Pub Date : 2016-10-01DOI: 10.1109/GCCE.2016.7800349
D. Sueaseenak
Electrocardiogram(ECG) signal is the most important of medical information used to diagnosis and indicates the condition of the heart in humans. In a design and construction of single-lead electrocardiogram measurement system using discrete component incorporated with data acquisition(DAQ) was a problem from the noise or external interference. The modern technology in signals processing used to perform a noise canceling in electrocardiography. In this paper, we propose the simulation study of modern signal processing technique to separate the single channel of ECG signals from noise and others interference. The ECG signal was performed a denoising using ICA. A useful ICA algorithm called FAS-TICA is a highperformance algorithm to divide multiple linear combinations of ECG and noise to statistically independent elements. Our experimental results indicate the robustness of ICA after applied ECG is higher than before applied ICA, since the correlation coefficient and SNR is improved with minimum error.
{"title":"Denoising of electrocardiogram measurement system based on statistical signal processing","authors":"D. Sueaseenak","doi":"10.1109/GCCE.2016.7800349","DOIUrl":"https://doi.org/10.1109/GCCE.2016.7800349","url":null,"abstract":"Electrocardiogram(ECG) signal is the most important of medical information used to diagnosis and indicates the condition of the heart in humans. In a design and construction of single-lead electrocardiogram measurement system using discrete component incorporated with data acquisition(DAQ) was a problem from the noise or external interference. The modern technology in signals processing used to perform a noise canceling in electrocardiography. In this paper, we propose the simulation study of modern signal processing technique to separate the single channel of ECG signals from noise and others interference. The ECG signal was performed a denoising using ICA. A useful ICA algorithm called FAS-TICA is a highperformance algorithm to divide multiple linear combinations of ECG and noise to statistically independent elements. Our experimental results indicate the robustness of ICA after applied ECG is higher than before applied ICA, since the correlation coefficient and SNR is improved with minimum error.","PeriodicalId":416104,"journal":{"name":"2016 IEEE 5th Global Conference on Consumer Electronics","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121553053","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 : 2016-10-01DOI: 10.1109/GCCE.2016.7800547
K. Chen, Yu-Shu Ni, Jhao-Yi Wang
Electrocardiography (ECG) is a widely used noninvasive clinical tool for the diagnosis of cardiovascular disease. However, the accuracy of ECG analysis significantly affect the diagnostic error rate of cardiovascular diseases. Therefore, in recent year, many Neural Network (NN)-based approaches were proposed to automatically analyze the ECG signal. However, these methods suffer from long computing time, which is inappropriate for the mobile real-time application. To solve this problem, we propose a Wavelet-based Artificial Neural Network (W-ANN) diagnosis flow in this paper. Based on the wavelet transform, the W-ANN can provide not only cleaner ECG input signal but lower computing time. The experimental results show that the proposed method can reduce 49% computing time with only 11.7% ECG diagnostic accuracy loss by involving the data from MIT-BIH arrhythmia database and real ECG signal measurement.
{"title":"Electrocardiogram diagnosis using wavelet-based artificial neural network","authors":"K. Chen, Yu-Shu Ni, Jhao-Yi Wang","doi":"10.1109/GCCE.2016.7800547","DOIUrl":"https://doi.org/10.1109/GCCE.2016.7800547","url":null,"abstract":"Electrocardiography (ECG) is a widely used noninvasive clinical tool for the diagnosis of cardiovascular disease. However, the accuracy of ECG analysis significantly affect the diagnostic error rate of cardiovascular diseases. Therefore, in recent year, many Neural Network (NN)-based approaches were proposed to automatically analyze the ECG signal. However, these methods suffer from long computing time, which is inappropriate for the mobile real-time application. To solve this problem, we propose a Wavelet-based Artificial Neural Network (W-ANN) diagnosis flow in this paper. Based on the wavelet transform, the W-ANN can provide not only cleaner ECG input signal but lower computing time. The experimental results show that the proposed method can reduce 49% computing time with only 11.7% ECG diagnostic accuracy loss by involving the data from MIT-BIH arrhythmia database and real ECG signal measurement.","PeriodicalId":416104,"journal":{"name":"2016 IEEE 5th Global Conference on Consumer Electronics","volume":"193 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122504380","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 : 2016-10-01DOI: 10.1109/GCCE.2016.7800330
Susumu Genma, Takahiro Ogawa, M. Haseyama
This paper presents an image retrieval method for insect identification based on saliency map and distance metric learning. First, the proposed method extracts regions of insects from target images by using saliency map and calculates visual features from the extracted insect regions. Next, in order to realize accurate retrieval of insects based on the calculated features, distance metric learning is newly adopted. Consequently, through users' evaluation in the retrieval, optimal distance can be obtained for the calculated visual features to obtain successful retrieval results, and the identification of insects becomes feasible. Experimental results show the effectiveness of our method.
