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Nonlocal Time-Resolved Terahertz Spectroscopy in the Near Field

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Nonlocal Time-Resolved Terahertz Spectroscopy in the Near Fi...

2022 IEEE Photonics Society Summer Topicals Meeting Series, SUM 2022

作者： Pizzuto, Angela Castro-Camus, Enrique Wilson, William Choi, Wonsik Li, Xiuling Mittleman, Daniel M. Brown University Department of Physics ProvidenceRI02912 United States University of Marburg Department of Physics Marburg Germany Centro de Investigaciones en Optica A. C. Guanajuato Leon37150 Mexico Harvard University Center for Nanoscale Systems CambridgeMA02138 United States University of Illinois at Urbana-Champaign Holonyak Micro and Nanotechnology Laboratory Department of Electrical and Computer Engineering ChampaignIL61820 United States School of Engineering Brown University ProvidenceRI02912 United States University of Texas Department of Electrical and Computer Engineering AustinTX78758 United States

ISBN: (数字)9781665434898

ISBN: (纸本)9781665434898

We describe scattering-type near-field terahertz imaging measurements in which the optical excitation spot is laterally displaced from the probed location. We demonstrate both non-local terahertz emission and non-local optical-pump, terahertz-probe measurements. This family of techniques are powerful new tools for studying anisotropic transport in nanostructures. © 2022 IEEE.

关键词： Terahertz spectroscopy

PAM-4 optical transmission beyond 224 Gbps based on an ultrahigh-bandwidth slow-light silicon modulator

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PAM-4 optical transmission beyond 224 Gbps based on an ultra...

2024 Conference on Lasers and Electro-Optics, CLEO 2024

作者： Han, Changhao Qin, Jun Yang, Qipeng Zheng, Zhao Shu, Haowen Zhang, Yunhao Wu, Yichen Sun, Yu Lu, Junde Zhou, Yan Ge, Zhangfeng Wang, Lei He, Zhixue Yu, Shaohua Hu, Weiwei Peng, Chao Bowers, John E. Wang, Xingjun State Key Laboratory of Advanced Optical Communications System and Networks School of Electronics Peking University Beijing100871 China Department of Electrical and Computer Engineering University of California Santa BarbaraCA93106 United States Key Laboratory of Information and Communication Systems Ministry of Information Industry Beijing Information Science and Technology University Beijing100192 China Frontiers Science Center for Nano-Optoelectronics Peking University Beijing100871 China Peng Cheng Laboratory Shenzhen518055 China Peking University Yangtze Delta Institute of Optoelectronics Nantong226010 China

ISBN: (纸本)9781957171395

关键词： Silicon photonics

SEE-MCAM: Scalable Multi-bit FeFET Content Addressable Memories for Energy Efficient Associative Search

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arXiv 2023年

作者： Shou, Shengxi Liu, Che-Kai Yun, Sanggeon Wan, Zishen Ni, Kai Imani, Mohsen Sharon Hu, X. Yang, Jianyi Zhuo, Cheng Yin, Xunzhao College of Information Science and Electronic Engineering Zhejiang University China School of Electrical and Computer Engineering Georgia Institute of Technology United States Department of Information and Computer Science University of California Irvine United States Department of Electrical and Microelectronic Engineering Rochester Institute of Technology United States Department of Computer Science and Engineering University of Notre Dame United States School of Micro-Nano Electronics Zhejiang University China Key Laboratory of Collaborative Sensing and Autonomous Unmanned Systems of Zhejiang Province China

Artificial intelligence has made remarkable advancements in recent years, leading to the development of algorithms and models capable of handling ever-increasing amounts of data. The computational demands of these algorithms necessitate circuit and architecture designs that go beyond the von-Neumann paradigm. Content addressable memories (CAMs), which implement parallel associative search functionality within memory blocks to overcome the memory wall bottleneck, have proven to be effective for data-intensive tasks. While current CAM designs have achieved higher storage density and energy efficiency than their CMOS-based counterparts by leveraging emerging nonvolatile memories (NVM), most of these implementations are limited to binary storage cells. In this work, we propose SEE-MCAM, scalable and compact multi-bit CAM (MCAM) designs that utilize the three-terminal ferroelectric FET (FeFET) as the proxy. By exploiting the multi-level-cell characteristics of FeFETs, our proposed SEE-MCAM designs enable multi-bit associative search functions and achieve better energy efficiency and performance than existing FeFET-based CAM designs. We validated the functionality of our proposed designs by achieving 3 bits per cell CAM functionality, resulting in 3× improvement in storage density. The area per bit of the proposed SEE-MCAM cell is 8% of the conventional CMOS CAM. We thoroughly investigated the scalability and robustness of the proposed design. Evaluation results suggest that the proposed 2FeFET-1T SEE-MCAM achieves 9.8× more energy efficiency and 1.6× less search latency compared to the CMOS CAM, respectively. When compared to existing MCAM designs, the proposed SEE-MCAM can achieve 8.7× and 4.9× more energy efficiency than ReRAM-based and FeFET-based MCAMs, respectively. Benchmarking results show that our approach provides up to 3 orders of magnitude improvement in speedup and energy efficiency over a GPU implementation in accelerating a novel quantized hyperdimensional

