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2021 IEEE International Symposium on Information Theory, ISIT 2021

作者： Nageswaran, Ajaykrishnan Narayan, Prakash University of Maryland Institute for Systems Research Department of Electrical and Computer Engineering College ParkMD20742 United States

ISBN: (纸本)9781538682098

A user generates n independent and identically distributed data rvs with a pmf that must be guarded from a querier. The querier must recover, with a prescribed accuracy, a given function of the data from each of n independent and identically distributed user-devised query responses. The user chooses the data pmf and the random query responses to maximize distribution privacy as gauged by the divergence between the pmf and the querier's best estimate of it based on the n query responses. Considering an arbitrary function, a basic achievable lower bound, that does not depend on n, is provided for distribution privacy. Next, upper (converse) and lower (achievable) bounds, dependent on n, are developed that converge to said basic bound as n grows. Explicit strategies for the user and the querier are identified. © 2021 IEEE.

关键词： Information theory

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Nearest Level Control Based Modular Multi-level Converters For Power Electronics Building Blocks Concept In Electric Ship System

Nearest Level Control Based Modular Multi-level Converters F...

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IEEE Transportation Electrification Conference and Expo (ITEC)

作者： Ali Moghassemi Laxman Timilsina S M Imrat Rahman Ali Arsalan Phani Kumar Chamarthi Gokhan Ozkan Behnaz Papari Christopher S. Edrington Zheyu Zhang Holcombe Department of Electrical and Computer Engineering Clemson University Clemson SC USA Department of Automotive Engineering Clemson University Greenville SC USA Dominion Energy Innovation Center Clemson University North Charleston SC USA Electrical Computer and Systems Engineering Rensselaer Polytechnic Institute Troy NY USA

ISBN: (数字)9798350317664

ISBN: (纸本)9798350317671

Power electronics building blocks (PEBBs) involve the integration of fundamental components into blocks with defined functionality, stacked in series and parallel, to extend converter power ratings to meet naval ship systems’ various power conversion needs. The PEBBs concept in literature is based on modular multilevel converters (MMCs). MMCs are promising candidates for medium and high-power system applications owing to their unique features, such as modularity/scalability/simplicity in structure, low switching losses, low quantization on voltage/current, high reliability, and high efficiency. However, a promising switching control method is required in MMCs to balance the capacitor voltage and suppress the circulating current. This paper presents the nearest level control (NLC) method for the PEBBs concept in modern electric ship systems to simultaneously improve capacitor voltage balancing, circulating current, and power quality. The simulation is conducted in MATLAB/Simulink software. A three-phase five-level MMC converter is considered for the simulation to analyze and compare the converter’s performance based on the proposed NLC and traditional sinusoidal pulse-width modulation (SPWM) switching methods.

关键词： Multilevel converters Capacitors Power quality Transportation Switching loss Switches Power electronics

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Ge2Sb2Se4Te Phase Change Material Monolithic Platform for Integrated Photonics

Ge2Sb2Se4Te Phase Change Material Monolithic Platform for In...

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CLEO: Science and Innovations in CLEO 2024, CLEO: S and I 2024 - Part of Conference on Lasers and Electro-Optics

作者： Acharjee, Niloy Sun, Hongyi Ghosh, Siddhartha Ríos Ocampo, Carlos A. Institute for Research in Electronics & Applied Physics University of Maryland College ParkMD20742 United States Department of Electrical & Computer Engineering University of Maryland College ParkMD20742 United States Department of Materials Science & Engineering University of Maryland College ParkMD20742 United States Space Systems - Northrop Grumman 1 Space Park Blvd Redondo BeachCA90278 United States

We demonstrate a monolithic Ge2Sb2Se4Te platform for tunable photonic integrated circuits. We fabricated and measured various on-chip components, including waveguides with preliminary 55.7±3.65 dB/cm propagation loss. Refractive index tuning is demonstrated via post-fabrication localized crystallization. © Optica Publishing Group 2024

关键词： Photonic integrated circuits

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An Efficient and Secure Post-Quantum Multi-Authority Ciphertext-Policy Attribute-Based Encryption Method Using Lattice

An Efficient and Secure Post-Quantum Multi-Authority Ciphert...

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2023 IEEE INFOCOM Conference on computer Communications Workshops, INFOCOM WKSHPS 2023

作者： Bagchi, Prithwi Bera, Basudeb Maheshwari, Raj Das, Ashok Kumar Yau, David K. Y. Sikdar, Biplab International Institute of Information Technology Center for Security Theory and Algorithmic Research Hyderabad500 032 India Singapore University of Technology and Design Information Systems Technology and Design Singapore487372 Singapore National University of Singapore Department of Electrical and Computer Engineering Singapore117583 Singapore

ISBN: (纸本)9781665494274

This article deals with designing an efficient post-quantum lattice based encryption scheme that relies on the multi-authority Ciphertext-Policy Attribute-Based Encryption (CP-ABE). The security of the proposed scheme is based on the hardness of the ring learning with errors (RLWE) problem. The construction of the proposed scheme is done using the Shamir's threshold secret sharing along with the Lagrange interpolation during the key generation and decryption processes in order to achieve the segmentation and restoration of private keys. A comparative study with the existing state of art schemes has been performed to show the feasibility and efficiency of the proposed scheme. Furthermore, experiments on the proposed scheme have been conducted to illustrate the computational time required during the key generation, encryption and decryption processes. © 2023 IEEE.

