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IEEE Conference on Vehicular Technology (VTC)

作者： Binbin Lu Xumin Huang Yuan Wu Liping Qian Dusit Niyato Tony Q. S. Quek Cheng-Zhong Xu State Key Lab of Internet of Things for Smart City Macau China School of Automation Guangdong University of Technology Guangzhou China Zhuhai- UM Science and Technology Research Institute Zhuhai China College of Information Engineering Zhejiang University of Technology Hangzhou China School of Computer Science and Engineering Nangyang Technological University Singapore Information Systems Technology and Design Pillar Singapore University of Technology and Design Singapore

ISBN: (数字)9798350387414

ISBN: (纸本)9798350387421

As an emerging technology, Digital Twin (DT) can provide a virtual presentation of the physical Intelligent Trans-portation systems (ITS) to enhance the applications of ITS such as cooperation perception. In cooperative perception, accurate location is crucial for selecting proper cooperative vehicles (CoVs) to improve the perception performance. However, due to the high mobility of vehicles, the deviation between DT and physical world may lead to non-negligible location errors, which raises the challenges for achieving efficient CoV selection in cooperative perception. In this paper, we propose a DT-empowered multi-vehicle cooperative perception system, in which the CoV selection and bandwidth allocation are jointly optimized to improve the performance of cooperative perception. Specifically, an asyn-chronous federated learning scheme is deployed in DT for location prediction to mitigate the effect of the deviation. Based on the prediction results, the problem of joint predictive scheduling and bandwidth allocation is then formulated as the average delay minimization problem while reaching the required performances. The adaptive CoV selection and bandwidth allocation algorithm based on deep reinforcement learning is proposed to find the optimal scheduling strategy. Simulation results demonstrate that the proposed algorithm achieves the lowest average delay while effectively guaranteeing the performance requirement.

关键词： Vehicular and wireless technologies Federated learning Simulation Optimal scheduling Channel allocation Prediction algorithms Minimization

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Polarization-Insensitive Metalenses at Wavelengths in Ultraviolet Region 23

Polarization-Insensitive Metalenses at Wavelengths in Ultrav...

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23rd IEEE Electronics Packaging Technology Conference, EPTC 2021

作者： Halim, Bishoy I. Kanwal, Saima Queen 'S University Electrical and Computer Engineering Electrical and Computer Engineering Dept. Kingston Canada Engineering Research Center of Optical Instrument and Systems Ministry of Education and Shanghai Key Lab of Modern Optical System University of Shanghai for Science and Technology Shanghai China

ISBN: (纸本)9781665416191

Metasurfaces in the ultraviolet band have attracted current research concern due to the expanding requirement for wearable UV optical and ultra-compact systems. The constraints of traditional plasmonic metasurfaces operating in transmission mode can be controlled by using an appropriate dielectric material. A metalens presents promising wavefront engineering for multiple applications. Metalenses have sophisticated advanced technology in the progress of miniaturized and integrated optical devices. However, metalenses using the Pancharatnam- Berry (PB) phase or resonance adjusting technique are limited to polarization reliance and for different applications, polarization-insensitive metalenses are extremely preferable. We present three designs of high-efficiency dielectric polarization-insensitive UV metalens using cylindrical nanopillars with focusing efficiency, offering full phase delay in a broadband spectrum of Ultraviolet light (380-400nm). The designed metalenses include titanium dioxide (cylindrical nanopillars on a glass substrate with geometrically different radii and present magnificent polarization-insensitive operation in the broadband UV band. It will considerably advance the miniaturization and integration of the UV photonic devices by limiting the use of Plasmonics structures that are undefended to the ohmic losses and absorption of the metals. The focusing efficiency of the designed metalenses is as high as 43%, 45%, and 47% for the designed wavelengths of metalenses lambda_{d}=396{n}{m}, 388 nm, and 380 nm, respectively. © 2021 IEEE.

关键词： Titanium dioxide

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Flexible Tactile Sensing Using PDMS/TiO2 Based Capacitor with MOSFET Structure

Flexible Tactile Sensing Using PDMS/TiO2 Based Capacitor wit...

