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2021 IEEE Globecom Workshops, GC Wkshps 2021

作者： Yazdinejad, Abbas Rabieinejad, Elnaz Dehghantanha, Ali Parizi, Reza M. Srivastava, Gautam University of Guelph Cyber Science Lab School of Computer Science ON Canada K.N.Toosi University of Technology Information Technology Department Tehran Iran Kennesaw State University College of Computing and Software Engineering GA United States Brandon University Department of Mathematics and Computer Science MB Canada

ISBN: (纸本)9781665423908

With the advancement of information and communication technology, Unmanned Aerial Vehicles (UAV), popularly known as drones, have also increased. The drones have been noted for their wide range of applications such as military, search and rescue operation, disaster detection and monitoring, agriculture, and delivery. Each type of drone has different characteristics and functionality based on its application, making them a security threat for some city zone. Therefore, there is an essential need for efficient drone management based on their type and application in different zones. To do this, we proposed a Machine learning (ML) based software Defined Network (SDN) drone management framework. In this framework, the SDN controller uses ML with the drone's radio frequency feature to detect its type and application and, according to its application, authenticate it and assign communication rules. SDN controller records authentication information in a DAG-based Distributed Ledger Technology (DLT) available for other SDN controllers. When a drone desires to migrate to another zone, the destination SDN controller can achieve authentication information by referring to DAG-based DLT, and there is no need for re-authentication. The experimental result shows authentication delay reduction in our proposed framework. Moreover, we adopted ML algorithms includes Decision Tree (DT), Random Forest (RF), Support Vector Machine (SVM), and Logistic Regression (LR), to evaluate our proposed framework in drone's type classification. The result shows that the RF algorithm shows the best performance with 92.81% accuracy in the classification of the drone's type. © 2021 IEEE.

关键词： Drones

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Multi-connectivity Enabled User-centric Association in Ultra-Dense mmWave Communication Networks

Multi-connectivity Enabled User-centric Association in Ultra...

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IEEE Conference on Vehicular Technology (VTC)

作者： Qing Xue Renlong Wei Shaodan Ma Yongjun Xu Li Yan Xuming Fang School of Communication and Information Engineering Chongqing University of Posts and Telecommunications Chongqing China State Key Laboratory of Internet of Things for Smart City University of Macau Macao SAR China Department of Electrical and Computer Engineering University of Macau Macao SAR China Key Lab of Information Coding & Transmission Southwest Jiaotong University Chengdu China

Signals over millimeter wave (mmWave) bands suffer from severe path loss and are easily blocked by obstacles, which greatly degrades the link quality and reliability of mmWave communications. One of the promising ways to overcome this challenge is multi-connectivity, which enables a user to associate with multiple small cells simultaneously. In this paper, we investigate the association problem of a given user to multiple mmWave base stations (mBSs), which we termed user-centric association. In particular, we consider an intelligent reflecting surface (IRS)-aided ultra-dense mmWave communication system, in which multiple distributed IRSs are deployed to expand the coverage of mmWave signals to blind spots. The user-centric association problem is formulated to maximize the user achievable sum-rate with respect to the mBS-user association, auxiliary IRS selection, and power allocation in a combinatorial manner. The original optimization problem is a mixed-integer nonlinear programming problem, which is NP-hard. To solve it, we first relax the formulated problem into a continuous one, then decouple it into three subproblems by utilizing decomposition technique, and finally propose an alternating iteration based algorithm to obtain the optimal solution. Numerical simulations show that the user sum-rate can be greatly improved by the joint optimization scheme.

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Joint Beamforming Design for Cooperative Double-RIS Aided mmWave Multi-User MIMO Communications

Joint Beamforming Design for Cooperative Double-RIS Aided mm...

