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IEEE International Conference on Electro-Information Technology

作者： Kourosh Jafari-Khouzani Hamid Soltanian-Zadeh Kost Elisevich Henry Ford Health System Radiology Image Analysis Laboratory Detroit MI USA Department of Computer Science Wayne State University Detroit MI USA Control and Intelligent Processing Center of Excellence ECE Department University of Tehran Tehran Iran Neurosurgery Department Henry Ford Health System Detroit MI USA

It has been previously shown that intensity and volume features of the hippocampus from MR images of the brain are useful in detecting the abnormality and consequently candidacy of the hippocampus for temporal lobe epilepsy (TLE) surgery. In this paper, we have studied and compared volume and texture features of hippocampus for TLE lateralization. Volume and some features describing the intensity non-uniformities are used. We use manual segmentation of the hippocampi from T1 weighted images and map them to the FLAIR images. Wavelet features as well as mean and variance of the hippocampus intensities of the FLAIR slices are used as texture features. A dataset of 55 patients with 28 right and 27 left abnormal sides was used in the experiments. Experimental results show that the volume and intensity features are useful in determining the abnormal side

关键词： Hippocampus Temporal lobe Epilepsy Image texture analysis Image segmentation Image analysis Shape Surgery Wavelet packets Radiology

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Select Informative Samples for Night-Time Vehicle Detection Benchmark in Urban Scenes

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Remote Sensing 2023年第17期15卷 4310-4310页

作者： Wang, Xiao Tu, Xingyue Al-Hassani, Baraa Lin, Chia-Wen Xu, Xin School of Computer Science and Technology Wuhan University of Science and Technology Wuhan430081 China Hubei Province Key Laboratory of Intelligent Information Processing and Real-Time Industrial System Wuhan University of Science and Technology Wuhan430081 China School of Computer Science Wuhan University Wuhan430081 China Department of Electrical Engineering Industrial Technology Research Institute National Tsing Hua University Hsinchu30013 Taiwan

Night-time vehicle detection plays a vital role due to the high incidence of abnormal events in our daily security field. However, existing studies mainly focus on vehicle detection in autonomous driving and traffic intersection scenes, but ignore urban scenes. There are vast differences between these scenes, such as viewpoint, position, illumination, etc. In this paper, the authors present a night-time vehicle detection dataset collected from urban scenes, named Vehicle Detection in Night-Time Urban Scene (VD-NUS). The VD-NUS dataset consists of more than 100 K challenging images, comprising a total of about 500 K labelled vehicles. This paper introduces a vehicle detection framework via an active auxiliary mechanism (AAM) to reduce the annotation workload. The proposed AAM framework can actively select the informative sample for annotation by estimating its uncertainty and locational instability. Furthermore, this paper proposes a computer-assisted detection module embedded in the AAM framework to help human annotators to rapidly and accurately label the selected data. AAM outperformed the baseline method (random sampling) by up to 0.91 AP and 3.0 MR−2 on the VD-NUS dataset. © 2023 by the authors.

关键词： Vehicles

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An adaptive multiplicative decomposition of non stationary multi-temporal satellite images: Application to urban changes detection

An adaptive multiplicative decomposition of non stationary m...

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International Image processing, Applications and systems Conference (IPAS)

作者： Ali Ben Abbes Houcine Essid Imed Riadh Farah Vincent Barra Research Labortaory in Computer Integrated Documentation and Arabized-Documentiel Genuises and Software National School of Computer Science Manouba-Tunisia Department Image and Information Processing Telecom Bretagne Technopôle France Laboratory of Computer Modeling and System Optimization UMR CNRS 6158 Univcrsité Blaise Pascal AUBIERE CEDEX

Nowadays, the process of change detection is regarded as an outstanding way for urban planning and design. The major concern of this paper is to investigate the non-stationary character of multi-temporal time series. To overcome this problem, we propose an adaptive multiplicative decomposition of non-stationary multi-temporal satellite image, which allows to decompose the series into three components: trend, seasonal and random, to properly model the evolution of land cover. We carried several experiments to validate our approach based on Landsat images covering the region of “Tres Cantos-Madrid” in Spain. The obtained results show the effectiveness of our proposed method comparing to some conventional methods.

