Pub Date : 2017-09-01DOI: 10.1109/ICSIPA.2017.8120608
V. Sowmya, D. Govind, K. Soman
In general, the three main modules of color image classification systems are: color-to-grayscale image conversion, feature extraction and classification. The color-to-grayscale image conversion is the important pre-processing step which must incorporate the significant and discriminative contrast and structure information in the converted grayscale images as in the original color image. All the existing techniques for color-to-grayscale image conversion preserves the significant contrast and structure information in the converted grayscale images in different manners. Hence, the present work is to analyze the significant and discriminative contrast and structure information preserved in the converted grayscale images using two different decolorization techniques called rgb2gray and singular value decomposition based color-to-grayscale image conversion (SVD) applied in the color image classification systems using the three different proposed features. The three different features for color image classification systems are proposed based on the combination of the existing dense SIFT features and the contrast & structure content computed using color-to-gray structure similarity index (C2G-SSIM) metric.
{"title":"Significance of contrast and structure features for an improved color image classification system","authors":"V. Sowmya, D. Govind, K. Soman","doi":"10.1109/ICSIPA.2017.8120608","DOIUrl":"https://doi.org/10.1109/ICSIPA.2017.8120608","url":null,"abstract":"In general, the three main modules of color image classification systems are: color-to-grayscale image conversion, feature extraction and classification. The color-to-grayscale image conversion is the important pre-processing step which must incorporate the significant and discriminative contrast and structure information in the converted grayscale images as in the original color image. All the existing techniques for color-to-grayscale image conversion preserves the significant contrast and structure information in the converted grayscale images in different manners. Hence, the present work is to analyze the significant and discriminative contrast and structure information preserved in the converted grayscale images using two different decolorization techniques called rgb2gray and singular value decomposition based color-to-grayscale image conversion (SVD) applied in the color image classification systems using the three different proposed features. The three different features for color image classification systems are proposed based on the combination of the existing dense SIFT features and the contrast & structure content computed using color-to-gray structure similarity index (C2G-SSIM) metric.","PeriodicalId":268112,"journal":{"name":"2017 IEEE International Conference on Signal and Image Processing Applications (ICSIPA)","volume":"54 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127155850","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 : 2017-09-01DOI: 10.1109/ICSIPA.2017.8120636
Aliyu Nuhu Shuaibu, A. Malik, I. Faye
Learning and recognizing 3-dimension (3D) adaptive features are important for crowd scene understanding in video surveillance research. Deep learning architectures such as Convolutional Neural Networks (CNN) have recently shown much success in various computer vision applications. Existing approaches such as hand-crafted method and 2D-CNN architectures are widely used in adaptive feature representations on image data. However, learning dynamic and temporal features in 3D scale features in videos remains an open problem. In this study, we proposed a novel technique 3D-scale Convolutional Neural Network (3DS-CNN), based on the decomposition of 3D feature maps into 2D spatio and 2D temporal feature representations. Extensive experiments on hundreds of video scene were demonstrated on publicly available crowd datasets. Quantitative and qualitative evaluations indicate that the proposed model display superior performance when compared to baseline approaches. The mean average precision of 95.30% was recorded on WWW crowd dataset.
{"title":"Adaptive feature learning CNN for behavior recognition in crowd scene","authors":"Aliyu Nuhu Shuaibu, A. Malik, I. Faye","doi":"10.1109/ICSIPA.2017.8120636","DOIUrl":"https://doi.org/10.1109/ICSIPA.2017.8120636","url":null,"abstract":"Learning and recognizing 3-dimension (3D) adaptive features are important for crowd scene understanding in video surveillance research. Deep learning architectures such as Convolutional Neural Networks (CNN) have recently shown much success in various computer vision applications. Existing approaches such as hand-crafted method and 2D-CNN architectures are widely used in adaptive feature representations on image data. However, learning dynamic and temporal features in 3D scale features in videos remains an open problem. In this study, we proposed a novel technique 3D-scale Convolutional Neural Network (3DS-CNN), based on the decomposition of 3D feature maps into 2D spatio and 2D temporal feature representations. Extensive experiments on hundreds of video scene were demonstrated on publicly available crowd datasets. Quantitative and qualitative evaluations indicate that the proposed model display superior performance when compared to baseline approaches. The mean average precision of 95.30% was recorded on WWW crowd dataset.","PeriodicalId":268112,"journal":{"name":"2017 IEEE International Conference on Signal and Image Processing Applications (ICSIPA)","volume":"64 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130694361","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 : 2017-09-01DOI: 10.1109/ICSIPA.2017.8120644
Devang S. Pandya, M. Zaveri
Due to rapid growth of digital contents, there has been an increasing demand of summarized videos to save time and other network resources. Automated soccer video analysis is a challenging due to involvement of more actors and rapid movements of players and camera. It is necessary first to detect various events of the video precisely before analyzing and labeling them. This paper proposes frame based approach for the automatic demarcation of events of the soccer video. However this task is very challenging due to variety of soccer leagues, various illumination and ground conditions. To overcome such issues we propose method which is invariant to such conditions and can successfully demarcate the soccer events. We exploit optical flow techniques to measure the motion. We introduce change in optical flow to extract the behavior of an event over the video span. Later, adaptive threshold is computed based on change in optical flow. We conducted number of simulations with variety of videos to validate the method. Proposed method achieves nearly 90% of accuracy and found robust in spite of illumination variation.
