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Real-time programmable metasurface for terahertz multifunctional wave front engineering

Light(Science & Applications) 2023年第9期12卷 1795-1806页

作者： Feng Lan Luyang Wang Hongxin Zeng Shixiong Liang Tianyang Song Wenxin Liu Pinaki Mazumder Ziqiang Yang Yaxin Zhang Daniel M.Mittleman Sichuan THz Communication Technology Engineering Research Center School of Electronic Science and EngineeringUniversity of Electronic Science and Technology of ChinaChengdu 611731China Yangtze Delta Region Institute(Huzhou) University of Electronic Science and Technology of ChinaHuzhou 313000China Zhangjiang Laboratory Shanghai 201204China National Key Laboratory of Solid-State Microwave Devices and Circuits Hebei Semiconductor Research InstituteShijiazhuang 050051China Aerospace Information Research Institute Chinese Academy of SciencesBeijing 100094China University of Chinese Academy of Sciences School of ElectronicElectrical and Communication EngineeringBeijing 101408China Department of Electrical Engineering and Computer Science University of MichiganAnn ArborMI 48109USA School of Engineering Brown UniversityProvidenceRI 02912USA

Terahertz(THz)technologies have become a focus of research in recent years due to their prominent role in envisioned future communication and sensing *** of the key challenges facing the field is the need for tools to enable agile engineering of THz wave ***,we describe a reconfigurable metasurface based on GaN technology with an array-of-subarrays *** subwavelength-spaced array,under the control of a 1-bit digital coding sequence,can switch between an enormous range of possible configurations,providing facile access to nearly arbitrary wave front control for signals near 0.34 *** demonstrate wide-angle beam scanning with 1°of angular precision over 70 GHz of bandwidth,as well as the generation of multi-beam and diffuse wave fronts,with a switching speed up to 100 *** device,offering the ability to rapidly reconfigure a propagating wave front for beam-forming or diffusively scattered wide-angle coverage of a scene,will open new realms of possibilities in sensing,imaging,and networking.

关键词： wave scattered arbitrary

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Optimal Multi-Constrained Workflow Scheduling for Cyber-Physical Systems in the Edge-Cloud Continuum

Optimal Multi-Constrained Workflow Scheduling for Cyber-Phys...

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IEEE Annual International computer Software and Applications Conference (COMPSAC)

作者： Andreas Kouloumpris Georgios L. Stavrinides Maria K. Michael Theocharis Theocharides Department of Electrical and Computer Engineering KIOS Research and Innovation Center of Excellence University of Cyprus Nicosia Cyprus

ISBN: (数字)9798350376968

ISBN: (纸本)9798350376975

The emerging edge-hub-cloud paradigm has enabled the development of innovative latency-critical cyber-physical applications in the edge-cloud continuum. However, this paradigm poses multiple challenges due to the heterogeneity of the devices at the edge of the network, their limited computational, commu-nication, and energy capacities, as well as their different sensing and actuating capabilities. To address these issues, we propose an optimal scheduling approach to minimize the overall latency of a workflow application in an edge-hub-cloud cyber-physical system. We consider multiple edge devices cooperating with a hub device and a cloud server. All devices feature heterogeneous multi core processors and various sensing, actuating, or other specialized capabilities. We present a comprehensive formulation based on continuous-time mixed integer linear programming, encapsulating multiple constraints often overlooked by existing approaches. We conduct a comparative experimental evaluation between our method and a well-established and effective scheduling heuristic, which we enhanced to consider the constraints of the specific problem. The results reveal that our technique outperforms the heuristic, achieving an average latency improvement of 13.54 % in a relevant real-world use case, under varied system configurations. In addition, the results demonstrate the scalability of our method under synthetic workflows of varying sizes, attaining a 33.03 % average latency decrease compared to the heuristic.

关键词： Program processors Processor scheduling Multicore processing Scalability Optimal scheduling Cyber-physical systems Software

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Passivity Preserving Safety-Critical Control of Switched Systems

Passivity Preserving Safety-Critical Control of Switched Sys...