关键词： Time series analysis Market research Satellites Urban areas Additives Standards Image processing

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Anchor-based spatio-temporal attention 3D convolutional networks for dynamic 3D point cloud sequences

arXiv

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arXiv 2020年

作者： Wang, Guangming Chen, Muyao Liu, Hanwen Yang, Yehui Liu, Zhe Wang, Hesheng The Department of Automation Institute of Medical Robotics Key Laboratory of System Control and Information Processing Ministry of Education Key Laboratory of Marine Intelligent Equipment System of Ministry of Education Shanghai Jiao Tong University Shanghai200240 China Beijing Advanced Innovation Center for Intelligent Robots and Systems Beijing Institute of Technology China The Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai200240 China The Department of Computer Science and Technology University of Cambridge

With the rapid development of measurwement technology, LiDAR and depth cameras are widely used in the perception of the 3D environment. Recent learning based methods for robot perception most focus on the image or video, but deep learning methods for dynamic 3D point cloud sequences are underexplored. Therefore, developing efficient and accurate perception method compatible with these advanced instruments is pivotal to autonomous driving and service robots. An Anchor-based Spatio-Temporal Attention 3D Convolution operation (ASTA3DConv) is proposed in this paper to process dynamic 3D point cloud sequences. The proposed convolution operation builds a regular receptive field around each point by setting several virtual anchors around each point. The features of neighborhood points are firstly aggregated to each anchor based on the spatio-temporal attention mechanism. Then, anchor-based 3D convolution is adopted to aggregate these anchors' features to the core points. The proposed method makes better use of the structured information within the local region and learns spatio-temporal embedding features from dynamic 3D point cloud sequences. Anchor-based Spatio-Temporal Attention 3D Convolutional Neural Networks (ASTA3DCNNs) are built for classification and segmentation tasks based on the proposed ASTA3DConv and evaluated on action recognition and semantic segmentation tasks. The experiments and ablation studies on MSRAction3D and Synthia datasets demonstrate the superior performance and effectiveness of our method for dynamic 3D point cloud sequences. Our method achieves the state-of-the-art performance among the methods with dynamic 3D point cloud sequences as input on MSRAction3D and Synthia datasets. Copyright © 2020, The Authors. All rights reserved.

关键词： Embeddings

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TIME PERIODIC SOLUTIONS TO THE EVOLUTIONARY OSEEN MODEL FOR A GENERALIZED NEWTONIAN INCOMPRESSIBLE FLUID

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Acta Mathematica Scientia 2023年第4期43卷 1645-1667页

作者：岑金夏 Stanislaw MIGóRSKI Emilio VILCHES 曾生达 School of Mathematical Sciences Zhejiang Normal UniversityJinhua 321004China Jagiellonian University in Krakow Chair of Optimization and Controlul.Lojasiewicza 630348 KrakowPoland Instituto de Ciencias de la Ingenier´ıa Universidad de O’HigginsAv.Libertador Bernardo OHiggins 6112841959 RancaguaChile Guangxi Colleges and Universities Key Laboratory of Complex System Optimization and Big Data Processing Yulin Normal UniversityYulin 537000China Department of Mathematics Nanjing University Nanjing 210093China Jagiellonian University in Krakow Faculty of Mathematics and Computer Scienceul.Lojasiewicza 630348 KrakowPoland

In this paper we study a nonstationary Oseen model for a generalized Newtonian incompressible fluid with a time periodic condition and a multivalued,nonmonotone friction ***,a variational formulation of the model is obtained;that is a nonlinear boundary hemivariational inequality of parabolic type for the velocity ***,an abstract first-order evolutionary hemivariational inequality in the framework of an evolution triple of spaces is *** mild assumptions,the nonemptiness and weak compactness of the set of periodic solutions to the abstract inequality are ***,a uniqueness theorem for the abstract inequality is established by using a monotonicity ***,we employ the theoretical results to examine the nonstationary Oseen model.

