Pub Date : 2013-05-12DOI: 10.1109/WOSSPA.2013.6602380
A. Ridi, Christophe Gisler, J. Hennebert
We report on the evaluation of signal processing and classification algorithms to automatically recognize electric appliances. The system is based on low-cost smart-plugs measuring periodically the electricity values and producing time series of measurements that are specific to the appliance consumptions. In a similar way as for biometric applications, such electric signatures can be used to identify the type of appliance in use. In this paper, we propose to use dynamic features based on time derivative and time second derivative features and we compare different classification algorithms including K-Nearest Neighbor and Gaussian Mixture Models. We use the recently recorded electric signature database ACS-Fl and its intersession protocol to evaluate our algorithm propositions. The best combination of features and classifiers shows 93.6% accuracy.
{"title":"Automatic identification of electrical appliances using smart plugs","authors":"A. Ridi, Christophe Gisler, J. Hennebert","doi":"10.1109/WOSSPA.2013.6602380","DOIUrl":"https://doi.org/10.1109/WOSSPA.2013.6602380","url":null,"abstract":"We report on the evaluation of signal processing and classification algorithms to automatically recognize electric appliances. The system is based on low-cost smart-plugs measuring periodically the electricity values and producing time series of measurements that are specific to the appliance consumptions. In a similar way as for biometric applications, such electric signatures can be used to identify the type of appliance in use. In this paper, we propose to use dynamic features based on time derivative and time second derivative features and we compare different classification algorithms including K-Nearest Neighbor and Gaussian Mixture Models. We use the recently recorded electric signature database ACS-Fl and its intersession protocol to evaluate our algorithm propositions. The best combination of features and classifiers shows 93.6% accuracy.","PeriodicalId":417940,"journal":{"name":"2013 8th International Workshop on Systems, Signal Processing and their Applications (WoSSPA)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126882621","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2013-05-12DOI: 10.1109/WOSSPA.2013.6602360
M. Boumehed, B. Alshaqaqi, A. Ouamri, M. Keche, Mohamed El Amine Ouis
This paper presents new local regions based level set model for segmenting multiple moving objects in video sequences captured by a stationary camera. The main idea evolves around the reformulation of well-known global energy in local way, by utilizing little squared windows centered on each point over a thin band surrounding the zero level set, hence the object contour can be reshaped into small local interior and exterior regions that lead to compute a family of adaptive local energies. Moreover, we propose to adapt the smoothness of the contours with an automatic stopping criterion. The proposed method has been tested on different real videos, and the experiment results demonstrate that our algorithm can segment effectively and accurately the moving objects.
{"title":"An effective segmentation of moving objects by a novel local regions-based level set","authors":"M. Boumehed, B. Alshaqaqi, A. Ouamri, M. Keche, Mohamed El Amine Ouis","doi":"10.1109/WOSSPA.2013.6602360","DOIUrl":"https://doi.org/10.1109/WOSSPA.2013.6602360","url":null,"abstract":"This paper presents new local regions based level set model for segmenting multiple moving objects in video sequences captured by a stationary camera. The main idea evolves around the reformulation of well-known global energy in local way, by utilizing little squared windows centered on each point over a thin band surrounding the zero level set, hence the object contour can be reshaped into small local interior and exterior regions that lead to compute a family of adaptive local energies. Moreover, we propose to adapt the smoothness of the contours with an automatic stopping criterion. The proposed method has been tested on different real videos, and the experiment results demonstrate that our algorithm can segment effectively and accurately the moving objects.","PeriodicalId":417940,"journal":{"name":"2013 8th International Workshop on Systems, Signal Processing and their Applications (WoSSPA)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125804939","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2013-05-12DOI: 10.1109/WOSSPA.2013.6602383
S. Al-Jazzar, T. Al-Naffouri
In this paper we introduce a self interference (SI) estimation and minimisation technique for amplify and forward relays. Relays are used to help forward signals between a transmitter and a receiver. This helps increase the signal coverage and reduce the required transmitted signal power. One problem that faces relays communications is the leaked signal from the relay's output to its input. This will cause an SI problem where the new received signal at the relay's input will be added with the unwanted leaked signal from the relay's output. A Solution is proposed in this paper to estimate and minimise this SI which is based upon using a tapped filter at the destination. To get the optimum weights for this tapped filter, some channel parameters must be estimated first. This is performed blindly at the destination without the need of any training. This channel parameter estimation method is named the blind-self-interference-channel-estimation (BSICE) method. The next step in the proposed solution is to estimate the tapped filter's weights. This is performed by minimising the mean squared error (MSE) at the destination. This proposed method is named the MSE-Optimum Weight (MSE-OW) method. Simulation results are provided in this paper to verify the performance of BSICE and MSE-OW methods.
