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2023 IEEE International Ultrasonics Symposium, IUS 2023

作者： Fang, Tianyang Fite, Austin Gromov, Mikhail Saniie, Jafar Research Laboratory Department of Electrical and Computer Engineering ChicagoIL United States

ISBN: (纸本)9798350346459

Decomposition and characterization of backscattered ultrasonic multiple interfering echoes is a critical step for analyzing the ultrasonic propagation path, propagation modes, coupling condition of the transducers and detecting defects within the propagation path. Chirplet Signal Decomposition (CSD) is an efficient way of analyzing ultrasonic echoes. However, CSD is computationally expensive and time-consuming for real-time signal processing applications. To address this problem, we present a System-on-Chip (SoC) implementation of the CSD algorithm, with the goal of speed optimization and real-time execution without sacrificing the accuracy of the signal decomposition and reconstruction. The implementation was tested on a Zynq Ultrascale+ series FPGA and achieved an echo estimation within two milliseconds. © 2023 IEEE.

关键词： System-on-chip

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Memory Enabled Segmentation of Terrain for Traversability based Reactive Navigation

Memory Enabled Segmentation of Terrain for Traversability ba...

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2023 IEEE International Conference on robotics and Biomimetics, ROBIO 2023

作者： Saucedo, Mario A.V. Patel, Akash Kanellakis, Christoforos Nikolakopoulos, George Luleå University of Technology Robotics & Ai Team Department of Computer Electrical and Space Engineering LuleåSE-97187 Sweden

ISBN: (纸本)9798350325706

This article presents a novel 2D traversability image estimation for local reactive navigation, that attributes the fusion of a novel Convolutional Neural Network (CNN) for coarse semantic segmentation on terrain roughness, with surface geometric normals. The proposed segmentation model consists of a U-Net based Encoder-Decoder architecture with a MobileNet V3 Large backbone for real-time performance. At the bottom layer, the bottleneck block commonly found in a U-Net has been enhanced with an Atrous Spatial Pyramid Pooling (ASPP) block. In addition, a SEResNet based decoder instead of the classical stacked convolution blocks of U-Net has been implemented, while a concatenation layer has been added at the output. Moreover, the development of a novel memory module to dynamically update the semantic segmentation image based on certainty heat maps is also shown. The efficacy of the proposed scheme has been evaluated in real-life environments such as indoors, outdoors and subterranean (SubT) environments on a Pioneer 3AT mobile robot. © 2023 IEEE.

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Environment Provisioning and Management for Cybersecurity Education

Environment Provisioning and Management for Cybersecurity Ed...

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2023 IEEE International Conference on Electro Information Technology, eIT 2023

作者： Ford, John Arnold, David Saniie, Jafar Research Laboratory Department of Electrical and Computer Engineering ChicagoIL United States

ISBN: (纸本)9781665493765

Hands-on learning environments and cyber ranges are popular tools in cybersecurity education. These resources provide students with practical assessments to strengthen their abilities and can assist in transferring material from the classroom to real-world scenarios. Additionally, virtualization environments, such as Proxmox, provide scalability and network flexibility that can be adapted to newly discovered threats. However, due to the increasing demand for cybersecurity skills and experience, learning environments must support an even greater number of students each term. Manual provisioning and management of environments for large student populations can consume valuable time for the instructor. To address this challenge, we developed an Environment Provisioning and Management Tool for cybersecurity education. Our solution interacts with the exposed Proxmox API to automate the process of user creation, server provisioning, and server destruction for a large set of users. Remote access will be managed by a pfSense firewall. Based on our testing, a six-machine user environment could be provisioned in 14.96 seconds and destroyed in 15.06 seconds. © 2023 IEEE.

关键词： Environmental management

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Digital Twins and Cyber-Physical Systems:A New Frontier in computer Modeling

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computer Modeling in engineering & Sciences 2025年第4期143卷 51-113页

作者： Vidyalakshmi G S Gopikrishnan Wadii Boulila Anis Koubaa Gautam Srivastava Department of Computer Science and Engineering SRM University APAmaravati522240India School of Computer Science and Engineering VIT-AP UniversityAmaravathi522240India Robotics and Internet-of-Things Laboratory Prince Sultan UniversityRiyadh12435Saudi Arabia Department of Math and Computer Science Brandon UniversityBrandonMB R7A6A9Canada Research Centre for Interneural Computing China Medical UniversityTaichung40402Taiwan

Cyber-Physical Systems(CPS)represent an integration of computational and physical elements,revolutionizing industries by enabling real-time monitoring,control,and optimization.A complementary technology,Digital Twin(DT),acts as a virtual replica of physical assets or processes,facilitating better decision making through simulations and predictive *** and DT underpin the evolution of Industry 4.0 by bridging the physical and digital *** survey explores their synergy,highlighting how DT enriches CPS with dynamic modeling,realtime data integration,and advanced simulation *** layered architecture of DTs within CPS is examined,showcasing the enabling technologies and tools vital for seamless *** study addresses key challenges in CPS modeling,such as concurrency and communication,and underscores the importance of DT in overcoming these *** in various sectors are analyzed,including smart manufacturing,healthcare,and urban planning,emphasizing the transformative potential of CPS-DT *** addition,the review identifies gaps in existing methodologies and proposes future research directions to develop comprehensive,scalable,and secure CPSDT *** synthesizing insights fromthe current literature and presenting a taxonomy of CPS and DT,this survey serves as a foundational reference for academics and *** findings stress the need for unified frameworks that align CPS and DT with emerging technologies,fostering innovation and efficiency in the digital transformation era.

