Pub Date : 2013-05-12DOI: 10.1109/WOSSPA.2013.6602355
C. O’Reilly, T. Nielsen
The domain of electroencephalography (EEG) has a long history of research and clinical applications. Its relative ease of deployment, low cost, and high availability have secured for this recording technique a place of choice in the neuroscience and neuromedecine armada. Given these qualities and its important user base, investigation of the central nervous system could largely benefit from the development of new analysis techniques for EEG recordings. This paper contributes to a better exploitation of EEG databases by proposing a new methodology to assess temporal displacements of transient activity of scalp action potential fields. This technique establishes relationships - through a cross-correlation analysis - between the time-frequency representations of different recording channels. An example application is given for the propagation of EEG sleep spindles but larger applicability to other transient waveforms recorded on dense arrays of sensors is also possible.
{"title":"Assessing the propagation of EEG transient activity","authors":"C. O’Reilly, T. Nielsen","doi":"10.1109/WOSSPA.2013.6602355","DOIUrl":"https://doi.org/10.1109/WOSSPA.2013.6602355","url":null,"abstract":"The domain of electroencephalography (EEG) has a long history of research and clinical applications. Its relative ease of deployment, low cost, and high availability have secured for this recording technique a place of choice in the neuroscience and neuromedecine armada. Given these qualities and its important user base, investigation of the central nervous system could largely benefit from the development of new analysis techniques for EEG recordings. This paper contributes to a better exploitation of EEG databases by proposing a new methodology to assess temporal displacements of transient activity of scalp action potential fields. This technique establishes relationships - through a cross-correlation analysis - between the time-frequency representations of different recording channels. An example application is given for the propagation of EEG sleep spindles but larger applicability to other transient waveforms recorded on dense arrays of sensors is also possible.","PeriodicalId":417940,"journal":{"name":"2013 8th International Workshop on Systems, Signal Processing and their Applications (WoSSPA)","volume":"53 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":"129755400","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.2013.6602401
C. O'Reilly, T. Nielsen
This communication investigates the impact on classifier evaluation of a high asymmetry between positive and negatives classes. It points out some necessary precautions when reporting classifier performances using threshold-dependent statistics defined with the confusion matrix. It stresses that, in highly unbalanced datasets, reporting the positive predictive value (PPV) is more appropriate than reporting specificity. More elaborate variables such as F-measure and Matthews' correlation coefficient may also provide a reliable portrait. It further concludes that, in many cases, only a small portion of the receiver operating characteristic (ROC) curve is actually useful, unless very low PPV are judged acceptable. Two remedies are proposed to complement the ROC curve: using a positive tradeoff curve (defined herein) or adding iso-PPV lines (i.e., lines of constant PPV) on the ROC graph. The observations reported in this study contribute to understanding of the impact of asymmetry on classifier performances. They also cast some doubt on the pertinence, when dealing with highly asymmetric problems, of using the area under the ROC curve for threshold-independent assessment of classifier performances.
{"title":"Revisiting the ROC curve for diagnostic applications with an unbalanced class distribution","authors":"C. O'Reilly, T. Nielsen","doi":"10.1109/WOSSPA.2013.6602401","DOIUrl":"https://doi.org/10.1109/WOSSPA.2013.6602401","url":null,"abstract":"This communication investigates the impact on classifier evaluation of a high asymmetry between positive and negatives classes. It points out some necessary precautions when reporting classifier performances using threshold-dependent statistics defined with the confusion matrix. It stresses that, in highly unbalanced datasets, reporting the positive predictive value (PPV) is more appropriate than reporting specificity. More elaborate variables such as F-measure and Matthews' correlation coefficient may also provide a reliable portrait. It further concludes that, in many cases, only a small portion of the receiver operating characteristic (ROC) curve is actually useful, unless very low PPV are judged acceptable. Two remedies are proposed to complement the ROC curve: using a positive tradeoff curve (defined herein) or adding iso-PPV lines (i.e., lines of constant PPV) on the ROC graph. The observations reported in this study contribute to understanding of the impact of asymmetry on classifier performances. They also cast some doubt on the pertinence, when dealing with highly asymmetric problems, of using the area under the ROC curve for threshold-independent assessment of classifier performances.","PeriodicalId":417940,"journal":{"name":"2013 8th International Workshop on Systems, Signal Processing and their Applications (WoSSPA)","volume":"71 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":"127559168","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.2013.6602333
W. Ouedraogo, A. Souloumiac, M. Jaidane, C. Jutten
Recently, we proposed an effective geometrical method for separating linear instantaneous mixtures of non-negative sources, termed Simplicial Cone Shrinking Algorithm for Unmixing Non-negative Sources (SCSA-UNS). The latter method operates in noiseless case, and estimates the mixing matrix and the sources by finding the minimum aperture simplicial cone, containing the scatter plot of mixed data. In this paper, we propose an extension of SCSA-UNS, to tackle the noisy mixtures, in the case where the sparsity degrees of the sources are known a priori. The idea is to progressively eliminate, the noisy mixed data points which are likely to significantly modify the scatter plot of noiseless mixed data and to lead to a bad estimation of the mixing matrix and the sources. Simulations on synthetic data show the effectiveness of the proposed method.
