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International Conference on Electronic Engineering and Informatics (EEI)

作者： Xianxiu Zhang Yuming Feng Yi Yang Baojie Zhang Key Laboratory of Intelligent Information Processing and Control of Chongqing Municipal Institutions of Higher Education School of Computer Science and Engineering Chongqing Three Gorges University Wanzhou Chongqing China School of Three Gorges Artificial Intelligence Chongqing Three Gorges University Wanzhou Chongqing China

ISBN: (数字)9798350353594

ISBN: (纸本)9798350353600

It is well known that people who know the presence of network viruses will defend against them, and the more computer viruses are detected and the more measures are taken. However, most previous computer virus propagation models have completely ignored the saturation effect. This paper investigates how the saturation effect impacts the transmission of electronic virus. For this purpose, a new transmission model of network virus is proposed, in which the virus is not very prevalent. Under certain conditions, there is a global asymptotical stable equilibrium point with no virus in this model. Finally, we give several numerical examples to exhibit that sufficient saturation effect is greatly conducive to containing network viruses.

关键词： Asymptotic stability Computer viruses Computational modeling Stability analysis Numerical models Informatics Viruses (medical) Numerical stability

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A Priori Based Deep Unfolding Method for mmWave Channel Estimation in MIMO Radar Aided V2X Communications

A Priori Based Deep Unfolding Method for mmWave Channel Esti...

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IEEE International Conference on Communications (ICC)

作者： Jiapan Yang Xiao Gong Bo Ai Wei Chen State Key Laboratory of Rail Traffic Control and Safety Beijing Jiaotong University China Frontiers Science Center for Smart High-speed Railway System Beijing China Beijing Engineering Research Center of High-speed Railway Broadband Mobile Communications Beijing China School of Information Engineering Zhengzhou University Zhengzhou China Key Laboratory of Railway Industry of Broadband Mobile Information Communications Beijing China

Due to the inherent high-mobility features in the Vehicles-to-Everything (V2X) scenarios, accurate channel estimation is essential to ensure the quality of communication services. Recently, multiple-input multiple-output (MIMO) radar has shown the potential to aid channel estimation. In this paper, we consider the MIMO radar aided V2X communication systems and propose a prior information aided deep learning method for channel estimation. Specifically, we use the MIMO radar to measure the angle information of moving vehicles. Based on the estimated angles, we obtain the non-zero position information of sparse angle-frequency channel. Then, by formulating the channel estimation as solving a group row sparse recovery problem, we propose a new shrinkage function and derive a priori assisted deep unfolding method. Experimental results show that the proposed method achieves the highest channel estimation accuracy compared with existing compressive sensing algorithms and deep-learning-based baseline methods.

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STMAD:A Simple Multimodal Anomaly Detection for Thyroid Medical information

STMAD:A Simple Multimodal Anomaly Detection for Thyroid Medi...

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International Congress on Image and Signal processing, BioMedical Engineering and Informatics (CISP-BMEI)

作者： Wentong Xu Chao Fu Jing Yu Caifeng Si Zuoyong Li Chaochao Li Haoyi Fan School of Computer and Artificial Intelligence Zhengzhou University Zhengzhou China Department of Ultrasound First Affiliated Hospital of Zhengzhou University Zhengzhou Henan China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control School of Computer and Big Data Minjiang University Fuzhou China

ISBN: (数字)9798331507398

ISBN: (纸本)9798331507404

Anomaly detection has been widely explored by training an out-of-distribution detector with only normal data. Medical image analysis, as a common tool, faces the challenge of how to further enhance the detection performance for thyroid cancer, especially with the application of various deep learning methods in the thyroid-related field. In this paper, we validate the effectiveness of sonographic feature descriptions (textual information) for thyroid cancer diagnosis. Building on this, we propose a simple multimodal self-supervised framework that leverages both textual and image modalities for thyroid cancer anomaly detection. Results on a private dataset demonstrate the effectiveness and superiority of our proposed method compared to advanced methods.