{"title":"Relay self interference minimisation using tapped filter","authors":"S. Al-Jazzar, T. Al-Naffouri","doi":"10.1109/WOSSPA.2013.6602383","DOIUrl":"https://doi.org/10.1109/WOSSPA.2013.6602383","url":null,"abstract":"In this paper we introduce a self interference (SI) estimation and minimisation technique for amplify and forward relays. Relays are used to help forward signals between a transmitter and a receiver. This helps increase the signal coverage and reduce the required transmitted signal power. One problem that faces relays communications is the leaked signal from the relay's output to its input. This will cause an SI problem where the new received signal at the relay's input will be added with the unwanted leaked signal from the relay's output. A Solution is proposed in this paper to estimate and minimise this SI which is based upon using a tapped filter at the destination. To get the optimum weights for this tapped filter, some channel parameters must be estimated first. This is performed blindly at the destination without the need of any training. This channel parameter estimation method is named the blind-self-interference-channel-estimation (BSICE) method. The next step in the proposed solution is to estimate the tapped filter's weights. This is performed by minimising the mean squared error (MSE) at the destination. This proposed method is named the MSE-Optimum Weight (MSE-OW) method. Simulation results are provided in this paper to verify the performance of BSICE and MSE-OW methods.","PeriodicalId":417940,"journal":{"name":"2013 8th International Workshop on Systems, Signal Processing and their Applications (WoSSPA)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114120395","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2013-05-12DOI: 10.1109/WOSSPA.2013.6602365
B. Bouledjfane, L. Bennacer, M. Kahli
Image sharpening is the essential preprocessing step when improving the angiographies retinal image quality. It is helpful for the vessel retinal analysis and for improving the quality of their. For this reason, we propose a new technique for image sharpening based on Unsharp Masking (UM), and Bidimensional Empirical Mode Decomposition (BEMD). Firstly, the image is decomposed into a set of bidimensional intrinsic mode functions (BIMFs) and the residual image. Afterward, a weighting mask is achieved from an edge map multiplied by a compensation factor. Then, we apply the weighting mask to the first mode. Finally, we perform the reconstruction of the sharpened image by summing the compensated BIMF1 with the rest of the other modes and the residual image. The proposed scheme is enhanced by means of deringing's step to overcome the overshooting introduced during image sharpening. The obtained results proved that the proposed approach is effective to sharpen retinal images.
{"title":"BEMD-Unsharp Masking for retinal angiography image sharpening","authors":"B. Bouledjfane, L. Bennacer, M. Kahli","doi":"10.1109/WOSSPA.2013.6602365","DOIUrl":"https://doi.org/10.1109/WOSSPA.2013.6602365","url":null,"abstract":"Image sharpening is the essential preprocessing step when improving the angiographies retinal image quality. It is helpful for the vessel retinal analysis and for improving the quality of their. For this reason, we propose a new technique for image sharpening based on Unsharp Masking (UM), and Bidimensional Empirical Mode Decomposition (BEMD). Firstly, the image is decomposed into a set of bidimensional intrinsic mode functions (BIMFs) and the residual image. Afterward, a weighting mask is achieved from an edge map multiplied by a compensation factor. Then, we apply the weighting mask to the first mode. Finally, we perform the reconstruction of the sharpened image by summing the compensated BIMF1 with the rest of the other modes and the residual image. The proposed scheme is enhanced by means of deringing's step to overcome the overshooting introduced during image sharpening. The obtained results proved that the proposed approach is effective to sharpen retinal images.","PeriodicalId":417940,"journal":{"name":"2013 8th International Workshop on Systems, Signal Processing and their Applications (WoSSPA)","volume":"150 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120949189","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2013-05-12DOI: 10.1109/WOSSPA.2013.6602331
D. Bouchoffra, F. Ykhlef
In this tutorial, we provide an in depth analysis of some important issues within the field of Machine Learning and Pattern Recognition. We intend to reflect recent developments and provide a comprehensive introduction to some fundamental issues pertaining to the field of machine learning and pattern recognition. We target advanced undergraduates or first year Ph.D. students as well as researchers and practitioners. The mathematical models covered during this tutorial include Machine Learning for Pattern Recognition, Hidden Markov Models and feature space Dimensionality Reduction. MATLAB projects are provided as experiments to the theory covered.
