检索结果-内蒙古大学图书馆

Effects of Interference on Beamforming-Enabled Vehicular Networks in Multipath Propagation Environments

学校读者我要写书评

暂无评论

Effects of Interference on Beamforming-Enabled Vehicular Net...

IEEE Conference on Vehicular Technology (VTC)

作者： Nivetha Kanthasamy Alexander M. Wyglinski Raghvendra Cowlagi Department of Electrical and Computer Engineering Worcester Polytechnic Institute Worcester MA Aerospace Engineering Worcester Polytechnic Institute Worcester MA

ISBN: (数字)9781728152073

ISBN: (纸本)9781728140537

This paper characterizes the impact of interference on vehicular communications employing beamforming links between transmitters and receivers operating within a multipath propagation environment. Different road scenarios, such as an intersection and a roundabout are considered to determine the performance characteristics of the vehicle communication link. By employing the results for different antenna array elements and by varying distance, we analyse the bit error rate (BER) of vehicles operating in multipath propagation environments with an interferer within the vicinity of an communication link.

关键词： Antenna arrays Interference Transmitters Roads Array signal processing Receiving antennas

Deep Direct Discriminative Decoders for High-dimensional Time-series Data Analysis

学校读者我要写书评

暂无评论

arXiv 2022年

作者： Rezaei, Mohammad R. Popovic, Milos R. Lankarany, Milad Yousefi, Ali Eden, Uri T. Krembil Brain Institute University Health Network Toronto Canada KITE Research Institute Rehabilitation Institute University Health Network Toronto Canada Department of Physiology University of Toronto Toronto Canada WorcesterMA United States Institute of Biomedical Engineering University of Toronto Toronto Canada Department of Mathematics and Statistics Boston University BostonMA United States Department of Computer Science Worcester Polytechnic Institute WorcesterMA United States

The state-space models (SSMs) are widely utilized in the analysis of time-series data. SSMs rely on an explicit definition of the state and observation processes. Characterizing these processes is not always easy and becomes a modeling challenge when the dimension of observed data grows or the observed data distribution deviates from the normal distribution. Here, we propose a new formulation of SSM for high-dimensional observation processes. We call this solution the deep direct discriminative decoder (D4). The D4 brings deep neural networks’ expressiveness and scalability to the SSM formulation letting us build a novel solution that efficiently estimates the underlying state processes through high-dimensional observation signal. We demonstrate the D4 solutions in simulated and real data such as Lorenz attractors, Langevin dynamics, random walk dynamics, and rat hippocampus spiking neural data and show that the D4 performs better than traditional SSMs and RNNs The D4 can be applied to a broader class of time-series data where the connection between high-dimensional observation and the underlying latent process is hard to characterize. © 2022, CC BY.

关键词： Normal distribution

Automatic extraction of secrets from the transistor jungle using laser-assisted side-channel attacks

学校读者我要写书评

暂无评论

arXiv 2021年

作者： Krachenfels, Thilo Kiyan, Tuba Tajik, Shahin Seifert, Jean-Pierre Technische Universität Berlin Germany Worcester Polytechnic Institute Department of Electrical and Computer Engineering United States Fraunhofer SIT

The security of modern electronic devices relies on secret keys stored on secure hardware modules as the root-of-trust (RoT). Extracting those keys would break the security of the entire system. As shown before, sophisticated side-channel analysis (SCA) attacks, using chip failure analysis (FA) techniques, can extract data from on-chip memory cells. However, since the chip’s layout is unknown to the adversary in practice, secret key localization and reverse engineering are onerous tasks. Consequently, hardware vendors commonly believe that the ever-growing physical complexity of the integrated circuit (IC) designs can be a natural barrier against potential adversaries. In this work, we present a novel approach that can extract the secret key without any knowledge of the IC’s layout, and independent from the employed memory technology as key storage. We automate the – traditionally very labor-intensive – reverse engineering and data extraction process. To that end, we demonstrate that black-box measurements captured using laser-assisted SCA techniques from a training device with known key can be used to profile the device for a later key prediction on other victim devices with unknown keys. To showcase the potential of our approach, we target keys on three different hardware platforms, which are utilized as RoT in different products. Copyright © 2021, The Authors. All rights reserved.