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IEEE SENSORS

作者： Kamalesh Tripathy Dhanranjan Kumar Karthick Thiyagarajan Mitradip Bhattacharjee Department of Electrical Engineeringand Computer Science i-Lab Indian Institute of Science Education and Research Bhopal Bhopal Madhya Pradesh India Smart Sensing and Robotics Laboratory (SensR Lab) Centre for Advanced Manufacturing Technology School of Engineering Design and Built Environment Western Sydney University Penrith NSW Australia

ISBN: (数字)9798350363517

ISBN: (纸本)9798350363524

In this paper, we present a flexible capacitive tactile sensor based on a polydimethylsiloxane (PDMS) elastomer, which is well-suited for a wide range of applications. The sensor incorporates an n-channel depletion-mode MOSFET to achieve an extended gate architecture. To enhance the sensing performance, TiO 2 nanoparticles are incorporated as nanofillers, resulting in an improved dielectric material ideal for highly sensitive capacitive pressure and tactile sensors. The 99PDMS-01TiO 2 nanocomposite (comprising 99 wt % PDMS and 1 wt % TiO 2 ) is utilized for promising flexible tactile sensor applications due to the synergistic effects between the PDMS matrix and TiO 2 nanoparticles, which enhance both the mechanical and sensing properties of the composite. The fabricated sensor exhibits a sensitivity of $\mathbf{30} \boldsymbol{\mu}\mathbf{A}/\mathbf{kPa}$ within the range of 0-10.5 kPa and a sensitivity of $\mathbf{9} \boldsymbol{\mu}\mathbf{A}/\mathbf{kPa}$ in the range of 10.5-30 kPa.

关键词： Nanoparticles MOSFET Sensitivity Nanocomposites Capacitors Tactile sensors Logic gates Robustness Sensors Titanium dioxide

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Towards a Deep Learning Pain-Level Detection Deployment at UAE for Patient-Centric-Pain Management and Diagnosis Support: Framework and Performance Evaluation

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Procedia computer science 2023年 220卷 339-347页

作者： Leila Ismail Muhammad Danish Waseem Clouds and Distributed Computing and Systems (CLOUDS) Lab School of Computing and Information Systems Faculty of Engineering and Information Technology The University of Melbourne Australia. Intelligent Distributed Computing and Systems (INDUCE) Research Laboratory Department of Computer Science and Software Engineering College of Information Technology United Arab Emirates University UAE. National Water and Energy Center United Arab Emirates University UAE.

The outbreak of the COVID-19 pandemic revealed the criticality of timely intervention in a situation exacerbated by a shortage in medical staff and equipment. Pain-level screening is the initial step toward identifying the severity of patient conditions. Automatic recognition of state and feelings help in identifying patient symptoms to take immediate adequate action and providing a patient-centric medical plan tailored to a patient's state. In this paper, we propose a framework for pain-level detection for deployment in the United Arab Emirates and assess its performance using the most used approaches in the literature. Our results show that a deployment of a pain-level deep learning detection framework is promising in identifying the pain level accurately.

关键词： computer Vision Deep Learning Image Processing Machine Learning Pain Detection Patient-Centric smart healthcare eHealth

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Watching the Air Rise: Learning-Based Single-Frame Schlieren Detection

Watching the Air Rise: Learning-Based Single-Frame Schlieren...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Florian Achermann Julian Andreas Haug Tobias Zumsteg Nicholas Lawrance Jen Jen Chung Andrey Kolobov Roland Siegwart Autonomous Systems Lab ETH Zürich Zürich Switzerland Robotic Perception and Autonomy Australia School of Electrical Engineering and Computer Science The University of Queensland Brisbane Australia Microsoft Research One Microsoft Way USA

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

Detecting air flows caused by phenomena such as heat convection is valuable in multiple scenarios, including leak identification and locating thermal updrafts for extending UAV flight duration. Unfortunately, the heat signature of these flows is often too subtle to be seen by a thermal camera. While convection also leads to fluctuations in air density and hence causes so-called schlieren – intensity and color variations in images – existing techniques such as Background-oriented schlieren (BOS) allow detecting them only against a known background and from a static camera, making these approaches unsuitable for moving vehicles. In this work we demonstrate the feasibility of visualizing air movement by predicting the corresponding schlieren-induced optical flow from a single greyscale image captured by a moving camera against an unfamiliar background. We first record and label a set of optical flows in an indoor setup using standard BOS techniques. We then train a convolutional neural network (CNN) by applying the previously collected optical flow distortions to a dataset containing a mixture of real and synthetically generated images to predict the two-dimensional optical flow from a single image. Finally, we evaluate our approach on the task of extracting the optical flow caused by schlieren from both a static and moving camera on previously unseen flow patterns and background images.

