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Time-varying Characteristics of mmWave Channel based on the Clustered Sparsity Model

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Time-varying Characteristics of mmWave Channel based on the ...

IEEE Conference on Vehicular technology (VTC)

作者： Lijun Yang Haitao Lu Xinchao Ge Zhixin Sun Pan Cao School of Internet of Things Nanjing University of Posts and Telecommunications Nanjing China RAN System Design Dept. ZTE Corporation Shenzhen China Shenzhen Key Laboratory of 5G RAN Security Technology Research and Application Shenzhen China School of Modern Posts Nanjing University of Posts and Telecommunications Nanjing China School of Physics Engineering and Computer Science University of Hertfordshire Hatfield UK

A better understanding of the mmWave channel coherence time will play a significant role in both communication and sensing. Traditional literatures consider that the channel coherence time in mmWave communication will be very short due to high frequency, which makes the channel estimation more difficult. However, we believe that the propagation characteristics of the mmWave channel will affect its coherence time, i.e., channel sparsity may limit its time variability. In this paper, we investigate the time-varying nature of mmWave channel by analyzing its Doppler spread based on the clustered sparsity model. After the derivation of Doppler spectrum DPS and the simulation analysis based on the two-cluster channel, we found that the maximum angle between clusters and the power distribution between clusters are two main factors limiting the Doppler spread. Finally, the realistic mmWave NYUSIM model is used for Doppler spread comparative analysis with Clarke’s model and find that Doppler spread of NYUSIM channel is far less than that estimated based on Clarke’s model. Thus, it is demonstrated that even if the mmWave frequency is high, the clustered sparsity characteristics makes its channel coherence time not as short as thought intuitively, which is meaningful for mmWave vehicular sensing and communication.

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TomoSAR-ALISTA: Efficient TomoSAR Imaging via Deep Unfolded Network

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arXiv 2022年

作者： Wang, Muhan Zhang, Zhe Wang, Yue Gao, Silin Qiu, Xiaolan Key Laboratory of Technology in Geo-spatial Information Processing and Application System Chinese Academy of Sciences Beijing100190 China Key Laboratory of Intelligent Aerospace Big Data Application Technology Suzhou215123 China Suzhou Aerospace Information Research Institute Suzhou215123 China School of Electronic Electrical and Communication Engineering University of Chinese Academy of Sciences Beijing100049 China Aerospace Information Research Institute Chinese Academy of Sciences Beijing100094 China Electrical and Computer Engineering Department George Mason University FairfaxVA22030 United States

Synthetic aperture radar (SAR) tomography (TomoSAR) has attracted remarkable interest for its ability in achieving three-dimensional reconstruction along the elevation direction from multiple observations. In recent years, compressed sensing (CS) technique has been introduced into TomoSAR considering for its super-resolution ability with limited samples. Whereas, the CS-based methods suffer from several drawbacks, including weak noise resistance, high computational complexity and complex parameter fine-tuning. Among the different CS algorithms, iterative soft-thresholding algorithm (ISTA) is widely used as a robust reconstruction approach, however, the parameters in the ISTA algorithm are manually chosen, which usually requires a time-consuming fine-tuning process to achieve the best performance. Aiming at efficient TomoSAR imaging, a novel sparse unfolding network named analytic learned ISTA (ALISTA) is proposed towards the TomoSAR imaging problem in this paper, and the key parameters of ISTA are learned from training data via deep learning to avoid complex parameter fine-tuning and significantly relieves the training burden. In addition, experiments verify that it is feasible to use traditional CS algorithms as training labels, which provides a tangible supervised training method to achieve better 3D reconstruction performance even in the absence of labeled data in real applications. Copyright © 2022, The Authors. All rights reserved.

关键词： Compressed sensing

Shadow detection of moving objects in traffic monitoring video 9

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Shadow detection of moving objects in traffic monitoring vid...

9th IEEE Joint International Information technology and Artificial Intelligence Conference, ITAIC 2020

作者： Zhang, Mingrui Zhao, Wenbing Li, Xiying Wang, Dan Beijing University of Technology College of Computer Science and Technology Beijing China Sun Yat-sen University Intelligent Transportation Research Center School of Intelligent System Engineering Guangzhou China Key Laboratory of Intelligent Transportation System of Guangdong Province Guangzhou China Key Laboratory of Video Image Intelligent Analysis and Application Technology Ministry of Public Security Guangzhou China

Moving object detection is an important application of computer vision. Commonly used foreground separation algorithms such as Gaussian mixture modeling, ViBe, frame difference method, etc., do not consider the color of shadow and recognize the shadow of a moving object as a part of the moving object. In many cases, the shadow detection effect is not good. Focus on the detection of moving object shadows in traffic surveillance videos, this paper improves the existing ViBe algorithm, considers the color characteristics of the shadows, recognizes the shadows as part of the background, gains a smaller amount of calculation and better effect of shadow detection with the advantages of ViBe. © 2020 IEEE.

