested to estimate the high-speed objects instant bounds in C-OTDR monitoring systems. In case of super-dynamic objects (trains, cars) is difficult to obtain the adequate estimate of the instantaneous object localization because of estimation lag. In other words, reliable estimation coordinates of monitored object requires taking some time for data observation collection by means of C-OTDR system, and only if the required sample volume will be collected the final decision could be issued. But it is contrary to requirements of many real applications. For example, in rail traffic management systems we need to get data off the dynamic objects localization in real time. The way to solve this problem is to use the set of statistical independent parameters of C-OTDR signals for obtaining the most reliable solution in real time. The parameters of this type we can call as 'signaling parameters' (SP). There are several the SP’s which carry information about dynamic objects instant localization for each of C-OTDR channels. The problem is that some of these parameters are very sensitive to dynamics of seismoacoustic emission sources but are non-stable. On the other hand, in case the SP is very stable it becomes insensitive as a rule. This report contains describing the method for SP’s co-processing which is designed to get the most effective dynamic objects localization estimates in the C-OTDR monitoring system framework. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=C-OTDR-system" title="C-OTDR-system">C-OTDR-system</a>, <a href="https://publications.waset.org/abstracts/search?q=co-processing%20of%20signaling%20parameters" title=" co-processing of signaling parameters"> co-processing of signaling parameters</a>, <a href="https://publications.waset.org/abstracts/search?q=high-speed%20objects%20localization" title="high-speed objects localization">high-speed objects localization</a>, <a href="https://publications.waset.org/abstracts/search?q=multichannel%20monitoring%20systems" title=" multichannel monitoring systems "> multichannel monitoring systems </a> </p> <a href="https://publications.waset.org/abstracts/32580/instant-location-detection-of-objects-moving-at-high-speed-in-c-otdr-monitoring-systems" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/32580.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">470</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">40572</span> Indoor Real-Time Positioning and Mapping Based on Manhattan Hypothesis Optimization</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Linhang%20Zhu">Linhang Zhu</a>, <a href="https://publications.waset.org/abstracts/search?q=Hongyu%20Zhu"> Hongyu Zhu</a>, <a href="https://publications.waset.org/abstracts/search?q=Jiahe%20Liu"> Jiahe Liu</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper investigated a method of indoor real-time positioning and mapping based on the Manhattan world assumption. In indoor environments, relying solely on feature matching techniques or other geometric algorithms for sensor pose estimation inevitably resulted in cumulative errors, posing a significant challenge to indoor positioning. To address this issue, we adopt the Manhattan world hypothesis to optimize the camera pose algorithm based on feature matching, which improves the accuracy of camera pose estimation. A special processing method was applied to image data frames that conformed to the Manhattan world assumption. When similar data frames appeared subsequently, this could be used to eliminate drift in sensor pose estimation, thereby reducing cumulative errors in estimation and optimizing mapping and positioning. Through experimental verification, it is found that our method achieves high-precision real-time positioning in indoor environments and successfully generates maps of indoor environments. This provides effective technical support for applications such as indoor navigation and robot control. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Manhattan%20world%20hypothesis" title="Manhattan world hypothesis">Manhattan world hypothesis</a>, <a href="https://publications.waset.org/abstracts/search?q=real-time%20positioning%20and%20mapping" title=" real-time positioning and mapping"> real-time positioning and mapping</a>, <a href="https://publications.waset.org/abstracts/search?q=feature%20matching" title=" feature matching"> feature matching</a>, <a href="https://publications.waset.org/abstracts/search?q=loopback%20detection" title=" loopback detection"> loopback detection</a> </p> <a href="https://publications.waset.org/abstracts/173745/indoor-real-time-positioning-and-mapping-based-on-manhattan-hypothesis-optimization" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/173745.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">61</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">40571</span> Design of a Real Time Closed Loop Simulation Test Bed on a General Purpose Operating System: Practical Approaches</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Pratibha%20Srivastava">Pratibha Srivastava</a>, <a href="https://publications.waset.org/abstracts/search?q=Chithra%20V.%20J."> Chithra V. J.</a>, <a href="https://publications.waset.org/abstracts/search?q=Sudhakar%20S."> Sudhakar S.</a>, <a href="https://publications.waset.org/abstracts/search?q=Nitin%20K.%20D."> Nitin K. D.