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Cute But Cunning: Circumventing Lognormal Statistic Complexities

IEEE Wireless Communications Letters 2025年第5期14卷 1331-1335页

作者： Rafael Nogueira Da Silva, Carlos Cecilia Luna Alvarado, Maria Daoud Yacoub, Michel Federal University of Triângulo Mineiro Department of Electrical Engineering Uberaba38064-200 Brazil University of Campinas Wireless Technology Laboratory School of Electrical and Computer Engineering Campinas13083-970 Brazil

Though widely used to model various natural phenomena, the lognormal distribution poses significant challenges in mathematical tractability. Its formulation alone is simple, elegant, and easily manageable. But its composition renders this distribution intricate to compute. These challenges hinder straightforward statistical analysis and often necessitate approximations, numerical methods, or simulations for practical use in wireless communications, finance, risk management, etc. In this letter, we aim to ease the analytical intractability of the lognormal distribution by providing asymptotically exact alternative formulations for its probability density function and, through these new formulas, derive some relevant, unprecedented expressions for cumulative distribution function, characteristic function, bit error rate, channel capacity, and composite fading (short-term-long-term). © 2012 IEEE.

关键词： Probability density function

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Can i Get There? Negotiated User-to-User Teleportations in Social VR

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IEEE Transactions on Visualization and computer Graphics 2025年第5期31卷 2320-2330页

作者： Wang, Miao Shu, Wen-Tong Li, Yi-Jun Li, Wanwan School of Computer Science and Engineering State Key Laboratory of Virtual Reality Technology and Systems Beihang University Beijing100191 China Zhongguancun Laboratory Beijing100094 China Tsinghua University Department of Computer Science and Technology Beijing100084 China Tandy School of Computer Science University of Tulsa Tulsa United States

The growing adoption of social virtual reality (VR) platforms underscores the importance of safeguarding personal VR space to maintain user privacy and security. Teleportation, a prevalent instantaneous locomotion method in VR, facilitates user engagement but can also inadvertently intrude upon personal VR space, thereby raising privacy concerns. This paper introduces three innovative negotiated teleportation techniques designed to secure user-to-user teleportation and protect personal space privacy, all under a unified small-group development framework. We have designed and evaluated three types of negotiated teleportation techniques: Sector technique for directional control, Distance technique for minimum social distance control, and Area technique for defining circular permissible teleportation areas. These techniques foster a collaborative approach to selecting teleportation points that respect personal space. To evaluate the efficacy of these techniques, we conducted a user study with 20 participants who performed social tasks within a virtual campus environment. The findings demonstrate that our techniques significantly enhance privacy protection and alleviate anxiety associated with unwanted proximity in social VR. © 1995-2012 IEEE.

关键词： Virtual environments

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Cost-Optimized Task Offloading for Dependent Applications in Collaborative Edge and Cloud Computing

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IEEE Internet of Things Journal 2025年第9期12卷 12975-12988页

作者： Yuan, Haitao Hu, Qinglong Wang, Shen Bi, Jing Buyya, Rajkumar Lu, Jinhu Yang, Jinhong Zhang, Jia Zhou, MengChu Beihang University School of Automation Science and Electrical Engineering Beijing100191 China City University of Hong Kong Department of Computer Science Kowloon Tong Hong Kong Beijing University of Technology School of Software Engineering Faculty of Information Technology Beijing100124 China Laboratory School of Computing and Information Systems MelbourneVIC3010 Australia CSSC Systems Engineering Research Institute Beijing100036 China Southern Methodist University Lyle School of Engineering Department of Computer Science DallasTX75205 United States New Jersey Institute of Technology Department of Electrical and Computer Engineering NewarkNJ07102 United States

