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Global Emerging Technology Blockchain Forum: Blockchain & Beyond (iGETblockchain), IEEE

作者： Hongchi Wu Binhao Fang Cheng Xiang Gregory Cohen André van Schaik Bharath Ramesh Department of Electrical and Computer Engineering Advanced Control Technology Lab National University of Singapore Singapore International Center for Neuromorphic Systems The MARCS Institute for Brain Behaviour and Development Western Sydney University Sydney Australia

Neuromorphic intelligent systems are motivated by the observation that biological organisms - from algae to primates - excel in swiftly sensing their environment, reacting promptly to its perils and opportunities. Furthermore, biological organisms function more resiliently than our most advanced machines, with a fraction of their power requirements. Taking inspiration from how primates and humans have successfully evolved higher cognitive intelligence within social constructs, this paper proposes neuromorphic systems to be built and governed on a public distributed ledger platform. However, following in the footsteps of generic AI research, neuromorphic benchmarks and algorithms are developed in isolation. Furthermore, as a relatively niche research field, there is limited access to the actual neuromorphic sensors and large publicly available curated data, exacerbating the slow research progress. Nonetheless, centralized neuromorphic datasets and algorithms pose a threat to secure closed-loop behavior and learning outcomes, both commonly modulated in biological organisms via social interactions. This paper makes the case for early adoption of distributed ledger technology by neuromorphic systems and benchmarks to avoid the pitfalls endured by AI research – showcasing competing event-based gesture recognition systems on the Ethereum smart contract platform. This shift towards real-world and dynamic systems on a distributed ledger platform will improve collaboration among neuromorphic researchers while enabling healthy competition via incentives. Smart contract protocols allow model behavior monitoring, setting new learning tasks and increase in baseline performance, and naturally provides a governance framework for evolving neuromorphic systems. The code is publicly made available at: https://***/BruceFang123.

关键词： Neuromorphics Distributed ledger Smart contracts Biological systems Benchmark testing Blockchains Sensors

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Enhanced electroencephalography effective connectivity in frontal low-gamma band correlates of emotional regulation after mindfulness training

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Journal of Neuroscience Research 2023年第6期101卷 901-915页

作者： Ng, Hei-Yin Hydra Wu, Changwei W. Huang, Feng-Ying Huang, Chih-Mao Hsu, Chia-Fen Chao, Yi-Ping Jung, Tzyy-Ping Chuang, Chun-Hsiang Research Center for Education and Mind Sciences College of Education National Tsing Hua University Hsinchu Taiwan Institute of Information Systems and Applications College of Electrical Engineering and Computer Science National Tsing Hua University Hsinchu Taiwan Department of Educational Psychology and Counseling College of Education National Tsing Hua University Hsinchu Taiwan Graduate Institute of Mind Brain and Consciousness Taipei Medical University Taipei Taiwan Brain and Consciousness Research Center Taipei Medical University-Shuang Ho Hospital New Taipei Taiwan Department of Education National Taipei University of Education Taipei Taiwan Department of Biological Science and Technology National Yang Ming Chiao Tung University Hsinchu Taiwan Center for Intelligent Drug Systems and Smart Bio-devices (IDS2B) National Yang Ming Chiao Tung University Hsinchu Taiwan Graduate Institute of Behavioral Sciences Chang Gung University Taoyuan Taiwan Department of Child Psychiatry Chang Gung Memorial Hospital at Linkou Taoyuan Taiwan Department of Computer Science and Information Engineering Chang Gung University Taoyuan Taiwan Department of Otolaryngology-Head and Neck Surgery Chang Gung Memorial Hospital at Linkou Taoyuan Taiwan Institute for Neural Computation and Institute of Engineering in Medicine University of California San Diego La Jolla CA United States

