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arXiv 2024年

作者： Xu, Minrui Niyato, Dusit Kang, Jiawen Xiong, Zehui Jamalipour, Abbas Fang, Yuguang Kim, Dong In Shen, Xuemin The School of Computer Science and Engineering Nanyang Technological University Singapore639798 Singapore The School of Automation Guangdong University of Technology Key Laboratory of Intelligent Information Processing and System Integration of IoT Ministry of Education Guangzhou510006 China Guangdong-HongKong-Macao Joint Laboratory for Smart Discrete Manufacturing Guangzhou510006 China The Pillar of Information Systems Technology and Design Singapore University of Technology and Design Singapore487372 Singapore The School of Electrical and Information Engineering University of Sydney SydneyNSW2006 Australia Department of Computer Science City University of Hong Kong Kowloon Hong Kong The Department of Electrical and Computer Engineering Sungkyunkwan University Suwon16419 Korea Republic of The Department of Electrical and Computer Engineering University of Waterloo WaterlooONN2L 3G1 Canada

Generative AI (GAI) can enhance the cognitive, reasoning, and planning capabilities of intelligent modules in the Internet of Vehicles (IoV) by synthesizing augmented datasets, completing sensor data, and making sequential decisions. In addition, the mixture of experts (MoE) can enable the distributed and collaborative execution of AI models without performance degradation between connected vehicles. In this survey, we explore the integration of MoE and GAI to enable Artificial General Intelligence in IoV, which can enable the realization of full autonomy for IoV with minimal human supervision and applicability in a wide range of mobility scenarios, including environment monitoring, traffic management, and autonomous driving. In particular, we present the fundamentals of GAI, MoE, and their interplay applications in IoV. Furthermore, we discuss the potential integration of MoE and GAI in IoV, including distributed perception and monitoring, collaborative decision-making and planning, and generative modeling and simulation. Finally, we present several potential research directions for facilitating the integration. © 2024, CC BY.

关键词： Integration

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CMOS Leaky Integrate-and-Fire Neurons with Local Dynamic Biasing and Inhibitory Self-Oscillation

CMOS Leaky Integrate-and-Fire Neurons with Local Dynamic Bia...

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IEEE Biomedical Circuits and systems (BIOCAS)

作者： Mannhee Cho Minil Kang Minseong Um Hangue Park Hyung-Min Lee School of Electrical Engineering Korea University Seoul Republic of Korea Department of Semiconductor System Engineering Korea University Seoul Republic of Korea Department of Biomedical Engineering Sungkyunkwan University Suwon Republic of Korea Department of Intelligent Precision Healthcare Convergence Sungkyunkwan University Suwon South Korea Department of Electrical and Computer Engineering Texas A&M University USA

This paper presents a CMOS-based neuromorphic circuit that can simulate self-oscillatory firing behaviors. Based on the leaky integrate-and-fire neuron model, a dynamic bias controller and an excitation integrator are added for controlling inhibitory connection between neurons as well as switching the operation mode with an integrated excitatory control signal. The proposed neuron circuit replicates extracellular fluidic biasing of membrane potential that oscillates at low frequency on top of the temporal integration of the action potential firing at high frequency. The proposed neuron can also replicate the synaptic fatigue by implementing inhibition declining over time, which leads the bias to oscillate. The proposed neuron model was implemented with 250nm CMOS process at 2.5V supply voltage. Each neuron consumes 46.16pJ energy/spike from leaky integration. Simulation results verified that a pair of proposed neuron cells can generate an oscillatory rhythm, where the bias waveform oscillates with the output spikes generated based on the bias level change. The proposed neuron circuit model can be utilized to reproduce more complicated oscillatory network behaviors of biological neurons.

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Establishment of the fundamental phase-to-polarization link in(ft)classical optics

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Fundamental Research 2021年第5期1卷 649-654页

作者： Xiaoyu Weng Xiumin Gao Guorong Sui Qiang Song Xiangmei Dong Junle Qu Songlin Zhuang Key Laboratory of Optoelectronic Devices and Systems of Ministry of Education and Guangdong Province College of Physics and Optoelectronic EngineeringShenzhen UniversityShenzhen 518060China Engineering Research Center of Optical Instrument and System Ministry of EducationShanghai Key Lab of Modem Optical SystemSchool of Optical-Electrical and Computer Engineering University of Shanghai for Science and Technology516 Jungong RoadShanghai 200093China

Vector polarization induced by a change in the scalar phase is far beyond our understanding of the relationship between polarization and phase in classical optics due to the entanglement of the inherent polarized modes of light *** overcome this limitation,we establish this miraculous relationship by the principle of phase vectorization,which can transform three particular phases into linear,circular and elliptical polarizations.A polarized-spatial light modulator based on the principle of phase vectorization can be achieved using a phase-only spatial light modulator,which not only enables pixel-level polarization manipulation of light beams in a real-time dynamic way but also simultaneously retains complete phase *** work demonstrates the phase-to-polarization link and reports the creation of a polarized-spatial light modulator,which will transform our view of the natural properties of light beams and pave the way for the era of vector optics.

