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International Conference on Automatic Face and Gesture Recognition

作者： Rijun Liao Zhu Li Shuvra S. Bhattacharyya George York Department of Computer Science and Electrical Engineering University of Missouri-Kansas City MO USA Department of Electrical and Computer Engineering and UMIACS University of Maryland College Park MD USA Department of Electrical and Computer Engineering and UAS Research Center US Air Force Academy Colorado Springs CO USA

ISBN: (数字)9798350394948

ISBN: (纸本)9798350394955

View transformation is crucial for gait recognition. Most existing methods use a view transformation models (VTM) or generative models (VAE or GAN) to achieve transformation. These approaches commonly adopt a paradigm of transforming a gait feature from one view to another. However, most existing methods attempt to use a single or multiple, large-view transformation model to directly transform a source view image to the target view image. Such transformations usually suffer from precision problems under large viewpoint variations due to the lack of fine view prediction. To overcome this challenge, we introduce a novel framework, ViewDiffGait, employing a view pyramid structure and diffusion models. ViewDiffGait is formulated as an iterative refinement generation task in a biologically interpretable way, capable of generating more accurate lateral view images from coarse to fine. Unlike the typical diffusion model that directly adds and removes Gaussian noise in the original image, the ViewDiffGait diffusion process involves a view pyramid structure to capture fine view transformations. The diffusion process adds view noise from the pyramid top to the bottom, while the denoising process removes view noise from the pyramid bottom to the top. We conducted extensive experiments on the CASIA-B and OUMVLP datasets, demonstrating that ViewDiffGait can generate more realistic images, remove variations effectively, and lead to high performance in real applications.

关键词： Face recognition Gaussian noise Noise reduction Diffusion processes Transforms Gesture recognition Diffusion models

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RSMA-Enabled Interference Management for Industrial Internet of Things Networks With Finite Blocklength Coding and Hardware Impairments

IEEE Transactions on Machine Learning in Communications and ...

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IEEE Transactions on Machine Learning in Communications and Networking 2024年 2卷 1319-1340页

作者： Mohamed, Nahed Belhadj Hassan, Md. Zoheb Kaddoum, Georges Université Laval Department of Electrical and Computer Engineering QuébecQCG1V 0A6 Canada MontrealQCH3C 1K3 Canada Lebanese American University Cyber Security Systems and Applied AI Research Center Beirut03797751 Lebanon

The increasing proliferation of industrial internet of things (IIoT) devices requires the development of efficient radio resource allocation techniques to optimize spectrum utilization. In densely populated IIoT networks, the interference that results from simultaneously scheduling multiple IIoT devices over the same radio resource blocks (RRBs) severely degrades a network's achievable capacity. This paper investigates an interference management problem for IIoT networks that considers both finite blocklength (FBL)-coded transmission and signal distortions induced by hardware impairments (HWIs) arising from practical, low-complexity radio-frequency front ends. We use the rate-splitting multiple access (RSMA) scheme to effectively schedule multiple IIoT devices in a cluster over the same RRB(s). To enhance the system's achievable capacity, a joint clustering and transmit power allocation (PA) problem is formulated. To tackle the optimization problem's inherent computational intractability due to its non-convex structure, a two-step distributed clustering and power management (DCPM) framework is proposed. First, the DCPM framework obtains a set of clustered devices for each access point by employing a greedy clustering algorithm while maximizing the clustered devices' signal-to-interference-plus-noise ratio. Then, the DCPM framework employs a multi-agent deep reinforcement learning (DRL) framework to optimize transmit PA among the clustered devices. The proposed DRL algorithm learns a suitable transmit PA policy that does not require precise information about instantaneous signal distortions. Our simulation results demonstrate that our proposed DCPM framework adapts seamlessly to varying channel conditions and outperforms several benchmark schemes with and without HWI-induced signal distortions. © 2023 CCBY.

关键词： Signal to noise ratio

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Survey of Navigational Perception Sensors’ Security in Autonomous Vehicles

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IEEE Access 2025年 13卷 104937-104965页

作者： Abhijeet Solanki Wesam Al Amiri Marim Mahmoud Blaine Swieder Syed Rafay Hasan Terry N. Guo Department of Electrical and Computer Engineering Tennessee Technological University Cookeville TN USA Department of Computer Science Tennessee Technological University Cookeville TN USA Center for Manufacturing Research Tennessee Technological University Cookeville TN USA