关键词： Side channel attack

How Simulation Helps Autonomous Driving: A Survey of Sim2real, Digital Twins, and Parallel Intelligence

学校读者我要写书评

暂无评论

arXiv 2023年

作者： Hu, Xuemin Li, Shen Huang, Tingyu Tang, Bo Huai, Rouxing Chen, Long The School of Artificial Intelligence Hubei University Hubei Wuhan430062 China The Department of Electrical and Computer Engineering Worcester Polytechnic Institute WorcesterMA01609 United States Beijing Huairou Academy of Parallel Sensing Beijing101499 China The State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100864 China The Waytous Inc. Beijing100083 China

Safety and cost are two important concerns for the development of autonomous driving technologies. From the academic research to commercial applications of autonomous driving vehicles, sufficient simulation and real world testing are required. In general, a large scale of testing in simulation environment is conducted and then the learned driving knowledge is transferred to the real world, so how to adapt driving knowledge learned in simulation to reality becomes a critical issue. However, the virtual simulation world differs from the real world in many aspects such as lighting, textures, vehicle dynamics, and agents' behaviors, etc., which makes it difficult to bridge the gap between the virtual and real worlds. This gap is commonly referred to as the reality gap (RG). In recent years, researchers have explored various approaches to address the reality gap issue, which can be broadly classified into three categories: transferring knowledge from simulation to reality (sim2real), learning in digital twins (DTs), and learning by parallel intelligence (PI) technologies. In this paper, we consider the solutions through the sim2real, DTs, and PI technologies, and review important applications and innovations in the field of autonomous driving. Meanwhile, we show the state-of-the-arts from the views of algorithms, models, and simulators, and elaborate the development process from sim2real to DTs and PI. The presentation also illustrates the far-reaching effects and challenges in the development of sim2real, DTs, and PI in autonomous driving. Copyright © 2023, The Authors. All rights reserved.

关键词： Textures

Grand Challenges in the Verification of Autonomous Systems

学校读者我要写书评

暂无评论

arXiv 2024年

作者： Leahy, Kevin Asgari, Hamid Dennis, Louise A. Feather, Martin S. Fisher, Michael Ibanez-Guzman, Javier Logan, Brian Olszewska, Joanna I. Redfield, Signe Robotics Engineering Department Worcester Polytechnic Institute WorcesterMA United States Complex and Autonomous Systems Research Group Thales UK Research Reading United Kingdom Jet Propulsion Laboratory California Institute of Technology PasadenaCA United States Department of Computer Science University of Manchester Manchester United Kingdom Renault Guyancourt France Department of Computing Science University of Aberdeen Aberdeen United Kingdom Department of Information and Computing Sciences Utrecht University Utrecht Netherlands School of Computing and Engineering University of the West of Scotland Glasgow United Kingdom U.S. Naval Research Laboratory WashingtonDC United States

Autonomous systems use independent decisionmaking with only limited human intervention to accomplish goals in complex and unpredictable environments. As the autonomy technologies that underpin them continue to advance, these systems will find their way into an increasing number of applications in an ever wider range of settings. If we are to deploy them to perform safety-critical or mission-critical roles, it is imperative that we have justified confidence in their safe and correct operation. Verification is the process by which such confidence is established. However, autonomous systems pose challenges to existing verification practices. This paper highlights viewpoints of the Roadmap Working Group of the IEEE Robotics and Automation Society Technical Committee for Verification of Autonomous Systems, identifying these grand challenges, and providing a vision for future research efforts that will be needed to address them. © 2024, CC BY.

