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Authentication through residual attention-based processing of tampered optical responses

Advanced Photonics 2024年第5期6卷 41-50页

作者： Blake Wilson Yuheng Chen Daksh Kumar Singh Rohan Ojha Jaxon Pottle Michael Bezick Alexandra Boltasseva Vladimir M.Shalaev Alexander V.Kildishev Purdue University Elmore Family School of Electrical and Computer EngineeringWest LafayetteIndianaUnited States Quantum Science Center Oak Ridge National LaboratoryOak RidgeTennesseeUnited States Purdue University School of Aeronautics and AstronauticsWest LafayetteIndianaUnited States Purdue University Department of Computer ScienceWest LafayetteIndianaUnited States

The global chip industry is grappling with dual challenges:a profound shortage of new chips and a surge of counterfeit chips valued at$75 billion,introducing substantial risks of malfunction and unwanted *** counteract this,we propose an optical anti-counterfeiting detection method for semiconductor devices that is robust under adversarial tampering features,such as malicious package abrasions,compromised thermal treatment,and adversarial *** new deep-learning approach uses a RAPTOR(residual,attention-based processing of tampered optical response)discriminator,showing the capability of identifying adversarial tampering to an optical,physical unclonable function based on randomly patterned arrays of gold *** semantic segmentation and labeled clustering,we efficiently extract the positions and radii of the gold nanoparticles in the random patterns from 1000 dark-field images in just 27 ms and verify the authenticity of each pattern using RAPTOR in 80 ms with 97.6%accuracy under difficult adversarial tampering *** demonstrate that RAPTOR outperforms the state-ofthe-art Hausdorff,Procrustes,and average Hausdorff distance metrics,achieving a 40.6%,37.3%,and 6.4%total accuracy increase,respectively.

关键词： machine learning plasmonics physical unclonable function anti-counterfeiting tampering detection

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Equity-Oriented Two-Stage Market Clearing With Differentiated LMPs to Mitigate Energy Overburdens on Disadvantaged Communities

IEEE Transactions on Energy Markets, Policy and Regulation

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IEEE Transactions on Energy Markets, Policy and Regulation 2025年

作者： Liu, Zexin Odonkor, Philip Wu, Lei Stevens Institute of Technology Department of Electrical and Computer Engineering HobokenNJ07030 United States Stevens Institute of Technology Department of Systems Engineering HobokenNJ07030 United States

The transition toward a sustainable electric energy system entails equitable access for consumers from all social groups. However, the current electricity market operation practice renders the same set of locational marginal prices (LMPs) to all consumers, neglecting disparate energy burdens of distinct social groups and inducing potential energy equity issues. To this end, this paper proposes an equity-oriented two-stage day-ahead network-constrained economic dispatch (T-NCED) model for wholesale energy market clearing. It offers stage-wise differentiated LMPs to loads of disparate energy burdens with enhanced energy equity. Specifically, the proposed T-NCED model minimizes the summed operation costs and LMP-based energy overburdens on disadvantaged communities;the first stage determines the optimal generation schedule to supply low-income loads (LILs) of disadvantaged communities and part of high-income loads (HILs) as variables to be optimized in this stage, to render equity-oriented LMPs for LILs with balanced operation costs and energy overburdens;the second stage is an incremental NCED problem which optimally increases power outputs of generators beyond their first-stage dispatch levels to satisfy the remaining HILs. The optimality of the proposed T-NCED model over the classical single-stage NCED (S-NCED) widely adopted by Independent System Operators and Regional Transmission Organizations is theoretically proved. Case studies on modified PJM 5-bus and IEEE 118-bus systems verify that the proposed T-NCED model with differentiated LMPs for loads of disparate energy burdens can effectively enhance energy equity, with insignificant generation deviations from the S-NCED results and reduced energy burdens on disadvantaged communities. © 2023 IEEE.

关键词： Electric industry

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Multidimensional Voronoi Constellations vs. Short Blocklength Probabilistic Shaping: A Comparison for Multilevel Coding Approach

Multidimensional Voronoi Constellations vs. Short Blocklengt...