关键词： Heating systems Training Training data Optical distortion Cameras Distortion Convolutional neural networks

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Hybrid Disassembly Line Balancing for Human-robot Collaborative Remanufacturing

Hybrid Disassembly Line Balancing for Human-robot Collaborat...

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IEEE International Conference on Automation Science and engineering (CASE)

作者： Feng Guo XiWang Guo MengChu Zhou Weitian Wang ShuJin Qin Qi Kang College of Information and Control Engineering Liaoning Petrochemical University Department of Electrical and Computer Engineering New Jersey Institute of Technology Newark NJ USA Collaborative Robotics and Smart Systems Laboratory (CRoSS Lab) Montclair State University Montclair NJ USA College of Economics and Management Shangqiu Normal University Shangqiu P R. China

ISBN: (数字)9798350358513

ISBN: (纸本)9798350358520

As technologies advance rapidly, the urgency of remanufacturing is escalating. Efficient disassembly processes are crucial at the outset of remanufacturing. This work proposes an innovative disassembly scheme aimed at amplifying efficiency through the utilization of parallelization and human-robot collaboration in the context of a hybrid disassembly line. A single-objective mixed-integer programming model is developed to optimize disassembly profit. A discrete salp swarm algorithm is proposed to solve it since it is NP-hard. We have proposed and integrated uniform variation and two-point crossover operators in this algorithm. After comparing its results with those of the exact solver and other intelligent optimization methods, we conclude its competitive performance in both solution quality and efficiency.

关键词： computer aided software engineering Automation Optimization methods Human-robot interaction Collaboration Programming

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Markerless Sagittal Gait Analysis of Cerebral Palsy in Children Using Pose Estimation Techniques

Markerless Sagittal Gait Analysis of Cerebral Palsy in Child...

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Innovation in Computing and engineering (ICE), International Conference on

作者： Vinay Kumar Vats Chandra Prakash Amandeep Kaur Rajesh Kumar Computer Science and Engineering National Institute of Technology Delhi India Computational Intelligence and Smart Motion Research (CISMR) Lab SVNIT Surat India Malaviya National Institute of Technology Jaipur India Department of Human Anatomy and Physiology Faculty of Health Sciences University of Johannesburg Johannesburg South Africa

ISBN: (数字)9798350380460

ISBN: (纸本)9798350380477

Cerebral Palsy (CP) is a neurological disorder affecting children’s motor function and posture. Early detection and monitoring of Cerebral palsy is crucial for effective intervention and improved quality of life. Gait analysis is a critical tool for assessing motor function in children with cerebral palsy, often requiring controlled environments and markers for accuracy. Markerless gait analysis has emerged as a promising tool for objectively evaluating gait abnormalities in children with CP. In this study, we propose a markerless methodology utilising pose estimation to analyse gait patterns from sagittal video recordings captured in unconstrained environments. The dataset consists of 49 children, including 10 typically developing children and 39 with cerebral palsy, focusing on side-view videos for the analysis. Given the motor disabilities of these children, conducting gait analysis in a constrained environment can alter their natural walking patterns, making our approach particularly advantageous. Then we identify the key gait events, heel strike and toe-off, to distinguish gait between cycles by tracing knee joint movements. This technique demonstrates the potential for practical, non-invasive gait assessment in natural settings, offering insights into the distinct gait characteristics of children with cerebral palsy compared to their typically developing peers.

关键词： Legged locomotion Neurological diseases Cerebral palsy Technological innovation Pose estimation Motors Motion control Gait recognition Video recording Monitoring

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BotScan: An Unsupervised Bot Detection Based on Adversarial Learning and Social Perception

BotScan: An Unsupervised Bot Detection Based on Adversarial ...

