检索结果-内蒙古大学图书馆

arXiv 2021年

作者： Zeng, Hong-Ye Ge, Song-Han Gao, Yu-Chong Zhou, De-Sen Zhou, Kang He, Xu-Ming Lou, Edmond Zheng, Rui School of Information Science and Technology ShanghaiTech University Shanghai201210 China Shanghai Institute of Microsystem and Infor mation Technology Chinese Academy of Sciences Shanghai200050 China University of Chinese Academy of Sciences Beijing100049 China Faculty of Engineering Electrical & Computer Engineering Dept. University of Alberta 8440-112 EdmontonT6G 2B7 Canada School of Information Science and Technology Shanghai Engineering Research Center of Intelligent Vision and Imaging ShanghaiTech University Shanghai201210 China

Objective: The spinous process angle (SPA) is one of the essential parameters to denote three-dimensional (3-D) deformity of spine. We propose an automatic segmentation method based on Stacked Hourglass Network (SHN) to detect the spinous processes (SP) on ultrasound (US) spine images and to measure the SPAs of clinical scoliotic subjects. Methods: The network was trained to detect vertebral SP and laminae as landmarks on 1200 ultrasound transverse images and validated on 100 images. All the processed transverse images with highlighted SP and laminae were reconstructed into a 3D image volume, and the SPAs were measured on the projected coronal images. The trained network was tested on 400 images by calculating the percentage of correct keypoints (PCK);and the SPA measurements were evaluated on 50 scoliotic subjects by comparing the results from US images and radiographs. Results: The trained network achieved a high average PCK (86.8%) on the test datasets, particularly the PCK of SP detection was 90.3%. The SPAs measured from US and radiographic methods showed good correlation (r>0.85), and the mean absolute differences (MAD) between two modalities were 3.3°, which was less than the clinical acceptance error (5°). Conclusion: The vertebral features can be accurately segmented on US spine images using SHN, and the measurement results of SPA from US data was comparable to the gold standard from radiography. Copyright © 2021, The Authors. All rights reserved.

关键词： Ultrasonic applications

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An Exoskeleton BCI System for Stroke Rehabilitation Using Multi-modality Training Mode

An Exoskeleton BCI System for Stroke Rehabilitation Using Mu...

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International Automatic control Conference

作者： Kai-Hsiang Su Chiao-Hsin Chen Chia-Hsin Chen Yong-Liang Zhang Li-Wei Ko Institute of Biological Science and Technology National Yang Ming Chiao Tung University (former National Chiao Tung University) Hsinchu Taiwan Center for Intelligent Drug Systems and Smart Bio-Devices (IDS2B) College of Biological Science and Technology National Yang Ming Chiao Tung University (former National Chiao Tung University) Hsinchu Taiwan Institute of Molecular Medicine and Bioengineering National Yang Ming Chiao Tung University (former National Chiao Tung University) Hsinchu Taiwan Rehabilitation Department of Kaohsiung Medical University Chung-Ho Memorial Hospital Institute of Electrical and Control Engineering National Yang Ming Chiao Tung University (former National Chiao Tung University) Hsinchu Taiwan Department of Electrical and Computer Engineering Brain Research Center National Yang Ming Chiao Tung University (former National Chiao Tung University) Hsinchu Taiwan

With the advent of the global aging era, the impact of stroke on humans is continuously increasing. Therefore, new therapeutic approaches to enhance patient recovery are receiving growing attention. Stroke is a severe neurological disorder that causes brain damage, requiring extended periods of rehabilitation for patients to return to normal life. Currently, common clinical methods largely rely on traditional passive limb rehabilitation, primarily targeting peripheral nerve training. Nowadays, an increasing number of people are recognizing the importance of post-stroke neuroplasticity. This study aims to activate patients' central nervous system and proposes a brain-machine interface (BCI) exoskeleton system based on human cognition. By designing three cognitive brainwave training models, including an attention-based standing-up model detecting θ-band power decrease, a relaxation-based sitting-down model detecting α-band power increase, and a walking model detecting MRCP, patients can actively control lower limb exoskeleton movement using *** study provides cross-validation accuracy through both normal subjects and stroke patients, with accuracies of 87.13±9.38 and 80.83±9.68, respectively. Envisioning a rehabilitation system guided by human cognition, our team has developed an intuitive BCI exoskeleton system.