关键词： Associative storage

Using Affect as a Communication Modality to Improve Human-Robot Communication in Robot-Assisted Search and Rescue Scenarios

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arXiv 2022年

作者： Akgun, Sami Alperen Ghafurian, Moojan Crowley, Mark Dautenhahn, Kerstin The Department of Systems Design Engineering University of Waterloo WaterlooONN2L 3G1 Canada The Department of Electrical and Computer Engineering University of Waterloo WaterlooONN2L 3G1 Canada The Department of Electrical and Computer Engineering and Systems Design Engineering University of Waterloo WaterlooONN2L 3G1 Canada

Emotions can provide a natural communication modality to complement the existing multi-modal capabilities of social robots, such as text and speech, in many domains. We conducted three online studies with 112, 223 and 151 participants to investigate the benefits of using emotions as a communication modality for Search And Rescue (SAR) robots. In the first experiment, we investigated the feasibility of conveying information related to SAR situations through robots' emotions, resulting in mappings from SAR situations to emotions. The second study used Affect Control Theory as an alternative method for deriving such mappings. This method is more flexible, e.g. allows for such mappings to be adjusted for different emotion sets and different robots. In the third experiment, we created affective expressions for an appearance-constrained outdoor field research robot using LEDs as an expressive channel. Using these affective expressions in a variety of simulated SAR situations, we evaluated the effect of these expressions on participants' (adopting the role of rescue workers) situational awareness. Our results and proposed methodologies provide (a) insights on how emotions could help conveying messages in the context of SAR, and (b) evidence on the effectiveness of adding emotions as a communication modality in a (simulated) SAR communication context. © 2022, CC BY.

关键词： Control theory

Computation Offloading in Air-Ground Integrated Vehicular Edge Computing Networks

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Computation Offloading in Air-Ground Integrated Vehicular Ed...

IEEE Globecom Workshops

作者： Shichao Li Hongbin Chen Fangqing Tan Ning Zhang Siyu Lin Tony Q. S. Quek School of Information and Communication Guilin University of Electronic Technology Guilin China Department of Electrical and Computer Engineering University of Windsor Windsor ON Canada School of Electronic and Information Engineering Beijing Jiaotong University Beijing China Information Systems Technology and Design Pillar Singapore University of Technology and Design Singapore

The integration of vehicle edge computing (VEC) and air-ground integrated network is considered as a key technology to achieve autonomous driving, where the tasks can be offloaded to the high altitude platform (HAP) through an unmanned aerial vehicle (UAV) as a relay. In this paper, we investigate a joint access equipment selection and resource allocation problem, with the objective of minimizing the total task offloading delay in the air-ground integrated VEC network. Considering the formulated problem is a non-convex problem, we decompose it into three subproblems: access equipment selection, joint bandwidth and computation resource allocation, and UAV trajectory design. Considering the first subproblem is an integer programming problem, the many-to-one matching method is utilized to solve it. Meanwhile, we utilize the CVX package and successive convex approximation (SCA) method to solve the last two subproblems, respectively. Considering the matching externality, we utilize coalition game method to deal with it. Based on the solutions of the three subproblems, the joint computation offloading and resource allocation (JCORA) algorithm has been proposed. Simulation results show that the proposed JCORA algorithm can reduce the total task offloading delay compared with other algorithms.

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Spear or Shield: Leveraging Generative AI to Tackle Security Threats of Intelligent Network Services

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arXiv 2023年

作者： Du, Hongyang Niyato, Dusit Kang, Jiawen Xiong, Zehui Lam, Kwok-Yan Fang, Yuguang Li, Yonghui School of Computer Science and Engineering Nanyang Technological University Singapore School of Automation Guangdong University of Technology China Pillar of Information Systems Technology and Design Singapore University of Technology and Design Singapore National Centre for Research in Digital Trust School of Computer Science and Engineering Nanyang Technological University Singapore Department of Computer Science City University of Hong Kong Hong Kong School of Electrical and Information Engineering University of Sydney SydneyNSW2006 Australia

Generative AI (GAI) models have been rapidly advancing, with a wide range of applications including intelligent networks and mobile AI-generated content (AIGC) services. Despite their numerous applications and potential, such models create opportunities for novel security challenges. In this paper, we examine the challenges and opportunities of GAI in the realm of the security of intelligent network AIGC services such as suggesting security policies, acting as both a "spear" for potential attacks and a "shield" as an integral part of various defense mechanisms. First, we present a comprehensive overview of the GAI landscape, highlighting its applications and the techniques underpinning these advancements, especially large language and diffusion models. Then, we investigate the dynamic interplay between GAI’s spear and shield roles, highlighting two primary categories of potential GAI-related attacks and their respective defense strategies within wireless networks. A case study illustrates the impact of GAI defense strategies on energy consumption in an image request scenario under data poisoning attack. Our results show that by employing an AI-optimized diffusion defense mechanism, energy can be reduced by 8.7%, and retransmission count can be decreased from 32 images, without defense, to just 6 images, showcasing the effectiveness of GAI in enhancing network security. © 2023, CC BY.