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Asian Control Conference

作者： Hong Lin Nuo Chen Yang Chen Xiang Li Cong Li Department of Electronic Engineering Adaptive Networks and Control Lab School of Information Science and Technology Fudan University China Shanghai Key Lab of Intelligent Information Processing School of Computer Science Fudan University China The Institute of Complex Networks and Intelligent Systems Shanghai Research Institute for Intelligent Autonomous Systems Tongji University China

ISBN: (数字)9789887581598

ISBN: (纸本)9798331540845

Within online social networks, the rapid expansion of automated accounts, known as social bots, poses a significant challenge. This dynamic has sparked a strategic contest between bots and detectors. Both sides continually learn and adjust their strategies to gain an edge in this silent war. To effectively manage risks from bot evolution, proactive detection methods have garnered widespread attention. In this work, we propose a graph-based and structure-aware adversarial learning framework for multi-category social bot detection. In detail, we adopt a categorical generative adversarial network to create synthetic evolved bot samples for adversarial learning, aiming to boost the detection performance. Besides, we design a user representation learning module, including local and remote feature extractors to mine neighborhood information and achieve multiple network structure representations. Results demonstrate that our model outperforms state-of-the-art approaches and exhibits strong robustness.

关键词： Representation learning Training Technological innovation Social networking (online) Image edge detection Detectors Feature extraction

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FediLive: A Framework for Collecting and Preprocessing Snapshots of Decentralized Online Social Networks 25

FediLive: A Framework for Collecting and Preprocessing Snaps...

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Companion Proceedings of the ACM on Web Conference 2025

作者： Shaojie Min Shaobin Wang Yaxiao Luo Min Gao Qingyuan Gong Yu Xiao Yang Chen Shanghai Key Lab of Intelligent Information Processing School of Computer Science Fudan University Shanghai China Research Institute of Intelligent Complex Systems Fudan University Shanghai China Department of Information and Communications Engineering Aalto University Espoo Finland

ISBN: (纸本)9798400713316

Decentralized online social networks such as Mastodon have emerged quickly during the past years, and offer unique opportunities to investigate user behavior, moderation strategies, and community evolution. However, their decentralized nature imposes challenges for data collection and preprocessing, particularly for obtaining a real-time snapshot in a timely manner. This paper introduces FediLive, a framework designed to rapidly collect and preprocess the live feeds from Mastodon, generate a comprehensive snapshot in real-time, including user-generated contents, interaction networks, and users' demographic attributes. Such a snapshot could further be leveraged for data analysis from different angles, leading to a deeper understanding of user activities on Mastodon. Using FediLive, we collected a 13-day snapshot of Mastodon, covering the publicly-visible activities of all Mastodon users. Our study demonstrates the usefulness of FediLive, and reveals its potential in facilitating data-driven analysis for decentralized online social networks.

关键词： decentralized online social networks

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Binding in hippocampal-entorhinal circuits enables compositionality in cognitive maps 38

Binding in hippocampal-entorhinal circuits enables compositi...

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38th Conference on Neural Information Processing systems, NeurIPS 2024

作者： Kymn, Christopher J. Mazelet, Sonia Thomas, Anthony Kleyko, Denis Frady, E. Paxon Sommer, Friedrich T. Olshausen, Bruno A. Redwood Center for Theoretical Neuroscience Helen Wills Neuroscience Institute UC Berkeley Berkeley United States Université Paris-Saclay ENS Paris-Saclay Gif-sur-Yvette France Department of Electrical and Computer Engineering UC Davis Davis United States Centre for Applied Autonomous Sensor Systems Örebro University Örebro Sweden Intelligent Systems Lab Research Institutes of Sweden Kista Sweden Intel Labs Santa Clara United States Herbert Wertheim School of Optometry & Vision Science UC Berkeley Berkeley United States

We propose a normative model for spatial representation in the hippocampal formation that combines optimality principles, such as maximizing coding range and spatial information per neuron, with an algebraic framework for computing in distributed representation. Spatial position is encoded in a residue number system, with individual residues represented by high-dimensional, complex-valued vectors. These are composed into a single vector representing position by a similarity-preserving, conjunctive vector-binding operation. Self-consistency between the representations of the overall position and of the individual residues is enforced by a modular attractor network whose modules correspond to the grid cell modules in entorhinal cortex. The vector binding operation can also associate different contexts to spatial representations, yielding a model for entorhinal cortex and hippocampus. We show that the model achieves normative desiderata including superlinear scaling of patterns with dimension, robust error correction, and hexagonal, carry-free encoding of spatial position. These properties in turn enable robust path integration and association with sensory inputs. More generally, the model formalizes how compositional computations could occur in the hippocampal formation and leads to testable experimental predictions. © 2024 Neural information processing systems foundation. All rights reserved.

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