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IC Backside Tamper Detection Using Impedance Sensing

IEEE Access 2025年 13卷 90706-90725页

作者： Tahoura Mosavirik Shahin Tajik Electrical and Computer Engineering Department Worcester Polytechnic Institute Worcester MA USA

The expansion of flip-chip technologies and lack of backside protection made integrated circuits (ICs) vulnerable to certain classes of physical attacks mounted from the IC’s backside, including laser-assisted probing, electromagnetic side-channel analysis, body biasing injection (BBI), and focused ion beam (FIB) editing. Existing countermeasures are expensive, incompatible with standard IC fabrication, and unsuitable for legacy systems such as field-programmable gate arrays (FPGAs), which are widely used in industrial and defense systems. In this paper, we demonstrate how impedance monitoring of the printed circuit board (PCB) and the IC package’s power distribution network (PDN) using on-chip circuit-based network analyzers can detect IC backside tampering. Our method is based on the fact that any attempt to expose the backside silicon substrate, such as the removal of the fan and heatsinks, or polishing it, leads to changes in the equivalent impedance of the package’s PDN, and hence scanning the package impedance will reveal if the package integrity has been violated. To validate our claims, we deployed an on-FPGA network analyzer on an AMD Zynq UltraScale+ MPSoC (16 nm), which is part of a multi-PCB system. We performed a series of backside tamper attacks, including the removal of cooling components and IC backside silicon milling/polishing, across different temperatures, and evaluated detection effectiveness leveraging the difference of means as the statistical metric.

关键词： Impedance Silicon Optical sensors Integrated optics Transistors Substrates Sensors Photonics Flip-chip devices Optical imaging

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Framework for Implementing Quantum Neural Networks in Wireless Communications

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IEEE Access 2025年 13卷 91655-91670页

作者： Salik Siddiqui Justin Holzer Joshua Malcarne Galahad M. B. Wernsing Alexander M. Wyglinski Electrical and Computer Engineering Department Worcester Polytechnic Institute Worcester MA USA

While quantum computing is already being employed in different domains to solve large scale and highly complex optimization problems, the wireless communications community has only recently begun exploring the potential of Quantum Machine Learning (QML). This paper provides a reference design approach that allows the wireless community to effectively leverage QML in optimizing their own wireless communications networks. Such an approach will become increasingly valuable as quantum computing matures and conventional computing methods reach their practical limits due to the high dimensionality of optimization problems. We present a detailed methodology enabling wireless communications practitioners to construct their own Quantum Neural Networks (QNN) implementations and benchmark their performance against conventional techniques. Optimization strategies for wireless communication channels using QNNs are explored. The resulting framework provides a generalizable, easy-to-adopt methodology for integrating QML into wireless networks, requiring minimal prior knowledge of quantum computing.

关键词： Quantum computing Wireless communication Optimization Neural networks Qubit Logic gates Quantum mechanics Machine learning computer architecture Wireless networks

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Robust 3D point clouds classification based on declarative defenders

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Neural Computing and Applications 2025年第3期37卷 1209-1221页

作者： Li, Kaidong Zhang, Tianxiao Zhong, Cuncong Zhang, Ziming Wang, Guanghui Department of Electrical Engineering & Computer Science University of Kansas 1520 West 15th St. LawrenceKS66045 United States Department of Electrical & Computer Engineering Worcester Polytechnic Institute 100 Institute Rd. WorcesterMA01609 United States Department of Computer Science Toronto Metropolitan University 350 Victoria Street TorontoONM5B 2K3 Canada

3D point cloud classification requires distinct models from 2D image classification due to the divergent characteristics of the respective input data. While 3D point clouds are unstructured and sparse, 2D images are structured and dense. Bridging the domain gap between these two data types is a non-trivial challenge to enable model interchangeability. Recent research using lattice point classifier (LPC) highlights the feasibility of cross-domain applicability. However, the lattice projection operation in LPC generates 2D images with disconnected projected pixels. In this paper, we explore three distinct algorithms for mapping 3D point clouds into 2D images. Through extensive experiments, we thoroughly examine and analyze their performance and defense mechanisms. Leveraging current large foundation models, we scrutinize the feature disparities between regular 2D images and projected 2D images. The proposed approaches demonstrate superior accuracy and robustness against adversarial attacks. The generative model-based mapping algorithms yield regular 2D images, further minimizing the domain gap from regular 2D classification tasks. The source code is available at https://***/KaidongLi/***. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Generative adversarial networks

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Spectroscopic photoacoustic denoising framework using hybrid analytical and data-free learning method