关键词：

Bumblebee-Inspired C-V2X Dynamic Spectrum Access Testbed Using OpenAirInterface

学校读者我要写书评

暂无评论

Bumblebee-Inspired C-V2X Dynamic Spectrum Access Testbed Usi...

IEEE Conference on Vehicular Technology (VTC)

作者： Kuldeep S. Gill Kevin N. Heath Sreeshti Chuke Aneela Haider Robert J. Gegear Elizabeth F. Ryder Alexander M. Wyglinski Department of Electrical and Computer Engineering Worcester Polytechnic Institute Worcester MA Department of Biology & Biotechnology Worcester Polytechnic Institute Worcester MA

ISBN: (数字)9781728152073

ISBN: (纸本)9781728140537

Cellular Vehicle-to-everything (C-V2X) was introduced by 3GPP in Release 14, which supports direct vehicle-to-vehicle (V2V) communication using sidelink channels. There are two modes in C-V2X for supporting V2V communication: Mode 3 and Mode 4. Both modes support V2V communications but differ on their spectrum allocation methodology. In out-of-coverage scenario defined under Mode 4, vehicles reserve spectrum resources using semi-persistent scheduling to enable direct communication. Currently, Intelligent Transportation System (ITS) spectrum at 5.9 GHz is emerging as an immediate resource to facilitate vehicular communication which has already been assigned for IEEE 802.11p. However, these dedicated channels will not be sufficient to support the connected vehicles in densely populated cities. Leveraging the existing underutilized wireless spectrum elsewhere, such as in digital television spectrum band is a likely solution. An intelligent scheme is required which can make adaptive decisions based on channel environment to efficiently use the secondary spectrum. Bumblebee behavioral model possess evolved decision-making mechanisms to adaptively solve similar problems while foraging in environments containing multiple floral resources (channels). Simulation results using bumblebee-based algorithm have shown that a memory system based on the averaging of stored channel energy information dramatically increased VDSA performance over a memoryless system in both urban and highway scenarios by 52% and 20%, respectively. In this paper, we propose a vehicular dynamic spectrum access (VDSA) solution based on bumblebee behavioral model for C-V2X and evaluate its performance using OpenAirInterface (OAI) LTE-Sidelink platform.

关键词： Digital TV Long Term Evolution Switches Sensors Dynamic spectrum access Protocols Bandwidth

Self-learn to explain siamese networks robustly

学校读者我要写书评

暂无评论

arXiv 2021年

作者： Chen, Chao Shen, Yifan Ma, Guixiang Kong, Xiangnan Rangarajan, Srinivas Zhang, Xi Xie, Sihong Computer Science and Engineering Dept Lehigh University BUPT University of Illinois at Chicago Worcester Polytechnic Institute Department of Chemical and Biomolecular Engineering Lehigh University

Learning to compare two objects are essential in applications, such as digital forensics, face recognition, and brain network analysis, especially when labeled data are scarce and imbalanced. As these applications make high-stake decisions and involve societal values like fairness and transparency, it is critical to explain the learned models. We aim to study post-hoc explanations of Siamese networks (SN) widely used in learning to compare. We characterize the instability of gradient-based explanations due to the additional compared object in SN, in contrast to architectures with a single input instance. We propose an optimization framework that derives global invariance from unlabeled data using self-learning to promote the stability of local explanations tailored for specific query-reference pairs. The optimization problems can be solved using gradient descent-ascent (GDA) for constrained optimization, or SGD for KL-divergence regularized unconstrained optimization, with convergence proofs, especially when the objective functions are nonconvex due to the Siamese architecture. Quantitative results and case studies on tabular and graph data from neuroscience and chemical engineering show that the framework respects the self-learned invariance while robustly optimizing the faithfulness and simplicity of the explanation. We further demonstrate the convergence of GDA experimentally. © 2021, CC BY.

关键词： Gradient methods

NDTNet: Optical Nondestructive Evaluation with Compact Convolutional Neural Network

学校读者我要写书评

暂无评论

NDTNet: Optical Nondestructive Evaluation with Compact Convo...

