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Robot-mediated intervention can assist children with autism to develop visual perspective taking skills

Paladyn 2021年第1期12卷 87-101页

作者： Lakatos, Gabriella Wood, Luke Jai Syrdal, Dag Sverre Robins, Ben Zaraki, Abolfazl Dautenhahn, Kerstin Adaptive Systems Research Group University of Hertfordshire HatfieldAL10 9AB United Kingdom Cardiff University CardiffCF24 3AA United Kingdom Departments of Electrical and Computer Engineering Systems Design Engineering University of Waterloo WaterlooON N2L 3G1 Canada

In this work, we tested a recently developed novel methodology to assist children with Autism Spectrum Disorder (ASD) improve their Visual Perspective Taking (VPT) and Theory of Mind (ToM) skills using the humanoid robot Kaspar. VPT is the ability to see the world from another person's perspective, drawing upon both social and spatial information. Children with ASD often find it difficultto understand that others might have perspectives, viewpoints and beliefs that are different from their own, which is a fundamental aspect of both VPT and ToM. The games we designed were implementedas the first attempt to study if these skills can be improved in children with ASD through interacting with a humanoid robot in a series of trials. The games involved a number of different actions with the common goal of helping the children to see the world from the robot's perspective. Childrenwith ASD were recruited to the study according to specific inclusion criteria that were determined in a previous pilot study. In order to measure the potential impact of the games on the children, three pre- and post-tests (Smarties, Sally-Anne and Charlie tests) were conducted with the children Our findings suggest that children with ASD can indeed benefit from this approach of robot-assistedtherapy. © 2021 Gabriella Lakatos et al., published by De Gruyter.

关键词： Anthropomorphic robots

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SELF-SUPERVISED GRAPH TRANSFORMER WITH CONTRASTIVE LEARNING FOR BRAIN CONNECTIVITY ANALYSIS TOWARDS IMPROVING AUTISM DETECTION

arXiv

引用

arXiv 2025年

作者： Leng, Yicheng Anwar, Syed Muhammad Rekik, Islem He, Sen Lee, Eung-Joo Department of Electrical and Computer Engineering University of Arizona Tucson United States Children’s National Hospital WashingtonDC United States Department of Computing Imperial College London London United Kingdom Department of Systems and Industrial Engineering University of Arizona Tucson United States

Functional Magnetic Resonance Imaging (fMRI) provides useful insights into the brain function both during task or rest. Representing fMRI data using correlation matrices is found to be a reliable method of analyzing the inherent connectivity of the brain in the resting and active states. Graph Neural Networks (GNNs) have been widely used for brain network analysis due to their inherent explainability capability. In this work, we introduce a novel framework using contrastive self-supervised learning graph transformers, incorporating a brain network transformer encoder with random graph alterations. The proposed network leverages both contrastive learning and graph alterations to effectively train the graph transformer for autism detection. Our approach, tested on Autism Brain Imaging Data Exchange (ABIDE) data, demonstrates superior autism detection, achieving an AUROC of 82.6 and an accuracy of 74%, surpassing current state-of-the-art methods. Copyright © 2025, The Authors. All rights reserved.

关键词： Self-supervised learning

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Evaluating Dry Electrodes and Bioinstrumentation for Wearable Arm ECG Acquisition

Evaluating Dry Electrodes and Bioinstrumentation for Wearabl...

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Challenges, Opportunities, Innovations and Applications in Electronic Textiles (E-Textiles), International Conference on the

作者： Suparna Veeturi Vignesh Ravichandran Alex Cerullo Shubham Chomal Yalda Shahriari Stephanie Carreiro Krishna Venkatasubramanian Kunal Mankodiya Dhaval Solanki Department of Electrical Computer and Biomedical Engineering University of Rhode Island USA Department of Emergency Medicine University of Massachusetts Chan Medical School USA Department of Mechanical Industrial and Systems Engineering University of Rhode Island Kingston RI USA Department of Computer Science and Statistics University of Rhode Island USA

