Pub Date : 2016-12-01DOI: 10.1109/ICEDSA.2016.7818497
Emir Džaferović, Sabahudin Vrtagic, Lejla Bandic, Jasmin Kevric, A. Subasi, S. Qaisar
It is estimated that there are millions of people with epilepsy around the world. Seizure detection and prediction systems are built to improve lifestyle of patients. Closed-loop systems are designed to predict and detect seizures and inform patient and caretakers. Ideally, wireless technologies are used in order not to interfere with patient's life. We build a prototype for closed-loop systems consisting of Mind Wave EEG capturing device and Android application communicating via Bluetooth. The application can store signals locally or send them to cloud and then process them for different applications such as BCI, Neurofeedback, epileptic seizure prediction, etc.
{"title":"Cloud-based mobile platform for EEG signal analysis","authors":"Emir Džaferović, Sabahudin Vrtagic, Lejla Bandic, Jasmin Kevric, A. Subasi, S. Qaisar","doi":"10.1109/ICEDSA.2016.7818497","DOIUrl":"https://doi.org/10.1109/ICEDSA.2016.7818497","url":null,"abstract":"It is estimated that there are millions of people with epilepsy around the world. Seizure detection and prediction systems are built to improve lifestyle of patients. Closed-loop systems are designed to predict and detect seizures and inform patient and caretakers. Ideally, wireless technologies are used in order not to interfere with patient's life. We build a prototype for closed-loop systems consisting of Mind Wave EEG capturing device and Android application communicating via Bluetooth. The application can store signals locally or send them to cloud and then process them for different applications such as BCI, Neurofeedback, epileptic seizure prediction, etc.","PeriodicalId":247318,"journal":{"name":"2016 5th International Conference on Electronic Devices, Systems and Applications (ICEDSA)","volume":"68 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123104271","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 : 2016-12-01DOI: 10.1109/ICEDSA.2016.7818540
Rida Gadhafi, M. Sanduleanu
A modified square patch antenna, for WiFi applications, operating at 5.5 GHz, is proposed in this article. A complementary square open loop resonator, having the resonance frequency at the upper 3 dB frequency of the patch resonance, is introduced on the patch to enhance the bandwidth more than twice that of the conventional square patch antenna operating at the same frequency. The concept of introducing a resonant slot at 3 dB frequency helps to eliminate the cross-polarized component seen in most of the classical designs due to the presence of resonance slots. Simulation results show that 2:1 VSWR bandwidth of the antenna is 7% whereas the conventional square patch antenna offers only 3% bandwidth. The proposed antenna has a compact size of 1.9 cm × 2 cm. It offers a uniform gain of 4.25 dB and a directivity of 6.1 dB at the frequency of operation.
{"title":"A modified square patch antenna with improved bandwidth performance for WiFi applications","authors":"Rida Gadhafi, M. Sanduleanu","doi":"10.1109/ICEDSA.2016.7818540","DOIUrl":"https://doi.org/10.1109/ICEDSA.2016.7818540","url":null,"abstract":"A modified square patch antenna, for WiFi applications, operating at 5.5 GHz, is proposed in this article. A complementary square open loop resonator, having the resonance frequency at the upper 3 dB frequency of the patch resonance, is introduced on the patch to enhance the bandwidth more than twice that of the conventional square patch antenna operating at the same frequency. The concept of introducing a resonant slot at 3 dB frequency helps to eliminate the cross-polarized component seen in most of the classical designs due to the presence of resonance slots. Simulation results show that 2:1 VSWR bandwidth of the antenna is 7% whereas the conventional square patch antenna offers only 3% bandwidth. The proposed antenna has a compact size of 1.9 cm × 2 cm. It offers a uniform gain of 4.25 dB and a directivity of 6.1 dB at the frequency of operation.","PeriodicalId":247318,"journal":{"name":"2016 5th International Conference on Electronic Devices, Systems and Applications (ICEDSA)","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116895121","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 : 2016-12-01DOI: 10.1109/ICEDSA.2016.7818513
Sunil T. Sonawane, M. Méribout
Halbach array is extensively used in process control industry targeting NMR (Nuclear Magnetic Resonance) technology as it has several advantages compared to non Halbach array methods; such as uniform magnetic field, light weight, low cost, and concentrated magnetic field. In this paper, we propose to design and assess a Halbach array targeting NMR application for a flowing multiphase flow process passing through a 2″ pipeline. The design methodology is based on Particle Swarm Optimization (PSO) algorithm, combined with a three dimensional (3D) finite element method-based software. The aim of the design is to generate simultaneously a concentric, uniform, and high intensity magnetic field inside the target sensing area in order to provide the most accurate results, while using a cheap and reasonably light weight hardware. Results of extensive 3D simulation results using an array of 12 permanent magnet elements of size (20mm × 20mm × 50mm size) indicate that an optimized magnetic field distribution of 0.9 Tesla maximal intensity and 606 ppm homogeneity could be achieved for an area of 40 mm diameter, which is adequate for our application.
