A Brain-Computer Interface (BCI) system can communicate without movement based on brain signals measured with Electroencephalography (EEG).
脑机接口(BCI)系统可以在没有运动的情况下根据脑电图(EEG)测量的大脑信号进行通信。
{"title":"The Introduction of Designing a Hybrid Brain Computer Interface System","authors":"Sorush Niknamian","doi":"10.2139/ssrn.3350821","DOIUrl":"https://doi.org/10.2139/ssrn.3350821","url":null,"abstract":"A Brain-Computer Interface (BCI) system can communicate\u0000without movement based on brain signals measured with\u0000Electroencephalography (EEG).","PeriodicalId":433297,"journal":{"name":"EngRN: Signal Processing (Topic)","volume":"55 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126787922","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}
In this paper, novel automatic attendance system is proposed by using machine learning and deep learning algorithms. Real-time face recognition algorithms are used and integrated with existing University management system which detects and recognize faces of students in real time while attending lectures. This new proposed system for automatic attendance system aims to be less time consuming in comparison to the existing system of marking the attendance. The designed system does not interrupt class in any manner. Therefore, it saves potential time of students as well as of teachers. From the experiment analysis it is found that the accuracy of proposed system is 97%. Hence proposed system doesn’t require any rectification and verification from teachers.
{"title":"Automatic Attendance System Using Deep Learning","authors":"Sunil Aryal, Rachhpal Singh, Arnav Sood, Gaurav Thapa","doi":"10.2139/ssrn.3352376","DOIUrl":"https://doi.org/10.2139/ssrn.3352376","url":null,"abstract":"In this paper, novel automatic attendance system is proposed by using machine learning and deep learning algorithms. Real-time face recognition algorithms are used and integrated with existing University management system which detects and recognize faces of students in real time while attending lectures. This new proposed system for automatic attendance system aims to be less time consuming in comparison to the existing system of marking the attendance. The designed system does not interrupt class in any manner. Therefore, it saves potential time of students as well as of teachers. From the experiment analysis it is found that the accuracy of proposed system is 97%. Hence proposed system doesn’t require any rectification and verification from teachers.<br>","PeriodicalId":433297,"journal":{"name":"EngRN: Signal Processing (Topic)","volume":"IA-12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126556510","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}
Face identification using feed forward technique is a very important technique to use in computer vision, machine learning, biometrics, pattern recognition, pattern analysis and digital image processing. It is a systematic method for training multi-layer convolutional neural network. It is a mathematical technique that is strong but not highly used in practical. Feed forward technique is using for extend gradient descent based delta learning rules. Feed forward technique are provides a computationally efficient method for changing the weight and bias. Face learning problem is to search for all hypothesis space defined to all weight values for all units in the networks. The error is replaced by P and the other category of the space corresponding to all of the associated weight with all of the units in the network. In this equation in the case of training a single unit the output attempts to find a hypothesis to minimize P. In face identification algorithm the automatically determined location of the different feature. This alignment is refined by optical view. Identification is performing by computing normalized correlation scores in many face identification scenarios the pose of the probe and registered database image are different.