关键词： Network security

Efficient Model Training in Edge Networks with Hierarchical Split Learning

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IEEE Transactions on Mobile Computing 2025年

作者： Zhang, Songge Wu, Wen Song, Lingyang Shen, Xuemin Peking University Shenzhen Graduate School School of Electronic and Computer Engineering Shenzhen518055 China Pengcheng Laboratory Frontier Research Center Shenzhen518055 China Peking University State Key Laboratory of Advanced Optical Communication Systems and Networks School of Electronics Beijing100871 China Xiangtan University Hunan Institute of Advanced Sensing and Information Technology Xiangtan China University of Waterloo Department of Electrical and Computer Engineering WaterlooN2L 3G1 Canada

In this paper, we propose an efficient model training scheme, named Group-based Hierarchical Split Learning (GHSL), which can accelerate the artificial intelligence (AI) training process in edge networks in a "first-sequential-then-parallel" manner. Specifically, the proposed scheme hierarchically splits an AI model into a user-side and server-side model, while dividing a number of users into multiple groups. Users in each group train user-side models with the interaction of the shared server-side model sequentially;different groups perform the above training process parallelly;the AI models of each group are aggregated into a global model. We also carry out the convergence analysis for the proposed scheme over non-independent and identically distributed data, which reveals that the convergence rate depends on user grouping. Furthermore, we propose a data-driven two-stage user grouping algorithm to minimize the overall training delay, taking user resource heterogeneity and the black-box training process into account. The proposed algorithm first utilizes the Gaussian process regression approach to determine the number of groups, and then employs the coalition game theory to determine the optimal user grouping decision. Comprehensive simulation results demonstrate that the proposed scheme can reduce training delay, user-side computational workload, and communication overhead by up to 19%, 53%, and 54%, respectively, comparing to state-of-the-art benchmarks. © 2002-2012 IEEE.

关键词： Game theory

Extraordinary Quality Factors in Dual-Band Polarization-Insensitive QuasiBound States in the Continuum

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arXiv 2024年

作者： Ghahremani, Maryam Zapata-Rodríguez, Carlos J. Photonics Research Laboratory Center of Excellence on Applied Electromagnetic Systems School of Electrical and Computer Engineering College of Engineering University of Tehran Tehran Iran Department of Optics and Optometry and Vision Sciences Faculty of Physics University of Valencia Dr. Moliner 50 Burjassot46100 Spain

In this study, we investigate a novel "dimerized" dielectric metasurface featuring dual-mode resonances governed by symmetry-protected bound states in the continuum (BICs). The metasurface design offers advantages such as insensitivity to incident light polarization and exceptionally high quality factors exceeding 105 for low and moderate structural deviations from the monoatomic array. By introducing mastered perturbations in the metasurface via the Brillouin zone folding method, without reducing symmetry, we explore the behavior of symmetry-protected BIC states and their polarization-independent responses. Through numerical simulations and analysis, we demonstrate the superiority of Q factors for specific BIC-based resonances, leading to precise control over interaction behaviors and light engineering in both near and far fields. Our findings contribute to the understanding of BIC resonance interactions and offer insights into the design of high-performance sensing applications and meta-devices with enhanced functionalities. © 2024, CC BY-NC-ND.

关键词： Incident light

Mixture of Experts for Network Optimization: A Large Language Model-enabled Approach

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arXiv 2024年

作者： Du, Hongyang Liu, Guangyuan Lin, Yijing Niyato, Dusit Kang, Jiawen Xiong, Zehui Kim, Dong In The School of Computer Science and Engineering Nanyang Technological University Singapore639798 Singapore The State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing100876 China 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 Sungkyunkwan University Suwon16419 Korea Republic of

Optimizing various wireless user tasks poses a significant challenge for networking systems because of the expanding range of user requirements. Despite advancements in Deep Reinforcement Learning (DRL), the need for customized optimization tasks for individual users complicates developing and applying numerous DRL models, leading to substantial computation resource and energy consumption and can lead to inconsistent outcomes. To address this issue, we propose a novel approach utilizing a Mixture of Experts (MoE) framework, augmented with Large Language Models (LLMs), to analyze user objectives and constraints effectively, select specialized DRL experts, and weigh each decision from the participating experts. Specifically, we develop a gate network to oversee the expert models, allowing a collective of experts to tackle a wide array of new tasks. Furthermore, we innovatively substitute the traditional gate network with an LLM, leveraging its advanced reasoning capabilities to manage expert model selection for joint decisions. Our proposed method reduces the need to train new DRL models for each unique optimization problem, decreasing energy consumption and AI model implementation costs. The LLM-enabled MoE approach is validated through a general maze navigation task and a specific network service provider utility maximization task, demonstrating its effectiveness and practical applicability in optimizing complex networking systems. © 2024, CC BY.

关键词： Reinforcement learning