Current Trends in Signal Processing
https://stmjournals.com/index.php?journal=CTSP
<p style="margin: 0px; padding: 5px; text-align: justify; color: #333333; font-size: 11.818181991577148px; line-height: 20px;"><strong style="margin: 0px; padding: 0px;">Current Trend in Signal Processing ( CTSP)</strong> is an international eJournal focused towards the rapid publication of fundamental research papers on areas of Signal Processing.</p><p style="margin: 0px; padding: 5px; text-align: justify; color: #333333; font-size: 11.818181991577148px; line-height: 20px;"><strong style="margin: 0px; padding: 0px;">Focus and Scope Covers</strong></p><ul style="margin: 0px; padding: 0px; color: #333333; font-size: 11.818181991577148px; line-height: 20px;"><li style="margin: 0px 0px 0px 25px; padding: 0px;">Electrical Engineering and Signaling</li><li style="margin: 0px 0px 0px 25px; padding: 0px;">Systems Engineering and Signaling</li><li style="margin: 0px 0px 0px 25px; padding: 0px;">Applied Mathematics and Signaling</li><li style="margin: 0px 0px 0px 25px; padding: 0px;">Control System Signals</li><li style="margin: 0px 0px 0px 25px; padding: 0px;">Telecommunication transmission signals</li><li style="margin: 0px 0px 0px 25px; padding: 0px;">Analog & digital signals</li><li style="margin: 0px 0px 0px 25px; padding: 0px;">Time varying measurement values & sensor data</li></ul><p style="margin: 0px; padding: 5px; text-align: justify; color: #333333; font-size: 11.818181991577148px; line-height: 20px;"> </p>
en-US
Current Trends in Signal Processing
2321 – 4252
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Stationary Wavelet and Recursive Least Square Filtering Based Fetal ECG Data Extraction from Composite Abdominal Signal
https://stmjournals.com/index.php?journal=CTSP&page=article&op=view&path%5B%5D=8483
<p class="MsoNormal" style="margin-bottom: .0001pt; text-align: justify; line-height: 150%;"><span style="font-family: "Times New Roman", serif;"><span style="font-size: 16px;">Abstract</span></span></p><p class="MsoNormal" style="margin-bottom: .0001pt; text-align: justify; line-height: 150%;"><span style="font-family: "Times New Roman", serif;"><span style="font-size: 16px;">This paper introduces a fast methodology for fetal ECG extraction based on stationary wavelet and noise canceler adaptive filter with the recursive least square filter. Firstly, signals are preprocessed by moving averaging filter for removing baseline wander. The stationary wavelet and the recursive least square filter is applied on preprocessed signal that effectively separates the maternal ECG and extracts fetal ECG (fECG) from the abdominal ECG. Finally, fetal ECG(fECG) is extracted with R peak detection using the Pan-Tompkins algorithm. The experiment has been conducted at MIT-BIH abdominal and direct fetal ECG database (adfecgdb) available on physionet. The obtained results are evaluated by the parameters of accuracy (Acc), sensitivity (Se), positive diagnostic value (PDV) and cross-correlation. The achieved results Acc=100%, Se=100%, PDV=100% based on counting the number of R-peak and cross correlation is 98.93% between direct fetal scalp recording and extracted fECG. Moreover, the results outperform the existing state of art works on fetal ECG extraction.</span></span></p><p class="MsoNormal" style="margin-bottom: .0001pt; text-align: justify; line-height: 150%;"><span style="font-family: "Times New Roman", serif;"><span style="font-size: 16px;">Keywords: Fetal electrocardiogram, fetal heart rate, adaptive filter, recursive least square filter (RLS), abdomen ECG, wavelet analysis</span></span></p><p class="MsoNormal" style="margin-bottom: .0001pt; text-align: justify; line-height: 150%;"><span style="font-family: "Times New Roman", serif;"><span style="font-size: 16px;">Cite this Article</span></span></p><p class="MsoNormal" style="margin-bottom: .0001pt; text-align: justify; line-height: 150%;"> </p><p class="MsoNormal" style="margin-bottom: .0001pt; text-align: justify; line-height: 150%;"><span style="font-family: "Times New Roman", serif;"><span style="font-size: 16px;">Priya Gupta, Brajinder Singh Saini, R.K. Sunkaria et al. Stationary Wavelet and Recursive Least Square Filtering Based Fetal ECG Data Extraction from Composite Abdominal Signal. Current Trends in Signal Processing. 2017; 7(2): 46–58p.</span></span></p>
Priya Gupta
Brajinder Singh Saini
R. K. Sunkaria
Anukul Pandey
Copyright (c)
2017-09-11
2017-09-11
7 2
46
58
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Inter-patient Heartbeat Classification Using Higher Order Statistics and Symbolic Dynamics of ECG Signal
https://stmjournals.com/index.php?journal=CTSP&page=article&op=view&path%5B%5D=8592
<div><span style="font-family: "Times New Roman", serif;"><span style="font-size: 16px;">Abstract</span></span></div><div><span style="font-family: "Times New Roman", serif;"><span style="font-size: 16px;">Computer assisted heartbeat classification techniques have gained significant importance in the recent years as they aid the cardiologists in identifying abnormal heartbeats in ECG recordings. These techniques are of vital importance especially for the long term ECG recordings where it is not feasible to manually analyze the numerous heartbeats. Electrocardiogram (ECG) is an electrical representation of human heart activities and change of these activities is reflected in the morphology of ECG signal. Anomalous activities of the heart are characterized by significant variation in the ECG heartbeat morphology and the time related aspects. In the present paper, the morphological and temporal features are explored for ECG heartbeat classification. A novel feature set is formulated by selecting symbolic dynamics, RR intervals and the higher order statistics (HOS). Subsequently the feature set is fed to the K-nearest neighbor (KNN) classifier that follows the inter-patient classification scheme for beat categorization. The proposed method is validated on MIT-BIH arrhythmia database and heartbeat samples of this database are grouped into five classes as Normal beats (N), Supraventricular beats (S), Ventricular beats (V), Fusion beats (F), and Unclassified beats (Q). The proposed method achieves an overall accuracy of 94.35%. The obtained results outperform existing state-of-art works reported in the literature for inter-patient heartbeat classification.</span></span></div><div><span style="font-family: "Times New Roman", serif;"><span style="font-size: 16px;">Keywords: Symbolic dynamics, heartbeat classification, K-nearest neighbors, higher order statistics</span></span></div><div><span style="font-family: "Times New Roman", serif;"><span style="font-size: 16px;"><div>Cite this Article</div><div>Krishnakant Chaubey, Barjinder Singh Saini, Atul Kumar Verma. Inter-patient Heartbeat Classification Using Higher Order Statistics and Symbolic Dynamics of ECG Signal. Current Trends in Signal Processing. 2017; 7(2): 37–45p.</div></span></span></div>
Krishnakant Chaubey
Barjinder Singh Saini
Atul Kumar Verma
Copyright (c)
2017-09-11
2017-09-11
7 2
37
45
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Homogeneous Video Transcoding Functions for High Efficiency Video Coding
https://stmjournals.com/index.php?journal=CTSP&page=article&op=view&path%5B%5D=8443
<p class="MsoNormal" style="margin-top: 0cm; margin-right: 23.8pt; margin-bottom: .0001pt; margin-left: 36.0pt; text-align: justify;"><span style="font-size: 14px;"><em>Abstract</em></span></p><p class="MsoNormal" style="margin-top: 0cm; margin-right: 23.8pt; margin-bottom: .0001pt; margin-left: 36.0pt; text-align: justify;"><span style="font-size: 14px;"><em>High efficiency video coding (HEVC) is the successor of H.264/AVC and it gives the higher compression ratio and high quality video than H.264. But, HEVC increases the computational complexity of video coding due to its flexible quad tree partitioning structure. Transcoding algorithms are very much required to associate the HEVC standard into real applications. Homogeneous video transcoding is used for conversion between the same video formats. A higher quality video at source can be transcoded according to the receiver requirements. There are different functions of homogeneous video transcoding which includes bit rate change, spatial resolution adjustment, and temporal resolution adjustment, single and multilayer video transcoding.</em></span></p><p class="MsoNormal" style="margin-top: 0cm; margin-right: 23.8pt; margin-bottom: .0001pt; margin-left: 36.0pt; text-align: justify;"><span style="font-size: 14px;"><em>Keywords: Bit rate, high efficiency video coding, spatial resolution, temporal resolution, transcoding</em></span></p><p class="MsoNormal" style="margin-top: 0cm; margin-right: 23.8pt; margin-bottom: .0001pt; margin-left: 36.0pt; text-align: justify;"><span style="font-size: 14px;"><em>Cite this Article</em></span></p><p class="MsoNormal" style="margin-top: 0cm; margin-right: 23.8pt; margin-bottom: .0001pt; margin-left: 36.0pt; text-align: justify;"> </p><p class="MsoNormal" style="margin-top: 0cm; margin-right: 23.8pt; margin-bottom: .0001pt; margin-left: 36.0pt; text-align: justify;"><span style="font-size: 14px;"><em>Dombale KL, Jagtap SK. Homogeneous Video Transcoding Functions for High Efficiency Video Coding. Current Trends in Signal Processing. 2017; 7(2): 32–36p.</em></span></p>
Kanchan Laxman Dombale
Sonal K Jagtap
Copyright (c)
2017-09-11
2017-09-11
7 2
32
36
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Estimation of Semi Blind Channel for Two-Way Relay by using CRB Pilot based and Random Pilot Based