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Asia Communications and Photonics Conference and Exhibition (ACP)

作者： Yajie Sheng Bin Chen Yi Lei Jingxin Deng Jiwei Xu Mengfan Fu Qunbi Zhuge Shen Li Department of Computer Science and Information Engineering Hefei University of Technology Hefei China Department of Electronic Engineering State Key Laboratory of Advanced Optical Communication Systems and Networks Shanghai Jiao Tong University Shanghai China Department of Electrical and Computer Engineering Centre of Optics Photonics and Lasers (COPL) Université Laval Québec Canada

Performance of concatenated multilevel coding with probabilistic shaping (PS) and Voronoi constellations (VCs) is analysed over AWGN channel. Numerical results show that VCs provide up to 1.3 dB SNR gains over PS-QAM ... 详细信息

ISBN: (数字)9798350379266

ISBN: (纸本)9798350379273

关键词： AWGN channels Wireless communication Optical fibers Probabilistic logic Optical fiber communication Encoding Complexity theory Gain Photonics Signal to noise ratio

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NeuroFusion-Trans: A Novel Transformer-Based EEG-EMG Fusion Model for Assistive Robotics

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IEEE Access 2025年 13卷 103245-103263页

作者： Tipu Sultan Guangping Liu Pascal Sikorski Samah Alshathri Walid El-Shafai Madi Babaiasl Aerospace and Mechanical Engineering Department Saint Louis University St. Louis MO USA Computer Science Department Saint Louis University St. Louis MO USA Department of Information Technology College of Computer and Information Sciences Princess Nourah bint Abdulrahman University P.O. Box 84428 Riyadh Saudi Arabia Computer Science Department Automated Systems and Computing Laboratory (ASCL) Prince Sultan University Riyadh Saudi Arabia Department of Electronics and Electrical Communications Engineering Faculty of Electronic Engineering Menoufia University Menouf Egypt

User intent recognition from multimodal neurophysiological signals, particularly electroencephalography (EEG) and electromyography (EMG), is critical for enhancing human-machine interaction in assistive robotics. Recent advances in neurophysiological signal processing have enabled enhanced user intent recognition for assistive robotics and human-machine interfaces. However, achieving high accuracy and real-time adaptability in electromyography (EMG) and electroencephalography (EEG)-based gesture recognition remains challenging due to temporal misalignment, weak cross-modality fusion, and lack of adaptive learning. This paper proposes NeuroFusion-Trans, a novel transformer-based framework that improves EEG-EMG gesture recognition by improving temporal resolution, using cross-modality attention, and integrating adaptive online learning. Temporal resolution enhancement ensures dynamic EEG-EMG synchronization for improved signal alignment. The cross-modality attention mechanism captures interdependencies between EEG and EMG signals, leading to more accurate intent classification. Adaptive online learning enables real-time personalization by dynamically adjusting to user-specific variations. The model is evaluated on two publicly available EEG-EMG upper-limb gesture datasets: Dataset 1 (5,296 for training, 1,324 for validation) and Dataset 2 (5,276 for training, 1,304 for validation). NeuroFusion-Trans achieves state-of-the-art performance, with an accuracy of 97% and 96% and Cohen’s Kappa of 0.97 and 0.95 after online adaptation, significantly outperforming baseline models such as CNN-LSTM, GRU, and LSTMNet. Ablation studies reveal that removing the cross-modality attention mechanism reduces accuracy by 6.1%, underscoring its importance in exploiting the EEG-EMG dependencies. Turning off synchronization leads to a 6.7% performance drop, demonstrating the necessity of real-time learning for robust intent recognition. Furthermore, NeuroFusion-Trans enhances EEG-EMG synchr

关键词： Brain modeling Electromyography Electroencephalography Adaptation models Real-time systems Synchronization Accuracy Transformers Robots Gesture recognition

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Versatile Test Reactor Open Digital engineering Ecosystem

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Insight 2025年第1期28卷 64-68页

作者： Ritter, Christopher Browning, Jeren Suyderhoud, Peter Hays, Ross Marshall, AnnMarie Han, Kevin Ashbocker, Taylor Darrington, John Nelson, Lee Idaho National Laboratory Department of Digital Systems & Engineering Versatile Test Reactor Program Idaho FallsID83401 United States Pivotal Solutions 3 Grace Avenue Suite 162 Great NeckNY11021 United States North Carolina State University Department of Civil and Environmental Engineering United States Department of Computer Science University of Illinois at Urbana-Champaign 205 N. Mathews Ave. UrbanaIL61801 United States