The adoption of autonomous vehicles (AVs) significantly increased in recent years, playing a crucial role in enhancing transportation safety and efficiency, expanding mobility options, and reducing user costs. However, as AVs become more connected and automated, they also become more susceptible to malicious attacks, making it imperative to prioritize their security. Specifically, AVs are vulnerable to a range of cyber-threats, leading to a growing body of literature on potential cyberattacks targeting AVs. A key focus of existing research is understanding these threats and proposing mitigation strategies, particularly those aimed at attacks on navigational perception sensors, which are critical to AV navigation and decision-making. AVs rely on an array of navigational perception sensors, including LiDAR, cameras, RADAR, global positioning system (GPS) sensors, and ultrasonic sensors, to ensure secure and safe operation. However, adversarial manipulation of these sensors can compromise an AV’s navigational perception system, potentially leading to catastrophic consequences. While existing surveys provide valuable insights on AV cybersecurity challenges, several gaps remain unexplored, such as hardware security issues in AVs, analyzing sensor fusion attacks and evaluating practical defense implementations. To address these gaps, this paper makes a novel contribution by offering a comprehensive review of sensor fusion vulnerabilities that have not been sufficiently addressed in previous literature, specifically focusing on the compounded effects of sensor manipulation in AVs. In addition, this paper introduces a new classification of sensor vulnerabilities, highlighting practical defense mechanisms based on real-world simulation and testbed environments, a key contribution not explored in earlier works. We also introduce a detailed classification of sensor vulnerabilities, categorize the attacks that exploit these vulnerabilities, and critically assess existing counte

关键词： Sensors Navigation Global Positioning System Laser radar Cameras Security Surveys Sensor fusion Sensor systems Autonomous vehicles

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A NovelMethod for Determining Tourism Carrying Capacity in a Decision-Making Context Using q−Rung Orthopair Fuzzy Hypersoft Environment

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computer Modeling in engineering & Sciences 2024年第2期138卷 1951-1979页

作者： Salma Khan Muhammad Gulistan NasreenKausar Seifedine Kadry Jungeun Kim Department of Mathematics and Statistics Hazara UniversityMansehra21120Pakistan Department of Mathematics Faculty of Arts and SciencesYildiz Technical UniversityEsenlerIstanbul34210Turkey Department of Applied Data Science Noroff University CollegeKristiansand4612Norway Artificial Intelligence Research Center(AIRC) Ajman UniversityAjman346United Arab Emirates Department of Electrical and Computer Engineering Lebanese American UniversityP.O.Box 13-5053ByblosLebanon Department of Software and CMPSI Konju National UniversityCheonan31080Korea

Tourism is a popular activity that allows individuals to escape their daily routines and explore new destinations for various reasons,including leisure,pleasure,or business.A recent study has proposed a unique mathematical concept called a q−Rung orthopair fuzzy hypersoft set(q−ROFHS)to enhance the formal representation of human thought processes and evaluate tourism carrying *** approach can capture the imprecision and ambiguity often present in human *** the advanced mathematical tools in this field,the study has also incorporated the Einstein aggregation operator and score function into the q−ROFHS values to supportmultiattribute decision-making *** implementing this technique,effective plans can be developed for social and economic development while avoiding detrimental effects such as overcrowding or environmental damage caused by tourism.A case study of selected tourism carrying capacity will demonstrate the proposed methodology.

关键词： q−Rung orthopair fuzzy hypersoft set decision-making tourism carrying capacity aggregation operator

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Performance Evaluation of Coffee Bean Binary Classification Through Deep Learning Techniques 1

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International Conference on Innovative Technologies in Intelligent Systems and Industrial Applications, CITISIA 2022

作者： Islamy, Fajrul Muchtar, Kahlil Arnia, Fitri Dawood, Rahmad Febriana, Alifya Elwirehardja, Gregorius Natanael Pardamean, Bens Department of Electrical and Computer Engineering Universitas Syiah Kuala Banda Aceh23111 Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia Computer Science Department Master of Computer Science Program BINUS Graduate Program Bina Nusantara University Jakarta11480 Indonesia

ISBN: (数字)9783031290787

ISBN: (纸本)9783031290770

Coffee beans are one of the high-value commodities in Indonesia, but the sorting method for the quality of coffee beans still uses visual methods and sieves with mechanical machines. This study aims to provide an alternative to classifying coffee beans using Deep Learning algorithms, such as the ResNet-18 and MobileNetV2. The dataset used is USK-COFFEE which consists of two classes, the normal coffee bean class, and the defect coffee bean class. The models were trained using the balanced and imbalanced datasets, meaning that two scenarios were performed. From the results of this study, the accuracy value for the ResNet-18 model was 91.50% when trained on the balanced dataset and 90.87% on the imbalanced dataset. It outperformed the MobileNetV2 architecture, which only achieved 0.8125 and 0.8112 accuracy scores when trained on the balanced and imbalanced datasets, respectively. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： computer vision

