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IEEE International Workshop on Robot and Human Communication (ROMAN)

作者： Delara Forghani Moojan Ghafurian Samira Rasouli Chrystopher L. Nehaniv Kerstin Dautenhahn Department of Electrical and Computer Engineering University of Waterloo Waterloo Ontario Canada Department of Systems Design Engineering University of Waterloo Waterloo Ontario Canada

Social robots have the potential to serve as coaches for public speaking training. To design successful social robots, it is important to understand the expectations and perceptions of prospective users of such robots. In this paper, we present thematic analyses of comments made by 168 participants in an online study where participants watched videos of agents in the role of a public speaking coach. The study had a between-participant design with three conditions: two conditions with a humanoid social robot in either (1) active listening mode, i.e., using non-verbal backchanneling, or (2) passive listening mode, and (3) a voice assistant agent. The themes identified and discussed can contribute to the development of social robots and other agents as public speaking coaches.

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Error Performance Analysis of UAV-Mounted RIS for NOMA systems with Practical Constraints

arXiv

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arXiv 2024年

作者： Khennoufa, Faical Abdellatif, Khelil Kara, Ferdi Yanikomeroglu, Halim Rabie, Khaled Elganimi, Taissir Y. Beddiaf, Safia LGEERE Laboratory Department of Electrical Engineering Echahid Hamma Lakhdar University El-Oued Algeria Laboratory Department of Systems and Computer Engineering Carleton University OttawaONK1S 5B6 Canada Department of Computer Engineering Zonguldak Bulent Ecevit University Zonguldak67100 Turkey The Department of Engineering Manchester Metropolitan University ManchesterM1 5GD United Kingdom The Department of Electrical and Electronic Engineering University of Tripoli Libya

Uncrewed aerial vehicles (UAVs) have attracted recent attention for sixth-generation (6G) networks due to their low cost and flexible deployment. In order to maximize the ever-increasing data rates, spectral efficiency, and wider coverage, technologies such as reconfigurable intelligent surface (RIS) and non-orthogonal multiple access (NOMA) are adapted with UAVs (UAV-RIS NOMA). However, the error performance of UAV-RIS NOMA has not been considered, yet. In this letter, we investigate the error probability of UAV-RIS NOMA systems. We also consider the practical constraints of hardware impairments (HWI) at the transceivers, inter-cell interference (ICI), and imperfect successive interference cancellation (SIC). The analytical derivations are validated by Monte-Carlo simulations. Our results demonstrate that our proposed system achieves higher performance gain (more than 5 dB with increasing the number of RIS elements) with less error probability compared to UAVs without RIS. Moreover, it is found that the HWI, ICI, and imperfect SIC have shown a negative impact on the system performance. © 2024, CC0.

关键词： Antennas

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PrivShape: Extracting Shapes in Time Series Under User-Level Local Differential Privacy

PrivShape: Extracting Shapes in Time Series Under User-Level...

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International Conference on Data engineering

作者： Yulian Mao Qingqing Ye Haibo Hu Qi Wang Kai Huang Department of Computer Science and Engineering Southern University of Science and Technology Department of Electrical and Electronic Engineering The Hong Kong Polytechnic University Research Institute of Trustworthy Autonomous Systems Southern University of Science and Technology School of Computer Science and Engineering Macau University of Science and Technology

ISBN: (数字)9798350317152

ISBN: (纸本)9798350317169

Time series have numerous applications in finance, healthcare, IoT, and smart city. In many of these applications, time series typically contain personal data, so privacy infringement may occur if they are released directly to the public. Recently, local differential privacy (LDP) has emerged as the state-of-the-art approach to protecting data privacy. However, existing works on LDP-based collections cannot preserve the shape of time series. A recent work, PatternLDP, attempts to address this problem, but it can only protect a finite group of elements in a time series due to ω-event level privacy guarantee. In this paper, we propose PrivShape, a trie-based mechanism under user-level LDP to protect all elements. PrivShape first transforms a time series to reduce its length, and then adopts trie-expansion and two-level refinement to improve utility. By extensive experiments on real-world datasets, we demonstrate that PrivShape outperforms PatternLDP when adapted for offline use, and can effectively extract frequent shapes.