关键词： Quantum cryptography

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A Data-Informed Analysis of Scalable Supervision for Safety in Autonomous Vehicle Fleets

arXiv

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arXiv 2024年

作者： Hickert, Cameron Yan, Zhongxia Wu, Cathy The Institute for Data Systems and Society Massachusetts Institute of Technology CambridgeMA02139 United States The Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology CambridgeMA02139 United States The Laboratory for Information & Decision Systems The Institute for Data Systems and Society The Department of Civil and Environmental Engineering Massachusetts Institute of Technology CambridgeMA02139 United States

Autonomous driving is a highly anticipated approach toward eliminating roadway fatalities. At the same time, the bar for safety is both high and costly to verify. This work considers the role of remotely-located human operators supervising a fleet of autonomous vehicles (AVs) for safety. Such a 'scalable supervision' concept was previously proposed to bridge the gap between still-maturing autonomy technology and the pressure to begin commercial offerings of autonomous driving. The present article proposes DISCES, a framework for Data-Informed Safety-Critical Event Simulation, to investigate the practicality of this concept from a dynamic network loading standpoint. With a focus on the safety-critical context of AVs merging into mixed-autonomy traffic, vehicular arrival processes at 1,097 highway merge points are modeled using microscopic traffic reconstruction with historical data from interstates across three California counties. Combined with a queuing theoretic model, these results characterize the dynamic supervision requirements and thereby scalability of the teleoperation approach. Across all scenarios we find reductions in operator requirements greater than 99% as compared to in-vehicle supervisors for the time period analyzed. The work also demonstrates two methods for reducing these empirical supervision requirements: (i) the use of cooperative connected AVs - which are shown to produce an average 3.67 orders-of-magnitude system reliability improvement across the scenarios studied - and (ii) aggregation across larger regions. Copyright © 2024, The Authors. All rights reserved.

关键词： Autonomous vehicles

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Position: Emergent Machina Sapiens Urge Rethinking Multi-Agent Paradigms

arXiv

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arXiv 2025年

作者： Li, Hepeng Liu, Yuhong Yan, Jun Department of Electrical & Computer Engineering University of Maine Orono United States Santa Clara University Santa Clara United States Concordia Institute for Information Systems Engineering Concordia University Montreal Canada

Artificially intelligent (AI) agents that are capable of autonomous learning and independent decision-making hold great promise for addressing complex challenges across domains like transportation, energy systems, and manufacturing. However, the surge in AI systems’ design and deployment driven by various stakeholders with distinct and unaligned objectives introduces a crucial challenge: how can uncoordinated AI systems coexist and evolve harmoniously in shared environments without creating chaos? To address this, we advocate for a fundamental rethinking of existing multi-agent frameworks, such as multi-agent systems and game theory, which are largely limited to predefined rules and static objective structures. We posit that AI agents should be empowered to dynamically adjust their objectives, make compromises, form coalitions, and safely compete or cooperate through evolving relationships and social feedback. Through this paper, we call for a shift toward the emergent, self-organizing, and context-aware nature of these systems. © 2025, CC BY-NC-ND.

关键词： Decision making

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Decentralized Acceleration-Based Bird-Inspired Flocking

Decentralized Acceleration-Based Bird-Inspired Flocking

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IEEE/RSJ International Conference on Intelligent Robots and systems (IROS)

作者： Luca Iacone Erwin Lejeune Tiziano Manoni Sabato Manfredi Dario Albani Autonomous Robotics Research Center Technology Innovation Institute Abu Dhabi UAE Department of Computer Science Vrije Universiteit Amsterdam The Netherlands Department Electrical Engineering and Information Technology Federico II University Naples Italy

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

In the following, we analyze and discuss the implementation of a novel approach for distributed flocking behavior applied to a group of Uncrewed Aerial Vehicle (UAV)s, also referred to as drones. Inspired by natural flocking phenomena observed in birds, which demonstrate coordinated movement in response to internal and external stimuli, we tackle the problem of robust and dynamic aerial motion for robots and design a control law based on a novel physical model. In contrast to previous works that rely on velocity or position-based references, this approach leverages an acceleration-based law to describe the collective dynamics of many interacting particles. As observed in the following, a third-order control possesses several advantages compared to first or second-order control, such as smoother transitions, better force balancing, and more responsive and dynamic behaviors. These advantages are thoroughly analyzed in the following, thanks to physics-based realistic simulations and field experiments with medium-sized UAVs in an unstructured outdoor environment.