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Constructing Epidermal Rete Ridges Using Composite Hydrogel to Enhance Multiple Signaling Pathways for the Maintenance of Epidermal Stem Cell Niche

SSRN

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SSRN 2023年

作者： Shen, Zhizhong Liu, Zixian Sun, Lei Li, Meng Han, Lu Wang, Jianming Wu, Xunwei Sang, Shengbo Shanxi Key Laboratory of Micro Nano Sensors & Artificial Intelligence Perception College of Information and Computer Taiyuan University of Technology Taiyuan030024 China Key Lab of Advanced Transducers and Intelligent Control System the Ministry of Education Taiyuan University of Technology Taiyuan030024 China Shanxi Research Institute of 6D Artificial Intelligence Biomedical Science Taiyuan030031 China General Hospital of TISCO North Street Xinghualing District Taiyuan030809 China Engineering Laboratory for Biomaterials and Tissue Regeneration Ningbo Stomatology Hospital Savaid Stomatology School Hangzhou Medical College Ningbo China Department of Tissue Engineering and Regeneration School and Hospital of Stomatology Cheeloo College of Medicine Shandong University Shandong Key Laboratory of Oral Tissue Regeneration Shandong Engineering Laboratory for Dental Materials and Oral Tissue Regeneration Shandong Jinan China Cutaneous Biology Research Center Massachusetts General Hospital Harvard Medical School BostonMA United States

The undulating microstructure rete ridge (RR) located at the junction between the dermis and epidermis plays a crucial role in improving skin mechanical properties and maintaining skin homeostasis. However, the investigation of RR microstructures are usually neglected in current tissue engineering for skin regeneration. Here, to create an epidermal model with RR microstructures, keratinocytes were inoculated onto a patterned GelMA-PEGDA hydrogel constructed using molding technology. Furthermore, grafting acryloylated Arg-Gly-Asp (RGD) peptides on the hydrogel surface significantly improved cell adhesion, fusion, and development. RT-PCR, Western blot, and immunofluorescence staining confirmed that cells on RR microstructures exhibited higher gene and protein expression associated with epidermal stem cells. RNA sequencing analysis of cells on RR microstructure showed higher gene expression profiles related to stem cell maintenance, basement membrane formation, and epidermal development. Furthermore, RT-PCR analysis of epidermal models of various dimensions demonstrated that smaller microstructures were more conducive to epidermal stem cell marker gene expression, which is analogous to human skin. Overall, we have successfully developed a method for integrating RR microstructures into an epidermal model that mimics natural skin to maintain epidermal stem cell niche, providing a valuable reference for researching skin regeneration within the fields of tissue engineering and regenerative medicine. © 2023, The Authors. All rights reserved.

关键词： Microstructure

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Proxy-bridged image reconstruction network for anomaly detection in medical images

arXiv

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arXiv 2021年

作者： Zhou, Kang Li, Jing Luo, Weixin Li, Zhengxin Yang, Jianlong Fu, Huazhu Cheng, Jun Liu, Jiang Gao, Shenghua School of Information Science and Technology ShanghaiTech University Shanghai201210 China Shanghai Institute of Microsystem and Information Technology Chinese Academy of Sciences Shanghai200050 China University of Chinese Academy of Sciences China Shanghai Engineering Research Center of Intelligent Vision and Imaging Shanghai201210 China Shanghai Engineering Research Center of Energy Efficient and Custom AI IC Shanghai201210 China School of Biomedical Engineering Shanghai Jiao Tong University Shanghai200030 China Singapore138632 Singapore Singapore138632 Singapore Department of Computer Science and Engineering Southern University of Science and Technology Guangdong518055 China Cixi Institute of Biomedical Engineering Chinese Academy of Sciences Zhejiang315201 China