Practicing mindfulness, focusing attention on the internal and external experiences occurring in the present moment with open and nonjudgement stance, can lead to the development of emotional regulation skills. Yet, the effective connectivity of brain regions during mindfulness has been largely unexplored. Studies have shown that mindfulness practice promotes functional connectivity in practitioners, potentially due to improved emotional regulation abilities and increased connectivity in the lateral prefrontal areas. To examine the changes in effective connectivity due to mindfulness training, we analyzed electroencephalogram (EEG) signals taken before and after mindfulness training, focusing on training-related effective connectivity changes in the frontal area. The mindfulness training group participated in an 8-week mindfulness-based stress reduction (MBSR) program. The control group did not take part. Regardless of the specific mindfulness practice used, low-gamma band effective connectivity increased globally after the mindfulness training. High-beta band effective connectivity increased globally only during Breathing. Moreover, relatively higher outgoing effective connectivity strength was seen during Resting and Breathing and Body-scan. By analyzing the changes in outgoing and incoming connectivity edges, both F7 and F8 exhibited strong parietal connectivity during Resting and Breathing. Multiple regression analysis revealed that the changes in effective connectivity of the right lateral prefrontal area predicted mindfulness and emotional regulation abilities. These results partially support the theory that the lateral prefrontal areas have top-down modulatory control, as these areas have high outflow effective connectivity, implying that mindfulness training cultivates better emotional regulation. © 2023 Wiley Periodicals LLC.

关键词： direct directed transfer function effective connectivity emotional regulation MBSR mindfulness

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Policy Evaluation in Distributional LQR

arXiv

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arXiv 2023年

作者： Wang, Zifan Gao, Yulong Wang, Siyi Zavlanos, Michael M. Abate, Alessandro Johansson, Karl H. Division of Decision and Control Systems School of Electrical Enginnering and Computer Science KTH Royal Institute of Technology Digital Futures StockholmSE-10044 Sweden Department of Computer Science University of Oxford OxfordOX13QD United Kingdom The School of Computation Information and Technology Technical University of Munich Munich80333 Germany The Department of Mechanical Engineering and Materials Science Duke University DurhamNC United States

Distributional reinforcement learning (DRL) enhances the understanding of the effects of the randomness in the environment by letting agents learn the distribution of a random return, rather than its expected value as in standard reinforcement learning. Meanwhile, a challenge in DRL is that the policy evaluation typically relies on the representation of the return distribution, which needs to be carefully designed. In this paper, we address this challenge for the special class of DRL problems that rely on a discounted linear quadratic regulator (LQR), which we call distributional LQR. Specifically, we provide a closed-form expression for the distribution of the random return, which is applicable for all types of exogenous disturbance as long as it is independent and identically distributed (i.i.d.). We show that the variance of the random return is bounded if the fourth moment of the exogenous disturbance is bounded. Furthermore, we investigate the sensitivity of the return distribution to model perturbations. While the proposed exact return distribution consists of infinitely many random variables, we show that this distribution can be well approximated by a finite number of random variables. The associated approximation error can be analytically bounded under mild assumptions. When the model is unknown, we propose a model-free approach for estimating the return distribution, supported by sample complexity guarantees. Finally, we extend our approach to partially observable linear systems. Numerical experiments are provided to illustrate the theoretical results. Copyright © 2023, The Authors. All rights reserved.

关键词： Reinforcement learning

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MealWatcher: A New Toolset to Record Meals for the Development of Automated Energy Intake Estimation

MealWatcher: A New Toolset to Record Meals for the Developme...

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IEEE EMBS International Conference on Information Technology Applications in Biomedicine (ITAB)

作者： Faria Armin James Jolly James Nguyen Lakshmi Rangaraju Daniel Regan Zoe Brown Sharon O'Toole Hollie Raynor Leslie Brick Elissa Jelalian J. Graham Thomas Stephanie P. Goldstein Adam Hoover Department of Electrical and Computer Engineering Clemson University Clemson SC USA Weight Control and Diabetes Research Center The Miriam Hospital Providence RI USA Department of Nutrition University of Tennessee Knoxville TN USA Department of Psychiatry and Human Behavior Alpert Medical School Brown University Providence RI USA Department of Psychiatry and Human Behavior Weight Control and Diabetes Research Center The Miriam Hospital/Brown Alpert Medical School Providence RI USA

ISBN: (数字)9798350351552

ISBN: (纸本)9798350351569

Classic tools for measuring energy intake, such as food diaries and 24 hour recalls, are burdensome to use and have significant measurement error. This hinders research and interventions in obesity treatment and comorbidities such as diabetes and heart disease. New tools are being developed to automate the measurement of energy intake, such as wearable devices like smartwatches. Towards this goal, several datasets have been collected and made publicly available that include hand motion. However, these datasets have been limited to the wrist part of hand motion, and have only been collected in controlled environments such as labs or cafeterias. In this work we describe a new toolset that supports data collection from a smartwatch and smart ring simultaneously, to be used in home, cafeteria, and free-living environments, with real-time user feedback to assist with energy intake estimation. This data collection is ongoing and will eventually encompass 600 subjects. This paper describes the toolset and preliminary results for 16 subjects, including a comparison of smart ring versus smartwatch intake gesture detection. The smart ring achieved an F1 score of 0.74 compared to an F1 score of 0.8 from the smartwatch. Finally, we describe the full set of experiments we intend to perform with the complete dataset.