关键词： nonstationary Oseen model Newtonian incompressible fluid hemivariational inequality periodic solution generalized subgradient

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Correntropy Supervised Non-negative Matrix Factorization

Correntropy Supervised Non-negative Matrix Factorization

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International Joint Conference on Neural Networks

作者： Wenju Zhang Naiyang Guan Dacheng Tao Bin Mao Xuhui Huang Zhigang Luo Science and Technology on Parallel and Distributed Processing Laboratory College of Computer National University of Defense Technology Changsha Hunan China 410073 Center for Quantum Computation and Intelligent Systems FEIT University of Technology Sydney Sydney NSW 2007 Australia College of Science National University of Defense Technology Changsha Hunan China 410073 Department of Computer Science and Technology College of Computer National University of Defense Technology Changsha Hunan China 410073

ISBN: (纸本)9781479919611

Non-negative matrix factorization (NMF) is a powerful dimension reduction method and has been widely used in many pattern recognition and computer vision problems. However, conventional NMF methods are neither robust enough as their loss functions are sensitive to outliers, nor discriminative because they completely ignore labels in a dataset. In this paper, we proposed a correntropy supervised NMF (CSNMF) to simultaneously overcome aforementioned deficiencies. In particular, CSNMF maximizes the correntropy between the data matrix and its reconstruction in low-dimensional space to inhibit outliers during learning the subspace, and narrows the minimizes the distances between coefficients of any two samples with the same class labels to enhance the subsequent classification performance. To solve CSNMF, we developed a multiplicative update rules and theoretically proved its convergence. Experimental results on popular face image datasets verify the effectiveness of CSNMF comparing with NMF, its supervised variants, and its robustified variants.

关键词： NMF subsequent classification NMF protocol matrix decomposition outliers Nonnegative LOSS FUNCTION images REDUCTION METHODS Dataset computer vision

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Motion Projection Consistency Based 3D Human Pose Estimation with Virtual Bones from Monocular Videos

arXiv

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arXiv 2021年

作者： Wang, Guangming Zeng, Honghao Wang, Ziliang Liu, Zhe Wang, Hesheng Department of Automation Key Laboratory of System Control and Information Processing Ministry of Education Key Laboratory of Marine Intelligent Equipment and System Shanghai Engineering Research Center of Intelligent Control and Management Shanghai Jiao Tong University Shanghai200240 China The Department of Computer Science and Technology University of Cambridge CambridgeCB3 0FD United Kingdom

Real-time 3D human pose estimation is crucial for human-computer interaction. It is cheap and practical to estimate 3D human pose only from monocular video. However, recent bone splicing based 3D human pose estimation method brings about the problem of cumulative error. In this paper, the concept of virtual bones is proposed to solve such a challenge. The virtual bones are imaginary bones between non-adjacent joints. They do not exist in reality, but they bring new loop constraints for the estimation of 3D human joints. The proposed network in this paper predicts real bones and virtual bones, simultaneously. The final length of real bones is constrained and learned by the loop constructed by the predicted real bones and virtual bones. Besides, the motion constraints of joints in consecutive frames are considered. The consistency between the 2D projected position displacement predicted by the network and the captured real 2D displacement by the camera is proposed as a new projection consistency loss for the learning of 3D human pose. The experiments on the Human3.6M dataset demonstrate the good performance of the proposed method. Ablation studies demonstrate the effectiveness of the proposed inter-frame projection consistency constraints and intra-frame loop constraints. Copyright © 2021, The Authors. All rights reserved.

