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IEEE International Workshop on Hardware-Oriented Security and Trust (HOST)

作者： Tasnuva Farheen Sourav Roy Jia Di Shahin Tajik Domenic Forte Department of Electrical and Computer Engineering University of Florida Department of Electrical and Computer Engineering University of Arkansas Department of Electrical and Computer Engineering Worcester Polytechnic Institute

ISBN: (数字)9798350373943

ISBN: (纸本)9798350373950

Security of a computing system can be compromised by physical attacks against the hardware. Side-channel attacks involve the observation of integrated circuit (IC) or system behavior to extract assets such as cryptographic keys. Fault injection attacks involve the manipulation of underlying circuits to provoke erroneous operations that lead to the escalation of privilege or leakage of secret information. Some side-channel and fault injection attacks force circuits to work at temperatures out of operating range. Several countermeasures have been proposed to detect temperature change. However, they do not perform well in wide temperature fluctuations, have a large overhead area, or introduce other reliability concerns. This paper proposes a calibratable Null Convention Logic-based (NCL) polymorphic sensor that changes its functionality with temperature and can sense whether the temperature goes below or above a carefully designed application-specific threshold. It can be used with a wide range of process technologies without requiring significant modification. Low area overhead, low power consumption, configurable nature, and easy integration make it ideal for ICs. Our results show that this sensor can work at various temperature ranges with reliable operation with ± 1 0 C confidence, 8 μm 2 area and 19.2 μW power overhead, which is approximately 40 $x$ less overhead compared to alternative techniques.

关键词： Temperature sensors Integrated circuits Temperature distribution Power demand Fluctuations Force Side-channel attacks

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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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Additively Manufactured Receiver Design for Wirelessly-Powered Biomedical Applications

Additively Manufactured Receiver Design for Wirelessly-Power...

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IEEE Biomedical Circuits and Systems (BIOCAS)

作者： Amin Hazrati Marangalou Miguel Arturo Gonzalez Ulkuhan Guler Department of Electrical and Computer Engineering Worcester Polytechnic Institute Worcester MA USA

This paper presents a miniaturized receiver prototype fabricated using an additive manufacturing electronics process. We aim to demonstrate the feasibility and advantages of using additive manufacturing for embedding the essential components of an inductive link in a compact form factor for miniaturized biomedical devices. We demonstrated various geometries of planar and helical coils, with average diameters of 20 mm. The wireless power transfer system comprises a class-E power amplifier operating at 6.78 MHz, a 10 µH transmitter coil, and a full-wave active rectifier fabricated in TSMC 180 nm CMOS process technology. The power delivered to a 500 Ω load at the output of the active rectifier varies from 2.7 mW to 42 mW for planar coils and 3 mW to 45 mW for helical coils, over a range of 1 cm to 4 cm. The transmitter’s power consumption is kept constant at 1.25 W throughout the experiments. Furthermore, in the case of helical coils, a lateral center-to-center misalignment of 1 cm at a distance of 2 cm between the receiver and transmitter results in a 10 mW drop in the output power.

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FDM Printing: a Fabrication Method for Fluidic Soft Circuits?

arXiv

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arXiv 2023年

作者： Kendre, Savita V. Wang, Lehong Wilke, Ethan Pacheco, Nicholas Fichera, Loris Nemitz, Markus P. Department of Robotics Engineering Worcester Polytechnic Institute WorcesterMA01609 United States Department of Computer Science Worcester Polytechnic Institute WorcesterMA01609 United States Department of Biomedical Engineering Worcester Polytechnic Institute WorcesterMA01609 United States Department of Mechanical and Materials Engineering Worcester Polytechnic Institute WorcesterMA01609 United States Department of Electrical and Computer Engineering Worcester Polytechnic Institute WorcesterMA01609 United States

Existing fluidic soft logic gates for the control of soft robots either rely on extensive manual fabrication processes or expensive printing techniques. In our work, we explore Fused Deposition Modeling for creating fully 3D printed fluidic logic gates. We print a soft bistable valve from thermoplastic polyurethane using a desktop FDM printer. We introduce a new printing nozzle for extruding tubing. Our fabrication strategy reduces the production time of soft bistable valves from 27 hours with replica molding to 3 hours with a FDM printer. Our rapid and cost-effective fabrication process for fluidic logic gates seeks to democratize fluidic circuitry for the control of soft robots. Copyright © 2023, The Authors. All rights reserved.

关键词： Cost effectiveness

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MAVR: Multi-Functional Point Cloud Annotations Using Virtual Reality

MAVR: Multi-Functional Point Cloud Annotations Using Virtual...

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IEEE Conference on High Performance Extreme Computing (HPEC)

作者： Xiao Zhang Zhanhong Huang Xinming Huang Department of Electrical & Computer Engineering and Department of Computer Science Worcester Polytechnic Institute Worcester MA USA

Learning-based point cloud perception methods rely on labeled data for training data-driven models, necessi-tating the development of precise and efficient tools for point cloud annotations. In this paper, we propose MAVR, a multi-functional annotation framework based on virtual reality (VR) technology, capable of accurately labeling point cloud data for diverse applications, including part segmentation and object detection. We begin by evaluating the user interface (UI) efficiency through interactive efficiency analysis. Subsequently, a comprehensive three-step process is introduced, which consists of pre-processing, point selection, and post-tagging. For 3D object part segmentation and scene perception, we propose two distinct tagging pipelines. Our experimental results on various datasets validate the effectiveness of MAVR in accurately annotating point clouds from different data sources within an immersive workspace.