{"title":"Frame based approach for automatic event boundary detection of soccer video using optical flow","authors":"Devang S. Pandya, M. Zaveri","doi":"10.1109/ICSIPA.2017.8120644","DOIUrl":"https://doi.org/10.1109/ICSIPA.2017.8120644","url":null,"abstract":"Due to rapid growth of digital contents, there has been an increasing demand of summarized videos to save time and other network resources. Automated soccer video analysis is a challenging due to involvement of more actors and rapid movements of players and camera. It is necessary first to detect various events of the video precisely before analyzing and labeling them. This paper proposes frame based approach for the automatic demarcation of events of the soccer video. However this task is very challenging due to variety of soccer leagues, various illumination and ground conditions. To overcome such issues we propose method which is invariant to such conditions and can successfully demarcate the soccer events. We exploit optical flow techniques to measure the motion. We introduce change in optical flow to extract the behavior of an event over the video span. Later, adaptive threshold is computed based on change in optical flow. We conducted number of simulations with variety of videos to validate the method. Proposed method achieves nearly 90% of accuracy and found robust in spite of illumination variation.","PeriodicalId":268112,"journal":{"name":"2017 IEEE International Conference on Signal and Image Processing Applications (ICSIPA)","volume":"61 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126018869","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 : 2017-09-01DOI: 10.1109/ICSIPA.2017.8120662
G. Chan, M. Awais, S. A. A. Shah, T. Tang, Cheng-Kai Lu, F. Mériaudeau
Diabetic Macular Edema (DME) is a common eye disease that causes irreversible vision loss for diabetic patients, if left untreated. Thus, early diagnosis of DME could help in early treatment and prevent blindness. This paper aims to create a framework based on deep learning for DME recognition on Spectral Domain Optical Coherence Tomography (SD-OCT) images through transfer learning. First, images are pre-processed: denoised using Block-Matching and 3-Dimension (BM3D) filtering and cropped through image boundary extraction. Later, features are extracted using CNN of AlexNet and finally images are classified using SVM classifier. The results are evaluated using 8-fold cross-validation. The experiments show that denoised and cropped images lead to better classification performances, exceeding previous other recent published works of 96% accuracy.
{"title":"Transfer learning for Diabetic Macular Edema (DME) detection on Optical Coherence Tomography (OCT) images","authors":"G. Chan, M. Awais, S. A. A. Shah, T. Tang, Cheng-Kai Lu, F. Mériaudeau","doi":"10.1109/ICSIPA.2017.8120662","DOIUrl":"https://doi.org/10.1109/ICSIPA.2017.8120662","url":null,"abstract":"Diabetic Macular Edema (DME) is a common eye disease that causes irreversible vision loss for diabetic patients, if left untreated. Thus, early diagnosis of DME could help in early treatment and prevent blindness. This paper aims to create a framework based on deep learning for DME recognition on Spectral Domain Optical Coherence Tomography (SD-OCT) images through transfer learning. First, images are pre-processed: denoised using Block-Matching and 3-Dimension (BM3D) filtering and cropped through image boundary extraction. Later, features are extracted using CNN of AlexNet and finally images are classified using SVM classifier. The results are evaluated using 8-fold cross-validation. The experiments show that denoised and cropped images lead to better classification performances, exceeding previous other recent published works of 96% accuracy.","PeriodicalId":268112,"journal":{"name":"2017 IEEE International Conference on Signal and Image Processing Applications (ICSIPA)","volume":"90 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115865626","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 : 2017-09-01DOI: 10.1109/ICSIPA.2017.8120656
A. Eltholth, M. Gaber, M. Fouda, H. Mansour
Filter Bank Multi-Carrier (FBMC), has been proposed to solve the problems of spectral efficiency and adjacent channel leakage power in OFDM. This solution has added implications for the Peak-to-Average Power Ratio (PAPR) problem. This paper aims to reduce PAPR in FBMC through convolutional constellation mapping. The idea is to give a different constellation point to the symbol if successive values of the same symbol occur in the state space, thus reducing the chances of coherent addition of subcarriers without increasing the constellation points or even the need for iterative mapping. Computer-based simulations show the superior performance of the proposed Convolutional Constellation Mapping (CCM) in reducing the PAPR of FBMC.
