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signals</title> <meta name="description" content="Search results for: existing FM signals"> <meta name="keywords" content="existing FM 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 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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="existing FM 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> 6244</div> </div> </div> </div> <h1 class="mt-3 mb-3 text-center" style="font-size:1.6rem;">Search results for: existing FM signals</h1> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">6244</span> Radio Based Location Detection </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=M.%20Pallikonda%20Rajasekaran">M. Pallikonda Rajasekaran</a>, <a href="https://publications.waset.org/abstracts/search?q=J.%20Joshapath"> J. Joshapath</a>, <a href="https://publications.waset.org/abstracts/search?q=Abhishek%20Prasad%20Shaw"> Abhishek Prasad Shaw</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Various techniques has been employed to find location such as GPS, GLONASS, Galileo, and Beidou (compass). This paper currently deals with finding location using the existing FM signals that operates between 88-108 MHz. The location can be determined based on the received signal strength of nearby existing FM stations by mapping the signal strength values using trilateration concept. Thus providing security to users data and maintains eco-friendly environment at zero installation cost as this technology already existing FM stations operating in commercial FM band 88-108 MHZ. Along with the signal strength based trilateration it also finds azimuthal angle of the transmitter by employing directional antenna like Yagi-Uda antenna at the receiver side. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=location" title="location">location</a>, <a href="https://publications.waset.org/abstracts/search?q=existing%20FM%20signals" title=" existing FM signals"> existing FM signals</a>, <a href="https://publications.waset.org/abstracts/search?q=received%20signal%20strength" title=" received signal strength"> received signal strength</a>, <a href="https://publications.waset.org/abstracts/search?q=trilateration" title=" trilateration"> trilateration</a>, <a href="https://publications.waset.org/abstracts/search?q=security" title=" security"> security</a>, <a href="https://publications.waset.org/abstracts/search?q=eco-friendly" title=" eco-friendly"> eco-friendly</a>, <a href="https://publications.waset.org/abstracts/search?q=direction" title=" direction"> direction</a>, <a href="https://publications.waset.org/abstracts/search?q=privacy" title=" privacy"> privacy</a>, <a href="https://publications.waset.org/abstracts/search?q=zero%20installation%20cost" title=" zero installation cost"> zero installation cost</a> </p> <a href="https://publications.waset.org/abstracts/30050/radio-based-location-detection" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/30050.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">518</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">6243</span> Epileptic Seizure Prediction Focusing on Relative Change in Consecutive Segments of EEG Signal</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mohammad%20Zavid%20Parvez">Mohammad Zavid Parvez</a>, <a href="https://publications.waset.org/abstracts/search?q=Manoranjan%20Paul"> Manoranjan Paul</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Epilepsy is a common neurological disorders characterized by sudden recurrent seizures. Electroencephalogram (EEG) is widely used to diagnose possible epileptic seizure. Many research works have been devoted to predict epileptic seizure by analyzing EEG signal. Seizure prediction by analyzing EEG signals are challenging task due to variations of brain signals of different patients. In this paper, we propose a new approach for feature extraction based on phase correlation in EEG signals. In phase correlation, we calculate relative change between two consecutive segments of an EEG signal and then combine the changes with neighboring signals to extract features. These features are then used to classify preictal/ictal and interictal EEG signals for seizure prediction. Experiment results show that the proposed method carries good prediction rate with greater consistence for the benchmark data set in different brain locations compared to the existing state-of-the-art methods. <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=epilepsy" title=" epilepsy"> epilepsy</a>, <a href="https://publications.waset.org/abstracts/search?q=phase%20correlation" title=" phase correlation"> phase correlation</a>, <a href="https://publications.waset.org/abstracts/search?q=seizure" title=" seizure"> seizure</a> </p> <a href="https://publications.waset.org/abstracts/38611/epileptic-seizure-prediction-focusing-on-relative-change-in-consecutive-segments-of-eeg-signal" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/38611.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">6242</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">6241</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">345</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">6240</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">6239</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">6238</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">6237</span> Path loss Signals Determination in a Selected Buildings in Kazaure</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Musefiu%20Aderinola">Musefiu Aderinola</a>, <a href="https://publications.waset.org/abstracts/search?q=F.