</a> </p> <p class="card-text"><strong>Abstract:</strong></p> A closed-loop system comprises of a controller, a response system, and an actuating system. The controller, which is the system under test for us, excites the actuators based on feedback from the sensors in a periodic manner. The sensors should provide the feedback to the System Under Test (SUT) within a deterministic time post excitation of the actuators. Any delay or miss in the generation of response or acquisition of excitation pulses may lead to control loop controller computation errors, which can be catastrophic in certain cases. Such systems categorised as hard real-time systems that need special strategies. The real-time operating systems available in the market may be the best solutions for such kind of simulations, but they pose limitations like the availability of the X Windows system, graphical interfaces, other user tools. In this paper, we present strategies that can be used on a general purpose operating system (Bare Linux Kernel) to achieve a deterministic deadline and hence have the added advantages of a GPOS with real-time features. Techniques shall be discussed how to make the time-critical application run with the highest priority in an uninterrupted manner, reduced network latency for distributed architecture, real-time data acquisition, data storage, and retrieval, user interactions, etc. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=real%20time%20data%20acquisition" title="real time data acquisition">real time data acquisition</a>, <a href="https://publications.waset.org/abstracts/search?q=real%20time%20kernel%20preemption" title=" real time kernel preemption"> real time kernel preemption</a>, <a href="https://publications.waset.org/abstracts/search?q=scheduling" title=" scheduling"> scheduling</a>, <a href="https://publications.waset.org/abstracts/search?q=network%20latency" title=" network latency"> network latency</a> </p> <a href="https://publications.waset.org/abstracts/117763/design-of-a-real-time-closed-loop-simulation-test-bed-on-a-general-purpose-operating-system-practical-approaches" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/117763.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">147</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">40570</span> Parallel Vector Processing Using Multi Level Orbital DATA</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Nagi%20Mekhiel">Nagi Mekhiel</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Many applications use vector operations by applying single instruction to multiple data that map to different locations in conventional memory. Transferring data from memory is limited by access latency and bandwidth affecting the performance gain of vector processing. We present a memory system that makes all of its content available to processors in time so that processors need not to access the memory, we force each location to be available to all processors at a specific time. The data move in different orbits to become available to other processors in higher orbits at different time. We use this memory to apply parallel vector operations to data streams at first orbit level. Data processed in the first level move to upper orbit one data element at a time, allowing a processor in that orbit to apply another vector operation to deal with serial code limitations inherited in all parallel applications and interleaved it with lower level vector operations. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Memory%20Organization" title="Memory Organization">Memory Organization</a>, <a href="https://publications.waset.org/abstracts/search?q=Parallel%20Processors" title=" Parallel Processors"> Parallel Processors</a>, <a href="https://publications.waset.org/abstracts/search?q=Serial%0D%0ACode" title=" Serial Code"> Serial Code</a>, <a href="https://publications.waset.org/abstracts/search?q=Vector%20Processing" title=" Vector Processing"> Vector Processing</a> </p> <a href="https://publications.waset.org/abstracts/59115/parallel-vector-processing-using-multi-level-orbital-data" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/59115.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">270</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">40569</span> Development of an EEG-Based Real-Time Emotion Recognition System on Edge AI</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=James%20Rigor%20Camacho">James Rigor Camacho</a>, <a href="https://publications.waset.org/abstracts/search?q=Wansu%20Lim"> Wansu Lim</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Over the last few years, the development of new wearable and processing technologies has accelerated in order to harness physiological data such as electroencephalograms (EEGs) for EEG-based applications. EEG has been demonstrated to be a source of emotion recognition signals with the highest classification accuracy among physiological signals. However, when emotion recognition systems are used for real-time classification, the training unit is frequently left to run offline or in the cloud rather than working locally on the edge. That strategy has hampered research, and the full potential of using an edge AI device has yet to be realized. Edge AI devices are computers with high performance that can process complex algorithms. It is capable of collecting, processing, and storing data on its own. It can also analyze and apply complicated algorithms like localization, detection, and recognition on a real-time application, making it a powerful embedded device. The NVIDIA Jetson series, specifically the Jetson Nano device, was used in the implementation. The cEEGrid, which is integrated to the open-source brain computer-interface platform (OpenBCI), is used to collect EEG signals. An EEG-based real-time emotion recognition system on Edge AI is proposed in this paper. To perform graphical spectrogram categorization of EEG signals and to predict emotional states based on input