Anomaly detection in medical images refers to the identification of abnormal images with only normal images in the training set. Most existing methods solve this problem with a self-reconstruction framework, which tends to learn an identity mapping and reduces the sensitivity to anomalies. To mitigate this problem, in this paper, we propose a novel Proxy-bridged Image Reconstruction Network (ProxyAno) for anomaly detection in medical images. Specifically, we use an intermediate proxy to bridge the input image and the reconstructed image. We study different proxy types, and we find that the superpixel-image (SI) is the best one. We set all pixels’ intensities within each superpixel as their average intensity, and denote this image as SI. The proposed ProxyAno consists of two modules, a Proxy Extraction Module and an Image Reconstruction Module. In the Proxy Extraction Module, a memory is introduced to memorize the feature correspondence for normal image to its corresponding SI, while the memorized correspondence does not apply to the abnormal images, which leads to the information loss for abnormal image and facilitates the anomaly detection. In the Image Reconstruction Module, we map an SI to its reconstructed image. Further, we crop a patch from the image and paste it on the normal SI to mimic the anomalies, and enforce the network to reconstruct the normal image even with the pseudo abnormal SI. In this way, our network enlarges the reconstruction error for anomalies. Extensive experiments on brain MR images, retinal OCT images and retinal fundus images verify the effectiveness of our method for both image-level and pixel-level anomaly detection. © 2021, CC BY.

关键词： Image reconstruction

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Notice of Retraction: Integrated Double Estimator Architecture for Reinforcement Learning

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IEEE Transactions on Cybernetics 2020年第5期52卷 1-12页

作者： Pingli Lv Xuesong Wang Yuhu Cheng Ziming Duan C. L. Philip Chen Engineering Research Center of Intelligent Control for Underground Space China University of Mining and Technology Xuzhou China School of Information and Electronics Engineering Jiangsu Vocational Institute of Architectural Technology Xuzhou China Faculty of Science and Technology the University of Macau Macau SAR 99999 China. He is now with the School of Computer Science and Engineering South China University of Technology Guangzhou 510006 China.

Retracted.

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Graph attention layer evolves semantic segmentation for road pothole detection: A benchmark and algorithms

arXiv

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arXiv 2021年

作者： Fan, Rui Wang, Hengli Wang, Yuan Liu, Ming Pitas, Ioannis Department of Control Science and Engineering College of Electronics and Information Engineering Tongji University Shanghai201804 China Shanghai Research Institute for Intelligent Autonomous Systems Shanghai201210 China Department of Electronic and Computer Engineering The Hong Kong University of Science and Technology Hong Kong SAR Hong Kong Industrial R&d Center SmartMore Shenzhen518000 China School of Informatics University of Thessaloniki Thessaloniki541 24 Greece

Existing road pothole detection approaches can be classified as computer vision-based or machine learning-based. The former approaches typically employ 2-D image analysis/ understanding or 3-D point cloud modeling and segmentation algorithms to detect (i.e., recognize and localize) road potholes from vision sensor data, e.g., RGB images and/or depth/disparity images. The latter approaches generally address road pothole detection using convolutional neural networks (CNNs) in an end-to-end manner. However, road potholes are not necessarily ubiquitous and it is challenging to prepare a large wellannotated dataset for CNN training. In this regard, while computer vision-based methods were the mainstream research trend in the past decade, machine learning-based methods were merely discussed. Recently, we published the first stereo visionbased road pothole detection dataset and a novel disparity transformation algorithm, whereby the damaged and undamaged road areas can be highly distinguished. However, there are no benchmarks currently available for state-of-the-art (SoTA) CNNs trained using either disparity images or transformed disparity images. Therefore, in this paper, we first discuss the SoTA CNNs designed for semantic segmentation and evaluate their performance for road pothole detection with extensive experiments. Additionally, inspired by graph neural network (GNN), we propose a novel CNN layer, referred to as graph attention layer (GAL), which can be easily deployed in any existing CNN to optimize image feature representations for semantic segmentation. Our experiments compare GAL-DeepLabv3+, our best-performing implementation, with nine SoTA CNNs on three modalities of training data: RGB images, disparity images, and transformed disparity images. The experimental results suggest that our proposed GAL-DeepLabv3+ achieves the best overall pothole detection accuracy on all training data modalities. The source code, dataset, and benchmark are publicly available at m