{"title":"Image retrieval for identification of insects based on saliency map and distance metric learning","authors":"Susumu Genma, Takahiro Ogawa, M. Haseyama","doi":"10.1109/GCCE.2016.7800330","DOIUrl":"https://doi.org/10.1109/GCCE.2016.7800330","url":null,"abstract":"This paper presents an image retrieval method for insect identification based on saliency map and distance metric learning. First, the proposed method extracts regions of insects from target images by using saliency map and calculates visual features from the extracted insect regions. Next, in order to realize accurate retrieval of insects based on the calculated features, distance metric learning is newly adopted. Consequently, through users' evaluation in the retrieval, optimal distance can be obtained for the calculated visual features to obtain successful retrieval results, and the identification of insects becomes feasible. Experimental results show the effectiveness of our method.","PeriodicalId":416104,"journal":{"name":"2016 IEEE 5th Global Conference on Consumer Electronics","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126476882","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 : 2016-10-01DOI: 10.1109/GCCE.2016.7800410
Yusuke Miyahara, Takahiro Uchiya, I. Takumi, Tetsuo Kinoshita
Recently, "Agent-Oriented Computing" has been attracting attention as an approach to designing a system with high response capabilities. To develop and operate an agent system effectively, an agent framework is used. An agent system developed on DASH[1], a kind of agent framework, requires us to install the agent framework as an operating infrastructure and to operate DASH to benefit from an agent system. This work and preparation puts a heavy strain on users who are not familiar with DASH. Therefore, it is difficult for people to benefit from services provided by an agent system. For that reason, this study was conducted to find some means of supporting general users who are unfamiliar with DASH, but who want to use an agent system. To those people, we hope to provide agent system benefits easily.
{"title":"Prototype of cloud-based agent framework for public use","authors":"Yusuke Miyahara, Takahiro Uchiya, I. Takumi, Tetsuo Kinoshita","doi":"10.1109/GCCE.2016.7800410","DOIUrl":"https://doi.org/10.1109/GCCE.2016.7800410","url":null,"abstract":"Recently, \"Agent-Oriented Computing\" has been attracting attention as an approach to designing a system with high response capabilities. To develop and operate an agent system effectively, an agent framework is used. An agent system developed on DASH[1], a kind of agent framework, requires us to install the agent framework as an operating infrastructure and to operate DASH to benefit from an agent system. This work and preparation puts a heavy strain on users who are not familiar with DASH. Therefore, it is difficult for people to benefit from services provided by an agent system. For that reason, this study was conducted to find some means of supporting general users who are unfamiliar with DASH, but who want to use an agent system. To those people, we hope to provide agent system benefits easily.","PeriodicalId":416104,"journal":{"name":"2016 IEEE 5th Global Conference on Consumer Electronics","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130586331","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 : 2016-10-01DOI: 10.1109/GCCE.2016.7800313
Chao-Yang Chang, Chung-Hsun Huang, Hui-Fu Chen, C. Yeh, Y. Chu, Tay-Jyi Lin
Overdrive technique is mandatory for liquid crystal display (LCD) to mitigate the motion blur phenomenon. As the display resolution increases, the image data should be highly compressed to reduce the usages of frame memory and bandwidth. Since current high quality compression algorithms inevitably require large embedded memory (line buffer) and complex computations, a low complexity line-buffer-free edge-preserved compression algorithm is proposed in this paper to reserve the edge information for overdrive technique of LCD. Performance evaluations show that the proposed edge-preserved compression achieves above 20% PSNR improvements as compared to the conventional DPCM compression up to 6:1 compression ratio.
