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IEEE International Test Conference

作者： Aleksa Deric Kyle Mitard Shahin Tajik Daniel Holcomb Department of Electrical and Computer Engineering University of Massachusetts Amherst Amherst Massachusetts Department of Electrical and Computer Engineering Worcester Polytechnic Institute Worcester Massachusetts

ISBN: (数字)9798331520137

ISBN: (纸本)9798331520144

Driven by a need for ever-increasing chip performance, a growing number of semiconductor companies are opting for all-inclusive System-on-Chip (SoC) architectures. Increasingly, the solution adopted to minimize the impact of silicon defects on manufacturing yield of larger dies has been to split a design into multiple smaller dies called chiplets, which are then brought together on a silicon interposer. Advanced 2.5D and 3D packaging techniques that enable this kind of integration also promise increased power efficiency and opportunities for heterogeneous ***, despite their advantages, chiplets are not without issues. Disaggregating a design into multiple separate dies introduces new security threats, including the possibility of tampering with and probing exposed data lines. In this paper we evaluate the exposure of chiplets to probing by applying laser contactless probing techniques to a chiplet-based AMD/Xilinx VU9P FPGA. First, we identify and map interposer wire drivers, and show that probing them is easier compared to probing internal nodes. Lastly, we demonstrate that delay-based sensors, which can be used to protect against physical probes, are insufficient to protect against laser probing.

关键词： Laser theory Three-dimensional displays Chiplets Packaging Silicon Sensors Delays System-on-chip Wire Security

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WIP: Federated Learning for Routing in Swarm Based Distributed Multi-Hop Networks

WIP: Federated Learning for Routing in Swarm Based Distribut...

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IEEE International Symposium on World of Wireless Mobile and Multimedia Networks (WoWMoM)

作者： Martha Cash Joseph Murphy Alexander Wyglinski Department of Electrical and Computer Engineering Worcester Polytechnic Institute Worcester MA

Unmanned Aerial Vehicles (UAVs) are a rapidly emerging technology offering fast and cost-effective solutions for many areas, including public safety, surveillance, and wireless networks. However, due to the highly dynamic network topology of UAVs, traditional mesh networking protocols, such as the Better Approach to Mobile Ad-hoc Networking (B.A.T.M.A.N.), are unsuitable. To this end, we investigate modifying the B.A.T.M.A.N. routing protocol with a machine learning (ML) model and propose implementing this solution using federated learning (FL). This work aims to aid the routing protocol to learn to predict future network topologies and preemptively make routing decisions to minimize network congestion. We also present an FL testbed built on a network emulator for future testing of the proposed ML aided B.A.T.M.A.N. routing protocol.

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DeepSC-Edge: Scientific Corrosion Segmentation with Edge-Guided and Class-Balanced Losses

DeepSC-Edge: Scientific Corrosion Segmentation with Edge-Gui...

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International Conference on Machine Learning and Applications (ICMLA)

作者： Biao Yin Nicholas Josselyn Thomas Considine John Kelley Berend Rinderspacher Robert Jensen James Snyder Ziming Zhang Elke Rundensteiner Department of Data Science Worcester Polytechnic Institute Worcester MA Department of Computer Science Worcester Polytechnic Institute Worcester MA Department of Electrical and Computer Engineering Worcester Polytechnic Institute Worcester MA

Corrosion is a prevalent issue in numerous industrial fields, causing expenses nearing $3 trillion or 4% of the GDP annually with safety threats and environmental pollution. To timely qualify and validate new corrosion-inhibiting materials on a large scale, accurate and efficient corrosion assessment is crucial. Yet it is hindered by a lack of automatic tools for expert-level corrosion segmentation of material science experimental images. Developing such tools is challenging due to limited domain-valid data, image artifacts visually similar to corrosion, various corrosion morphology, strong class imbalance, and millimeter-precision corrosion boundaries. To help the community address these challenges, we curate the first expert-level segmentation annotations for a real-world image dataset [1] for scientific corrosion segmentation. In addition, we design a deep learning based model, called DeepSC-Edge that achieves guidance of ground-truth edge learning by adopting a novel loss that avoids over-fitting to edges. It also is enriched by integrating a class-balanced loss that improves segmentation with small area but crucial edges of interest for scientific corrosion assessment. Our dataset and methods pave the way to advanced deep-learning models for corrosion assessment and generation – promoting new research to connect computer vision and material science discovery. Once the appropriate approvals have been cleared, we expect to release the code and data at: https://***/

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Modeling, Estimation, and Bounds for Precision Two-way Time Transfer and Ranging

Modeling, Estimation, and Bounds for Precision Two-way Time ...