https://stmjournals.com/index.php?journal=CTSP&page=article&op=view&path%5B%5D=8366
<p class="MsoNormal" style="margin-left: .5in; text-align: justify; mso-layout-grid-align: none; text-autospace: none;"><span style="font-size: 14px;"><em>Abstract</em></span></p><p class="MsoNormal" style="margin-left: .5in; text-align: justify; mso-layout-grid-align: none; text-autospace: none;"><span style="font-size: 14px;"><em>Main purpose of semi blind channel estimation scheme for frequency selective channel estimation for multi-input and multi-output (MIMO) on relay networks for using high bandwidth efficient use of spectrum. This scheme motivation is low complexity of system to allow for exchange information users to users with the using of an intermediate relay node. because of superposition of signals at the relay node section receiving signal at the user terminals is affected by multiple impairments, i.e., channel gains, timing offsets, throughput, modulation error rate and carrier frequency offsets that need to be estimated and compensated in the channel section. We derived semi-blind estimator is based on the Gaussian maximum likelihood (GML) criterion using of CRBs based and Random pilot and using data symbols for Gaussian-distributed nuisance parameters. In estimation of the individual channels, we adopt a superimposed pilot symbols strategy on a relay. Main estimation is designed pilot-based vectors of the terminal section and Cramer-Rao bounds on relay to optimize the estimating performance. Furthermore, we derive the semi-blind and pilot-based Cramer-Rao bounds (CRBs) to characterize the Mean Squared error (MSE) performance of semi-blind channel estimation schemes and comparison between both CRB and random pilot based. Finally, we demonstrate simulation result to show that the proposed method provides substantial improvements in estimation accuracy over the conventional pilot-based estimation and that it approaches the semi-blind CRB and random pilot based estimation. Simulation results show that the performance of the proposed estimators is very close to the derived semi-blind CRLBs and random pilot based achieves significantly lower MSE compared to other estimation.</em></span></p><p class="MsoNormal" style="margin-left: .5in; text-align: justify; mso-layout-grid-align: none; text-autospace: none;"><span style="font-size: 14px;"><em>Keywords: MIMO (Multiple-Input Multiple-Output), CRB (Cramer-Rao bounds), GML (Gaussian maximum likelihood), MSE (Mean Squared error), TWRN (Two-way relaying network)</em></span></p><p class="MsoNormal" style="margin-left: .5in; text-align: justify; mso-layout-grid-align: none; text-autospace: none;"><span style="font-size: 14px;"><em>Cite this Article</em></span></p><p class="MsoNormal" style="margin-left: .5in; text-align: justify; mso-layout-grid-align: none; text-autospace: none;"> </p><p class="MsoNormal" style="margin-left: .5in; text-align: justify; mso-layout-grid-align: none; text-autospace: none;"><span style="font-size: 14px;"><em>Hemant Gavaskar, Neetendra Singh Dhakad. Estimation of Semi Blind Channel for Two-Way Relay by using CRB Pilot based and Random Pilot Based. Current Trends in Signal Processing. 2017; 7(2): 22–31p.</em></span></p>
Hemant Gavaskar
Neetendra Singh Dhakad
Copyright (c)
2017-09-11
2017-09-11
7 2
22
31
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An Improved Power Line Interference Reduction Approach Based on Combination of Mirror Extension and IIR filtering Through a Data Driven Mechanism
https://stmjournals.com/index.php?journal=CTSP&page=article&op=view&path%5B%5D=8569
<div><div>Abstract</div><div>Electrocardiogram (ECG) is a clinical sign monitoring measurement of the cardiac abnormalities. Like other biomedical signals, the ECG signal is also contaminated by various kinds of noise and artifacts such as power line interference, base line wandering, muscle artifacts and electrode artifacts. The present paper proposes a scheme for power line interference (PLI) reduction from ECG signal. It makes use of the concept of mirror method, empirical mode decomposition (EMD) and infinite impulse response (IIR) filtering. The three stage process has multiple advantages: (a) Mirror method removes the end effect problem in EMD. (b) EMD effectively separates the 50 Hz interference from ECG signal. (c)The 50 Hz interference and side lobes noise in intrinsic mode function (IMF’s) are further reduced using the IIR filtering technique. The proposed scheme was validated on simulated and real ECG records acquired from MIT-BIH arrhythmia database using metrics of Signal-to-noise ratio (SNR), mean square error (MSE), and percentage root mean square error difference (PRD). Typically achieved values are SNR = 69.38, MSE = 1.015×10−6, and PRD = 6.8810×10−6 on an average for an ECG signal corrupted by 5% noise level. Moreover, a comparison with the conventional EMD and other recent state of art works clearly validates the superiority of the proposed method.</div><div><br /></div><div>Keywords: Electrocardiogram (ECG), Empirical mode decomposition (EMD), Intrinsic mode function (IMF), Infinite impulse response (IIR)</div><div><br /></div><div><div>Cite this Article</div><div>Poonam Poonia, B. S. Saini, Taranjit Kaur. An Improved Power Line Interference Reduction Approach Based on Combination of Mirror Extension and IIR Filtering Through a Data Driven Mechanism. Current Trends in Signal Processing. 2017; 7(2): 13–21p.</div></div></div>
Poonam Poonia
B. S. Saini
Taranjit Kaur
Copyright (c)
2017-09-11
2017-09-11
7 2
13
21
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A Survey: Video Stabilization Techniques for Handheld Videos
https://stmjournals.com/index.php?journal=CTSP&page=article&op=view&path%5B%5D=8617
<div><span style="font-family: "Times New Roman", serif;"><span style="font-size: 14px;"><em>Abstract</em></span></span></div><div><span style="font-family: "Times New Roman", serif;"><span style="font-size: 14px;"><em>Taking selfies and videos from multimedia devices such as mobile phone cameras, smart phones, and camcorders is a very recent trend. Video stabilization is an important technology which aims at removing annoying shaky motion from videos. Video sequences captured by handheld cameras exhibit various spatiotemporal distortions which cause unpleasant visual experience. To overcome this problem and to get rid of unwanted camera motions various video stabilization techniques are designed which reduces shaky camera movements. The paper presents the review of the various techniques of video stabilization.</em></span></span></div><div><span style="font-family: "Times New Roman", serif;"><span style="font-size: 14px;"><em>Keywords: Video stabilization, motion estimation, motion smoothing, motion compensation</em></span></span></div><div><span style="font-family: "Times New Roman", serif;"><span style="font-size: 14px;"><em><div>Cite this Article</div><div>Himali B Ghorpade, Sonal Jagtap. A Survey: Video Stabilization Techniques for Handheld Videos. Current Trends in Signal Processing. 2017; 7(2): 8–12p.</div></em></span></span></div><!--[endif] -->
Himali B. Ghorpade
Sonal K. Jagtap
Copyright (c)
2017-09-11
2017-09-11
7 2
8
12
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A Low Delay Two Channel Quadrature Mirror Filter Bank using IIR Filter
https://stmjournals.com/index.php?journal=CTSP&page=article&op=view&path%5B%5D=8364
<p class="MsoNormal" style="margin-bottom: .0001pt; text-align: justify; line-height: normal; mso-layout-grid-align: none; text-autospace: none;"><span style="font-family: "Times New Roman", serif;"><span style="font-size: 16px;">Abstract</span></span></p><p class="MsoNormal" style="margin-bottom: .0001pt; text-align: justify; line-height: normal; mso-layout-grid-align: none; text-autospace: none;"><span style="font-family: "Times New Roman", serif;"><span style="font-size: 16px;">IIR filters have the advantage of low delay, high selectivity and stopband attenuation as compared to their counterpart FIR filters. In the present work, a low delay two Channel Quadrature Mirror Filter (QMF) bank using IIR filter has been proposed. The IIR prototype filter has been designed by approximating the FIR lowpass filter using the least square approximation. The length of the IIR filter has been selected using the bi-section method. The performance of the proposed design method has been evaluated in terms of mean square error in the pass band (Ep), mean square error in the stop band (Es), peak reconstruction error (PRE) and stop-band first side lobe level (A1). It has been observed that the IIR QMF filter bank outperformed to its counterpart with lesser number of filter taps.</span></span></p><p class="MsoNormal" style="margin-bottom: .0001pt; text-align: justify; line-height: normal; mso-layout-grid-align: none; text-autospace: none;"><span style="font-family: "Times New Roman", serif;"><span style="font-size: 16px;">Keywords: IIR QMF bank, low delay, Bi-section method, peak reconstruction error, least square approximation</span></span></p><p class="MsoNormal" style="margin-bottom: .0001pt; text-align: justify; line-height: normal; mso-layout-grid-align: none; text-autospace: none;"><span style="font-family: "Times New Roman", serif;"><span style="font-size: 16px;">Cite this Article</span></span></p><p class="MsoNormal" style="margin-bottom: .0001pt; text-align: justify; line-height: normal; mso-layout-grid-align: none; text-autospace: none;"> </p><p class="MsoNormal" style="margin-bottom: .0001pt; text-align: justify; line-height: normal; mso-layout-grid-align: none; text-autospace: none;"><span style="font-family: "Times New Roman", serif;"><span style="font-size: 16px;">Aman Kumar, Ramesh Kumar Sunkaria. A Low Delay Two Channel Quadrature Mirror Filter Bank using IIR Filter. Current Trends in Signal Processing. 2017; 7(2): 1–7p.</span></span></p>