{"title":"Halbach array design targeting nuclear magnetic resonance","authors":"Sunil T. Sonawane, M. Méribout","doi":"10.1109/ICEDSA.2016.7818513","DOIUrl":"https://doi.org/10.1109/ICEDSA.2016.7818513","url":null,"abstract":"Halbach array is extensively used in process control industry targeting NMR (Nuclear Magnetic Resonance) technology as it has several advantages compared to non Halbach array methods; such as uniform magnetic field, light weight, low cost, and concentrated magnetic field. In this paper, we propose to design and assess a Halbach array targeting NMR application for a flowing multiphase flow process passing through a 2″ pipeline. The design methodology is based on Particle Swarm Optimization (PSO) algorithm, combined with a three dimensional (3D) finite element method-based software. The aim of the design is to generate simultaneously a concentric, uniform, and high intensity magnetic field inside the target sensing area in order to provide the most accurate results, while using a cheap and reasonably light weight hardware. Results of extensive 3D simulation results using an array of 12 permanent magnet elements of size (20mm × 20mm × 50mm size) indicate that an optimized magnetic field distribution of 0.9 Tesla maximal intensity and 606 ppm homogeneity could be achieved for an area of 40 mm diameter, which is adequate for our application.","PeriodicalId":247318,"journal":{"name":"2016 5th International Conference on Electronic Devices, Systems and Applications (ICEDSA)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129362263","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 : 2016-12-01DOI: 10.1109/ICEDSA.2016.7818530
Dana Al Akil, R. Shubair
Wireless telemonitoring of physiological signals is an evolving direction in personalized medicine and home-based e-Health. There are several constraints in designing such systems. The three important constraints are energy consumption, data compression and device cost. Compressive Sensing (CS) is an emerging data compression technique that overcomes those constraints. Nevertheless, the non-sparsity of physiological signals presents a major issue to the existing compressive sensing algorithms. This research proposes to use a developed compressive sensing algorithm which has the ability to recover such non-sparse physiological signals. This algorithm is Block Sparse Bayesian Learning (BSBL). The proposed algorithm and the conventional CS algorithm were used to compress Fetal ECG (FECG) signals. Results showed that using BSBL to recover non-sparse FECG is more efficient comparing with the conventional CS algorithm, SL0.
{"title":"On the efficient application of compressive sensing of physiological signals in medical diagnostics","authors":"Dana Al Akil, R. Shubair","doi":"10.1109/ICEDSA.2016.7818530","DOIUrl":"https://doi.org/10.1109/ICEDSA.2016.7818530","url":null,"abstract":"Wireless telemonitoring of physiological signals is an evolving direction in personalized medicine and home-based e-Health. There are several constraints in designing such systems. The three important constraints are energy consumption, data compression and device cost. Compressive Sensing (CS) is an emerging data compression technique that overcomes those constraints. Nevertheless, the non-sparsity of physiological signals presents a major issue to the existing compressive sensing algorithms. This research proposes to use a developed compressive sensing algorithm which has the ability to recover such non-sparse physiological signals. This algorithm is Block Sparse Bayesian Learning (BSBL). The proposed algorithm and the conventional CS algorithm were used to compress Fetal ECG (FECG) signals. Results showed that using BSBL to recover non-sparse FECG is more efficient comparing with the conventional CS algorithm, SL0.","PeriodicalId":247318,"journal":{"name":"2016 5th International Conference on Electronic Devices, Systems and Applications (ICEDSA)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123954076","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 : 2016-12-01DOI: 10.1109/ICEDSA.2016.7818493
Paul O. Fisher, S. Al-Sarawi
A comparative analysis between well established and more recent solid state power amplifier (SSPA) combining techniques is presented. Based on this analysis, a new Figure of Merit (FOM) methodology is presented that can form part of a process to optimise SSPA design within the constraints of service, technology and performance requirements. Also, this formalises the process of determining the most appropriate SSPA design path, from the earliest stages, based on the above constraints as well as providing a detailed technology comparison to allow optimum technology selection.
