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Evaluation of DCS1800/CDMA-EVDO dual-mode and dual-link UE system performance based on the high isolation handset antenna

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Progress In Electromagnetics Research C 2012年 31卷 67-81页

作者： Gao, A.-M. Kong, L.-L. Jiang, W. Ding, J.-P. Jiang, Y. School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shanghai 200240 China Key Laboratory of Optic-electronic Information Acquisition and Manipulation Ministry of Education Anhui University Hefei 230039 China Key Lab of Department of Optoelectronic Information for Image processing University of Electronic Science and Technology of China Chengdu 610054 China

This paper investigates the performance of DCS1800/CD-MA-EVDO dual-mode dual-link (DM-DL) UE and presents an effective solution which is based on our designed high isolation antenna for the UE local electromagnetic interference (EMI). To this end, a DCS1800/CDMA-EVDO DM-DL UE model is firstly brought out, together with its system model, interference analysis and performance evaluation. Simulated results show that the DCS1800 (CDMA-EVDO) local interference will cause a sharp deterioration of the CDMA-EVDO (DCS1800) receiving sensitivity. To solve this problem, then, a compact and high isolation double-band handset antenna is given with its structure designed and performance validated by measurement. On the basis of this antenna and existing UE bandpass filter, we introduce a solution called "passive isolation to eliminate the local interference. Finally, the performance of DCS1800/CDMA-EVDO DM-DL UE is researched again by our solution, simulated results indicate that the isolation effect is quite good and the DM-DL UE can work normally.

关键词： Electromagnetic pulse

Iwin: Human-Object Interaction Detection via Transformer with Irregular Windows

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arXiv 2022年

作者： Tu, Danyang Min, Xiongkuo Duan, Huiyu Guo, Guodong Zhai, Guangtao Shen, Wei Institute of Image Communication and Network Engineering Shanghai Jiao Tong University China Institute of Deep Learning Baidu Research Beijing China MoE Key Lab of Artificial Intelligence AI Institute Shanghai Jiao Tong University China

This paper presents a new vision Transformer, named Iwin Transformer, which is specifically designed for human-object interaction (HOI) detection, a detailed scene understanding task involving a sequential process of human/object detection and interaction recognition. Iwin Transformer is a hierarchical Transformer which progressively performs token representation learning and token agglomeration within irregular windows. The irregular windows, achieved by augmenting regular grid locations with learned offsets, 1) eliminate redundancy in token representation learning, which leads to efficient human/object detection, and 2) enable the agglomerated tokens to align with humans/objects with different shapes, which facilitates the acquisition of highly-abstracted visual semantics for interaction recognition. The effectiveness and efficiency of Iwin Transformer are verified on the two standard HOI detection benchmark datasets, HICO-DET and V-COCO. Results show our method outperforms existing Transformers-based methods by large margins (3.7 mAP gain on HICO-DET and 2.0 mAP gain on V-COCO) with fewer training epochs (0.5×). Copyright © 2022, The Authors. All rights reserved.

关键词： Semantics

End-to-End Human-Gaze-Target Detection with Transformers

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arXiv 2022年

In this paper, we propose an effective and efficient method for Human-Gaze-Target (HGT) detection, i.e., gaze following. Current approaches decouple the HGT detection task into separate branches of salient object detection and human gaze prediction, employing a two-stage framework where human head locations must first be detected and then be fed into the next gaze target prediction sub-network. In contrast, we redefine the HGT detection task as detecting human head locations and their gaze targets, simultaneously. By this way, our method, named Human-Gaze-Target detection TRansformer or HGTTR, streamlines the HGT detection pipeline by eliminating all other additional components. HGTTR reasons about the relations of salient objects and human gaze from the global image context. Moreover, unlike existing two-stage methods that require human head locations as input and can predict only one human's gaze target at a time, HGTTR can directly predict the locations of all people and their gaze targets at one time in an end-to-end manner. The effectiveness and robustness of our proposed method are verified with extensive experiments on the two standard benchmark datasets, GazeFollowing and VideoAttentionTarget. Without bells and whistles, HGTTR outperforms existing state-of-the-art methods by large margins (6.4 mAP gain on GazeFollowing and 10.3 mAP gain on VideoAttentionTarget) with a much simpler architecture. Copyright © 2022, The Authors. All rights reserved.

