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frequency signal analysis</title> <meta name="description" content="Search results for: time and frequency signal analysis"> <meta name="keywords" content="time and frequency signal analysis"> <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 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method="get" 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="time and frequency signal analysis"> <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> 42468</div> </div> </div> </div> <h1 class="mt-3 mb-3 text-center" style="font-size:1.6rem;">Search results for: time and frequency signal analysis</h1> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">42468</span> Signal On-Off Ratio and Output Frequency Analysis of Semiconductor Electron-Interference Device</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Tomotaka%20Aoki">Tomotaka Aoki</a>, <a href="https://publications.waset.org/abstracts/search?q=Isao%20Tomita"> Isao Tomita</a> </p> <p class="card-text"><strong>Abstract:</strong></p> We examined the on-off ratio and frequency components of output signals from an electron-interference device made of GaAs/AlₓGa₁₋ₓAs by solving the time-dependent Schrödinger's equation on conducting electrons in the channel waveguide of the device. For electron-wave modulation, a periodic voltage of frequency f was applied to the channel. Furthermore, we examined the voltage-amplitude dependence of the signals in time and frequency domains and found that large applied voltage deformed the output-signal waveform and created additional side modes (frequencies) near the modulation frequency f and that there was a trade-off between on-off ratio and side-mode creation. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=electrical%20conduction" title="electrical conduction">electrical conduction</a>, <a href="https://publications.waset.org/abstracts/search?q=electron%20interference" title=" electron interference"> electron interference</a>, <a href="https://publications.waset.org/abstracts/search?q=frequency%20spectrum" title=" frequency spectrum"> frequency spectrum</a>, <a href="https://publications.waset.org/abstracts/search?q=on-off%20ratio" title=" on-off ratio"> on-off ratio</a> </p> <a href="https://publications.waset.org/abstracts/145444/signal-on-off-ratio-and-output-frequency-analysis-of-semiconductor-electron-interference-device" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/145444.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">121</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">42467</span> Analysis of the Impact of Refractivity on Ultra High Frequency Signal Strength over Gusau, North West, Nigeria</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=B.%20G.%20Ayantunji">B. G. Ayantunji</a>, <a href="https://publications.waset.org/abstracts/search?q=B.%20Musa"> B. Musa</a>, <a href="https://publications.waset.org/abstracts/search?q=H.%20Mai-Unguwa"> H. Mai-Unguwa</a>, <a href="https://publications.waset.org/abstracts/search?q=L.%20A.%20Sunmonu"> L. A. Sunmonu</a>, <a href="https://publications.waset.org/abstracts/search?q=A.%20S.%20Adewumi"> A. S. Adewumi</a>, <a href="https://publications.waset.org/abstracts/search?q=L.%20Sa%27ad"> L. Sa'ad</a>, <a href="https://publications.waset.org/abstracts/search?q=A.%20Kado"> A. Kado</a> </p> <p class="card-text"><strong>Abstract:</strong></p> For achieving reliable and efficient communication system, both terrestrial and satellite communication, surface refractivity is critical in planning and design of radio links. This study analyzed the impact of atmospheric parameters on Ultra High Frequency (UHF) signal strength over Gusau, North West, Nigeria. The analysis exploited meteorological data measured simultaneously with UHF signal strength for the month of June 2017 using a Davis Vantage Pro2 automatic weather station and UHF signal strength measuring devices respectively. The instruments were situated at the premise of Federal University, Gusau (6° 78' N, 12° 13' E). The refractivity values were computed using ITU-R model. The result shows that the refractivity value attained the highest value of 366.28 at 2200hr and a minimum value of 350.66 at 2100hr local time. The correlation between signal strength and refractivity is 0.350; Humidity is 0.532 and a negative correlation of -0.515 for temperature. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=refractivity" title="refractivity">refractivity</a>, <a href="https://publications.waset.org/abstracts/search?q=UHF%20%28ultra%20high%20frequency%29%20signal%20strength" title=" UHF (ultra high frequency) signal strength"> UHF (ultra high frequency) signal strength</a>, <a href="https://publications.waset.org/abstracts/search?q=free%20space" title=" free space"> free space</a>, <a href="https://publications.waset.org/abstracts/search?q=automatic%20weather%20station" title=" automatic weather station"> automatic weather station</a> </p> <a href="https://publications.waset.org/abstracts/83775/analysis-of-the-impact-of-refractivity-on-ultra-high-frequency-signal-strength-over-gusau-north-west-nigeria" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/83775.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">197</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">42466</span> Vibroacoustic Modulation with Chirp Signal</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Dong%20Liu">Dong Liu</a> </p> <p class="card-text"><strong>Abstract:</strong></p> By sending a high-frequency probe wave and a low-frequency pump wave to a specimen, the vibroacoustic method evaluates the defect’s severity according to the modulation index of the received signal. Many studies experimentally proved the significant sensitivity of the modulation index to the tiny contact type defect. However, it has also been found that the modulation index was highly affected by the frequency of probe or pump waves. Therefore, the chirp signal has been introduced to the VAM method since it can assess multiple frequencies in a relatively short time duration, so the robustness of the VAM method could be enhanced. Consequently, the signal processing method needs to be modified accordingly. Various studies utilized different algorithms or combinations of algorithms for processing the VAM signal method by chirp excitation. These signal process methods were compared and used for processing a VAM signal acquired from the steel samples. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=vibroacoustic%20modulation" title="vibroacoustic modulation">vibroacoustic modulation</a>, <a href="https://publications.waset.org/abstracts/search?q=nonlinear%20acoustic%20modulation" title=" nonlinear acoustic modulation"> nonlinear acoustic modulation</a>, <a href="https://publications.waset.org/abstracts/search?q=nonlinear%20acoustic%20NDT%26E" title=" nonlinear acoustic NDT&E"> nonlinear acoustic NDT&E</a>, <a href="https://publications.waset.org/abstracts/search?q=signal%20processing" title=" signal processing"> signal processing</a>, <a href="https://publications.waset.org/abstracts/search?q=structural%20health%20monitoring" title=" structural health monitoring"> structural health monitoring</a> </p> <a href="https://publications.waset.org/abstracts/155764/vibroacoustic-modulation-with-chirp-signal" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/155764.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">99</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">42465</span> Construction of Graph Signal Modulations via Graph Fourier Transform and Its Applications</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Xianwei%20Zheng">Xianwei Zheng</a>, <a href="https://publications.waset.org/abstracts/search?q=Yuan%20Yan%20Tang"> Yuan Yan Tang</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Classical window Fourier transform has been widely used in signal processing, image processing, machine learning and pattern recognition. The related Gabor transform is powerful enough to capture the texture information of any given dataset. Recently, in the emerging field of graph signal processing, researchers devoting themselves to develop a graph signal processing theory to handle the so-called graph signals. Among the new developing theory, windowed graph Fourier transform has been constructed to establish a time-frequency analysis framework of graph signals. The windowed graph Fourier transform is defined by using the translation and modulation operators of graph signals, following the similar calculations in classical windowed Fourier transform. Specifically, the translation and modulation operators of graph signals are defined by using the Laplacian eigenvectors as follows. For a given graph signal, its translation is defined by a similar manner as its definition in classical signal processing. Specifically, the translation operator can be defined by using the Fourier atoms; the graph signal translation is defined similarly by using the Laplacian eigenvectors. The modulation of the graph can also be established by using the Laplacian eigenvectors. The windowed graph Fourier transform based on these two operators has been applied to obtain time-frequency representations of graph signals. Fundamentally, the modulation operator is defined similarly to the classical modulation by multiplying a graph signal with the entries in each Fourier atom. However, a single