ISBN: (数字)9783000804298

ISBN: (纸本)9798331516871

Arm-based electrocardiogram (ECG) offers a more comfortable and user-friendly alternative to traditional chest ECG for wearable cardiac monitoring in daily health applications. However, arm ECG faces challenges such as capturing extremely low-amplitude signals in the microvolt range and significant electromyogram (EMG) noise contamination. The aim of this study was to evaluate the performance of dry electrodes for arm and chest ECG monitoring using gold standard BIOPAC and proposed mDAQ systems. This study consisted of two phases. In Phase 1, we evaluated different dry electrode materials, including silver knit fabric and gold-plated electrodes, with six healthy adult participants ( $\mathrm{N}{=3}$ males and $\mathrm{N}{=3}$ females) having upper arm circumference ranging from 27 to 42 cm. We used a BIOPAC system as a gold standard to collect arm ECG, upper arm EMG, and reference chest ECG during standardized movement tasks. Results showed that silver knit electrodes demonstrated superior QRS energy ${(0.705}\pm {0.151)}$ , surpassing chest gel floating ECG QRS energy ${(0.328}\pm {0.026)}$ , with lower EMG interference across movement conditions. In Phase 2, we developed a bioinstrumentation system (mDAQ) optimized for dry electrodes, demonstrating reliable ECG acquisition in both chest Lead I and arm configurations. -We validated our bioinstrumentation with sixteen healthy adult participants ( $\mathrm{N}{=8}$ male and $\mathrm{N}= \text{female}$ ). The mDAQ system maintained high signal consistency with a skewness effect size of 0.963 and showed a mean heart rate bias of only −1.842 bpm compared to the BIOPAC system, confirming its viability for wearable ECG monitoring applications.

关键词： Electrodes Smart textiles Silver Technological innovation Noise Electrocardiography Reliability engineering Electromyography Biomedical monitoring Monitoring

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Trikarenos: Design and Experimental Characterization of a Fault-Tolerant 28nm RISC-V-based SoC

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IEEE Transactions on Nuclear Science 2025年

作者： Rogenmoser, Michael Wiese, Philip Forlin, Bruno Endres Gurkaynak, Frank K. Rech, Paolo Menicucci, Alessandra Ottavi, Marco Benini, Luca Integrated Systems Laboratory ETH Zurich Switzerland Computer Architecture for Embedded Systems University of Twente Netherlands Department of Industrial Engineering University of Trento Italy Delft University of Technology Department of Space Engineering Netherlands University of Rome Department of Electronic Engineering Tor Vergata Italy Information Engineering at the University of Bologna Department of Electrical Electronic Italy

RISC-V-based fault-tolerant system-on-chip (SoC) designs are critical for the new generation of automotive and space SoC architectures. However, reliability assessment requires characterization under controlled radiation doses to accurately quantify the fault tolerance of the fabricated designs. This work analyzes the Trikarenos design, a SoC implemented in TSMC 28nm, for single event upset (SEU) vulnerability under atmospheric neutron and 200MeV proton radiation, comparing these results to simulation-based fault injection. All faults in error correction codes (ECC) protected memory are corrected by a scrubber, showing an estimated cross-section per bit of up to 1.09 × 10−14 cm2 bit−1. Furthermore, the triple-core lockstep (TCLS) mechanism implemented in Trikarenos is validated and is shown to correct errors affecting a cross-section up to 3.23 × 10−11 cm2, with the remaining uncorrectable vulnerability below 5.36 × 10−12 cm2. When augmenting the experimental analysis of fabricated chips with gate-level fault injection in simulation, 99.10% of injections into the SoC produced correct results, while 100% of injections in the TCLS-protected cores were handled *** 12.28%of all injected faults leading to a TCLS recovery, this indicates an approximate effective flip-flop cross-section of up to 1.28 × 10−14 cm2/FF. © 1963-2012 IEEE.

关键词： Radiation damage

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Stress Pattern Recognition Using Spatio Temporal Features Based on EEG Sport Simulation

Stress Pattern Recognition Using Spatio Temporal Features Ba...

引用

IEEE industrial Electronics and Applications Conference (IEACon)

作者： Diah Risqiwati Hideyuki Ooshima Hiroki Sato Adhi Dharma Wibawa Evi Septiana Pane Mauridhi Hery Purnomo Department of Electrical Engineering Institut Teknologi Sepuluh Nopember Surabaya Indonesia Department of Informatics Universitas Muhammadiyah Malang Malang Indonesia Department of Systems Science and Engineering Shibaura Institute of Technology Saitama Japan Dept of Electrical Engineering Medical Technology Institut Teknologi Sepuluh Nopember Surabaya Indonesia Industrial Training and Education of Surabaya Ministry of Industry RI Surabaya Indonesia Dept of Electrical Engineering Dept of Computer Engineering Institut Teknologi Sepuluh Nopember Surabaya Indonesia

ISBN: (数字)9798350382976

ISBN: (纸本)9798350382983

Several emotional factors that can induce stress affect participants performance during the competition, such as choking under pressure. This study aims to evaluate the performance of golf players under psychological pressure by creating a scenario in a golf putting simulation test. We forced each participant to compete by making a monetary reward scenario based on their winning score to increase each participant's stress level. Their winning score is classified as poor or good by measuring the distance to the target achievement. The objective is to detect the stress pattern by observing the prominent features and window segments in the alpha band. Our approach is processing the EEG data by wavelet transform and extracting the data into spatio temporal features in the central and occipital lobe. Our investigation found that the maximum amplitude and band power feature values were higher when the participants performed well, and this pattern is reversed when the participant's performance is poor. Meanwhile, based on brain area activity in a good performance, we found that central area activity is high from the first to the fourth segment. In contrast, occipital area activity is high only until the third segment. Following these parts, brain activity decreases compared to its previous state. This study also discovered that the significant segment window is located in the tenth segment, with a P-value < 0.05 between good and poor performance. Thus, this analysis's results can be used to recommend neurofeedback training for athletes to prevent choking under pressure.