{"title":"Mathematical models for machine learning and pattern recognition","authors":"D. Bouchoffra, F. Ykhlef","doi":"10.1109/WOSSPA.2013.6602331","DOIUrl":"https://doi.org/10.1109/WOSSPA.2013.6602331","url":null,"abstract":"In this tutorial, we provide an in depth analysis of some important issues within the field of Machine Learning and Pattern Recognition. We intend to reflect recent developments and provide a comprehensive introduction to some fundamental issues pertaining to the field of machine learning and pattern recognition. We target advanced undergraduates or first year Ph.D. students as well as researchers and practitioners. The mathematical models covered during this tutorial include Machine Learning for Pattern Recognition, Hidden Markov Models and feature space Dimensionality Reduction. MATLAB projects are provided as experiments to the theory covered.","PeriodicalId":417940,"journal":{"name":"2013 8th International Workshop on Systems, Signal Processing and their Applications (WoSSPA)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123360650","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2013-05-12DOI: 10.1109/WOSSPA.2013.6602406
B. Koussa, S. Bachir, C. Perrine, C. Duvanaud, R. Vauzelle
Systems based on Orthogonal Frequency Division Multiplexing (OFDM) take advantages of multicarrier modulations. Properties in term of dynamic spectrum adjustment make OFDM a promising technology for Cognitive Radio systems. An OFDM modulation suffers from high power peaks, which significantly degrade the power efficiency and the linearity of the transmitter. In this paper, the Tone Reservation method is used to reduce the Peak-To-Average Power-Ratio (PAPR) in OFDM systems. We intend to consider an adaptive algorithm to improve speed convergence and PAPR reduction gain, taking into account the complexity of the algorithm. The proposed approach is based on the Conjugate-gradient method which is a powerful gradient descent algorithm, reliable and efficient on a wide range of optimization problems. The radio-frequency Power Amplifier (PA) being the most sensitive element to envelope fluctuations, a class AB 2.4 GHz-2 W InGaP PA is used to evaluate the performance of the proposed method in terms of Bit Error Rate and Error Vector Magnitude.
{"title":"Flexible OFDM system for peak power reduction in OFDM-based Cognitive Radio context","authors":"B. Koussa, S. Bachir, C. Perrine, C. Duvanaud, R. Vauzelle","doi":"10.1109/WOSSPA.2013.6602406","DOIUrl":"https://doi.org/10.1109/WOSSPA.2013.6602406","url":null,"abstract":"Systems based on Orthogonal Frequency Division Multiplexing (OFDM) take advantages of multicarrier modulations. Properties in term of dynamic spectrum adjustment make OFDM a promising technology for Cognitive Radio systems. An OFDM modulation suffers from high power peaks, which significantly degrade the power efficiency and the linearity of the transmitter. In this paper, the Tone Reservation method is used to reduce the Peak-To-Average Power-Ratio (PAPR) in OFDM systems. We intend to consider an adaptive algorithm to improve speed convergence and PAPR reduction gain, taking into account the complexity of the algorithm. The proposed approach is based on the Conjugate-gradient method which is a powerful gradient descent algorithm, reliable and efficient on a wide range of optimization problems. The radio-frequency Power Amplifier (PA) being the most sensitive element to envelope fluctuations, a class AB 2.4 GHz-2 W InGaP PA is used to evaluate the performance of the proposed method in terms of Bit Error Rate and Error Vector Magnitude.","PeriodicalId":417940,"journal":{"name":"2013 8th International Workshop on Systems, Signal Processing and their Applications (WoSSPA)","volume":"120 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126144783","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2013-05-12DOI: 10.1109/WOSSPA.2013.6602356
M. Adnane, A. Belouchrani
Premature ventricular contraction (PVC) detection is an important task in critical care medicine. However, making this task automatic is not that simple. In this paper, we are describing a method for PVC arrhythmia detection. This method is based on the use of wavelet detail coefficients to discriminate between normal beats and abnormal beats (PVCs). The proposed method was tested against selected records of the MIT-BIH Arrhythmia Database (MITDB). Results are very satisfactory and show that it is possible to detect PVC arrhythmia using wavelet detail coefficients applied to QRS complexes.
