Pub Date : 2017-10-01DOI: 10.1109/CISP-BMEI.2017.8301957
Fan Wu, U. KinTak
To improve the quality of low-light image, we proposed a new HSI based enhancement algorithm. This new algorithm enhances the luminance of low-light level images while preserving image contrast and details. First, the original RGB image is converted into HSI color space, then the intensity and saturation components are processed with different enhancement methods, but the hue component remains unchanged, the segmentation exponential enhancement algorithm is applied to saturation component S, then apply the histogram equalization to intensity component I and then the intensity component I is divided into high and low frequency sub-bands with wavelet transform, the Retinex algorithm is applied to the low frequency sub-band to adjust image luminance while the improved fuzzy enhancement is applied to the high frequency sub-band to enhance image details. Finally, reconstruct the component I with inverse wavelet transform, and the reconstructed component I will be synthesized with H and the enhanced S components to get a clear RGB image. By taking advantage of HSI color space and the improved enhancement algorithm, the enhancement of low illumination color image has been achieved. According to the experiment results, this algorithm can obviously improve the visual effect of low light color image.
{"title":"Low-Light image enhancement algorithm based on HSI color space","authors":"Fan Wu, U. KinTak","doi":"10.1109/CISP-BMEI.2017.8301957","DOIUrl":"https://doi.org/10.1109/CISP-BMEI.2017.8301957","url":null,"abstract":"To improve the quality of low-light image, we proposed a new HSI based enhancement algorithm. This new algorithm enhances the luminance of low-light level images while preserving image contrast and details. First, the original RGB image is converted into HSI color space, then the intensity and saturation components are processed with different enhancement methods, but the hue component remains unchanged, the segmentation exponential enhancement algorithm is applied to saturation component S, then apply the histogram equalization to intensity component I and then the intensity component I is divided into high and low frequency sub-bands with wavelet transform, the Retinex algorithm is applied to the low frequency sub-band to adjust image luminance while the improved fuzzy enhancement is applied to the high frequency sub-band to enhance image details. Finally, reconstruct the component I with inverse wavelet transform, and the reconstructed component I will be synthesized with H and the enhanced S components to get a clear RGB image. By taking advantage of HSI color space and the improved enhancement algorithm, the enhancement of low illumination color image has been achieved. According to the experiment results, this algorithm can obviously improve the visual effect of low light color image.","PeriodicalId":6474,"journal":{"name":"2017 10th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI)","volume":"1237 1","pages":"1-6"},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81916687","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 : 2017-10-01DOI: 10.1109/CISP-BMEI.2017.8302064
Y. Shima
An algorithm to detect car distance from a pair of stereo images is presented. It is useful for drivers to avoid collisions and ensure safety to keep the car at a constant distance from the car ahead. The conventional distance detection method is based on image matching; the proposed algorithm is based on key-point matching. Key points are extracted from a stereo image pair by using Speeded Up Robust Features (SURF). The distance is calculated from 3D binocular disparity, the difference of position at the object.
{"title":"Inter-vehicle distance detection based on keypoint matching for stereo images","authors":"Y. Shima","doi":"10.1109/CISP-BMEI.2017.8302064","DOIUrl":"https://doi.org/10.1109/CISP-BMEI.2017.8302064","url":null,"abstract":"An algorithm to detect car distance from a pair of stereo images is presented. It is useful for drivers to avoid collisions and ensure safety to keep the car at a constant distance from the car ahead. The conventional distance detection method is based on image matching; the proposed algorithm is based on key-point matching. Key points are extracted from a stereo image pair by using Speeded Up Robust Features (SURF). The distance is calculated from 3D binocular disparity, the difference of position at the object.","PeriodicalId":6474,"journal":{"name":"2017 10th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI)","volume":"121 1","pages":"1-6"},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79854199","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 : 2017-10-01DOI: 10.1109/CISP-BMEI.2017.8302217
F. N. Almirantearena
In the processing of biological signals of the electrocardiogram (ECG) and Arterial Diameter Variation (ADV) there are several methods for the extraction of cardiovascular event characteristics. In this case, the canonical polyadic decomposition of CANDECOMP/PARAFAC (CP) tensors is used in the processing of the mixed signals of ECG-ADV; the ECG-ADV signals are extracted from each patient simultaneously and the detection quality of the ECG complex is verified. To do this, the ECG complex and the systolic wave of the ADV wave are aligned with Gaussian noise, and then the tensors were constructed for both signals. Five algorithms of CP were applied and the quality of the factorization of each one of the algorithms was checked with four indices. Both signals were shown to be non-collinear, and the algorithms that have the minimum number of iterations at the convergence were determined.