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2022 IEEE Aerospace Conference, AERO 2022

作者： Bidigare, Patrick Obranovich, Charlie Raeman, David Chang, Dan Brown, D. Richard Synoptic Engineering ArlingtonVA22201 United States Worcester Polytechnic Institute Electrical and Computer Engineering Department WorcesterMA01609 United States

ISBN: (数字)9781665437608

ISBN: (纸本)9781665437608

Over-the-air precision two-way time transfer and ranging (PTTR) uses the signals exchanged between two RF transceivers to estimate the range between them and the offset between their clocks. Our paper makes three significant contributions to the estimation and position, navigation, and timing literature. First, we introduce a propagation channel model characterized by its amplitude (attenuation), phase shift, and propagation delay. This three-parameter formulation is quite practical for modeling signal exchange over line-of-sight channels with arbitrary additive wide-sense stationary noise that also captures electronic effects. Second, we derive the Cramer-Rao Lower Bound (CRLB) for this three parameter channel model. We show that there is a canonical definition of phase shift for which the CRLB is a diagonal matrix. The derived CRLB depends on simple properties of the signal and noise power spectral density (PSD) related to whitened bandwidth and whitened center frequency as well as the signal to noise ratio. Third, we derive a maximum likelihood estimator (MLE) for the amplitude, phase shift, and delay given the reference waveform, received waveform samples, and noise PSD. A novel aspect of the derived MLE is that it is implemented entirely in the frequency domain, which makes the MLE more computationally efficient than a typical time-domain implementation. Using an example reference waveform, we show that the derived MLE achieves an empirical covariance close to the CRLB for sufficiently high signal to noise ratios. The estimation results of our paper are directly relevant to over-the-air PTTR applications operating in direct-path, line-of-sight propagation environments. The generality of the waveform and noise formulations allow our estimator and bounds to be applied to arbitrary PTTR ranging waveforms in colored interference environments. We present a system architecture utilizing this estimator that enables PTTR on existing wireless networks with no changes to

关键词： Frequency domain analysis

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Improved IChOA-Based Reinforcement Learning for Secrecy Rate Optimization in Smart Grid Communications

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computers, Materials & Continua 2024年第11期81卷 2819-2843页

作者： Mehrdad Shoeibi Mohammad Mehdi Sharifi Nevisi Sarvenaz Sadat Khatami Diego Martín Sepehr Soltani Sina Aghakhani The WPI Business School Worcester Polytechnic InstituteWorcesterMA 01609-2280USA Department of Computer Science Escuela de Ingeniería Informática de SegoviaUniversidad de ValladolidSegovia40005Spain Department of Data Science Engineering University of HoustonHoustonTX 77204USA Department of Industrial Engineering College of EngineeringUniversity of HoustonHoustonTX 77204USA Department of Industrial and Manufacturing Systems Engineering Iowa State UniversityAmesIA 50011USA

In the evolving landscape of the smart grid(SG),the integration of non-organic multiple access(NOMA)technology has emerged as a pivotal strategy for enhancing spectral efficiency and energy ***,the open nature of wireless channels in SG raises significant concerns regarding the confidentiality of critical control messages,especially when broadcasted from a neighborhood gateway(NG)to smart meters(SMs).This paper introduces a novel approach based on reinforcement learning(RL)to fortify the performance of *** by the need for efficient and effective training of the fully connected layers in the RL network,we employ an improved chimp optimization algorithm(IChOA)to update the parameters of the *** integrating the IChOA into the training process,the RL agent is expected to learn more robust policies faster and with better convergence properties compared to standard optimization *** can lead to improved performance in complex SG environments,where the agent must make decisions that enhance the security and efficiency of the *** compared the performance of our proposed method(IChOA-RL)with several state-of-the-art machine learning(ML)algorithms,including recurrent neural network(RNN),long short-term memory(LSTM),K-nearest neighbors(KNN),support vector machine(SVM),improved crow search algorithm(I-CSA),and grey wolf optimizer(GWO).Extensive simulations demonstrate the efficacy of our approach compared to the related works,showcasing significant improvements in secrecy capacity rates under various network *** proposed IChOA-RL exhibits superior performance compared to other algorithms in various aspects,including the scalability of the NOMA communication system,accuracy,coefficient of determination(R2),root mean square error(RMSE),and convergence *** our dataset,the IChOA-RL architecture achieved coefficient of determination of 95.77%and accuracy of 97.41%in validation *** was accompanied by the lowes

关键词： Smart grid communication secrecy rate optimization reinforcement learning improved chimp optimization algorithm

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An Attention-based Self-Supervised Approach to Monocular Depth Estimation from UAV captured Video Sequences

An Attention-based Self-Supervised Approach to Monocular Dep...