%20A.%20Amuda"> F. A. Amuda</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Outages of GSM signals may be experienced at some indoor locations even when there are strong outdoor receptions. This is often traced to the building penetration loss, which account for increased attenuation of received GSM signals level when a mobile signal device is moved indoor from outdoor. In this work, measurement of two existing GSM operators signal level were made outside and inside two selected buildings- mud and block which represent the prevalent building types in Kazaure, Jigawa State, Nigeria. A gionee P2 mobile phone with RF signal tracker software installed in it was used and the result shows that an average loss of 10.62dBm and 4.25dBm for mud and block buildings respectively. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=penetration%20loss" title="penetration loss">penetration loss</a>, <a href="https://publications.waset.org/abstracts/search?q=outdoor%20reception" title=" outdoor reception"> outdoor reception</a>, <a href="https://publications.waset.org/abstracts/search?q=Gionee%20P2" title=" Gionee P2"> Gionee P2</a>, <a href="https://publications.waset.org/abstracts/search?q=RF%20signal%20tracker" title=" RF signal tracker"> RF signal tracker</a>, <a href="https://publications.waset.org/abstracts/search?q=mud%20and%20block%20building" title=" mud and block building"> mud and block building</a> </p> <a href="https://publications.waset.org/abstracts/49651/path-loss-signals-determination-in-a-selected-buildings-in-kazaure" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/49651.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">302</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">6236</span> Implementation in Python of a Method to Transform One-Dimensional Signals in Graphs</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Luis%20Andrey%20Fajardo%20Fajardo">Luis Andrey Fajardo Fajardo</a> </p> <p class="card-text"><strong>Abstract:</strong></p> We are immersed in complex systems. The human brain, the galaxies, the snowflakes are examples of complex systems. An area of interest in Complex systems is the chaos theory. This revolutionary field of science presents different ways of study than determinism and reductionism. Here is where in junction with the Nonlinear DSP, chaos theory offer valuable techniques that establish a link between time series and complex theory in terms of complex networks, so that, the study of signals can be explored from the graph theory. Recently, some people had purposed a method to transform time series in graphs, but no one had developed a suitable implementation in Python with signals extracted from Chaotic Systems or Complex systems. That’s why the implementation in Python of an existing method to transform one dimensional chaotic signals from time domain to graph domain and some measures that may reveal information not extracted in the time domain is proposed. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Python" title="Python">Python</a>, <a href="https://publications.waset.org/abstracts/search?q=complex%20systems" title=" complex systems"> complex systems</a>, <a href="https://publications.waset.org/abstracts/search?q=graph%20theory" title=" graph theory"> graph theory</a>, <a href="https://publications.waset.org/abstracts/search?q=dynamical%20systems" title=" dynamical systems"> dynamical systems</a> </p> <a href="https://publications.waset.org/abstracts/25768/implementation-in-python-of-a-method-to-transform-one-dimensional-signals-in-graphs" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/25768.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">509</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">6235</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">6234</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">6233</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">6232</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">6231</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">6230</span> EEG Signal Processing Methods to Differentiate Mental States</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Sun%20H.%20Hwang">Sun H. Hwang</a>, <a href="https://publications.waset.org/abstracts/search?q=Young%20E.%20Lee"> Young E. Lee</a>, <a href="https://publications.waset.org/abstracts/search?q=Yunhan%20Ga"> Yunhan Ga</a>, <a href="https://publications.waset.org/abstracts/search?q=Gilwon%20Yoon"> Gilwon Yoon</a> </p> <p class="card-text"><strong>Abstract:</strong></p> EEG is a very complex signal with noises and other bio-potential interferences. EOG is the most distinct interfering signal when EEG signals are measured and analyzed. It is very important how to process raw EEG signals in order to obtain useful information. In this study, the EEG signal processing techniques such as EOG filtering and outlier removal were examined to minimize unwanted EOG signals and other noises. The two different mental states of resting and focusing were examined through EEG analysis. A focused state was induced by letting subjects to watch a red dot on the white screen. EEG data for 32 healthy subjects were measured. EEG data after 60-Hz notch filtering were processed by a commercially available EOG filtering and our presented algorithm