Aman Kumar
Ramesh Kumar Sunkaria
Copyright (c)
2017-09-11
2017-09-11
7 2
1
7
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Trans multiplexer, As a bliss to SATCOM System
https://stmjournals.com/index.php?journal=CTSP&page=article&op=view&path%5B%5D=8428
<p class="MsoNormal" style="margin-top: 0in; margin-right: 32.0pt; margin-bottom: .0001pt; margin-left: .5in; text-align: justify; text-justify: inter-ideograph; line-height: 97%; mso-pagination: none; mso-layout-grid-align: none; punctuation-wrap: simple; text-autospace: none;"><em><span style="font-size: 10.0pt; line-height: 97%; font-family: "Times New Roman","serif"; mso-bidi-font-family: "Times New Roman"; mso-bidi-theme-font: minor-bidi;">The paper describes the importantance and application of trans multiplexer (TMUX) in Satellite Communication System. Now a days, the communication system is completely based on multi user system. Generally cellular communication can be done with a fixed frequency bands which is narrow band system where as satellite communication is based on wide band system where different standards can be used simultaneously. In this paper the overall idea of how signal processing is done at Transmitter and receiver end is described. Trans multiplexer is a system which is used to convert TDM signal to FDM at the transmitter end and from FDM to TDM at receiver end. Discussion is done by taking example of a single side, that is at receiver side.</span></em><span style="font-size: 12.0pt; line-height: 97%; font-family: "Times New Roman","serif"; mso-bidi-font-family: "Times New Roman"; mso-bidi-theme-font: minor-bidi;"></span></p>
Aparajita Biswal
Copyright (c)
2017-04-29
2017-04-29
7 2
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Microcontroller based Electrocardiography System with Adaptive Baseline Wander Removal
https://stmjournals.com/index.php?journal=CTSP&page=article&op=view&path%5B%5D=8316
<div><span style="font-family: "Times New Roman", serif;"><span style="font-size: 16px;">In this paper an affordable, user-friendly, and re-programmable electrocardiography system is presented. The proposed system is based on open-source Arduino/Genuino platform which can be re-programmed and up-graded as per the requirement of the end user. This system has the facility to display, store and share the acquired data via internet with a cardiologist, for the remote assistance. The proposed system is equipped with an adaptive median filter based baseline wander removal algorithm (AMF-BWR), implemented on microcontroller.</span></span></div><div><span style="font-family: "Times New Roman", serif;"><span style="font-size: 16px;"><br /></span></span></div><div><span style="font-family: "Times New Roman", serif;"><span style="font-size: 16px;">Keywords: Electrocardiography, Arduino/Genuino, Cortex-M3 microcontroller, remote health monitoring, AMF-BWR</span></span></div>
Lakhan Dev Sharma
Ramesh Kumar Sunkaria
Copyright (c)
2017-04-28
2017-04-28
7 2
29
34
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Study of Cooperative Spectrum Sensing Techniques in Cognitive Radio Networks
https://stmjournals.com/index.php?journal=CTSP&page=article&op=view&path%5B%5D=8427
<p class="MsoNormal" style="margin-top: 0in; margin-right: .5in; margin-bottom: .0001pt; margin-left: .5in; text-align: justify; line-height: normal; mso-pagination: none; mso-layout-grid-align: none; punctuation-wrap: simple; text-autospace: none;"><em><span style="font-size: 10pt; font-family: "Times New Roman", serif;">Cognitive radio is key enabling technology that enables next generation communication networks, also known as dynamic spectrum access network, to utilize the spectrum more efficiently in an opportunistic fashion without interfering with the licensed user. Its roots came from the discovery by regulatory bodies in various countries, which found that most of the radio frequency spectrum was inefficiently utilized. This was the reason for allowing unlicensed users to utilize licensed band; whenever it would not cause any interference [1]. Over the past decades various spectrum sensing techniques have been proposed in cognitive radio networks. In this paper, a brief survey of all the major contributions in the field of cooperative spectrum sensing for cognitive radio network have been discussed. This paper will also provide an overview of the elements used in cooperative sensing.</span></em></p><p class="MsoNormal" style="margin-top: 0in; margin-right: .5in; margin-bottom: .0001pt; margin-left: .5in; text-align: justify; line-height: normal; mso-pagination: none; mso-layout-grid-align: none; text-autospace: none;"><em><span style="font-size: 10pt; font-family: "Times New Roman", serif;"> </span></em></p><p class="MsoNormal" style="margin-top: 0in; margin-right: .5in; margin-bottom: .0001pt; margin-left: .5in; text-align: justify; line-height: normal; mso-pagination: none; mso-layout-grid-align: none; text-autospace: none;"><strong><em><span style="font-size: 10pt; font-family: "Times New Roman", serif;">Keywords: </span></em></strong><em><span style="font-size: 10pt; font-family: "Times New Roman", serif;">Cognitive radio, spectrum sensing, cooperative sensing</span></em></p><p class="MsoNormal" style="margin-bottom: .0001pt; text-align: justify; text-justify: inter-ideograph; line-height: 114%; mso-pagination: none; mso-layout-grid-align: none; punctuation-wrap: simple; text-autospace: none;"> </p><p class="MsoNormal" style="margin-bottom: .0001pt; text-align: justify; line-height: normal; mso-pagination: none; mso-layout-grid-align: none; text-autospace: none;"><span style="font-family: "Times New Roman", serif;"> </span><strong><span style="font-size: 12pt; font-family: "Times New Roman", serif;">Cite this Article</span></strong></p> <p class="MsoNormal" style="margin-bottom: .0001pt; text-align: justify; line-height: normal;"><span style="font-family: "Times New Roman", serif;">Mamta Bhatt, Vartika Srivastava, Shipra Nigam. Study of Cooperative Spectrum Sensing Techniques in Cognitive Radio Networks. <em>Current Trends in Signal Processing</em>. 2017; 7(1):</span><strong></strong></p>
Mamta Bhatt
Vartika Srivatava
Shipra Nigam
Copyright (c)
2017-04-28
2017-04-28
7 2
35
41
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A Strip Feed Compact UWB Antenna with Triple Band Notched
https://stmjournals.com/index.php?journal=CTSP&page=article&op=view&path%5B%5D=8426
<p>In <em style="text-align: justify;"><span style="font-size: 10.0pt; mso-bidi-font-size: 11.0pt; font-family: "Times New Roman","serif";">this paper, a microstrip feed Ultrawide Band (UWB) antenna with triple notch band characteristic has been proposed. The proposed antenna has U-shaped stub on the radiating patch. The proposed antenna reduces the interference between UWB and WiMAX/WLAN/ITU. The propose antenna has a compact size of 26 x 27 mm<sup>2</sup>. The proposed antenna achieved an operating bandwidth (VSWR<2) from 2.68 to 12 GHz except notch band 3-3.85 GHz (WiMAX), 5-5.9 GHz (WLAN) and 8-8.8 GHz (ITU). The proposed antenna exhibits omnidirectional radiation patterns except notch bands, and makes it suitable for UWB applications.</span></em></p><p> </p><p class="MsoNormal" style="margin-top: 0in; margin-right: .5in; margin-bottom: .0001pt; margin-left: .5in; text-align: justify; line-height: normal;"><strong><em><span style="font-size: 10.0pt; mso-bidi-font-size: 11.0pt; font-family: "Times New Roman","serif";">Keywords: </span></em></strong><em><span style="font-size: 10.0pt; mso-bidi-font-size: 11.0pt; font-family: "Times New Roman","serif";">UWB, notch band, WiMAX, WLAN, ITU</span></em></p><p class="MsoNormal" style="margin-top: 0in; margin-right: .5in; margin-bottom: .0001pt; margin-left: .5in; text-align: justify; line-height: normal;"> </p><p class="MsoNormal" style="margin-bottom: .0001pt; text-align: justify; line-height: normal;"><strong><span style="font-size: 12.0pt; mso-bidi-font-size: 11.0pt; font-family: "Times New Roman","serif";">Cite this Article</span></strong></p><p class="MsoNormal" style="margin-top: 0in; margin-right: .5in; margin-bottom: .0001pt; margin-left: .5in; text-align: justify; line-height: normal;"> </p><p class="MsoNormal" style="margin-bottom: .0001pt; text-align: justify; line-height: normal;"><span style="font-family: "Times New Roman","serif";">Ajeet Pratap Singh, Khanna R, Hardeep Singh<em>. </em>A Strip Feed Compact UWB Antenna with Triple Band Notched<em>. Current Trends in Signal Processing</em>. 2017; 7(1):</span></p><p class="MsoListParagraph" style="margin-top: 0in; margin-right: .5in; margin-bottom: .0001pt; margin-left: .5in; mso-add-space: auto; text-align: justify; line-height: normal;"><em></em></p>
Ajeet Pratap Singh
R. Khanna
Hardeep Singh
Copyright (c)
2017-04-28
2017-04-28
7 2
1
7
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Investigation of Surface Electromyography Signal for an Upper Limb Amputee
https://stmjournals.com/index.php?journal=CTSP&page=article&op=view&path%5B%5D=8037