关键词： Deep learning Training Image analysis Thyroid cancer Signal processing Feature extraction Informatics Anomaly detection Medical diagnostic imaging Thyroid

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W-net: Simultaneous segmentation of multi-anatomical retinal structures using a multi-task deep neural network

arXiv

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arXiv 2020年

作者： Zhao, Hongwei Peng, Chengtao Liu, Lei Li, Bin School of Information Science and Technology University of Science and Technology of China Hefei Anhui230022 China Department of Precision Machinery and Instrumentation University of Science and Technology of China HefeiAnhui230022 China CAS Key Laboratory of Technology in Geo-spatial Information Processing and Application System University of Science and Technology of China Hefei Anhui230026 China

Segmentation of multiple anatomical structures is of great importance in medical image analysis. In this study, we proposed a W-net to simultaneously segment both the optic disc (OD) and the exudates in retinal images based on the multi-task learning (MTL) scheme. We introduced a class-balanced loss and a multi-task weighted loss to alleviate the imbalanced problem and to improve the robustness and generalization property of the W-net. We demonstrated the effectiveness of our approach by applying five-fold cross-validation experiments on two public datasets e_ophtha_EX and DiaRetDb1. We achieved F1-score of 94.76% and 95.73% for OD segmentation, and 92.80% and 94.14% for exudates segmentation. To further prove the generalization property of the proposed method, we applied the trained model on the DRIONS-DB dataset for OD segmentation and on the MESSIDOR dataset for exudate segmentation. Our results demonstrated that by choosing the optimal weights of each task, the MTL based W-net outperformed separate models trained individually on each task. Code and pre-trained models will be available at: https://***/FundusResearch/MTL_for_OD_and_***. Copyright © 2020, The Authors. All rights reserved.

关键词： Deep neural networks

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Deep Joint Source-Channel Coding for CSI Feedback: An End-to-End Approach

arXiv

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arXiv 2022年

作者： Xu, Jialong Ai, Bo Wang, Ning Chen, Wei State Key Laboratory of Rail Traffic Control and Safety Beijing Jiaotong University Beijing100044 China State Key Laboratory of Rail Traffic Control and Safety The School of Electronic and Information Engineering The Frontiers Science Center for Smart High-Speed Railway System Beijing Jiaotong University Beijing100044 China Research Center of Networks and Communications Peng Cheng Laboratory Shenzhen518055 China Henan Joint International Research Laboratory of Intelligent Networking and Data Analysis Zhengzhou University Zhengzhou450001 China School of Information Engineering Zhengzhou University Zhengzhou450001 China

The increased throughput brought by MIMO technology relies on the knowledge of channel state information (CSI) acquired in the base station (BS). To make the CSI feedback overhead affordable for the evolution of MIMO technology (e.g., massive MIMO and ultra-massive MIMO), deep learning (DL) is introduced to deal with the CSI compression task. Based on the separation principle in existing communication systems, DL based CSI compression is used as source coding. However, this separate source-channel coding (SSCC) scheme is inferior to the joint source-channel coding (JSCC) scheme in the finite blocklength regime. In this paper, we propose a deep joint source-channel coding (DJSCC) based framework for the CSI feedback task. In particular, the proposed method can simultaneously learn from the CSI source and the wireless channel. Instead of truncating CSI via Fourier transform in the delay domain in existing methods, we apply non-linear transform networks to compress the CSI. Furthermore, we adopt an SNR adaption mechanism to deal with the wireless channel variations. The extensive experiments demonstrate the validity, adaptability, and generality of the proposed framework. © 2022, CC BY.