data properties, machine learning-based classifiers were used. Until the emotional state was identified, the EEG signals were analyzed using the K-Nearest Neighbor (KNN) technique, which is a supervised learning system. In EEG signal processing, after each EEG signal has been received in real-time and translated from time to frequency domain, the Fast Fourier Transform (FFT) technique is utilized to observe the frequency bands in each EEG signal. To appropriately show the variance of each EEG frequency band, power density, standard deviation, and mean are calculated and employed. The next stage is to identify the features that have been chosen to predict emotion in EEG data using the K-Nearest Neighbors (KNN) technique. Arousal and valence datasets are used to train the parameters defined by the KNN technique.Because classification and recognition of specific classes, as well as emotion prediction, are conducted both online and locally on the edge, the KNN technique increased the performance of the emotion recognition system on the NVIDIA Jetson Nano. Finally, this implementation aims to bridge the research gap on cost-effective and efficient real-time emotion recognition using a resource constrained hardware device, like the NVIDIA Jetson Nano. On the cutting edge of AI, EEG-based emotion identification can be employed in applications that can rapidly expand the research and implementation industry's use. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=edge%20AI%20device" title="edge AI device">edge AI device</a>, <a href="https://publications.waset.org/abstracts/search?q=EEG" title=" EEG"> EEG</a>, <a href="https://publications.waset.org/abstracts/search?q=emotion%20recognition%20system" title=" emotion recognition system"> emotion recognition system</a>, <a href="https://publications.waset.org/abstracts/search?q=supervised%20learning%20algorithm" title=" supervised learning algorithm"> supervised learning algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=sensors" title=" sensors"> sensors</a> </p> <a href="https://publications.waset.org/abstracts/147311/development-of-an-eeg-based-real-time-emotion-recognition-system-on-edge-ai" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/147311.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">40568</span> Lab Bench for Synthetic Aperture Radar Imaging System</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Karthiyayini%20Nagarajan">Karthiyayini Nagarajan</a>, <a href="https://publications.waset.org/abstracts/search?q=P.%20V.%20Ramakrishna"> P. V. Ramakrishna </a> </p> <p class="card-text"><strong>Abstract:</strong></p> Radar Imaging techniques provides extensive applications in the field of remote sensing, majorly Synthetic Aperture Radar (SAR) that provide high resolution target images. This paper work puts forward the effective and realizable signal generation and processing for SAR images. The major units in the system include camera, signal generation unit, signal processing unit and display screen. The real radio channel is replaced by its mathematical model based on optical image to calculate a reflected signal model in real time. Signal generation realizes the algorithm and forms the radar reflection model. Signal processing unit provides range and azimuth resolution through matched filtering and spectrum analysis procedure to form radar image on the display screen. The restored image has the same quality as that of the optical image. This SAR imaging system has been designed and implemented using MATLAB and Quartus II tools on Stratix III device as a System (Lab Bench) that works in real time to study/investigate on radar imaging rudiments and signal processing scheme for educational and research purposes. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=synthetic%20aperture%20radar" title="synthetic aperture radar">synthetic aperture radar</a>, <a href="https://publications.waset.org/abstracts/search?q=radio%20reflection%20model" title=" radio reflection model"> radio reflection model</a>, <a href="https://publications.waset.org/abstracts/search?q=lab%20bench" title=" lab bench"> lab bench</a>, <a href="https://publications.waset.org/abstracts/search?q=imaging%20engineering" title=" imaging engineering"> imaging engineering</a> </p> <a href="https://publications.waset.org/abstracts/29485/lab-bench-for-synthetic-aperture-radar-imaging-system" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/29485.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">497</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">40567</span> Design and Development of Fleet Management System for Multi-Agent Autonomous Surface Vessel</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Zulkifli%20Zainal%20Abidin">Zulkifli Zainal Abidin</a>, <a href="https://publications.waset.org/abstracts/search?q=Ahmad%20Shahril%20Mohd%20Ghani"> Ahmad Shahril Mohd Ghani</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Agent-based systems technology has been addressed as a new paradigm for conceptualizing, designing, and implementing software systems. Agents are sophisticated systems that act autonomously across open and distributed environments in solving problems. Nevertheless, it is impractical to rely on a single agent to do all computing processes in solving complex problems. An increasing number of applications lately require multiple agents to work together. A multi-agent system (MAS) is a loosely coupled network of agents that interact to solve problems that are beyond the individual capacities or knowledge of each problem solver. However, the network of MAS still requires a main system to govern or oversees the operation