Modern design of nuclear facilities represents unique challenges: enabling the design of complex advanced concepts, supporting geographically dispersed teams, and supporting first-of-a-kind system development. Errors made early in design can introduce silent errors. These errors can cascade causing unknown risk of complex engineering programs. The Versatile Test Reactor (VTR) Program uses digital-engineering principles for design, procurement, construction, and operation to reduce risk and improve efficiencies. Digital engineering is an integrated, model-based approach which connects proven digital tools such as building information management (BIM), project controls, and systems-engineering software tools into a cohesive environment. The VTR team hypothesizes using these principals can lead to similar risk and cost reductions and schedule efficiencies observed in other engineering industries. This research investigates the use of a digital engineering ecosystem in the design of a 300-MWt sodium-cooled fast reactor. This ecosystem was deployed to over 200 engineers and used to deliver the conceptual design of the VTR. We conclude that initial results show significant reductions in user latency (1000x at peak use), the possibility of direct finite-element-analysis (FEA) integrations to computer-aided design (CAD) tools, and nuclear reactor system design descriptions (SDDs) that we can fully link throughout design in data-driven requirements-management software. These early results led to the VTR maintaining milestone performance during the COVID-19 pandemic. Copyright © 2022 by Idaho National laboratory. Published by INCOSE with permission.

关键词： Sodium-cooled fast reactors

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Sensor Fault Detection and Isolation in Autonomous Nonlinear systems Using Neural Network-Based Observers

Sensor Fault Detection and Isolation in Autonomous Nonlinear...

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European Control Conference (ECC)

作者： John Cao M. Umar B. Niazi Matthieu Barreau Karl Henrik Johansson Division of Decision and Control Systems Digital Futures KTH Royal Institute of Technology Stockholm Sweden Department of Electrical Engineering and Computer Science Laboratory for Information & Decision Systems Massachusetts Institute of Technology Cambridge MA USA

ISBN: (数字)9783907144107

ISBN: (纸本)9798331540920

This paper presents a novel observer-based approach to detect and isolate faulty sensors in nonlinear systems. The proposed sensor fault detection and isolation (s-FDI) method applies to a general class of nonlinear systems. Our focus is on s-FDI for two types of faults: complete failure and sensor degradation. The key aspect of this approach lies in the utilization of a neural network-based Kazantzis-Kravaris/Luenberger (KKL) observer. The neural network is trained to learn the dynamics of the observer, enabling accurate output predictions of the system. Sensor faults are detected by comparing the actual output measurements with the predicted values. If the difference surpasses a theoretical threshold, a sensor fault is detected. To identify and isolate which sensor is faulty, we compare the numerical difference of each sensor measurement with an empirically derived threshold. We derive both theoretical and empirical thresholds for detection and isolation, respectively. Notably, the proposed approach is robust to measurement noise and system uncertainties. Its effectiveness is demonstrated through numerical simulations of sensor faults in a network of Kuramoto oscillators.

关键词： Uncertainty Fault detection Noise Observers Numerical simulation Sensor systems Sensors

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A Hybrid Joint Source-Channel Coding Scheme for Mobile Multi-Hop Networks

A Hybrid Joint Source-Channel Coding Scheme for Mobile Multi...

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IEEE International Conference on Communications (ICC)

作者： Chenghong Bian Yulin Shao Deniz Gündüz Department of Electrical and Electronic Engineering Imperial College London Department of Electrical and Computer Engineering State Key Laboratory of Internet of Things for Smart City University of Macau Macau S.A.R.

ISBN: (数字)9781728190549

ISBN: (纸本)9781728190556

We propose a novel hybrid joint source-channel coding (JSCC) scheme for robust image transmission over multihop networks. In the considered scenario, a mobile user wants to deliver an image to its destination over a mobile cellular network. We assume a practical setting, where the links between the nodes belonging to the mobile core network are stable and of high quality, while the link between the mobile user and the first node (e.g., the access point) is potentially time-varying with poorer quality. In recent years, neural network based JSCC schemes (called DeepJSCC) have emerged as promising solutions to overcome the limitations of separation-based fully digital schemes. However, relying on analog transmission, DeepJSCC suffers from noise accumulation over multi-hop networks. Moreover, most of the hops within the mobile core network may be high-capacity wireless connections, calling for digital approaches. To this end, we propose a hybrid solution, where DeepJSCC is adopted for the first hop, while the received signal at the first relay is digitally compressed and forwarded through the mobile core network. We show through numerical simulations that the proposed scheme is able to outperform both the fully analog and fully digital schemes. Thanks to DeepJSCC it can avoid the cliff effect over the first hop, while also avoiding noise forwarding over the mobile core network thank to digital transmission. We believe this work paves the way for the practical deployment of DeepJSCC solutions in 6G and future wireless networks.