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Photoacoustics 2025年 44卷 100729页

作者： Lin, Fangzhou Gao, Shang Tang, Yichuan Ma, Xihan Murakami, Ryo Zhang, Ziming Obayemi, John D. Soboyejo, Winston O. Zhang, Haichong K. Department of Robotics Engineering Worcester Polytechnic Institute 100 Institute Road WorcesterMA01609 United States Department of Electrical & Computer Engineering Worcester Polytechnic Institute 100 Institute Road WorcesterMA01609 United States 60 Prescott Street WorcesterMA01605 United States Department of Mechanical & Materials Engineering Worcester Polytechnic Institute 100 Institute Road WorcesterMA01609 United States Department of Computer Science Worcester Polytechnic Institute 100 Institute Road WorcesterMA01609 United States

Spectroscopic photoacoustic (sPA) imaging uses multiple wavelengths to differentiate and quantify chromophores based on their unique optical absorption spectra. This technique has been widely applied in areas such as vascular mapping, tumor detection, and therapeutic monitoring. However, PA imaging is highly susceptible to noise, leading to a low signal-to-noise ratio (SNR) and compromised image quality. Furthermore, low SNR in spectral data adversely affects spectral unmixing outcomes, hindering accurate quantitative PA imaging. Traditional denoising techniques like frame averaging, though effective in improving SNR, can be impractical for dynamic imaging scenarios due to reduced frame rates. Advanced methods, including learning-based approaches and analytical algorithms, have demonstrated promise but often require extensive training data and parameter tuning. Moreover, spectral information preservation is unclear, limiting their adaptability for clinical usage. Additionally, training data is not always accessible for learning-based methods. In this work, we propose a Spectroscopic Photoacoustic Denoising (SPADE) framework using hybrid analytical and data-free learning method. This framework integrates a data-free learning-based method with an efficient BM3D-based analytical approach while preserving spectral integrity, providing noise reduction, and ensuring that functional information is maintained. The SPADE framework was validated through simulation, phantom, in vivo, and ex vivo studies. These studies demonstrated that SPADE improved image SNR by over 15 dB in high noise cases and preserved spectral information (R > 0.8), outperforming conventional methods, especially in low SNR conditions. SPADE presents a promising solution for preserving the accuracy of quantitative PA imaging in clinical applications where noise reduction and spectral preservation are critical. © 2025 The Authors

关键词： Image denoising

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5DGWO-GAN:A Novel Five-Dimensional Gray Wolf Optimizer for Generative Adversarial Network-Enabled Intrusion Detection in IoT Systems

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computers, Materials & Continua 2025年第1期82卷 881-911页

作者： Sarvenaz Sadat Khatami Mehrdad Shoeibi Anita Ershadi Oskouei Diego Martín Maral Keramat Dashliboroun Department of Data Science Engineering University of HoustonHoustonTX 77204USA The WPI Business School Worcester Polytechnic InstituteWorcesterMA 01609USA School of Systems and Enterprises Stevens Institute of TechnologyHobokenNJ 07030USA Department of Computer Science Escuela de Ingeniería Informática de SegoviaUniversidad de ValladolidSegovia40005Spain Ernest G.Welch School of Art&Design Georgia State UniversityAtlantaGA 30303USA

The Internet of Things(IoT)is integral to modern infrastructure,enabling connectivity among a wide range of devices from home automation to industrial control *** the exponential increase in data generated by these interconnected devices,robust anomaly detection mechanisms are *** detection in this dynamic environment necessitates methods that can accurately distinguish between normal and anomalous behavior by learning intricate *** paper presents a novel approach utilizing generative adversarial networks(GANs)for anomaly detection in IoT ***,optimizing GANs involves tuning hyper-parameters such as learning rate,batch size,and optimization algorithms,which can be challenging due to the non-convex nature of GAN loss *** address this,we propose a five-dimensional Gray wolf optimizer(5DGWO)to optimize GAN *** 5DGWO introduces two new types of wolves:gamma(γ)for improved exploitation and convergence,and theta(θ)for enhanced exploration and escaping local *** proposed system framework comprises four key stages:1)preprocessing,2)generative model training,3)autoencoder(AE)training,and 4)predictive model *** generative models are utilized to assist the AE training,and the final predictive models(including convolutional neural network(CNN),deep belief network(DBN),recurrent neural network(RNN),random forest(RF),and extreme gradient boosting(XGBoost))are trained using the generated data and AE-encoded *** evaluated the system on three benchmark datasets:NSL-KDD,UNSW-NB15,and *** conducted on diverse IoT datasets show that our method outperforms existing anomaly detection strategies and significantly reduces false *** 5DGWO-GAN-CNNAE exhibits superior performance in various metrics,including accuracy,recall,precision,root mean square error(RMSE),and convergence *** proposed 5DGWO-GAN-CNNAE achieved the lowest RMSE values across the NSL-KDD,UNSW-NB15,and

关键词： Internet of things intrusion detection generative adversarial networks five-dimensional binary gray wolf optimizer deep learning

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Liver Necrotic Mapping Using Diffusing Illumination Photoacoustic Imaging

Liver Necrotic Mapping Using Diffusing Illumination Photoaco...