{"title":"Decomposition of tensors of cardio-vascular signals using CANDECOMP / PARAFAC algorithms","authors":"F. N. Almirantearena","doi":"10.1109/CISP-BMEI.2017.8302217","DOIUrl":"https://doi.org/10.1109/CISP-BMEI.2017.8302217","url":null,"abstract":"In the processing of biological signals of the electrocardiogram (ECG) and Arterial Diameter Variation (ADV) there are several methods for the extraction of cardiovascular event characteristics. In this case, the canonical polyadic decomposition of CANDECOMP/PARAFAC (CP) tensors is used in the processing of the mixed signals of ECG-ADV; the ECG-ADV signals are extracted from each patient simultaneously and the detection quality of the ECG complex is verified. To do this, the ECG complex and the systolic wave of the ADV wave are aligned with Gaussian noise, and then the tensors were constructed for both signals. Five algorithms of CP were applied and the quality of the factorization of each one of the algorithms was checked with four indices. Both signals were shown to be non-collinear, and the algorithms that have the minimum number of iterations at the convergence were determined.","PeriodicalId":6474,"journal":{"name":"2017 10th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI)","volume":"6 1","pages":"1-6"},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84108047","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 : 2017-10-01DOI: 10.1109/CISP-BMEI.2017.8301979
Zeyu Sun, Juhua Zhang
With the development and improvement of imaging technology in the medical field, image technology, which provides important scientific basis for disease analysis, has become an indispensable part of disease diagnosis. Therefore, how to dig out valuable information in these images and help doctors to make diagnosis more accurately and quickly have always been the concern of researchers. In this paper, we have made some improvements to the FCN network and incorporated Inception Architecture into it to build several convolutional neural networks. In our experiments, we trained the networks in IBSR dataset and contrasted the results with some classical methods. The results demonstrate that our improved network has high efficiency and accuracy in segmentation of MRI brain images.
{"title":"Brain tissue segmentation based on convolutional neural networks","authors":"Zeyu Sun, Juhua Zhang","doi":"10.1109/CISP-BMEI.2017.8301979","DOIUrl":"https://doi.org/10.1109/CISP-BMEI.2017.8301979","url":null,"abstract":"With the development and improvement of imaging technology in the medical field, image technology, which provides important scientific basis for disease analysis, has become an indispensable part of disease diagnosis. Therefore, how to dig out valuable information in these images and help doctors to make diagnosis more accurately and quickly have always been the concern of researchers. In this paper, we have made some improvements to the FCN network and incorporated Inception Architecture into it to build several convolutional neural networks. In our experiments, we trained the networks in IBSR dataset and contrasted the results with some classical methods. The results demonstrate that our improved network has high efficiency and accuracy in segmentation of MRI brain images.","PeriodicalId":6474,"journal":{"name":"2017 10th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI)","volume":"28 1","pages":"1-6"},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84541174","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 : 2017-10-01DOI: 10.1109/CISP-BMEI.2017.8302007
Yi Li, Xiaodong Liu
In this paper, a maximum similarity criterion is proposed which is adapted to a new fuzzy face recognition method (namely, 2DFMS). The similarity degree between faces is defined by a nonlinear function. Based on this similarity, an improvement fuzzy membership function is obtained by applying k-nearest neighbor. Then, 2DFMS extracts the features from face images directly so that it will not suffer from the SSS problem. Finally, in the projected space, the test image is identified according to a specific classifier, which is based on a maximum similarity criterion. The whole algorithm is implemented on ORL and Yale face database to demonstrate the effectiveness and robustness.