关键词： Wavelet transforms Training Time-frequency analysis Psychology Human factors Feature extraction Electroencephalography Occipital Lobe Time-domain analysis Neurofeedback

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Federated Cybersecurity Testbed as a Service (Fctaas): A Framework to Federate Cybersecurity Testbeds

SSRN

引用

SSRN 2023年

作者： Mamun, Muntasir Lin, Yu-Zheng Almazyad, Ibrahim Shao, Sicong Satam, Shalaka Hariri, Salim Satam, Pratik Department of Systems and Industrial Engineering University of Arizona TucsonAZ85721 United States Department of Electrical and Computer Engineering University of Arizona TucsonAZ85719 United States School of Electrical Engineering and Computer Science University of North Dakota Grand ForksND58203 United States The University of Arizona United States

Rapid technological changes are transforming our society with the emergence of new fields like Autonomous Vehicles and Smart Manufacturing, posing new research questions and challenges on system design, operations, security, and training. Researchers rely on testbeds to create experimental scenarios to solve these research challenges. These testbeds aid in data collection, analysis, and observation of the research problem and measure the efficacy of the proposed solution. However, the rapid pace of modern innovation makes it challenging and cost prohibitive for these testbeds to represent state-of-the-art scenarios, which require expensive upgrades and the addition of new capabilities. In addition, a small select community of researchers has access to these specialized testbeds, with the testbeds not used optimally through their small operational *** paper presents FCTaaS: Federated Cybersecurity Testbed as a Service, a scalable framework that allows two separate cybersecurity testbeds to be part of one experiment, overcoming the testbed's geographical boundary to address today's research challenges. This federation allows multiple geographically isolated testbeds to work together to experiment using a Virtual Private Network (VPN), while providing researchers interfaces for testbed discovery and remote experiment design. Our experimental evaluation stress tests the FCTaaS by evaluating its performance in Denial of Service Scenarios on smart infrastructure. The limiting network utilization of 49\%, and low overhead of 1\%, even under such resource-intensive attack scenarios, showcases the efficacy of the FCTaaS to allow improved experimentation. © 2023, The Authors. All rights reserved.

关键词： Cybersecurity

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A Comparative Analysis of SiC MOSFET Performance in a Traction Inverter

A Comparative Analysis of SiC MOSFET Performance in a Tracti...

引用

IEEE Asia-Pacific Conference on Circuits and systems

作者： Chih-Chiang Wu Uma Sankar Rout Yu-Long Wang Yi-Ling Lin Jwu-Sheng Hu Mechanical and Mechatronics Systems Research Laboratories Industrial Technology Research Institute Hsinchu R.O.C. Department of Electrical Engineering&Computer Science National Yang Ming Chiao Tung University

ISBN: (数字)9798350378771

ISBN: (纸本)9798350378788

This study rigorously tests and verifies silicon carbide (SiC) power modules within a developed 800 V electric vehicle traction inverter. SiC traction inverters offer high efficiency, power density, and compact size. The developed SiCbased traction inverter is thoroughly evaluated on the established platform with various switching frequency, achieving a peak drive efficiency exceeding 98% at 6 kHz and a power density of $50 \mathrm{~kW} / \mathrm{L}$.

关键词： Power system measurements MOSFET Silicon carbide Density measurement Circuits and systems Switching frequency Asia Multichip modules Electric vehicles Inverters

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Investigation of Polycystic Ovary Syndrome (PCOS) Diagnosis Using Machine Learning Approaches

Investigation of Polycystic Ovary Syndrome (PCOS) Diagnosis ...