<p class="MsoNormal" style="text-align: justify;"><span style="font-size: 12px;">Surface electromyography (SEMG) is a method to measure the muscle activity of the skin. Sensors placed on the skin detect the electrical current or signal generated by active muscles. Lot of the research has focuses on the detection of signal from upper limb amputee with activity of triceps and biceps muscles. Our main aim of this study is to process the acquired signal from the different muscles of the shoulder of trans-humeral amputee who has no muscles activity below shoulder and extract the signal by different features extraction techniques. Afterward, classify the different muscles’ activation with the movement of arm and identify the best movement with respect to active muscle. The analysed data is directly useful for the researcher to design of prosthetic arms for near shoulder amputee for opening and closing of hand activity.</span></p><p class="MsoNormal" style="text-align: justify;"><span style="font-size: 12px;">Keywords: Surface electromyography, signal,muscles, feature extraction, myoelectric signal, arm movements</span></p>
Amanpreet Kaur
Ravinder Agarwal
Amod Kumar
Copyright (c)
2017-04-28
2017-04-28
7 2
19
28
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Constrained Online Non Negative Matrix Factorization (CONMF) for Visual Tracking
https://stmjournals.com/index.php?journal=CTSP&page=article&op=view&path%5B%5D=7634
<p class="MsoBodyText"><span style="font-size: 13.3333px;"><strong><em>Visual tracking is the process of locating a moving object (or multiple objects) over time using a camera. It is one of the most important components in numerous applications such as military, secure control, crime prevention systems, access control and biometric identification etc. of computer vision. In visual tracking, holistic and part-based representations are both popular choices to model target appearance. The former is known for great efficiency and convenience while the latter for robustness against local appearance or shape variations. Non-negative matrix factorization (NMF) is a feature extraction technique. To adjust NMF to the tracking context, sparsity and smoothness constraints are added to the non-negativity. Putting these ingredients together with a particle filter framework, we proposed the tracker, constrained online non-negative matrix factorization (CONMF). It is possible to achieve robustness to challenging appearance variations and non-trivial deformations in real world. In past decade, there has been made progress for problem of recognizing faces under several unfavorable situations such as changing illumination in an uncontrolled environment. While tracking an object, problems such as sparsity and smoothness appear. By evaluating the tracker on various benchmark sequences containing targets undergoing large variations in scale, pose or illumination. Experiments on real video, including both, indoor and outdoor scenes will demonstrate the effectiveness and robustness of the approach that are subjected to occlusion and alteration in addition to scale.</em></strong></span></p><p class="MsoBodyText"><span style="font-size: 13.3333px;"><strong><em>Keywords: Illumination, non-negative matrix factorization, object recognition</em></strong></span></p>
Smita Deepak Khandagale
Gargi Sameer Phadke
Copyright (c)
2017-04-28
2017-04-28
7 2
8
18
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Performance Evaluation of Direction of Arrival Estimation in Smart Antenna
https://stmjournals.com/index.php?journal=CTSP&page=article&op=view&path%5B%5D=7763
<p class="MsoNormal" style="margin-top: 0in; margin-right: .5in; margin-bottom: .0001pt; margin-left: .5in; text-align: justify;"><em><span style="font-size: 10pt;">Direction of arrival (DOA) estimation has typically played a key role in signal processing. Its task is to find the directions impinging on an array antenna to increase the performance of the received signal. To use DOA estimation methods that are applicable to most environments has become the key of technique implementation. The traditional algorithms can get superior performance for DOA estimation in the rich receiving conditions, but they are not fit for adverse environment such as low SNR, small number of array elements or snapshots, etc. In order to improve the performance of DOA estimation, the modified method based on wavelet operator is used. Moreover, the method is applied on DOA estimation algorithm multiple signal classification (MUSIC) to get new algorithm WMUSIC. Undoubtedly, the wavelet-based method expands the application range of traditional DOA estimation algorithms and has widely practical prospects in future. Further results are improved to minimize the errors in angle estimation by using genetic algorithm. <em></em></span></em></p> <p class="MsoNormal" style="margin-top: 0in; margin-right: .5in; margin-bottom: .0001pt; margin-left: .5in; text-align: justify;"><em><span style="font-size: 10pt;"> </span></em></p> <p class="MsoNormal" style="margin-top: 0in; margin-right: .5in; margin-bottom: .0001pt; margin-left: .5in; text-align: justify;"><strong><em><span style="font-size: 10pt;">Keywords: </span></em></strong><em><span style="font-size: 10pt;">Direction of arrival, MUSIC, SNR</span></em></p> <p class="MsoNormal" style="text-align: justify;"><span style="font-size: 11pt;"> </span><strong>Cite this Article</strong></p> <p class="MsoNormal" style="text-align: justify;"><span style="font-size: 11pt;">Jadhav SS, Ganage DG, Wagh SA. Performance Evaluation of Direction of Arrival Estimation in Smart Antenna. </span><em><span style="font-size: 11pt;">Current Trends in Signal Processing</span></em><span style="font-size: 11pt;">. 2016; 6(3): 39–48p.</span></p><!--[endif] -->
S.S. Jadhav
D.G. Ganage
S.A. Wagh
Copyright (c)
2016-12-07
2016-12-07
7 2
39
48
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Heater Automation Using LabVIEW
https://stmjournals.com/index.php?journal=CTSP&page=article&op=view&path%5B%5D=7877
<p class="MsoNormal" style="margin-top: 0in; margin-right: .5in; margin-bottom: .0001pt; margin-left: .5in; text-align: justify; text-justify: inter-ideograph; line-height: normal;"><em><span style="font-size: 10pt; font-family: "Times New Roman", serif;">Water heating system is an age-old tradition being commonly used from domestic sectors to production sectors; it is a need-based requirement to develop all time and effective water heating system for use in day-to-day life. With reference to lime lighting the deficiencies in general water heating systems and improved versions of sensor operated systems, this model has been designed. The scope of the work is specifically with reference to common household water heating systems operated using LabVIEW software, facilitating absence of an individual. </span></em><em><span style="font-size: 10pt; font-family: "Times New Roman", serif;">Water heaters without control of water level indicator and energy cut off orientation will cause short circuits in domestic, commercial environments. Intelligent application of sensors used in the devices will balance the total losses of different kinds and ensure utmost satisfactory service to the domestic as well as commercial users. In the public interest and routing out the common man’s water heating problems, the model will deliver the efficiency oriented water heating systems with cost effectiveness.</span></em></p><p class="MsoNormal" style="margin-top: 0in; margin-right: .5in; margin-bottom: .0001pt; margin-left: .5in; text-align: justify; text-justify: inter-ideograph; line-height: normal;"><em><span style="font-size: 10pt; font-family: "Times New Roman", serif;"> </span></em></p><p class="MsoNormal" style="mso-margin-top-alt: auto; mso-margin-bottom-alt: auto; text-align: justify; line-height: normal; tab-stops: center 3.25in right 6.5in;"> </p><p class="MsoNormal" style="margin-top: 0in; margin-right: .5in; margin-bottom: .0001pt; margin-left: .5in; text-align: justify; text-justify: inter-ideograph; line-height: normal;"><strong><em><span style="font-size: 10pt; font-family: "Times New Roman", serif;">Keywords:</span></em></strong><em><span style="font-size: 10pt; font-family: "Times New Roman", serif;"> LabVIEW, heating element, tank, pump, temperature sensor, set point, geyser</span></em></p><p class="MsoNormal" style="margin-top: 0in; margin-right: .5in; margin-bottom: .0001pt; margin-left: .5in; text-align: justify; text-justify: inter-ideograph; line-height: normal;"> </p><p class="MsoNormal" style="margin-bottom: .0001pt; text-align: justify; text-justify: inter-ideograph; line-height: normal;"><strong><span style="font-size: 12pt; font-family: "Times New Roman", serif;">Cite this Article</span></strong><strong></strong></p><p class="MsoNormal" style="margin-top: 0in; margin-right: .5in; margin-bottom: .0001pt; margin-left: .5in; text-align: justify; text-justify: inter-ideograph; line-height: normal;"> </p><p class="MsoNormal" style="margin-bottom: .0001pt; text-align: justify; text-justify: inter-ideograph; line-height: normal;"><span style="font-family: "Times New Roman", serif;">Dinesh Kumar N, Ch Aditya Kumar VB. Heater Automation Using LabVIEW. </span><em><span style="font-family: "Times New Roman", serif;">Current Trends in Signal Processing</span></em><span style="font-family: "Times New Roman", serif;">. 2016; 6(3): 28–38p.</span></p>
N. Dinesh Kumar
Ch V.B. Aditya Kumar
Copyright (c)
2016-12-07
2016-12-07
7 2
28
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Congenital Heart Disease (CHD) Detection Technique Based on Analysis and Classification of ECG signal
https://stmjournals.com/index.php?journal=CTSP&page=article&op=view&path%5B%5D=7831