{"title":"A robust geometrical method for blind separation of noisy mixtures of non-negatives sources","authors":"W. Ouedraogo, A. Souloumiac, M. Jaidane, C. Jutten","doi":"10.1109/WOSSPA.2013.6602333","DOIUrl":"https://doi.org/10.1109/WOSSPA.2013.6602333","url":null,"abstract":"Recently, we proposed an effective geometrical method for separating linear instantaneous mixtures of non-negative sources, termed Simplicial Cone Shrinking Algorithm for Unmixing Non-negative Sources (SCSA-UNS). The latter method operates in noiseless case, and estimates the mixing matrix and the sources by finding the minimum aperture simplicial cone, containing the scatter plot of mixed data. In this paper, we propose an extension of SCSA-UNS, to tackle the noisy mixtures, in the case where the sparsity degrees of the sources are known a priori. The idea is to progressively eliminate, the noisy mixed data points which are likely to significantly modify the scatter plot of noiseless mixed data and to lead to a bad estimation of the mixing matrix and the sources. Simulations on synthetic data show the effectiveness of the proposed method.","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":"130848311","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.2013.6602385
N. Settouti, Mostafa El Habib Daho, Mohammed El Amine Lazouni, M. A. Chikh
The semi-supervised learning has been widely applied in many fields such as medical diagnosis, pattern recognition. The semi supervised learning methods are used to employ unlabelled data in addition to labelled data for better classification of large data sets, where only a small number of labelled examples is available. Ensemble Methods are considered as an effective solution to the problem of dimensionality and can improve the robustness and generalization ability of individual learners. In this paper, we are particularly interested in the overall algorithm Random Forest semi-supervised named Co-Forest for the classification of large biological data. The algorithm is evaluated on its ability to correctly predict the labels of unlabelled examples, and its robustness when the number of labelled examples available decreases.
{"title":"Random forest in semi-supervised learning (Co-Forest)","authors":"N. Settouti, Mostafa El Habib Daho, Mohammed El Amine Lazouni, M. A. Chikh","doi":"10.1109/WOSSPA.2013.6602385","DOIUrl":"https://doi.org/10.1109/WOSSPA.2013.6602385","url":null,"abstract":"The semi-supervised learning has been widely applied in many fields such as medical diagnosis, pattern recognition. The semi supervised learning methods are used to employ unlabelled data in addition to labelled data for better classification of large data sets, where only a small number of labelled examples is available. Ensemble Methods are considered as an effective solution to the problem of dimensionality and can improve the robustness and generalization ability of individual learners. In this paper, we are particularly interested in the overall algorithm Random Forest semi-supervised named Co-Forest for the classification of large biological data. The algorithm is evaluated on its ability to correctly predict the labels of unlabelled examples, and its robustness when the number of labelled examples available decreases.","PeriodicalId":417940,"journal":{"name":"2013 8th International Workshop on Systems, Signal Processing and their Applications (WoSSPA)","volume":"48 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":"124564078","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.2013.6602417
L. Mitiche, Amel Baha Houda Adamou-Mitiche
In this paper, an efficient approach is proposed for the synthesis of an economical digital 2D-IIR filter with high information efficiency, by means of model reduction. As a result a linear phase IIR filter whose frequency response is very close to that of the initial filter and is very suitable for directional filtering and image processing applications.