{"title":"Convolutional constellation mapping technique for PAPR reduction in filter bank multi-carrier","authors":"A. Eltholth, M. Gaber, M. Fouda, H. Mansour","doi":"10.1109/ICSIPA.2017.8120656","DOIUrl":"https://doi.org/10.1109/ICSIPA.2017.8120656","url":null,"abstract":"Filter Bank Multi-Carrier (FBMC), has been proposed to solve the problems of spectral efficiency and adjacent channel leakage power in OFDM. This solution has added implications for the Peak-to-Average Power Ratio (PAPR) problem. This paper aims to reduce PAPR in FBMC through convolutional constellation mapping. The idea is to give a different constellation point to the symbol if successive values of the same symbol occur in the state space, thus reducing the chances of coherent addition of subcarriers without increasing the constellation points or even the need for iterative mapping. Computer-based simulations show the superior performance of the proposed Convolutional Constellation Mapping (CCM) in reducing the PAPR of FBMC.","PeriodicalId":268112,"journal":{"name":"2017 IEEE International Conference on Signal and Image Processing Applications (ICSIPA)","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133929816","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 : 2017-09-01DOI: 10.1109/ICSIPA.2017.8120581
Anthony Ngozichukwuka Uwaechia, N. Mahyuddin
In this paper, the problem of the deterministic pilot allocation for sparse channel estimation in Orthogonal Frequency Division Multiplexing (OFDM) system is investigated. This method is based on mutual coherence minimization of the measurement matrix associated with the OFDM system pilot subcarriers. It is known that if the set of pilot pattern is a Cyclic Difference Set (CDS), the mutual coherence of the measurement matrix is minimized. However, CDS in most practical OFDM system is not available. Few research efforts have tackled the problem of pilot allocation by proposing methods that lead to suboptimal solutions in order to ignore the computationally complex exhaustive search method. This contribution, however, proposes two pilot allocation design schemes for the construction of deterministic partial Fourier matrices satisfying the Restricted Isometry Property (RIP) namely, the Generic Random Search (GRS) and Progressive Search (PS) based on bounding the mutual coherence between different columns of the measurement matrix. Simulation results show that the two proposed pilot allocation design schemes are effective and offer a better channel estimation performance in terms of MSE when compared to former pilot allocation design methods.
{"title":"New pilot allocation design schemes for sparse channel estimation in OFDM system","authors":"Anthony Ngozichukwuka Uwaechia, N. Mahyuddin","doi":"10.1109/ICSIPA.2017.8120581","DOIUrl":"https://doi.org/10.1109/ICSIPA.2017.8120581","url":null,"abstract":"In this paper, the problem of the deterministic pilot allocation for sparse channel estimation in Orthogonal Frequency Division Multiplexing (OFDM) system is investigated. This method is based on mutual coherence minimization of the measurement matrix associated with the OFDM system pilot subcarriers. It is known that if the set of pilot pattern is a Cyclic Difference Set (CDS), the mutual coherence of the measurement matrix is minimized. However, CDS in most practical OFDM system is not available. Few research efforts have tackled the problem of pilot allocation by proposing methods that lead to suboptimal solutions in order to ignore the computationally complex exhaustive search method. This contribution, however, proposes two pilot allocation design schemes for the construction of deterministic partial Fourier matrices satisfying the Restricted Isometry Property (RIP) namely, the Generic Random Search (GRS) and Progressive Search (PS) based on bounding the mutual coherence between different columns of the measurement matrix. Simulation results show that the two proposed pilot allocation design schemes are effective and offer a better channel estimation performance in terms of MSE when compared to former pilot allocation design methods.","PeriodicalId":268112,"journal":{"name":"2017 IEEE International Conference on Signal and Image Processing Applications (ICSIPA)","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134276449","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 : 2017-09-01DOI: 10.1109/ICSIPA.2017.8120600
Tanvira Ismail, L. J. Singh
Accurate dialect identification technique helps in improving the speech recognition systems that exist in most of the present day electronic devices and is also expected to help in providing new services in the field of e-health and telemedicine which is especially important for older and homebound people. The accuracy of a dialect identification system is highly dependent on its speech corpora. Therefore, in this paper, we describe how speech corpora have been developed for Goalparia dialect and languages it is similar to i.e. Assamese and Bengali. Finally, identification of Goalparia dialect, Assamese and Bengali languages have been done using the developed speech corpora in order to evaluate it.