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Non-invasive estimation of the powder size distribution from a single speckle image

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Light(Science & Applications) 2024年第10期13卷 2221-2230页

作者： Qihang Zhang Ajinkya Pandit Zhiguang Liu Zhen Guo Shashank Muddu Yi Wei Deborah Pereg Neda Nazemifard Charles Papageorgiou Yihui Yang Wenlong Tang Richard D.Braatz Allan S.Myerson George Barbastathis Department of Electrical Engineering and Computer Science Massachusetts Institute of TechnologyCambridgeMA 02139USA Singapore-MIT Alliance for Research and Technology(SMART)Centre Singapore 117543Singapore Department of Chemical Engineering Massachusetts Institute of TechnologyCambridgeMA 02139USA Department of Mechanical Engineering Massachusetts Institute of TechnologyCambridgeMA 02139USA Process Chemistry Development Takeda Pharmaceuticals International Co40 Landsdowne StCambridgeMA 02139USA ShinrAI Center for AI/ML Data Sciences InstitutesTakeda Pharmaceuticals International Co650 E Kendall StCambridgeMA 02142USA Present address:Department of Precision Instruments Tsinghua UniversityBeijing 100084China

Non-invasive characterization of powders may take one of two approaches:imaging and counting individual particles;or relying on scattered light to estimate the particle size distribution(PSD)of the *** former approach runs into practical difficulties,as the system must conform to the working distance and other restrictions of the imaging *** latter approach requires an inverse map from the speckle autocorrelation to the particle *** principle relies on the pupil function determining the basic sidelobe shape,whereas the particle size spread modulates the sidelobe *** recently showed that it is feasible to invert the speckle autocorrelation and obtain the PSD using a neural network,trained efficiently through a physics-informed semi-generative *** this work,we eliminate one of the most time-consuming steps of our previous method by engineering the pupil *** judiciously blocking portions of the pupil,we sacrifice some photons but in return we achieve much enhanced sidelobes and,hence,higher sensitivity to the change of the size *** result is a 60×reduction in total acquisition and processing time,or 0.25 seconds per frame in our *** real-time operation in our system is not only more appealing toward rapid industrial adoption,it also paves the way for quantitative characterization of complex spatial or temporal dynamics in drying,blending,and other chemical and pharmaceutical manufacturing processes.

关键词： speckle size distribution

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A Hybrid Deep Learning Approach to Classify the Plant Leaf Species

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computers, Materials & Continua 2023年第9期76卷 3897-3920页

作者： Javed Rashid Imran Khan Irshad Ahmed Abbasi Muhammad Rizwan Saeed Mubbashar Saddique Mohamed Abbas Department of CS&SE Islamic International UniversityIslamabad44000Pakistan Department of IT Services University of OkaraOkara56310Pakistan Faculty of Science and Arts Belqarn University of BishaSabtul Alaya61985Saudi Arabia Department of CS University of OkaraOkara56310Pakistan Department of Computer Science&Engineering University of Engineering&Technology LahoreNarowalCampusNarowal51601Pakistan Electrical Engineering Department College of EngineeringKing Khalid UniversityAbha61421Saudi Arabia Research Center for Advanced Materials Sciences(RCAMS) King Khalid UniversityAbhaSaudi Arabia

Many plant species have a startling degree of morphological similarity,making it difficult to split and categorize them *** plant species can be challenging to classify and segment using deep *** using deep learning architectures has helped improve classification accuracy,the resulting models often need to be more flexible and require a large dataset to *** the sake of taxonomy,this research proposes a hybrid method for categorizing guava,potato,and java *** new approaches are used to formthe hybridmodel suggested *** guava,potato,and java plum plant species have been successfully segmented using the first model built on the MobileNetV2-UNET *** a second model,we use a Plant Species Detection Stacking Ensemble Deep Learning Model(PSD-SE-DLM)to identify potatoes,java plums,and *** proposed models were trained using data collected in Punjab,Pakistan,consisting of images of healthy and sick leaves from guava,java plum,and *** datasets are known as PLSD and *** levels of 99.84%and 96.38%were achieved for the suggested PSD-SE-DLM and MobileNetV2-UNET models,respectively.

关键词： Plant leaf species stacking ensemble model guava potato java plum MobileNetV2-UNET hybrid deep learning segmentation

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MSMB-GCN: Multi-scale Multi-branch Fusion Graph Convolutional Networks for 3D Human Pose Estimation

MSMB-GCN: Multi-scale Multi-branch Fusion Graph Convolutiona...

