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Modeling the effect of hatched grounds on the radio-frequency performance of microstrip lines on flexible substrate

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Modeling the effect of hatched grounds on the radio-frequenc...

作者： Abdelatty, Mohamed Y. Umar, Ashraf Obeidat, Abdullah S. Enakerakpo, Emuobosan Alhendi, Mohammed Poliks, Mark D. Dept. of Systems Science and Industrial Engineering State University of New York Binghamton United States Dept. of Electrical and Computer Engineering State University of New York Binghamton United States

Cross-hatching of PCB grounds refers to a process in which certain ground planes appear as copper lattices;regular openings are placed at regular intervals. Nowadays, the efficacy of ground hatching on rigid PCBs is minimal. However, it is becoming essential for flexible hybrid electronics. Utilizing hatched grounds for flexible applications could offer some benefits. Hatched grounds can perform dual roles, act as ground surfaces, and offer structural support. Hatched grounds are more durable for bending and stretching applications. Also, hatched grounds are desirable in terms of material conservation. In this paper, we focus on studying how hatched grounds affect the RF performance of a straight microstrip line. Simulations show that cross-hatched grounds with more than 50% filling produces good radio-frequency performance. We will study the effect hatching pattern as well as the density of the hatching on the RF performance of the microstrips. The theoretical modeling is considered in this work and fabricating aerosol jet silver ink microstrip lines on PET substrates will be considered as an extension of this work. © 2022 IMAPS-International Microelectronics and Packaging Society. All rights reserved.

关键词： Microstrip lines

Guaranteeing Service in Connected Microgrids: Storage Planning and Optimal Power Sharing Policy

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arXiv 2024年

作者： Dey, Arnab Khatana, Vivek Mani, Ankur Salapaka, Murti V. Department of Electrical and Computer Engineering University of Minnesota Twin Cities United States Department of Industrial and Systems Engineering University of Minnesota Twin Cities United States

The integration of renewable energy sources (RES) into power distribution grids poses challenges to system reliability due to the inherent uncertainty in their power production. To address this issue, battery energy sources (BESs) are being increasingly used as a promising solution to counter the uncertainty associated with RES power production. During the overall system planning stage, the optimal capacity of the BES has to be decided. In the operational phase, policies on when to charge the BESs and when to use them to support loads must be determined so that the BES remains within its operating range, avoiding depletion of charge on one hand and remaining within acceptable margins of maximum charge on the other. In this paper, a stochastic control framework is used to determine battery capacity, for microgrids, which ensures that during the operational phase, BESs' operating range is respected with pre-specified high probability. We provide an explicit analytical expression of the required BESs energy capacity for a single microgrid with RES as the main power source. Leveraging insights from the single microgrid case, the article focuses on the design and planning of BESs for the two-microgrid scenario. In this setting, microgrids are allowed to share power while respecting the capacity constraints imposed by the power lines. We characterize the optimal power transfer policy between the microgrids and the optimal BES capacity for multiple microgrids. This provides the BES savings arising from connecting the microgrids. © 2024, CC BY.

关键词： Power distribution planning

RESIST: Resilient Decentralized Learning Using Consensus Gradient Descent

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arXiv 2025年

作者： Fang, Cheng Dixit, Rishabh Bajwa, Waheed U. Gürbüzbalaban, Mert Department of Electrical and Computer Engineering Rutgers University New BrunswickNJ United States Department of Mathematics University of California San DiegoCA United States Departments of Electrical and Computer Engineering and Statistics Rutgers University New BrunswickNJ United States Departments of Electrical and Computer Engineering Management Science and Information Systems and Statistics Rutgers University New BrunswickNJ United States

Empirical risk minimization (ERM) is a cornerstone of modern machine learning (ML), supported by advances in optimization theory that ensure efficient solutions with provable algorithmic convergence rates, which measure the speed at which optimization algorithms approach a solution, and statistical learning rates, which characterize how well the solution generalizes to unseen data. Privacy, memory, computational, and communications constraints increasingly necessitate data collection, processing, and storage across network-connected devices. In many applications, these networks operate in decentralized settings where a central server cannot be assumed, requiring decentralized ML algorithms that are both efficient and resilient. Decentralized learning, however, faces significant challenges, including an increased attack surface for adversarial interference during decentralized learning processes. This paper focuses on the man-in-the-middle (MITM) attack, wherein adversaries exploit communication vulnerabilities between devices to inject malicious updates during training, potentially causing models to deviate significantly from their intended ERM solutions. To address this challenge, we propose RESIST (Resilient dEcentralized learning using conSensus gradIent deScenT), an optimization algorithm designed to be robust against adversarially compromised communication links, where transmitted information may be arbitrarily altered before being received. Unlike existing adversarially robust decentralized learning methods, which often (i) guarantee convergence only to a neighborhood of the solution, (ii) lack guarantees of linear convergence for strongly convex problems, or (iii) fail to ensure statistical consistency as sample sizes grow, RESIST overcomes all three limitations. It achieves algorithmic and statistical convergence for strongly convex, Polyak–Lojasiewicz, and nonconvex ERM problems by employing a multistep consensus gradient descent framework and robust statis