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IEEE Conference on Decision and Control

作者： Qingkai Meng Andreas Kasis Marios M. Polycarpou Department of Electrical and Computer Engineering KIOS Research and Innovation Center of Excellence University of Cyprus Nicosia Cyprus

ISBN: (数字)9798350316339

ISBN: (纸本)9798350316346

System safety refers to the property of the state trajectories to remain within some predefined set at all times. Integrating safety and stability offers significant advantages, such as resilience to disturbances and enhanced reliability and predictability. This paper combines control barrier functions and passivity methodologies, in the context of switched systems, to simultaneously ensure stability and safety. We derive conditions under which the passivity of switched systems is preserved under control barrier function-based switched safety-critical control. This enables the construction of a suitable design framework for observer-based output feedback controllers and switching laws, that achieve simultaneous stability and safety guarantees, without imposing any assumption on the safety of individual subsystems. Furthermore, we show that the simultaneous passivity and safety of interconnected switched systems under feedback and parallel configurations can be guaranteed by applying the developed conditions on each local switched subsystem. The applicability of the developed theoretical results is validated through a planar moving body example.

关键词： Switched systems Simulation Switches Control systems Stability analysis Safety Trajectory Reliability Output feedback Resilience

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Applications of Particle Swarm Optimization Algorithms in Coordination between Power System Stabilizer and Flexible AC Transmission 21

Applications of Particle Swarm Optimization Algorithms in Co...

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21st IEEE International Symposium on Intelligent Systems and Informatics, SISY 2023

作者： Moazzami, Majid Shahgholian, Ghazanfar Heydaran-Daroogheh-Amnyieh, Zahra Mosavi, Amirhosein Fathollahi, Arman Islamic Azad University Najafabad Branch Smart Microgrid Research Center Najafabad Iran Islamic Azad University Najafabad Branch Department of Electrical Engineering Najafabad Iran Islamic Azad University Dolatabad Branch Department of Computer Engineering Dolatabad Iran Obuda University John von Neumann Faculty of Informatics Budapest Hungary Aarhus University Department of Electrical and Computer Engineering Aarhus Denmark

ISBN: (纸本)9798350343366

Interconnected power systems require the damping of oscillations to ensure secure operation. Traditional power system stabilizers (PSSs) may, however, not be able to sufficiently dampen inter-Area oscillations in multi-machine energy networks. PSSs and flexible AC transmission systems (FACTS) devices are frequently utilized to improve system stability. Meta-heuristic algorithms using search strategies are utilized to find near-optimal solutions. These strategies typically rely on a fitness function to evaluate candidate solutions and are inspired by natural systems. In this work, we furnish a concise overview of particle swarm optimization algorithms applied for the coordinated arrangement of PSS and FACTS controllers, presenting new directions for researchers working on meta-heuristic algorithms and coordination design of FACTS and PSS controllers for inter-Area oscillation damping in power systems. © 2023 IEEE.

关键词： Soft computing

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WPT Enabled SP3T Switch for Broadband Antenna Switching Applications

WPT Enabled SP3T Switch for Broadband Antenna Switching Appl...

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2022 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting, AP-S/URSI 2022

作者： Quddious, Abdul Bilal, Asif Darvazehban, Amin Rezaeieh, Sasan Ahdi Iezekiel, Stavros Antoniades, Marco A. University of Cyprus KIOS Research Center for Excellence Nicosia Cyprus University of Cyprus EMPHASIS Research Centre Nicosia Cyprus University of Cyprus Department of Electrical and Computer Engineering Nicosia Cyprus The University of Queensland School of Information Technology and Electrical Engineering Brisbane Australia Ryerson University Department of Electrical Computer & Biomedical Engineering TorontoON Canada

ISBN: (数字)9781665496582

ISBN: (纸本)9781665496582

This paper presents a battery-less single pole triple throw (SP3T) switch enabled through wireless power transfer (WPT) for a frequency range of 0.5-6 GHz. A set of three rectifying circuits, each connected to their independent antenna elements (rectennas) were designed. These rectennas were designed to operate at 2.3 GHz, 2.45 GHz, and 2.6 GHz, respectively. Different frequencies were used to avoid any interference between received signals. The three RF output ports of the switch were biased using each rectenna. A minimum of 1.8 V is required to actuate each RF port that was rectified when the input power ranges from 0-2 dBm with RF-to-DC power conversion efficiency (PCE) which is around 50% for each rectifier. The proposed battery-less SP3T switch paves the way to reconfigure microwave devices and antennas for a broad range of applications. © 2022 IEEE.