关键词： Channel state information

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An end-to-end foreground-aware network for person re-identification

arXiv

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arXiv 2019年

作者： Liu, Yiheng Zhou, Wengang Liu, Jianzhuang Qi, Guojun Tian, Qi Li, Houqiang CAS Key Laboratory of Technology in Geo-spatial Information Processing and Application System Department of Electronic Engineering and Information Science University of Science and Technology of China Hefei230027 China Noah’s Ark Lab Huawei Technologies Company Limited Shenzhen518129 China Huawei Cloud EI Product Department Cloud & AI Huawei Technologies

Person re-identification is a crucial task of identifying pedestrians of interest across multiple surveillance camera views. For person re-identification, a pedestrian is usually represented with features extracted from a rectangular image region that inevitably contains the scene background, which incurs ambiguity to distinguish different pedestrians and degrades the accuracy. Thus, we propose an end-to-end foreground-aware network to discriminate against the foreground from the background by learning a soft mask for person re-identification. In our method, in addition to the pedestrian ID as supervision for the foreground, we introduce the camera ID of each pedestrian image for background modeling. The foreground branch and the background branch are optimized collaboratively. By presenting a target attention loss, the pedestrian features extracted from the foreground branch become more insensitive to backgrounds, which greatly reduces the negative impact of changing backgrounds on pedestrian matching across different camera views. Notably, in contrast to existing methods, our approach does not require an additional dataset to train a human landmark detector or a segmentation model for locating the background regions. The experimental results conducted on three challenging datasets, i.e., Market-1501, DukeMTMC-reID, and MSMT17, demonstrate the effectiveness of our approach. Copyright © 2019, The Authors. All rights reserved.

关键词： Cameras

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An MR Safe Rotary Encoder Based on Eccentric Sheave and FBG Sensors

An MR Safe Rotary Encoder Based on Eccentric Sheave and FBG ...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Shaoping Huang Anzhu Gao Zicong Wu Chuqian Lou Yanjun Wang Guang-Zhong Yang The Institute of Medical Robotics and School of Biomedical Engineering Shanghai Jiao Tong University Shanghai China Shanghai Jiao Tong University The Key Laboratory of System Control and Information Processing Ministry of Education Shanghai China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai China The Institute of Medical Robotics Shanghai Jiao Tong University Shanghai China The Imperial College London and Empa-Swiss Federal Laboratories for Materials Science and Technology

MRI-guided robotic systems are emerging platforms for minimally invasive intervention because of high positioning accuracy and excellent tissue contrast. MR safe encoders are critical components for closed-loop robotic control. This paper develops an MR safe absolute rotary encoder based on eccentric sheave and FBG sensors. The eccentric sheave transforms the rotational motion of the shaft to the bending deflection of the beam on which FBG sensors are integrated. A model is built by establishing the relationship of the kinematics of the sheave, the mechanical properties of the beam with unknown length, and the strain model of two Fiber Bragg Grating (FBG) sensors. A Pseudo-Rigid Body (PRB) 3R model is used to solve a set of constrained equations for accurate rotary encoding. A prototype is built to calibrate the parameters and validate the accuracy of the encoder and its MR compatibility. Results show that the maximum angular error is 1.6°, and the RMS error is 0.46°. MRI shows that no noticeable artifacts are observed, and the Signal to Noise Ratio (SNR) is not affected. The results demonstrate the potential of the proposed method for it to be integrated with MR safe robots with easy fabrication, compact structures, and continuous measurement.

关键词： Mechanical sensors Shafts Analytical models Kinematics Transforms Fiber gratings Robot sensing systems

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Fuzzy-logic based adaptive weighting filter for strap-down inertial navigation systems

Fuzzy-logic based adaptive weighting filter for strap-down i...

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World Congress on Intelligent control and Automation (WCICA)

作者： Wenjun Lv Yu Kang Zhijun Li Yun-Bo Zhao Department of Automation & CAS Key Laboratory of Technology in Geo-spatial Information Processing and Application System University of Science and Technology of China Hefei Anhui Province China College of Automation Science and Engineering South China University of Technology Guangzhou Guangdong Province China College of Information Engineering Zhejiang University of Technology Hangzhou Zhejiang Province China

Strap-down inertial navigation systems are widely applicable in both military and civil industries. The outputs of gyroscope and accelerometer are polluted by noises. Different form the conventional technologies, the weighting filter presented in this work is superior to both the complementary filter and Kalman filter. Furthermore, to eliminate the negative effects brought by the variation of noise statistical characteristics, a fuzzy-logic based regulator is introduced to adjust the weight coefficient adaptively. Finally, experiments are conducted to validate the efficiency of the filter.

