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Novel 3D Geometry-Based Stochastic Models for Non-Isotropic MIMO Vehicle-to-Vehicle Channels

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arXiv 2023年

作者： Yuan, Yi Wang, Cheng-Xiang Cheng, Xiang Ai, Bo Laurenson, David I. The Joint Research Institute for Signal and Image Processing School of Engineering & Physical Sciences Heriot-Watt University EdinburghEH14 4AS United Kingdom The Research Institute for Modern Communications School of Electronics Engineering & Computer Science Peking University Beijing100871 China The State Key Laboratory of Rail Traffic Control and Safety Beijing Jiaotong University Beijing100044 China The Joint Research Institute for Signal and Image Processing Institute for Digital Communications University of Edinburgh EdinburghEH9 3JL United Kingdom

This paper proposes a novel three-dimensional (3D) theoretical regular-shaped geometry-based stochastic model (RS-GBSM) and the corresponding sum-of-sinusoids (SoS) simulation model for non-isotropic multiple-input multiple-output (MIMO) vehicle-to-vehicle (V2V) Ricean fading channels. The proposed RS-GBSM, combining line-of-sight (LoS) components, a two-sphere model, and an elliptic-cylinder model, has the ability to study the impact of the vehicular traffic density (VTD) on channel statistics, and jointly considers the azimuth and elevation angles by using the von Mises Fisher distribution. Moreover, a novel parameter computation method is proposed for jointly calculating the azimuth and elevation angles in the SoS channel simulator. Based on the proposed 3D theoretical RS-GBSM and its SoS simulation model, statistical properties are derived and thoroughly investigated. The impact of the elevation angle in the 3D model on key statistical properties is investigated by comparing with those of the corresponding two-dimensional (2D) model. It is demonstrated that the 3D model is more accurate to characterize real V2V channels, in particular for pico cell scenarios. Finally, close agreement is achieved between the theoretical model, SoS simulation model, and simulation results, demonstrating the utility of the proposed models. © 2023, CC BY.

关键词： Stochastic systems

3D FACE REPRESENTATION AND RECOGNITION BY INTRINSIC SHAPE DESCRIPTION MAPS

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3D FACE REPRESENTATION AND RECOGNITION BY INTRINSIC SHAPE DE...

IEEE International Conference on Acoustics, Speech, and Signal processing

作者： Zhe Guo Yanning Zhang Yong Xia Zenggang Lin Dagan Feng Shaanxi Provincial Key Lab of Speech & Image Information Processing (SAIIP) School of Computer Science Northwestern Polytechnical University Xi'an China Biomedical and Multimedia Information Technology (BMIT) Research Group School of Information Technologies University of Sydney Australia

ISBN: (纸本)9781424442959

We present a novel method for 3D face recognition, in which the 3D facial surface is first mapped into a 2D domain with specified resolution through a global optimization by constrained conformal geometric maps. The Intrinsic Shape Description Map (ISDM) is then constructed through a modeling technique capable to express geometric and appearance information of the 3D face. Hence the 3D surface matching problem can be simplified to a 2D image matching problem, which greatly reduces the computational complexity. Finally, the Intrinsic Shape Description Feature (ISDF) of ISDM and the discrimination analysis can be calculated. Experimental results implemented on GavabDB demonstrate that our proposed method significantly outperforms the existing methods with respect to pose variation.

关键词： Face recognition Conformal Geometric Maps Intrinsic Shape Description Map

RTM3D: Real-time monocular 3D detection from object keypoints for autonomous driving

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arXiv 2020年

作者： Li, Peixuan Zhao, Huaici Liu, Pengfei Cao, Feidao Shenyang Institute of Automation Chinese Academy of Sciences Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences University of Chinese Academy of Sciences Key Laboratory of Opto-Electronic Information Processing Chinese Academy of Sciences Key Lab of Image Understanding and Computer Vision Liaoning Province

In this work, we propose an efficient and accurate monocular 3D detection framework in single shot. Most successful 3D detectors take the projection constraint from the 3D bounding box to the 2D box as an important component. Four edges of a 2D box provide only four constraints and the performance deteriorates dramatically with the small error of the 2D detector. Different from these approaches, our method predicts the nine perspective keypoints of a 3D bounding box in image space, and then utilize the geometric relationship of 3D and 2D perspectives to recover the dimension, location, and orientation in 3D space. In this method, the properties of the object can be predicted stably even when the estimation of keypoints is very noisy, which enables us to obtain fast detection speed with a small architecture. Training our method only uses the 3D properties of the object without the need for external networks or supervision data. Our method is the first real-time system for monocular image 3D detection while achieves state-ofthe-art performance on the KITTI benchmark. Code will be released at https://***/Banconxuan/RTM3D. Copyright © 2020, The Authors. All rights reserved.