关键词： Hands Wrist Performance evaluation Obesity Energy measurement Estimation Biomedical measurement Data collection Real-time systems Wearable devices

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Partially Occluded Face Expression Recognition with CBAM-Based Residual Network for Teaching Scene

Partially Occluded Face Expression Recognition with CBAM-Bas...

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Chinese Automation Congress (CAC)

作者： Yanxing Bai Luefeng Chen Min Li Min Wu Witold Pedrycz Kaoru Hirota School of Automation China University of Geosciences Wuhan China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration School of Automation Ministry of Education China University of Geosciences Wuhan China Department of Electrical and Computer Engineering University of Alberta Edmonton AB Canada Systems Research Institute Polish Academy of Sciences Warsaw Poland Department of Computer Engineering Istinye University Sariyer/Istanbul Turkey Tokyo Institute of Technology Yokohama Japan

In this paper, a deep residual network based on convolutional block attention module (CBAM) is proposed, which is utilized for feature extraction of partially occluded face expression data. The proposed method overcomes the problem of localized occlusion face feature extraction by focusing on the regions and channels containing important information in the occluded face data through CBAM. Multi-task cascaded convolutional networks (MTCNN) are firstly utilized to localize the key regions of face emotion, and then deep emotion features are extracted by CBAM-ResNet network. The final emotion labels are generated. The effectiveness of this paper's method is verified on the RAF-DB dataset and the occluded CK+ dataset. The experimental accuracy in the RAF-DB dataset is 76.3%, which is 3.74% and 1.64% higher than the accuracy produced by the method of RGBT, and the WLS-RF, respectively. Application experiments are carried out in the real teaching scenario, which verifies the applicability of the algorithm in the real teaching scene.

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Embedding Temporal Convolutional Networks for Energy-Efficient PPG-Based Heart Rate Monitoring

arXiv

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arXiv 2022年

作者： Burrello, Alessio Pagliari, Daniele Jahier Maria Rapa, Pierangelo Semilia, Matilde Risso, Matteo Polonelli, Tommaso Poncino, Massimo Benini, Luca Benatti, Simone Department of Electrical Electronic and Information Engineering University of Bologna Emilia-Romagna Bologna40136 Italy Department of Control and Computer Engineering Politecnico di Torino Torino10129 Italy PBL Center Zurich8092 Switzerland Department of Information Technology and Electrical Engineering The ETH Zurich Zurich8092 Switzerland Department of Sciences and Methods for Engineering University of Modena e Reggio Emilia Emilia-Romagna Modena42123 Italy

Photoplethysmography (PPG) sensors allow for non-invasive and comfortable heart-rate (HR) monitoring, suitable for compact wrist-worn devices. Unfortunately, Motion Artifacts (MAs) severely impact the monitoring accuracy, causing high variability in the skin-to-sensor interface. Several data fusion techniques have been introduced to cope with this problem, based on combining PPG signals with inertial sensor data. Until know, both commercial and reasearch solutions are computationally efficient but not very robust, or strongly dependent on hand-tuned parameters, which leads to poor generalization performance. In this work, we tackle these limitations by proposing a computationally lightweight yet robust deep learning-based approach for PPG-based HR estimation. Specifically, we derive a diverse set of Temporal Convolutional Networks (TCN) for HR estimation, leveraging Neural Architecture Search (NAS). Moreover, we also introduce ActPPG, an adaptive algorithm that selects among multiple HR estimators depending on the amount of MAs, to improve energy efficiency. We validate our approaches on two benchmark datasets, achieving as low as 3.84 Beats per Minute (BPM) of Mean Absolute Error (MAE) on PPGDalia, which outperforms the previous state-of-the-art. Moreover, we deploy our models on a low-power commercial microcontroller (STM32L4), obtaining a rich set of Pareto optimal solutions in the complexity vs. accuracy space. © 2022, CC BY.