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2023 IEEE International Conference on Robotics and Biomimetics, ROBIO 2023

作者： Ji, Shanshan Meng, Qiwei Wang, Wen Lin, Zheyuan Li, Te Wan, Minhong Zhang, Chunlong Gu, Jason Research Institute of Interdisciplinary Innovation Zhejiang Laboratory Research Center for Intelligent Robotics Hangzhou311100 China Dalhousie University Department of Electrical and Computer Engineering HalifaxNSB3H 4R2 Canada

ISBN: (纸本)9798350325706

In human-robot interaction (HRI), human pose estimation is a necessary technology for the robot to perceive the dynamic environment and make interactive actions. Recently, graph convolutional networks (GCNs) have been increasingly used for 2D to 3D pose estimation tasks since the skeleton topologies can be viewed as graph structures. In this paper, we propose a novel graph convolutional network architecture, Multi-scale Multi-branch Fusion Graph Convolutional Networks (MSMB-GCN), for 3D Human Pose Estimation(3D HPE) task. The proposed model consists of multiple GCN blocks with a multi-branch architecture. This multi-branch architecture enables the model to get multi-scale features for human skeletal representations. The group of GCN blocks, which has strong multi-level feature extraction capabilities, allows the model to learn global and local features, lower-level and higher-level features. Experiment results on the HumanPose benchmark demonstrate that our model outperforms the state-of-the-art and ablation studies validate the effectiveness of our approach. © 2023 IEEE.

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Assessing the electrical Energy Consumption for Designing and Developing an Energy Management System in an Educational Building in Romania 58

Assessing the Electrical Energy Consumption for Designing an...

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58th International Universities Power engineering Conference, UPEC 2023

作者： Farkas, Timea Chicco, Gianfranco Czumbil, Levente Ceclan, Andrei Berciu, Alexandru-George Polycarpou, Alexis Micu, Dan D. Technical University of Cluj-Napoca Energy Transition Research Center Cluj-Napoca Romania Politecnico di Torino Dipartimento Energia 'Galileo Ferraris' Torino Italy Frederick University Department of Electrical Engineering Computer Engineering and Informatics Nicosia Cyprus

ISBN: (纸本)9798350316834

The building sector along with more and more inside or nearby-supplied EV mobility are a key pillar in the efforts of decarbonisation and achieving a Net Zero Emissions scenario by 2050 and even earlier. Without immediate action in the energy management and control automated systems, the building sector will not align with the decarbonisation goals. Energy management systems play a crucial role in managing energy usage and rely on forecasting algorithms and energy management schemes. A case study on a demo site pilot was conducted on the main building of the Faculty of Building engineering Services at the Technical University of Cluj-Napoca, Romania. The study aims to assess electrical energy consumption and bidirectional flows, and to identify key correlations between factors, using multilinear regression as well as consumption pattern identification. Two scenarios are proposed and assessed. © 2023 IEEE.

关键词： Energy efficiency

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A Neural Interface System-on-Chip for Nerve Signal Recording and Analysis of Human Gesture 8

A Neural Interface System-on-Chip for Nerve Signal Recording...

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8th IEEE International Conference on Advanced Robotics and Mechatronics, ICARM 2023

作者： Liu, Junhuo Li, Zhijun Gu, Jason Feng, Ying Li, Guoxin College of Artificial Intelligence Anhui University Hefei230601 China Institute of Artificial Intelligence Hefei Comprehensive National Science Center Hefei230088 China University of Science and Technology of China Department of Automation Hefei230026 China Dalhousie University Department of Electrical and Computer Engineering HalifaxNSCanada Canada School of Automation Science and Engineering South China University of Technology Guangzhou510640 China

ISBN: (纸本)9798350300178

The intensity of surface electromyography (sEMG) reflects the state of muscle activity, and the monitoring of neural activity is accomplished by recording and analyzing sEMG, so as to help the elderly and patients with muscle injury live normally. However, the current products in the literature or on the market cannot support the integration of signal recording and decision making simultaneously. To solve this challenge, a novel neural interface system-on-chip (SoC) is developed, which includes a neural signal recorder, a hardware integrated classifier with signal screening capability and a series of communication interfaces for data transmission. In addition, function circuits consisting of the controller and the clock generator are designed to provide operation instructions and the necessary reference timing. The prototype of the system was developed by using Verilog on FPGA. In the experiments, five volunteer subjects with healthy upper limbs were invited to participate in the verification of model parameter training and real-time gesture recognition. The experimental results show that the average recognition accuracy reach to 98.14%. which is better than the existing model of the same classifier. © 2023 IEEE.

关键词： System-on-chip

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