关键词： E-learning

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Prediction of hard rock TBM penetration rate using least square support vector machine

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IFAC Proceedings Volumes 2013年第13期46卷 347-352页

作者： Yang Ge Jingcheng Wang Kang Li Department of Automation Shanghai Jiao Tong University Shanghai and Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai Jiao Tong University Shanghai 200240 China (Tel: 86-21-34205374 ) School of Electronics Electrical Engineering and Computer Science Queen's University Belfast UK

The prediction of tunnel boring machine (TBM) penetration rate is helpful to plan construction time and to control cost. This paper studies on modeling the penetration rate and the rock mass parameters via least square support vector machine (LS-SVM). These rock properties include uniaxial compressive strength (UCS), Brazilian tensile strength (BTS), peak slope index (PSI), distance between planes of weakness (DPW) and the alpha angle. The correlation between the rock parameters and the measured rate of penetration (ROP) is first investigated via LS-SVM. Then the necessity of normalizing the rock parameters is discussed. Finally, comparisons between the LS-SVM and other methods are presented to demonstrate the applicability of LS-SVM for predicting ROP.

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RBP-Former: Joint Prediction of RNA-protein Binding Sites on Full-length RNA Transcripts for Multiple RBPs

RBP-Former: Joint Prediction of RNA-protein Binding Sites on...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Yichong Li Xiaojian Liu Fan Cheng Xiaoyong Pan Yang Yang Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai China Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Shanghai China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai China Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Shanghai China

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

RNA-binding proteins (RBPs) are essential for gene expression, and the complex RNA-protein interaction mechanisms require analysis of global RNA information. Therefore, accurate prediction of RBP binding sites on full-length RNA transcripts is crucial for understanding these mechanisms and their roles in diseases. While machine learning methods can predict RBP binding to RNA fragments, extending this to full-length transcripts presents challenges due to sequence length and data imbalance. In this paper, we introduce RBP-Former, a binding site joint prediction model designed specifically for full-length RNA transcripts that can be used for multiple RBPs. This model processes information at both coarse and fine-grained levels to fully exploit sequence data and its interactions with multiple RBPs. We develop multi-level imbalance learning strategies, achieving favorable results on imbalanced data. Our method outperforms existing methods in predicting binding sites on full-length RNA transcripts for multiple RBPs, demonstrating its effectiveness in handling imbalanced label and sample distributions.

关键词： Proteins Protein engineering Accuracy RNA Machine learning Predictive models Data models Gene expression Bioinformatics Diseases

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RTC: Link schedule based MAC design in multi-hop wireless network

RTC: Link schedule based MAC design in multi-hop wireless ne...

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International Conference on Quality of Service in Heterogeneous Wired/Wireless Networks (QSHINE)

作者： Xuan Dong Yinjia Huo Chunsheng Zhu Shaohe Lv Wenxiang Li Xiaodong Wang Department of Electrical and Computer Engineering The University of British Columbia Vancouver BC Canada National Laboratory of Parallel and Distributed Processing National University of Defense Technology Changsha China Engineering Research Center for Metallurgical Automation and Detecting Technology of Ministry of Education Wuhan University of Science and Technology Wuhan China

ISBN: (纸本)9781479982172

The performance of an ad-hoc network is greatly limited by collisions due to hidden terminals. In this paper, we propose a receiver tracking contention (RTC) scheme, which achieves high throughput by allowing the receivers to assist for channel contention. In RTC, link is the basic unit for channel access contention. Specifically, transmitter is used to contend for the channel and receiver is used to announce the potential collision. Based on INT message coding scheme, transmitter and its corresponding receiver can be well coordinated. In such mechanism, hidden terminals are avoided and exposed terminals are encouraged to transmit simultaneously. Based on OFDM modulation, RTC packets several subcarriers as subcontention unit and operates channel contention over multiple subcontention units. Furthermore, each subcontention unit maintains a transmission set, where collision-free links are allowed to merged into the transmission set In this case, the transmission set of subcontention unit can be aggregated after each contention period. When the subcontention unit i is the smallest index of non-empty subcontention unit, the transmission set of unit i will win the channel contention and transmitters of unit i will start to transmit in the following data transmission period. Analysis and simulation results show that RTC achieves a notable throughput gain over Back2f as high as 190% through simulation.

关键词： Receivers OFDM Transmitters Electronics packaging

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