关键词： Polarization Vector optics Polarized-spatial light modulator

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Egcnet: Edge-Guided Context Real-Time Semantic Segmentation Network

SSRN

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SSRN 2023年

作者： Yu, Kaili Chen, Zhenxue Ye, Mengting Guo, Yixin Wu, Q.M. Jonathan School of Control Science and Engineering Shandong University Jinan250061 China School of Control Science and Engineering Shandong University Engineering Research Center of Intelligent Unmanned System Ministry of Education Jinan250061 China Department of Electrical and Computer Engineering University of Windsor WindsorONN9B 3P4 Canada

Semantic segmentation is widely needed in many application areas. Driven by the development of deep learning, semantic segmentation methods have made significant progress in accuracy, robustness, and speed. Based on the short-term dense concatenate network, this paper proposes an edge-guided context real-time semantic segmentation network (EGCNet), which further improves the detail branch, extracts edge information from feature maps, and enhances the network’s ability to extract edge information through edge-assisted training. Because the top-level feature map contains minimal detailed information, the network only extracts the boundary of the low-level feature map such that the useful information is utilized as much as possible while reducing the computational load. In addition, we improve the attention-refining module by embedding position information, allowing it to capture the target structure more accurately. EGCNet achieves 71 FPS and 78.6\% mIoU on the Cityscapes dataset at a low computational cost, making it an efficient real-time semantic segmentation network. © 2023, The Authors. All rights reserved.

关键词： Semantic Segmentation

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Unleashing the Power of Edge-Cloud Generative AI in Mobile Networks: A Survey of AIGC Services

arXiv

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arXiv 2023年

作者： Xu, Minrui Du, Hongyang Niyato, Dusit Kang, Jiawen Xiong, Zehui Mao, Shiwen Han, Zhu Jamalipour, Abbas Kim, Dong In Shen, Xuemin Leung, Victor C.M. Poor, H. Vincent The School of Computer Science and Engineering Nanyang Technological University Singapore639798 Singapore The School of Automation Guangdong University of Technology Key Laboratory of Intelligent Information Processing and System Integration of IoT Ministry of Education Guangzhou510006 China Guangdong-HongKong-Macao Joint Laboratory for Smart Discrete Manufacturing Guangzhou510006 China The Pillar of Information Systems Technology and Design Singapore University of Technology and Design Singapore487372 Singapore The Department of Electrical and Computer Engineering Auburn University AuburnAL36849-5201 United States The Department of Electrical and Computer Engineering University of Houston HoustonTX77004 United States The Department of Computer Science and Engineering Kyung Hee University Seoul446-701 Korea Republic of The School of Electrical and Information Engineering University of Sydney SydneyNSW2006 Australia The Department of Electrical and Computer Engineering Sungkyunkwan University Suwon16419 Korea Republic of The Department of Electrical and Computer Engineering University of Waterloo WaterlooONN2L 3G1 Canada The College of Computer Science and Software Engineering Shenzhen University Shenzhen518061 China The Department of Electrical and Computer Engineering The University of British Columbia VancouverBCV6T 1Z4 Canada The Department of Electrical and Computer Engineering Princeton University PrincetonNJ08544 United States

Artificial Intelligence-Generated Content (AIGC) is an automated method for generating, manipulating, and modifying valuable and diverse data using AI algorithms creatively. This survey paper focuses on the deployment of AIGC applications, e.g., ChatGPT and Dall-E, at mobile edge networks, namely mobile AIGC networks, that provide personalized and customized AIGC services in real time while maintaining user privacy. We begin by introducing the background and fundamentals of generative models and the lifecycle of AIGC services at mobile AIGC networks, which includes data collection, training, fine-tuning, inference, and product management. We then discuss the collaborative cloud-edge-mobile infrastructure and technologies required to support AIGC services and enable users to access AIGC at mobile edge networks. Furthermore, we explore AIGC-driven creative applications and use cases for mobile AIGC networks. Additionally, we discuss the implementation, security, and privacy challenges of deploying mobile AIGC networks. Finally, we highlight some future research directions and open issues for the full realization of mobile AIGC networks. © 2023, CC BY.