{"title":"Development of speech corpora for Goalparia dialect and similar languages","authors":"Tanvira Ismail, L. J. Singh","doi":"10.1109/ICSIPA.2017.8120600","DOIUrl":"https://doi.org/10.1109/ICSIPA.2017.8120600","url":null,"abstract":"Accurate dialect identification technique helps in improving the speech recognition systems that exist in most of the present day electronic devices and is also expected to help in providing new services in the field of e-health and telemedicine which is especially important for older and homebound people. The accuracy of a dialect identification system is highly dependent on its speech corpora. Therefore, in this paper, we describe how speech corpora have been developed for Goalparia dialect and languages it is similar to i.e. Assamese and Bengali. Finally, identification of Goalparia dialect, Assamese and Bengali languages have been done using the developed speech corpora in order to evaluate it.","PeriodicalId":268112,"journal":{"name":"2017 IEEE International Conference on Signal and Image Processing Applications (ICSIPA)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117080896","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 : 2017-09-01DOI: 10.1109/ICSIPA.2017.8120591
Umma Hany, L. Akter
In this paper, we propose distance and path loss based weighted centroid localization (WCL) algorithms for video capsule endoscope (VCE) using the received signal strength indicator (RSSI). We evaluate the performance of both algorithms considering real channel characteristics of human body. One of the major challenge in RSSI based VCE localization is the shadow fading and multi-path propagation effects of non-homogeneous medium of human body for which the measured RSSI is highly random resulting in high localization error. Again, due to the complex environment of experiment, accurate estimation of the channel parameters is quite difficult. We evaluate the performance of both algorithms in presence of randomness in path loss and estimation errors in channel parameters. To address the randomness issue, we estimate the smoothed path loss using moving averaging filter. Then, we introduce 10–50% errors in channel parameters to analyze the performance of both algorithms. We develop a simulation tool using MATLAB to visualize the results and to compare the performance. We observe significant improvement in performance by applying moving averaging method of smoothed path loss estimation using both algorithms. We also observe that the accuracy of distance based WCL decreases significantly in presence of errors in channel parameters. Whereas path loss based WCL is robust to the errors in channel parameters as it estimates the positions by using the estimated path loss directly without prior precise knowledge of channel parameters.
{"title":"Performance of distance based and path loss based weighted centroid localization algorithms for video capsule endoscope","authors":"Umma Hany, L. Akter","doi":"10.1109/ICSIPA.2017.8120591","DOIUrl":"https://doi.org/10.1109/ICSIPA.2017.8120591","url":null,"abstract":"In this paper, we propose distance and path loss based weighted centroid localization (WCL) algorithms for video capsule endoscope (VCE) using the received signal strength indicator (RSSI). We evaluate the performance of both algorithms considering real channel characteristics of human body. One of the major challenge in RSSI based VCE localization is the shadow fading and multi-path propagation effects of non-homogeneous medium of human body for which the measured RSSI is highly random resulting in high localization error. Again, due to the complex environment of experiment, accurate estimation of the channel parameters is quite difficult. We evaluate the performance of both algorithms in presence of randomness in path loss and estimation errors in channel parameters. To address the randomness issue, we estimate the smoothed path loss using moving averaging filter. Then, we introduce 10–50% errors in channel parameters to analyze the performance of both algorithms. We develop a simulation tool using MATLAB to visualize the results and to compare the performance. We observe significant improvement in performance by applying moving averaging method of smoothed path loss estimation using both algorithms. We also observe that the accuracy of distance based WCL decreases significantly in presence of errors in channel parameters. Whereas path loss based WCL is robust to the errors in channel parameters as it estimates the positions by using the estimated path loss directly without prior precise knowledge of channel parameters.","PeriodicalId":268112,"journal":{"name":"2017 IEEE International Conference on Signal and Image Processing Applications (ICSIPA)","volume":"69 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125287123","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 : 2017-09-01DOI: 10.1109/ICSIPA.2017.8120629
W. Ung, T. Tang, F. Mériaudeau, Esther Gunaseli M. Ebenezer
Cognitive rehabilitation has been proposed as an alternative treatment for Alzheimer's disease (AD) as it helps to preserve brain functionality. However, gains of cognitive training or rehabilitation may be eliminated due to cognitive overload and mental fatigue. This paper reports the development of a functional near-infrared spectroscopy (fNIRS) — brain-computer interface (BCI) that can adjust task difficulty adaptively. The aim is to have participants trained at their optimal level of difficulty and workload to maximize their gains. One patient with mild AD and one healthy control were recruited to test the functionality of proposed fNIRS-BCI system. The fNIRS-BCI system is able to process fNIRS signals in real time and adjust task difficulty accordingly. The healthy control was able to proceed to higher task levels, as compared to the mild AD patient. The fNIRS-BCI system has the potential as a tool to examine the efficacy of cognitive rehabilitation as an alternative treatment for AD.