Laplacian eigenvector entry cannot play a similar role as the Fourier atom. This definition ignored the relationship between the translation and modulation operators. In this paper, a new definition of the modulation operator is proposed and thus another time-frequency framework for graph signal is constructed. Specifically, the relationship between the translation and modulation operations can be established by the Fourier transform. Specifically, for any signal, the Fourier transform of its translation is the modulation of its Fourier transform. Thus, the modulation of any signal can be defined as the inverse Fourier transform of the translation of its Fourier transform. Therefore, similarly, the graph modulation of any graph signal can be defined as the inverse graph Fourier transform of the translation of its graph Fourier. The novel definition of the graph modulation operator established a relationship of the translation and modulation operations. The new modulation operation and the original translation operation are applied to construct a new framework of graph signal time-frequency analysis. Furthermore, a windowed graph Fourier frame theory is developed. Necessary and sufficient conditions for constructing windowed graph Fourier frames, tight frames and dual frames are presented in this paper. The novel graph signal time-frequency analysis framework is applied to signals defined on well-known graphs, e.g. Minnesota road graph and random graphs. Experimental results show that the novel framework captures new features of graph signals. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=graph%20signals" title="graph signals">graph signals</a>, <a href="https://publications.waset.org/abstracts/search?q=windowed%20graph%20Fourier%20transform" title=" windowed graph Fourier transform"> windowed graph Fourier transform</a>, <a href="https://publications.waset.org/abstracts/search?q=windowed%20graph%20Fourier%20frames" title=" windowed graph Fourier frames"> windowed graph Fourier frames</a>, <a href="https://publications.waset.org/abstracts/search?q=vertex%20frequency%20analysis" title=" vertex frequency analysis"> vertex frequency analysis</a> </p> <a href="https://publications.waset.org/abstracts/63133/construction-of-graph-signal-modulations-via-graph-fourier-transform-and-its-applications" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/63133.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">341</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">42464</span> The Lubrication Regimes Recognition of a Pressure-Fed Journal Bearing by Time and Frequency Domain Analysis of Acoustic Emission Signals</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=S.%20Hosseini">S. Hosseini</a>, <a href="https://publications.waset.org/abstracts/search?q=M.%20Ahmadi%20Najafabadi"> M. Ahmadi Najafabadi</a>, <a href="https://publications.waset.org/abstracts/search?q=M.%20Akhlaghi"> M. Akhlaghi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The health of the journal bearings is very important in preventing unforeseen breakdowns in rotary machines, and poor lubrication is one of the most important factors for producing the bearing failures. Hydrodynamic lubrication (HL), mixed lubrication (ML), and boundary lubrication (BL) are three regimes of a journal bearing lubrication. This paper uses acoustic emission (AE) measurement technique to correlate features of the AE signals to the three lubrication regimes. The transitions from HL to ML based on operating factors such as rotating speed, load, inlet oil pressure by time domain and time-frequency domain signal analysis techniques are detected, and then metal-to-metal contacts between sliding surfaces of the journal and bearing are identified. It is found that there is a significant difference between theoretical and experimental operating values that are obtained for defining the lubrication regions. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=acoustic%20emission%20technique" title="acoustic emission technique">acoustic emission technique</a>, <a href="https://publications.waset.org/abstracts/search?q=pressure%20fed%20journal%20bearing" title=" pressure fed journal bearing"> pressure fed journal bearing</a>, <a href="https://publications.waset.org/abstracts/search?q=time%20and%20frequency%20signal%20analysis" title=" time and frequency signal analysis"> time and frequency signal analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=metal-to-metal%20contact" title=" metal-to-metal contact"> metal-to-metal contact</a> </p> <a href="https://publications.waset.org/abstracts/101940/the-lubrication-regimes-recognition-of-a-pressure-fed-journal-bearing-by-time-and-frequency-domain-analysis-of-acoustic-emission-signals" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/101940.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">155</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">42463</span> Analysis of EEG Signals Using Wavelet Entropy and Approximate Entropy: A Case Study on Depression Patients</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Subha%20D.%20Puthankattil">Subha D. Puthankattil</a>, <a href="https://publications.waset.org/abstracts/search?q=Paul%20K.%20Joseph"> Paul K. Joseph</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Analyzing brain signals of the patients suffering from the state of depression may lead to interesting observations in the signal parameters that is quite different from a normal control. The present study adopts two different methods: Time frequency domain and nonlinear method for the analysis of EEG signals acquired from depression patients and age and sex matched normal controls. The time frequency domain analysis is realized using wavelet entropy and approximate entropy is employed for the nonlinear method of analysis. The ability of the signal processing technique and the nonlinear method in differentiating the physiological aspects of the brain state are revealed using Wavelet entropy and Approximate entropy. <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=depression" title=" depression"> depression</a>, <a href="https://publications.waset.org/abstracts/search?q=wavelet%20entropy" title=" wavelet entropy"> wavelet entropy</a>, <a href="https://publications.waset.org/abstracts/search?q=approximate%20entropy" title=" approximate entropy"> approximate entropy</a>, <a href="https://publications.waset.org/abstracts/search?q=relative%20wavelet%20energy" title=" relative wavelet energy"> relative wavelet energy</a>, <a href="https://publications.waset.org/abstracts/search?q=multiresolution%20decomposition" title=" multiresolution decomposition"> multiresolution decomposition</a> </p> <a href="https://publications.waset.org/abstracts/11836/analysis-of-eeg-signals-using-wavelet-entropy-and-approximate-entropy-a-case-study-on-depression-patients" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/11836.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">332</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">42462</span> Optimal ECG Sampling Frequency for Multiscale Entropy-Based HRV</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Manjit%20Singh">Manjit Singh</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Multiscale entropy (MSE) is an extensively used index to provide a general understanding of multiple complexity of physiologic mechanism of heart rate variability (HRV) that operates on a wide range of time scales. Accurate selection of electrocardiogram (ECG) sampling frequency is an essential concern for clinically significant HRV quantification; high ECG sampling rate increase memory requirements and processing time, whereas low sampling rate degrade signal quality and results in clinically misinterpreted HRV. In this work, the impact of ECG sampling frequency on MSE based HRV have been quantified. MSE measures are found to be sensitive to ECG sampling frequency and effect of sampling frequency will be a function of time scale. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=ECG%20%28electrocardiogram%29" title="ECG (electrocardiogram)">ECG (electrocardiogram)</a>, <a href="https://publications.waset.org/abstracts/search?q=heart%20rate%20variability%20%28HRV%29" title=" heart rate variability (HRV)"> heart rate variability (HRV)</a>, <a href="https://publications.waset.org/abstracts/search?q=multiscale%20entropy" title=" multiscale entropy"> multiscale entropy</a>, <a href="https://publications.waset.org/abstracts/search?q=sampling%20frequency" title=" sampling frequency"> sampling frequency</a> </p> <a href="https://publications.waset.org/abstracts/78603/optimal-ecg-sampling-frequency-for-multiscale-entropy-based-hrv" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/78603.