<p class="MsoNormal" style="margin-top: 0in; margin-right: .5in; margin-bottom: .0001pt; margin-left: .5in; text-align: justify; line-height: normal;"><em><span style="font-size: 10.0pt; mso-bidi-font-size: 11.0pt; font-family: "Times New Roman","serif";">As per American Heart Association information, every year 125 babies out of 1000 are born with congenital heart disease (CHD). It was estimated that 36000 children are live born with CHD each year in the European Union. Child with CHD may have hole in heart up to 3–5 mm as small defects and for large defects it may go up to 5–8 mm. Depending upon the size of hole in the upper or lower chamber of heart shows diseases such as atrial septal defect (ASD), ventricular septal defect (VSD), patent ductus arteriosus (PDA) and tetralogy of fallot (TOF). Child patients with CHD report difficulties in several areas of daily life such as sports, study, travelling, and driving. It affects the efficiency and functionality of heart. It also affects the autonomic control of heart rate. The main focus would be to detect CHD in the pediatric population and preventive measures that can be proposed to improve the quality of life. Nowadays, we still fill infancy in the analysis of pediatric ECG. In our research work, we aimed at wave detection of CHD and its ECG analysis with different classification techniques so that child with CHD can be diagnosed accurately. We then attempted to justify the recommendation for promising future directions in signal processing and database creation.</span></em></p><p class="MsoNormal" style="margin-top: 0in; margin-right: .5in; margin-bottom: .0001pt; margin-left: .5in; text-align: justify; line-height: normal;"><strong><em><span style="font-size: 10.0pt; mso-bidi-font-size: 11.0pt; font-family: "Times New Roman","serif"; mso-fareast-font-family: "Times New Roman";"> </span></em></strong></p><p class="MsoNormal" style="margin-top: 0in; margin-right: .5in; margin-bottom: .0001pt; margin-left: .5in; text-align: justify; line-height: normal;"><strong><em><span style="font-size: 10.0pt; mso-bidi-font-size: 11.0pt; font-family: "Times New Roman","serif"; mso-fareast-font-family: "Times New Roman";">Keywords</span></em></strong><em><span style="font-size: 10.0pt; mso-bidi-font-size: 11.0pt; font-family: "Times New Roman","serif"; mso-fareast-font-family: "Times New Roman";">: </span></em><em><span style="font-size: 10.0pt; mso-bidi-font-size: 11.0pt; font-family: "Times New Roman","serif";">atrial septal defect (</span></em><em><span style="font-size: 10.0pt; mso-bidi-font-size: 11.0pt; font-family: "Times New Roman","serif"; mso-fareast-font-family: "Times New Roman";">ASD), </span></em><em><span style="font-size: 10.0pt; mso-bidi-font-size: 11.0pt; font-family: "Times New Roman","serif";">ventricular septal defect (</span></em><em><span style="font-size: 10.0pt; mso-bidi-font-size: 11.0pt; font-family: "Times New Roman","serif"; mso-fareast-font-family: "Times New Roman";">VSD), </span></em><em><span style="font-size: 10.0pt; mso-bidi-font-size: 11.0pt; font-family: "Times New Roman","serif";">patent ductus arteriosus</span></em><em><span style="font-size: 10.0pt; mso-bidi-font-size: 11.0pt; font-family: "Times New Roman","serif"; mso-fareast-font-family: "Times New Roman";"> (PDA), </span></em><em><span style="font-size: 10.0pt; mso-bidi-font-size: 11.0pt; font-family: "Times New Roman","serif";">tetralogy of fallot (</span></em><em><span style="font-size: 10.0pt; mso-bidi-font-size: 11.0pt; font-family: "Times New Roman","serif"; mso-fareast-font-family: "Times New Roman";">TOF)</span></em></p><p class="MsoNormal" style="margin-top: 0in; margin-right: .5in; margin-bottom: .0001pt; margin-left: .5in; text-align: justify; line-height: normal;"> </p><p class="MsoNormal" style="margin-bottom: .0001pt; text-align: justify; line-height: normal;"><strong><span style="font-size: 12.0pt; mso-bidi-font-size: 11.0pt; font-family: "Times New Roman","serif"; mso-fareast-font-family: "MS Mincho";">Cite this Article</span></strong></p><p class="MsoNormal" style="margin-top: 0in; margin-right: .5in; margin-bottom: .0001pt; margin-left: .5in; text-align: justify; line-height: normal;"> </p><p class="MsoNormal" style="margin-bottom: .0001pt; text-align: justify; line-height: normal;"><span style="font-family: "Times New Roman","serif";">Kamble PB, Ingole DT. Congenital Heart Disease (CHD) Detection Technique Based on Analysis and Classification of ECG Signal</span><span style="font-family: "Times New Roman","serif"; mso-fareast-font-family: "MS Mincho";">. <em>Current Trends in Signal Processing</em>. 2016; 6(3): 23–27p.</span><strong></strong></p><p class="MsoNormal" style="text-align: justify; line-height: 150%;"> </p><p class="MsoNormal" style="margin-bottom: .0001pt; text-align: justify; line-height: normal;"><span style="font-family: "Times New Roman","serif"; mso-fareast-font-family: "Times New Roman";"> </span></p>
Pravin Baburao Kamble
D.T. Ingole
Copyright (c)
2016-11-07
2016-11-07
7 2
23
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Non-Invasive Extraction of Fetal Electrocardiogram and Maternal Electrocardiogram from Mixed Source Signal
https://stmjournals.com/index.php?journal=CTSP&page=article&op=view&path%5B%5D=7724
<p class="MsoNormal" style="margin-top: 0in; margin-right: .5in; margin-bottom: .0001pt; margin-left: .5in; text-align: justify;"><em><span style="font-size: 10.0pt; mso-bidi-font-size: 11.0pt; mso-fareast-font-family: Calibri; mso-bidi-language: EN-US;">This paper is to bring in self-regulating methodology to obtain Fetal Electrocardiogram (FECG) and Maternal Electrocardiogram (MECG) from the mixed source signal. The Abdominal Electrocardiogram (AECG) is treated as an input signal to achieve the above-mentioned goal. FECG and MECG can be differentiated according to the power of a signal. FECG are weak signals as compared to that of MECG. But, FECG signals are engrossed in various other interference signals and thus its extraction has gained significance in the medical field. Various other methods and research work were done in this field. This paper is focused on non-invasive methods and by implementing independent component analysis. Various mathematical calculations are used in order to get accurate FECG signal. Then after ‘R’ peaks can be calculated and later fetal heart rate which will help to know the condition of the fetus in mother’s womb.</span></em></p><p class="MsoNormal" style="margin-top: 0in; margin-right: .5in; margin-bottom: .0001pt; margin-left: .5in; text-align: justify;"><em><span style="font-size: 10.0pt; mso-bidi-font-size: 11.0pt; mso-fareast-font-family: Calibri; mso-bidi-language: EN-US;"> </span></em></p><p class="MsoNormal" style="margin-top: 0cm; margin-right: 23.85pt; margin-bottom: .0001pt; margin-left: 36.0pt; text-align: justify; tab-stops: 144.0pt;"> </p><p class="MsoNormal" style="margin-top: 0in; margin-right: .5in; margin-bottom: .0001pt; margin-left: .5in; text-align: justify;"><strong><em><span style="font-size: 10.0pt; mso-bidi-font-size: 11.0pt;">Keywords: </span></em></strong><em><span style="font-size: 10.0pt; mso-bidi-font-size: 11.0pt;">Abdominal electrocardiogram, blind source separation, fetal electrocardiogram, independent component analysis, maternal electrocardiogram</span></em></p><p class="MsoNormal" style="margin-top: 0in; margin-right: .5in; margin-bottom: .0001pt; margin-left: .5in; text-align: justify;"> </p><p class="papertitle" style="margin-bottom: .0001pt; text-align: justify;"><strong><span style="font-size: 12.0pt; mso-bidi-font-size: 11.0pt; mso-fareast-font-family: "MS Mincho";">Cite this Article</span></strong></p><p class="MsoNormal" style="margin-top: 0in; margin-right: .5in; margin-bottom: .0001pt; margin-left: .5in; text-align: justify;"> </p><p class="MsoNormal" style="text-align: justify;"><span style="font-size: 11.0pt;">Sai S. Kulkarni, Supriya O. Rajankar. </span><span style="font-size: 11.0pt; mso-fareast-font-family: "MS Mincho";">Non-Invasive Extraction of Fetal Electrocardiogram and Maternal Electrocardiogram from Mixed Source Signal. <em>Current Trends in Signal Processing.</em> 2016; 6(3): 16–22p.</span><strong></strong></p>
Sai Sadanand Kulkarni
Supriya O. Rajankar
Copyright (c)
2016-11-07
2016-11-07
7 2
16
22
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Elimination of Noise on ECG Signal Using Adaptive Filter
https://stmjournals.com/index.php?journal=CTSP&page=article&op=view&path%5B%5D=7786
<!--[if gte mso 9]><xml> <o:OfficeDocumentSettings> <o:TargetScreenSize>800x600</o:TargetScreenSize> </o:OfficeDocumentSettings> </xml><![endif]--> <p class="MsoNormal" style="margin-top: 0in; margin-right: .5in; margin-bottom: .0001pt; margin-left: .5in; text-align: justify;"><em><span style="font-size: 10pt;">There are various biomedical signals present in the human body, by examining these biomedical signal one can check the health condition of a person who is clinically fit or not. Electrocardiogram (ECG) is one of them. ECG plays an important role in the primary diagnosis, prognosis and survival analysis of heart diseases. This ECG signal is corrupted by various noises like power line interference, baseline wandering etc. One way to remove this noise is filtering the signal. This paper will present the performance of adaptive filter on ECG signal. Diseases can be found by mixing power line interference with the ECG signal. We have used the advanced adaptive filtering technique with Least Mean Square Algorithm for the removal of power line interference from the ECG signal. Also we have changed different parameter of adaptive filter and we analyzed the performance of algorithm in terms of Signal to Noise Ratio (SNR) improvement. </span></em></p><p class="MsoNormal" style="margin-top: 0in; margin-right: .5in; margin-bottom: .0001pt; margin-left: .5in; text-align: justify;"><em><span style="font-size: 10pt;"> </span></em></p><p class="MsoNormal" style="margin-top: 0in; margin-right: 23.85pt; margin-bottom: .0001pt; margin-left: .5in; text-align: justify; tab-stops: 2.0in;"> </p><p class="MsoNormal" style="margin-top: 0in; margin-right: .5in; margin-bottom: .0001pt; margin-left: .5in; text-align: justify;"><strong><em><span style="font-size: 10pt;">Keywords: </span></em></strong><em><span style="font-size: 10pt;">ECG Signal, </span></em><em><span style="font-size: 10pt;">Adaptive Filter, Least Mean Square (LMS), signal to noise ratio (SNR)</span></em></p><p class="MsoNormal" style="margin-top: 0in; margin-right: .5in; margin-bottom: .0001pt; margin-left: .5in; text-align: justify;"> </p><p class="MsoNormal" style="text-align: justify;"><strong>Cite this Article</strong></p><p class="MsoNormal" style="margin-top: 0in; margin-right: .5in; margin-bottom: .0001pt; margin-left: .5in; text-align: justify;"> </p><p class="MsoNormal" style="text-align: justify;"><span style="font-size: 11pt;">Shabnam Tahmin, Zeenat Afroze. Elimination of Noise on ECG Signal Using Adaptive Filter</span><span style="font-size: 11pt;">. </span><em><span style="font-size: 11pt;">Current Trends in Signal Processing</span></em><span style="font-size: 11pt;">. 