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arXiv 2023年

作者： Ma, Zhe Wu, Wen Gao, Feifei Shen, Xuemin The Institute for Artificial Intelligence Tsinghua University State Key Lab of Intelligent Technologies and Systems Beijing National Research Center for Information Science and Technology Department of Automation Tsinghua University Beijing100084 China The Frontier Research Center Peng Cheng Laboratory Guangdong Shenzhen518055 China The Department of Electrical and Computer Engineering University of Waterloo WaterlooONN2L 3G1 Canada

In this paper, we investigate the joint device activity and data detection in massive machine-type communications (mMTC) with a one-phase non-coherent scheme, where data bits are embedded in the pilot sequences and the base station simultaneously detects active devices and their embedded data bits without explicit channel estimation. Due to the correlated sparsity pattern introduced by the noncoherent transmission scheme, the traditional approximate message passing (AMP) algorithm cannot achieve satisfactory performance. Therefore, we propose a deep learning (DL) modified AMP network (DL-mAMPnet) that enhances the detection performance by effectively exploiting the pilot activity correlation. The DL-mAMPnet is constructed by unfolding the AMP algorithm into a feedforward neural network, which combines the principled mathematical model of the AMP algorithm with the powerful learning capability, thereby benefiting from the advantages of both techniques. Trainable parameters are introduced in the DL-mAMPnet to approximate the correlated sparsity pattern and the large-scale fading coefficient. Moreover, a refinement module is designed to further advance the performance by utilizing the spatial feature caused by the correlated sparsity pattern. Simulation results demonstrate that the proposed DL-mAMPnet can significantly outperform traditional algorithms in terms of the symbol error rate performance. © 2023, CC BY-NC-SA.

关键词： Message passing

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Distributed Online Convex Optimization with Adversarial Constraints: Reduced Cumulative Constraint Violation Bounds under Slater’s Condition

arXiv

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arXiv 2023年

作者： Yi, Xinlei Li, Xiuxian Yang, Tao Xie, Lihua Hong, Yiguang Chai, Tianyou Johansson, Karl H. Lab for Information & Decision Systems Massachusetts Institute of Technology CambridgeMA02139 United States Department of Control Science and Engineering College of Electronics and Information Engineering Tongji University Shanghai201804 China Shanghai Research Institute for Intelligent Autonomous Systems China The State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang110819 China School of Electrical and Electronic Engineering Nanyang Technological University 50 Nanyang Avenue Singapore639798 Singapore Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Sweden Digital Futures Stockholm10044 Sweden

This paper considers distributed online convex optimization with adversarial constraints. In this setting, a network of agents makes decisions at each round, and then only a portion of the loss function and a coordinate block of the constraint function are privately revealed to each agent. The loss and constraint functions are convex and can vary arbitrarily across rounds. The agents collaborate to minimize network regret and cumulative constraint violation. A novel distributed online algorithm is proposed and it achieves an O(T max{c,1−c}) network regret bound and an O(T 1−c/2) network cumulative constraint violation bound, where T is the number of rounds and c ∈ (0, 1) is a user-defined trade-off parameter. When Slater’s condition holds (i.e, there is a point that strictly satisfies the inequality constraints), the network cumulative constraint violation bound is reduced to O(T 1−c). Moreover, if the loss functions are strongly convex, then the network regret bound is reduced to O(log(T)), and the network cumulative constraint violation bound is reduced to O(√log(T)T) and O(log(T)) without and with Slater’s condition, respectively. To the best of our knowledge, this paper is the first to achieve reduced (network) cumulative constraint violation bounds for (distributed) online convex optimization with adversarial constraints under Slater’s condition. Finally, the theoretical results are verified through numerical simulations. Copyright © 2023, The Authors. All rights reserved.