关键词： Object detection

Fast signal recovery from saturated measurements by linear loss and nonconvex penalties

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arXiv 2018年

作者： He, Fan Huang, Xiaolin Liu, Yipeng Yan, Ming Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University The MOE Key Laboratory of System Control and Information Processing Shanghai200240 China School of Information and Communication Engineering University of Electronic Science and Technology of China Chengdu611731 China The Department of Computational Mathematics Science and Engineering Michigan State University MI United States

Sign information is the key to overcoming the inevitable saturation error in compressive sensing systems, which causes information loss and results in bias. For sparse signal recovery from saturation, we propose to use a linear loss to improve the effectiveness from existing methods that utilize hard constraints/hinge loss for sign consistency. Due to the use of linear loss, an analytical solution in the update progress is obtained, and some nonconvex penalties are applicable, e.g., the minimax concave penalty, the 0 norm, and the sorted 1 norm. Theoretical analysis reveals that the estimation error can still be bounded. Generally, with linear loss and nonconvex penalties, the recovery performance is significantly improved, and the computational time is largely saved, which is verified by the numerical experiments. Copyright © 2018, The Authors. All rights reserved.

关键词： Compressed sensing

Multi-level graph convolutional network with automatic graph learning for hyperspectral image classification

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arXiv 2020年

作者： Wan, Sheng Gong, Chen Pan, Shirui Yang, Jie Yang, Jian PCA Lab Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education Jiangsu Key Laboratory of Image Video Understanding for Social Security School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing210094 China Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Shanghai200240 China Faculty of Information Technology Monash University ClaytonVIC3800 Australia

Nowadays, deep learning methods, especially the Graph Convolutional Network (GCN), have shown impressive performance in hyperspectral image (HSI) classification. However, the current GCN-based methods treat graph construction and image classification as two separate tasks, which often results in suboptimal performance. Another defect of these methods is that they mainly focus on modeling the local pairwise importance between graph nodes while lack the capability to capture the global contextual information of HSI. In this paper, we propose a Multi-level GCN with Automatic Graph Learning method (MGCN-AGL) for HSI classification, which can automatically learn the graph information at both local and global levels. By employing attention mechanism to characterize the importance among spatially neighboring regions, the most relevant information can be adaptively incorporated to make decisions, which helps encode the spatial context to form the graph information at local level. Moreover, we utilize multiple pathways for local-level graph convolution, in order to leverage the merits from the diverse spatial context of HSI and to enhance the expressive power of the generated representations. To reconstruct the global contextual relations, our MGCN-AGL encodes the long range dependencies among image regions based on the expressive representations that have been produced at local level. Then inference can be performed along the reconstructed graph edges connecting faraway regions. Finally, the multi-level information is adaptively fused to generate the network output. In this means, the graph learning and image classification can be integrated into a unified framework and benefit each other. Extensive experiments have been conducted on three real-world hyperspectral datasets, which are shown to outperform the state-of-the-art methods. Copyright © 2020, The Authors. All rights reserved.

关键词： image classification

Data-driven Sparse Bayesian Learning Discovery of Hysteresis Effects in Electrospinning Processes

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Data-driven Sparse Bayesian Learning Discovery of Hysteresis...

第34届中国控制与决策会议

作者： Hongwei Sun Yasen Wang Yaozhong Zheng Xiang Huang Hai-Tao Zhang School of Artificial Intelligence and Automation and Key Laboratory of Image Processing and Intelligent ControlSchool of AutomationHuazhong University of Science and Technology State Key Lab of Digital Manufacturing Equipment and Technology Huazhong University of Science and Technology School of Mechanical Science and Engineering Huazhong University of Science and Technology

Electrospinning is an efficient and feasible method to fabricate hairline fibers from polymers or ***,the hysteresis nonlinearity among pump flow and jet diameter during electrospinning process strongly hinders the improvement of its *** alleviate this effect,it is an urgent yet challenging mission to discover hysteresis effects in near-field Electrospinning *** this work,a nonlinear autoregressive exogenous model is developed for described electrospinning ***,a pure data-driven sparse B ayesian learning(SBL) method is applied to distill the pump flow-jet diameter hysteresis ***,extensive experiments are conducted to verify the discovered effects by the SBL identification method.