{"title":"A new figure of merit methodology for power amplifier combining technologies","authors":"Paul O. Fisher, S. Al-Sarawi","doi":"10.1109/ICEDSA.2016.7818493","DOIUrl":"https://doi.org/10.1109/ICEDSA.2016.7818493","url":null,"abstract":"A comparative analysis between well established and more recent solid state power amplifier (SSPA) combining techniques is presented. Based on this analysis, a new Figure of Merit (FOM) methodology is presented that can form part of a process to optimise SSPA design within the constraints of service, technology and performance requirements. Also, this formalises the process of determining the most appropriate SSPA design path, from the earliest stages, based on the above constraints as well as providing a detailed technology comparison to allow optimum technology selection.","PeriodicalId":247318,"journal":{"name":"2016 5th International Conference on Electronic Devices, Systems and Applications (ICEDSA)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125701066","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 : 2016-12-01DOI: 10.1109/ICEDSA.2016.7818563
M. Ibrahim, Adel Al-Jumaily
Feature extraction is playing a major role in bio signal processing. Feature identification and selection has two approaches. The common approach is engineering handcraft which is based on user experience and application area. While the other approach is feature learning that based on making the system identify and select the best features suit the application. The idea behind feature learning is to avoid dealing with any feature extraction or reduction algorithms and to train the suggested model on learning with avoiding the exposure to feature extraction which is mainly based on researcher experience. In this paper, Independent component analysis (ICA) will be implemented as a feature learning technique to learn the model extract the features from the input data. Deep learning approach will be proposed by implementing ICA to learn features. In the proposed model, the raw data will be read then represented by using different signal representation as Spectrogram, Wavelet and Wavelet Packet. Then, the new represented data will be fed to Independent component analysis layer to generate features and finally, the performance of the suggested scheme will be evaluated by applying different classifiers such as Support Vector Machine, Extreme Learning Machine and Discriminate Analysis. And As an improving step for the results, classifier fusion layer will be implemented to select the most accurate result for both training and testing set. Classifier fusion layer resulted in a promising training and testing accuracies. On the other side, Feature Selection is the process of selecting subset of features from a pool of features.
{"title":"ICA based feature learning and feature selection","authors":"M. Ibrahim, Adel Al-Jumaily","doi":"10.1109/ICEDSA.2016.7818563","DOIUrl":"https://doi.org/10.1109/ICEDSA.2016.7818563","url":null,"abstract":"Feature extraction is playing a major role in bio signal processing. Feature identification and selection has two approaches. The common approach is engineering handcraft which is based on user experience and application area. While the other approach is feature learning that based on making the system identify and select the best features suit the application. The idea behind feature learning is to avoid dealing with any feature extraction or reduction algorithms and to train the suggested model on learning with avoiding the exposure to feature extraction which is mainly based on researcher experience. In this paper, Independent component analysis (ICA) will be implemented as a feature learning technique to learn the model extract the features from the input data. Deep learning approach will be proposed by implementing ICA to learn features. In the proposed model, the raw data will be read then represented by using different signal representation as Spectrogram, Wavelet and Wavelet Packet. Then, the new represented data will be fed to Independent component analysis layer to generate features and finally, the performance of the suggested scheme will be evaluated by applying different classifiers such as Support Vector Machine, Extreme Learning Machine and Discriminate Analysis. And As an improving step for the results, classifier fusion layer will be implemented to select the most accurate result for both training and testing set. Classifier fusion layer resulted in a promising training and testing accuracies. On the other side, Feature Selection is the process of selecting subset of features from a pool of features.","PeriodicalId":247318,"journal":{"name":"2016 5th International Conference on Electronic Devices, Systems and Applications (ICEDSA)","volume":"80 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115094337","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 : 2016-12-01DOI: 10.1109/ICEDSA.2016.7818488
N. Özbek, I. Eker
In this study, a novel fractional fuzzy proportional-integral proportional-derivative (PI-PD) based modified Smith predictor (SP) is presented for controlling of an industrial air heating system with time delay. The performance of the proposed controller is validated with real-time experimental applications. Furthermore, a performance comparison is demonstrated via results of real-time applications through a set of performance indices.