{"title":"Premature ventricular contraction arrhythmia detection using wavelet coefficients","authors":"M. Adnane, A. Belouchrani","doi":"10.1109/WOSSPA.2013.6602356","DOIUrl":"https://doi.org/10.1109/WOSSPA.2013.6602356","url":null,"abstract":"Premature ventricular contraction (PVC) detection is an important task in critical care medicine. However, making this task automatic is not that simple. In this paper, we are describing a method for PVC arrhythmia detection. This method is based on the use of wavelet detail coefficients to discriminate between normal beats and abnormal beats (PVCs). The proposed method was tested against selected records of the MIT-BIH Arrhythmia Database (MITDB). Results are very satisfactory and show that it is possible to detect PVC arrhythmia using wavelet detail coefficients applied to QRS complexes.","PeriodicalId":417940,"journal":{"name":"2013 8th International Workshop on Systems, Signal Processing and their Applications (WoSSPA)","volume":"718 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116521836","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2013-05-12DOI: 10.1109/WOSSPA.2013.6602397
F. Douak, N. Benoudjit, F. Melgani
In this work, we investigate a new design of a multiblock general regression neural network applied to wind speed prediction in Algeria. The idea in our proposed method is to minimize the error of the prediction for wind speed in such a way as to minimize the quantity of training samples used, and thus to reduce the costs related to the training sample collection. For this reason, we propose to select the most significant sample among a large number of training samples by using multiblock general regression neural network (MBGRNN). This paper presents experimental results on six different real wind speed measurement stations in Algeria namely, Alger, Djelfa, Bechar, Oran, Sétif and In Aménas. The wind speed data covers a period of ten years between 2001 and 2010.
在这项工作中，我们研究了一种应用于阿尔及利亚风速预测的多块通用回归神经网络的新设计。我们提出的方法的思想是通过最小化训练样本的数量来最小化风速预测的误差，从而减少与训练样本收集相关的成本。为此，我们提出使用多块广义回归神经网络(multiblock general regression neural network, MBGRNN)从大量训练样本中选择最显著的样本。本文介绍了阿尔及利亚Alger、Djelfa、Bechar、Oran、ssamtif和in amsamims六个不同实际风速测量站的实验结果。风速数据涵盖了从2001年到2010年的十年间。
{"title":"Design of a multiblock general regression neural network for wind speed prediction in Algeria","authors":"F. Douak, N. Benoudjit, F. Melgani","doi":"10.1109/WOSSPA.2013.6602397","DOIUrl":"https://doi.org/10.1109/WOSSPA.2013.6602397","url":null,"abstract":"In this work, we investigate a new design of a multiblock general regression neural network applied to wind speed prediction in Algeria. The idea in our proposed method is to minimize the error of the prediction for wind speed in such a way as to minimize the quantity of training samples used, and thus to reduce the costs related to the training sample collection. For this reason, we propose to select the most significant sample among a large number of training samples by using multiblock general regression neural network (MBGRNN). This paper presents experimental results on six different real wind speed measurement stations in Algeria namely, Alger, Djelfa, Bechar, Oran, Sétif and In Aménas. The wind speed data covers a period of ten years between 2001 and 2010.","PeriodicalId":417940,"journal":{"name":"2013 8th International Workshop on Systems, Signal Processing and their Applications (WoSSPA)","volume":"57 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126247586","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2013-05-12DOI: 10.1109/WOSSPA.2013.6602329
A. Mohammad-Djafari
Bayesian approach is nowadays commonly used for inverse problems. Simple prior laws (Gaussian, Generalized Gaussian, Gauss-Markov and more general Markovian priors) are common in modeling and in their use in Bayesian inference methods. But, we need still more appropriate prior models which can account for non station-narities in signals and for the presence of the contours and homogeneous regions in images. Recently, we proposed a family of hierarchical prior models, called Gauss-Markov-Potts, which seems to be more appropriate for many applications in Imaging systems such as X ray Computed Tomography (CT) or Microwave imaging in Non Destructive Testing (NDT). In this tutorial paper, first some backgrounds on the Bayesian inference and the tools for assignment of priors and doing efficiently the Bayesian computation is presented. Then, more specifically hiearachical models and particularly the Gauss-Markov-Potts family of prior models are presented. Finally, their real applications in image restoration, in different practical Computed Tomography (CT) or other imaging systems are presented.