关键词： Economic and social effects

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Ameliorated Golden Jackal Optimization (Agjo) with Enhanced Movement and Multi-Angle Position Updating Strategy

SSRN

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SSRN 2023年

作者： Wahab, Magd Abdel Bai, Jianfu Khatir, Samir Abualigah, Laith Soete Laboratory Department of Electrical Energy Metals Mechanical Constructions and Systems Faculty of Engineering and Architecture Ghent University Belgium Computer Science Department Prince Hussein Bin Abdullah Faculty for Information Technology Al al-Bayt University Mafraq25113 Jordan Department of Electrical and Computer Engineering Lebanese American University Byblos13-5053 Lebanon Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman19328 Jordan MEU Research Unit Middle East University Amman11831 Jordan Applied Science Research Center Applied Science Private University Amman11931 Jordan School of Computer Sciences Universiti Sains Malaysia Pulau Pinang11800 Malaysia School of Engineering and Technology Sunway University Malaysia Petaling Jaya27500 Malaysia College of Engineering Yuan Ze University Taiwan

Golden jackal optimization (GJO), a lately published meta-heuristic optimization algorithm, is inspired by the foraging behavior of pairs of golden jackals and shows an acceptable optimization performance. However, GJO exists a shortage in balancing exploration and exploitation, as it completely focused on exploitation in the later iterations. This study proposes a new variant of GJO, named Ameliorated Golden jackal optimization (AGJO). AGJO uses three strategies to alleviate the imbalance between the exploration and exploitation of GJO: the enhanced movement strategy, the global search strategy, and the multi-angle position update strategy for prey. An environmental disturbance factor is added to the third strategy to strengthen GJO's ability to evade the local optimal solution. The performance of AGJO is tested and compared with GJO and seven well-known meta-heuristic algorithms for 23 classical benchmark functions, CEC 2017 and CEC 2019. These results determined that AGJO performs better over 90% of these functions compared to GJO. AGJO is also highly competitive in terms of optimization capability and convergence speed compared with other algorithms. Finally, AGJO is applied to optimize five engineering design problems with constraints. Results show that AGJO is a more potential algorithm for solving real problems with constraints than GJO and other algorithms. © 2023, The Authors. All rights reserved.

关键词： Optimization

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RDA: An Accelerated Collision Free motion Planner for Autonomous Navigation in Cluttered Environments

arXiv

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arXiv 2022年

作者： Han, Ruihua Wang, Shuai Wang, Shuaijun Zhang, Zeqing Zhang, Qianru Eldar, Yonina C. Hao, Qi Pan, Jia The Department of Computer Science and Engineering Southern University of Science and Technology Guangdong Shenzhen China The Department of Computer Science The University of Hong Kong Hong Kong Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Guangdong Shenzhen China The Department of Computer Science and Engineering Harbin Institute of Technology Guangdong Shenzhen China The Weizmann Institute of Science Rehovot Israel The Department of Computer Science and Engineering The Shenzhen Key Laboratory of Robotics and Computer Vision The Sifakis Research Institute for Trustworthy Autonomous Systems Southern University of Science and Technology Guangdong Shenzhen China

Autonomous motion planning is challenging in multi-obstacle environments due to nonconvex collision avoidance constraints. Directly applying numerical solvers to these nonconvex formulations fails to exploit the constraint structures, resulting in excessive computation time. In this paper, we present an accelerated collision-free motion planner, namely regularized dual alternating direction method of multipliers (RDADMM or RDA for short), for the model predictive control (MPC) based motion planning problem. The proposed RDA addresses nonconvex motion planning via solving a smooth biconvex reformulation via duality and allows the collision avoidance constraints to be computed in parallel for each obstacle to reduce computation time significantly. We validate the performance of the RDA planner through path-tracking experiments with car-like robots in both simulation and real-world settings. Experimental results show that the proposed method generates smooth collision-free trajectories with less computation time compared with other benchmarks and performs robustly in cluttered environments. The source code is available at https://***/hanruihua/ RDA_planner. Copyright © 2022, The Authors. All rights reserved.