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Photons Plus Ultrasound: Imaging and Sensing 2025

作者： Gao, Shang Hua, Yanbo Bhagavatula, Sharath K. Liu, Guigen Jonas, Oliver Zhang, Haichong K. Department of Robotics Engineering Worcester Polytechnic Institute MA United States Department of Biomedical Engineering Worcester Polytechnic Institute MA United States Department of Computer Science Worcester Polytechnic Institute MA United States Department of Radiology Brigham and Women’s Hospital Harvard Medical School MA United States

ISBN: (纸本)9781510683860

Liver cancer treatments often face challenges, particularly when tumors are considered surgically unresectable. Percutaneous radiofrequency ablation (RFA) offers a minimally invasive alternative but can have higher recurrence rates due to incomplete ablations. Standard non-contrast ultrasound imaging cannot distinguish ablation-induced necrosis, limiting ability to monitor treatment and ensure complete tumor coverage during the ablation procedure. Photoacoustic (PA) imaging, which uses light to generate ultrasound signals, has shown promise in distinguishing between ablated and normal tissues through spectroscopic PA (sPA) imaging. However, conventional light delivery methods are unsuitable for minimally invasive procedures due to bulky fiber bundles. Optical fibers, though suitable for less invasive insertion, have limited illumination volume because of the confined light delivery only at the fiber tip. This study introduces a diffusing fiber approach for effective light delivery within the liver, enhancing the PA excitation area compared to conventional tip illumination. The diffusing fiber was tested in both phantom and ex vivo liver tissue studies. Results demonstrated that the diffusing fiber provided a wider PA excitation area and more homogeneous illumination. In an ex vivo porcine liver, the diffusing fiber accurately mapped the ablated region with minimal deviation from gross pathological measurements. Our findings suggest that this diffusing fiber approach could significantly improve the accuracy of PA imaging for liver ablation, potentially reducing recurrence rates by ensuring complete tumor ablation and minimizing complications by enabling real time monitoring. This innovation paves the way for more reliable and effective minimally invasive treatments for liver cancer. © 2025 SPIE.

关键词： Photoacoustic microscopy

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A Language Anchor-Guided Method for Robust Noisy Domain Generalization

arXiv

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arXiv 2025年

作者： Dai, Zilin Wang, Lehong Lin, Fangzhou Wang, Yidong Li, Zhigang Yamada, Kazunori D. Zhang, Ziming Lu, Wang Department of Computer Science Worcester Polytechnic Institute WorcesterMA01609 United States Department of Electrical Engineering Worcester Polytechnic Institute WorcesterMA01609 United States Department of Robotics Engineering Worcester Polytechnic Institute WorcesterMA01609 United States Carnegie Mellon University PittsburghPA15213 United States Tohoku University Sendai980-8572 Japan Tsinghua University Beijing100190 China Peking University Beijing100871 China

Real-world machine learning applications are often hindered by two critical challenges: distribution shift and label noise. Networks inherently tend to overfit to redundant, uninformative features present in the training distribution, which undermines their ability to generalize effectively to the target domain’s distribution. The presence of noisy data further exacerbates this issue by inducing additional overfitting to noise, causing existing domain generalization methods to fail in effectively distinguishing invariant features from spurious ones. To address these challenges, we propose Anchor Alignment and Adaptive Weighting (A3W), a novel algorithm based on sample reweighting guided by natural language processing (NLP) anchors that seeks to extract representative features. In particular, A3W leverages semantic representations derived from natural language models to serve as a source of domain-invariant prior knowledge. We also introduce a weighted loss function that dynamically adjusts the contribution of each sample based on its distance to the corresponding NLP anchor, thereby improving the model’s resilience to noisy labels. Extensive experiments on benchmark datasets demonstrate that A3W outperforms state-of-the-art domain generalization methods, yielding significant improvements in both accuracy and robustness across various datasets and noise levels. © 2025, CC BY.

