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检索条件"任意字段=International Congress on Image and Signal Processing, BioMedical Engineering and Informatics"

共 2036 条记录，以下是71-80 订阅

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Research on Epileptic EEG signal Based on Partial Mutual Information and Partial Measure of Conditional Mutual Information from the Mixed Embedding 17

Research on Epileptic EEG Signal Based on Partial Mutual Inf...

引用

17th international congress on image and signal processing, biomedical engineering and informatics, CISP-BMEI 2024

作者： Guo, Suolian Lu, Canai Luo, Yuman Xia, Zhangyang Dai, Jiafei Wang, Jun Suzhou New District Technology Service Center of Medical Device Suzhou China Nanjing University of Posts and Telecommunications Smart Health Big Data Analysis and Location Services Engineering Research Center of Jiangsu Province Nanjing China Jinling Hospital Medical School of Nanjing University Department of Neurology Nanjing China

ISBN: (纸本)9798331507398

Epilepsy is a common neurological disorder, and a practical approach to analyzing EEG signals in epilepsy is essential for the study of epilepsy. To explore the differences in the coupling relationship between epileptic Electroencephalo-gram(EEG) signals and healthy EEG signals, we analyze the coupling relationship using symbolic partial mutual information and partial measure of conditional mutual information from the mixed embedding(pmime) method. It is shown that the coupling degree of EEG signals in healthy individuals is significantly higher than that of epileptic EEG signals. Symbolized partial mutual information and the pmime method can effectively distinguish between healthy and epileptic EEG signals. This study may be helpful in the diagnosis and treatment of patients with epilepsy in clinical practice. © 2024 IEEE.

关键词： Electroencephalography

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EEN:Edge Enhanced Network for Object Detection from SAR images 17

EEN:Edge Enhanced Network for Object Detection from SAR Imag...

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17th international congress on image and signal processing, biomedical engineering and informatics, CISP-BMEI 2024

作者： Zhang, Han Dong, Likai Zhang, Yuqian Zang, Junyuan Han, Mingzhi Xiao, Chenxin Xu, Tao School of Information Science and Engineering University of Jinan China No.1 Geological Team of Shandong Provincial Bureau of Geology and Mineral Resources China

ISBN: (纸本)9798331507398

Object detection in Synthetic Aperture Radar(SAR) imagery is becoming increasingly important due to its ability to function effectively in conditions where visible remote sensing is limited by weather factors. Despite these advantages, SAR images are frequently marred by coherent speckle noise and distinct edge features, which pose considerable challenges for object detection algorithms. We present an Edge Enhanced Network (EEN) designed to enhance the performance of SAR image object detection algorithms. The proposed network comprises two integral components: the Edge Enhancement Module (EEM) and the Context Fusion Module (CFM). The EEM applies specialized edge convolution to elements within SAR images, effectively amplifying edge features while reducing the influence of coherent speckle noise. The CFM primarily integrates contextual features, thereby enhancing multi-scale feature representation and bolstering the model's generalization capability. To validate the effectiveness of the EEN, this study conducts experiments using mainstream object detection networks, including YOLOX and RtmDet. Comparative and ablation experiments are carried out on the SSDD, RSDD-SAR, and SAR-AIRcraft datasets. The experimental results confirm the effectiveness of the EEN. © 2024 IEEE.

关键词： Radar imaging

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Design and Implementation of Cardiac Resynchronization Therapy Device for Canine 17

Design and Implementation of Cardiac Resynchronization Thera...

引用

17th international congress on image and signal processing, biomedical engineering and informatics, CISP-BMEI 2024

作者： Guo, Jinwei Chen, Xiang Zhang, Lu Zhao, Jiao Jin, Hua Chen, Xiaolong Li, Jin The Key Laboratory of Biomedical Information Engineering of Ministry of Education Institute of Health and Rehabilitation Science School of Life Science and Technology Xi'an Jiaotong University Shaanxi Xi'an710049 China The Key Laboratory of Neuro-informatics & Rehabilitation Engineering of Ministry of Civil Affairs Shaanxi Xi'an710049 China School of Microelectronics Xi'an Jiaotong University Shaanxi Xi'an710049 China Lepu Medical Electronic Instruments co. Ltd Shaanxi Xi'an710116 China

ISBN: (纸本)9798331507398

As a treatment device for heart failure, the cardiac resynchronization therapy pacemaker achieves the treatment of heart failure by improving symptoms of asynchronous contractions. This study designed and implemented a low-cost cardiac resynchronization therapy pacemaker for canine, which can filter and amplify intracavity electrical sensing signals separately and achieve programmable amplification of 10-100 times;It can also perform intracavity biphasic pacing pulse stimulation and achieve controllable pulse width of 0.5-2ms and pulse voltage amplitude of 0.8-8V. The circuit evaluation results show that the sensing and pacing functions are stably, and the adjustment range of working parameters meets the experimental needs of canine animals. The programmable function can achieve multi-platform testing requirements without the development environment. The completed canine cardiac resynchronization therapy pacemaker can meet the needs of establishing conventional heart failure models and conducting animal experiments on cardiac resynchronization therapy. © 2024 IEEE.