关键词： Landforms

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Dynamic event-triggered H ∞ state estimation for discrete-time complex-valued memristive neural networks with mixed time delays

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Neural Networks 2025年 190卷 107631页

作者： Yufei Liu Bo Shen Hongjian Liu Tingwen Huang Hailong Tan Jie Sun School of Electrical Engineering Anhui Polytechnic University Wuhu 241000 China Key Laboratory of Advanced Perception and Intelligent Control of High-End Equipment Ministry of Education Anhui Polytechnic University Wuhu 241000 China College of Information Science and Technology Donghua University Shanghai 201620 China Engineering Research Center of Digitalized Textile and Fashion Technology Ministry of Education Shanghai 201620 China School of Mathematics-Physics and Finance Anhui Polytechnic University Wuhu 241000 China Faculty of Computer Science and Control Engineering Shenzhen University of Advanced Technology Shenzhen 518107 China School of Electrical Engineering Shanghai Dianji University Shanghai 201306 China

This paper explores the H ∞ state estimation problem for a category of discrete-time complex-valued memristive neural networks (CVMNNs). Regarding the studied CVMNNs, the phenomena of the distributed delay and time-varying delay are taken into account so as to describe the system more practically. Firstly, for further effective analysis, the examined CVMNNs are converted to an augmented system that integrates both the real and imaginary dynamics about the initial CVMNNs. To alleviate the communication burden, a representative dynamic event-triggered scheme is employed, for the first time, in the state estimator design of discrete-time CVMNNs. By establishing the Lyapunov functional, a sufficient condition is derived to assure the asymptotical stability of the estimation error system. Subsequently, the explicit expression of the desired estimator is obtained by resolving several matrix inequalities. Ultimately, the efficacy of the designed state estimator is substantiated through a simulation example.

关键词： Complex-valued memristive neural networks Discrete-time Dynamic event-triggered mechanism Mixed time delays State estimation

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An Optimal Wireless Transmission Strategy based on Coherent Beamforming and Successive Interference Cancellation for Edge Computing

An Optimal Wireless Transmission Strategy based on Coherent ...

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International Conference on Mobile Ad-hoc and Sensor Networks, MSN

作者： Zhehao Li Lei Shi Yi Shi Yuqi Fan Zhenchun Wei Liaoyuan Wu School of Computer Science and Information Engineering HeFei University of Technology Hefei China Ministry of Education Engineering Research Center of Safety Critical Industrial Measurement and Control Technology Hefei China Intelligent Automation Inc. 15400 Calhoun Drive Rockville USA

ISBN: (数字)9781728199160

ISBN: (纸本)9781728192369

In general, edge devices and edge servers in edge computing environment communicate with each other by wireless network, which put forward a high requirement for end-to-end wireless communication performance. In this paper, we propose an optimal strategy by combining the coherent beamforming (CB) technique and the successive interference cancellation (SIC) technique for improving the performance of the edge device communications. CB technique can be used for expanding the transmitter's transmitting range, while SIC technique can be used for improving the receiver's receiving ability. However, when these two techniques are used jointly, interference will occur between transmitters and receivers, which makes the CB-SIC strategy hard to be designed. We first give the mathematical model based on CB-SIC and show it is difficult to solve directly. Then, we design a heuristic algorithm called time slot loop allocation (TSLA) algorithm. TSLA is based on greedy strategy to obtain an approximate optimal solution. By using TSLA, the whole scheduling time will be divided into many time slots. In each time slot, we try to make as many edge devices as possible to transmit data to the server. These can increase the overall data throughput. In simulation, we compare CB-SIC wireless network with CB only, SIC only, and traditional multi-hop network. Simulation results show that the TSLA algorithm can improve the end-to-end communication performance in edge computing environment.