2016; 6(3): 9–15p.</span></p> <!--[if gte mso 9]><xml> <w:WordDocument> <w:View>Normal</w:View> <w:Zoom>0</w:Zoom> <w:TrackMoves /> <w:TrackFormatting /> <w:PunctuationKerning /> <w:ValidateAgainstSchemas /> <w:SaveIfXMLInvalid>false</w:SaveIfXMLInvalid> <w:IgnoreMixedContent>false</w:IgnoreMixedContent> <w:AlwaysShowPlaceholderText>false</w:AlwaysShowPlaceholderText> <w:DoNotPromoteQF /> <w:LidThemeOther>EN-US</w:LidThemeOther> <w:LidThemeAsian>X-NONE</w:LidThemeAsian> <w:LidThemeComplexScript>X-NONE</w:LidThemeComplexScript> <w:Compatibility> <w:BreakWrappedTables /> <w:SnapToGridInCell /> <w:WrapTextWithPunct /> <w:UseAsianBreakRules /> <w:DontGrowAutofit /> <w:SplitPgBreakAndParaMark /> <w:EnableOpenTypeKerning /> <w:DontFlipMirrorIndents /> <w:OverrideTableStyleHps 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Shabnam Tahmin
Zeenat Afroze
Copyright (c)
2016-11-07
2016-11-07
7 2
9
15
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Efficient Partial Update Algorithm over Wireless Networks
https://stmjournals.com/index.php?journal=CTSP&page=article&op=view&path%5B%5D=7493
<!--[if gte mso 9]><xml> <o:OfficeDocumentSettings> <o:RelyOnVML /> <o:AllowPNG /> </o:OfficeDocumentSettings> </xml><![endif]--> <p class="MsoNormal" style="margin-top: 0in; margin-right: .5in; margin-bottom: .0001pt; margin-left: .5in; line-height: normal;"><em><span style="font-size: 10.0pt; mso-bidi-font-size: 11.0pt; font-family: "Times New Roman","serif";">Adaptive partial update algorithm is developed based on incremental method. The proposed algorithm applies in real time changing environment. The proposed algorithm responds to linear estimation with nodes in co – operative manner and less number of computations. The algorithm has powerful advantage is that it requires less number of coefficient and reduced computational and communication complexity in wireless sensor network. It is efficient because it has power of solving distributed estimation and optimization by learning mechanism. In wireless sensor networks there are various applications that involve phenomenon in which space parameter are varying like surveillance, environment monitoring, battle field, precision agriculture and medical application. In this paper three algorithms; sequential partial update, stochastic partial update and max partial update are compared in terms of mean square error (MSE). Performance characteristic and complexity analysis of each algorithm are compared with MATLAB simulation.</span></em></p><p class="MsoNormal" style="margin-top: 0in; margin-right: .5in; margin-bottom: .0001pt; margin-left: .5in; line-height: normal;"><em><span style="font-size: 10.0pt; mso-bidi-font-size: 11.0pt; font-family: "Times New Roman","serif";"> </span></em></p><p class="MsoNormal" style="margin-right: 0in; line-height: normal;"> </p><p class="MsoNormal" style="margin-top: 0in; margin-right: .5in; margin-bottom: .0001pt; margin-left: .5in; line-height: normal;"><strong><em><span style="font-size: 10.0pt; mso-bidi-font-size: 11.0pt; font-family: "Times New Roman","serif";">Keywords:</span></em></strong><em><span style="font-size: 10.0pt; mso-bidi-font-size: 11.0pt; font-family: "Times New Roman","serif";"> Incremental network, Sequential partial update, stochastic partial update, Max partial update, MSE</span></em></p><p class="MsoNormal" style="margin-top: 0in; margin-right: .5in; margin-bottom: .0001pt; margin-left: .5in; line-height: normal;"> </p><p class="MsoNormal" style="margin-right: 0in; line-height: normal;"><strong><span style="font-size: 12.0pt; mso-bidi-font-size: 11.0pt; font-family: "Times New Roman","serif"; mso-fareast-font-family: "MS Mincho";">Cite this Article</span></strong><strong><span style="font-size: 12.0pt; mso-bidi-font-size: 11.0pt; font-family: "Times New Roman","serif";"></span></strong></p><p class="MsoNormal" style="margin-top: 0in; margin-right: .5in; margin-bottom: .0001pt; margin-left: .5in; line-height: normal;"> </p><p class="MsoNormal" style="margin-right: 0in; line-height: normal;"><span style="font-family: "Times New Roman","serif";">Rupali Dewda,<sup> </sup>Megha Gupta. Efficient Partial Update Algorithm over Wireless Networks, </span><em><span style="font-family: "Times New Roman","serif"; mso-fareast-font-family: "MS Mincho";">Current Trends in Signal Processing</span></em><span style="font-family: "Times New Roman","serif"; mso-fareast-font-family: "MS Mincho";">. 2016; 6(3): 1–8p.</span><strong></strong></p> <!--[if gte mso 9]><xml> <w:WordDocument> <w:View>Normal</w:View> <w:Zoom>0</w:Zoom> <w:TrackMoves /> <w:TrackFormatting /> <w:PunctuationKerning /> <w:ValidateAgainstSchemas /> <w:SaveIfXMLInvalid>false</w:SaveIfXMLInvalid> <w:IgnoreMixedContent>false</w:IgnoreMixedContent> <w:AlwaysShowPlaceholderText>false</w:AlwaysShowPlaceholderText> <w:DoNotPromoteQF /> <w:LidThemeOther>EN-US</w:LidThemeOther> <w:LidThemeAsian>X-NONE</w:LidThemeAsian> <w:LidThemeComplexScript>X-NONE</w:LidThemeComplexScript> <w:Compatibility> <w:BreakWrappedTables /> <w:SnapToGridInCell /> <w:WrapTextWithPunct /> <w:UseAsianBreakRules /> <w:DontGrowAutofit /> 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Rupali Dewda
Vijay Barfa
Copyright (c)
2016-11-07
2016-11-07
7 2
1
8
-
Signal Transmission in Underwater Using Passive Time Reversal for Single and Multiuser
https://stmjournals.com/index.php?journal=CTSP&page=article&op=view&path%5B%5D=7456
<p class="MsoNormal" style="margin: 0in 0.5in 0.0001pt; text-align: justify;"><em><span style="font-size: 10pt; font-family: "Times New Roman", serif;">This paper describes passive time reversal communications, which enables multiple users to send information simultaneously to the time reversal array. Experimental results at 3.5 kHz with a 1 kHz bandwidth demonstrate that as many as six users can transmit information over a 4 km range in a 120 m deep water using quaternary phase-shift keying (QPSK) modulation, achieving an aggregate data rate of 6 kb/s. Moreover, the same data rate has been achieved at 20 km range by three users using 16-quadrature amplitude modulation (16-QAM).</span></em></p><p class="MsoNormal" style="margin: 0in 0.5in 0.0001pt; text-align: justify;"><em><span style="font-size: 10pt; font-family: "Times New Roman", serif;"> </span></em></p><p class="MsoNormal" style="margin-bottom: 0.0001pt; text-align: justify;"> </p><p class="MsoNormal" style="margin: 0in 0.5in 0.0001pt; text-align: justify;"><strong><em><span style="font-size: 10pt; font-family: "Times New Roman", serif;">Keywords</span></em></strong><span style="font-size: 10pt; font-family: "Times New Roman", serif;">: <em>Active time reversal, intersymbol interference (ISI), multiple-input-multiple-output (MIMO), multiuser communication, passive time reversal, time reversal communication</em></span></p><p class="MsoNormal" style="margin: 0in 0.5in 0.0001pt; text-align: justify;"> </p><p class="MsoNormal" style="margin-bottom: 0.0001pt; text-align: justify;"><strong><span style="font-size: 12pt; font-family: "Times New Roman", serif;">Cite this Article</span></strong></p><p class="MsoNormal" style="margin: 0in 0.5in 0.0001pt; text-align: justify;"> </p><p class="MsoNormal" style="margin-bottom: 0.0001pt; text-align: justify;"><span style="font-family: "Times New Roman", serif;">Sathya Narayanan P, Shanmuga Kani J, Kabilan P. Signal Transmission in Underwater Using Passive Time Reversal for Single and Multiuser</span><span style="font-family: "Times New Roman", serif;" lang="EN-IN">. </span><em><span style="font-family: "Times New Roman", serif;">Current Trends in Signal Processing.</span></em><span style="font-family: "Times New Roman", serif;"> 2016; 6(2): 49–54p.</span></p>
Mr. P. Sathya Narayanan
J. Shanmuga Kani
P. Kabilan
Copyright (c)
2016-08-07
2016-08-07
7 2
49
54
-
Application of Compressive Sensing for Sampling and Reconstruction of MRI Images
https://stmjournals.com/index.php?journal=CTSP&page=article&op=view&path%5B%5D=7532