关键词： Adversarial machine learning

Task Shift: From Classification to Regression in Overparameterized Linear Models

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arXiv 2025年

作者： LaBonte, Tyler Lai, Kuo-Wei Muthukumar, Vidya H. Milton Stewart School of Industrial and Systems Engineering Georgia Institute of Technology United States School of Electrical and Computer Engineering Georgia Institute of Technology United States

Modern machine learning methods have recently demonstrated remarkable capability to generalize under task shift, where latent knowledge is transferred to a different, often more difficult, task under a similar data distribution. We investigate this phenomenon in an overparameterized linear regression setting where the task shifts from classification during training to regression during evaluation. In the zero-shot case, wherein no regression data is available, we prove that task shift is impossible in both sparse signal and random signal models for any Gaussian covariate distribution. In the few-shot case, wherein limited regression data is available, we propose a simple postprocessing algorithm which asymptotically recovers the ground-truth predictor. Our analysis leverages a fine-grained characterization of individual parameters arising from minimum-norm interpolation which may be of independent interest. Our results show that while minimum-norm interpolators for classification cannot transfer to regression a priori, they experience surprisingly structured attenuation which enables successful task shift with limited additional data. Copyright © 2025, The Authors. All rights reserved.

关键词： Zero-shot learning

Self-Supervised Graph Transformer with Contrastive Learning for Brain Connectivity Analysis Towards Improving Autism Detection 22

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Self-Supervised Graph Transformer with Contrastive Learning ...

22nd IEEE International Symposium on Biomedical Imaging, ISBI 2025

作者： Leng, Yicheng Anwar, Syed Muhammad Rekik, Islem He, Sen Lee, Eung-Joo University of Arizona Department of Electrical and Computer Engineering Tucson United States Children's National Hospital WashingtonDC United States Imperial College London Department of Computing London United Kingdom University of Arizona Department of Systems and Industrial Engineering Tucson United States

ISBN: (纸本)9798331520526

Functional Magnetic Resonance Imaging (fMRI) provides useful insights into the brain function both during task or rest. Representing fMRI data using correlation matrices is found to be a reliable method of analyzing the inherent connectivity of the brain in the resting and active states. Graph Neural Networks (GNNs) have been widely used for brain network analysis due to their inherent explainability capability. In this work, we introduce a novel framework using contrastive self-supervised learning graph transformers, incorporating a brain network transformer encoder with random graph alterations. The proposed network leverages both contrastive learning and graph alterations to effectively train the graph transformer for autism detection. Our approach, tested on Autism Brain Imaging Data Exchange (ABIDE) data, demonstrates superior autism detection, achieving an AUROC of 82.6 and an accuracy of 74%, surpassing current state-of-the-art methods. © 2025 IEEE.

关键词： Functional neuroimaging

Investigating Factors Affecting Electric Vehicle Users’ Procrastination Charging Behavior

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Proceedings of the Human Factors and Ergonomics Society Annual Meeting 2023年第1期67卷 1295 - 1301页

作者： Mehrsa Khaleghikarahrodi Jeremy Herrera Santos Fatima Lynch-Smith Gretchen A. Macht Department of Mechanical Industrial & Systems Engineering University of Rhode Island Kingston RI USA Department of Electrical Computer & Biomedical Engineering University of Rhode Island Kingston RI USA

The existence of electric vehicle users who procrastinate their charging activities – delay in charging until a critically low state of charge (i.e., almost empty battery) – is acknowledged in the previous survey studies and data-driven analyses. However, the underlying reasons affecting such behavior have not been explored. This study attempts to justify whether procrastination in charging is due to larger battery capacities and users’ close proximity to charging stations. The results indicate that battery sizes can have a significant effect on EV users’ procrastination charging behavior. This study found that users with battery capacities greater than 21kWh tend to procrastinate charging since they can travel longer distances. The findings will be able to serve a broad range of EV industry stakeholders due to the emerging market and continuous improvement for achieving mass EV adoption.

关键词： Electric Vehicle User Behavior Procrastination Battery Size

Automated Warehouse 5G Infrastructure Modeling Using Variational Autoencoders

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Automated Warehouse 5G Infrastructure Modeling Using Variati...

