2021 14th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI)最新文献_第4页

Malware Visualization Based on Deep Learning 基于深度学习的恶意软件可视化

2021 14th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI) Pub Date : 2021-10-23 DOI: 10.1109/CISP-BMEI53629.2021.9624362

Zhuojun Ren, Ting Bai

引用次数: 1

A Capacitor-Free Low-Dropout Regulator with Low Line Regulation Rate and High Stability 一种具有低线路调节率和高稳定性的无电容低差稳压器

2021 14th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI) Pub Date : 2021-10-23 DOI: 10.1109/CISP-BMEI53629.2021.9624416

Yimin Liang, Shengxi Diao

引用次数: 1

Improving the image quality of machine vision thread detection 提高机器视觉线程检测的图像质量

2021 14th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI) Pub Date : 2021-10-23 DOI: 10.1109/CISP-BMEI53629.2021.9624452

Yunqi Zhang, Weimin Wei

{"title":"Improving the image quality of machine vision thread detection","authors":"Yunqi Zhang, Weimin Wei","doi":"10.1109/CISP-BMEI53629.2021.9624452","DOIUrl":"https://doi.org/10.1109/CISP-BMEI53629.2021.9624452","url":null,"abstract":"In this paper, the disadvantage of lack of effective information in threaded workpiece imaging with industrial cameras is analyzed against the research background of external thread measurement in the vision measurement technique, aiming at solving the problems of poor anti-interference ability and low level of automation in the existing vision detection algorithms. A thread image measurement device is also designed for the experiment. In terms of algorithm, the current thread parameter measurement method based on machine vision is easily disturbed by dust, grease and other factors in the industrial measurement environment, which leads to higher image noises. In view of the above problems and based on the combination of machine vision and deep learning technology, this paper proposes a method of measuring external threads with high automation and certain anti-interference ability. Firstly, this paper adopts the U-Net model, and incorporates it with the Attention Augment mechanism and residual learning module to form AA ResU-Net model, so as to improve the ability of learning the features of the target. In addition, in this paper, defect removal and sub-pixel processing are carried out on the thread edge, which further improves the measurement accuracy and meets the needs of industrial detection.","PeriodicalId":131256,"journal":{"name":"2021 14th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI)","volume":"68 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131813508","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

A calibration-free P300 BCI system using an on-line updating classifier based on reinforcement learning 基于强化学习的在线更新分类器的无标定P300 BCI系统

2021 14th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI) Pub Date : 2021-10-23 DOI: 10.1109/CISP-BMEI53629.2021.9624451

J. Guo, Zhihua Huang

{"title":"A calibration-free P300 BCI system using an on-line updating classifier based on reinforcement learning","authors":"J. Guo, Zhihua Huang","doi":"10.1109/CISP-BMEI53629.2021.9624451","DOIUrl":"https://doi.org/10.1109/CISP-BMEI53629.2021.9624451","url":null,"abstract":"P300 brain-computer interface (BCI) is a very promising technology. However, the time-consuming calibration before every use reduces the convenience of P300 BCI. In recent years, researchers are increasingly concerning the studies on calibration-free P300 BCI. In this study, we designed an on-line updating classifier for recognizing P300 based on the fundament of reinforcement learning and developed a calibration-free P300 BCI system. The on-line updating classifier can be randomly initiated and quickly optimized by learning from the system feedback. The system uses the latest classifier to recognize P300, acquires the user's feedback, and calls an on-line updating algorithm to optimize the classifier. By this system, any user can start using P300 BCI without having to collect the training data. To verify this system, we recruited 7 volunteers as the subjects to participate in a brain-controlled Chinese Pinyin experiment based on this system. The mean accuracies of recognizing P300, Pinyin symbols and Chinese words in the on-line experiment were 84.69% and 80.55% respectively. The results show the effectiveness of our on-line updating classifier and the feasibility of the developed system.","PeriodicalId":131256,"journal":{"name":"2021 14th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI)","volume":"136 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132220101","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: 1

A Lightweight Convolutional Neural Network for Personal Identification Based on Code-Modulated Visual-Evoked Potentials 基于码调制视觉诱发电位的轻量级卷积神经网络

2021 14th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI) Pub Date : 2021-10-23 DOI: 10.1109/CISP-BMEI53629.2021.9624212

Jing Li, Zhihua Huang

{"title":"A Lightweight Convolutional Neural Network for Personal Identification Based on Code-Modulated Visual-Evoked Potentials","authors":"Jing Li, Zhihua Huang","doi":"10.1109/CISP-BMEI53629.2021.9624212","DOIUrl":"https://doi.org/10.1109/CISP-BMEI53629.2021.9624212","url":null,"abstract":"At present, personal identification based on code-modulated visual-evoked potentials is increasingly attracting people's attentions. Some convolutional neural networks (CNN) have been applied to recognize biomarkers based on code-modulated visual-evoked potentials (c-VEP) for personal identification. However, the ordinary CNNs encountered difficulties in grasping the basic characteristics of c-VEP to achieve a satisfactory performance. In this study, we proposed a lightweight convolutional neural network (LCNN) to recognize the c-VEP biomarkers in the tasks of personal identification. LCNN is composed of two parallel sub-nets, which correspond respectively to two profiles of a c-VEP sample and both include two blocks. The two blocks both contain a two-step convolutional sequence. The LCNN model is fitted by minimizing the categorical cross-entropy loss function. The goal of LCNN is to specifically handle the Electroencephalogram (EEG) data in the tasks of personal identification based on c-VEP. We recruited 20 subjects to participate in our personal identification experiments based on c-VEP. In the EEG dataset of the 20 subjects, LCNN reached the recognition accuracy of 99%. The result shows that the design of LCNN is suitable for recognizing the c-VEP biomarkers.","PeriodicalId":131256,"journal":{"name":"2021 14th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI)","volume":"28 2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134345176","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: 1