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IEEE Conference on Vehicular Technology (VTC)

作者： Renlong Wei Qing Xue Yongjun Xu Li Yan Shaodan Ma School of Communications and Information Engineering Chongqing University of Posts and Telecommunications Chongqing China State Key Laboratory of Internet of Things for Smart City University of Macau Macao SAR China Key Lab of Information Coding & Transmission Southwest Jiaotong University Chengdu China Department of Electrical and Computer Engineering University of Macau Macao SAR China

To alleviate the blockage effect and explore the potential of reconfigurable intelligent surface (RIS) assisted communication, we investigate the cooperative double-RIS assisted multi-user millimeter wave (mmWave) multiple-input multiple-output (MIMO) communications. To improve system performance, we jointly optimize the transmit beamforming matrix at the base station and the phase shift matrices at RISs to maximize the system sum rate, which is an intractable non-convex problem. To solve the problem, we propose an efficient alternating optimization algorithm based on the techniques of weighted minimum mean square error (WMMSE), Lagrange multiplier and majorization-minimization (MM). Simulation results validate the effectiveness of the proposed algorithm, as well as the superiority of double-RIS in improving system performance.

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Late Breaking Results: An Effective Legalization Algorithm for Heterogeneous FPGAs with Complex Constraints 21

Late Breaking Results: An Effective Legalization Algorithm f...

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Proceedings of the 58th Annual ACM/IEEE Design Automation Conference

作者： Zhipeng Huang Haokai Sun Huimin Wang Ziran Zhu Jun Yu Jianli Chen Center for Discrete Mathematics and Theoretical Computer Science Fuzhou University Fuzhou China Software Engineering Institute East China Normal University Shanghai China State Key Lab of ASIC & System Fudan University Shanghai China National ASIC System Engineering Center School of Electronic Science and Engineering Southeast University Nanjing China Center for Discrete Mathematics and Theoretical Computer Science Fuzhou University Fuzhou China and State Key Lab of ASIC & System Fudan University Shanghai China

ISBN: (纸本)9781665432740

The modern FPGA placement problem has become much more challenging than ever with various emerging design constraints, such as the location (including relative location (RLOC) and location range) and chain constraints, which have not been considered in the literature. In this paper, we propose a combinatorial algorithm for FPGA legalization with location and chain constraints. We first identify a virtual range to cluster an instance with the RLOC constraint and formulate minimum cost integer linear programming. Besides, we use an adaptive algorithm to deal with the chain-aware legalization problem for better quality and runtime trade-offs. Finally, a legalization algorithm based on minimum cost maximum flow (MCMF) is used to improve the solution quality further. Compared with the state-of-the-art work, experimental results show that our proposed algorithm can achieve respectively 4.4% and 4.9% smaller average and maximum movements, 1.7% smaller routed wirelength, and 7.6% shorter routing runtime.

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Mortar: Morphing the Bit Level Sparsity for General Purpose Deep Learning Acceleration

Mortar: Morphing the Bit Level Sparsity for General Purpose ...

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Asia and South Pacific Design Automation Conference

作者： Yunhung Gao Hongyan Li Kevin Zhang Xueru Yu Hang Lu School of Electronics Engineering and Computer Science Peking University Beijing China State Key Lab of Computer Architecture Institute of Computing Technology CAS University of Chinese Academy of Sciences Beijing China Shanghai Integrated Circuits R&D Center Co. Ltd Shanghai China

Vanilla Deep Neural Networks (DNN) after training are represented with native floating-point 32 (fp32) weights. We observe that the bit-level sparsity of these weights is very abundant in the mantissa and can be directly exploited to speed up model inference. In this paper, we propose Mortar, an off-line/on-line collaborated approach for fp32 DNN acceleration, which includes two parts: first, an off-line bit sparsification algorithm to construct the target formulation by “mantissa morphing”, which maintains higher model accuracy while increasing bit-level sparsity; second, the associating hardware accelerator architecture to speed up the on-line fp32 inference through manipulating the enlarged bit sparsity. We highlight the following results by evaluating various deep learning tasks, including image classification, object detection, video understanding, video & image super-resolution, etc.: We (1) increase bit-level sparsity up to 1.28~2.51x with only a negligible -0.09~0.23% accuracy loss, (2) maintain on average 3.55% higher model accuracy while increasing more bit-level sparsity than the baseline, (3)and our hardware accelerator outperforms up to 4.8x over the baseline, with an area of 0.031 mm 2 and power of 68.58mW.