{"title":"Maximum similarity degree for 2D fuzzy face recognition","authors":"Yi Li, Xiaodong Liu","doi":"10.1109/CISP-BMEI.2017.8302007","DOIUrl":"https://doi.org/10.1109/CISP-BMEI.2017.8302007","url":null,"abstract":"In this paper, a maximum similarity criterion is proposed which is adapted to a new fuzzy face recognition method (namely, 2DFMS). The similarity degree between faces is defined by a nonlinear function. Based on this similarity, an improvement fuzzy membership function is obtained by applying k-nearest neighbor. Then, 2DFMS extracts the features from face images directly so that it will not suffer from the SSS problem. Finally, in the projected space, the test image is identified according to a specific classifier, which is based on a maximum similarity criterion. The whole algorithm is implemented on ORL and Yale face database to demonstrate the effectiveness and robustness.","PeriodicalId":6474,"journal":{"name":"2017 10th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI)","volume":"15 1","pages":"1-6"},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84881940","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 : 2017-10-01DOI: 10.1109/CISP-BMEI.2017.8302212
Chuchu Ding, Jiafei Dai, J. Wang, Danqin Xing, Yiyi He, Jiaqin Wang, F. Hou
The study of brain networks usually analyze the difference of the statistical data and topological structure between pathological with normal brain, or study the difference of complex networks in different physiological state. This paper presents an inner composition alignment algorithm (IOTA) to study the complexity and differences of the brain function network of different ages in beta rhythm, study the topological characteristics in younger and old by computing the characteristics through algorithm: the average path length, clustering coefficient, the average node degree and inner composition alignment algorithm coefficient.
{"title":"Analysis of brain functional networks based on inner composition alignment","authors":"Chuchu Ding, Jiafei Dai, J. Wang, Danqin Xing, Yiyi He, Jiaqin Wang, F. Hou","doi":"10.1109/CISP-BMEI.2017.8302212","DOIUrl":"https://doi.org/10.1109/CISP-BMEI.2017.8302212","url":null,"abstract":"The study of brain networks usually analyze the difference of the statistical data and topological structure between pathological with normal brain, or study the difference of complex networks in different physiological state. This paper presents an inner composition alignment algorithm (IOTA) to study the complexity and differences of the brain function network of different ages in beta rhythm, study the topological characteristics in younger and old by computing the characteristics through algorithm: the average path length, clustering coefficient, the average node degree and inner composition alignment algorithm coefficient.","PeriodicalId":6474,"journal":{"name":"2017 10th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI)","volume":"144 12 1","pages":"1-5"},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83062406","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 : 2017-10-01DOI: 10.1109/CISP-BMEI.2017.8302320
Yongshang Cheng, Chongchong Ding
In the open network environment, Web service has a strong dynamic nature and the optimal service combination scheme that produced in design stage may become invalid. Therefore, the single optimal service combination scheme is difficult to meet the individual needs of users, which will reduce the utilization of resource and the satisfaction of users. To solve this problem, this paper improves nectar selection strategy of the artificial bee colony algorithm. In addition, the paper designs a new neighborhood search formula and scout bee operation strategy, which effectively prevents the artificial bee colony algorithm from converging prematurely. After that, combined with Pareto strategy, it improves a Web services combination optimization method that is based on Pareto multi-objective artificial bee colony algorithm. This method will recommend a group of Pareto optimal solutions to users instead of recommending a single optimal solution to users. In this way, it can deal with the instability of combinational services in the dynamic environment and the different needs of users. Finally, the paper uses the relevant experiments to verify the feasibility and effectiveness of service combination optimization method improved in this paper.
{"title":"Optimization of web services composition using artificial bee colony algorithm","authors":"Yongshang Cheng, Chongchong Ding","doi":"10.1109/CISP-BMEI.2017.8302320","DOIUrl":"https://doi.org/10.1109/CISP-BMEI.2017.8302320","url":null,"abstract":"In the open network environment, Web service has a strong dynamic nature and the optimal service combination scheme that produced in design stage may become invalid. Therefore, the single optimal service combination scheme is difficult to meet the individual needs of users, which will reduce the utilization of resource and the satisfaction of users. To solve this problem, this paper improves nectar selection strategy of the artificial bee colony algorithm. In addition, the paper designs a new neighborhood search formula and scout bee operation strategy, which effectively prevents the artificial bee colony algorithm from converging prematurely. After that, combined with Pareto strategy, it improves a Web services combination optimization method that is based on Pareto multi-objective artificial bee colony algorithm. This method will recommend a group of Pareto optimal solutions to users instead of recommending a single optimal solution to users. In this way, it can deal with the instability of combinational services in the dynamic environment and the different needs of users. Finally, the paper uses the relevant experiments to verify the feasibility and effectiveness of service combination optimization method improved in this paper.","PeriodicalId":6474,"journal":{"name":"2017 10th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI)","volume":"20 1","pages":"1-6"},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77841206","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 : 2017-10-01DOI: 10.1109/CISP-BMEI.2017.8302011
S. Bo, Yongju Jing
Target detection is a fundamental problem in remote sensing images analysis. Multi-class classifiers are usually used in target detection. However, one-class classifier requires only the training samples of positive class, which has obvious advantages in specific target extraction. Based on one-class classification, the river target detection in remote sensing image is studied in this paper. The target detection process is divided into two phases: coarse screening and fine detection. In the screening phase, most non-target areas are excluded based on one-class classification. The fine detection phase extracts complex features from the target candidate regions and detects the river target by feature matching method. Based on one-class classification, the proposed method reduces the time complexity in target detection.