Prediction of LVEF using BiLSTM and Swarm Decomposition-based 24-h HRV Components 基于BiLSTM和群分解的24 h HRV分量预测LVEF

2021 14th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI) Pub Date : 2021-10-23 DOI: 10.1109/CISP-BMEI53629.2021.9624338

M. Alkhodari, G. Apostolidis, H. F. Jelinek, L. Hadjileontiadis, A. Khandoker

{"title":"Prediction of LVEF using BiLSTM and Swarm Decomposition-based 24-h HRV Components","authors":"M. Alkhodari, G. Apostolidis, H. F. Jelinek, L. Hadjileontiadis, A. Khandoker","doi":"10.1109/CISP-BMEI53629.2021.9624338","DOIUrl":"https://doi.org/10.1109/CISP-BMEI53629.2021.9624338","url":null,"abstract":"In this study, we investigated the effectiveness of using hourly Bi-Directional Long Short-Term Memory (BiLSTM) classifiers to predict left ventricle ejection fraction (LVEF) groups of CAD patients using their heart rate variability and Swarm Decomposition components. The 24-hour segmentation of patients' HRV data was performed using Cosinor Analysis. The novel Swarm Decomposition algorithm was then applied on the per-hour HRV data to extract the corresponding oscillatory components (HRV-OCs). The OCs represent the four bands in an HRV data, namely the ultra-low frequency (ULF), very-low frequency (VLF), low frequency (LF), and high frequency (HF). The training and classification process followed a leave-one-out scheme and was done for each per-hour HRV-OC. The highest prediction accuracy of LVEF was observed when using the VLF and HF components of HRV at an early morning hour (03-00-04:00 - average accuracy: 75.6%) and an evening hour (18:00-19:00 - average accuracy: 72.7%), respectively. In addition, the classifier achieved high sensitivity levels in predicting the borderline group (up to 76.7%), which is usually ambiguous and hard to diagnose in clinical practice.","PeriodicalId":131256,"journal":{"name":"2021 14th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI)","volume":"88 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134445779","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

Cervical Cell Image Classification Based On Multiple Attention Fusion 基于多注意力融合的宫颈细胞图像分类

2021 14th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI) Pub Date : 2021-10-23 DOI: 10.1109/CISP-BMEI53629.2021.9624420

Xin Su, Jun Shi, Yusheng Peng, Liping Zheng

引用次数: 3

Breast tumor diagnosis using radiofrequency signals based ultrasound multifeature maps combined with radiomics analysis 基于射频信号的超声多特征图与放射组学分析相结合诊断乳腺肿瘤

2021 14th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI) Pub Date : 2021-10-23 DOI: 10.1109/CISP-BMEI53629.2021.9624456

Qingmin Wang, X. Jia, Tianlei Xiao, Z. Yao, Jianqiao Zhou, Jinhua Yu

{"title":"Breast tumor diagnosis using radiofrequency signals based ultrasound multifeature maps combined with radiomics analysis","authors":"Qingmin Wang, X. Jia, Tianlei Xiao, Z. Yao, Jianqiao Zhou, Jinhua Yu","doi":"10.1109/CISP-BMEI53629.2021.9624456","DOIUrl":"https://doi.org/10.1109/CISP-BMEI53629.2021.9624456","url":null,"abstract":"Breast cancer is a high incidence of malignancy in women, with a higher mortality rate. Accurate screening is helpful to early detection and improve the treatment success rate and patient survival rate. This study is based on low-cost ultrasound, using ultrasound multifeature maps based on the original radiofrequency (RF) signals and radiomics analysis method to evaluate the benign and malignant of breast tumors. The three ultrasound multifeature maps of breast tumor are composed of direct energy attenuation coefficient (AC), standard deviation of image intensity (SD) and Rician distribution parameters (RD). From the above multifeature maps, high-throughput radiomics features were extracted, then sparse representation method was used for feature selection, and then support vector machine was used to predict the benign and malignant of breast tumors. Eight groups of comparative experiments were established by using ultrasound gray-scale image, single ultrasound feature map and two ultrasound feature maps. The results from 164 patients with breast tumor showed that the AUC, accuracy and sensitivity of the radiomics classification model with feature maps of AC, SD and RD can reach 93.61%, 93.94% and 100%, respectively. The use of RF based ultrasound multifeature maps combined with radiomics could effectively predict the benign and malignant of breast tumors in this study.","PeriodicalId":131256,"journal":{"name":"2021 14th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114992465","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: 1

Hierarchical classification and visualization with multiple feature ranking criteria 基于多特征排序标准的分层分类和可视化

2021 14th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI) Pub Date : 2021-10-23 DOI: 10.1109/CISP-BMEI53629.2021.9624378

Qun Wang, Xuegang Wang, Zhiguo Zhou, Di Sheng, Duozheng Sheng

引用次数: 0

An Improved Image Super-Resolution Reconstruction Method Based On LapSRN 一种改进的基于LapSRN的图像超分辨率重建方法

2021 14th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI) Pub Date : 2021-10-23 DOI: 10.1109/CISP-BMEI53629.2021.9624453

Lei Kong, L. Jiao, Feng Jia, Kai Sun

引用次数: 0