关键词： Cardiac resynchronization therapy

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Cognition-Based Verification of Target Existence in Radar Detection 16

Cognition-Based Verification of Target Existence in Radar De...

引用

16th international congress on image and signal processing, biomedical engineering and informatics, CISP-BMEI 2023

作者： Tan, Donglin Wang, Junfeng Shanghai Jiao Tong University School of Electronic Information and Electrical Engineering Shanghai China

ISBN: (纸本)9798350330755

The signal-to-noise ratio (SNR) is an important factor in radar target detection. Designing an appropriate transmitted waveform to improve the SNR of the echo is always an interesting topic. For radar detection, a novel and simple waveform design method to maximize the SNR of the echo is presented. This method consists of two parts: the search part and the verification part. This method first finds the frequency where the target is most sensitive to the signal, and then transmits a signal with this frequency to improve the SNR of the echo and verify the detection results. This method can maximize the SNR of the radar echo under the same energy conditions in radar detection. According to computer simulation results, the proposed method is effective. © 2023 IEEE.

关键词： signal to noise ratio

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Predicting Microbial Community Productivity Based-On Genomic Sequences with Graph Neural Network 17

Predicting Microbial Community Productivity Based-On Genomic...

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17th international congress on image and signal processing, biomedical engineering and informatics, CISP-BMEI 2024

作者： Hou, Jiaheng Geng, Peter X. Wu, Zhikai Fan, Wenlin Zhu, Huaiqiu College of Future Technology Center for Quantitative Biology Peking University Department of Biomedical Engineering Beijing100871 China

ISBN: (纸本)9798331507398

Artificial microbial community design has gained increasing attention due to its economic and environmental benefits. However, it is challenging considering the complex microbial interactions and emergent properties arising from metabolic networks. Although flux balance analysis (FBA), based on genome-scale metabolic models (GEMs), is a well-known approach for simulating community phenotypes, its inefficiency in handling communities with a large number of functional genes limits its utility. In this paper, we introduce a novel graph neural network model that incorporates growth-coupling effects to predict the productivity of microbial communities, which is impractical for prior techniques. Utilizing 83 high-quality bacterial GEMs, we trained and tested our model on a large number of bacterial communities containing two or three strains, each undergoing a random genetic modification (either knock-outs or knock-ins) within the community GEM. Our model demonstrates high concordance with FBA, achieving an average area under the curve (AUC) of 0.98, and a F1 score of 0.82. Notably, it operates approximately 6,415 times faster than FBA. Our model indicates that graph-based machine learning algorithms can significantly accelerate productivity prediction and hold potential to advance microbial community design, thereby guiding microbial engineering strategies. © 2024 IEEE.

关键词： Biotic

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Crack detection and spatial marking of ancient buildings by near-view and far-view combination 16

Crack detection and spatial marking of ancient buildings by ...

引用

16th international congress on image and signal processing, biomedical engineering and informatics, CISP-BMEI 2023

作者： Zhao, Min Guo, Chunlong Lei, Gang Zhu, Lingjian Gao, Heng Xi'an University of Technology Department of Precision Instrument Engineering Shaanxi Xi'an China Xi 'An City Wall Management Committee Shaanxi Xi'an China

ISBN: (纸本)9798350330755

In the protection of ancient buildings, not only crack parameters need to be detected but also crack positions need to be known in the spatial structure. As the existing image detection methods cannot consider both detection accuracy and field of view scope, a detection scheme that combines near-view images and far-view images is proposed. Near-view image realizes crack identification and parameter extraction. A region growth algorithm based on local Otsu and local mean is proposed to improve the efficiency of crack extraction. A mapping method based on ASIFT and homography matrix is proposed to realize the crack mapping from near-view images to far-view images and complete the crack marking of far-view images. The 3D reconstruction of the processed far-view images is carried out by the multi-view geometry method to realize the marking of cracks in the spatial structure. Through the actual measurement of two ancient buildings in the Da Ci'en Temple in Xi'an, the effectiveness of crack identification is verified. The detection accuracy of the average crack width is less than 0.1 mm, and the spatial marking of cracks is realized. The crack detection of ancient buildings on stereo vision is realized, which can effectively guide the restoration of ancient buildings and has important reference value for the protection of ancient buildings. © 2023 IEEE.

关键词： Crack detection

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MITD: A Multi-Dimensional Insider Threat Detection Framework Based On Deep Learning 17

MITD: A Multi-Dimensional Insider Threat Detection Framework...