International Symposium on Networks, computers and Communications (ISNCC)

作者： Rahul Gulia Amlan Ganguly Andres Kwasinski Michael E. Kuhl Ehsan Rashedi Clark Hochgraf Department of Computer Engineering Rochester Institute of Technology Rochester NY Department of Industrial and Systems Engineering Rochester Institute of Technology Rochester NY Department of Electrical and Computer Engineering Technology Rochester Institute of Technology Rochester NY

ISBN: (数字)9798350364910

ISBN: (纸本)9798350364927

The next decade is poised for a transformative shift in wireless communication technologies, driven by the increasing demand for data-intensive applications. Innovations in signal processing, network architecture estimation and design, and spectrum utilization will be critical in realizing the potential of 5G and beyond. These advancements will enable the seamless integration of emerging technologies and empower the digital transformation of industries and society. In this paper, we introduce a Variational Autoencoder (VAE) based model for indoor radio propagation modeling within an automated Industry4.0 warehouse or factory floor in the 5G wireless bands. We detail the creation of training data tensors, the architecture of our proposed VAE model, and its training using tensors that capture the impact of various interacting objects within a 5G-enabled smart warehouse infrastructure. The model is trained on multiple warehouse configurations to predict the signal-to-interference-plus-noise ratio (SINR) for both the validation dataset and unknown warehouse configurations. We present reconstruction error heatmaps to demonstrate the accuracy of our model and analyze its performance in different complex warehouse environments.

关键词： Analytical models Adaptation models Accuracy 5G mobile communication Indoor radio communication Training data Interference Predictive models Data models Signal to noise ratio

Robust Online Epistemic Replanning of Multi-Robot Missions

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arXiv 2024年

作者： Bramblett, Lauren Miloradović, Branko Sherman, Patrick Papadopoulos, Alessandro V. Bezzo, Nicola Departments of Systems and Information Engineering and Electrical and Computer Engineering University of Virginia CharlottesvilleVA22904 United States Division of Intelligent Future Technologies Mälardalen University Sweden

As Multi-Robot systems (MRS) become more affordable and computing capabilities grow, they provide significant advantages for complex applications such as environmental monitoring, underwater inspections, or space exploration. However, accounting for potential communication loss or the unavailability of communication infrastructures in these application domains remains an open problem. Much of the applicable MRS research assumes that the system can sustain communication through proximity regulations and formation control or by devising a framework for separating and adhering to a predetermined plan for extended periods of disconnection. The latter technique enables an MRS to be more efficient, but breakdowns and environmental uncertainties can have a domino effect throughout the system, particularly when the mission goal is intricate or time-sensitive. To deal with this problem, our proposed framework has two main phases: i) a centralized planner to allocate mission tasks by rewarding intermittent rendezvous between robots to mitigate the effects of the unforeseen events during mission execution, and ii) a decentralized replanning scheme leveraging epistemic planning to formalize belief propagation and a Monte Carlo tree search for policy optimization given distributed rational belief updates. The proposed framework outperforms a baseline heuristic and is validated using simulations and experiments with aerial vehicles. Copyright © 2024, The Authors. All rights reserved.

关键词： Multipurpose robots

Assessing the Performance of AI-Generated Code: A Case Study on GitHub Copilot

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Assessing the Performance of AI-Generated Code: A Case Study...

International Symposium on Software Reliability engineering (ISSRE)

作者： Shuang Li Yuntao Cheng Jinfu Chen Jifeng Xuan Sen He Weiyi Shang School of Computer Science Wuhan University Wuhan China Department of Systems and Industrial Engineering University of Arizona Tucson United States Department of Electrical and Computer Engineering University of Waterloo Waterloo Canada

ISBN: (数字)9798350353884

ISBN: (纸本)9798350353891

The integration of Large Language Models (LLMs) into software development tools like GitHub Copilot holds the promise of transforming code generation processes. While AI-driven code generation presents numerous advantages for software development, code generated by large language models may introduce challenges related to security, privacy, and copyright issues. However, the performance implications of AI-generated code remain insufficiently explored. This study conducts an empirical analysis focusing on the performance regressions of code generated by GitHub Copilot across three distinct datasets: HumanEval, AixBench, and MBPP. We adopt a comprehensive methodology encompassing static and dynamic performance analyses to assess the effectiveness of the generated code. Our findings reveal that although the generated code is functionally correct, it frequently exhibits performance regressions compared to code solutions crafted by humans. We further investigate the code-level root causes responsible for these performance regressions. We identify four major root causes, i.e., inefficient function calls, inefficient looping, inefficient algorithm, and inefficient use of language features. We further identify a total of ten sub-categories of root causes attributed to the performance regressions of generated code. Additionally, we explore prompt engineering as a potential strategy for optimizing performance. The outcomes suggest that meticulous prompt designs can enhance the performance of AI-generated code. This research offers valuable insights contributing to a more comprehensive understanding of AI-assisted code generation.

关键词： Privacy Codes Large language models Focusing Software reliability Performance analysis Prompt engineering Security Software development management