based on the removal of outliers. The ratio of beta wave to theta wave was used as a parameter for determining the degree of focusing. The results show that our algorithm was more appropriate than the existing EOG filtering. <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=focus" title=" focus"> focus</a>, <a href="https://publications.waset.org/abstracts/search?q=mental%20state" title=" mental state"> mental state</a>, <a href="https://publications.waset.org/abstracts/search?q=outlier" title=" outlier"> outlier</a>, <a href="https://publications.waset.org/abstracts/search?q=signal%20processing" title=" signal processing"> signal processing</a> </p> <a href="https://publications.waset.org/abstracts/62057/eeg-signal-processing-methods-to-differentiate-mental-states" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/62057.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">283</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">6229</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">6228</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">6227</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">6226</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">6225</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 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">6224</span> Artificial Intelligence Based Analysis of Magnetic Resonance Signals for the Diagnosis of Tissue Abnormalities</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Kapila%20Warnakulasuriya">Kapila Warnakulasuriya</a>, <a href="https://publications.waset.org/abstracts/search?q=Walimuni%20Janaka%20Mendis"> Walimuni Janaka Mendis</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this study, an artificial intelligence-based approach is developed to diagnose abnormal tissues in human or animal bodies by analyzing magnetic resonance signals. As opposed to the conventional method of generating an image from the magnetic resonance signals, which are then evaluated by a radiologist for the diagnosis of abnormalities, in the discussed approach, the magnetic resonance signals are analyzed by an artificial intelligence algorithm without having to generate or analyze an image. The AI-based program compares magnetic resonance signals with millions of possible magnetic resonance waveforms which can be generated from various types of normal tissues. Waveforms generated by abnormal tissues are then identified, and images of the abnormal tissues are generated with the possible location of them in the body for further diagnostic tests. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=magnetic%20resonance" title="magnetic resonance">magnetic resonance</a>, <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=magnetic%20waveform%20analysis" title=" magnetic waveform analysis"> magnetic waveform analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=abnormal%20tissues" title=" abnormal tissues"> abnormal tissues</a> </p> <a href="https://publications.waset.org/abstracts/164140/artificial-intelligence-based-analysis-of-magnetic-resonance-signals-for-the-diagnosis-of-tissue-abnormalities" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/164140.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">90</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">6223</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">6222</span> Remaining Useful Life Estimation of Bearings Based on Nonlinear Dimensional Reduction Combined with Timing Signals</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Zhongmin%20Wang">Zhongmin Wang</a>, <a href="https://publications.waset.org/abstracts/search?q=Wudong%20Fan"> Wudong Fan</a>, <a href="https://publications.waset.org/abstracts/search?q=Hengshan%20Zhang"> Hengshan Zhang</a>, <a href="https://publications.waset.org/abstracts/search?q=Yimin%20Zhou"> Yimin Zhou</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In data-driven prognostic methods, the prediction accuracy of the estimation for remaining useful life of bearings mainly depends on the performance of health indicators, which are usually fused some statistical features extracted from vibrating signals. However, the existing health indicators have the following two drawbacks: (1) The differnet ranges of the statistical features have the different contributions to construct the health indicators, the expert knowledge is required to extract the features. (2) When convolutional neural networks are utilized to tackle time-frequency features of signals, the time-series of signals are not considered. To overcome these drawbacks, in this study, the method combining convolutional neural network with gated recurrent unit is proposed to extract the time-frequency image features. The extracted features are utilized to construct health indicator and predict remaining useful life of bearings. First, original signals are converted into time-frequency images by using continuous wavelet transform so as to form the original feature sets. Second, with convolutional and pooling layers of convolutional neural networks, the most sensitive features of time-frequency images are selected from the original feature sets. Finally, these selected features are fed into the gated recurrent unit to construct the health indicator. The results state that the proposed method shows the enhance performance than the related studies which have used the same bearing dataset provided by PRONOSTIA. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=continuous%20wavelet%20transform" title="continuous wavelet transform">continuous wavelet transform</a>, <a href="https://publications.waset.org/abstracts/search?q=convolution%20neural%20net-work" title=" convolution neural net-work"> convolution neural net-work</a>, <a href="https://publications.waset.org/abstracts/search?q=gated%20recurrent%20unit" title=" gated recurrent unit"> gated recurrent unit</a>, <a href="https://publications.waset.org/abstracts/search?q=health%20indicators" title=" health indicators"> health indicators</a>, <a href="https://publications.waset.org/abstracts/search?q=remaining%20useful%20life" title=" remaining useful life"> remaining useful life</a> </p> <a href="https://publications.waset.org/abstracts/108324/remaining-useful-life-estimation-of-bearings-based-on-nonlinear-dimensional-reduction-combined-with-timing-signals" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/108324.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">133</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">6221</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">6220</span> On the Bootstrap P-Value Method in Identifying out of Control Signals in Multivariate Control Chart</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=O.%20Ikpotokin">O. Ikpotokin</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In any production process, every product is aimed to attain a certain standard, but the presence of assignable cause of variability affects our process, thereby leading to low quality of product. The ability to identify and remove this type of variability reduces its overall effect, thereby improving the quality of the product. In case of a univariate control chart signal, it is easy to detect the problem and give a solution since it is related to a single quality characteristic. However, the problems involved in the use of multivariate control chart are the violation of multivariate normal assumption and the difficulty in identifying the quality characteristic(s) that resulted in the out of control signals. The purpose of this paper is to examine the use of non-parametric control chart (the bootstrap approach) for obtaining control limit to overcome the problem of multivariate distributional assumption and the p-value method for detecting out of control signals. Results from a performance study show that the proposed bootstrap method enables the setting of control limit that can enhance the detection of out of control signals when compared, while the p-value method also enhanced in identifying out of control variables. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=bootstrap%20control%20limit" title="bootstrap control limit">bootstrap control limit</a>, <a href="https://publications.waset.org/abstracts/search?q=p-value%20method" title=" p-value method"> p-value method</a>, <a href="https://publications.waset.org/abstracts/search?q=out-of-control%20signals" title=" out-of-control signals"> out-of-control signals</a>, <a href="https://publications.waset.org/abstracts/search?q=p-value" title=" p-value"> p-value</a>, <a href="https://publications.waset.org/abstracts/search?q=quality%20characteristics" title=" quality characteristics"> quality characteristics</a> </p> <a href="https://publications.waset.org/abstracts/77853/on-the-bootstrap-p-value-method-in-identifying-out-of-control-signals-in-multivariate-control-chart" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/77853.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">347</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">6219</span> An Effective Noise Resistant Frequency Modulation Continuous-Wave Radar Vital Sign Signal Detection Method</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Lu%20Yang">Lu Yang</a>, <a href="https://publications.waset.org/abstracts/search?q=Meiyang%20Song"> Meiyang Song</a>, <a href="https://publications.waset.org/abstracts/search?q=Xiang%20Yu"> Xiang Yu</a>, <a href="https://publications.waset.org/abstracts/search?q=Wenhao%20Zhou"> Wenhao Zhou</a>, <a href="https://publications.waset.org/abstracts/search?q=Chuntao%20Feng"> Chuntao Feng</a> </p> <p class="card-text"><strong>Abstract:</strong></p> To address the problem that the FM continuous-wave radar (FMCW) extracts human vital sign signals which are susceptible to noise interference and low reconstruction accuracy, a new detection scheme for the sign signals is proposed. Firstly, an improved complete ensemble empirical modal decomposition with adaptive noise (ICEEMDAN) algorithm is applied to decompose the radar-extracted thoracic signals to obtain several intrinsic modal functions (IMF) with different spatial scales, and then the IMF components are optimized by a BP neural network improved by immune genetic algorithm (IGA). The simulation results show that this scheme can effectively separate the noise and accurately extract the respiratory and heartbeat signals and improve the reconstruction accuracy and signal-to-noise ratio of the sign signals. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=frequency%20modulated%20continuous%20wave%20radar" title="frequency modulated continuous wave radar">frequency modulated continuous wave radar</a>, <a href="https://publications.waset.org/abstracts/search?q=ICEEMDAN" title=" ICEEMDAN"> ICEEMDAN</a>, <a href="https://publications.waset.org/abstracts/search?q=BP%20neural%20network" title=" BP neural network"> BP neural network</a>, <a href="https://publications.waset.org/abstracts/search?q=vital%20signs%20signal" title=" vital signs signal"> vital signs signal</a> </p> <a href="https://publications.waset.org/abstracts/150638/an-effective-noise-resistant-frequency-modulation-continuous-wave-radar-vital-sign-signal-detection-method" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/150638.