<!--[if gte mso 9]><xml> <w:WordDocument> <w:View>Normal</w:View> <w:Zoom>0</w:Zoom> <w:TrackMoves /> <w:TrackFormatting /> <w:PunctuationKerning /> <w:ValidateAgainstSchemas /> <w:SaveIfXMLInvalid>false</w:SaveIfXMLInvalid> <w:IgnoreMixedContent>false</w:IgnoreMixedContent> <w:AlwaysShowPlaceholderText>false</w:AlwaysShowPlaceholderText> <w:DoNotPromoteQF /> <w:LidThemeOther>EN-IN</w:LidThemeOther> <w:LidThemeAsian>X-NONE</w:LidThemeAsian> <w:LidThemeComplexScript>X-NONE</w:LidThemeComplexScript> <w:Compatibility> <w:BreakWrappedTables /> <w:SnapToGridInCell /> <w:WrapTextWithPunct /> <w:UseAsianBreakRules /> <w:DontGrowAutofit /> <w:SplitPgBreakAndParaMark /> <w:DontVertAlignCellWithSp /> <w:DontBreakConstrainedForcedTables /> <w:DontVertAlignInTxbx /> <w:Word11KerningPairs /> <w:CachedColBalance /> </w:Compatibility> <m:mathPr> <m:mathFont m:val="Cambria Math" /> <m:brkBin m:val="before" /> <m:brkBinSub m:val="--" /> <m:smallFrac m:val="off" /> <m:dispDef /> <m:lMargin m:val="0" /> <m:rMargin m:val="0" /> <m:defJc m:val="centerGroup" /> <m:wrapIndent m:val="1440" /> <m:intLim m:val="subSup" /> <m:naryLim m:val="undOvr" /> </m:mathPr></w:WordDocument> </xml><![endif]--> <p class="MsoNormal" style="margin: 0in 0.5in 0.0001pt; text-align: justify;"><em><span style="font-size: 10pt; font-family: "Times New Roman", serif;">In recent years, a new theory of compressive sensing has evolved which asserts that super resolved signals and images can be recovered with far fewer samples than that demanded by the Nyquist sampling theorem. It is required that the signal being sensed has a low information-rate meaning that it is sparse in original or some transform domain. Former approaches capture the complete signal and process it to extract the information. The new paradigm captures lesser number of non-adaptive linear measurements of the signal and exploits sophisticated algorithms to determine its information content. In this work, we apply the compressive sensing theory as a general purpose signal compression scheme. Transform coding is applied along with compressive sensing on MRI images. The final transform coefficients provide alternative representation of the original signal, which is less redundant and appropriate for compression. Reconstruction of the signal is a linear optimization process which studies the sparsity in the transform domain, governed by the fact that higher the sparsity, better the recovery. Performances are analyzed using performance parameters viz. peak signal to noise ratio (PSNR), root mean square error (RMSE) and CoC (Correlation Coefficient). These values are extensively computed by varying the compression ratio (CR). Comparison is made between wavelet transform and discrete cosine transform implementation.</span></em></p><p class="MsoNormal" style="margin: 0in 0.5in 0.0001pt; text-align: justify;"><em><span style="font-size: 10pt; font-family: "Times New Roman", serif;"> </span></em></p><p class="MsoNormal" style="margin-bottom: .0001pt; text-align: justify; line-height: normal; mso-layout-grid-align: none; text-autospace: none;"><strong><em><span style="font-size: 10pt; line-height: 115%; font-family: Calibri, sans-serif;">Keywords: </span></em></strong><em><span style="font-size: 10.0pt; mso-bidi-font-size: 11.0pt; line-height: 115%; font-family: "Calibri","sans-serif"; mso-ascii-theme-font: minor-latin; mso-fareast-font-family: "Times New Roman"; mso-fareast-theme-font: minor-fareast; mso-hansi-theme-font: minor-latin; mso-bidi-font-family: "Times New Roman"; mso-bidi-theme-font: minor-bidi; mso-ansi-language: EN-US; mso-fareast-language: EN-US; mso-bidi-language: AR-SA; mso-bidi-font-weight: bold;">Compressive sensing (CS), sparsity, incoherence, L<sub>1</sub>-minimization, basis pursuit (BP), wavelet transform, discrete cosine transform</span></em></p><p class="MsoNormal" style="margin-bottom: .0001pt; text-align: justify; line-height: normal; mso-layout-grid-align: none; text-autospace: none;"> </p><p class="MsoNormal" style="margin-bottom: 0.0001pt; text-align: justify;"><strong><span style="font-size: 12.0pt; mso-bidi-font-size: 11.0pt; font-family: "Times New Roman","serif";">Cite this Article<em></em></span></strong></p><p class="MsoNormal" style="margin-bottom: .0001pt; text-align: justify; line-height: normal; mso-layout-grid-align: none; text-autospace: none;"> </p><p class="MsoNormal" style="margin-bottom: 0.0001pt; text-align: justify;"><span style="font-family: "Times New Roman","serif";">Mit Patel, Hardik Modi, Mayur Sevak <em>et al</em>. Application of Compressive Sensing for Sampling and Reconstruction of MRI Images. <em>Current Trends in Signal Processing</em>. 2016; 6(2): 42–48p.</span><strong></strong></p> <!--[if gte mso 9]><xml> <w:LatentStyles DefLockedState="false" DefUnhideWhenUsed="true" DefSemiHidden="true" DefQFormat="false" DefPriority="99" LatentStyleCount="267"> <w:LsdException Locked="false" Priority="0" SemiHidden="false" UnhideWhenUsed="false" QFormat="true" Name="Normal" /> <w:LsdException Locked="false" Priority="9" SemiHidden="false" UnhideWhenUsed="false" QFormat="true" Name="heading 1" /> <w:LsdException Locked="false" Priority="9" QFormat="true" Name="heading 2" /> <w:LsdException Locked="false" Priority="9" QFormat="true" Name="heading 3" /> <w:LsdException Locked="false" Priority="9" QFormat="true" Name="heading 4" /> <w:LsdException Locked="false" Priority="9" QFormat="true" Name="heading 5" /> <w:LsdException Locked="false" Priority="9" QFormat="true" Name="heading 6" /> <w:LsdException 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Mit Patel
Hardik Modi
Mayur Sevak
Himanshu Patel
Copyright (c)
2016-08-07
2016-08-07
7 2
42
48
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Hybrid Approach between LMS and Self-Correcting Adaptive Algorithm for Real Time Audio Noise Cancellation
https://stmjournals.com/index.php?journal=CTSP&page=article&op=view&path%5B%5D=7359
<p class="MsoNormal" style="margin: 0in 0.5in 0.0001pt; text-align: justify;"><em><span style="font-size: 10.0pt; mso-bidi-font-size: 11.0pt; font-family: "Times New Roman","serif";">In present era of public address systems one of the most important issue is removing the noise signal from original signal. In various types of noise, which annoys audience, one of the most annoying problems is active feedback causing howling effect. This effect can be removed with the help of adaptive filters. There are various popular adaptive algorithms like LMS, NLMS, VLMS commonly used for this purpose. As public address system is a real time system, the delay in system should be minimum and for that the convergence time of adaptive filter is an important parameter. This paper presents comparison between LMS and self-corrective algorithm in SIMULINK and based on results, the hybrid approach is proposed which gives faster convergence time then parent algorithms. The proposed hybrid algorithm is implemented on FPGA platform altera cyclone II.</span></em></p><p class="MsoNormal" style="margin: 0in 0.5in 0.0001pt; text-align: justify;"><em><span style="font-size: 10.0pt; mso-bidi-font-size: 11.0pt; font-family: "Times New Roman","serif";"> </span></em></p><p class="MsoNormal" style="margin: 2.4pt 0in; text-align: justify;"> </p><p class="MsoNormal" style="margin: 0in 0.5in 0.0001pt; text-align: justify;"><strong><em><span style="font-size: 10.0pt; mso-bidi-font-size: 11.0pt; font-family: "Times New Roman","serif";">Keywords: </span></em></strong><em><span style="font-size: 10.0pt; mso-bidi-font-size: 11.0pt; font-family: "Times New Roman","serif";">PAS, adaptive filter, LMS, self-corrective algorithm, FPGA</span></em></p><p class="MsoNormal" style="margin: 0in 0.5in 0.0001pt; text-align: justify;"> </p><p class="MsoNormal" style="margin-bottom: 0.0001pt; text-align: justify;"><strong><span style="font-size: 12.0pt; mso-bidi-font-size: 11.0pt; font-family: "Times New Roman","serif"; mso-fareast-font-family: "MS Mincho";">Cite this Article </span></strong></p><p class="MsoNormal" style="margin: 0in 0.5in 0.0001pt; text-align: justify;"> </p><p class="MsoNormal" style="margin-bottom: 0.0001pt; text-align: justify;"><span style="font-family: "Times New Roman","serif";">Hemang Patel</span><span style="font-family: "Times New Roman","serif"; mso-fareast-font-family: "MS Mincho";">, </span><span style="font-family: "Times New Roman","serif";">Arpan Desai</span><span style="font-family: "Times New Roman","serif"; mso-fareast-font-family: "MS Mincho";">. </span><span style="font-family: "Times New Roman","serif"; mso-bidi-font-weight: bold;">Hybrid Approach between LMS and Self-Correcting Adaptive Algorithm for Real Time Audio Noise Cancellation. </span><em><span style="font-family: "Times New Roman","serif"; mso-fareast-font-family: "MS Mincho";">Current Trends in Signal Processing</span></em><span style="font-family: "Times New Roman","serif"; mso-fareast-font-family: "MS Mincho";">. 2016; 6(2): 36–41p.</span><strong></strong></p>
Hemang Nareshbhai Patel
Arpan Desai
Copyright (c)
2016-08-07
2016-08-07
7 2
36
41
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Deploying Optimal Number of Sensors and Damage Detection in Structural Health Monitoring Using SEM-GA Method
https://stmjournals.com/index.php?journal=CTSP&page=article&op=view&path%5B%5D=7214