引用

Sustainable Technologies for Industry 4.0 (STI), International Conference on

作者： Al Zadid Sultan Bin Habib Md Asif Bin Syed Md. Ekramul Islam Tanpia Tasnim Lane Department of Computer Science and Electrical Engineering West Virginia University Morgantown WV USA Department of Industrial and Management Systems Engineering West Virginia University Morgantown WV USA Department of Computer Science & Engineering Stamford University Bangladesh Dhaka Bangladesh Department of Computer Science and Engineering Green University of Bangladesh Narayanganj Dhaka Bangladesh

Polycystic Ovarian Syndrome (PCOS) is a widespread hormone problem for women of childbearing age. Women with PCOS may not ovulate; they might have high levels of androgens and have many small cysts on the ovaries. It can cause missed or irregular menstrual periods, excess hair growth, acne, infertility, and weight gain. Machine Learning (ML) can effectively diagnose this disease at an earlier stage as tons of medical data are available now. Traditional approaches to detect PCOS encompass a combination of clinical evaluation, medical history assessment, physical examination, and laboratory tests. These approaches aim to identify the characteristic symptoms and hormonal imbalances associated with PCOS. Physical examination requires good resources and costs time and money. In recent times, data-driven techniques have substantially advanced disease prediction within the medical field. We aim to utilize ML approaches, incorporating unique feature selection algorithms, to predict PCOS. This paper introduces a data-driven approach to PCOS diagnosis, combining Feature engineering and ML. Several feature selection approaches have been considered to select sets of features for training the ML model, including CatBoost, Extreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LGBM), AdaBoost, Random Forest (RF). Results demonstrate that AdaBoost, with ten features selected by RF Feature Importance and Highest Correlation (HC), provides the highest test accuracy.

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Neural-Network-Based Recursive State Estimation for Nonlinear Networked systems With Binary-Encoding Mechanisms

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IEEE Transactions on Neural Networks and Learning systems 2025年第6期36卷 10072-10083页

作者： Yuhan Zhang Zidong Wang Lei Zou Wei Qian Shuxin Du College of Electrical Engineering and Automation Shandong University of Science and Technology Qingdao China Department of Computer Science Brunel University of London Middlesex Uxbridge U.K. College of Information Science and Technology Donghua University Shanghai China Engineering Research Center of Digitalized Textile and Fashion Technology Ministry of Education Shanghai China School of Electrical Engineering and Automation Henan Polytechnic University Jiaozuo China Huzhou Key Laboratory of Intelligent Sensing and Optimal Control for Industrial Systems School of Engineering Huzhou University Huzhou China

This work addresses the problem of recursive state estimation for networked control systems with unknown nonlinearities and binary-encoding mechanisms (BEMs). To enhance transmission reliability and reduce network resource consumption, BEMs are used to convert measurement signals into binary bit strings (BBSs) of limited length, which are then transmitted to the estimator through noisy communication channels. During transmission, random bit errors may occur in the BBSs due to channel noise. For the considered nonlinear networked control systems affected by random bit errors, a neural-network (NN)-based recursive estimation strategy is proposed, where an NN with a time-varying tuning scalar is employed to approximate the unknown nonlinearity of the networked control systems. By using the proposed strategy, the upper bounds of the estimation error of the system state and the trace of the estimation error of the NN weight (NNW) are first derived. These bounds are then minimized by recursively designing both the estimator gain matrix and the tuning scalar of the NNW. Finally, the effectiveness of the proposed estimation strategy is demonstrated through a numerical example.

关键词： Artificial neural networks State estimation Encoding Noise Networked control systems Electronic mail Symmetric matrices Estimation error Tuning Recursive estimation

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Optimal Linear Deception Attacks on Remote State Estimation with Constrained Alarm Rates: A Low-Dimensional Case

Optimal Linear Deception Attacks on Remote State Estimation ...

引用

IEEE Conference on Decision and Control

作者： Jun Shang Hanwen Zhang Jing Zhou Tongwen Chen Department of Control Science and Engineering Shanghai Research Institute for Intelligent Autonomous Systems National Key Laboratory of Autonomous Intelligent Unmanned Systems and Frontiers Science Center for Intelligent Autonomous Systems Tongji University Shanghai China Key Laboratory of Knowledge Automation for Industrial Processes of Ministry of Education School of Automation and Electrical Engineering University of Science and Technology Beijing Beijing China Department of Electrical and Computer Engineering University of Alberta Edmonton AB Canada

ISBN: (数字)9798350316339

ISBN: (纸本)9798350316346

This study addresses linear attacks on remote state estimation within the context of a constrained alarm rate. Smart sensors, which are equipped with local Kalman filters, transmit innovations instead of raw measurements through a wireless communication network. This transmission is vulnerable to malicious data interception and manipulation by attackers. The aim of this research is to identify the optimal attack strategy that degrades the system performance while adhering to stealthiness constraints. A notable innovation of this paper is the direct association of the attack’s stealthiness with the alarm rate, diverging from traditional approaches that rely on the covariance of the innovation or the Kullback–Leibler divergence, which are conventional metrics that have been extensively explored in previous studies. Our findings reveal that the optimal attack strategy exhibits some structural characteristics in systems of low dimensions. The performance of the proposed attack strategy is demonstrated through numerical examples.

关键词： Wireless communication Measurement Technological innovation System performance Kalman filters State estimation Intelligent sensors

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