关键词： Deep learning

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Fully Decentralized Design of Initialization-free Distributed Network Size Estimation

arXiv

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arXiv 2024年

作者： Lee, Donggil Kim, Taekyoo Lee, Seungjoon Shim, Hyungbo Center for Intelligent and Interactive Robotics Korea Institute of Science and Technology Seoul02792 Korea Republic of The Train Control and Communication Team Korea Railroad Research Institute Uiwang16105 Korea Republic of NAVER Labs Seongnam13161 Korea Republic of ASRI Department of Electrical and Computer Engineering Seoul National University Seoul08826 Korea Republic of

In this paper, we propose a distributed scheme for estimating the network size, which refers to the total number of agents in a network. By leveraging a synchronization technique for multi-agent systems, we devise an agent dynamics that ensures convergence to an equilibrium point located near the network size regardless of its initial condition. Our approach is based on an assumption that each agent has a unique identifier, and an estimation algorithm for obtaining the largest identifier value. By adopting this approach, we successfully implement the agent dynamics in a fully decentralized manner, ensuring accurate network size estimation even when some agents join or leave the network. © 2024, CC BY-NC-ND.

关键词： Multi agent systems

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Applications of MXenes in human-like sensors and actuators

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Nano Research 2023年第4期16卷 5767-5795页

作者： Jinbo Pang Songang Peng Chongyang Hou Xiao Wang Ting Wang Yu Cao Weijia Zhou Ding Sun Kai Wang Mark H.Rümmeli Gianaurelio Cuniberti Hong Liu Institute for Advanced Interdisciplinary Research(iAIR) Collaborative Innovation Center of Technology and Equipment for Biological Diagnosis and Therapy in Universities of ShandongUniversity of JinanJinan 250022China Institute for Materials Science and Max Bergmann Center of Biomaterials Technische Universität DresdenDresden 01069Germany Center for Advancing Electronics Dresden Technische Universität DresdenDresden 01069Germany Dresden Center for Computational Materials Science Technische Universität DresdenDresden 01062Germany Dresden Center for Intelligent Materials(GCL DCIM) Technische Universität DresdenDresden 01062Germany High-Frequency High-Voltage Device and Integrated Circuits R&D Center Institute of MicroelectronicsChinese Academy of SciencesBeijing 100029China Key Laboratory of Microelectronic Devices&Integrated Technology Institute of MicroelectronicsChinese Academy of SciencesBeijing 100029China Shenzhen Key Laboratory of Nanobiomechanics Shenzhen Institute of Advanced TechnologyChinese Academy of SciencesShenzhen 518055China State Key Laboratory of Biobased Material and Green Papermaking Qilu University of TechnologyShandong Academy of SciencesJinan 250353China School of Bioengineering Qilu University of TechnologyShandong Academy of ScienceJinan 250353China Key Laboratory of Modern Power System Simulation and Control&Renewable Energy Technology(Ministry of Education) Northeast Electric Power UniversityJilin 132012China School of Electrical Engineering Northeast Electric Power UniversityJilin 132012China School of Electrical Engineering Weihai Innovation Research InstituteQingdao UniversityQingdao 266000China School of Electrical and Computer Engineering Jilin Jianzhu UniversityChangchun 130118China Institute for Complex Materials Leibniz Institute for Solid State and Materials Research Dresden(IFW Dresden)20 Helmholtz StrasseDresden 01069Germany College of Energy Soochow Institute for Energy and Materials Innovations Soochow UniversitySuzhou 215006China Key L

Human beings perceive the world through the senses of sight,hearing,smell,taste,touch,space,and *** first five senses are prerequisites for people to *** sensing organs upload information to the nervous systems,including the brain,for interpreting the surrounding ***,the brain sends commands to muscles reflexively to react to stimuli,including light,gas,chemicals,sound,and ***,as an emerging two-dimensional material,has been intensively adopted in the applications of various sensors and *** this review,we update the sensors to mimic five primary senses and actuators for stimulating muscles,which employ MXene-based film,membrane,and composite with other functional ***,a brief introduction is delivered for the structure,properties,and synthesis methods of ***,we feed the readers the recent reports on the MXene-derived image sensors as artificial retinas,gas sensors,chemical biosensors,acoustic devices,and tactile sensors for electronic ***,the actuators of MXene-based composite are ***,future opportunities are given to MXene research based on the requirements of artificial intelligence and humanoid robot,which may induce prospects in accompanying healthcare and biomedical engineering applications.