关键词： motion planning

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Specialty optical fibers for advanced sensing applications

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Opto-Electronic Science 2023年第2期2卷 1-26页

作者： Huanhuan Liu Dora Juan Juan Hu Qizhen Sun Lei Wei Kaiwei Li Changrui Liao Bozhe Li Cong Zhao Xinyong Dong Yuhan Tang Yihong Xiao Gerd Keiser Perry Ping Shum Guangdong Key Laboratory of Integrated Optoelectronics Intellisense Department of Electronic and Electrical EngineeringSouthern University of Science and TechnologyShenzhen 518055China Institute for Infocomm Research(I2R) Agency for ScienceTechnology and Research(A*STAR)1 Fusionopolis Way#21-01Connexis South TowerSingapore 138632Singapore School of Electrical and Electronic Engineering Nanyang Technological University50 Nanyang AvenueSingapore 639798Singapore School of Optical and Electronic Information National Engineering Laboratory for Next Generation Internet Access SystemWuhan National Laboratory for OptoelectronicsHuazhong University of Science and TechnologyWuhan 430074China Key Laboratory of Bionic Engineering of Ministry of Education Jilin UniversityChangchun 130022China Key Laboratory of Optoelectronic Devices and Systems of Ministry of Education/Guangdong Province College of Physics and Optoelectronic EngineeringShenzhen UniversityShenzhen 518060China Shenzhen Key Laboratory of Photonic Devices and Sensing Systems for Internet of Things Guangdong and Hong Kong Joint Research Centre for Optical Fibre SensorsShenzhen UniversityShenzhen 518060China Guangdong Laboratory of Artificial Intelligence and Digital Economy(SZ) Shenzhen 518107China Guangdong Provincial Key Laboratory of Information Photonics Technology School of Information EngineeringGuangdong University of TechnologyGuangzhou 510006China Department of Electrical&Computer Engineering Boston UniversityBoston 02215USA Pengcheng Laboratory Shenzhen 518055China

Optical fiber technology has changed the world by enabling extraordinary growth in world-wide communications and *** rapid development and wide deployment of optical fiber sensors are driven by their excellent sensing performance with outstanding flexibility,functionality,and ***,the research on specialty optical fibers is playing a critical role in enabling and proliferating the optical fiber sensing *** paper overviews recent developments in specialty optical fibers and their sensing *** specialty optical fibers are reviewed based on their innovations in special structures,special materials,and technologies to realize lab in/on a *** overview of sensing applications in various fields is *** prospects and emerging research areas of specialty optical fibers are also discussed.

关键词： specialty optical fibers photonic crystal fiber multifunctional multi-material fibers lab in/on fiber

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EFaR 2023: Efficient Face Recognition Competition

EFaR 2023: Efficient Face Recognition Competition

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IEEE International Joint Conference on Biometrics (IJCB)

作者： Jan Niklas Kolf Fadi Boutros Jurek Elliesen Markus Theuerkauf Naser Damer Mohamad Alansari Oussama Abdul Hay Sara Alansari Sajid Javed Naoufel Werghi Klemen Grm Vitomir Štruc Fernando Alonso-Fernandez Kevin Hernandez Diaz Josef Bigun Anjith George Christophe Ecabert Hatef Otroshi Shahreza Ketan Kotwal Sébastien Marcel Iurii Medvedev Bo Jin Diogo Nunes Ahmad Hassanpour Pankaj Khatiwada Aafan Ahmad Toor Bian Yang Fraunhofer Institute for Computer Graphics Research IGD Germany TU Darmstadt Germany Department of Electrical and Computer Engineering Khalifa University Abu Dhabi United Arab Emirates Laboratory for Machine Intelligence Faculty of Electrical Engineering University of Ljubljana Slovenia Halmstad University Sweden Idiap Research Institute Martigny Switzerland École Polytechnique Fédérale de Lausanne (EPFL) Lausanne Switzerland Université de Lausanne (UNIL) Lausanne Switzerland Institute of Systems and Robotics University of Coimbra Coimbra Portugal Department of Information Security and Communication Technology eHealth and Welfare Security Group Norwegian University of Science and Technology Norway

This paper presents the summary of the Efficient Face Recognition Competition (EFaR) held at the 2023 International Joint Conference on Biometrics (IJCB 2023). The competition received 17 submissions from 6 different teams. To drive further development of efficient face recognition models, the submitted solutions are ranked based on a weighted score of the achieved verification accuracies on a diverse set of benchmarks, as well as the deployability given by the number of floating-point operations and model size. The evaluation of submissions is extended to bias, cross-quality, and large-scale recognition benchmarks. Overall, the paper gives an overview of the achieved performance values of the submitted solutions as well as a diverse set of baselines. The submitted solutions use small, efficient network architectures to reduce the computational cost, some solutions apply model quantization. An outlook on possible techniques that are underrepresented in current solutions is given as well.