{"title":"One-class classification based river detection in remote sensing image","authors":"S. Bo, Yongju Jing","doi":"10.1109/CISP-BMEI.2017.8302011","DOIUrl":"https://doi.org/10.1109/CISP-BMEI.2017.8302011","url":null,"abstract":"Target detection is a fundamental problem in remote sensing images analysis. Multi-class classifiers are usually used in target detection. However, one-class classifier requires only the training samples of positive class, which has obvious advantages in specific target extraction. Based on one-class classification, the river target detection in remote sensing image is studied in this paper. The target detection process is divided into two phases: coarse screening and fine detection. In the screening phase, most non-target areas are excluded based on one-class classification. The fine detection phase extracts complex features from the target candidate regions and detects the river target by feature matching method. Based on one-class classification, the proposed method reduces the time complexity in target detection.","PeriodicalId":6474,"journal":{"name":"2017 10th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI)","volume":"32 1","pages":"1-5"},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81165427","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 : 2017-10-01DOI: 10.1109/CISP-BMEI.2017.8302183
Min-Xiong Zhou, Ke Zan, X. Min, Liang Wang, Chao Ma, Xu Yan
This study attempts to compare a new fast SE-EPI T2 mapping method with the conventional CPMG method, which showed potential in prostate cancer evaluation and also other applications. The SE-EPI method has advantages in faster acquisition than CPMG method and can be integrated into scan of diffusion imaging with slightly increase of acquisition time. Voxel-by-voxel and region-of-interest (ROI) based analysis were performed to compare these two methods. The result showed that the T2 values of SE-EPI method is relatively lower than those of CPMG method, while a strong correlation was found: voxel-by-voxel analysis showed that the correlation coefficient was up to 0.84; in ROI-based analysis, correlation coefficient was up to 0.87. In addition, the clinical validation showed that the T2 maps of SE-EPI at b = 0 and CPMG-based T2 have similar statistical significance in differentiating between benign prostatic hyperplasia (BPH) and prostate cancer (PCa) with mean and standard deviation of 81.6 ± 13.8 vs 72.2 ± 8.5 (SE-EPI) and 108.9 ± 37.8 vs 87.5 ± 10.6 (CPMG). Thus, this work suggests that the SE-EPI method could be considered as a candidate for fast T2 mapping, and be used alone or combined with diffusion in multi-parametric analysis.