关键词： Real time systems

EDGE DETECTION USING SUPPORT VALUE TRANSFROM

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EDGE DETECTION USING SUPPORT VALUE TRANSFROM

2007 International Conference on Machine Learning and Cybernetics(IEEE第六届机器学习与控制论国际会议)

作者： SHENG ZHENG JINWEN TIAN JIAN LIU Electronics and Information Engineering Department Huazhong University of Science and Technology W State Education Commission Key Lab for Image Processing and Intelligent Control HUST Wuhan 430074 Electronics and Information Engineering Department Huazhong University of Science and Technology W

An innovative edge detection algorithm using the support value transform is presented in this *** on the support value transform, the multi scale support value images are extracted from the *** resolution of an image is directly related to the proper scale for edge detection, and the second level support value image is used to locate the edge positions by their zero *** experiments are carried out and sharp image edges are obtained from a variety of sample *** with many other existing methods, including LoG and Canny detectors, the proposed algorithm is superior to the LOG and Roberts approach.

关键词： Edge detection mapped least squares support vector machine (mapped LS-SVM) support value transform (SVT) zero crossings

Compressive tracking moving cells in time-lapse image sequences

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Compressive tracking moving cells in time-lapse image sequen...

IEEE International Conference on Orange Technologies (ICOT)

作者： Chen Ding Ying Li Yongsheng Pan Tao Zhou Pengcheng Gao Yong Xia Center for Multidisciplinary Convergence Computing (CMCC) Northwestern Polytechnical University Xi'an China Shaanxi Key Lab of Speech & Image Information Processing (SAIIP) Northwestern Polytechnical University Xi'an China School of Science Ningxia Medical University Yinchuan China

ISBN: (纸本)9781467382380

Tracking the motion of cells in time-lapse image sequences plays a pivotal role in both research settings and clinical practices. In spite of their prevalence, automated cell tracking approaches are still facing several major challenges, including the effectiveness of cell detection, accuracy of tracking and high computational complexity. In this paper, we propose a segmentation-based compressive tracking (SBCT) algorithm for moving cells. This algorithm consists three major steps, including detecting the bounding box of each cell, extracting image features in each bounding box using compressive sensing, and identifying the correspondence between cells in adjacent frames using a trained naive Bayes classifier. The proposed SBCT algorithm has been evaluated against seven state-of-the-art cell tracking approaches on two time-lapse images sequences provided by the 2014 cell tracking challenge. Our results suggest that the proposed algorithm can successfully tracking moving cells with relatively high accuracy and low computational complexity.

关键词： Tracking image segmentation Feature extraction image sequences Classification algorithms Compressed sensing Shape

Revisiting Stereo Triangulation in UAV Distance Estimation

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arXiv 2023年

作者： Zhuang, Jiafan Yuan, Duan Yan, Rihong Huang, Weixin Li, Wenji Fan, Zhun The Department of Electronic Engineering Shantou University Guangdong Shantou515063 China The Key Lab of Digital Signal and Image Processing of Guangdong Province Guangdong Shantou515063 China The College of Electronic Engineering Shantou University Guangdong Shantou515063 China

Distance estimation plays an important role for path planning and collision avoidance of swarm UAVs. However, the lack of annotated data seriously hinders the related studies. In this work, we build and present a UAVDE dataset for UAV distance estimation, in which distance between two UAVs is obtained by UWB sensors. During experiments, we surprisingly observe that the stereo triangulation cannot stand for UAV scenes. The core reason is the position deviation issue due to long shooting distance and camera vibration, which is common in UAV scenes. To tackle this issue, we propose a novel position correction module, which can directly predict the offset between the observed positions and the actual ones and then perform compensation in stereo triangulation calculation. Besides, to further boost performance on hard samples, we propose a dynamic iterative correction mechanism, which is composed of multiple stacked PCMs and a gating mechanism to adaptively determine whether further correction is required according to the difficulty of data samples. We conduct extensive experiments on UAVDE, and our method can achieve a significant performance improvement over a strong baseline (by reducing the relative difference from 49.4% to 9.8%), which demonstrates its effectiveness and superiority. The code and dataset are available at https://***/duanyuan13/PCM. Copyright © 2023, The Authors. All rights reserved.

关键词： Unmanned aerial vehicles (UAV)

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

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)