{"title":"A low complexity edge-preserved image compression algorithm for LCD overdrive","authors":"Chao-Yang Chang, Chung-Hsun Huang, Hui-Fu Chen, C. Yeh, Y. Chu, Tay-Jyi Lin","doi":"10.1109/GCCE.2016.7800313","DOIUrl":"https://doi.org/10.1109/GCCE.2016.7800313","url":null,"abstract":"Overdrive technique is mandatory for liquid crystal display (LCD) to mitigate the motion blur phenomenon. As the display resolution increases, the image data should be highly compressed to reduce the usages of frame memory and bandwidth. Since current high quality compression algorithms inevitably require large embedded memory (line buffer) and complex computations, a low complexity line-buffer-free edge-preserved compression algorithm is proposed in this paper to reserve the edge information for overdrive technique of LCD. Performance evaluations show that the proposed edge-preserved compression achieves above 20% PSNR improvements as compared to the conventional DPCM compression up to 6:1 compression ratio.","PeriodicalId":416104,"journal":{"name":"2016 IEEE 5th Global Conference on Consumer Electronics","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131379348","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 : 2016-10-01DOI: 10.1109/GCCE.2016.7800355
Hailong Liu, T. Taniguchi, Kazuhito Takenaka, Yusuke Tanaka, T. Bando
Analyzing driving behavior data is essential for developing driver assistance systems. Statistical segmentation is one of the important methods to realize the analysis. Driving behavior data actually include undesirable defects caused by external environment and sensor failures. Defects in the data cause a huge negative effect on the segmentation. In this paper, we showed that a feature extraction method based on a deep sparse autoencoder with fixed point (DSAE-FP) could reduce the negative effect of defective data in a driving behavior segmentation task. In the experiments, we used sticky hierarchical Dirichlet process hidden Markov model to segment the driving behavior. We compared the segmentation results using hidden features extracted by DSAE-FP and other comparative methods. Experimental results showed that segmentation results of non-defective dataset and defective dataset turned out most similar when DSAE-FP was used.
{"title":"Reducing the negative effect of defective data on driving behavior segmentation via a deep sparse autoencoder","authors":"Hailong Liu, T. Taniguchi, Kazuhito Takenaka, Yusuke Tanaka, T. Bando","doi":"10.1109/GCCE.2016.7800355","DOIUrl":"https://doi.org/10.1109/GCCE.2016.7800355","url":null,"abstract":"Analyzing driving behavior data is essential for developing driver assistance systems. Statistical segmentation is one of the important methods to realize the analysis. Driving behavior data actually include undesirable defects caused by external environment and sensor failures. Defects in the data cause a huge negative effect on the segmentation. In this paper, we showed that a feature extraction method based on a deep sparse autoencoder with fixed point (DSAE-FP) could reduce the negative effect of defective data in a driving behavior segmentation task. In the experiments, we used sticky hierarchical Dirichlet process hidden Markov model to segment the driving behavior. We compared the segmentation results using hidden features extracted by DSAE-FP and other comparative methods. Experimental results showed that segmentation results of non-defective dataset and defective dataset turned out most similar when DSAE-FP was used.","PeriodicalId":416104,"journal":{"name":"2016 IEEE 5th Global Conference on Consumer Electronics","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129928053","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 : 2016-10-01DOI: 10.1109/GCCE.2016.7800350
T. Hase, T. Sakao
This paper describes a method for distinguishing various home appliances with separate remote controllers and integrating control with a single remote controller. We propose that images of home appliances can be obtained with a camera, after which the appliances can be distinguished from each other by a convolutional neural network. We developed an experimental system to evaluate our proposed method and demonstrated that the system could distinguish different home appliances with a high accuracy of 99%.
{"title":"Integrated remote controller distinguishing home appliances by deep learning","authors":"T. Hase, T. Sakao","doi":"10.1109/GCCE.2016.7800350","DOIUrl":"https://doi.org/10.1109/GCCE.2016.7800350","url":null,"abstract":"This paper describes a method for distinguishing various home appliances with separate remote controllers and integrating control with a single remote controller. We propose that images of home appliances can be obtained with a camera, after which the appliances can be distinguished from each other by a convolutional neural network. We developed an experimental system to evaluate our proposed method and demonstrated that the system could distinguish different home appliances with a high accuracy of 99%.","PeriodicalId":416104,"journal":{"name":"2016 IEEE 5th Global Conference on Consumer Electronics","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120996866","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 : 2016-10-01DOI: 10.1109/GCCE.2016.7800331
Naoki Saito, Takahiro Ogawa, Satoshi Asamizu, M. Haseyama
In this paper, we propose a tourism category classification method based on estimation of reliable decision. The proposed method performs tourism category classification using location, visual, and textual tag features obtained from tourism images in image sharing services. As the biggest contribution of this paper, the proposed method performs successful classification based on two classification results obtained from a fuzzy K-nearest neighbor algorithm using the location features and a decision level fusion approach using the visual and textual tag features. The proposed method enables estimation of reliable decision from above two classifiers.