关键词： Performance evaluation Silicon carbide Heuristic algorithms Wireless networks Throughput Servers Edge computing

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Assessing bone quality of spine on children with scoliosis using ultrasound reflection fai method - A preliminary study

arXiv

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arXiv 2021年

作者： Song, Sheng Chen, Hongbo Li, Conger Lou, Edmond Le, Lawrence H. Zheng, Rui School of Information Science and Technology ShanghaiTech University Shanghai201210 China Shanghai Institute of Microsystem and Information Technology Chinese Academy of Sciences Shanghai200050 China University of Chinese Academy of Sciences Beijing100049 China School of Physical Science and Technology ShanghaiTech University Shanghai201210 China Department of Electrical and Computer Engineering University of Alberta Edmonton Canada Department of Radiology and Diagnostic Imaging University of Alberta Edmonton Canada Shanghai Engineering Research Center of Intelligent Vision and Imaging ShanghaiTech University Shanghai201210 China

Osteopenia is indicated as a common phenomenon in patients who have scoliosis. Quantitative ultrasound (QUS) has been used to assess skeletal status for decades, and recently ultrasound imaging using reflection signals from vertebra were as well applied to measure spinal curvatures on children with scoliosis. The objectives of this study are to develop a new method which can robustly extract a parameter from ultrasound spinal data for estimating bone quality of scoliotic patients and to investigate the potential for the parameter on predicting curve progression. The frequency amplitude index (FAI) was calculated based on the spectrum of the original radio frequency (RF) signals reflected from the tissue-vertebra interface. The correlation between FAI and reflection coefficient was validated using decalcified bovine bone samples in vitro, and the FAIs of scoliotic subjects were investigated in vivo referring to BMI, Cobb angles and curve progression status. The results showed that the intra-rater measures were highly reliable between different trials (ICC=0.997). The FAI value was strongly correlated to the reflection coefficient of bone tissue (R2=0.824), and the lower FAI indicated the higher risk of curve progression for the non-mild cases. This preliminary study reported that the FAI method can provide a feasible and promising approach to assess bone quality and monitor curve progression of the patients who have AIS. Copyright © 2021, The Authors. All rights reserved.

关键词： Reflection

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DeepACC:Automate Chromosome Classification based on Metaphase Images using Deep Learning Framework Fused with Prior Knowledge

arXiv

引用

arXiv 2020年

作者： Luo, Chunlong Yu, Tianqi Luo, Yufan Wang, Manqing Yu, Fuhai Li, Yinhao Tian, Chan Qiao, Jie Xiao, Li Key Laboratory of Intelligent Information Processing Advanced Computer Research Center Institute of Computing Technology Chinese Academy of Sciences Beijing China Peking University Third Hospital Beijing China School of Computer and Control Engineering University of Chinese Academy of China

Chromosome classification is an important but difficult and tedious task in karyotyping. Previous methods only classify manually segmented single chromosome, which is far from clinical practice. In this work, we propose a detection based method, DeepACC, to locate and fine classify chromosomes simultaneously based on the whole metaphase image. We firstly introduce the Additive Angular Margin Loss to enhance the discriminative power of model. To alleviate batch effects, we transform decision boundary of each class case-by-case through a siamese network which make full use of prior knowledges that chromosomes usually appear in pairs. Furthermore, we take the clinically seven group criterion as a prior knowledge and design an additional Group Inner-Adjacency Loss to further reduce inter-class similarities. 3390 metaphase images from clinical laboratory are collected and labelled to evaluate the performance. Results show that the new design brings encouraging performance gains comparing to the state-of-the-art baselines. Copyright © 2020, The Authors. All rights reserved.

关键词： Chromosomes

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