关键词： Noise Gyroscopes Accelerometers Kalman filters Accuracy Mathematical model Educational institutions

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A Single-Cell Clustering Algorithm Based on Structure Perturbation Non-Negative Matrix Factorization

A Single-Cell Clustering Algorithm Based on Structure Pertur...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Jiang, Hanjing Wang, Meineng Zhang, Luping Huang, Yu-An Li, Yangyuan Huang, Yabing Huazhong University of Science and Technology Key Laboratory of Image Information Processing and Intelligent Control Education Ministry of China Institute of Artificial Intelligence School of Artificial Intelligence and Automation Hubei Wuhan430074 China China University of Mining and Technology School of Computer Science and Technology Xuzhou221116 China Yichun University School of Mathematics and Computer Science Yichun336000 China East China University of Technology School of Information Engineering Nanchang330013 China Northwestern Polytechnical University School of Computer Science Xi'an710129 China Renmin Hospital Wuhan University Department of Pathology Wuhan430060 China

ISBN: (纸本)9798350386226

The advent of single-cell RNA sequencing (scRNA-seq) has facilitated the acquisition of high-resolution data regarding cell heterogeneity across various tissues. A fundamental and critical step in the analysis of scRNA-seq data is cell type identification. An appropriate graph construction strategy can reflect the topological structure between cells and allows for detailed exploration of the relationships between cells and genes. Given the sparsity of scRNA-seq data and the pronounced noise generated by shallow sequencing, choosing an appropriate graph construction strategy has become a significant challenge. In this paper, we propose a single-cell clustering method, named Sc-PNNMF, based on structural perturbation of nonnegative matrix factorization. Sc-PNNMF employs a structural perturbation algorithm to optimize the gene expression matrix. The optimized matrix guides the matrix factorization process, effectively overcoming the impact of data sparsity and significant noise in gene expression data on the graph construction strategy. We compared the clustering abilities of Sc-PNNMF with five other methods on ten real datasets. © 2024 IEEE.

关键词： Non-negative matrix factorization

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Metal Migration Induced Breakdown from Gate Contact in Bulk FinFET Devices

Metal Migration Induced Breakdown from Gate Contact in Bulk ...

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International Symposium on Physical & Failure Analysis of Integrated Circuits

作者： Xin Yang Yihong Qing Kuei-Shu Chang-Liao Yuchong Qiao Chaolun Wang Zhiwei Liu Luoyong Li Chihang Tsai Yongren Wu Yazhen Xie Weisong Yu Xing Wu Shanghai Key Laboratory of Multidimensional Information Processing East China Normal University Shanghai China Center for Advanced Semiconductor & Integrated Micro-System University of Electronic Science and Technology of China Chengdu China Engineering & System Science National Tsing Hua University Taiwan China China Chinaisti (shanghai) Testing Technology Co. Ltd Shanghai China

Bulk fin field-effect transistor (FinFET) devices with excellent gate control ability are promising to succeed the planar devices under Moore’s law. With the scaling down of the feature size, the reliability of the FinFET device becomes an urgent issue. Also, the miniaturized three-dimensional structure of bulk FinFET makes the failure characterization more challenging. In this paper, we carried out the direct current (DC) breakdown and transmission line pulse (TLP) breakdown in the bulk FinFET. Corresponding microstructural and chemical elements’ differences between gate contact and gate electrode are characterized by transmission electron microscopy (TEM). For both experiments, the fin nearest to gate contact is completely damaged under electrical stressing. The destruction of DC is more serious, which has a large area fusion with metal migration. The TLP damage is local and tends to form a void. The gate contact structure is intact with slight elements’ migration. This work paves a guideline for the reliability improvements of FinFET.

关键词： Integrated circuits Electric breakdown Transmission electron microscopy Contacts Metals Logic gates FinFETs

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