关键词： Security systems

Rsh-Bu: Revocable Secret Handshakes with Backward Unlinkability from Vlr Group Signatures

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SSRN

SSRN 2023年

作者： Wen, Yamin Wan, Min Zhao, Junying Gong, Zheng Deng, Yuqiao School of Statistics and Mathematics Guangdong University of Finance and Economics Guangzhou510320 China Guangdong Provincial Key Laboratory of Information Security Technology Sun Yat-sen University Guangzhou510006 China Big Data and Education Statistics Application Laboratory Guangdong University of Finance and Economics Guangzhou510320 China School of Information Guangdong University of Finance and Economics Guangzhou510320 China School of Computer Science South China Normal University Guangzhou510631 China

Secret handshake scheme is a bi-directional authentication method that enables two participants from the same organization to identify both sides in private. A new generic construction of secret handshakes is proposed in this paper, which is basically derived from verifier-local revocation group signature (VLR-GS). Accordingly, a secret handshake scheme instance is illustrated from a short VLR-GS with backward unlinkability. Our scheme implements an efficient revocation mechanism that also ensures traceability and unlinkability. In addition, the past behaviors of revoked users still keep confidential because of the mechanism of backward unlinkability. We also strengthen the communication protection between Group Authority (GA) and members, such that group members will not be forged by malicious GA. Compared with the previous secret handshake schemes, our scheme greatly reduces the communication and computation overhead, so it is especially suitable for mobile environments. Due to the difficulty of Decision Linear (DLIN) and q-Strong Diffie-Hellman (q-SDH) problems, our proposal can be proved to be secure under the random oracle model. © 2023, The Authors. All rights reserved.

关键词： Authentication

When Adversarial Example Attacks Meet Vertical Federated Learning

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When Adversarial Example Attacks Meet Vertical Federated Lea...

Symposia and Workshops on Ubiquitous, Autonomic and Trusted Computing, UIC-ATC

作者： Dan Meng Zhihui Fu Chao Kong Yue Qi Guitao Cao OPPO Research Institute Shenzhen China School of Computer and Information Anhui Polytechnic University Wuhu China Shanghai Key Laboratory of Trustworthy Computing East China Normal University Shanghai China MoE Engineering Research Center of SH/HW Co-design Technology and Application ECNU Shanghai China

Federated learning (FL) is proposed to enable efficient machine learning while protecting the privacy of user data. In non-federated scenarios, machine learning models are vulnerable to adversarial attacks. The attacker can construct elaborate adversarial examples, which can greatly reduce the performance of the victim model. It is worth exploring how FL models differ from traditional models in face of complex adversarial attacks. We are also curious about the robustness of different FL modeling methods against adversarial attacks, providing suggestions for selecting suitable FL models in practical applications. Consequently, we perform adversarial example attacks on vertical federated learning and design experiments to study how vertical federated models perform under adversarial example attacks. The simulation results show that federated models are more robust than non-federated models. Besides, in vertical federation learning, decision trees and XGBoost are much more robust than logistic regression and neural networks.

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Collaborative Knowledge Graph Fusion by Exploiting the Open Corpus

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arXiv 2022年

作者： Wang, Yue Wan, Yao Bai, Lu Cui, Lixin Xu, Zhuo Li, Ming Yu, Philip S. Hancock, Edwin R. Central University of Finance and Economics Beijing China Wuhan China School of Artificial Intelligence Beijing Normal University Beijing China The Key Laboratory of Intelligent Education Technology and Application of Zhejiang Province Zhejiang Normal University Jinhua China Department of Computer Science University of Illinois Chicago United States Department of Computer Science University of York United Kingdom

To alleviate the challenges of building Knowledge Graphs (KG) from scratch, a more general task is to enrich a KG using triples from an open corpus, where the obtained triples contain noisy entities and relations. It is challenging to enrich a KG with newly harvested triples while maintaining the quality of the knowledge representation. This paper proposes a system to refine a KG using information harvested from an additional corpus. To this end, we formulate our task as two coupled sub-tasks, namely join event extraction (JEE) and knowledge graph fusion (KGF). We then propose a Collaborative Knowledge Graph Fusion Framework to allow our sub-tasks to mutually assist one another in an alternating manner. More concretely, the explorer carries out the JEE supervised by both the ground-truth annotation and an existing KG provided by the supervisor. The supervisor then evaluates the triples extracted by the explorer and enriches the KG with those that are highly ranked. To implement this evaluation, we further propose a Translated Relation Alignment Scoring Mechanism to align and translate the extracted triples to the prior KG. Experiments verify that this collaboration can both improve the performance of the JEE and the KGF. Copyright © 2022, The Authors. All rights reserved.