{"title":"Dynamic optimization of mental workload in fNIRS-BCI system for cognitive rehabilitation","authors":"W. Ung, T. Tang, F. Mériaudeau, Esther Gunaseli M. Ebenezer","doi":"10.1109/ICSIPA.2017.8120629","DOIUrl":"https://doi.org/10.1109/ICSIPA.2017.8120629","url":null,"abstract":"Cognitive rehabilitation has been proposed as an alternative treatment for Alzheimer's disease (AD) as it helps to preserve brain functionality. However, gains of cognitive training or rehabilitation may be eliminated due to cognitive overload and mental fatigue. This paper reports the development of a functional near-infrared spectroscopy (fNIRS) — brain-computer interface (BCI) that can adjust task difficulty adaptively. The aim is to have participants trained at their optimal level of difficulty and workload to maximize their gains. One patient with mild AD and one healthy control were recruited to test the functionality of proposed fNIRS-BCI system. The fNIRS-BCI system is able to process fNIRS signals in real time and adjust task difficulty accordingly. The healthy control was able to proceed to higher task levels, as compared to the mild AD patient. The fNIRS-BCI system has the potential as a tool to examine the efficacy of cognitive rehabilitation as an alternative treatment for AD.","PeriodicalId":268112,"journal":{"name":"2017 IEEE International Conference on Signal and Image Processing Applications (ICSIPA)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125806563","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 : 2017-09-01DOI: 10.1109/ICSIPA.2017.8120599
R. Kannan, Nur Izzati Abdul Samad, M. Romlie, N. M. Nor, L. Kumar
Electric vehicles and hybrid electric vehicles are seen as the future of the automotive industry with its aim to replace the conventional combustion engine vehicle. The conventional Multi-Input Multi-Output topology used in the electric and hybrid electric vehicle applications. The weaknesses of this topology are the complexity of circuit which increases the size of the converter and overall cost. In this research, the novel idea would be to implement the Single-Input Multi-Output DC-DC converter topology in an electric vehicle. The proposed idea will be able to overcome the downsides of the conventional method of the DC-DC converter used in electric vehicle and thus benefiting users. The limitations of this research would be the implementation of the system in a real electric vehicle. The circuit designed will be simulated, fabricated and evaluated.
{"title":"Design and execution of single input multiple output DC-DC converter","authors":"R. Kannan, Nur Izzati Abdul Samad, M. Romlie, N. M. Nor, L. Kumar","doi":"10.1109/ICSIPA.2017.8120599","DOIUrl":"https://doi.org/10.1109/ICSIPA.2017.8120599","url":null,"abstract":"Electric vehicles and hybrid electric vehicles are seen as the future of the automotive industry with its aim to replace the conventional combustion engine vehicle. The conventional Multi-Input Multi-Output topology used in the electric and hybrid electric vehicle applications. The weaknesses of this topology are the complexity of circuit which increases the size of the converter and overall cost. In this research, the novel idea would be to implement the Single-Input Multi-Output DC-DC converter topology in an electric vehicle. The proposed idea will be able to overcome the downsides of the conventional method of the DC-DC converter used in electric vehicle and thus benefiting users. The limitations of this research would be the implementation of the system in a real electric vehicle. The circuit designed will be simulated, fabricated and evaluated.","PeriodicalId":268112,"journal":{"name":"2017 IEEE International Conference on Signal and Image Processing Applications (ICSIPA)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128505749","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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