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Pattern Recognition and Artificial Intelligence (PRAI), International Conference on

作者： Yasmina Benkhoui Tahar El-Korchi Reinhold Ludwig Electrical and Computer Engineering Department Worcester Polytechnic Institute Civil and Environmental Engineering Department Worcester Polytechnic Institute

UAV-based inspections represent a promising solution to contactless civil infrastructure condition assessment, especially bridges. In this context, depth estimation is crucial as it helps predict the distance to objects, plan flight trajectories, and avoid obstacles. Recently, learning-based methods made significant progress in monocular depth estimation. However, supervised approaches require a large amount of annotated depth data which remains challenging to collect. As an alternative, researchers have been investigating self-supervised learning, where the depth estimation network is trained by projecting a view to nearby views and minimizing the re-projection photometric loss. However, current methods lack accuracy and robustness since they do not exploit structural information, leading to inaccurate spatial representations and surface discontinuities. In addition, monocular depth estimation suffers from scale ambiguity, and external or prior information is needed for scale recovery. This work sought to overcome these issues by adapting the standard joint network of estimating depth and pose based on a self-learned depth prediction network from monocular videos. The proposed method introduces a soft attention mechanism and a recurrent block. The attention mechanism guides the neural network’s attention to the global structures and local attributes and recovers the spatial relationship of visual features, whereas the recurrent block leverages sequence frames to capture temporal information in addition to spatial information. Our experiments and ablation study on AeroBridge dataset demonstrate that these modifications efficiently capture spatio-temporal information and generate competitive qualitative and quantitative results compared to state-of-the-art methods confirming the practical applicability of our design in inspection tasks.

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Counterfeit Chip Detection using Scattering Parameter Analysis

Counterfeit Chip Detection using Scattering Parameter Analys...

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IEEE Design and Diagnostics of Electronic Circuits and Systems (DDECS)

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

The increase in the number of counterfeit and recycled microelectronic chips in recent years has created significant security and safety concerns in various applications. Hence, detecting such counterfeit chips in electronic systems is critical before deployment in the field. Unfortunately, the conventional verification tools using physical inspection and side-channel methods are costly, unscalable, error-prone, and often incompatible with legacy systems. This paper introduces a generic non-invasive and low-cost counterfeit chip detection based on characterizing the impedance of the system’s power delivery network (PDN). Our method relies on the fact that the impedance of the counterfeit and recycled chips differs from the genuine ones. To sense such impedance variations confidently, we deploy scattering parameters, frequently used for impedance characterization of RF/microwave circuits. Our proposed approach can directly be applied to soldered chips on the system’s PCB and does not require any modifications on the legacy systems. To validate our claims, we perform extensive measurements on genuine and aged samples from two families of STMicroelectronics chips to assess the effectiveness of the proposed approach.

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Outlier Detection and Removal Signal Processing for Wearable Transcutaneous Oxygen Sensor

Outlier Detection and Removal Signal Processing for Wearable...

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IEEE MIT Undergraduate Research Technology Conference (URTC)

作者： Abigail Leonardi Ciara Murphy Sydney Hobson Vanshika Rohera Ulkuhan Guler Electrical and Computer Engineering Department Worcester Polytechnic Institute Worcester MA USA

ISBN: (数字)9798350309652

ISBN: (纸本)9798350309669

Respiratory diseases impact millions of people worldwide. In the smart and connected health era, there is a pressing need for reliable medical devices for remote monitoring of patients with respiratory disorders and patients with chronic diseases in general. For respiratory monitoring, there exist widely-used miniaturized photoplethysmography devices to measure peripheral blood oxygen saturation. More recently, transcutaneous oxygen monitors have been designed in a small form factor to be used as wearable devices to enable remote and continuous measurement of transcutaneous oxygen. In this study, we focus on processing the raw data collected from a noninvasive transcutaneous blood gas monitor that is in the development phase. Post-processing should be applied to remove the outliers in the measurements and smooth out the measurement noise. Various filters to remove outliers were tested in MATLAB, including the Hampel, rmoutliers, filloutliers, and moving average filters. The Hampel filter yielded the best results while maintaining the integrity of the data. The fillmissing function in MATLAB using the previous or linear interpolation methods is used to replace outlier data. The comparison of the raw data and the result of the data processing is presented.