关键词： Cyber physical systems digital twin efficiency Industry 4.0 robustness and intelligence

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Hyper-Drive: Visible-Short Wave Infrared Hyperspectral Imaging Datasets for Robots in Unstructured Environments 13

Hyper-Drive: Visible-Short Wave Infrared Hyperspectral Imagi...

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13th Workshop on Hyperspectral Imaging and Signal Processing: Evolution in Remote Sensing, WHISPERS 2023

作者： Hanson, Nathaniel Pyatski, Benjamin Hibbard, Samuel Dimarzio, Charles Padir, Taskin Institute for Experiential Robotics United States Northeastern University Electrical and Computer Engineering Department BostonMA United States

ISBN: (纸本)9798350395570

Hyperspectral sensors have enjoyed widespread use in the realm of remote sensing;however, they require additional considerations for reliable functionality onboard mobile robots. In this work, we introduce a first-of-its-kind system architecture with snapshot hyperspectral cameras and point spectrometers to efficiently generate composite datacubes from a moving robot base. Our system collects and registers datacubes spanning the visible to shortwave infrared (660-1700 nm) spectrum while simultaneously capturing the ambient solar spectrum reflected off a white reference tile. We collect and disseminate a large dataset of more than 500 labeled datacubes from on-road and off-road terrain compliant with the ATLAS ontology to further the integration hyperspectral imaging (HSI). Our analysis of these data demonstrates that HSI is beneficial in terrain class separability and is a significant opportunity to increase understanding of scene composition from a robot-centric context. All code and data are open source online: https://***/hyper_drive_data © 2023 IEEE.

关键词： Hyperspectral imaging

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Heterogeneity Tolerance in IoT Botnet Attack Classification

Heterogeneity Tolerance in IoT Botnet Attack Classification

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2023 IEEE International Conference on Electro Information Technology, eIT 2023

作者： Kalenowski, Samuel Arnold, David Gromov, Mikhail Saniie, Jafar Research Laboratory Department of Electrical and Computer Engineering ChicagoIL United States

ISBN: (纸本)9781665493765

Due to the rapid adoption of Internet of Things (IoT) technologies, many networks are composed of a patchwork of devices designed by different software and hardware developers. In addition to the heterogeneity of IoT networks, the general rush-to-market produced products with poor adherence to core cybersecurity principles. Coupled together, these weaknesses leave organizations vulnerable to attack by botnets, such as Mirai and Gafgyt. Infected devices pose a threat to both internal and external devices as they attempt to add new devices to the collective or to perpetrate targeted attacks within the network or against third parties. Artificial Intelligence (AI) tools for intrusion detection are popular platforms for detecting indicators of botnet infiltration. However, when training AI tools, the heterogeneity of the network hampers detection and classification accuracy due to the differences in device architecture and network layout. To investigate this challenge, we explored the application of a Neural Network (NN) to the N-BaIoT dataset. The NN achieved 94% classification accuracy when trained using data from all devices in the network. Further, we examined the model's transferability by training on a single device and applying it to data from all devices. This resulted in a noticeable decline in classification accuracy. However, when considering cyberattack detection the model retained a very high true positive rate of 99.6%. © 2023 IEEE.

关键词： Internet of things

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IoT-Enabled Smart Bike Helmet with an AI-Driven Collision Avoidance System

IoT-Enabled Smart Bike Helmet with an AI-Driven Collision Av...

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2023 IEEE International Conference on Electro Information Technology, eIT 2023

作者： Solus, Jacob Rakotondraibe, Maureen Yu, Xinrui Yi, Won-Jae Gromov, Mikhail Saniie, Jafar Research Laboratory Department of Electrical and Computer Engineering ChicagoIL United States

ISBN: (纸本)9781665493765

This paper presents a system design for a smart bike helmet with multiple safety features that are intended to empower bicycle riders to proactively avoid potential sources of danger or injury. A Smart Sensor/Actuator Node (SSAN), driven by an Arduino Uno single-board microcontroller, contains input sensors and actuators to provide riders the ability to send and receive warnings promptly on their helmet. A Vision Node, driven by an NVIDIA Jetson Nano and a cable pin-connected camera, executes AI object detection algorithms for any dangerous objects that are out of sight of the rider and sends alerts to the SSAN as needed. By combining safety features of the SSAN and Vision Node while continuously sending data to an IoT-enabled backend web server, the safety operation of a typical bike ride can be substantially improved. © 2023 IEEE.