A collaborative system that includes mobile devices (MDs), edge nodes (ENs), and the cloud is needed where ENs at the network edge can run offloaded tasks of MDs with limited resources and energy for timely processing for latency-sensitive applications. Unlike existing studies, we formulate a total cost minimization problem for the system for applications, which can be divided into several interdependent subtasks. Each subtask can be executed in MDs, ENs, and the cloud. This work formulates a mixed-integer nonlinear program to minimize the total system cost. To address it, a novel meta-heuristic optimization algorithm called Genetic Simulated-annealing-based Particle swarm optimization with Auto-Encoder (GSPAE) is proposed, which innovatively combines feature extraction of deep learning and global search of meta-heuristic optimization. Genetic operations provide diverse solutions, the Metropolis acceptance of annealing offers a robust global search, and autoencoders (AEs) extract distribution characteristics of particles toward high-quality regions for fast convergence. Thus, GSPAE optimizes the associations between ENs and MDs and the scheduling of subtasks among MDs, ENs, and the cloud. Experiments with large-scale Google cluster datasets show that compared to state-of-the-art benchmark methods, GSPAE reduces the total cost by at least 17% while strictly meeting limits of application latency, available energy, computing, and communication resources of ENs and MDs. © 2014 IEEE.

关键词： Mobile edge computing

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Resilient Operation of DC Standalone Microgrids: Experimental Validation of Hybrid RNN-PI Control for Pulse Load Management

Resilient Operation of DC Standalone Microgrids: Experimenta...

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2025 IEEE Green Technologies Conference, GreenTech 2025

作者： Aghmadi, Ahmed Mohammed, Osama A. Florida International University Energy Systems Research Laboratory Department of Electrical and Computer Engineering MiamiFL33174 United States

ISBN: (纸本)9798331520182

Effective energy storage system (ESS) management is critical for enhancing the performance of standalone DC microgrids, particularly when integrating renewable energy sources such as photovoltaic (PV) or wind systems. This research describes a new hybrid control technique to improve DC microgrid operations' efficiency and resiliency, especially when handling critical loads. This research presents a hybrid closed-loop control technique that uses Recurrent Neural Networks (RNNs) and Proportional-Integral (PI) controllers to address the complex issues of dynamic load situations and transient disturbances. The RNN evaluates voltage errors to generate accurate reference currents, which the PI controllers then adjust to improve the performance of the bidirectional converter. Unlike standard PI-PI controls, our hybrid approach is particularly effective at regulating pulsed power loads (PPLs) and enhancing system response to transitory situations. The experimental results show that the RNN-PI control scheme significantly improves voltage regulation and transient responsiveness, exceeding conventional performance and resilience techniques. This approach provides a convincing solution for enhancing microgrid systems' stability and efficiency in various real-world applications. © 2025 IEEE.

关键词： Electric load distribution

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Prior-Boosted GRL: Microarchitecture Design Space Exploration via Graph Representation Learning

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IEEE Transactions on computer-Aided Design of Integrated Circuits and systems 2025年第3期44卷 1141-1154页

作者： Wu, Zheng Shen, Jinyi Yi, Xiaoling Shang, Li Yang, Fan Zeng, Xuan Fudan University State Key Laboratory of Integrated Circuits and Systems The School of Microelectronics Shanghai200433 China Fudan University State Key Laboratory of Integrated Circuits and Systems Shanghai200433 China KU Leuven Department of Electrical Engineering Microelectronics Circuits and Systems Leuven3000 Belgium Fudan University State Key Laboratory of Integrated Circuits and Systems The School of Computer Science Shanghai200433 China

The design space exploration (DSE) of contemporary microprocessors faces a significant challenge of high-computational cost. In this context, we introduce Prior-boosted graph representation learning (GRL), a novel framework for the DSE of the microarchitectures the microprocessors underpinned by graph embeddings. Using GRL, Prior-boosted GRL constructs a compact and continuous vector space for design representation. This framework is further boosted by an efficient sampling algorithm informed by prior knowledge, which is instrumental in generating a superior set of initial designs to accelerate the exploration process. A well-designed ensemble surrogate model is combined with the multiobjective Bayesian optimization to explore the design space holistically within this graph-embedding domain. Rigorous experimental evaluations conducted on the RISC-V Berkeley-Out-of-Order Machine (BOOM) platform demonstrate that Prior-boosted GRL substantially surpasses preceding methods, achieving a 107.79% enhancement in Pareto front quality compared to the state-of-the-art DSE algorithm. It also outstrips manual designs on performance, power, and area metrics. As of this writing, Prior-boosted GRL holds the first place in the ICCAD 2022 CAD Contest evaluation platform. © 2024 IEEE.