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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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MediatorDNN: Contention Mitigation for Co-Located DNN Inference Jobs

MediatorDNN: Contention Mitigation for Co-Located DNN Infere...

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IEEE International Conference on Cloud Computing, CLOUD

作者： Seyed Morteza Nabavinejad Sherief Reda Tian Guo Computer Science Department Worcester Polytechnic Institute Worcester MA School of Engineering Brown University Providence RI

ISBN: (数字)9798350368536

ISBN: (纸本)9798350368543

With the increase in computing power of cutting-edge hardware platforms, it is a common practice to run multiple jobs on a single machine for improved resource utilization and throughput. However, this leads to inevitable resource contention among co-located jobs, impacting their performance. The resource contention can worsen due to fluctuations in resource utilization of jobs caused by variations in their input workload. To tackle the co-location contention for DNN inference jobs, we propose MediatorDNN, which considers contention and resource utilization variation when co-locating DNN inference jobs. It profiles each DNN, monitors microarchitectural metrics such as memory bandwidth and cache access pattern, and high-level resource utilization like CPU utilization. Based on profiling results and leveraging Modern Portfolio Theory (MPT), MediatorDNN decides on the co-location of jobs. Experimental results with various DNNs on two hardware platforms show that MediatorDNN improves throughput by up to 108% (21 % on average) compared to an approach only considering contention and ignoring resource utilization variation.

关键词： Measurement Cloud computing Microarchitecture Prevention and mitigation Artificial neural networks Throughput Hardware

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FairCIM: Fair Interference Mitigation by DNN Switching for Latency-Sensitive Inference Jobs

FairCIM: Fair Interference Mitigation by DNN Switching for L...

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Autonomic Computing and Self-Organizing Systems (ACSOS),International Conference on

作者： Seyed Morteza Nabavinejad Sherief Reda Tian Guo Computer Science Department Worcester Polytechnic Institute Worcester MA School of Engineering Brown University Providence RI

ISBN: (数字)9798350363876

ISBN: (纸本)9798350363883

It is a common practice to co-locate several jobs, such as DNN inference ones, on a single machine to improve resource utilization, energy efficiency, and overall performance. However, individual job performance might degrade when these jobs compete for shared resources. This degradation can in particular affect latency-sensitive jobs bound by Service Level Agreements (SLA), and result in SLA violations. To tackle this issue and mitigate the impact of interference on latency, we design a system called FairCIM. For certain applications such as image classification, various DNNs with varying accuracy and computational complexity are proposed. Upon detection of SLA violations, FairCIM dynamically switches between those DNNs to maintain the latency in the presence of co-location interference. To ensure fairness, FairCIM selects the jobs for DNN switching based on their resource utilization. We conduct experiments using diverse DNNs, datasets, and hardware platforms to evaluate the FairCIM effectiveness. Compared to another approach that uses a resource exchange mechanism, FairCIM’s flexibility achieved by DNN switching substantially reduces SLA violations and improves the effective accuracy by up to 244%.

关键词： Accuracy Prevention and mitigation Artificial neural networks Switches Interference Hardware Resource management Faces Service level agreements Image classification

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On Contrastive Learning for Domain Generalization

On Contrastive Learning for Domain Generalization

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International Conference on Machine Learning and Applications (ICMLA)

作者： Thuan Nguyen D. Richard Brown Department of Engineering Engineering Technology and Surveying East Tennessee State University TN Department of Electrical and-Computer Engineering Worcester Polytechnic Institute Worcester MA

ISBN: (数字)9798350374889

ISBN: (纸本)9798350374896

Given the training data and labels from several seen domains, Domain Generalization (DG) aims to learn models that generalize well on unlabeled data from unseen domains. Due to the distribution of data and/or labels may vary between domains, current DG methods are mainly based on the theme of domain-invariant representation learning which decomposes the learning process into two steps: (1) finding a representation function from the input space to the representation/feature space to learn the so-called domain-invariant features, i.e., the features that are statistically stable between domains, and (2) designing a classifier on top of these domain-invariant features which is mutually optimal for all domains. Recent works on DG show that Contrastive Learning (CL) which was originally proposed to learn a representation function such that similar samples are pulled closer while dissimilar samples are pushed far away in the representation/feature space, appears as a promising solution for domain-invariant features learning. In this paper, we first establish the fundamental theory to justify why CL might be a potential approach for DG by showing that CL, under particular settings, allows a mechanism to minimize a loss function that enforces an optimal classifier for all training domains. Based on these foundations, we revisit the recent works that apply CL for DG to indicate their limitations and suggest a modification by combining the CL method with current DG methods for better out-of-domain generalization. Our numerical results point out that our proposed algorithm can achieve state-of-the-art performance on multiple DG benchmark datasets.

关键词： Training Representation learning Machine learning algorithms Training data Contrastive learning Benchmark testing Data models Classification algorithms

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

arXiv

引用

arXiv 2023年

作者： Cash, Martha Murphy, Joseph Wyglinski, Alexander Department of Electrical and Computer Engineering Worcester Polytechnic Institute WorcesterMA United States

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. © 2023, CC BY.

关键词： Machine learning

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