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SPP-CNN: An Efficient Framework for Network Robustness Prediction

arXiv

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arXiv 2023年

作者： Wu, Chengpei Lou, Yang Wang, Lin Li, Junli Li, Xiang Chen, Guanrong The College of Computer Science Sichuan Normal University Chengdu610066 China The Key Laboratory of System Control and Information Processing Ministry of Education Shanghai200240 China The Department of Computer Science National Yang Ming Chiao Tung University Hsinchu300 Taiwan The Department of Automation Shanghai Jiao Tong University Shanghai200240 China The Institute of Complex Networks and Intelligent Systems Shanghai Research Institute for Intelligent Autonomous Systems Tongji University Shanghai201210 China The Department of Control Science and Engineering Tongji University Shanghai200240 China The Department of Electrical Engineering City University of Hong Kong Hong Kong

This paper addresses the robustness of a network to sustain its connectivity and controllability against malicious attacks. This kind of network robustness is typically measured by the time-consuming attack simulation, which returns a sequence of values that record the remaining connectivity and controllability after a sequence of node- or edge-removal attacks. For improvement, this paper develops an efficient framework for network robustness prediction, the spatial pyramid pooling convolutional neural network (SPP-CNN). The new framework installs a spatial pyramid pooling layer between the convolutional and fully-connected layers, overcoming the common mismatch issue in the CNN-based prediction approaches and extending its generalizability. Extensive experiments are carried out by comparing SPP-CNN with three state-of-the-art robustness predictors, namely a CNN-based and two graph neural networks-based frameworks. Synthetic and real-world networks, both directed and undirected, are investigated. Experimental results demonstrate that the proposed SPP-CNN achieves better prediction performances and better generalizability to unknown datasets, with significantly lower time-consumption, than its counterparts. © 2023, CC BY.

关键词： Complex networks

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2 kV, 0.7 mΩ•cm2 Vertical Ga2O3 Superjunction Schottky Rectifier with Dynamic Robustness

2 kV, 0.7 mΩ•cm2 Vertical Ga2O3 Superjunction Schottky Rec...

引用

International Electron Devices Meeting (IEDM)

作者： Yuan Qin Matthew Porter Ming Xiao Zhonghao Du Hongming Zhang Yunwei Ma Joseph Spencer Boyan Wang Qihao Song Kohei Sasaki Chia-Hung Lin Ivan Kravchenko Dayrl P Briggs Dale K Hensley Marko Tadjer Han Wang Yuhao Zhang Center for Power Electronics Systems (CPES) Virginia Polytechnic Institute and State University Blacksburg VA USA Ming Hsieh Department of Electrical and Computer Engineering University of Southern California Los Angeles CA USA U.S. Naval Research Laboratory Washington DC USA Novel Crystal Technology Sayama Japan Oak Ridge National Laboratory Oak Ridge TN USA

We report the first experimental demonstration of a vertical superjunction device in ultra-wide bandgap (UWBG) Ga 2 O 3 . The device features 1.8 μm wide, 2×10 17 cm -3 doped n-Ga 2 O 3 pillars wrapped by the charge-balanced p-type nickel oxide (NiO). The sidewall NiO is sputtered through a novel self-align process. Benefited from the high doping in Ga 2 O 3 , the superjunction Schottky barrier diode (SJ-SBD) achieves a ultra-low specific on-resistance (R ON,SP ) of 0.7 mΩ•cm 2 with a low turn-on voltage of 1 V and high breakdown voltage (BV) of 2000 V. The R ON,SP ~BV trade-off is among the best in all WBG and UWBG power SBDs. The device also shows good thermal stability with BV > 1.8 kV at 175 °C. In the unclamped inductive switching tests, the device shows a dynamic BV of 2.2 kV and no degradation under 1.7 kV repetitive switching, verifying the fast acceptor depletion in NiO under dynamic switching. Such high-temperature and switching robustness are reported for the first time in a heterogeneous superjunction. These results show the great potential of UWBG superjunction power devices.