关键词： Graph embeddings

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Flexible, Super-stable, and Multifunctional Capacitive Pressure Sensors with Tilted Micropillar Array by Inclined Laser Ablation and Demolding

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IEEE Sensors Journal 2025年第6期25卷 9367-9374页

作者： Xu, Tianlong Li, Kaixin Zhou, Yihao Zhang, Dawei Sheng, Bin College of Optical Electrical and Computer Engineering Unversity of Shanghai for Science and Technology Shanghai 200093 China. Shanghai Key Laboratory of Modern Optical Systems Engineering Research Center of Optical Instruments and System Shanghai 200093 China

Flexible capacitive pressure sensors have garnered considerable interest across diverse applications, including medical monitoring, electronic skin, and robotic tactile systems, owing to their straightforward fabrication process, low energy consumption, and robust stability. In this paper, we introduce a new inclined laser ablation (ILA) combined with demolding technique for the fabrication of flexible capacitive sensors featuring a microstructured dielectric layer composed of a tilted micropillar array (TMA). The design parameters of TMA were systematically optimized, including the tilt angle of the micropillars, array gap, aspect ratio, and bottom film thickness. The resulting capacitive pressure sensor, equipped with 75° tilted micropillar arrays (aspect ratio of 5:1), demonstrates exceptional sensitivity (1.12 kPa-1) within the low-pressure range (0-2 kPa), rapid response and recovery times, a low pressure detection limit of 0.25 Pa, and transient response stability factor as high as 243.9. The proposed capacitive pressure sensor was validated for the detection and recognition of human motions when attached to various body parts. Additionally, the sensors also demonstrated significant potential for diverse applications, including tactile sensing, and Morse code communication. © 2025 IEEE.

关键词： Aspect ratio

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Monolithic 3-D Integration of Diverse Memories: Resistive Switching (RRAM) and Gain Cell (GC) Memory Integrated on Si CMOS

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IEEE Transactions on Electron Devices 2025年第5期72卷 2685-2690页

作者： Liu, Shuhan Radway, Robert M. Wang, Xinxin Moro, Filippo Nodin, Jean-Francois Jana, Koustav Yan, Lixian Du, Shuting Upton, Luke R. Chen, Wei-Chen Kang, Jimin Chen, Jian Li, Haitong Andrieu, Francois Vianello, Elisa Raina, Priyanka Mitra, Subhasish Wong, H.-S. Philip Stanford University Department of Electrical Engineering StanfordCA94305 United States University of Pennsylvania School of Engineering and Applied Science Department of Electrical and Systems Engineering PhiladelphiaPA19104 United States Université Grenoble Alpes CEA-Leti Grenoble38058 France Purdue University Department of Electrical and Computer Engineering West LafayetteIN47907 United States

The future memory is massive, diverse, and tightly integrated with computing. This research presents tight integration, both physically and architecturally, of two on-chip memory technologies, resistive switching random access memory (RRAM) and gain cell (GC) memory. HfO2 RRAM and indium tin oxide (ITO) GC memory are monolithically integrated on 130-nm Si CMOS technology to form a joint memory that enables low-energy training and low-standby-power inference for edge devices. High-bandwidth on-chip data transfer can have a bandwidth that is 90x state-of-the-art (SoTA) HBM3E and 211x PCIe 7.0, enabled by high-density monolithic 3-D interconnections between memory arrays and high-speed transfer circuit within the integrated joint memory macro. Fabricated atomic layer deposition (ALD) ITO FET exhibits positive VTHof 0.67 V, excellent subthreshold slope (SS) of 65 mV/dec, high ON-current of 20 μA/μm, and low OFF-current of 5 x 10-18 A/μm, as extracted from >5000 s retention. The joint memory macro consumes 78% less standby power and 95% less training energy for MobileBERT compared to SRAM with iso-capacity. This RRAM-GC joint memory facilitates efficient continual learning in edge devices, addressing the challenges of a resource-constrained environment while supporting adaptive artificial intelligence (AI) model updates. © 1963-2012 IEEE.