{"title":"New procedure in designing 2D-IIR filters based on 2D-FIR filters approximation","authors":"L. Mitiche, Amel Baha Houda Adamou-Mitiche","doi":"10.1109/WOSSPA.2013.6602417","DOIUrl":"https://doi.org/10.1109/WOSSPA.2013.6602417","url":null,"abstract":"In this paper, an efficient approach is proposed for the synthesis of an economical digital 2D-IIR filter with high information efficiency, by means of model reduction. As a result a linear phase IIR filter whose frequency response is very close to that of the initial filter and is very suitable for directional filtering and image processing applications.","PeriodicalId":417940,"journal":{"name":"2013 8th International Workshop on Systems, Signal Processing and their Applications (WoSSPA)","volume":"48 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":"114660016","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.2013.6602344
Soumia Sid Ahmed, Z. Messali, M. Chouiter
Since 3D Electron Tomography Reconstruction is currently under development, several software are appearing in this field. Hereby, we present a study of recent applications used in electron tomography. We have highlighted EFTEM and TomoJ Plug-in under ImageJ, Tomography Toolbox (Tom toolbox) under MATLAB and eTomo Under IMOD. We have also applied preprocessing and reconstruction steps for real data (biological samples) by using one of the programs aforementioned, therefore according to the acquired results, we have compared these programs in terms of Electron Tomography Reconstruction. The goal of this comparison is to develop hybrid software that takes the advantages of all previous software. Furthermore, we have summarized Advantages and disadvantages of the introduced software.
{"title":"Public software: TomoJ, eTomo and tomtoolbox for stem and EFTEM tomography of biological samples","authors":"Soumia Sid Ahmed, Z. Messali, M. Chouiter","doi":"10.1109/WOSSPA.2013.6602344","DOIUrl":"https://doi.org/10.1109/WOSSPA.2013.6602344","url":null,"abstract":"Since 3D Electron Tomography Reconstruction is currently under development, several software are appearing in this field. Hereby, we present a study of recent applications used in electron tomography. We have highlighted EFTEM and TomoJ Plug-in under ImageJ, Tomography Toolbox (Tom toolbox) under MATLAB and eTomo Under IMOD. We have also applied preprocessing and reconstruction steps for real data (biological samples) by using one of the programs aforementioned, therefore according to the acquired results, we have compared these programs in terms of Electron Tomography Reconstruction. The goal of this comparison is to develop hybrid software that takes the advantages of all previous software. Furthermore, we have summarized Advantages and disadvantages of the introduced software.","PeriodicalId":417940,"journal":{"name":"2013 8th International Workshop on Systems, Signal Processing and their Applications (WoSSPA)","volume":"158 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":"114909333","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.2013.6602415
Mustapha Chouiha, M. Djeddou
We describe in this paper the solutions adopted to fulfill the MIL-STD-188-110B standard HF modem requirements. We present the designs details of all the blocks of the standard such as channel encoding/decoding, matched filtering, digital modulation/demodulation, synchronization, and equalization. We deal also with channel modeling and provide performance analysis of the developed chain of communication.
{"title":"MIL-STD-188-110B standard HF modem design using Agilent ADS","authors":"Mustapha Chouiha, M. Djeddou","doi":"10.1109/WOSSPA.2013.6602415","DOIUrl":"https://doi.org/10.1109/WOSSPA.2013.6602415","url":null,"abstract":"We describe in this paper the solutions adopted to fulfill the MIL-STD-188-110B standard HF modem requirements. We present the designs details of all the blocks of the standard such as channel encoding/decoding, matched filtering, digital modulation/demodulation, synchronization, and equalization. We deal also with channel modeling and provide performance analysis of the developed chain of communication.","PeriodicalId":417940,"journal":{"name":"2013 8th International Workshop on Systems, Signal Processing and their Applications (WoSSPA)","volume":"642 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":"117096818","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.2013.6602339
M. Tursun, Guzalnur Amrulla
This paper describes a computer vision system to detect and count moving vehicles on roads. The system uses a real-time traffic video surveillance camera mounted over roads and computes the total number of vehicles which passed the road. Moving vehicle image is extracted using `double difference image `algorithm and counting is accomplished by tracking vehicle movements within a tracking zone, called virtual loop. The system was tested on a video surveillance record file of a road that has a medium-level traffic volume.