<p class="MsoNormal" style="margin: 0in 0.5in 0.0001pt; text-align: justify;"><em><span style="font-size: 10.0pt; mso-bidi-font-size: 11.0pt; font-family: "Times New Roman","serif"; mso-bidi-font-weight: bold;">Detecting damages is the most important criterion in any engineering creation—be it a machine or a building. Among the engineering creation, civil engineering structures need a continuous monitoring to check their operations, performance and the health status of the structures. Damage detection cannot be done manually every time. Automated systems have to be developed in order to monitor the health of the structure periodically.<strong> </strong>Hence, structural health monitoring (SHM) aims to develop automated systems for the continuous monitoring, inspection, and damage detection of structures with minimum labour involvement. In order to achieve this, sensors are deployed to enable predictive monitoring of the general health of a structure, be it a building, a bridge, or other structure whose integrity translates into the safety of those who utilizes and depends upon it. One of the fundamental requirements of SHM is sensor location optimization. To make efficient placement in SHM, a</span></em><em><span style="font-size: 10.0pt; mso-bidi-font-size: 11.0pt; font-family: "Times New Roman","serif"; mso-bidi-language: TA; mso-bidi-font-weight: bold;"> hybrid<strong> </strong>optimization strategy named </span></em><em><span style="font-size: 10.0pt; mso-bidi-font-size: 11.0pt; font-family: "Times New Roman","serif"; mso-bidi-language: TA;">strain energy method-genetic algorithm (SEM-GA) is proposed. This approach firstly selects mode shape of the structures. Then, the modal strain energy method is adopted to conduct the initial sensor placement. Finally, the genetic algorithm (GA) is utilized to determine the optimal number and</span></em><span style="font-size: 10.0pt; mso-bidi-font-size: 11.0pt; font-family: "Times New Roman","serif"; mso-bidi-language: TA; mso-bidi-font-weight: bold;"> <em>locations of the<strong> </strong>sensors, which uses the root mean square of off-diagonal elements in the modal assurance criterion matrix as the fitness function. Further, for damage detection, damages are induced in a certain storey and the modal strain energy change is studied.</em></span><em><span style="font-size: 10.0pt; mso-bidi-font-size: 11.0pt; font-family: "Times New Roman","serif"; mso-bidi-font-weight: bold;"></span></em></p> <p class="MsoNormal" style="margin: 0in 0.5in 0.0001pt; text-align: justify;"><span style="font-size: 10.0pt; mso-bidi-font-size: 11.0pt; font-family: "Times New Roman","serif"; mso-bidi-font-weight: bold;"> </span></p> <p class="MsoNormal" style="margin: 0in 0.5in 0.0001pt; text-align: justify;"><strong><em><span style="font-size: 10.0pt; mso-bidi-font-size: 11.0pt; font-family: "Times New Roman","serif";">Keywords: </span></em></strong><em><span style="font-size: 10.0pt; mso-bidi-font-size: 11.0pt; font-family: "Times New Roman","serif";">Structural health monitoring, optimal sensor placement, damage detection</span></em></p><p class="MsoNormal" style="margin: 0in 0.5in 0.0001pt; text-align: justify;"> </p><p class="MsoNormal" style="margin-bottom: 0.0001pt; text-align: justify;"><strong><span style="font-size: 12.0pt; mso-bidi-font-size: 11.0pt; font-family: "Times New Roman","serif"; mso-fareast-font-family: "MS Mincho";">Cite this Article</span></strong><strong><span style="font-size: 12.0pt; mso-bidi-font-size: 11.0pt; font-family: "Times New Roman","serif";"></span></strong></p><p class="MsoNormal" style="margin: 0in 0.5in 0.0001pt; text-align: justify;"> </p><p class="MsoNormal" style="margin-bottom: 0.0001pt; text-align: justify;"><span style="font-family: "Times New Roman","serif";">Sai Harshita MM</span><span style="font-family: "Times New Roman","serif"; mso-fareast-font-family: "MS Mincho";">, </span><span style="font-family: "Times New Roman","serif";">Sakthieswaran</span><span style="font-family: "Times New Roman","serif"; mso-fareast-font-family: "MS Mincho";"> N.</span><em><span style="font-family: "Times New Roman","serif"; mso-bidi-font-weight: bold;"> </span></em><span style="font-family: "Times New Roman","serif"; mso-bidi-font-weight: bold; mso-bidi-font-style: italic;">Deploying Optimal Number of Sensors and Damage Detection in Structural Health Monitoring Using SEM-GA Method</span><span style="font-family: "Times New Roman","serif"; mso-fareast-font-family: "MS Mincho";">.<em> Current Trends in Signal Processing</em>. 2016; 6(2): 28–35p.</span></p>
Sai Harshita M M
N Sakthieswaran
Copyright (c)
2016-08-07
2016-08-07
7 2
28
35
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Smart Traffic Control System with Overriding Technology
https://stmjournals.com/index.php?journal=CTSP&page=article&op=view&path%5B%5D=7388
<p class="Abstract" style="margin-top: 0in; margin-right: .5in; margin-bottom: .0001pt; margin-left: .5in; text-indent: 0in;"><em><span style="font-size: 10pt;">Traffic directing and monitoring is becoming a difficult task now a days. Present day traffic light control system is based on a fixed time breaks of green, orange and red signals and thus the waiting time of vehicle is more and uses more fuel. Hence, ejection of CO from the vehicles at that region is more. Another major problem is the possibility of emergency vehicles to get stuck in traffic jam. This paper’s aim is to design a safe and efficient movement of traffic by reducing the effect of CO at that area and allot the right way, which minimizes the delay. Also, it allows the facility for clearance of the emergency vehicle, if any, by providing early warning along with direction from which vehicle is approaching. In this system a CO sensor is used which is used to sense the CO emissions from vehicles. Based on CO levels, the traffic light indication will be given in the appropriate directions. The status will be shown in LCD. Pedestrian crossing system is also included in this system for safe and efficient flow of traffic.</span></em><em><span style="font-size: 10pt;"></span></em></p><p class="keywords" style="margin-top: 0in; margin-right: .5in; margin-bottom: .0001pt; margin-left: .5in; text-indent: 0in;"><span style="font-size: 10pt;"> </span></p><p class="Abstract" style="text-indent: 0in;"> </p><p class="keywords" style="margin-top: 0in; margin-right: .5in; margin-bottom: .0001pt; margin-left: .5in; text-indent: 0in;"><span style="font-size: 10pt;">Keywords: </span><span style="font-size: 10pt;">Traffic control system, CO, emergency vehicle clearance</span></p><p class="keywords" style="margin-top: 0in; margin-right: .5in; margin-bottom: .0001pt; margin-left: .5in; text-indent: 0in;"> </p><p class="MsoNormal" style="text-align: justify;"><strong><span style="font-size: 12pt;">Cite this Article</span></strong><span style="font-size: 11pt;"></span></p><p class="keywords" style="margin-top: 0in; margin-right: .5in; margin-bottom: .0001pt; margin-left: .5in; text-indent: 0in;"> </p><p class="MsoNormal" style="text-align: justify;"><span style="font-size: 11pt;">Keerthana MS, Minnu Manjooran, Hima IS <em>et al</em>. Smart Traffic Control System with Overriding Technology</span><span style="font-size: 11pt;">. </span><em><span style="font-size: 11pt;">Current Trends in Signal Processing</span></em><span style="font-size: 11pt;">. 2016; 6(2): 24–27p.</span></p>
Keerthana M S
Minnu Manjooran
Hima I.S.
T.D. Subash
Midhila P.B
Copyright (c)
2016-08-07
2016-08-07
7 2
24
27
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A Review on Wi-Vi Technology
https://stmjournals.com/index.php?journal=CTSP&page=article&op=view&path%5B%5D=7370
<p class="MsoNormal" style="margin-top: 0in; margin-right: .5in; margin-bottom: .0001pt; margin-left: .5in; text-align: justify;"><em>Wi-Vi is a new emerging technology that captures moving objects behind a wall or obstacle. Previous attempts used array of spaced antennas to track the moving objects through walls. This would be very expensive and bulky. So, in its place Wi-Vi is used which uses only one receiver. Wi-Vi technology is similar to radar and sonar imaging. But Wi-Vi uses very low power Wi-Fi signals for the transmission. The Wi-Fi signal penetrates through the wall and reflects off any human moving behind the wall. Wi-Vi system cancels out the random reflections, and keeps only the reflecting signals from moving object. Under this technology, a human does not carry any wireless device to communicate commands or short messages to a receiver using simple gestures. This paper mainly concentrates on two innovations: (1) MIMO based technology to avoid reflections from static objects; (2) use of ISAR for tracking the human motion. Wi-Vi can be built into a smart phone or special handheld devices. It can be used in law enforcement, emergency situations and personal security. Wi-Vi can be envisioned, promising a new technology in virtual reality.</em></p><p class="keywords" style="margin-top: 0in; margin-right: .5in; margin-bottom: .0001pt; margin-left: .5in; text-indent: 0in;"><span style="font-size: 10pt;"> </span></p><p class="MsoNormal" style="text-align: justify;"> </p><p class="keywords" style="margin-top: 0in; margin-right: .5in; margin-bottom: .0001pt; margin-left: .5in; text-indent: 0in;"><span style="font-size: 10.0pt; mso-bidi-font-size: 11.0pt; mso-fareast-font-family: "MS Mincho"; mso-no-proof: no;">Keywords: </span><span style="font-size: 10pt;">Wireless, MIMO, ISAR, radar and sonar imaging</span></p><p class="keywords" style="margin-top: 0in; margin-right: .5in; margin-bottom: .0001pt; margin-left: .5in; text-indent: 0in;"> </p><p class="MsoNormal" style="text-align: justify;"><strong><span style="font-size: 12.0pt; mso-bidi-font-size: 11.0pt; mso-fareast-font-family: "MS Mincho";">Cite this Article</span></strong><span style="font-size: 12.0pt; mso-bidi-font-size: 11.0pt; mso-fareast-font-family: "MS Mincho";"></span></p><p class="keywords" style="margin-top: 0in; margin-right: .5in; margin-bottom: .0001pt; margin-left: .5in; text-indent: 0in;"><span style="font-size: 11.0pt; font-family: "Times New Roman","serif"; mso-fareast-font-family: "MS Mincho"; mso-ansi-language: EN-US; mso-fareast-language: EN-US; mso-bidi-language: AR-SA;">Nimmi Ignatious Perumadan, Solwin Johnson. A Review on Wi-Vi Technology. <em>Current Trends in Signal Processing</em>. 2016; 6(2): 16–23p.</span></p>
Nimmi Ignatious Perumadan
Solwin Johnson
Copyright (c)
2016-08-07
2016-08-07
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