关键词： Image coding Image communication Wireless networks Simulation Noise Semantics Spread spectrum communication

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Distribution-Free Guarantees for systems with Decision-Dependent Noise

arXiv

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arXiv 2024年

作者： Zhang, Heling Ratliff, Lillian J. Dong, Roy The Department of Electrical and Computer Engineering University of Illinois at Urbana-Champaign ChampaignIL61801 United States The Department of Electrical and Computer Engineering University of Washington SeattleWA98195 United States The Department of Industrial and Enterprise Systems Engineering University of Illinois at Urbana-Champaign ChampaignIL61801 United States

In many real-world dynamicalsystems, obtaining precise models of system uncertainty remains a challenge. It may be difficult to estimate noise distributions or robustness bounds, especially when the distributions/robustness bounds vary with different control inputs in unknown ways. Addressing this challenge, this paper presents a novel iterative method tailored for systems with decision-dependent noise without prior knowledge of the distributions. Our approach finds the open-loop control law that minimizes the worst-case loss, given that the noise induced by this control lies in its (1 − p)-confidence set for a predetermined p. At each iteration, we use a quantile method inspired by conformal prediction to empirically estimate the confidence set shaped by the preceding control law. These derived confidence sets offer distribution-free guarantees on the system's noise, guiding a robust control formulation that targets worst-case loss minimization. Under specific regularity conditions, our method is shown to converge to a near-optimal open-loop control. While our focus is on open-loop controls, the adaptive, data-driven nature of our approach suggests its potential applicability across diverse scenarios and extensions. © 2024, CC BY.

关键词： Open loop control

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Comparison of Machine Learning Classifiers for Exchange Rate Trend Forecasting 13

Comparison of Machine Learning Classifiers for Exchange Rate...

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13th International Conference on Information, Intelligence, systems and Applications, IISA 2022

作者： Sarmas, Elissaios Koutsellis, Themistoklis Ververidis, Christopher Papapolyzos, Thomas Choumas, Stylianos Bitsikas, Anastasios Doukas, Haris National Technical University of Athens Decision Support Systems Laboratory School of Electrical and Computer Engineering Athens Greece Hypertech Sa Athens Greece CAPITAL.GR Sa Athens Greece

ISBN: (数字)9781665463904

ISBN: (纸本)9781665463904

The market of foreign exchange is one of the largest markets worldwide. However, predicting the price of exchange currency pairs is a very difficult problem due to the fact that exchange rate time series demonstrate a highly non-linear and non-stationary behavior, being affected by a series of parameters which are difficult to model efficiently. This study attempts to compare five machine learning and neural network classifiers: Logistic Regression model, Support Vector Classifier, Gaussian Naive Bayes, Random Forest and Multi-layer Perceptron. The most highly correlated features are evaluated and compared for predicting the day ahead trend of the Euro-United States Dollar (EUR-USD) currency pair. Results indicate that model selection is not as significant as the combination of the most important features for the accuracy of the prediction. © 2022 IEEE.

关键词： Forecasting

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Asynchronous Distributed Optimization via ADMM with Efficient Communication

Asynchronous Distributed Optimization via ADMM with Efficien...

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IEEE Conference on Decision and Control

作者： Apostolos I. Rikos Wei Jiang Themistoklis Charalambous Karl H. Johansson Department of Electrical and Computer Engineering Division of Systems Engineering Boston University Boston MA US Hong Kong China Department of Electrical and Computer Engineering School of Engineering University of Cyprus Nicosia Cyprus Department of Electrical Engineering and Automation School of Electrical Engineering Aalto University Espoo Finland Division of Decision and Control Systems KTH Royal Institute of Technology Stockholm Sweden

In this paper, we focus on an asynchronous distributed optimization problem. In our problem, each node is endowed with a convex local cost function, and is able to communicate with its neighbors over a directed communication network. Furthermore, we assume that the communication channels between nodes have limited bandwidth, and each node suffers from processing delays. We present a distributed algorithm which combines the Alternating Direction Method of Multipliers (ADMM) strategy with a finite time quantized averaging algorithm. In our proposed algorithm, nodes exchange quantized valued messages and operate in an asynchronous fashion. More specifically, during every iteration of our algorithm each node (i) solves a local convex optimization problem (for the one of its primal variables), and (ii) utilizes a finite-time quantized averaging algorithm to obtain the value of the second primal variable (since the cost function for the second primal variable is not decomposable). We show that our algorithm converges to the optimal solution at a rate of O (1/ k) (where $k$ is the number of time steps) for the case where the local cost function of every node is convex and not-necessarily differentiable. Finally, we demonstrate the operational advantages of our algorithm against other algorithms from the literature.

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