关键词： Edge computing

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Reconfigurable Distributed FPGA Cluster Design for Deep Learning Accelerators

Reconfigurable Distributed FPGA Cluster Design for Deep Lear...

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2023 IEEE International Conference on Electro Information Technology, eIT 2023

作者： Johnson, Hans Fang, Tianyang Perez-Vicente, Alejandro Saniie, Jafar Research Laboratory Department of Electrical and Computer Engineering ChicagoIL United States

ISBN: (纸本)9781665493765

We propose a distributed system based on low-power embedded FPGAs designed for edge computing applications focused on exploring distributing scheduling optimizations for Deep Learning (DL) workloads to obtain the best performance regarding latency and power efficiency. Our cluster was modular throughout the experiment, and we have implementations that consist of up to 12 Zynq-7020 chip-based boards as well as 5 UltraScale+ MPSoC FPGA boards connected through an ethernet switch, and the cluster will evaluate configurable Deep Learning Accelerator (DLA) Versatile Tensor Accelerator (VTA). This adaptable distributed architecture is distinguished by its capacity to evaluate and manage neural network workloads in numerous configurations which enables users to conduct multiple experiments tailored to their specific application needs. The proposed system can simultaneously execute diverse Neural Network (NN) models, arrange the computation graph in a pipeline structure, and manually allocate greater resources to the most computationally intensive layers of the NN graph. © 2023 IEEE.

关键词： Field programmable gate arrays (FPGA)

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A passive,reusable,and resonating wearable sensing system for ondemand,non-invasive,and wireless molecular stress biomarker detection

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Nano Research 2024年第8期17卷 7542-7556页

作者： Shingirirai Chakoma Xiaochang Pei Huiting Qin Anita Ghandehari Sahar NajafiKhoshnoo Jerome Rajendran Rahim Esfandyarpour Department of Electrical Engineering and Computer Science University of CaliforniaIrvineCA 92697USA Laboratory for Integrated Nano BioElectronics Innovation The Henry Samueli School of EngineeringUniversity of CaliforniaIrvineCA 92697USA Henry Samueli School of Engineering University of CaliforniaIrvineCA 92697USA

The significant impact of stress on health necessitates accurate assessment methods,where traditional questionnaires lack reliability and *** advancements like wearables with electrocardiogram(ECG)and galvanic skin response(GSR)sensors face accuracy and artifact *** biosensors detecting cortisol,a critical stress hormone,present a promising ***,existing cortisol assays,requiring saliva,urine,or blood,are complex,expensive,and unsuitable for continuous *** study introduces a passive,molecularly imprinted polymer-radio-frequency(MIP-RF)wearable sensing system for real-time,non-invasive sweat cortisol *** system is wireless,flexible,battery-free,reusable,environmentally stable,and designed for long-term monitoring,using an inductance-capacitance *** transducer translates cortisol concentrations into resonant frequency shifts with high sensitivity(~160 kHz/(log(μM)))across a physiological range of 0.025–1μ*** with near-field communication(NFC)for wireless and battery-free operation,and threedimensional(3D)-printed microfluidic channel for in-situ sweat collection,it enables daily activity cortisol level *** of cortisol circadian rhythm through morning and evening measurements demonstrates its effectiveness in tracking and monitoring sweat cortisol levels.A 28-day stability test and the use of cost-effective 3D nanomaterials printing enhance its economic viability and *** innovation paves the way for a new era in realistic,on-demand health monitoring outside the laboratory,leveraging wearable technology for molecular stress biomarker detection.

关键词： cortisol wearable flexible sensors three-dimensional(3D)nanomaterials printing reusable environmentally stable

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Learning Cross-Modal Audiovisual Representations with Ladder Networks for Emotion Recognition 48

Learning Cross-Modal Audiovisual Representations with Ladder...

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48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Goncalves, Lucas Busso, Carlos Laboratory Department of Electrical and Computer Engineering RichardsonTX75080 United States

ISBN: (纸本)9781728163277

Representation learning is a challenging, but essential task in audiovisual learning. A key challenge is to generate strong cross-modal representations while still capturing discriminative information contained in unimodal features. Properly capturing this information is important to increase accuracy and robustness in audiovisual tasks. Focusing on emotion recognition, this study proposes novel cross-modal ladder networks to capture modality-specific information while building strong cross-modal representations. Our method utilizes representations from a backbone network to implement unsupervised auxiliary tasks to reconstruct intermediate layer representations across the acoustic and visual networks. The skip connections between the cross-modal encoder and decoder provide powerful modality-specific and multimodal representations for emotion recognition. Our model on the CREMA-D corpus achieves high performance with precision, recall, and F1 scores over 80% on a six-class problem. © 2023 IEEE.

关键词： audiovisual emotion recognition ladder networks representation learning

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