{"title":"A fractional fuzzy PI-PD based modified smith predictor for controlling of FOPDT process","authors":"N. Özbek, I. Eker","doi":"10.1109/ICEDSA.2016.7818488","DOIUrl":"https://doi.org/10.1109/ICEDSA.2016.7818488","url":null,"abstract":"In this study, a novel fractional fuzzy proportional-integral proportional-derivative (PI-PD) based modified Smith predictor (SP) is presented for controlling of an industrial air heating system with time delay. The performance of the proposed controller is validated with real-time experimental applications. Furthermore, a performance comparison is demonstrated via results of real-time applications through a set of performance indices.","PeriodicalId":247318,"journal":{"name":"2016 5th International Conference on Electronic Devices, Systems and Applications (ICEDSA)","volume":"52 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117268954","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 : 2016-12-01DOI: 10.1109/ICEDSA.2016.7818555
Ijjou Tizgui, H. Bouzahir, Fatima El Guezar, B. Benaid
In this paper, an estimation of the available and produced power in three wind farms (Akhfennir, Tarfaya and Fem El oued) in Southern Morocco is studied. Wind characteristics are analyzed. The Weibull distribution is used to model the wind speed at these wind parks, Fem El oued is the more windy park and where wind speed is more uniform. The monthly estimated available power density is more important in Fem El Oued, so, the investment in terms of increasing the number of wind turbines in this park can be profitable. The average usable power is estimated, Fem El oued has the lowest production because there is less number of wind turbines.
本文研究了摩洛哥南部三个风电场(Akhfennir, Tarfaya和Fem El oued)的可用和生产功率的估计。分析了风的特性。使用威布尔分布来模拟这些风电场的风速，Fem El oued是风力更大的风电场，风速更均匀。在Fem El Oued中，每月估计的可用功率密度更重要，因此，在增加该园区风力涡轮机数量方面的投资是有利可图的。平均可用功率估计，Fem El oued的产量最低，因为风力涡轮机的数量较少。
{"title":"Estimation of electricity production for a Moroccan wind farm","authors":"Ijjou Tizgui, H. Bouzahir, Fatima El Guezar, B. Benaid","doi":"10.1109/ICEDSA.2016.7818555","DOIUrl":"https://doi.org/10.1109/ICEDSA.2016.7818555","url":null,"abstract":"In this paper, an estimation of the available and produced power in three wind farms (Akhfennir, Tarfaya and Fem El oued) in Southern Morocco is studied. Wind characteristics are analyzed. The Weibull distribution is used to model the wind speed at these wind parks, Fem El oued is the more windy park and where wind speed is more uniform. The monthly estimated available power density is more important in Fem El Oued, so, the investment in terms of increasing the number of wind turbines in this park can be profitable. The average usable power is estimated, Fem El oued has the lowest production because there is less number of wind turbines.","PeriodicalId":247318,"journal":{"name":"2016 5th International Conference on Electronic Devices, Systems and Applications (ICEDSA)","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126110270","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 : 2016-12-01DOI: 10.1109/ICEDSA.2016.7818548
Avinash More
In energy-constrain wireless sensor networks, maintaining k — coverage degree requested by an application while maximizing the network lifetime is a major challenge. Existing literature on k — coverage does not consider residual energy levels and actual battery discharge rate of ACTIVE nodes. OBSP (Optimized Backoff Sleep Protocol) considers the residual energy level information and battery discharge rate but ignores the k-coverage degree. This paper proposes k-CGP (k-Coverage Guarantee Protocol) based on battery discharge curve using polynomial regression for different coverage degrees (k). k-CGP determines the optimal wakeup rate of sleeping nodes by computing Optimal Sleep Time derived from battery discharge curve using polynomial regression and Received Signal Strength Indicator for distance estimation. The coverage redundancy is computed by using equi-distance test. Due to this, a sufficient number of ACTIVE nodes could be maintained while achieving lesser energy consumption in the network. Simulation results show that k-CGP achieves higher energy savings and sensing area coverage as compared to PEAS.