关键词： Privacy Differential privacy Shape Smart cities Frequency-domain analysis Time series analysis Transforms

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IRKO:An Improved Runge-Kutta Optimization Algorithm for Global Optimization Problems

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computers, Materials & Continua 2022年第3期70卷 4803-4827页

作者： R.Manjula Devi M.Premkumar Pradeep Jangir Mohamed Abdelghany Elkotb Rajvikram Madurai Elavarasan Kottakkaran Sooppy Nisar Department of Computer Science and Engineering Kongu Engineering CollegePerundurai638060Tamil NaduIndia Department of Electrical&Electronics Engineering Dayananda Sagar College of EngineeringBengaluru560078KarnatakaIndia Rajasthan Rajya Vidyut Prasaran Nigam Sikar332025RajasthanIndia Mechanical Engineering Department College of EngineeringKing Khalid UniversityAbha61421Saudi Arabia Mechanical Engineering Department Faculty of EngineeringKafrelsheikh UniversityEgypt Clean and Resilient Energy Systems(CARES)Laboratory Texas A&MUniversityGalveston77553TXUSA College of Arts and Sciences Prince Sattam bin Abdulaziz UniversityWadi Aldawaser11991Saudi Arabia

Optimization is a key technique for maximizing or minimizing functions and achieving optimal cost,gains,energy,mass,and so *** order to solve optimization problems,metaheuristic algorithms are *** of these techniques are influenced by collective knowledge and natural *** is no such thing as the best or worst algorithm;instead,there are more effective algorithms for certain ***,in this paper,a new improved variant of a recently proposed metaphorless Runge-Kutta Optimization(RKO)algorithm,called Improved Runge-Kutta Optimization(IRKO)algorithm,is suggested for solving optimization *** IRKO is formulated using the basic RKO and local escaping operator to enhance the diversification and intensification capability of the basic RKO *** performance of the proposed IRKO algorithm is validated on 23 standard benchmark functions and three engineering constrained optimization *** outcomes of IRKO are compared with seven state-of-the-art algorithms,including the basic RKO *** to other algorithms,the recommended IRKO algorithm is superior in discovering the optimal results for all selected optimization *** runtime of IRKO is less than 0.5 s for most of the 23 benchmark problems and stands first for most of the selected problems,including real-world optimization problems.

关键词： engineering design global optimization local escaping operator metaheuristics Runge-Kutta optimization algorithm

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Machine Learning Techniques in Additive Manufacturing: A Review

Machine Learning Techniques in Additive Manufacturing: A Rev...

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Artificial Intelligence, Blockchain, and Internet of Things (AIBThings), IEEE International Conference on

作者： Ahmed Alsalem Mohamed Zohdy Department of Industrial and Systems Engineering Oakland University Rochester MI USA Department of Electrical and Computer Engineering Oakland University Rochester MI USA

Additive manufacturing (AM) is a 3D printing process widely used in industries such as automotive, healthcare, aerospace, and consumer goods. It creates different 3D objects by adding different layers over one another. AM manufacturing processes are capable enough to produce complex parts, consisting of intricate designs and geometries, characterized by well-established grain structure and properties without expensive tooling in reduced lead time and at low cost. It is a multi-physical process, including various parameters that impact the final product's quality. Moreover, Machine Learning (ML) techniques are suitable approaches aimed at understanding and predicting the complex processes of AM. In this study, an overview of AM and the challenges in the AM is presented, as well as the ML approaches to address the challenges in AM processes are also discussed. The recently introduced ML techniques in AM processes are evaluated explicitly. To be more precise, limited training data, material choice, and standardization regarding data pose major challenges that need to be addressed when applying ML techniques to AM methods.