of the agents in order to achieve a unified goal. We had developed a fleet management system (FMS) in order to manage the fleet of agents, plan route for the agents, perform real-time data processing and analysis, and issue sets of general and specific instructions to the agents. This FMS should be able to perform real-time data processing, communicate with the autonomous surface vehicle (ASV) agents and generate bathymetric map according to the data received from each ASV unit. The first algorithm is developed to communicate with the ASV via radio communication using standard National Marine Electronics Association (NMEA) protocol sentences. Next, the second algorithm will take care of the path planning, formation and pattern generation is tested using various sample data. Lastly, the bathymetry map generation algorithm will make use of data collected by the agents to create bathymetry map in real-time. The outcome of this research is expected can be applied on various other multi-agent systems. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=autonomous%20surface%20vehicle" title="autonomous surface vehicle">autonomous surface vehicle</a>, <a href="https://publications.waset.org/abstracts/search?q=fleet%20management%20system" title=" fleet management system"> fleet management system</a>, <a href="https://publications.waset.org/abstracts/search?q=multi%20agent%20system" title=" multi agent system"> multi agent system</a>, <a href="https://publications.waset.org/abstracts/search?q=bathymetry" title=" bathymetry"> bathymetry</a> </p> <a href="https://publications.waset.org/abstracts/72389/design-and-development-of-fleet-management-system-for-multi-agent-autonomous-surface-vessel" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/72389.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">271</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">40566</span> Design and Implementation of a Lab Bench for Synthetic Aperture Radar Imaging System</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Karthiyayini%20Nagarajan">Karthiyayini Nagarajan</a>, <a href="https://publications.waset.org/abstracts/search?q=P.%20V.%20RamaKrishna"> P. V. RamaKrishna</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Radar Imaging techniques provides extensive applications in the field of remote sensing, majorly Synthetic Aperture Radar(SAR) that provide high resolution target images. This paper work puts forward the effective and realizable signal generation and processing for SAR images. The major units in the system include camera, signal generation unit, signal processing unit and display screen. The real radio channel is replaced by its mathematical model based on optical image to calculate a reflected signal model in real time. Signal generation realizes the algorithm and forms the radar reflection model. Signal processing unit provides range and azimuth resolution through matched filtering and spectrum analysis procedure to form radar image on the display screen. The restored image has the same quality as that of the optical image. This SAR imaging system has been designed and implemented using MATLAB and Quartus II tools on Stratix III device as a System(lab bench) that works in real time to study/investigate on radar imaging rudiments and signal processing scheme for educational and research purposes. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=synthetic%20aperture%20radar" title="synthetic aperture radar">synthetic aperture radar</a>, <a href="https://publications.waset.org/abstracts/search?q=radio%20reflection%20model" title=" radio reflection model"> radio reflection model</a>, <a href="https://publications.waset.org/abstracts/search?q=lab%20bench" title=" lab bench"> lab bench</a> </p> <a href="https://publications.waset.org/abstracts/29475/design-and-implementation-of-a-lab-bench-for-synthetic-aperture-radar-imaging-system" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/29475.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">468</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">40565</span> A Fundamental Study for Real-Time Safety Evaluation System of Landing Pier Using FBG Sensor</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Heungsu%20Lee">Heungsu Lee</a>, <a href="https://publications.waset.org/abstracts/search?q=Youngseok%20Kim"> Youngseok Kim</a>, <a href="https://publications.waset.org/abstracts/search?q=Jonghwa%20Yi"> Jonghwa Yi</a>, <a href="https://publications.waset.org/abstracts/search?q=Chul%20Park"> Chul Park</a> </p> <p class="card-text"><strong>Abstract:</strong></p> A landing pier is subjected to safety assessment by visual inspection and design data, but it is difficult to check the damage in real-time. In this study, real - time damage detection and safety evaluation methods were studied. As a result of structural analysis of the arbitrary landing pier structure, the inflection point of deformation and moment occurred at 10%, 50%, and 90% of pile length. The critical value of Fiber Bragg Grating (FBG) sensor was set according to the safety factor, and the FBG sensor application method for real - time safety evaluation was derived. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=FBG%20sensor" title="FBG sensor">FBG sensor</a>, <a href="https://publications.waset.org/abstracts/search?q=harbor%20structure" title=" harbor structure"> harbor structure</a>, <a href="https://publications.waset.org/abstracts/search?q=maintenance" title=" maintenance"> maintenance</a>, <a href="https://publications.waset.org/abstracts/search?q=safety%20evaluation%20system" title=" safety evaluation system"> safety evaluation system</a> </p> <a href="https://publications.waset.org/abstracts/84883/a-fundamental-study-for-real-time-safety-evaluation-system-of-landing-pier-using-fbg-sensor" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/84883.