{"title":"Machine Learning Approaches for Face Identification Feed Forward Algorithms","authors":"A. Tiwari, R. Shukla","doi":"10.2139/ssrn.3350264","DOIUrl":"https://doi.org/10.2139/ssrn.3350264","url":null,"abstract":"Face identification using feed forward technique is a very important technique to use in computer vision, machine learning, biometrics, pattern recognition, pattern analysis and digital image processing. It is a systematic method for training multi-layer convolutional neural network. It is a mathematical technique that is strong but not highly used in practical. Feed forward technique is using for extend gradient descent based delta learning rules. Feed forward technique are provides a computationally efficient method for changing the weight and bias. Face learning problem is to search for all hypothesis space defined to all weight values for all units in the networks. The error is replaced by P and the other category of the space corresponding to all of the associated weight with all of the units in the network. In this equation in the case of training a single unit the output attempts to find a hypothesis to minimize P. In face identification algorithm the automatically determined location of the different feature. This alignment is refined by optical view. Identification is performing by computing normalized correlation scores in many face identification scenarios the pose of the probe and registered database image are different.","PeriodicalId":433297,"journal":{"name":"EngRN: Signal Processing (Topic)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123712023","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}
Rainy image restoration is considered asone of the most important image restorations aspects to improve the outdoor vision. Many fields have used this kind of restorations such as driving assistant, environment monitoring, animals monitoring, computer vision, face recognition, object recognition and personal photos. Image restoration simply means how to remove the noise from the images. Most of the images have some noises from the environment. Moreover, image quality assessment plays an important role in the valuation of image enhancement algorithms. In this research, we will use a total variation to remove rain streaks from a single image. It shows a good performance compared to other methods, using some measurements MSE, PSNR, and VIF for an image with references and BRISQUE for an image without references
{"title":"Removing Rain Streaks From Single Images Using Total Variation","authors":"Samer Shorman, S. A. Pitchay","doi":"10.5121/IJMA.2018.10616","DOIUrl":"https://doi.org/10.5121/IJMA.2018.10616","url":null,"abstract":"Rainy image restoration is considered asone of the most important image restorations aspects to improve the outdoor vision. Many fields have used this kind of restorations such as driving assistant, environment monitoring, animals monitoring, computer vision, face recognition, object recognition and personal photos. Image restoration simply means how to remove the noise from the images. Most of the images have some noises from the environment. Moreover, image quality assessment plays an important role in the valuation of image enhancement algorithms. In this research, we will use a total variation to remove rain streaks from a single image. It shows a good performance compared to other methods, using some measurements MSE, PSNR, and VIF for an image with references and BRISQUE for an image without references","PeriodicalId":433297,"journal":{"name":"EngRN: Signal Processing (Topic)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133815938","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}
Increased downscaling of CMOS circuits with respect to feature size and threshold voltage has a result of dramatically increasing in leakage current. So, leakage power reduction is an important design issue for active and standby modes as long as the technology scaling increased. In this paper, a simultaneous active and standby energy optimization methodology is proposed for 22 nm sub-threshold CMOS circuits. In the first phase, we investigate the dual threshold voltage design for active energy per cycle minimization. A slack based genetic algorithm is proposed to find the optimal reverse body bias assignment to set of noncritical paths gates to ensure low active energy per cycle with the maximum allowable frequency at the optimal supply voltage. The second phase, determine the optimal reverse body bias that can be applied to all gates for standby power optimization at the optimal supply voltage determined from the first phase. Therefore, there exist two sets of gates and two reverse body bias values for each set. The reverse body bias is switched between these two values in response to the mode of operation. Experimental results are obtained for some ISCAS-85 benchmark circuits such as 74L85, 74283, ALU74181, and 16 bit RCA. The optimized circuits show significant energy saving ranged (from 14.5% to 42.28%) and standby power saving ranged (from 62.8% to 67%).
{"title":"Simultaneous Optimization of Standby and Active Energy for Sub-threshold Circuits","authors":"Ali T. Shaheen, S. Taha","doi":"10.5121/VLSIC.2016.7601","DOIUrl":"https://doi.org/10.5121/VLSIC.2016.7601","url":null,"abstract":"Increased downscaling of CMOS circuits with respect to feature size and threshold voltage has a result of dramatically increasing in leakage current. So, leakage power reduction is an important design issue for active and standby modes as long as the technology scaling increased. In this paper, a simultaneous active and standby energy optimization methodology is proposed for 22 nm sub-threshold CMOS circuits. In the first phase, we investigate the dual threshold voltage design for active energy per cycle minimization. A slack based genetic algorithm is proposed to find the optimal reverse body bias assignment to set of noncritical paths gates to ensure low active energy per cycle with the maximum allowable frequency at the optimal supply voltage. The second phase, determine the optimal reverse body bias that can be applied to all gates for standby power optimization at the optimal supply voltage determined from the first phase. Therefore, there exist two sets of gates and two reverse body bias values for each set. The reverse body bias is switched between these two values in response to the mode of operation. Experimental results are obtained for some ISCAS-85 benchmark circuits such as 74L85, 74283, ALU74181, and 16 bit RCA. The optimized circuits show significant energy saving ranged (from 14.5% to 42.28%) and standby power saving ranged (from 62.8% to 67%).<br>","PeriodicalId":433297,"journal":{"name":"EngRN: Signal Processing (Topic)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131073301","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}
Graphical abstractDisplay Omitted A spatial representations for Arabic characters are proposed.A new approach to the skeletonization of handwriting images documents is introduced.Experiments were performed on the IFN/ENIT databases.The approach is successful even when using handwritten upper case English characters. Segmentation is the most challenging part of Arabic handwriting recognition due to the unique characteristics of Arabic writing that allow the same shape to denote different characters. An Arabic handwriting recognition system cannot be successful without using an appropriate segmentation method. In this paper, a very effective and efficient off-line Arabic handwriting recognition approach is proposed. The proposed approach has three stages. Firstly, all characters are simplified to single-pixel-thin images that preserve the fundamental writing characteristics. Secondly, the image pixels are normalized into horizontal and vertical lines only. Therefore, the different writing styles can be unified and the shapes of characters are standardized. Finally, these orthogonal lines are coded as unique vectors; each vector represents one letter of a word. To evaluate the proposed techniques, we have tested our approach on two different datasets. Our experimental results show that the proposed approach has superior performance over the state-of-the-art approaches.