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">165</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">6218</span> An ANOVA-based Sequential Forward Channel Selection Framework for Brain-Computer Interface Application based on EEG Signals Driven by Motor Imagery</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Forouzan%20Salehi%20Fergeni">Forouzan Salehi Fergeni</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Converting the movement intents of a person into commands for action employing brain signals like electroencephalogram signals is a brain-computer interface (BCI) system. When left or right-hand motions are imagined, different patterns of brain activity appear, which can be employed as BCI signals for control. To make better the brain-computer interface (BCI) structures, effective and accurate techniques for increasing the classifying precision of motor imagery (MI) based on electroencephalography (EEG) are greatly needed. Subject dependency and non-stationary are two features of EEG signals. So, EEG signals must be effectively processed before being used in BCI applications. In the present study, after applying an 8 to 30 band-pass filter, a car spatial filter is rendered for the purpose of denoising, and then, a method of analysis of variance is used to select more appropriate and informative channels from a category of a large number of different channels. After ordering channels based on their efficiencies, a sequential forward channel selection is employed to choose just a few reliable ones. Features from two domains of time and wavelet are extracted and shortlisted with the help of a statistical technique, namely the t-test. Finally, the selected features are classified with different machine learning and neural network classifiers being k-nearest neighbor, Probabilistic neural network, support-vector-machine, Extreme learning machine, decision tree, Multi-layer perceptron, and linear discriminant analysis with the purpose of comparing their performance in this application. Utilizing a ten-fold cross-validation approach, tests are performed on a motor imagery dataset found in the BCI competition III. Outcomes demonstrated that the SVM classifier got the greatest classification precision of 97% when compared to the other available approaches. The entire investigative findings confirm that the suggested framework is reliable and computationally effective for the construction of BCI systems and surpasses the existing methods. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=brain-computer%20interface" title="brain-computer interface">brain-computer interface</a>, <a href="https://publications.waset.org/abstracts/search?q=channel%20selection" title=" channel selection"> channel selection</a>, <a href="https://publications.waset.org/abstracts/search?q=motor%20imagery" title=" motor imagery"> motor imagery</a>, <a href="https://publications.waset.org/abstracts/search?q=support-vector-machine" title=" support-vector-machine"> support-vector-machine</a> </p> <a href="https://publications.waset.org/abstracts/186038/an-anova-based-sequential-forward-channel-selection-framework-for-brain-computer-interface-application-based-on-eeg-signals-driven-by-motor-imagery" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/186038.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">50</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">6217</span> Brainwave Classification for Brain Balancing Index (BBI) via 3D EEG Model Using k-NN Technique</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=N.%20Fuad">N. Fuad</a>, <a href="https://publications.waset.org/abstracts/search?q=M.%20N.%20Taib"> M. N. Taib</a>, <a href="https://publications.waset.org/abstracts/search?q=R.%20Jailani"> R. Jailani</a>, <a href="https://publications.waset.org/abstracts/search?q=M.%20E.%20Marwan"> M. E. Marwan </a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, the comparison between k-Nearest Neighbor (kNN) algorithms for classifying the 3D EEG model in brain balancing is presented. The EEG signal recording was conducted on 51 healthy subjects. Development of 3D EEG models involves pre-processing of raw EEG signals and construction of spectrogram images. Then, maximum PSD values were extracted as features from the model. There are three indexes for the balanced brain; index 3, index 4 and index 5. There are significant different of the EEG signals due to the brain balancing index (BBI). Alpha-α (8–13 Hz) and beta-β (13–30 Hz) were used as input signals for the classification model. The k-NN classification result is 88.46% accuracy. These results proved that k-NN can be used in order to predict the brain balancing application. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=power%20spectral%20density" title="power spectral density">power spectral density</a>, <a href="https://publications.waset.org/abstracts/search?q=3D%20EEG%20model" title=" 3D EEG model"> 3D EEG model</a>, <a href="https://publications.waset.org/abstracts/search?q=brain%20balancing" title=" brain balancing"> brain balancing</a>, <a href="https://publications.waset.org/abstracts/search?q=kNN" title=" kNN"> kNN</a> </p> <a href="https://publications.waset.org/abstracts/11285/brainwave-classification-for-brain-balancing-index-bbi-via-3d-eeg-model-using-k-nn-technique" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/11285.