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17th international congress on image and signal processing, biomedical engineering and informatics, CISP-BMEI 2024

作者： Peng, Lijuan Feng, Jiaxuan Huang, Yuxin Zhou, Hongcheng Qin, Haoyang Qiu, Feng School of Computer Science and Technology Southwest University of Science and Technology Mianyang621010 China School of Mechanical Engineering and Vehicle Engineering Beijing Institute of Technology Beijing100081 China Automation Research Institute Co. Ltd. China South Industries Group Corporation Mianyang China

ISBN: (纸本)9798331507398

In this paper, we propose a multi-dimensional insider threat detection framework (MITD), which aims to improve the accuracy of detection by comprehensively analyzing user behavior patterns. We combine statistical features and behavioral sequences and use sequence pattern recognition, graph structure information mining, and adaptive feature extraction methods. It can reveal user behavior from multiple dimensions. Then, we intelligently fuse the extracted multi-dimensional features to generate a comprehensive feature representation. It is further fused with statistical features through two strategies: concat and bitwise addition. Experimental results based on the CERT r4.2 dataset show that the MITD framework significantly improves in terms of accuracy, precision, recall, and F1 score, reaching 93.2% accuracy and 93.5% recall. The results show that the MITD framework effectively improves the performance of insider threat detection and has great potential in practical applications. © 2024 IEEE.

关键词： Feature extraction

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Ballistic Object Trajectory Separation Point Estimation Using Fourth Order Runge-Kutta Method and Extended Kalman Filter 17

Ballistic Object Trajectory Separation Point Estimation Usin...

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17th international congress on image and signal processing, biomedical engineering and informatics, CISP-BMEI 2024

作者： Yao, Jiaqi Wu, Qinchen Wang, Chongze Sun, Jinping School of Electronic and Information Engineering Beihang University Beijing China School of Mechanical Electrical & Information Engineering Shandong University Weihai China

ISBN: (纸本)9798331507398

Ballistic missiles enter the free-flight phase upon the cessation of their booster engines, traversing the outer atmosphere for approximately 20 minutes. During this period, separation release events occur, deploying multiple warheads and decoys, presenting the optimal window for tracking, identification, and interception. However, due to the curvature of the Earth, it is challenging to ascertain the precise timing of these separation release events until the ballistic target reaches a certain altitude detectable by ground-based radar. This paper proposes a method for estimating the separation point of ballistic objects. Initially, the trajectory information of the warheads and decoys detected by radar is enhanced through Extended Kalman filtering and smoothing to improve trajectory accuracy. Subsequently, employing the fourth-order Runge-Kutta method, trajectory backtracking is initiated from the initial points of the trajectories. The Mahalanobis distance between the trajectories of the decoys and warheads is computed to determine the separation release relationship and estimate the timing of decoy separation release. Simulation results validate the effectiveness of the proposed method. © 2024 IEEE.

关键词： Extended Kalman filters

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Combining Multi-Scale Feature Fusion and Attention Mechanism to Achieve Pulmonary Nodule Classification 17

Combining Multi-Scale Feature Fusion and Attention Mechanism...

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17th international congress on image and signal processing, biomedical engineering and informatics, CISP-BMEI 2024

作者： He, Naixin Dong, Zhengyu Li, Lin Wang, Jinxia Ma, Chaoqing School of Computer and Control Engineering Yantai University China Yantaishan Hospital Affiliated Binzhou Medical University China

ISBN: (纸本)9798331507398

Lung cancer is the deadliest form of cancer, with most cases originating from small malignant nodules. However, the early symptoms of malignant nodules are not obvious, which can lead to misdiagnosis and delay the optimal treatment time. Therefore, differentiating between benign and malignant lung nodules is crucial for the early diagnosis and treatment of lung cancer. The advent of convolutional neural networks has made it possible to achieve rapid classification of pulmonary nodules. Most classification networks use a convolution kernel size of 3, but due to the limited receptive field, the classification of benign and malignant nodules remains inaccurate. To address this, we use larger convolution kernels to increase the receptive field, capture more detailed feature information, and enhance the ability of model to represent complex patterns and details. Furthermore, we fuse features from multiple scales to mitigate the impact of scale differences. We also employ an attention mechanism to obtain global information, highlight important areas, and suppress irrelevant parts. We use the Lung image Database Consortium and image Database Resource Initiative (LIDC-IDRI) dataset to evaluate our model. Compared to other classification methods, our approach achieved superior performance, with an accuracy of 0.9855 and an F1score of 0.9901. © 2024 IEEE.

关键词： Lung cancer

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HiCVAE: A Variational Auto-Encoder Framework for Simulating Hi-C Data 16

HiCVAE: A Variational Auto-Encoder Framework for Simulating ...

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16th international congress on image and signal processing, biomedical engineering and informatics, CISP-BMEI 2023

作者： Zhao, Xuemin Duan, Ran Yao, Shaowen Yunnan University School of Information Science and Engineering Kunming China Yunnan University Engineering Research Center of Cyberspace Kunming China

ISBN: (纸本)9798350330755

Hi-C technology generates substantial sequencing data, which is further complicated by challenges such as the sparsity of samples, spurious associations, and noise. These factors contribute to discrepancies between the sequencing data and the actual structural organization. Moreover, the limited available data samples aggravate the issue. This paper presents a novel method for generating simulated Hi-C data by implementing the variational auto-encoder model. The resultant Hi-C interaction matrix exhibits structural similarity to real data, enabling validation of Hi-C analysis techniques and software, completion of data voids, and streamlining of hypothesis verification experiments. © 2023 IEEE.

关键词： signal encoding

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