关键词： Deep learning Training Mortar Superresolution software algorithms Inference algorithms software

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Block-Wise Index Modulation and Receiver Design for High-Mobility OTFS Communications

arXiv

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arXiv 2023年

作者： Qian, Mi Ji, Fei Ge, Yao Wen, Miaowen Cheng, Xiang Poor, H. Vincent The School of Electronic and Information Engineering South China University of Technology Guangzhou510641 China The Continental-NTU Corporate Lab Nanyang Technological University Singapore The State Key Laboratory of Advanced Optical Communication Systems and Networks Department of Electronics School of Electronics Engineering and Computer Science Peking University Beijing100871 China The Department of Electrical and Computer Engineering Princeton University PrincetonNJ08544 United States

As a promising technique for high-mobility wireless communications, orthogonal time frequency space (OTFS) has been proved to enjoy excellent advantages with respect to traditional orthogonal frequency division multiplexing (OFDM). Although multiple studies have considered index modulation (IM) based OTFS (IM-OTFS) schemes to further improve system performance, a challenging and open problem is the development of effective IM schemes and efficient receivers for practical OTFS systems that must operate in the presence of channel delays and Doppler shifts. In this paper, we propose two novel block-wise IM schemes for OTFS systems, named delay-IM with OTFS (DeIM-OTFS) and Doppler-IM with OTFS (DoIM-OTFS), where a block of delay/Doppler resource bins are activated simultaneously. Based on a maximum likelihood (ML) detector, we analyze upper bounds on the average bit error rates for the proposed DeIM-OTFS and DoIM-OTFS schemes, and verify their performance advantages over the existing IM-OTFS systems. We also develop a multi-layer joint symbol and activation pattern detection (MLJSAPD) algorithm and a customized message passing detection (CMPD) algorithm for our proposed DeIM-OTFS and DoIM-OTFS systems with low complexity. Simulation results demonstrate that our proposed MLJSAPD and CMPD algorithms can achieve desired performance with robustness to the imperfect channel state information (CSI). © 2023, CC BY.

关键词： Message passing

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Optimizing execution for pipelined-based distributed deep learning in a heterogeneously networked GPU cluster

Optimizing execution for pipelined-based distributed deep le...

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作者： Zhang, Jinghui Zhan, Jun Li, Jiange Jin, Jiahui Qian, Lei School of Computer Science and Engineering Southeast University Nanjing China State Key Lab of Mathematical Engineering and Advanced Computing Wuxi China

Exorbitant resources (computing and memory) are required to train a deep neural network (DNN). Often researchers deploy an approach that uses distributed parallel training to acquire larger models faster on GPUs. This approach has its detriments, though;on one hand, a GPU's expanded capacity to compute also produces bigger bottlenecks in inter-GPU's communications during model training, and multi-GPU systems lead to complex connectivity. Workload schedulers then end up having to consider hardware topology and requirements for workload communication, in hopes of allocating GPU resources to optimize execution time and improve usage in a heterogeneous environment. On the other hand, the high memory requirements to train a DNN model make running the training processes on GPUs onerous. To contend with this, we introduce two execution optimization methods based on pipeline-hybrid parallelism (using both data and model parallelism) in a GPU cluster with heterogeneous networking. First, we propose a model partition algorithm that accelerates pipeline-hybrid parallelism training between heterogeneously network-connected GPUs. Second, we introduce a cost-balanced recomputing algorithm to reduce memory usage in the pipeline mode. Experiments show that our solution (Pipe-Torch) averages a speedup of 1.4× compared with data parallelism, and reduces the memory footprint while maintaining pipelined load-balanced training. © 2020 John Wiley & Sons Ltd

关键词： Pipelines

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Multi-Head Feature Pyramid Networks for Breast Mass Detection

Multi-Head Feature Pyramid Networks for Breast Mass Detectio...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Hexiang Zhang Zhenghua Xu Dan Yao Shuo Zhang Junyang Chen Thomas Lukasiewicz State Key Laboratory of Reliability and Intelligence of Electrical Equipment School of Health Sciences and Biomedical Engineering Hebei University of Technology Tianjin China College of Computer Science and Software Engineering and Guangdong Laboratory of Artificial Intelligence and Digital Economy (SZ) Shenzhen University Shenzhen China Institute of Logic and Computation TU Wien Vienna Austria Department of Computer Science University of Oxford Oxford United Kingdom