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Silicon photonics for high-capacity data communications

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Photonics research 2022年第9期10卷 I0002-I0030页

作者： YAOCHENG SHI YONG ZHANG YATING WAN YU YU YUGUANG ZHANG XIAO HU XI XIAO HONGNAN XU LONG ZHANG BINGCHENG PAN Centre for Optical and Electromagnetic Research State Key Laboratory for Modern Optical InstrumentationCollege of Optical Science and EngineeringZhejiang UniversityZijingang CampusHangzhou 310058China State Key Laboratory of Advanced Optical Communication Systems and Networks Department of Electronic EngineeringShanghai Jiao Tong UniversityShanghai 200240China Institute for Energy Efficiency University of California Santa BarbaraSanta BarbaraCalifornia 93106USA Electrical and Computer Engineering Department King Abdullah University of Science and Technology(KAUST)Thuwal 23955-6900Saudi Arabia Wuhan National Laboratory for Optoelectronics&School of Optical and Electronic Information Huazhong University of Science and TechnologyWuhan 430074China National Information Optoelectronics Innovation Center China Information and Communication Technologies Group Corporation(CICT)Wuhan 430074China State Key Laboratory of Optical Communication Technologies and Networks China Information and Communication Technologies Group Corporation(CICT)Wuhan 430074China Department of Electronic Engineering The Chinese University of Hong KongShatinNew TerritoriesHong KongChina

In recent years,optical modulators,photodetectors,(de)multiplexers,and heterogeneously integrated lasers based on silicon optical platforms have been *** performance of some devices even surpasses the traditional III-V and photonic integrated circuit(PIC)platforms,laying the foundation for large-scale photonic *** photonic technology can overcome the limitations of traditional transceiver technology in high-speed transmission networks to support faster interconnection between data *** this article,we will review recent progress for silicon *** first part gives an overview of recent achievements in silicon *** second part introduces the silicon photonic building blocks,including low-loss waveguides,passive devices,modulators,photodetectors,heterogeneously integrated lasers,and so *** the third part,the recent progress on high-capacity silicon photonic transceivers is *** the fourth part,we give a review of high-capacity silicon photonic networks on chip.

关键词： interconnection passive networks

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Model-Based Reinforcement Learning for Quantized Federated Learning Performance Optimization

Model-Based Reinforcement Learning for Quantized Federated L...

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GLOBECOM 2022 - 2022 IEEE Global Communications Conference

作者： Nuocheng Yang Sihua Wang Mingzhe Chen Christopher G. Brinton Changchuan Yin Walid Saad Shuguang Cui Beijing Laboratory of Advanced Information Network Beijing University of Posts and Telecommunications Beijing China State Key Laboratory Of Networking And Switching Technology Beijing University of Posts and Telecommunications Beijing China Department of Electrical and Computer Engineering Institute for Data Science and Computing University of Miami Coral Gables FL USA School of Electrical and Computer Engineering Purdue University West Lafayette IN USA Bradley Department of Electrical and Computer Engineering Virginia Tech Arlington VA USA Shenzhen Research Institute of Big Data (SRIBD) and the Future Network of Intelligence Institute (FNii) Chinese University of Hong Kong Shenzhen China

ISBN: (纸本)9781665435413

This paper considers improving wireless communication and computation efficiency in federated learning (FL) via model quantization. In the proposed bitwidth FL scheme, edge devices train and transmit quantized versions of their local FL model parameters to a coordinating server, which, in turn, aggregates them into a quantized global model and synchronizes the devices. With the goal of jointly determining the set of participating devices in each training iteration and the bitwidths employed at the devices, we pose an optimization problem for minimizing the training loss of quantized FL under a device sampling budget and delay requirement. Our analytical results show that the improvement of FL training loss between two consecutive iterations depends on not only the device selection and quantization scheme, but also on several parameters inherent to the model being learned. As a result, we propose, a model-based reinforcement learning (RL) method to optimize action selection over iterations. Compared to model-free RL, the proposed approach leverages the derived mathematical characterization of the FL training process to discover an effective device selection and quantization scheme without imposing additional device communication overhead. Numerical evaluations show that the proposed FL framework can achieve the same classification performance while reducing the number of training iterations needed for convergence by 20% compared to model-free RL-based FL.

关键词： Training Performance evaluation Wireless communication Quantization (signal) Computational modeling Reinforcement learning Mathematical models

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