关键词： Rectennas

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Synergising Human-like Responses and Machine Intelligence for Planning in Disaster Response

Synergising Human-like Responses and Machine Intelligence fo...

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International Joint Conference on Neural Networks (IJCNN)

作者： Savvas Papaioannou Panayiotis Kolios Christos G. Panayiotou Marios M. Polycarpou Department of Electrical and Computer Engineering KIOS Research and Innovation Center of Excellence University of Cyprus Nicosia Cyprus

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

In the rapidly changing environments of disaster response, planning and decision-making for autonomous agents involve complex and interdependent choices. Although recent advancements have improved traditional artificial intelligence (AI) approaches, they often struggle in such settings, particularly when applied to agents operating outside their well-defined training parameters. To address these challenges, we propose an attention-based cognitive architecture inspired by Dual Process Theory (DPT). This framework integrates, in an online fashion, rapid yet heuristic (human-like) responses (System 1) with the slow but optimized planning capabilities of machine intelligence (System 2). We illustrate how a supervisory controller can dynamically determine in real-time the engagement of either system to optimize mission objectives by assessing their performance across a number of distinct attributes. Evaluated for trajectory planning in dynamic environments, our framework demonstrates that this synergistic integration effectively manages complex tasks by optimizing multiple mission objectives.

关键词： Training Trajectory planning Disasters Decision making Process control Real-time systems Planning

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Advanced machine learning in neuroimaging studies via federated learning

Advanced machine learning in neuroimaging studies via federa...

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2024 IEEE International Conference on Big Data, BigData 2024

作者： Basodi, Sunitha Romero, Javier Panta, Sandeep Martin, Dylan Plis, Sergey Sarwate, Anand D. Calhoun, Vince D. Georgia State University Georgia Institute of Technology Emory University Tri-Institutional Center for Translational Research in Neuroimaging and Data Science AtlantaGA United States Rutgers University Department of Electrical & Computer Engineering NJ United States

ISBN: (纸本)9798350362480

Federated analysis can help perform large-scale analyses using neuroimaging datasets across various research groups overcoming the limitations of institutional data-sharing policies, privacy or regulatory concerns as it requires no data sharing. In this work, we employ a federated neuromark algorithm to generate independent component analysis (ICA) time courses data from functional magnetic resonance imaging (fMRI) data and feed it to a federated deep neural network (DNN) model to perform classification of schizophrenia patients versus controls. © 2024 IEEE.

关键词： Federated learning

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Mini-InternVL: a flexible-transfer pocket multi-modal model with 5% parameters and 90% performance

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Visual Intelligence 2024年第1期2卷 1-17页

作者： Gao, Zhangwei Chen, Zhe Cui, Erfei Ren, Yiming Wang, Weiyun Zhu, Jinguo Tian, Hao Ye, Shenglong He, Junjun Zhu, Xizhou Lu, Lewei Lu, Tong Qiao, Yu Dai, Jifeng Wang, Wenhai Shanghai AI Laboratory Shanghai 200232 China School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shanghai 200240 China School of Computer Science Nanjing University Nanjing 210023 China Tsinghua Shenzhen International Graduate School Tsinghua University Shenzhen 518055 China School of Computer Science Fudan University Shanghai 200433 China SenseTime Research Shanghai 200233 China Department of Electronic Engineering Tsinghua University Beijing 100084 China Beijing National Research Center for Information Science and Technology Department of Automation Tsinghua University Beijing 100084 China Department of Information Engineering The Chinese University of Hong Kong 999077 Hong Kong