关键词： Interpolation Filters Noise measurement Biomedical monitoring Wearable devices Blood Signal integrity

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Loss Distillation via Gradient Matching for Point Cloud Completion with Weighted Chamfer Distance

Loss Distillation via Gradient Matching for Point Cloud Comp...

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

作者： Fangzhou Lin Haotian Liu Haoying Zhou Songlin Hou Kazunori D Yamada Gregory S. Fischer Yanhua Li Haichong K. Zhang Ziming Zhang Department of Robotics Engineering Worcester Polytechnic Institute Worcester MA USA Graduate School of Information Sciences Tohoku University Sendai Japan Dell Technologies Hopkinton MA USA Department of Computer Science Worcester Polytechnic Institute Worcester MA USA Department of Electrical & Computer Engineering Worcester Polytechnic Institute Worcester MA USA

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

3D point clouds enhanced the robot’s ability to perceive the geometrical information of the environments, making it possible for many downstream tasks such as grasp pose detection and scene understanding. The performance of these tasks, though, heavily relies on the quality of data input, as incomplete can lead to poor results and failure cases. Recent training loss functions designed for deep learning-based point cloud completion, such as Chamfer distance (CD) and its variants (e.g. HyperCD [1]), imply a good gradient weighting scheme can significantly boost performance. However, these CD-based loss functions usually require data-related parameter tuning, which can be time-consuming for data-extensive tasks. To address this issue, we aim to find a family of weighted training losses (weighted CD) that requires no parameter tuning. To this end, we propose a search scheme, Loss Distillation via Gradient Matching, to find good candidate loss functions by mimicking the learning behavior in backpropagation between HyperCD and weighted CD. Once this is done, we propose a novel bilevel optimization formula to train the backbone network based on the weighted CD loss. We observe that: (1) with proper weighted functions, the weighted CD can always achieve similar performance to HyperCD, and (2) the Landau weighted CD, namely Landau CD, can outperform HyperCD for point cloud completion and lead to new state-of-the-art results on several benchmark datasets. Our demo code is available at https://***/Zhang-VISLab/IROS2024-LossDistillationWeightedCD.

关键词： Point cloud compression Training Three-dimensional displays Laser radar Benchmark testing Iterative algorithms Tuning Optimization Intelligent robots Convergence

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A Behavioral Model for Transcutaneous Carbon Dioxide Measurement Using Time-Correlated Single Photon Counting Technique

A Behavioral Model for Transcutaneous Carbon Dioxide Measure...

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Annual International Conference of the IEEE engineering in Medicine and Biology Society (EMBC)

作者： Isil Isiksalan Hakan B. Karli Tuna B. Tufan John Matthews Bige D. Unluturk Ulkuhan Guler Department of Electrical and Computer Engineering Worcester Polytechnic Institute Worcester MA USA Department of Electrical and Computer Engineering Michigan State University Michigan MI USA

ISBN: (数字)9798350371499

ISBN: (纸本)9798350371505

Millimeter-size carbon dioxide (CO 2 )-sensitive luminescence films emerge as practical sensors in miniaturized biomedical devices for measuring transcutaneous CO 2 . The intensity and lifetime of the excited luminophores in the film carry critical information about the CO 2 values in the surrounding environment. It has been proven that lifetime-based measurement is superior to intensity-based measurement because of its robustness to confounding factors. Addressing the challenge of accurately determining the rapid nanosecond-scale lifetime of CO 2 -sensitive luminophores, we introduce a behavioral model using a time-correlated single photon counting system equipped with silicon photomultipliers. We included nonidealities such as dark count rate, afterpulsing, and crosstalk to this model. To recover the lifetime from photon counts, we applied denoising and fitting techniques. With hyperparameter optimization to determine various post-processing parameters, we improved accuracy in recovery for lifetime values ranging from 3 ns to 7 ns. Our results demonstrate the applicability of the proposed model and techniques in real-world scenarios.

关键词： Semiconductor device modeling Accuracy Films Carbon dioxide Biomedical measurement Silicon Sensors System-on-chip Photonics Single-photon avalanche diodes

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