关键词： Knowledge graph

Fuzzy Knowledge Distillation from High-Order TSK to Low-Order TSK

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arXiv 2023年

作者： Zhang, Xiongtao Yin, Zezong Jiang, Yunliang Jiang, Yizhang Sun, Danfeng Liu, Yong The Zhejiang Province Key Laboratory of Smart Management&Application of Modern Agricultural Resources Huzhou University Huzhou313000 China The School of Information Engineering Huzhou University Huzhou313000 China The School of Computer Science and Technology Zhejiang Normal University Jinhua321004 China The School of Artificial Intelligence and Computer Science Jiangnan University Wuxi214122 China Ministry of Education Wuhan430074 China The School of Computer Science Hangzhou Dianzi University Hangzhou310018 China The College of Control Science and Enginneering Zhejiang University Hangzhou310007 China

High-order Takagi-Sugeno-Kang (TSK) fuzzy classifiers possess powerful classification performance yet have fewer fuzzy rules, but always be impaired by its exponential growth training time and poorer interpretability owing to High-order polynomial used in consequent part of fuzzy rule, while Low-order TSK fuzzy classifiers run quickly with high interpretability, however they usually require more fuzzy rules and perform relatively not very well. Address this issue, a novel TSK fuzzy classifier embeded with knowledge distillation in deep learning called HTSK-LLM-DKD is proposed in this study. HTSK-LLM-DKD achieves the following distinctive characteristics: 1) It takes High-order TSK classifier as teacher model and Low-order TSK fuzzy classifier as student model, and leverages the proposed LLM-DKD (Least Learning Machine based Decoupling Knowledge Distillation) to distill the fuzzy dark knowledge from High-order TSK fuzzy classifier to Low-order TSK fuzzy classifier, which resulting in Low-order TSK fuzzy classifier endowed with enhanced performance surpassing or at least comparable to High-order TSK classifier, as well as high interpretability;specifically 2) The Negative Euclidean distance between the output of teacher model and each class is employed to obtain the teacher logits, and then it compute teacher/student soft labels by the softmax function with distillating temperature parameter;3) By reformulating the Kullback-Leibler divergence, it decouples fuzzy dark knowledge into target class knowledge and non-target class knowledge, and transfers them to student model. The advantages of HTSK-LLM-DKD are verified on the benchmarking UCI datasets and a real dataset Cleveland heart disease, in terms of classification performance and model interpretability. Copyright © 2023, The Authors. All rights reserved.

关键词： Distillation

FEDLAB: A FLEXIBLE FEDERATED LEARNING FRAMEWORK

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arXiv 2021年

作者： Zeng, Dun Liang, Siqi Hu, Xiangjing Wang, Hui Xu, Zenglin School of Computer Science and Engineering University of Electronic Science and Technology of China China Rich Media Big Data Analytics and Application Key Laboratory Shenzhen Research Institute The Chinese University of Hong Kong Hong Kong School of Computer Science and Technology Harbin Institute of Technology Shenzhen China Peng Cheng Laboratory Shenzhen China

Federated learning (FL) is a machine learning field in which researchers try to facilitate model learning process among multiparty without violating privacy protection regulations. Considerable effort has been invested in FL optimization and communication related researches. In this work, we introduce FedLab, a lightweight open-source framework for FL simulation. The design of FedLab focuses on FL algorithm effectiveness and communication efficiency. Also, FedLab is scalable in different deployment scenario. We hope FedLab could provide flexible API as well as reliable baseline implementations, and relieve the burden of implementing novel approaches for researchers in FL community. The source code is available at https://***/SMILELab-FL/FedLab. © 2021, CC BY.

关键词： Learning systems

Data-Driven Fast Real-Time Flood Forecasting Model for Processing Concept Drift 1

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9th EAI International Conference on Cloud Computing, CloudComp 2019 and the 4th EAI International Conference on Smart Grid and Innovative Frontiers in Telecommunications, SmartGIFT 2019

作者： Yan, Le Feng, Jun Wu, Yirui Hang, Tingting Computer and Information College Hohai University Nanjing211100 China National Key Lab for Novel Software Technology Nanjing University Nanjing China Key Laboratory of Unmanned Aerial Vehicle Development & Data Application of Anhui Higher Education Institutes Wanjiang University of Technology Maanshan243031 China

ISBN: (数字)9783030485139

ISBN: (纸本)9783030485122

The hydrological data of small and medium watershed develops with the passage of time. The rainfall-runoff patterns in these data often develop over time, and the models established for the analysis of such data will soon not be applicable. In view of the problem that adaptability and accuracy of the existing data-driven flood real-time forecasting model in medium and small watershed with concept drift. We update the data-driven model using incremental training based on support vector machine (SVM) and gated recurrent unit (GRU) model respectively. According to the rapid real-time flood forecasting test results of the Tunxi watershed, Anhui Province, China, the fast real-time flood forecast data-driven model with incremental update can more accurately predict the moment when the flood begins to rise and the highest point of flood stream-flow, and it is an effective tool for real-time flood forecasting in small and medium watersheds. © 2020, ICST Institute for computer Sciences, Social Informatics and Telecommunications Engineering.

关键词： Floods