关键词： Electrospinning hysteresis nonlinear autoregressive exogenous(NARX) sparse Bayesian learning(SBL)

Automatic needle segmentation using 3D Gray-level Hough Transform in 3D ultrasound images

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Journal of Computational Information Systems 2008年第5期4卷 2021-2026页

作者： Qiu, Wu Ding, Mingyue Yuchi, Ming Image Processing and Intelligence Control Key Lab. of Education Ministry of China Institute of Pattern Recognition and Artificial Intelligence Huazhong University of Science and Technology Wuhan 430074 China Department of Biomedical Engineering School of Life Science and Technology Huazhong University of Science and Technology Wuhan 430074 China

This paper describes a novel 3D needle segmentation algorithm for 3DUS data. The algorithm includes the 3D Gray-level Hough Transform (3DGHT), which is based on the representation (ψ, θ, ρ, α) of straight lines in 3D space and Local Dimensionality Estimator (GST). Different from the traditional Hough transformation the process of binarization is not needed, because the proposed algorithm adopts the original gray level image directly. We tested it with the synthetic data and water phantom. The results show that our algorithm works well in 3D US images with angular deviation less than 1° and position deviation less than 1 mm. © 2008 Binary Information Press.

关键词： Hough transforms

Combining visible light and infrared imaging for efficient detection of respiratory infections such as covid-19 on portable device

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arXiv 2020年

作者： Jiang, Zheng Hu, Menghan Fan, Lei Pan, Yaling Tang, Wei Zhai, Guangtao Lu, Yong Institute of Image Communication and Information Processing Shanghai Jiao Tong University China Shanghai Key Labora. of Multidim. Infor. Proce East China Normal University China Rui Jin Hospital/Lu Wan Branch School of Medicine Shanghai Jiao Tong University

Coronavirus Disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronaviruses 2 (SARS-CoV-2) has become a serious global epidemic in the past few months and caused huge loss to human society worldwide. For such a large-scale epidemic, early detection and isolation of potential virus carriers is essential to curb the spread of the epidemic. Recent studies have shown that one important feature of COVID-19 is the abnormal respiratory status caused by viral infections. During the epidemic, many people tend to wear masks to reduce the risk of getting sick. Therefore, in this paper, we propose a portable non-contact method to screen the health condition of people wearing masks through analysis of the respiratory characteristics. The device mainly consists of a FLIR one thermal camera and an Android phone. This may help identify those potential patients of COVID-19 under practical scenarios such as pre-inspection in schools and hospitals. In this work, we perform the health screening through the combination of the RGB and thermal videos obtained from the dual-mode camera and deep learning architecture. We first accomplish a respiratory data capture technique for people wearing masks by using face recognition. Then, a bidirectional GRU neural network with attention mechanism is applied to the respiratory data to obtain the health screening result. The results of validation experiments show that our model can identify the health status on respiratory with the accuracy of 83.7% on the real-world dataset. The abnormal respiratory data and part of normal respiratory data are collected from Ruijin Hospital Affiliated to The Shanghai Jiao Tong University Medical School. Other normal respiratory data are obtained from healthy people around our researchers. This work demonstrates that the proposed portable and intelligent health screening device can be used as a pre-scan method for respiratory infections, which may help fight the current COVID-19 epidemic. The demo

关键词： Coronavirus

image Encryption Using 2D Sine-Piecewise Linear Chaotic Map

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Image Encryption Using 2D Sine-Piecewise Linear Chaotic Map

International Conference on Wavelet Analysis and Pattern Recognition (ICWAPR)

作者： Yan-Ru Zhong Hua-Yi Liu Xi-Yan Sun Ru-Shi Lan Xiao-Nan Luo School of Computer Science and Information Security Guilin University of Electronic Technology Guilin China School of Information and Communication Guilin University of Electronic Technology Guilin China Guangxi Colleges and Universities Key Laboratory of Intelligent Processing of Computer Image and Graphics Guilin University of Electronic Technology Guilin China

In this paper, a new image encryption algorithm is proposed by integrating Sine and piecewise linear chaotic maps. In order to realize the effect of encrypted image, the security key and 2D Sine-piecewise linear chaotic map (SPLCM) are used to encrypt the image by using random sequence and random matrix, and then using the replacement operation and the diffusion operation of the original image. The proposed image encryption algorithm is simple and practical, and the simulation results show that this algorithm is able to encrypt different types of digital images into unidentifiable random images. The security analysis also shows that this algorithm has a higher security level.

关键词： Encryption Two dimensional displays Digital images Chaotic communication