{"title":"An Effective Approach to Offline Arabic Handwriting Recognition","authors":"Jafaar Al Abodi, Xue Li","doi":"10.2139/ssrn.3624010","DOIUrl":"https://doi.org/10.2139/ssrn.3624010","url":null,"abstract":"Graphical abstractDisplay Omitted A spatial representations for Arabic characters are proposed.A new approach to the skeletonization of handwriting images documents is introduced.Experiments were performed on the IFN/ENIT databases.The approach is successful even when using handwritten upper case English characters. Segmentation is the most challenging part of Arabic handwriting recognition due to the unique characteristics of Arabic writing that allow the same shape to denote different characters. An Arabic handwriting recognition system cannot be successful without using an appropriate segmentation method. In this paper, a very effective and efficient off-line Arabic handwriting recognition approach is proposed. The proposed approach has three stages. Firstly, all characters are simplified to single-pixel-thin images that preserve the fundamental writing characteristics. Secondly, the image pixels are normalized into horizontal and vertical lines only. Therefore, the different writing styles can be unified and the shapes of characters are standardized. Finally, these orthogonal lines are coded as unique vectors; each vector represents one letter of a word. To evaluate the proposed techniques, we have tested our approach on two different datasets. Our experimental results show that the proposed approach has superior performance over the state-of-the-art approaches.","PeriodicalId":433297,"journal":{"name":"EngRN: Signal Processing (Topic)","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127392693","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}
Morphological active contours for image segmentation have become popular due to their low computational complexity coupled with their accurate approximation of the partial differential equations involved in the energy minimization of the segmentation process. In this paper, a morphological active contour which mimics the energy minimization of the popular Chan-Vese Active Contour without Edges is coupled with a morphological edge-driven segmentation term to accurately segment natural images. By using morphological approximations of the energy minimization steps, the algorithm has a low computational complexity. Additionally, the coupling of the edge-based and region-based segmentation techniques allows the proposed method to be robust and accurate. We will demonstrate the accuracy and robustness of the algorithm using images from the Weizmann Segmentation Evaluation Database and report on the segmentation results using the Sorensen-Dice similarity coefficient.
{"title":"A Hybrid Morphological Active Contour for Natural Images","authors":"Victoria L. Fox, M. Milanova, S. Al-Ali","doi":"10.2139/ssrn.3775362","DOIUrl":"https://doi.org/10.2139/ssrn.3775362","url":null,"abstract":"Morphological active contours for image segmentation have become popular due to their low computational complexity coupled with their accurate approximation of the partial differential equations involved in the energy minimization of the segmentation process. In this paper, a morphological active contour which mimics the energy minimization of the popular Chan-Vese Active Contour without Edges is coupled with a morphological edge-driven segmentation term to accurately segment natural images. By using morphological approximations of the energy minimization steps, the algorithm has a low computational complexity. Additionally, the coupling of the edge-based and region-based segmentation techniques allows the proposed method to be robust and accurate. We will demonstrate the accuracy and robustness of the algorithm using images from the Weizmann Segmentation Evaluation Database and report on the segmentation results using the Sorensen-Dice similarity coefficient.","PeriodicalId":433297,"journal":{"name":"EngRN: Signal Processing (Topic)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134329382","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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