关键词： RRAM

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Coded Beam Training for RIS-Assisted Wireless Communications

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IEEE Transactions on Wireless Communications 2025年第5期24卷 3854-3867页

作者： Chen, Yuhao Dai, Linglong Tsinghua University State Key Laboratory of Space Network and Communications Department of Electronic Engineering Beijing100084 China Massachusetts Institute of Technology Department of Electrical Engineering and Computer Science CambridgeMA02139 United States

Reconfigurable intelligent surface (RIS) is considered as one of the key technologies for future 6G communications. To fully unleash the performance of RIS, accurate channel state information (CSI) is crucial. Beam training is widely utilized to acquire the CSI. However, before aligning the beam correctly to establish stable connections, the signal-to-noise ratio (SNR) at UE is inevitably low, which reduces the beam training accuracy. To deal with this problem, we exploit the coded beam training framework for RIS systems, which leverages the error correction capability of channel coding to improve the beam training accuracy under low SNR. Specifically, we first extend the coded beam training framework to RIS systems by decoupling the base station-RIS channel and the RIS-user channel. For this framework, codewords that accurately steer to multiple angles is essential for fully unleashing the error correction capability. In order to realize effective codeword design in RIS systems, we then propose a new codeword design criterion, based on which we propose a relaxed Gerchberg-Saxton (GS) based codeword design scheme by considering the constant modulus constraints of RIS elements. In addition, considering the two dimensional structure of RIS, we further propose a dimension reduced encoder design scheme, which can not only guarentee a better beam shape, but also enable a stronger error correction capability. Simulation results reveal that the proposed scheme can realize effective and accurate beam training in low SNR scenarios. © 2002-2012 IEEE.

关键词： Error correction

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Robust Authentication: Leveraging Hardware Fingerprints and AI to Enhance Security Against Spoofing

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IEEE Access 2025年 13卷 86912-86926页

作者： Nimmy, K. Jain, Kurunandan Sachin, S.M. Abekaesh, P.A. Venkitasubramaniam, Parv Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Amritapuri India Amrita Vishwa Vidyapeetham Center for Cybersecurity Systems and Networks Amritapuri India Lehigh University Department of Electrical & Computer Engineering United States

Fingerprint authentication blends biometric precision with user convenience, leveraging unique ridge patterns for fast, reliable, and tamper-resistant identity verification. However, it remains vulnerable to spoofing attacks, where adversaries create synthetic fingerprints using materials like gelatin. To mitigate this, multi-factor authentication (MFA) enhances security by verifying both the fingerprint and the user’s device, restricting access to authorized hardware and complicating attacks. Although Photo Response Non-Uniformity (PRNU) has been explored for device authentication, its reliability diminishes under varying lighting conditions. To address this, we propose an AI-driven approach to generate robust hardware fingerprints from fingerprint scanners, enabling high-accuracy device identification. Our method, trained on 2,000 fingerprint images from 10 scanners, achieves an impressive 98% accuracy in identifying the source scanner. By integrating fingerprint biometrics, hardware fingerprints, and passwords, our novel MFA protocol significantly enhances security against spoofing and forgery attacks. Through security analysis, we demonstrate its effectiveness in preventing unauthorized access. Finally, we validate our authentication framework using an AI-powered binary classifier model, showcasing its practicality for real-world security applications. © 2013 IEEE.

关键词： AI-based classification hardware fingerprint multifactor authentication (MFA) mutual authentication protocol photo-response non-uniformity (PRNU)

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Guest Editorial: Special Issue on Human-Machine Fusion Decision-Making for Emergency Handling

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IEEE Transactions on Automation Science and engineering 2025年 22卷 4427-4433页

作者： Wu, Edmond Q. Li, Jianqiang Chen, Guimin Yuce, Mehmet R. Islam, Shafiqul Ser, Javier Del Yu, Hui Liu, Peter Xiaoping Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai200240 China College of Computer and Software Engineering Shenzhen University Shenzhen518060 China State Key Laboratory for Manufacturing Xi'an Jiaotong University Xi'an710049 China Department of Electrical and Computer Systems Engineering Monash University MelbourneVIC3800 Australia Xavier University of Louisiana New OrleansLA70125 United States Tecnalia Institute Bizkaia48160 Spain School of Creative Technologies University of Portsmouth PortsmouthPO1 2UP United Kingdom Department of Systems and Computer Engineering Carleton University OttawaONKIS 5B6 Canada

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