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FORS-EMG: A Novel sEMG Dataset for Hand Gesture Recognition Across Multiple Forearm Orientations

arXiv

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arXiv 2024年

作者： Rumman, Umme Ferdousi, Arifa Saha, Bipin Hossain, Md. Sazzad Islam, Md. Johirul Ahmad, Shamim Reaz, Mamun Bin Ibne Islam, Md. Rezaul The Department of Computer Science and Engineering Varendra University Rajshahi Bangladesh The Department of Electrical Electronic and Systems Engineering Universiti Kebangsaan Malaysia Selangor Bangi Malaysia The Department of Physics Rajshahi University of Engineering and Technology Rajshahi6204 Bangladesh The Department of Electrical and Electronic Engineering University of Rajshahi Rajshahi6205 Bangladesh The Department of Computer Science and Engineering University of Rajshahi Rajshahi6205 Bangladesh The Department of Electrical and Electronic Engineering Independent University Dhaka Bangladesh

Surface electromyography (sEMG) signals hold significant potential for gesture recognition and robust prosthetic hand development. However, sEMG signals are affected by various physiological and dynamic factors, including forearm orientation, electrode displacement, and limb position. Most existing sEMG datasets lack these dynamic considerations. This study introduces a novel multichannel sEMG dataset to evaluate commonly used hand gestures across three distinct forearm orientations. The dataset was collected from nineteen able-bodied subjects performing twelve hand gestures in three forearm orientations-supination, rest, and pronation. Eight MFI EMG electrodes were strategically placed at the elbow and mid-forearm to record high-quality EMG signals. Signal quality was validated through Signal-to-Noise Ratio (SNR) and Signal-to-Motion artifact Ratio (SMR) metrics. Hand gesture classification performance across forearm orientations was evaluated using machine learning classifiers, including LDA, SVM, and KNN, alongside five feature extraction methods: TDD, TSD, FTDD, AR-RMS, and SNTDF. Furthermore, deep learning models such as 1D CNN, RNN, LSTM, and hybrid architectures were employed for a comprehensive analysis. Notably, the LDA classifier achieved the highest F1 score of 88.58% with the SNTDF feature set when trained on hand gesture data of resting and tested across gesture data of all orientations. The promising results from extensive analyses underscore the proposed dataset's potential as a benchmark for advancing gesture recognition technologies, clinical sEMG research, and human-computer interaction applications. The dataset is publicly available in MATLAB format. Access the dataset here. © 2024, CC BY-SA.

关键词： Signal to noise ratio

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Integrating Artificial Intelligence into Electric Vehicle Energy systems: A Survey 1

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10th IFIP International Workshop on Artificial Intelligence for Knowledge Management, AI4KMES 2023

作者： Hua, Weiqi Mullen, Daniel Wahid, Abdul Sitabkhan, Khadija Mason, Karl Department of Electronic Electrical and Systems Engineering University of Birmingham BirminghamB15 2TT United Kingdom School of Engineering University of Edinburgh EdinburghEH9 3DW United Kingdom School of Computer Science University of Galway GalwayH91 FYH2 Ireland

ISBN: (数字)9783031610691

ISBN: (纸本)9783031610684

Electric vehicles play an important role in the global transition to "Net Zero" and the decarbonisation of point source emissions from road transport, as their market share continues to grow each year. However, integrating these electric vehicles into existing electricity grids, refuelling stations and supply chains presents significant challenges. But most challenges can be effectively addressed through the implementation of artificial intelligence solutions. This paper starts with an introduction to artificial intelligence and an investigation of the challenges associated with the widespread adoption of electric vehicles in society. Then, the paper analyses in detail how artificial intelligence can facilitate the smooth integration of electric vehicles. In addition, the paper provides a detailed investigation of the life-cycle emissions of electric vehicles and examines the limitations of existing research in this area and suggests potential avenues for future research. © IFIP International Federation for Information Processing 2024.

关键词： Renewable energy

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A NOVEL MODEL FOR THE MATERIAL SELECTION OF SMART OBJECTS FOR HAND-REHABILITATION: A CASE STUDY 24

A NOVEL MODEL FOR THE MATERIAL SELECTION OF SMART OBJECTS FO...