{"title":"Bayesian inference with hierarchical prior models for inverse problems in imaging systems","authors":"A. Mohammad-Djafari","doi":"10.1109/WOSSPA.2013.6602329","DOIUrl":"https://doi.org/10.1109/WOSSPA.2013.6602329","url":null,"abstract":"Bayesian approach is nowadays commonly used for inverse problems. Simple prior laws (Gaussian, Generalized Gaussian, Gauss-Markov and more general Markovian priors) are common in modeling and in their use in Bayesian inference methods. But, we need still more appropriate prior models which can account for non station-narities in signals and for the presence of the contours and homogeneous regions in images. Recently, we proposed a family of hierarchical prior models, called Gauss-Markov-Potts, which seems to be more appropriate for many applications in Imaging systems such as X ray Computed Tomography (CT) or Microwave imaging in Non Destructive Testing (NDT). In this tutorial paper, first some backgrounds on the Bayesian inference and the tools for assignment of priors and doing efficiently the Bayesian computation is presented. Then, more specifically hiearachical models and particularly the Gauss-Markov-Potts family of prior models are presented. Finally, their real applications in image restoration, in different practical Computed Tomography (CT) or other imaging systems are presented.","PeriodicalId":417940,"journal":{"name":"2013 8th International Workshop on Systems, Signal Processing and their Applications (WoSSPA)","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121448397","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2013-05-12DOI: 10.1109/WOSSPA.2013.6602405
B. Boashash, M. Ghafoor
This paper describes the advancements, updates and improvements made in the new Time Frequency Signal Analysis TFSAP toolbox as compared with the previous TFSA toolbox version. The updates and improvements done in TFSA toolbox are in-line with the latest research done in recent few years in the field of time-frequency based signal analysis. TFSA Toolbox has proved in the past to be an efficient and popular tool for analyzing non-stationary signals. The new TFSAP Toolbox is an updated Matlab toolbox that extends the functionality of previous TFSA toolboxes. It provides additional options for generating new time-frequency distributions, and synthesizing a signal from its time-frequency distribution. It also includes options for analyzing real-life signals such as biomedical, speech and radar signals. Several demo scripts are also included in the new version to demonstrate the main functionality of the toolbox and to coach new users to use TFSA toolbox for advanced signal processing applications dealing with non-stationarities. The new version is renamed TFSAP 6.2; it can be downloaded for free as a service to the community.
{"title":"Time frequency signal analysis and processing toolbox update 6.2: An enhanced research platform with new advanced high-resolution TFDs","authors":"B. Boashash, M. Ghafoor","doi":"10.1109/WOSSPA.2013.6602405","DOIUrl":"https://doi.org/10.1109/WOSSPA.2013.6602405","url":null,"abstract":"This paper describes the advancements, updates and improvements made in the new Time Frequency Signal Analysis TFSAP toolbox as compared with the previous TFSA toolbox version. The updates and improvements done in TFSA toolbox are in-line with the latest research done in recent few years in the field of time-frequency based signal analysis. TFSA Toolbox has proved in the past to be an efficient and popular tool for analyzing non-stationary signals. The new TFSAP Toolbox is an updated Matlab toolbox that extends the functionality of previous TFSA toolboxes. It provides additional options for generating new time-frequency distributions, and synthesizing a signal from its time-frequency distribution. It also includes options for analyzing real-life signals such as biomedical, speech and radar signals. Several demo scripts are also included in the new version to demonstrate the main functionality of the toolbox and to coach new users to use TFSA toolbox for advanced signal processing applications dealing with non-stationarities. The new version is renamed TFSAP 6.2; it can be downloaded for free as a service to the community.","PeriodicalId":417940,"journal":{"name":"2013 8th International Workshop on Systems, Signal Processing and their Applications (WoSSPA)","volume":"55 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133573360","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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