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">218</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">40564</span> Efficient Frequent Itemset Mining Methods over Real-Time Spatial Big Data</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Hamdi%20Sana">Hamdi Sana</a>, <a href="https://publications.waset.org/abstracts/search?q=Emna%20Bouazizi"> Emna Bouazizi</a>, <a href="https://publications.waset.org/abstracts/search?q=Sami%20Faiz"> Sami Faiz</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In recent years, there is a huge increase in the use of spatio-temporal applications where data and queries are continuously moving. As a result, the need to process real-time spatio-temporal data seems clear and real-time stream data management becomes a hot topic. Sliding window model and frequent itemset mining over dynamic data are the most important problems in the context of data mining. Thus, sliding window model for frequent itemset mining is a widely used model for data stream mining due to its emphasis on recent data and its bounded memory requirement. These methods use the traditional transaction-based sliding window model where the window size is based on a fixed number of transactions. Actually, this model supposes that all transactions have a constant rate which is not suited for real-time applications. And the use of this model in such applications endangers their performance. Based on these observations, this paper relaxes the notion of window size and proposes the use of a timestamp-based sliding window model. In our proposed frequent itemset mining algorithm, support conditions are used to differentiate frequents and infrequent patterns. Thereafter, a tree is developed to incrementally maintain the essential information. We evaluate our contribution. The preliminary results are quite promising. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=real-time%20spatial%20big%20data" title="real-time spatial big data">real-time spatial big data</a>, <a href="https://publications.waset.org/abstracts/search?q=frequent%20itemset" title=" frequent itemset"> frequent itemset</a>, <a href="https://publications.waset.org/abstracts/search?q=transaction-based%20sliding%20window%20model" title=" transaction-based sliding window model"> transaction-based sliding window model</a>, <a href="https://publications.waset.org/abstracts/search?q=timestamp-based%20sliding%20window%20model" title=" timestamp-based sliding window model"> timestamp-based sliding window model</a>, <a href="https://publications.waset.org/abstracts/search?q=weighted%20frequent%20patterns" title=" weighted frequent patterns"> weighted frequent patterns</a>, <a href="https://publications.waset.org/abstracts/search?q=tree" title=" tree"> tree</a>, <a href="https://publications.waset.org/abstracts/search?q=stream%20query" title=" stream query"> stream query</a> </p> <a href="https://publications.waset.org/abstracts/102447/efficient-frequent-itemset-mining-methods-over-real-time-spatial-big-data" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/102447.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">161</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">40563</span> A NoSQL Based Approach for Real-Time Managing of Robotics's Data</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Gueidi%20Afef">Gueidi Afef</a>, <a href="https://publications.waset.org/abstracts/search?q=Gharsellaoui%20Hamza"> Gharsellaoui Hamza</a>, <a href="https://publications.waset.org/abstracts/search?q=Ben%20Ahmed%20Samir"> Ben Ahmed Samir</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper deals with the secret of the continual progression data that new data management solutions have been emerged: The NoSQL databases. They crossed several areas like personalization, profile management, big data in real-time, content management, catalog, view of customers, mobile applications, internet of things, digital communication and fraud detection. Nowadays, these database management systems are increasing. These systems store data very well and with the trend of big data, a new challenge’s store demands new structures and methods for managing enterprise data. The new intelligent machine in the e-learning sector, thrives on more data, so smart machines can learn more and faster. The robotics are our use case to focus on our test. The implementation of NoSQL for Robotics wrestle all the data they acquire into usable form because with the ordinary type of robotics; we are facing very big limits to manage and find the exact information in real-time. Our original proposed approach was demonstrated by experimental studies and running example used as a use case. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=NoSQL%20databases" title="NoSQL databases">NoSQL databases</a>, <a href="https://publications.waset.org/abstracts/search?q=database%20management%20systems" title=" database management systems"> database management systems</a>, <a href="https://publications.waset.org/abstracts/search?q=robotics" title=" robotics"> robotics</a>, <a href="https://publications.waset.org/abstracts/search?q=big%20data" title=" big data"> big data</a> </p> <a href="https://publications.waset.org/abstracts/65886/a-nosql-based-approach-for-real-time-managing-of-roboticss-data" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/65886.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> 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