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">42461</span> A Study on the Different Components of a Typical Back-Scattered Chipless RFID Tag Reflection </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Fatemeh%20Babaeian">Fatemeh Babaeian</a>, <a href="https://publications.waset.org/abstracts/search?q=Nemai%20Chandra%20Karmakar"> Nemai Chandra Karmakar</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Chipless RFID system is a wireless system for tracking and identification which use passive tags for encoding data. The advantage of using chipless RFID tag is having a planar tag which is printable on different low-cost materials like paper and plastic. The printed tag can be attached to different items in the labelling level. Since the price of chipless RFID tag can be as low as a fraction of a cent, this technology has the potential to compete with the conventional optical barcode labels. However, due to the passive structure of the tag, data processing of the reflection signal is a crucial challenge. The captured reflected signal from a tag attached to an item consists of different components which are the reflection from the reader antenna, the reflection from the item, the tag structural mode RCS component and the antenna mode RCS of the tag. All these components are summed up in both time and frequency domains. The effect of reflection from the item and the structural mode RCS component can distort/saturate the frequency domain signal and cause difficulties in extracting the desired component which is the antenna mode RCS. Therefore, it is required to study the reflection of the tag in both time and frequency domains to have a better understanding of the nature of the captured chipless RFID signal. The other benefits of this study can be to find an optimised encoding technique in tag design level and to find the best processing algorithm the chipless RFID signal in decoding level. In this paper, the reflection from a typical backscattered chipless RFID tag with six resonances is analysed, and different components of the signal are separated in both time and frequency domains. Moreover, the time domain signal corresponding to each resonator of the tag is studied. The data for this processing was captured from simulation in CST Microwave Studio 2017. The outcome of this study is understanding different components of a measured signal in a chipless RFID system and a discovering a research gap which is a need to find an optimum detection algorithm for tag ID extraction. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=antenna%20mode%20RCS" title="antenna mode RCS">antenna mode RCS</a>, <a href="https://publications.waset.org/abstracts/search?q=chipless%20RFID%20tag" title=" chipless RFID tag"> chipless RFID tag</a>, <a href="https://publications.waset.org/abstracts/search?q=resonance" title=" resonance"> resonance</a>, <a href="https://publications.waset.org/abstracts/search?q=structural%20mode%20RCS" title=" structural mode RCS"> structural mode RCS</a> </p> <a href="https://publications.waset.org/abstracts/103734/a-study-on-the-different-components-of-a-typical-back-scattered-chipless-rfid-tag-reflection" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/103734.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">200</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">42460</span> Analysis of Matching Pursuit Features of EEG Signal for Mental Tasks Classification </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Zin%20Mar%20Lwin">Zin Mar Lwin</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Brain Computer Interface (BCI) Systems have developed for people who suffer from severe motor disabilities and challenging to communicate with their environment. BCI allows them for communication by a non-muscular way. For communication between human and computer, BCI uses a type of signal called Electroencephalogram (EEG) signal which is recorded from the human„s brain by means of an electrode. The electroencephalogram (EEG) signal is an important information source for knowing brain processes for the non-invasive BCI. Translating human‟s thought, it needs to classify acquired EEG signal accurately. This paper proposed a typical EEG signal classification system which experiments the Dataset from “Purdue University.” Independent Component Analysis (ICA) method via EEGLab Tools for removing artifacts which are caused by eye blinks. For features extraction, the Time and Frequency features of non-stationary EEG signals are extracted by Matching Pursuit (MP) algorithm. The classification of one of five mental tasks is performed by Multi_Class Support Vector Machine (SVM). For SVMs, the comparisons have been carried out for both 1-against-1 and 1-against-all methods. <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=EEG" title=" EEG"> EEG</a>, <a href="https://publications.waset.org/abstracts/search?q=ICA" title=" ICA"> ICA</a>, <a href="https://publications.waset.org/abstracts/search?q=SVM" title=" SVM"> SVM</a> </p> <a href="https://publications.waset.org/abstracts/19307/analysis-of-matching-pursuit-features-of-eeg-signal-for-mental-tasks-classification" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/19307.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">278</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">42459</span> Signal Processing of Barkhausen Noise Signal for Assessment of Increasing Down Feed in Surface Ground Components with Poor Micro-Magnetic Response</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Tanmaya%20Kumar%20Dash">Tanmaya Kumar Dash</a>, <a href="https://publications.waset.org/abstracts/search?q=Tarun%20Karamshetty"> Tarun Karamshetty</a>, <a href="https://publications.waset.org/abstracts/search?q=Soumitra%20Paul"> Soumitra Paul</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The Barkhausen Noise Analysis (BNA) technique has been utilized to assess surface integrity of steels. But the BNA technique is not very successful in evaluating surface integrity of ground steels that exhibit poor micro-magnetic response. A new approach has been proposed for the processing of BN signal with Fast Fourier transforms while Wavelet transforms has been used to remove noise from the BN signal, with judicious choice of the ‘threshold’ value, when the micro-magnetic response of the work material is poor. In the present study, the effect of down feed induced upon conventional plunge surface grinding of hardened bearing steel has been investigated along with an ultrasonically cleaned, wet polished and a sample ground with spark out technique for benchmarking. Moreover, the FFT analysis has been established, at different sets of applied voltages and applied frequency and the pattern of the BN signal in the frequency domain is analyzed. The study also depicts the wavelet transforms technique with different levels of decomposition and different mother wavelets, which has been used to reduce the noise value in BN signal of materials with poor micro-magnetic response, in order to standardize the procedure for all BN signals depending on the frequency of the applied voltage. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=barkhausen%20noise%20analysis" title="barkhausen noise analysis">barkhausen noise analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=grinding" title=" grinding"> grinding</a>, <a href="https://publications.waset.org/abstracts/search?q=magnetic%20properties" title=" magnetic properties"> magnetic properties</a>, <a href="https://publications.waset.org/abstracts/search?q=signal%20processing" title=" signal processing"> signal processing</a>, <a href="https://publications.waset.org/abstracts/search?q=micro-magnetic%20response" title=" micro-magnetic response"> micro-magnetic response</a> </p> <a href="https://publications.waset.org/abstracts/29867/signal-processing-of-barkhausen-noise-signal-for-assessment-of-increasing-down-feed-in-surface-ground-components-with-poor-micro-magnetic-response" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/29867.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">667</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">42458</span> Classification of Cochannel Signals Using Cyclostationary Signal Processing and Deep Learning</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Bryan%20Crompton">Bryan Crompton</a>, <a href="https://publications.waset.org/abstracts/search?q=Daniel%20Giger"> Daniel Giger</a>, <a href="https://publications.waset.org/abstracts/search?q=Tanay%20Mehta"> Tanay Mehta</a>, <a href="https://publications.waset.org/abstracts/search?q=Apurva%20Mody"> Apurva Mody</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The task of classifying radio frequency (RF) signals has seen recent success in employing deep neural network models. In this work, we present a combined signal processing and machine learning approach to signal classification for cochannel anomalous signals. The power spectral density and cyclostationary signal processing features of a captured signal are computed and fed into a neural net to produce a classification decision. Our combined signal preprocessing and machine learning approach allows for simpler neural networks with fast training times and small computational resource requirements for inference with longer preprocessing time. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=signal%20processing" title="signal processing">signal processing</a>, <a href="https://publications.waset.org/abstracts/search?q=machine%20learning" title=" machine learning"> machine learning</a>, <a href="https://publications.waset.org/abstracts/search?q=cyclostationary%20signal%20processing" title=" cyclostationary signal processing"> cyclostationary signal processing</a>, <a href="https://publications.waset.org/abstracts/search?q=signal%20classification" title=" signal classification"> signal classification</a> </p> <a href="https://publications.waset.org/abstracts/164958/classification-of-cochannel-signals-using-cyclostationary-signal-processing-and-deep-learning" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/164958.