{"title":"A video based real-time vehicle counting system using optimized virtual loop method","authors":"M. Tursun, Guzalnur Amrulla","doi":"10.1109/WOSSPA.2013.6602339","DOIUrl":"https://doi.org/10.1109/WOSSPA.2013.6602339","url":null,"abstract":"This paper describes a computer vision system to detect and count moving vehicles on roads. The system uses a real-time traffic video surveillance camera mounted over roads and computes the total number of vehicles which passed the road. Moving vehicle image is extracted using `double difference image `algorithm and counting is accomplished by tracking vehicle movements within a tracking zone, called virtual loop. The system was tested on a video surveillance record file of a road that has a medium-level traffic volume.","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":"130155523","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.2013.6602368
M. Guermoui, M. L. Mekhalfi, K. Ferroudji
Over the last decade, computerized heart screening techniques have been increasingly receiving attention. In general, one can say that such techniques can be categorized as: with, or without the so-called Electrocardiogram (ECG) signal. Considering this latter strategy, we devote this paper with the intention to design an algorithm that provides with heart sounds known as Phonocardiograms (PGC) investigation for further definition of the present pathology if any. A novel algorithm for heart sounds segmentation is also presented. The decision making is accomplished by means of support vector machines (SVM) classifier which is fed by characteristic features extracted from PCGs basing on wavelet filter banks coefficients so that PCG signals are classified into five classes: normal heart sound (NHS), aortic stenosis (AS), aortic insufficiency (Al) mitral stenosis (MS), and mitral insufficiency (MI). The SVM was trained on a low-dimensional feature space, and tested on relatively a big dataset in order to show its generalization capability.
{"title":"Heart sounds analysis using wavelets responses and support vector machines","authors":"M. Guermoui, M. L. Mekhalfi, K. Ferroudji","doi":"10.1109/WOSSPA.2013.6602368","DOIUrl":"https://doi.org/10.1109/WOSSPA.2013.6602368","url":null,"abstract":"Over the last decade, computerized heart screening techniques have been increasingly receiving attention. In general, one can say that such techniques can be categorized as: with, or without the so-called Electrocardiogram (ECG) signal. Considering this latter strategy, we devote this paper with the intention to design an algorithm that provides with heart sounds known as Phonocardiograms (PGC) investigation for further definition of the present pathology if any. A novel algorithm for heart sounds segmentation is also presented. The decision making is accomplished by means of support vector machines (SVM) classifier which is fed by characteristic features extracted from PCGs basing on wavelet filter banks coefficients so that PCG signals are classified into five classes: normal heart sound (NHS), aortic stenosis (AS), aortic insufficiency (Al) mitral stenosis (MS), and mitral insufficiency (MI). The SVM was trained on a low-dimensional feature space, and tested on relatively a big dataset in order to show its generalization capability.","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":"131428089","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.2013.6602404
N. Saulig, V. Sucic, B. Boashash, D. Seršić
Separation of different signal components, produced by one or more sources, is a problem encountered in many signal processing applications. This paper proposes a fully automatic undetermined blind source separation method, based on a peak detection and extraction technique from a signal time-frequency distribution (TFD). Information on the local number of components is obtained from the TFD Short-term Rényi entropy. It also allows to detect components time supports in the time-frequency plane, with no need for predefined thresholds on the components amplitude. This approach allows to extract different signal components without prior knowledge about the signal. The method is also used as a quality criterion to compare Quadratic TFDs. Results for synthetic and real data are reported for different TFDs, including the recently introduced Extended Modified B distribution.
{"title":"A comparison of quadratic TFDs for entropy based detection of components time supports in multicomponent nonstationary signal mixtures","authors":"N. Saulig, V. Sucic, B. Boashash, D. Seršić","doi":"10.1109/WOSSPA.2013.6602404","DOIUrl":"https://doi.org/10.1109/WOSSPA.2013.6602404","url":null,"abstract":"Separation of different signal components, produced by one or more sources, is a problem encountered in many signal processing applications. This paper proposes a fully automatic undetermined blind source separation method, based on a peak detection and extraction technique from a signal time-frequency distribution (TFD). Information on the local number of components is obtained from the TFD Short-term Rényi entropy. It also allows to detect components time supports in the time-frequency plane, with no need for predefined thresholds on the components amplitude. This approach allows to extract different signal components without prior knowledge about the signal. The method is also used as a quality criterion to compare Quadratic TFDs. Results for synthetic and real data are reported for different TFDs, including the recently introduced Extended Modified B distribution.","PeriodicalId":417940,"journal":{"name":"2013 8th International Workshop on Systems, Signal Processing and their Applications (WoSSPA)","volume":"58 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":"131425888","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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