关键词： Adversarial machine learning

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Cross-Modality Registration using Bone Surface Pointcloud for Robotic Ultrasound-Guided Spine Surgery

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Journal of Medical Robotics Research 2025年第0期

作者： Ma, Xihan Zhang, Xiao Wang, Yang Nycz, Christopher J. Sungarian, Arno Ji, Songbai Huang, Xinming Zhang, Haichong K. Department of Robotics Engineering Worcester Polytechnic Institute 100 Institute Rd. Worcester 01609 MA United States Department of Electrical and Computer Engineering Worcester Polytechnic Institute 100 Institute Rd. Worcester 01609 MA United States Memorial Medical Center University of Massachusetts 55 N Lake Ave. Worcester 01655 MA United States Department of Biomedical Engineering Worcester Polytechnic Institute 100 Institute Rd. Worcester 01609 MA United States

Image guidance using preoperative magnetic resonance imaging (MRI) and intraoperative ultrasound (US) can improve the outcome of spine surgery. Employing a robotic US system (RUSS) allows the automated acquisition of large 3D US volumes, facilitating accurate registration. However, such registration remains challenging due to the cross-modality discrepancy. To address this issue, we present a pipeline that extracts spine pointclouds from MRI and 3D US to perform per-vertebra registration. Experiments showed a registration accuracy of 1.82 mm in terms of residual root mean square error and 7.02 mm in terms of Chamfer distance. The pipeline exhibits superior robustness to suboptimal initial conditions compared with the two baseline methods. It also demonstrated good time efficiency under real-time conditions, demonstrating the potential applicability in RUSS-guided spine surgeries. © World Scientific Publishing Company.

关键词： cross-modality registration image-guided intervention robotic ultrasound Spine surgery ultrasound

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SAFE: Sound Analysis for Fall Event detection using machine learning

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Smart Health 2025年 35卷

作者： Garcia, Antony Huang, Xinming Department of Electrical and Computer Engineering Worcester Polytechnic Institute Worcester 01609 MA United States Department of Electrical Engineering Technological University of Panama Panama City 0819 Panama

This study evaluates the application of machine learning (ML) and deep learning (DL) algorithms for fall detection using sound signals. The work is supported by the Sound Analysis for Fall Events (SAFE) dataset, comprising 950 audio samples, including 475 fall events recorded with a grappling dummy to simulate realistic scenarios. Decision tree-based ML algorithms achieved a classification accuracy of 93% at lower sampling rates, indicating that critical features are preserved despite reduced resolution. DL models, using spectrogram-based feature extraction, reached accuracies up to 99%, surpassing traditional ML methods in performance. Linear models also achieved high accuracy (up to 97%) in various spectrogram techniques, emphasizing the separability of audio features. These results establish the viability of sound-based fall detection systems as efficient and accurate solutions. © 2024

关键词： Audio classification Deep learning Fall detection Feature extraction Machine learning Spectrogram analysis

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Adaptive Knowledge Graphs Enhance Medical Question Answering: Bridging the Gap Between LLMs and Evolving Medical Knowledge

arXiv

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arXiv 2025年

作者： Rezaei, Mohammad Reza Fard, Reza Saadati Parker, Jayson L. Krishnan, Rahul G. Lankarany, Milad Department of Biomedical Engineering University of Toronto Canada Department of Computer Science Worcester Polytechnic Institute United States Department of Biology University of Toronto Mississauga Canada Department of Computer Science University of Toronto Canada Vector Institute Canada

Large Language Models (LLMs) have greatly advanced medical Question Answering (QA) by leveraging vast clinical data and medical literature. However, the rapid evolution of medical knowledge and the labor-intensive process of manually updating domain-specific resources can undermine the reliability of these systems. We address this challenge with Adaptive Medical Graph-RAG (AMG-RAG), a comprehensive framework that automates the construction and continuous updating of Medical Knowledge Graphs (MKGs), integrates Chain-of-Thought (CoT) reasoning, and retrieves current external evidence (e.g., PubMed, WikiSearch). By dynamically linking new findings and complex medical concepts, AMG-RAG not only boosts accuracy but also enhances interpretability for medical queries. Evaluations on the MEDQA and MEDMCQA benchmarks demonstrate the effectiveness of AMG-RAG, achieving an F1 score of 74.1% on MEDQA and an accuracy of 66.34% on MEDMCQA—surpassing both comparable models and those 10 to 100 times larger. Importantly, these improvements are achieved without increasing computational overhead, underscoring the critical impact of automated knowledge graph generation and external evidence retrieval in delivering up-to-date, trustworthy medical insights. © 2025, CC BY.

关键词： Knowledge graph

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