IEEE Instrumentation and Measurement Technology Conference

作者： Raunak Borwankar Reinhold Ludwig Electrical and Computer Engineering Department Worcester Polytechnic Institute Worcester USA

ISBN: (数字)9781728144603

ISBN: (纸本)9781728144610

The inspection of high volume parts is critical for quality assurance in manufacturing industries. In this article, we put forth NDTNet, a compact convolution neural network (CCNN) based surface flaw inspection of metallic components. The compactness of CNN is achieved by model compression techniques like parameter pruning and quantization, which reduces its size by a factor of 18. In order to account for loss due to model compression, the CNN is trained with iterative parameter pruning and simulated quantization. The compact CNN is trained on over 120,000 images of size 224-by-224-by-3. The statistical evaluation of the proposed compact CNN shows it can achieve accuracy, precision and recall in the range of 96-98% for different compression techniques. The total evaluation time i.e. image acquisition and inference is about 10 seconds. Therefore, the proposed compact CNN is suitable for high volume metallic component inspection in real-time with high accuracy and speed.

关键词： Convolution neural networks Nondestructive testing Quantization Network pruning Metallic surface Quantization METAL SURFACE Nondestructive Testing Compact Compact Pattern HIGH CAPACITY Manufacturing industry Neural network image samples

Dual Action Electrochemical Bandage Operated by a Programmable Multimodal Wearable Potentiostat

学校读者我要写书评

暂无评论

SSRN

SSRN 2024年

作者： Bozyel, Ibrahim Fleming, Derek Won-Jun, Kim Rosen, Peter F. Gelston, Suzanne Ozdemir, Dilara Ay, Suat U. Patel, Robin Beyenal, Haluk The Gene and Linda Voiland School of Chemical Engineering and Bioengineering Washington State University PullmanWA United States Division of Clinical Microbiology Mayo Clinic RochesterMN United States Department of Electrical and Computer Engineering Worcester Polytechnic Institute WorcesterMA United States Division of Infectious Diseases Mayo Clinic RochesterMN United States The Gene and Linda Voiland School of Chemical Engineering and Bioengineering Washington State University 1505 Stadium Way Room 105 P.O. Box 646515 PullmanWA99164-6515 United States

Electrochemical bandages (e-bandages) can be applied to biofilm-infected wounds to generate reactive oxygen species, such as hypochlorous acid (HOCl) or hydrogen peroxide (H2O2). The e-bandage-generated HOCl or H2O2 kills biofilms in vitro and in infected wounds on mice. The HOCl-generating e-bandage is more active against biofilms in vitro, although this distinction is less apparent in vivo. The H2O2-generating e-bandage, more than the HOCl-generating e-bandage, is associated with improved healing of infected wounds. A strategy in which H2O2 and HOCl are generated alternately—for dual action—was explored. The goal was to develop a programmable multimodal wearable potentiostat (PMWP) that could be programmed to generate HOCl or H2O2, as needed. An ultralow-power microcontroller unit managed operation of the PMWP. The system was operated with a 260‑mAh capacity coin battery and weighed 4.6 grams, making it suitable for small animal experiments or human use. The overall cost of a single wearable potentiostat was $6.50 (USD). The device was verified using established electrochemical systems and functioned comparably to a commercial potentiostat. To determine antimicrobial effectiveness, PMWP-controlled e-bandages were tested against clinical isolates of four prevalent chronic wound bacterial pathogens, methicillin-resistant Staphylococcus aureus (MRSA), Pseudomonas aeruginosa, Acinetobacter baumannii, and Enterococcus faecium, and one fungal pathogen of emerging concern, Candida auris. PMWP-controlled e-bandages exhibited broad-spectrum activity against biofilms of all study isolates tested when programmed to deliver HOCl followed by H2O2. These results show that the PMWP operates effectively and is suitable for animal testing. © 2024, The Authors. All rights reserved.

关键词： Biofilms