关键词： Life cycle

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Optimal Energy Shaping Control for a Backdrivable Hip Exoskeleton

Optimal Energy Shaping Control for a Backdrivable Hip Exoske...

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American Control Conference (ACC)

作者： Jiefu Zhang Jianping Lin Vamsi Peddinti Robert D. Gregg Electrical Engineering and Computer Science University of Michigan Ann Arbor MI USA State Key Laboratory of Mechanical System and Vibration School of Mechanical Engineering Shanghai Jiao Tong University Shanghai China Robotics University of Michigan Ann Arbor MI USA

Task-dependent controllers widely used in exoskeletons track predefined trajectories, which overly constrain the volitional motion of individuals with remnant voluntary mobility. Energy shaping, on the other hand, provides task-invariant assistance by altering the human body’s dynamic characteristics in the closed loop. While human-exoskeleton systems are often modeled using Euler-Lagrange equations, in our previous work we modeled the system as a port-controlled-Hamiltonian system, and a task-invariant controller was designed for a knee-ankle exoskeleton using interconnection-damping assignment passivity-based control. In this paper, we extend this framework to design a controller for a backdrivable hip exoskeleton to assist multiple tasks. A set of basis functions that contains information of kinematics is selected and corresponding coefficients are optimized, which allows the controller to provide torque that fits normative human torque for different activities of daily life. Human-subject experiments with two able-bodied subjects demonstrated the controller’s capability to reduce muscle effort across different tasks.

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Integral Inequalities for the Analysis of Distributed Parameter systems: A Complete Characterization via the Least-Squares Principle

Integral Inequalities for the Analysis of Distributed Parame...

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American Control Conference (ACC)

作者： Qian Feng Alexandre Seuret Sing Kiong Nguang Feng Xiao School of Control and Computer Engineering North China Electric Power University Beijing China Dpto de Ingeniería de Sistemas y Automática Universidad de Sevilla Camino de los Descubrimientos Sevilla Spain Department of Electrical Computer and Software Engineering The University of Auckland New Zealand State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources North China Electric Power University Beijing China

ISBN: (数字)9798350382655

ISBN: (纸本)9798350382662

A wide variety of integral inequalities (IIs) have been developed and studied for the stability analysis of distributed parameter systems using the Lyapunov functional approach. However, no unified mathematical framework has been proposed that could characterize the similarity and connection between these IIs, as most of them was introduced in a dispersed manner for the analysis of specific types of systems. Additionally, the extent to which the generality of these IIs can be expanded and the optimality of their lower bounds (LBs) remains open questions. In this study, we introduce two general classes of IIs that can generalize nearly all IIs in the literature. The integral kernels of the LBs of our IIs can contain an unlimited number of weighted £2 functions that are linearly independent in a Lebesgue sense. Moreover, we not only estalish the equivalence relations between the LBs of our IIs, but also demonstrate that these LBs are guaranteed by the least squares principle, implying asymptotic convergence to the upper bound when the kernels functions constitutes a Schauder basis of the underlying Hilbert space. Owing to their general structures, our IIs are applicable in a variety of contexts, such as the stability analysis of coupled PDE-ODE systems or cybernetic systems with delay structures.

关键词： Upper bound Integral equations Stability analysis Hilbert space Linear matrix inequalities Delays Kernel

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First Demonstration of HZO/β-Ga2O3 Ferroelectric FinFET with Improved Memory Window

arXiv

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arXiv 2024年

作者： Park, Seohyeon Yoo, Jaewook Song, Hyeojun Lee, Hongseung Lim, Seongbin Kim, Soyeon Park, Minah Kim, Bongjoong Heo, Keun Ye, Peide D. Bae, Hagyoul Electronic Engineering Jeonbuk National University Jeonju54896 Korea Republic of School of Electrical and Computer Engineering Purdue University West LafayetteIN68588 United States Department of Semiconductor Science & Technology Jeonbuk National University Jeonju54896 Korea Republic of Department of Mechanical & System Design Engineering Hongik University Seoul04066 Korea Republic of

We have experimentally demonstrated the effectiveness of beta-gallium oxide (β-Ga2O3) ferroelectric fin field-effect transistors (Fe-FinFETs) for the first time. Atomic layer deposited (ALD) hafnium zirconium oxide (HZO) is used as the ferroelectric layer. The HZO/β-Ga2O3 Fe-FinFETs have wider counterclockwise hysteresis loops in the transfer characteristics than that of conventional planar FET, achieving record-high memory window (MW) of 13.9 V in a single HZO layer. When normalized to the actual channel width, FinFETs show an improved ION/IOFF ratio of 2.3×107 and a subthreshold swing value of 110 mV/dec. The enhanced characteristics are attributed to the low-interface state density (Dit), showing good interface properties between the β-Ga2O3 and HZO layer. The enhanced polarization due to larger electric fields across the entire ferroelectric layer in FinFETs is validated using Sentaurus TCAD. After 5×106 program/erase (PGM/ERS) cycles, the MW was maintained at 9.2 V, and the retention time was measured up to 3×104 s with low degradation. Therefore, the ultrawide bandgap (UWBG) Fe-FinFET was shown to be one of the promising candidates for high-density non-volatile memory devices. © 2024, CC BY.