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">486</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">6216</span> Effect of Carbon Amount of Dual-Phase Steels on Deformation Behavior Using Acoustic Emission</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ramin%20Khamedi">Ramin Khamedi</a>, <a href="https://publications.waset.org/abstracts/search?q=Isa%20Ahmadi"> Isa Ahmadi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this study acoustic emission (AE) signals obtained during deformation and fracture of two types of ferrite-martensite dual phase steels (DPS) specimens have been analyzed in frequency domain. For this reason two low carbon steels with various amounts of carbon were chosen, and intercritically heat treated. In the introduced method, identifying the mechanisms of failure in the various phases of DPS is done. For this aim, AE monitoring has been used during tensile test of several DPS with various volume fraction of the martensite (VM) and attempted to relate the AE signals and failure mechanisms in these steels. Different signals, which referred to 2-3 micro-mechanisms of failure due to amount of carbon and also VM have been seen. By Fast Fourier Transformation (FFT) of signals in distinct locations, an excellent relationship between peak frequencies in these areas and micro-mechanisms of failure were seen. The results were verified by microscopic observations (SEM). <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=acoustic%20emission" title="acoustic emission">acoustic emission</a>, <a href="https://publications.waset.org/abstracts/search?q=dual%20phase%20steels" title=" dual phase steels"> dual phase steels</a>, <a href="https://publications.waset.org/abstracts/search?q=deformation" title=" deformation"> deformation</a>, <a href="https://publications.waset.org/abstracts/search?q=failure" title=" failure"> failure</a>, <a href="https://publications.waset.org/abstracts/search?q=fracture" title=" fracture"> fracture</a> </p> <a href="https://publications.waset.org/abstracts/10129/effect-of-carbon-amount-of-dual-phase-steels-on-deformation-behavior-using-acoustic-emission" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/10129.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">403</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">6215</span> Efficient Utilization of Unmanned Aerial Vehicle (UAV) for Fishing through Surveillance for Fishermen</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=T.%20Ahilan">T. Ahilan</a>, <a href="https://publications.waset.org/abstracts/search?q=V.%20Aswin%20Adityan"> V. Aswin Adityan</a>, <a href="https://publications.waset.org/abstracts/search?q=S.%20Kailash"> S. Kailash</a> </p> <p class="card-text"><strong>Abstract:</strong></p> UAV’s are small remote operated or automated aerial surveillance systems without a human pilot aboard. UAV’s generally finds its use in military and special operation application, a recent growing trend in UAV’s finds its application in several civil and non military works such as inspection of power or pipelines. The objective of this paper is the augmentation of a UAV in order to replace the existing expensive sonar (sound navigation and ranging) based equipment amongst small scale fisherman, for whom access to sonar equipment are restricted due to limited economic resources. The surveillance equipment’s present in the UAV will relay data and GPS location onto a receiver on the fishing boat using RF signals, using which the location of the schools of fishes can be found. In addition to this, an emergency beacon system is present for rescue operations and drone recovery. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=UAV" title="UAV">UAV</a>, <a href="https://publications.waset.org/abstracts/search?q=Surveillance" title=" Surveillance"> Surveillance</a>, <a href="https://publications.waset.org/abstracts/search?q=RF%20signals" title=" RF signals"> RF signals</a>, <a href="https://publications.waset.org/abstracts/search?q=fishing" title=" fishing"> fishing</a>, <a href="https://publications.waset.org/abstracts/search?q=sonar" title=" sonar"> sonar</a>, <a href="https://publications.waset.org/abstracts/search?q=GPS" title=" GPS"> GPS</a>, <a href="https://publications.waset.org/abstracts/search?q=video%20stream" title=" video stream"> video stream</a>, <a href="https://publications.waset.org/abstracts/search?q=school%20of%20fish" title=" school of fish"> school of fish</a> </p> <a href="https://publications.waset.org/abstracts/34394/efficient-utilization-of-unmanned-aerial-vehicle-uav-for-fishing-through-surveillance-for-fishermen" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/34394.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">457</span> </span> </div> </div> <ul class="pagination"> <li class="page-item disabled"><span class="page-link">&lsaquo;</span></li> <li class="page-item active"><span class="page-link">1</span></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=existing%20FM%20signals&page=2">2</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=existing%20FM%20signals&page=3">3</a></li> <li class="page-item"><a class="page-link" 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