Analysis of X-ray images is one of the main tools to diagnose breast cancer. The ability to quickly and accurately detect the location of masses from the huge amount of image data is the key to reducing the morbidity and mortality of breast cancer. Currently, the main factor limiting the accuracy of breast mass detection is the unequal focus on the mass boxes, leading the network to focus too much on larger masses at the expense of smaller ones. In the paper, we propose the multi-head feature pyramid module (MHFPN) to solve the problem of unbalanced focus of target boxes during feature map fusion and design a multi-head breast mass detection network (MBMDnet). Experimental studies show that, comparing to the SOTA detection baselines, our method improves by 6.58% (in AP@50) and 5.4% (in TPR@50) on the commonly used IN-breast dataset, while about 6-8% improvements (in AP@20) are also observed on the public MIAS and BCS-DBT datasets.

关键词： Limiting Feature extraction Breast cancer Acoustics Speech processing X-ray imaging

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Iterative Poisson Surface Reconstruction (iPSR) for Unoriented Points

arXiv

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arXiv 2022年

作者： Hou, Fei Wang, Chiyu Wang, Wencheng Qin, Hong Qian, Chen He, Ying State Key Laboratory of Computer Science Institute of Software Chinese Academy of Sciences University of Chinese Academy of Sciences Beijing China Department of Computer Science Stony Brook University New York United States SenseTime Research Tetras.AI Beijing China School of Computer Science and Engineering S-Lab Nanyang Technological University Singapore

Poisson surface reconstruction (PSR) remains a popular technique for reconstructing watertight surfaces from 3D point samples thanks to its efficiency, simplicity, and robustness. Yet, the existing PSR method and subsequent variants work only for oriented points. This paper intends to validate that an improved PSR, called iPSR, can completely eliminate the requirement of point normals and proceed in an iterative manner. In each iteration, iPSR takes as input point samples with normals directly computed from the surface obtained in the preceding iteration, and then generates a new surface with better quality. Extensive quantitative evaluation confirms that the new iPSR algorithm converges in 5-30 iterations even with randomly initialized normals. If initialized with a simple visibility based heuristic, iPSR can further reduce the number of iterations. We conduct comprehensive comparisons with PSR and other powerful implicit-function based methods. Finally, we confirm iPSR's effectiveness and scalability on the AIM@SHAPE dataset and challenging (indoor and outdoor) scenes. Code and data for this paper are at https://***/houfei0801/ipsr. © 2022, CC BY-NC-SA.

关键词： Iterative methods

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MvCo-DoT: Multi-View Contrastive Domain Transfer Network for Medical Report Generation

MvCo-DoT: Multi-View Contrastive Domain Transfer Network for...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Ruizhi Wang Xiangtao Wang Zhenghua Xu Wenting Xu Junyang Chen Thomas Lukasiewicz State Key Laboratory of Reliability and Intelligence of Electrical Equipment School of Health Sciences and Biomedical Engineering Hebei University of Technology Tianjin China College of Computer Science and Software Engineering and Guangdong Laboratory of Artificial Intelligence and Digital Economy (SZ) Shenzhen University Shenzhen China Institute of Logic and Computation TU Wien Vienna Austria Department of Computer Science University of Oxford Oxford United Kingdom

In clinical scenarios, multiple medical images with different views are usually generated at the same time, and they have high semantic consistency. However, the existing medical report generation methods cannot exploit the rich multi-view mutual information of medical images. Therefore, in this work, we propose the first multi-view medical report generation model, called MvCo-DoT. Specifically, MvCo-DoT first propose a multi-view contrastive learning (MvCo) strategy to help the deep reinforcement learning based model utilize the consistency of multi-view inputs for better model learning. Then, to close the performance gaps of using multi-view and single-view inputs, a domain transfer network is further proposed to ensure MvCo-DoT achieve almost the same performance as multi-view inputs using only single-view inputs. Extensive experiments on the IU X-Ray public dataset show that MvCo-DoT outperforms the SOTA medical report generation baselines in all metrics.

关键词： Deep learning Training Semantics Reinforcement learning Signal processing Task analysis Speech processing

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