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Tenth International Conference on Digital Image Processing (ICDIP 2018)最新文献

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Measuring heart-rate using wireless power charging receiver coil 使用无线充电接收器线圈测量心率
Tenth International Conference on Digital Image Processing (ICDIP 2018) Pub Date : 2018-08-28 DOI: 10.1117/12.2503044
Kang-Hwi Lee, Seong-Su Lee, Sang-Min Kim, Kyeong-Seop Kim, Jeong-Whan Lee
{"title":"Measuring heart-rate using wireless power charging receiver coil","authors":"Kang-Hwi Lee, Seong-Su Lee, Sang-Min Kim, Kyeong-Seop Kim, Jeong-Whan Lee","doi":"10.1117/12.2503044","DOIUrl":"https://doi.org/10.1117/12.2503044","url":null,"abstract":"In this paper, we propose a method of measuring mechanical activity of the heart using flat coil via wireless power charging receiver coil in a mobile communication devices such as mobile phone. The basic principle of the proposed method is based on the variation of magnetic inductance of coil antenna near biological tissues caused by the eddy current according to the Faraday’s law of magnetic induction. In order to recover the movement of ventricular muscle of the heart, FM demodulation scheme was applied. The correlation between the cardiac cycle and the measured heart activities was compared with standard ECG lead II signals. As a result, the period of the measured signals almost close to the intrinsic period of R-R intervals of the heart. The proposed method shows the possibility of using wireless power charging receiver coil embedded in the personal communication devices such as mobile phone and tablet device as a new heart-rate sensor.","PeriodicalId":174730,"journal":{"name":"Tenth International Conference on Digital Image Processing (ICDIP 2018)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116780731","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}
引用次数: 0
Synthetic aperture radar target identification based on incremental kernel extreme learning machine 基于增量核极值学习机的合成孔径雷达目标识别
Tenth International Conference on Digital Image Processing (ICDIP 2018) Pub Date : 2018-08-09 DOI: 10.1117/12.2502994
Guo Chenlong, Zhou Hongyi
{"title":"Synthetic aperture radar target identification based on incremental kernel extreme learning machine","authors":"Guo Chenlong, Zhou Hongyi","doi":"10.1117/12.2502994","DOIUrl":"https://doi.org/10.1117/12.2502994","url":null,"abstract":"Batch learning method is usually adopted for traditional SAR target identification, but training data of a system cannot be completely acquired at one time in practical application. When a new training sample is added, the batch training method needs to retrain the whole system. In order to solve this problem, cholesky factorization principle was adopted in this paper to promote extreme learning machine to an incremental learning form and apply it in the classifier training for SAR target identification. Moreover, in allusion to disadvantageous approximation capability of traditional single kernel function, a multi-scale wavelet kernel function was established to improve classification performance thereof. Experiment results show: when new SAR target sample is obtained, this algorithm only needs to update output weight value to update the system, without any retraining; it has extremely fast speed, with identification rate higher than that of traditional kernel extreme learning machine, SVM algorithm, etc., thus becoming a good choice for the online updating of SAR target identification system.","PeriodicalId":174730,"journal":{"name":"Tenth International Conference on Digital Image Processing (ICDIP 2018)","volume":"83 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124118960","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}
引用次数: 2
Smoke detection in video using dissipation function and support vector machine 基于耗散函数和支持向量机的视频烟雾检测
Tenth International Conference on Digital Image Processing (ICDIP 2018) Pub Date : 2018-08-09 DOI: 10.1117/12.2502874
zhang jiaohao, Zhao Junhao, Z. Jia, Zhang Qiang, cui hong
{"title":"Smoke detection in video using dissipation function and support vector machine","authors":"zhang jiaohao, Zhao Junhao, Z. Jia, Zhang Qiang, cui hong","doi":"10.1117/12.2502874","DOIUrl":"https://doi.org/10.1117/12.2502874","url":null,"abstract":"A method for smoke detection in video is proposed. The camera monitoring the scene is assumed to be stationary. With the atmospheric scattering model, dissipation function is reflected the transmissivity between the background objects in the scene and the camera. Dark channel prior and fast bilateral filter are used for estimating dissipation function which is only the function of the depth of field. Based on dissipation function, visual background extractor (ViBe) can be used for detecting smoke as a result of smoke’s motion characteristics as well as detecting other moving targets. Later on, various characteristics of smoke are extracted. Color feature and high-frequency energy based on wavelet transformation is selected to constitute the final recognition vectors, and Support vector machine (SVM) is used as a classification model. The final experimental results show that the accuracy rate of this method for smoke detection can reach 90.1%.","PeriodicalId":174730,"journal":{"name":"Tenth International Conference on Digital Image Processing (ICDIP 2018)","volume":"66 6","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114129800","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}
引用次数: 0
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