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24th International Conference on engineering Design, ICED 2023

作者： Bos, Mikaël Ivar Karahanoǧlu, Armaǧan Haarman, Juliet Nizamis, Kostas Chair of Systems Engineering and Multidisciplinary Design Department of Design Production and Management Faculty of Engineering Technology University of Twente Enschede Netherlands Interaction Design Research Group Department of Design Production and Management Faculty of Engineering Technology University of Twente Enschede Netherlands Human Media Interaction Group Faculty of Electrical Engineering Mathematics and Computer Science University of Twente Enschede Netherlands

Home hand rehabilitation for stroke is becoming increasingly important due to logistic and financial challenges. Developing Daily-life Integrated Hand-rehabilitation Products (DIHP) aims to enable the application of at-home rehabilitation. The materials of these products are essential for their success, however, selecting materials for DIHP has not been investigated yet. Previous research on material selection showed that it is done strictly on material properties or based on a human-centered approach. Hence, in this study, we propose a hybrid model for choosing materials for DIHP. To achieve this, we first combined the findings of previous material selection processes into a comprehensive material selection model. We applied this model in a case study, in which we first selected three materials based on their properties. Following, we 3d printed a DIHP out of the chosen materials and tested the feeling of the materials with multiple expert groups. Our findings suggest that the proposed material selection method is promising and highlights that our comprehensive model provides more insights when compared to a strict material property-based selection. © The Author(s), 2023. Published by Cambridge University Press.

关键词： 3D printing

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Unsupervised Video Domain Adaptation with Masked Pre-Training and Collaborative Self-Training

Unsupervised Video Domain Adaptation with Masked Pre-Trainin...

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Conference on computer Vision and Pattern Recognition (CVPR)

作者： Arun Reddy William Paul Corban Rivera Ketul Shah Celso M. de Melo Rama Chellappa Johns Hopkins University Applied Physics Laboratory Department of Electrical & Computer Engineering Johns Hopkins University DEVCOM U.S. Army Research Laboratory

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

In this work, we tackle the problem of unsupervised domain adaptation (UDA) for video action recognition. Our approach, which we call UNITE, uses an image teacher model to adapt a video student model to the target domain. UNITE first employs self-supervised pretraining to promote discriminative feature learning on target domain videos using a teacher-guided masked distillation objective. We then perform self-training on masked target data, using the video student model and image teacher model together to generate improved pseudolabels for unlabeled target videos. Our self-training process successfully leverages the strengths of both models to achieve strong transfer performance across domains. We evaluate our approach on multiple video domain adaptation benchmarks and observe significant improvements upon previously reported results.

关键词： Representation learning Adaptation models computer vision Computational modeling Collaboration Benchmark testing Data models

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β-GaO Lateral Schottky Barrier Diodes with > 10 kV Breakdown Voltage and Anode engineering

IEEE Electron Device Letters

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IEEE Electron Device Letters 2023年第10期44卷 1684-1687页

作者： Wang, Chenlu Yan, Qinglong Zhang, Chaoqun Su, Chunxu Zhang, Kun Sun, Sihan Liu, Zhihong Zhang, Weihang Alghamdi, Sami Ghandourah, Emad Zhang, Chunfu Zhang, Jincheng Zhou, Hong Hao, Yue Xidian University Key Laboratory of Wide Band Gap Semiconductor Materials and Devices School of Microelectronics Xi'an710071 China Guangzhou Institute of Technology Xidian University Guangzhou Wide Band Gap Semiconductor Innovation Center Guangzhou510555 China King Abdulaziz University Department of Electrical and Computer Engineering Jeddah21589 Saudi Arabia

In this letter, we demonstrated β-Ga2O3 lateral Schottky barrier diodes (SBDs) with breakdown voltage (BV) over 10 kV via anode engineering techniques. Postanode deposition annealing (PAA) was implemented to enhance the Schottky barrier height (SBH) to minimize the leakage current and suppress the interface state density (Dit) by over 1 order of magnitude. It is found that the Dit was extracted to be only 3 × 1010 cm-2eV-1 at the energy level near the conduction band after the PAA treatment. Benefiting from the PAA and the coupled as well as the carefully designed dual field-plate (FP) structure, the BV of the SBD was enhanced from about 4 kV to over 10 kV while the turn-on voltage (Von) for those SBDs remained approximately at 1 V. These findings show the immense potential of anode engineered Ga2O3 diodes for future high-voltage and high-power electronic systems. © 1980-2012 IEEE.

关键词： Deposition

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