Multi-modal large language models (MLLMs) have demonstrated impressive performance in vision-language tasks across a wide range of domains. However, the large model scale and associated high computational cost pose significant challenges for training and deploying MLLMs on consumer-grade GPUs or edge devices, thereby hindering their widespread application. In this work, we introduce Mini-InternVL, a series of MLLMs with parameters ranging from 1 billion to 4 billion, which achieves 90% of the performance with only 5% of the parameters. This significant improvement in efficiency and effectiveness makes our models more accessible and applicable in various real-world scenarios. To further promote the adoption of our models, we are developing a unified adaptation framework for Mini-InternVL, which enables our models to transfer and outperform specialized models in downstream tasks, including autonomous driving, medical image processing, and remote sensing. We believe that our models can provide valuable insights and resources to advance the development of efficient and effective MLLMs. © The Author(s) 2024.

关键词： Knowledge distillation Lightweight multi-modal large language model Vision-language model Visual instruction tuning

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Fluctuation and dissipation in quantum nonlinear optics

Fluctuation and dissipation in quantum nonlinear optics

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2024 Conference on Lasers and Electro-Optics, CLEO 2024

作者： Uddin, Shiekh Zia Rivera, Nicholas Seyler, Devin Salamin, Yannick Sloan, Jamison Xu, Shutao Sander, Michelle Y. Soljačić, Marin Department of Physics Massachusetts Institute of Technology CambridgeMA02139 United States Research Laboratory of Electronics Massachusetts Institute of Technology CambridgeMA02139 United States Department of Physics Harvard University CambridgeMA02138 United States Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology CambridgeMA02139 United States Department of Electrical and Computer Engineering BU Photonics Center Boston University BostonMA02215 United States

We present a new fluctuation-dissipation relation that shows how quantum noise transforms under engineered nonlinear dissipation found in many important nonlinear optical devices and validate the theory with experimen... 详细信息

ISBN: (纸本)9781957171395

关键词： Quantum noise

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TTSlow: Slow Down Text-to-Speech With Efficiency Robustness Evaluations

IEEE Transactions on Audio, Speech and Language Processing

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IEEE Transactions on Audio, Speech and Language Processing 2025年 33卷 693-704页

作者： Xiaoxue Gao Yiming Chen Xianghu Yue Yu Tsao Nancy F. Chen Institute for Infocomm Research Agency for Science Technology and Research (A*STAR) Singapore Department of Electrical and Computer Engineering National University of Singapore Singapore Research Center for Information Technology Innovation Academia Sinica Taipei Taiwan Department of Electrical Engineering Chung Yuan Christian University Taoyuan City Taiwan

Text-to-speech (TTS) has been extensively studied for generating high-quality speech with textual inputs, playing a crucial role in various real-time applications. For real-world deployment, ensuring stable and timely generation in TTS models against minor input perturbations is of paramount importance. Therefore, evaluating the robustness of TTS models against such perturbations, commonly known as adversarial attacks, is highly desirable. In this paper, we propose TTSlow, a novel adversarial approach specifically tailored to slow down the speech generation process in TTS systems. To induce long TTS waiting time, we design novel efficiency-oriented adversarial loss to encourage endless generation process. TTSlow encompasses two attack strategies targeting both text inputs and speaker embedding. Specifically, we propose TTSlow-text, which utilizes a combination of homoglyphs-based and swap-based perturbations, along with TTSlow-spk, which employs a gradient optimization attack approach for speaker embedding. TTSlow serves as the first attack approach targeting a wide range of TTS models, including autoregressive and non-autoregressive TTS ones, thereby advancing exploration in audio security. Extensive experiments are conducted to evaluate the inference efficiency of TTS models, and in-depth analysis of generated speech intelligibility is performed using Gemini. The results demonstrate that TTSlow can effectively slow down two TTS models across three publicly available datasets.

关键词： Perturbation methods Speech processing Robustness Computational modeling Security Optimization Text to speech Speech recognition Predictive models Real-time systems

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