关键词： Navigation Robot kinematics Perturbation methods Dynamics Life estimation Autonomous aerial vehicles Trajectory Vehicle dynamics Drones Testing

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RFFCE: Residual Feature Fusion and Confidence Evaluation Network for 6DoF Pose Estimation

RFFCE: Residual Feature Fusion and Confidence Evaluation Net...

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IEEE International Conference on robotics and Automation (ICRA)

作者： Qiwei Meng Shanshan Ji Shiqiang Zhu Tianlei Jin Te Li Jason Gu Wei Song Research Center for Intelligent Robotics Zhejiang Lab Research Institute of Interdisciplinary Innovation Hangzhou China Zhejiang Engineering Research Center for Intelligent Robotics Hangzhou China Department of Electrical and Computer Engineering Dalhousie University Halifax NS Canada

In this paper, we propose a novel RGBD-based object 6DoF pose estimation network - RFFCE. It is a two-stage method that firstly leverages deep neural networks for feature extraction and object points matching, and then the geometric principles are utilized for final pose computation. Our approach consists of three primary innovations: residual feature fusion for representative RGBD feature extraction; confidence evaluation and confidence-based paired points offsets regression for self-evaluation and self-optimization respectively. Their effectiveness is verified through an ablation study, and our RFFCE achieves the SOTA performance on LineMOD, Occlusion-LineMOD and YCB-Video datasets. Additionally, we also conduct a real-world object grasping experiment for visualization and qualitative evaluation of the RFFCE.

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AXI-REALM: A Lightweight and Modular Interconnect Extension for Traffic Regulation and Monitoring of Heterogeneous Real-Time SoCs

AXI-REALM: A Lightweight and Modular Interconnect Extension ...

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Design, Automation and Test in Europe Conference and Exhibition

作者： Thomas Benz Alessandro Ottaviano Robert Balas Angelo Garofalo Francesco Restuccia Alessandro Biondi Luca Benini Integrated Systems Laboratory ETH Zurich Switzerland Department of Electrical Electronic and Information Engineering University of Bologna Italy Department of Computer Science and Engineering UC San Diego San Diego CA USA Department of Excellence in Robotics & AI Scuola Superiore Sant'Anna Pisa Italy

ISBN: (数字)9783981926385

ISBN: (纸本)9798350348606

The increasing demand for heterogeneous functionality in the automotive industry and the evolution of chip manufac-turing processes have led to the transition from federated to integrated critical real-time embedded systems (CRTESs). This leads to higher integration challenges of conventional timing predictability techniques due to access contention on shared resources, which can be resolved by providing system-level observability and controllability in hardware. We focus on the interconnect as a shared resource and propose AXI-REALM, a lightweight, modular, and technology - independent real-time extension to industry-standard AXI4 interconnects, available open-source. AXI-REALM uses a credit-based mechanism to distribute and control the bandwidth in a multi-subordinate system on periodic time windows, proactively prevents denial of service from malicious actors in the system, and tracks each manager's access and interference statistics for optimal budget and period selection. We provide detailed performance and implementation cost assessment in a 12nm node and an end-to-end functional case study implementing AXI-REALM into an open-source Linux-capable RISC-V SoC. In a system with a general-purpose core and a hardware accelerator's DMA engine causing interference on the interconnect, AXI-REALM achieves fair bandwidth distribution among managers, allowing the core to recover 68.2 % of its performance compared to the case without contention. Moreover, near-ideal performance (above 95 %) can be achieved by distributing the available bandwidth in favor of the core, improving the worst-case memory access latency from 264 to below eight cycles. Our approach minimizes buffering compared to other solutions and introduces only 2.45 % area overhead compared to the original SoC.

关键词： Bandwidth Interference Real-time systems Hardware Regulation Timing System-on-chip

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OffRIPP: Offline RL-based Informative Path Planning

arXiv

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arXiv 2024年

作者： Gadipudi, Srikar Babu Deolasee, Srujan Kailas, Siva Luo, Wenhao Sycara, Katia Kim, Woojun The Department of Electrical Engineering Indian Institute of Technology Madras India The Robotics Institute Carnegie Mellon University United States The School of Interactive Computing Georgia Institute of Technology United States The Department of Computer Science University of North Carolina at Charlotte United States

Informative path planning (IPP) is a crucial task in robotics, where agents must design paths to gather valuable information about a target environment while adhering to resource constraints. Reinforcement learning (RL) has been shown to be effective for IPP, however, it requires environment interactions, which are risky and expensive in practice. To address this problem, we propose an offline RL-based IPP framework that optimizes information gain without requiring real-time interaction during training, offering safety and cost-efficiency by avoiding interaction, as well as superior performance and fast computation during execution—key advantages of RL. Our framework leverages batch-constrained reinforcement learning to mitigate extrapolation errors, enabling the agent to learn from pre-collected datasets generated by arbitrary algorithms. We validate the framework through extensive simulations and real-world experiments. The numerical results show that our framework outperforms the baselines, demonstrating the effectiveness of the proposed approach. Copyright © 2024, The Authors. All rights reserved.

关键词： Reinforcement learning

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