关键词： MXenes sensors actuators gas sensors biosensors tactile sensors sound sensors artificial retina

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Enhancement of sensitivity near exceptional points in dissipative qubit-resonator systems

arXiv

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arXiv 2025年

作者： Han, Pei-Rong Wu, Fan Huang, Xin-Jie Wu, Huai-Zhi Zou, Chang-Ling Yi, Wei Zhang, Mengzhen Li, Hekang Xu, Kai Zheng, Dongning Fan, Heng Wen, Jianming Yang, Zhen-Biao Zheng, Shi-Biao School of Physics and Mechanical and Electrical Engineering Longyan University Longyan364012 China Fujian Key Laboratory of Quantum Information and Quantum Optics College of Physics and Information Engineering Fuzhou University Fuzhou350108 China CAS Key Laboratory of Quantum Information University of Science and Technology of China Hefei230026 China CAS Center for Excellence in Quantum Information and Quantum Physics University of Science and Technology of China Hefei230026 China Pritzker School of Molecular Engineering University of Chicago ChicagoIL60637 United States Institute of Physics Chinese Academy of Sciences Beijing100190 China CAS Center for Excellence in Topological Quantum Computation University of Chinese Academy of Sciences Beijing100190 China Department of Electrical and Computer Engineering Binghamton University BinghamtonNY13902 United States Department of Physics Kennesaw State University MariettaGA30060 United States

Dissipation usually plays a negative role in quantum metrological technologies, which aim to improve measurement precision by leveraging quantum effects that are vulnerable to environment-induced decoherence. Recently, it has been demonstrated that dissipation can actually be used as a favorable resource for enhancing the susceptibility of signal detection. However, demonstrations of such enhancement for detecting physical quantities in open quantum systems are still lacking. Here we propose and demonstrate a protocol for realizing such non-Hermitian quantum sensors for probing the coupling between a qubit and a resonator subjecting to energy dissipations. The excitation-number conversion associated with the no-jump evolution trajectory enables removal of the noisy outcomes with quantum jumps, implementing the exceptional point (EP), where the Rabi splitting exhibits a divergent behavior in response to a tiny variation of the effective coupling. The sensitivity enhancement near the EP is confirmed by both theoretical calculation and experimental measurement. Copyright © 2025, The Authors. All rights reserved.

关键词： Qubits

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Broad-Deep Network-Based Fuzzy Emotional Inference Model with Personal Information for Intention Understanding

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IFAC-PapersOnLine 2023年第2期56卷 7071-7076页

作者： Min Li Luefeng Chen Min Wu Min Ding Kaoru Hirota Witold Pedrycz School of Automation China University of Geosciences Wuhan 430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan 430074 China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan 430074 China Tokyo Institute of Technology Yokohama 226-8502 Japan Department of Electrical and Computer Engineering University of Alberta Edmonton AB T6R 2G7 Canada

A broad-deep fusion network-based fuzzy emotional inference model with personal information (BDFEI) is proposed for emotional intention understanding in human-robot interaction. It aims to understand students’ intentions in the university teaching scene. Specifically, feature extraction is carried out by convolution and maximum pooling, and then the ridge regression algorithm is used for emotional behavior recognition, which reduce the influence of the complex structure and slow network updates in deep learning. Multivariate analysis of variance is used to select the key personal information affecting the intention and obtain the coefficient of influence degree. Finally, fuzzy inference is used to understand the intention. According to the recognition results, the accuracy on FABO database of our proposal is 1.89%, 12.21% and 0.78% higher than those of the Residual Network combined with geodesic flow kernel (ResNet-101+GFK), a fuzzy deep neural network with sparse autoencoder (FDNNSA), and an affect recognition on a video-skeleton of bimodal information with a hierarchical classification fusion strategy (HCFS), respectively, indicating that our proposal can effectively capture the emotional intention of students in the teaching scene.

关键词： Convolution Neural Networks Broad Learning Emotional Intention Human-Robot Interaction

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