本研究试图将一种新的快速SE-EPI T2定位方法与传统的CPMG方法进行比较，该方法在前列腺癌评估和其他方面具有潜在的应用前景。SE-EPI方法比CPMG方法具有更快的采集速度，可以集成到扩散成像扫描中，但采集时间略有增加。采用逐体素和基于感兴趣区域(ROI)的分析对这两种方法进行比较。结果表明，SE-EPI方法的T2值相对低于CPMG方法，但存在较强的相关性:逐体素分析显示相关系数高达0.84;在基于roi的分析中，相关系数高达0.87。此外，临床验证显示，b = 0时SE-EPI T2图谱与CPMG T2图谱对良性前列腺增生(BPH)与前列腺癌(PCa)的鉴别具有相似的统计学意义，其均值和标准差分别为81.6±13.8 vs 72.2±8.5 (SE-EPI)和108.9±37.8 vs 87.5±10.6 (CPMG)。因此，这项工作表明SE-EPI方法可以被认为是快速T2映射的候选方法，可以单独使用或与多参数分析中的扩散相结合。
{"title":"Validation of fast SE-EPI T2 mapping with reference to conventional CPMG T2 mapping, and its application in prostate cancer","authors":"Min-Xiong Zhou, Ke Zan, X. Min, Liang Wang, Chao Ma, Xu Yan","doi":"10.1109/CISP-BMEI.2017.8302183","DOIUrl":"https://doi.org/10.1109/CISP-BMEI.2017.8302183","url":null,"abstract":"This study attempts to compare a new fast SE-EPI T2 mapping method with the conventional CPMG method, which showed potential in prostate cancer evaluation and also other applications. The SE-EPI method has advantages in faster acquisition than CPMG method and can be integrated into scan of diffusion imaging with slightly increase of acquisition time. Voxel-by-voxel and region-of-interest (ROI) based analysis were performed to compare these two methods. The result showed that the T2 values of SE-EPI method is relatively lower than those of CPMG method, while a strong correlation was found: voxel-by-voxel analysis showed that the correlation coefficient was up to 0.84; in ROI-based analysis, correlation coefficient was up to 0.87. In addition, the clinical validation showed that the T2 maps of SE-EPI at b = 0 and CPMG-based T2 have similar statistical significance in differentiating between benign prostatic hyperplasia (BPH) and prostate cancer (PCa) with mean and standard deviation of 81.6 ± 13.8 vs 72.2 ± 8.5 (SE-EPI) and 108.9 ± 37.8 vs 87.5 ± 10.6 (CPMG). Thus, this work suggests that the SE-EPI method could be considered as a candidate for fast T2 mapping, and be used alone or combined with diffusion in multi-parametric analysis.","PeriodicalId":6474,"journal":{"name":"2017 10th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI)","volume":"23 1","pages":"1-5"},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89530988","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 : 2017-10-01DOI: 10.1109/CISP-BMEI.2017.8302321
Kun-wu Cao, Hui Yang, G. Zheng, Ying Ren, Wei Ge
The method of depolarized dynamic light scattering is an effective means to characterize the two-dimensional information (i.e. the length and the diameter) of particles in dilute solution. However, due to the large aspect ratio of short carbon nanotubes, the signal-to-noise ratio of the system is low and the measurement repeatability is poor. To solve this problem, multi-angle depolarized dynamic light scattering(MA-DDLS) is proposed. The reproducibility of the measurement system is improved by linear fitting of the results measured at different angles. Firstly, the basic principle of multi-angle dynamic light scattering measurement of short carbon nanotube was studied. Then, set up an experimental measurement system. The short carbon nanotube measuring system, which is based on photon counting, is developed by LabVIEW. Finally, the measurement is executed using standard carbon nanotube, and compared with the single angle measurement method. The result show that the measurement repeatability of the multi-angle depolarized dynamic light scattering method is superior to the single angle measurement method.
{"title":"Length and diameter characterization of short carbon nanotubes by light scattering method","authors":"Kun-wu Cao, Hui Yang, G. Zheng, Ying Ren, Wei Ge","doi":"10.1109/CISP-BMEI.2017.8302321","DOIUrl":"https://doi.org/10.1109/CISP-BMEI.2017.8302321","url":null,"abstract":"The method of depolarized dynamic light scattering is an effective means to characterize the two-dimensional information (i.e. the length and the diameter) of particles in dilute solution. However, due to the large aspect ratio of short carbon nanotubes, the signal-to-noise ratio of the system is low and the measurement repeatability is poor. To solve this problem, multi-angle depolarized dynamic light scattering(MA-DDLS) is proposed. The reproducibility of the measurement system is improved by linear fitting of the results measured at different angles. Firstly, the basic principle of multi-angle dynamic light scattering measurement of short carbon nanotube was studied. Then, set up an experimental measurement system. The short carbon nanotube measuring system, which is based on photon counting, is developed by LabVIEW. Finally, the measurement is executed using standard carbon nanotube, and compared with the single angle measurement method. The result show that the measurement repeatability of the multi-angle depolarized dynamic light scattering method is superior to the single angle measurement method.","PeriodicalId":6474,"journal":{"name":"2017 10th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI)","volume":"1 1","pages":"1-5"},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89235126","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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