{"title":"Battery regression for guaranteed k-coverage in distributed sensor networks","authors":"Avinash More","doi":"10.1109/ICEDSA.2016.7818548","DOIUrl":"https://doi.org/10.1109/ICEDSA.2016.7818548","url":null,"abstract":"In energy-constrain wireless sensor networks, maintaining k — coverage degree requested by an application while maximizing the network lifetime is a major challenge. Existing literature on k — coverage does not consider residual energy levels and actual battery discharge rate of ACTIVE nodes. OBSP (Optimized Backoff Sleep Protocol) considers the residual energy level information and battery discharge rate but ignores the k-coverage degree. This paper proposes k-CGP (k-Coverage Guarantee Protocol) based on battery discharge curve using polynomial regression for different coverage degrees (k). k-CGP determines the optimal wakeup rate of sleeping nodes by computing Optimal Sleep Time derived from battery discharge curve using polynomial regression and Received Signal Strength Indicator for distance estimation. The coverage redundancy is computed by using equi-distance test. Due to this, a sufficient number of ACTIVE nodes could be maintained while achieving lesser energy consumption in the network. Simulation results show that k-CGP achieves higher energy savings and sensing area coverage as compared to PEAS.","PeriodicalId":247318,"journal":{"name":"2016 5th International Conference on Electronic Devices, Systems and Applications (ICEDSA)","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125068087","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 : 2016-12-01DOI: 10.1109/ICEDSA.2016.7818521
Dana Al Akil, R. Shubair
Compressive Sensing (CS) techniques have emerged with the increasing demand of high data rate transmissions. Recently, block sparse Bayesian learning (BSBL) framework was introduced which has a superior performance over conventional CS methods. In this paper, the BSBL-Expectation Maximization (BSBL-EM) and BSBL-Bound Optimization (BSBL-BO) methods were deployed. The performance, mainly quality and speed, of recovering a block sparse signal was analyzed. Results showed that the two algorithms performance is almost the same in terms of NMSE. However, BSBL-BO achieved better efficiency since the required recovery time was less than BSBL-EM. To further investigate the algorithms performance, they were deployed to recover a real world FECG segment. They achieved a satisfactory quality where the distortion is negligible and does not affect the clinical diagnosis. Nevertheless, using BSBL-BO is more suitable for wireless tele-monitoring based systems since it is more efficient.
{"title":"Robust BSBL recovery method of physiological signals with application to fetal ECG","authors":"Dana Al Akil, R. Shubair","doi":"10.1109/ICEDSA.2016.7818521","DOIUrl":"https://doi.org/10.1109/ICEDSA.2016.7818521","url":null,"abstract":"Compressive Sensing (CS) techniques have emerged with the increasing demand of high data rate transmissions. Recently, block sparse Bayesian learning (BSBL) framework was introduced which has a superior performance over conventional CS methods. In this paper, the BSBL-Expectation Maximization (BSBL-EM) and BSBL-Bound Optimization (BSBL-BO) methods were deployed. The performance, mainly quality and speed, of recovering a block sparse signal was analyzed. Results showed that the two algorithms performance is almost the same in terms of NMSE. However, BSBL-BO achieved better efficiency since the required recovery time was less than BSBL-EM. To further investigate the algorithms performance, they were deployed to recover a real world FECG segment. They achieved a satisfactory quality where the distortion is negligible and does not affect the clinical diagnosis. Nevertheless, using BSBL-BO is more suitable for wireless tele-monitoring based systems since it is more efficient.","PeriodicalId":247318,"journal":{"name":"2016 5th International Conference on Electronic Devices, Systems and Applications (ICEDSA)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129748317","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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