{"title":"A tourism category classification method based on estimation of reliable decision","authors":"Naoki Saito, Takahiro Ogawa, Satoshi Asamizu, M. Haseyama","doi":"10.1109/GCCE.2016.7800331","DOIUrl":"https://doi.org/10.1109/GCCE.2016.7800331","url":null,"abstract":"In this paper, we propose a tourism category classification method based on estimation of reliable decision. The proposed method performs tourism category classification using location, visual, and textual tag features obtained from tourism images in image sharing services. As the biggest contribution of this paper, the proposed method performs successful classification based on two classification results obtained from a fuzzy K-nearest neighbor algorithm using the location features and a decision level fusion approach using the visual and textual tag features. The proposed method enables estimation of reliable decision from above two classifiers.","PeriodicalId":416104,"journal":{"name":"2016 IEEE 5th Global Conference on Consumer Electronics","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121356751","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 : 2016-10-01DOI: 10.1109/GCCE.2016.7800508
T. Ozaki, Naokazu Uchida, H. Mineno
The number of small stores, such as convenience stores, is increasing, and their electric power consumption is also increasing. Disaggregation technology, which identifies the statuses of devices by measuring one current at the circuit breaker board, is a preferred solution to save energy. In this research, we propose an EMS with the disaggregation technology that can estimate electric power consumption. We have tested it at a convenience store. The result shows that the system estimated the electric power consumption to be less than 15% of the MAPE for most appliances.
{"title":"Development of electric power disaggregation system for chain stores","authors":"T. Ozaki, Naokazu Uchida, H. Mineno","doi":"10.1109/GCCE.2016.7800508","DOIUrl":"https://doi.org/10.1109/GCCE.2016.7800508","url":null,"abstract":"The number of small stores, such as convenience stores, is increasing, and their electric power consumption is also increasing. Disaggregation technology, which identifies the statuses of devices by measuring one current at the circuit breaker board, is a preferred solution to save energy. In this research, we propose an EMS with the disaggregation technology that can estimate electric power consumption. We have tested it at a convenience store. The result shows that the system estimated the electric power consumption to be less than 15% of the MAPE for most appliances.","PeriodicalId":416104,"journal":{"name":"2016 IEEE 5th Global Conference on Consumer Electronics","volume":"247 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121239989","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 : 2016-10-01DOI: 10.1109/GCCE.2016.7800352
Kazu Mishiba, Y. Oyamada, K. Kondo
Image retargeting methods change the size of images to an arbitrary resolution while protecting visually important regions from distortion. Since retargeting methods deform contents of an image based on these importance, importance calculation methods suitable for image retargeting is needed. In this paper, we propose a framework to improve an importance map by using depth and segmentation information obtained from light field images. Depth information is used for considering the distance of objects from a camera. Segmentation information is used for maintaining visual consistency. Experimental results show that importance improved by our framework leads to better retargeting results.
{"title":"A framework for improvement of importance map for image retargeting assisted by light field images","authors":"Kazu Mishiba, Y. Oyamada, K. Kondo","doi":"10.1109/GCCE.2016.7800352","DOIUrl":"https://doi.org/10.1109/GCCE.2016.7800352","url":null,"abstract":"Image retargeting methods change the size of images to an arbitrary resolution while protecting visually important regions from distortion. Since retargeting methods deform contents of an image based on these importance, importance calculation methods suitable for image retargeting is needed. In this paper, we propose a framework to improve an importance map by using depth and segmentation information obtained from light field images. Depth information is used for considering the distance of objects from a camera. Segmentation information is used for maintaining visual consistency. Experimental results show that importance improved by our framework leads to better retargeting results.","PeriodicalId":416104,"journal":{"name":"2016 IEEE 5th Global Conference on Consumer Electronics","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129008912","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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