关键词： Energy gap

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Data-Driven Learning and Control with Event-Triggered Measurements

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IEEE Transactions on Automatic Control 2025年

作者： Feng, Shilun Shi, Dawei Chen, Tongwen Shi, Ling Beijing Institute of Technology State Key Laboratory of Intelligent Control and Decision of Complex Systems MIIT Key Laboratory of Servo Motion System Drive and Control School of Automation Beijing100081 China University of Alberta Department of Electrical and Computer Engineering EdmontonABT6G 1H9 Canada Hong Kong University of Science and Technology Department of Electronic and Computer Engineering Clear Water Bay Kowloon Hong Kong

Event-triggered control has attracted considerable attention for its effectiveness in resource-restricted applications. To make event-triggered control as an end-to-end solution, a key issue is how to effectively learn unknown system dynamics from event-triggered measurements and consequently, develop a learning-based event-triggered controller. Existing works learn system dynamics based on periodic time-triggered measurements, and it is yet to know how to learn a controller with performance guarantee based on event-triggered measurements. To address this issue, we consider the problem of learning an event-triggered state feedback controller for an unknown linear system based on event-triggered state measurements in this work. In particular, we first analyze the event-triggered measurements within a set-membership framework. We prove that the estimation error belongs to a bounded ellipsoid determined by the historical measurements and the event-triggering condition. Subsequently, we demonstrate that all admissible systems compatible with the collected data samples can be explicitly represented in the form of quadratic matrix inequalities using the state estimates. With the acquired set of admissible systems, a co-design problem for the data-driven controller and event-triggering condition is solved using the linear matrix inequality technique, with guaranteed closed-loop stability and L2-gain performance. Finally, numerical examples and comparisons are provided to illustrate the effectiveness of the proposed event-triggered learning and control approach. © 1963-2012 IEEE.

关键词： Networked control systems

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HMAMP: Hypervolume-Driven Multi-Objective Antimicrobial Peptides Design

arXiv

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arXiv 2024年

作者： Wang, Li Liu, Yiping Fu, Xiangzheng Ye, Xiucai Shi, Junfeng Yen, Gary G. Zeng, Xiangxiang The College of Computer Scienceand Electronic Engineering Hunan University Hunan Changsha China The School of Electrical and Computer Engineering Oklahoma State University StillwaterOK74078 United States The School of System Information and Engineering University of Tsukuba Ibaraki Japan Neher’s Biophysics Laboratory for Innovative Drug Discovery State Key Laboratory of Quality Research in Chinese Medicine Macau Institute for Applied Research in Medicine and Health Macau University of Science and Technology 999078 China

Antimicrobial peptides (AMPs) have exhibited unprecedented potential as biomaterials in combating multidrugresistant bacteria. Despite the increasing adoption of artificial intelligence for novel AMP design, challenges pertaining to conflicting attributes such as activity, hemolysis, and toxicity have significantly impeded the progress of researchers. This paper introduces a paradigm shift by considering multiple attributes in AMP design. Presented herein is a novel approach termed Hypervolume-driven Multi-objective Antimicrobial Peptide Design (HMAMP), which prioritizes the simultaneous optimization of multiple attributes of AMPs. By synergizing reinforcement learning and a gradient descent algorithm rooted in the hypervolume maximization concept, HMAMP effectively expands exploration space and mitigates the issue of pattern collapse. This method generates a wide array of prospective AMP candidates that strike a balance among diverse attributes. Furthermore, we pinpoint knee points along the Pareto front of these candidate AMPs. Empirical results across five benchmark models substantiate that HMAMP-designed AMPs exhibit competitive performance and heightened diversity. A detailed analysis of the helical structures and molecular dynamics simulations for ten potential candidate AMPs validates the superiority of HMAMP in the realm of multiobjective AMP design. The ability of HMAMP to systematically craft AMPs considering multiple attributes marks a pioneering milestone, establishing a universal computational framework for the multi-objective design of AMPs. Copyright © 2024, The Authors. All rights reserved.

关键词： Reinforcement learning

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