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">107</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">42457</span> The Time-Frequency Domain Reflection Method for Aircraft Cable Defects Localization</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Reza%20Rezaeipour%20Honarmandzad">Reza Rezaeipour Honarmandzad</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper introduces an aircraft cable fault detection and location method in light of TFDR keeping in mind the end goal to recognize the intermittent faults adequately and to adapt to the serial and after-connector issues being hard to be distinguished in time domain reflection. In this strategy, the correlation function of reflected and reference signal is used to recognize and find the airplane fault as per the qualities of reflected and reference signal in time-frequency domain, so the hit rate of distinguishing and finding intermittent faults can be enhanced adequately. In the work process, the reflected signal is interfered by the noise and false caution happens frequently, so the threshold de-noising technique in light of wavelet decomposition is used to diminish the noise interference and lessen the shortcoming alert rate. At that point the time-frequency cross connection capacity of the reference signal and the reflected signal based on Wigner-Ville appropriation is figured so as to find the issue position. Finally, LabVIEW is connected to execute operation and control interface, the primary capacity of which is to connect and control MATLAB and LABSQL. Using the solid computing capacity and the bottomless capacity library of MATLAB, the signal processing turn to be effortlessly acknowledged, in addition LabVIEW help the framework to be more dependable and upgraded effectively. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=aircraft%20cable" title="aircraft cable">aircraft cable</a>, <a href="https://publications.waset.org/abstracts/search?q=fault%20location" title=" fault location"> fault location</a>, <a href="https://publications.waset.org/abstracts/search?q=TFDR" title=" TFDR"> TFDR</a>, <a href="https://publications.waset.org/abstracts/search?q=LabVIEW" title=" LabVIEW"> LabVIEW</a> </p> <a href="https://publications.waset.org/abstracts/35568/the-time-frequency-domain-reflection-method-for-aircraft-cable-defects-localization" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/35568.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">476</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">42456</span> Subband Coding and Glottal Closure Instant (GCI) Using SEDREAMS Algorithm</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Harisudha%20Kuresan">Harisudha Kuresan</a>, <a href="https://publications.waset.org/abstracts/search?q=Dhanalakshmi%20Samiappan"> Dhanalakshmi Samiappan</a>, <a href="https://publications.waset.org/abstracts/search?q=T.%20Rama%20Rao"> T. Rama Rao</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In modern telecommunication applications, Glottal Closure Instants location finding is important and is directly evaluated from the speech waveform. Here, we study the GCI using Speech Event Detection using Residual Excitation and the Mean Based Signal (SEDREAMS) algorithm. Speech coding uses parameter estimation using audio signal processing techniques to model the speech signal combined with generic data compression algorithms to represent the resulting modeled in a compact bit stream. This paper proposes a sub-band coder SBC, which is a type of transform coding and its performance for GCI detection using SEDREAMS are evaluated. In SBCs code in the speech signal is divided into two or more frequency bands and each of these sub-band signal is coded individually. The sub-bands after being processed are recombined to form the output signal, whose bandwidth covers the whole frequency spectrum. Then the signal is decomposed into low and high-frequency components and decimation and interpolation in frequency domain are performed. The proposed structure significantly reduces error, and precise locations of Glottal Closure Instants (GCIs) are found using SEDREAMS algorithm. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=SEDREAMS" title="SEDREAMS">SEDREAMS</a>, <a href="https://publications.waset.org/abstracts/search?q=GCI" title=" GCI"> GCI</a>, <a href="https://publications.waset.org/abstracts/search?q=SBC" title=" SBC"> SBC</a>, <a href="https://publications.waset.org/abstracts/search?q=GOI" title=" GOI"> GOI</a> </p> <a href="https://publications.waset.org/abstracts/56336/subband-coding-and-glottal-closure-instant-gci-using-sedreams-algorithm" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/56336.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">356</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">42455</span> Visualization Tool for EEG Signal Segmentation</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Sweeti">Sweeti</a>, <a href="https://publications.waset.org/abstracts/search?q=Anoop%20Kant%20Godiyal"> Anoop Kant Godiyal</a>, <a href="https://publications.waset.org/abstracts/search?q=Neha%20Singh"> Neha Singh</a>, <a href="https://publications.waset.org/abstracts/search?q=Sneh%20Anand"> Sneh Anand</a>, <a href="https://publications.waset.org/abstracts/search?q=B.%20K.%20Panigrahi"> B. K. Panigrahi</a>, <a href="https://publications.waset.org/abstracts/search?q=Jayasree%20Santhosh"> Jayasree Santhosh</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This work is about developing a tool for visualization and segmentation of Electroencephalograph (EEG) signals based on frequency domain features. Change in the frequency domain characteristics are correlated with change in mental state of the subject under study. Proposed algorithm provides a way to represent the change in the mental states using the different frequency band powers in form of segmented EEG signal. Many segmentation algorithms have been suggested in literature having application in brain computer interface, epilepsy and cognition studies that have been used for data classification. But the proposed method focusses mainly on the better presentation of signal and that’s why it could be a good utilization tool for clinician. Algorithm performs the basic filtering using band pass and notch filters in the range of 0.1-45 Hz. Advanced filtering is then performed by principal component analysis and wavelet transform based de-noising method. Frequency domain features are used for segmentation; considering the fact that the spectrum power of different frequency bands describes the mental state of the subject. Two sliding windows are further used for segmentation; one provides the time scale and other assigns the segmentation rule. The segmented data is displayed second by second successively with different color codes. Segment’s length can be selected as per need of the objective. Proposed algorithm has been tested on the EEG data set obtained from University of California in San Diego’s online data repository. Proposed tool gives a better visualization of the signal in form of segmented epochs of desired length representing the power spectrum variation in data. The algorithm is designed in such a way that it takes the data points with respect to the sampling frequency for each time frame and so it can be improved to use in real time visualization with desired epoch length. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=de-noising" title="de-noising">de-noising</a>, <a href="https://publications.waset.org/abstracts/search?q=multi-channel%20data" title=" multi-channel data"> multi-channel data</a>, <a href="https://publications.waset.org/abstracts/search?q=PCA" title=" PCA"> PCA</a>, <a href="https://publications.waset.org/abstracts/search?q=power%20spectra" title=" power spectra"> power spectra</a>, <a href="https://publications.waset.org/abstracts/search?q=segmentation" title=" segmentation"> segmentation</a> </p> <a href="https://publications.waset.org/abstracts/37186/visualization-tool-for-eeg-signal-segmentation" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/37186.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">397</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">42454</span> Application of Local Mean Decomposition for Rolling Bearing Fault Diagnosis Based On Vibration Signals</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Toufik%20Bensana">Toufik Bensana</a>, <a href="https://publications.waset.org/abstracts/search?q=Slimane%20Mekhilef"> Slimane Mekhilef</a>, <a href="https://publications.waset.org/abstracts/search?q=Kamel%20Tadjine"> Kamel Tadjine</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Vibration analysis has been frequently applied in the condition monitoring and fault diagnosis of rolling element bearings. Unfortunately, the vibration signals collected from a faulty bearing are generally non stationary, nonlinear and with strong noise interference, so it is essential to obtain the fault features correctly. In this paper, a novel numerical analysis method based on local mean decomposition (LMD) is proposed. LMD decompose the signal into a series of product functions (PFs), each of which is the product of an envelope signal and a purely frequency modulated FM signal. The envelope of a PF is the instantaneous amplitude (IA) and the derivative of the unwrapped phase of a purely flat frequency demodulated (FM) signal is the IF. After that the fault characteristic frequency of the roller bearing can be extracted by performing spectrum analysis to the instantaneous amplitude of PF component containing dominant fault information. The results show the effectiveness of the proposed technique in fault detection and diagnosis of rolling element bearing. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=fault%20diagnosis" title="fault diagnosis">fault diagnosis</a>, <a href="https://publications.waset.org/abstracts/search?q=condition%20monitoring" title=" condition monitoring"> condition monitoring</a>, <a href="https://publications.waset.org/abstracts/search?q=local%20mean%20decomposition" title=" local mean decomposition"> local mean decomposition</a>, <a href="https://publications.waset.org/abstracts/search?q=rolling%20element%20bearing" title=" rolling element bearing"> rolling element bearing</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/31381/application-of-local-mean-decomposition-for-rolling-bearing-fault-diagnosis-based-on-vibration-signals" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/31381.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">397</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">42453</span> Signal Processing Approach to Study Multifractality and Singularity of Solar Wind Speed Time Series</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Tushnik%20Sarkar">Tushnik Sarkar</a>, <a href="https://publications.waset.org/abstracts/search?q=Mofazzal%20H.%20Khondekar"> Mofazzal H. Khondekar</a>, <a href="https://publications.waset.org/abstracts/search?q=Subrata%20Banerjee"> Subrata Banerjee</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper investigates the nature of the fluctuation of the daily average Solar wind speed time series collected over a period of 2492 days, from 1<sup>st </sup>January, 1997 to 28<sup>th</sup> October, 2003. The degree of self-similarity and scalability of the Solar Wind Speed signal has been explored to characterise the signal fluctuation. Multi-fractal Detrended Fluctuation Analysis (MFDFA) method has been implemented on the signal which is under investigation to perform this task. Furthermore, the singularity spectra of the signals have been also obtained to gauge the extent of the multifractality of the time series signal. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=detrended%20fluctuation%20analysis" title="detrended fluctuation analysis">detrended fluctuation analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=generalized%20hurst%20exponent" title=" generalized hurst exponent"> generalized hurst exponent</a>, <a href="https://publications.waset.org/abstracts/search?q=holder%20exponents" title=" holder exponents"> holder exponents</a>, <a href="https://publications.waset.org/abstracts/search?q=multifractal%20exponent" title=" multifractal exponent"> multifractal exponent</a>, <a href="https://publications.waset.org/abstracts/search?q=multifractal%20spectrum" title=" multifractal spectrum"> multifractal spectrum</a>, <a href="https://publications.waset.org/abstracts/search?q=singularity%20spectrum" title=" singularity spectrum"> singularity spectrum</a>, <a href="https://publications.waset.org/abstracts/search?q=time%20series%20analysis" title=" time series analysis"> time series analysis</a> </p> <a href="https://publications.waset.org/abstracts/62350/signal-processing-approach-to-study-multifractality-and-singularity-of-solar-wind-speed-time-series" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/62350.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">393</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">42452</span> Analysis and Modeling of Vibratory Signals Based on LMD for Rolling Bearing Fault Diagnosis</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Toufik%20Bensana">Toufik Bensana</a>, <a href="https://publications.waset.org/abstracts/search?q=Slimane%20Mekhilef"> Slimane Mekhilef</a>, <a href="https://publications.waset.org/abstracts/search?q=Kamel%20Tadjine"> Kamel Tadjine</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The use of vibration analysis has been established as the most common and reliable method of analysis in the field of condition monitoring and diagnostics of rotating machinery. Rolling bearings cover a broad range of rotary machines and plays a crucial role in the modern manufacturing industry. Unfortunately, the vibration signals collected from a faulty bearing are generally non-stationary, nonlinear and with strong noise interference, so it is essential to obtain the fault features correctly. In this paper, a novel numerical analysis method based on local mean decomposition (LMD) is proposed. LMD decompose the signal into a series of product functions (PFs), each of which is the product of an envelope signal and a purely frequency modulated FM signal. The envelope of a PF is the instantaneous amplitude (IA) and the derivative of the unwrapped phase of a purely flat frequency demodulated (FM) signal is the IF. After that, the fault characteristic frequency of the roller bearing can be extracted by performing spectrum analysis to the instantaneous amplitude of PF component containing dominant fault information. the results show the effectiveness of the proposed technique in fault detection and diagnosis of rolling element bearing. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=fault%20diagnosis" title="fault diagnosis">fault diagnosis</a>, <a href="https://publications.waset.org/abstracts/search?q=local%20mean%20decomposition" title=" local mean decomposition"> local mean decomposition</a>, <a href="https://publications.waset.org/abstracts/search?q=rolling%20element%20bearing" title=" rolling element bearing"> rolling element bearing</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/30908/analysis-and-modeling-of-vibratory-signals-based-on-lmd-for-rolling-bearing-fault-diagnosis" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/30908.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">408</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">42451</span> A High Time Resolution Digital Pulse Width Modulator Based on Field Programmable Gate Array’s Phase Locked Loop Megafunction</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Jun%20Wang">Jun Wang</a>, <a href="https://publications.waset.org/abstracts/search?q=Tingcun%20Wei"> Tingcun Wei</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The digital pulse width modulator (DPWM) is the crucial building block for digitally-controlled DC-DC switching converter, which converts the digital duty ratio signal into its analog counterpart to control the power MOSFET transistors on or off. With the increase of switching frequency of digitally-controlled DC-DC converter, the DPWM with higher time resolution is required. In this paper, a 15-bits DPWM with three-level hybrid structure is presented; the first level is composed of a7-bits counter and a comparator, the second one is a 5-bits delay line, and the third one is a 3-bits digital dither. The presented DPWM is designed and implemented using the PLL megafunction of FPGA (Field Programmable Gate Arrays), and the required frequency of clock signal is 128 times of switching frequency. The simulation results show that, for the switching frequency of 2 MHz, a DPWM which has the time resolution of 15 ps is achieved using a maximum clock frequency of 256MHz. The designed DPWM in this paper is especially useful for high-frequency digitally-controlled DC-DC switching converters. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=DPWM" title="DPWM">DPWM</a>, <a href="https://publications.waset.org/abstracts/search?q=digitally-controlled%20DC-DC%20switching%20converter" title=" digitally-controlled DC-DC switching converter"> digitally-controlled DC-DC switching converter</a>, <a href="https://publications.waset.org/abstracts/search?q=FPGA" title=" FPGA"> FPGA</a>, <a href="https://publications.waset.org/abstracts/search?q=PLL%20megafunction" title=" PLL megafunction"> PLL megafunction</a>, <a href="https://publications.waset.org/abstracts/search?q=time%20resolution" title=" time resolution"> time resolution</a> </p> <a href="https://publications.waset.org/abstracts/50826/a-high-time-resolution-digital-pulse-width-modulator-based-on-field-programmable-gate-arrays-phase-locked-loop-megafunction" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/50826.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">480</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">42450</span> Frequency-Dependent and Full Range Tunable Phase Shifter</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Yufu%20Yin">Yufu Yin</a>, <a href="https://publications.waset.org/abstracts/search?q=Tao%20Lin"> Tao Lin</a>, <a href="https://publications.waset.org/abstracts/search?q=Shanghong%20Zhao"> Shanghong Zhao</a>, <a href="https://publications.waset.org/abstracts/search?q=Zihang%20Zhu"> Zihang Zhu</a>, <a href="https://publications.waset.org/abstracts/search?q=Xuan%20Li"> Xuan Li</a>, <a href="https://publications.waset.org/abstracts/search?q=Wei%20Jiang"> Wei Jiang</a>, <a href="https://publications.waset.org/abstracts/search?q=Qiurong%20Zheng"> Qiurong Zheng</a>, <a href="https://publications.waset.org/abstracts/search?q=Hui%20Wang"> Hui Wang</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, a frequency-dependent and tunable phase shifter is proposed and numerically analyzed. The key devices are the dual-polarization binary phase shift keying modulator (DP-BPSK) and the fiber Bragg grating (FBG). The phase-frequency response of the FBG is employed to determine the frequency-dependent phase shift. The simulation results show that a linear phase shift of the recovered output microwave signal which depends on the frequency of the input RF signal is achieved. In addition, by adjusting the power of the RF signal, the full range phase shift from 0° to 360° can be realized. This structure shows the spurious free dynamic range (SFDR) of 70.90 dB·Hz<sup>2/3</sup> and 72.11 dB·Hz<sup>2/3</sup> under different RF powers. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=microwave%20photonics" title="microwave photonics">microwave photonics</a>, <a href="https://publications.waset.org/abstracts/search?q=phase%20shifter" title=" phase shifter"> phase shifter</a>, <a href="https://publications.waset.org/abstracts/search?q=spurious%20free%20dynamic%20range" title=" spurious free dynamic range"> spurious free dynamic range</a>, <a href="https://publications.waset.org/abstracts/search?q=frequency-dependent" title=" frequency-dependent"> frequency-dependent</a> </p> <a href="https://publications.waset.org/abstracts/95223/frequency-dependent-and-full-range-tunable-phase-shifter" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/95223.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">296</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">42449</span> Monitoring of Spectrum Usage and Signal Identification Using Cognitive Radio</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=O.%20S.%20Omorogiuwa">O. S. Omorogiuwa</a>, <a href="https://publications.waset.org/abstracts/search?q=E.%20J.%20Omozusi"> E. J. Omozusi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The monitoring of spectrum usage and signal identification, using cognitive radio, is done to identify frequencies that are vacant for reuse. It has been established that ‘internet of things’ device uses secondary frequency which is free, thereby facing the challenge of interference from other users, where some primary frequencies are not being utilised. The design was done by analysing a specific frequency spectrum, checking if all the frequency stations that range from 87.5-108 MHz are presently being used in Benin City, Edo State, Nigeria. From the results, it was noticed that by using Software Defined Radio/Simulink, we were able to identify vacant frequencies in the range of frequency under consideration. Also, we were able to use the significance of energy detection threshold to reuse this vacant frequency spectrum, when the cognitive radio displays a zero output (that is decision H0), meaning that the channel is unoccupied. Hence, the analysis was able to find the spectrum hole and identify how it can be reused. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=spectrum" title="spectrum">spectrum</a>, <a href="https://publications.waset.org/abstracts/search?q=interference" title=" interference"> interference</a>, <a href="https://publications.waset.org/abstracts/search?q=telecommunication" title=" telecommunication"> telecommunication</a>, <a href="https://publications.waset.org/abstracts/search?q=cognitive%20radio" title=" cognitive radio"> cognitive radio</a>, <a href="https://publications.waset.org/abstracts/search?q=frequency" title=" frequency"> frequency</a> </p> <a href="https://publications.waset.org/abstracts/93900/monitoring-of-spectrum-usage-and-signal-identification-using-cognitive-radio" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/93900.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">224</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">42448</span> Linear Frequency Modulation-Frequency Shift Keying Radar with Compressive Sensing</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ho%20Jeong%20Jin">Ho Jeong Jin</a>, <a href="https://publications.waset.org/abstracts/search?q=Chang%20Won%20Seo"> Chang Won Seo</a>, <a href="https://publications.waset.org/abstracts/search?q=Choon%20Sik%20Cho"> Choon Sik Cho</a>, <a href="https://publications.waset.org/abstracts/search?q=Bong%20Yong%20Choi"> Bong Yong Choi</a>, <a href="https://publications.waset.org/abstracts/search?q=Kwang%20Kyun%20Na"> Kwang Kyun Na</a>, <a href="https://publications.waset.org/abstracts/search?q=Sang%20Rok%20Lee"> Sang Rok Lee</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, a radar signal processing technique using the LFM-FSK (Linear Frequency Modulation-Frequency Shift Keying) is proposed for reducing the false alarm rate based on the compressive sensing. The LFM-FSK method combines FMCW (Frequency Modulation Continuous Wave) signal with FSK (Frequency Shift Keying). This shows an advantage which can suppress the ghost phenomenon without the complicated CFAR (Constant False Alarm Rate) algorithm. Moreover, the parametric sparse algorithm applying the compressive sensing that restores signals efficiently with respect to the incomplete data samples is also integrated, leading to reducing the burden of ADC in the receiver of radars. 24 GHz FMCW signal is applied and tested in the real environment with FSK modulated data for verifying the proposed algorithm along with the compressive sensing. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=compressive%20sensing" title="compressive sensing">compressive sensing</a>, <a href="https://publications.waset.org/abstracts/search?q=LFM-FSK%20radar" title=" LFM-FSK radar"> LFM-FSK radar</a>, <a href="https://publications.waset.org/abstracts/search?q=radar%20signal%20processing" title=" radar signal processing"> radar signal processing</a>, <a href="https://publications.waset.org/abstracts/search?q=sparse%20algorithm" title=" sparse algorithm"> sparse algorithm</a> </p> <a href="https://publications.waset.org/abstracts/51309/linear-frequency-modulation-frequency-shift-keying-radar-with-compressive-sensing" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/51309.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">482</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">42447</span> Analysis of Injection-Lock in Oscillators versus Phase Variation of Injected Signal</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=M.%20Yousefi">M. Yousefi</a>, <a href="https://publications.waset.org/abstracts/search?q=N.%20Nasirzadeh"> N. Nasirzadeh</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, behavior of an oscillator under injection of another signal has been investigated. Also, variation of output signal amplitude versus injected signal phase variation, the effect of varying the amplitude of injected signal and quality factor of the oscillator has been investigated. The results show that the locking time depends on phase and the best locking time happens at 180-degrees phase. Also, the effect of injected lock has been discussed. Simulations show that the locking time decreases with signal injection to bulk. Locking time has been investigated versus various phase differences. The effect of phase and amplitude changes on locking time of a typical LC oscillator in 180 nm technology has been investigated. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=analysis" title="analysis">analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=oscillator" title=" oscillator"> oscillator</a>, <a href="https://publications.waset.org/abstracts/search?q=injection-lock%20oscillator" title=" injection-lock oscillator"> injection-lock oscillator</a>, <a href="https://publications.waset.org/abstracts/search?q=phase%20modulation" title=" phase modulation"> phase modulation</a> </p> <a href="https://publications.waset.org/abstracts/53354/analysis-of-injection-lock-in-oscillators-versus-phase-variation-of-injected-signal" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/53354.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">348</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">42446</span> Analysis of Vibratory Signals Based on Local Mean Decomposition (LMD) for Rolling Bearing Fault Diagnosis</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Toufik%20Bensana">Toufik Bensana</a>, <a href="https://publications.waset.org/abstracts/search?q=Medkour%20Mihoub"> Medkour Mihoub</a>, <a href="https://publications.waset.org/abstracts/search?q=Slimane%20Mekhilef"> Slimane Mekhilef</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The use of vibration analysis has been established as the most common and reliable method of analysis in the field of condition monitoring and diagnostics of rotating machinery. Rolling bearings cover a broad range of rotary machines and plays a crucial role in the modern manufacturing industry. Unfortunately, the vibration signals collected from a faulty bearing are generally nonstationary, nonlinear and with strong noise interference, so it is essential to obtain the fault features correctly. In this paper, a novel numerical analysis method based on local mean decomposition (LMD) is proposed. LMD decompose the signal into a series of product functions (PFs), each of which is the product of an envelope signal and a purely frequency modulated FM signal. The envelope of a PF is the instantaneous amplitude (IA), and the derivative of the unwrapped phase of a purely flat frequency demodulated (FM) signal is the IF. After that, the fault characteristic frequency of the roller bearing can be extracted by performing spectrum analysis to the instantaneous amplitude of PF component containing dominant fault information. The results show the effectiveness of the proposed technique in fault detection and diagnosis of rolling element bearing. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=fault%20diagnosis" title="fault diagnosis">fault diagnosis</a>, <a href="https://publications.waset.org/abstracts/search?q=rolling%20element%20bearing" title=" rolling element bearing"> rolling element bearing</a>, <a href="https://publications.waset.org/abstracts/search?q=local%20mean%20decomposition" title=" local mean decomposition"> local mean decomposition</a>, <a href="https://publications.waset.org/abstracts/search?q=condition%20monitoring" title=" condition monitoring"> condition monitoring</a> </p> <a href="https://publications.waset.org/abstracts/53190/analysis-of-vibratory-signals-based-on-local-mean-decomposition-lmd-for-rolling-bearing-fault-diagnosis" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/53190.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">389</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">42445</span> THz Phase Extraction Algorithms for a THz Modulating Interferometric Doppler Radar</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Shaolin%20Allen%20Liao">Shaolin Allen Liao</a>, <a href="https://publications.waset.org/abstracts/search?q=Hual-Te%20Chien"> Hual-Te Chien</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Various THz phase extraction algorithms have been developed for a novel THz Modulating Interferometric Doppler Radar (THz-MIDR) developed recently by the author. The THz-MIDR differs from the well-known FTIR technique in that it introduces a continuously modulating reference branch, compared to the time-consuming discrete FTIR stepping reference branch. Such change allows real-time tracking of a moving object and capturing of its Doppler signature. The working principle of the THz-MIDR is similar to the FTIR technique: the incoming THz emission from the scene is split by a beam splitter/combiner; one of the beams is continuously modulated by a vibrating mirror or phase modulator and the other split beam is reflected by a reflection mirror; finally both the modulated reference beam and reflected beam are combined by the same beam splitter/combiner and detected by a THz intensity detector (for example, a pyroelectric detector). In order to extract THz phase from the single intensity measurement signal, we have derived rigorous mathematical formulas for 3 Frequency Banded (FB) signals: 1) DC Low-Frequency Banded (LFB) signal; 2) Fundamental Frequency Banded (FFB) signal; and 3) Harmonic Frequency Banded (HFB) signal. The THz phase extraction algorithms are then developed based combinations of 2 or all of these 3 FB signals with efficient algorithms such as Levenberg-Marquardt nonlinear fitting algorithm. Numerical simulation has also been performed in Matlab with simulated THz-MIDR interferometric signal of various Signal to Noise Ratio (SNR) to verify the algorithms. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=algorithm" title="algorithm">algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=modulation" title=" modulation"> modulation</a>, <a href="https://publications.waset.org/abstracts/search?q=THz%20phase" title=" THz phase"> THz phase</a>, <a href="https://publications.waset.org/abstracts/search?q=THz%20interferometry%20doppler%20radar" title=" THz interferometry doppler radar"> THz interferometry doppler radar</a> </p> <a href="https://publications.waset.org/abstracts/48964/thz-phase-extraction-algorithms-for-a-thz-modulating-interferometric-doppler-radar" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/48964.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">42444</span> Design of a Phemt Buffer Amplifier in Mm-Wave Band around 60 GHz</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Maryam%20Abata">Maryam Abata</a>, <a href="https://publications.waset.org/abstracts/search?q=Moulhime%20El%20Bekkali"> Moulhime El Bekkali</a>, <a href="https://publications.waset.org/abstracts/search?q=Said%20Mazer"> Said Mazer</a>, <a href="https://publications.waset.org/abstracts/search?q=Catherine%20Algani"> Catherine Algani</a>, <a href="https://publications.waset.org/abstracts/search?q=Mahmoud%20Mehdi"> Mahmoud Mehdi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> One major problem of most electronic systems operating in the millimeter wave band is the signal generation with a high purity and a stable carrier frequency. This problem is overcome by using the combination of a signal with a low frequency local oscillator (LO) and several stages of frequency multipliers. The use of these frequency multipliers to create millimeter-wave signals is an attractive alternative to direct generation signal. Therefore, the isolation problem of the local oscillator from the other stages is always present, which leads to have various mechanisms that can disturb the oscillator performance, thus a buffer amplifier is often included in oscillator outputs. In this paper, we present the study and design of a buffer amplifier in the mm-wave band using a 0.15μm pHEMT from UMS foundry. This amplifier will be used as a part of a frequency quadrupler at 60 GHz. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mm-wave%20band" title="Mm-wave band">Mm-wave band</a>, <a href="https://publications.waset.org/abstracts/search?q=local%20oscillator" title=" local oscillator"> local oscillator</a>, <a href="https://publications.waset.org/abstracts/search?q=frequency%20quadrupler" title=" frequency quadrupler"> frequency quadrupler</a>, <a href="https://publications.waset.org/abstracts/search?q=buffer%20amplifier" title=" buffer amplifier"> buffer amplifier</a> </p> <a href="https://publications.waset.org/abstracts/26079/design-of-a-phemt-buffer-amplifier-in-mm-wave-band-around-60-ghz" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/26079.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">545</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">42443</span> A Self-Adaptive Stimulus Artifacts Removal Approach for Electrical Stimulation Based Muscle Rehabilitation</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Yinjun%20Tu">Yinjun Tu</a>, <a href="https://publications.waset.org/abstracts/search?q=Qiang%20Fang"> Qiang Fang</a>, <a href="https://publications.waset.org/abstracts/search?q=Glenn%20I.%20Matthews"> Glenn I. Matthews</a>, <a href="https://publications.waset.org/abstracts/search?q=Shuenn-Yuh%20Lee"> Shuenn-Yuh Lee</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper reports an efficient and rigorous self-adaptive stimulus artifacts removal approach for a mixed surface EMG (Electromyography) and stimulus signal during muscle stimulation. The recording of EMG and the stimulation of muscles were performing simultaneously. It is difficult to generate muscle fatigue feature from the mixed signal, which can be further used in closed loop system. A self-adaptive method is proposed in this paper, the stimulation frequency was calculated and verified firstly. Then, a mask was created based on this stimulation frequency to remove the undesired stimulus. 20 EMG signal recordings were analyzed, and the ANOVA (analysis of variance) approach illustrated that the decreasing trend of median power frequencies was successfully generated from the 'cleaned' EMG signal. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=EMG" title="EMG">EMG</a>, <a href="https://publications.waset.org/abstracts/search?q=FES" title=" FES"> FES</a>, <a href="https://publications.waset.org/abstracts/search?q=stimulus%20artefacts" title=" stimulus artefacts"> stimulus artefacts</a>, <a href="https://publications.waset.org/abstracts/search?q=self-adaptive" title=" self-adaptive"> self-adaptive</a> </p> <a href="https://publications.waset.org/abstracts/78969/a-self-adaptive-stimulus-artifacts-removal-approach-for-electrical-stimulation-based-muscle-rehabilitation" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/78969.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">399</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">42442</span> Bidirectional Long Short-Term Memory-Based Signal Detection for Orthogonal Frequency Division Multiplexing With All Index Modulation</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mahmut%20Yildirim">Mahmut Yildirim</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper proposed the bidirectional long short-term memory (Bi-LSTM) network-aided deep learning (DL)-based signal detection for Orthogonal frequency division multiplexing with all index modulation (OFDM-AIM), namely Bi-DeepAIM. OFDM-AIM is developed to increase the spectral efficiency of OFDM with index modulation (OFDM-IM), a promising multi-carrier technique for communication systems beyond 5G. In this paper, due to its strong classification ability, Bi-LSTM is considered an alternative to the maximum likelihood (ML) algorithm, which is used for signal detection in the classical OFDM-AIM scheme. The performance of the Bi-DeepAIM is compared with LSTM network-aided DL-based OFDM-AIM (DeepAIM) and classic OFDM-AIM that uses (ML)-based signal detection via BER performance and computational time criteria. Simulation results show that Bi-DeepAIM obtains better bit error rate (BER) performance than DeepAIM and lower computation time in signal detection than ML-AIM. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=bidirectional%20long%20short-term%20memory" title="bidirectional long short-term memory">bidirectional long short-term memory</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=maximum%20likelihood" title=" maximum likelihood"> maximum likelihood</a>, <a href="https://publications.waset.org/abstracts/search?q=OFDM%20with%20all%20index%20modulation" title=" OFDM with all index modulation"> OFDM with all index modulation</a>, <a href="https://publications.waset.org/abstracts/search?q=signal%20detection" title=" signal detection"> signal detection</a> </p> <a href="https://publications.waset.org/abstracts/183512/bidirectional-long-short-term-memory-based-signal-detection-for-orthogonal-frequency-division-multiplexing-with-all-index-modulation" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/183512.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">72</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">42441</span> Gestalt in Music and Brain: A Non-Linear Chaos Based Study with Detrended/Adaptive Fractal Analysis</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Shankha%20Sanyal">Shankha Sanyal</a>, <a href="https://publications.waset.org/abstracts/search?q=Archi%20Banerjee"> Archi Banerjee</a>, <a href="https://publications.waset.org/abstracts/search?q=Sayan%20Biswas"> Sayan Biswas</a>, <a href="https://publications.waset.org/abstracts/search?q=Sourya%20Sengupta"> Sourya Sengupta</a>, <a href="https://publications.waset.org/abstracts/search?q=Sayan%20Nag"> Sayan Nag</a>, <a href="https://publications.waset.org/abstracts/search?q=Ranjan%20Sengupta"> Ranjan Sengupta</a>, <a href="https://publications.waset.org/abstracts/search?q=Dipak%20Ghosh"> Dipak Ghosh</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The term ‘gestalt’ has been widely used in the field of psychology which defined the perception of human mind to group any object not in part but as a 'unified' whole. Music, in general, is polyphonic - i.e. a combination of a number of pure tones (frequencies) mixed together in a manner that sounds harmonious. The study of human brain response due to different frequency groups of the acoustic signal can give us an excellent insight regarding the neural and functional architecture of brain functions. Hence, the study of music cognition using neuro-biosensors is becoming a rapidly emerging field of research. In this work, we have tried to analyze the effect of different frequency bands of music on the various frequency rhythms of human brain obtained from EEG data. Four widely popular Rabindrasangeet clips were subjected to Wavelet Transform method for extracting five resonant frequency bands from the original music signal. These frequency bands were initially analyzed with Detrended/Adaptive Fractal analysis (DFA/AFA) methods. A listening test was conducted on a pool of 100 respondents to assess the frequency band in which the music becomes non-recognizable. Next, these resonant frequency bands were presented to 20 subjects as auditory stimulus and EEG signals recorded simultaneously in 19 different locations of the brain. The recorded EEG signals were noise cleaned and subjected again to DFA/AFA technique on the alpha, theta and gamma frequency range. Thus, we obtained the scaling exponents from the two methods in alpha, theta and gamma EEG rhythms corresponding to different frequency bands of music. From the analysis of music signal, it is seen that loss of recognition is proportional to the loss of long range correlation in the signal. From the EEG signal analysis, we obtain frequency specific arousal based response in different lobes of brain as well as in specific EEG bands corresponding to musical stimuli. In this way, we look to identify a specific frequency band beyond which the music becomes non-recognizable and below which in spite of the absence of other bands the music is perceivable to the audience. This revelation can be of immense importance when it comes to the field of cognitive music therapy and researchers of creativity. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=AFA" title="AFA">AFA</a>, <a href="https://publications.waset.org/abstracts/search?q=DFA" title=" DFA"> DFA</a>, <a href="https://publications.waset.org/abstracts/search?q=EEG" title=" EEG"> EEG</a>, <a href="https://publications.waset.org/abstracts/search?q=gestalt%20in%20music" title=" gestalt in music"> gestalt in music</a>, <a href="https://publications.waset.org/abstracts/search?q=Hurst%20exponent" title=" Hurst exponent"> Hurst exponent</a> </p> <a href="https://publications.waset.org/abstracts/71039/gestalt-in-music-and-brain-a-non-linear-chaos-based-study-with-detrendedadaptive-fractal-analysis" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/71039.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">332</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">42440</span> The Principle Probabilities of Space-Distance Resolution for a Monostatic Radar and Realization in Cylindrical Array</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Anatoly%20D.%20Pluzhnikov">Anatoly D. Pluzhnikov</a>, <a href="https://publications.waset.org/abstracts/search?q=Elena%20N.%20Pribludova"> Elena N. Pribludova</a>, <a href="https://publications.waset.org/abstracts/search?q=Alexander%20G.%20Ryndyk"> Alexander G. Ryndyk</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In conjunction with the problem of the target selection on a clutter background, the analysis of the scanning rate influence on the spatial-temporal signal structure, the generalized multivariate correlation function and the quality of the resolution with the increase pulse repetition frequency is made. The possibility of the object space-distance resolution, which is conditioned by the range-to-angle conversion with an increased scanning rate, is substantiated. The calculations for the real cylindrical array at high scanning rate are presented. The high scanning rate let to get the signal to noise improvement of the order of 10 dB for the space-time signal processing. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=antenna%20pattern" title="antenna pattern">antenna pattern</a>, <a href="https://publications.waset.org/abstracts/search?q=array" title=" array"> array</a>, <a href="https://publications.waset.org/abstracts/search?q=signal%20processing" title=" signal processing"> signal processing</a>, <a href="https://publications.waset.org/abstracts/search?q=spatial%20resolution" title=" spatial resolution"> spatial resolution</a> </p> <a href="https://publications.waset.org/abstracts/98259/the-principle-probabilities-of-space-distance-resolution-for-a-monostatic-radar-and-realization-in-cylindrical-array" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/98259.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">42439</span> Preventive Maintenance of Rotating Machinery Based on Vibration Diagnosis of Rolling Bearing</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=T.%20Bensana">T. Bensana</a>, <a href="https://publications.waset.org/abstracts/search?q=S.%20Mekhilef"> S. Mekhilef</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The methodology of vibration based condition monitoring technology has been developing at a rapid stage in the recent years suiting to the maintenance of sophisticated and complicated machines. The ability of wavelet analysis to efficiently detect non-stationary, non-periodic, transient features of the vibration signal makes it a demanding tool for condition monitoring. This paper presents a methodology for fault diagnosis of rolling element bearings based on wavelet envelope power spectrum technique is analysed in both the time and frequency domains. In the time domain the auto-correlation of the wavelet de-noised signal is applied to evaluate the period of the fault pulses. However, in the frequency domain the wavelet envelope power spectrum has been used to identify the fault frequencies with the single sided complex Laplace wavelet as the mother wavelet function. Results show the superiority of the proposed method and its effectiveness in extracting fault features from the raw vibration signal. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=preventive%20maintenance" title="preventive maintenance">preventive maintenance</a>, <a href="https://publications.waset.org/abstracts/search?q=fault%20diagnostics" title=" fault diagnostics"> fault diagnostics</a>, <a href="https://publications.waset.org/abstracts/search?q=rolling%20element%20bearings" title=" rolling element bearings"> rolling element bearings</a>, <a href="https://publications.waset.org/abstracts/search?q=wavelet%20de-noising" title=" wavelet de-noising"> wavelet de-noising</a> </p> <a href="https://publications.waset.org/abstracts/18460/preventive-maintenance-of-rotating-machinery-based-on-vibration-diagnosis-of-rolling-bearing" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/18460.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">379</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=time%20and%20frequency%20signal%20analysis&page=2">2</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=time%20and%20frequency%20signal%20analysis&page=3">3</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=time%20and%20frequency%20signal%20analysis&page=4">4</a></li> <li class="page-item"><a class="page-link" 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