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International Conference on Smart Computing (SMARTCOMP)

作者： Luca Sciullo Federico Montori Ivan Zyrianoff Lorenzo Gigli Davide Tinti Marco Di Felice Department of Computer Science and Engineering University of Bologna Italy Advanced Research Center on Electronic Systems "Ercole De Castro" University of Bologna Italy

Mobile crowdsensing (MCS) is an emerging paradigm that leverages the pervasive presence of mobile devices to collect and analyze data from the environment. However, the choice of a push- or pull-based architecture for MCS can result in a loss of flexibility and limitations for the creators of the campaigns (crowdsourcers). To address this issue, we propose a hybrid push-pull architecture for MCS campaigns that leverages the W3C Web of Things (WoT) to standardize the interfaces and interactions of devices through well-consolidated Web technologies. Furthermore, we present the design and implementation of a WoT-enabled Android application for MCS. We evaluate our proposal through simulations in a vehicular scenario based on a real dataset, showing that the hybrid architecture provides greater flexibility to crowdsourcers, supporting simultaneously the push and pull paradigms.

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Optimizing IoT-based Human Activity Recognition on Extreme Edge Devices

Optimizing IoT-based Human Activity Recognition on Extreme E...

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International Conference on Smart Computing (SMARTCOMP)

作者： Angelo Trotta Federico Montori Giacomo Vallasciani Luciano Bononi Marco Di Felice Department of Computer Science and Engineering University of Bologna Italy Advanced Research Center on Electronic Systems "Ercole De Castro" University of Bologna Italy

Wearable Internet of Things (IoT) devices with inertial sensors can enable personalized and fine-grained Human Activity Recognition (HAR). While activity classification on the Extreme Edge (EE) can reduce latency and maximize user privacy, it must tackle the unique challenges posed by the constrained environment. Indeed, Deep Learning (DL) techniques may not be applicable, and data processing can become burdensome due to the lack of input systems. In this paper, we address those issues by proposing, implementing, and validating an EE-aware HAR system. Our system incorporates a feature selection mechanism to reduce the data dimensionality in input, and an unsupervised feature separation and classification technique based on Self-Organizing Maps (SOMs). We developed the system on an M5Stack IoT prototype board and implemented a new SOM library for the Arduino SDK. Experimental results on two HAR datasets show that our proposed solution is able to overcome other unsupervised approaches and achieve performance close to state-of-art DL techniques while generating a model small enough to fit the limited memory capabilities of EE devices.

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Low Complexity Single-Snapshot DoA Estimation via Bayesian Compressive Sensing

Low Complexity Single-Snapshot DoA Estimation via Bayesian C...

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IEEE National Conference on Radar

作者： Ignacio Roldan Lucas Lamberti Francesco Fioranelli Alexander Yarovoy Department of Microelectronics Microwave Sensing Signals and Systems Group Faculty of Electrical Engineering Mathematics and Computer Science Delft University of Technology Delft The Netherlands

The problem of single-snapshot direction of arrival (DoA) estimation with antenna arrays has been considered. A sectorized approach based on Bayesian Compressive Sensing (BCS) has been proposed. In this method, the angular space is discretized, defining many non-overlapping small grids which cover the desired large angular space. First, a BCS estimation is run in each of the sectors to estimate the DoA of the signals. Then, a second stage is performed to correct the inconsistencies at the edges due to signal leaking between sectors. The performance of the method has been analyzed via extensive Monte-Carlo simulations in which the number of targets, their Radar Cross Section (RCS), and their location have been varied in a large extent, and the targets were observed by a Frequency Modulated Continuous Wave (FMCW) radar with an 86-element Uniform Linear Array (ULA). The results are compared with state-of-the-art methods in terms of estimation accuracy and resolution. Moreover, an analysis of the computational time, critical for many real-time applications, is presented, which shows a reduction of 20 times in the computational time compared with the standard BCS. Finally, the method has also been validated using experimental data collected with a commercial automotive radar.

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VCOACH: A Virtual Coaching System Based on Visual Streaming

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Procedia computer science 2023年 222卷 207-216页

作者： Fatma Youssef Victor Parque Walid Gomaa Department of Computer Science and Engineering Egypt-Japan University of Science and Technology Alexandria 21934 Egypt. Department of Modern Mechanical Engineering Waseda University Tokyo 169-8050 Japan. Cyber-Physical Systems Lab Egypt-Japan University of Science and Technology Alexandria 21934 Egypt. Faculty of Engineering Alexandria University Alexandria 21544 Egypt.

A virtual coaching system is key for monitoring the performance during exercise regimes and rendering timely feedback to avoid potential physical harm. Ideally, the core technologies behind such a system imply exercise recognition/assessment for performance evaluation and subject identification for retrieval/update of personal profiles. In this work, we develop techniques based on learning models for inferences regarding exercise recognition, prediction of the subject's biometrics, exercise evaluation, and simultaneous recognition and evaluation. We address four indoor exercises: squats, push-ups, shoulder-press, and lunges. Different sorts of exercise evaluation are provided: (1) the overall-binary assessment, with a detailed assessment rendering the exact mistakes performed, and (2) the score-based evaluation giving a metric in the range of 0-10 for each aspect of the exercise. Thermal and RGB imagery are examined with various descriptors, such as raw images, Gait Energy Image (GEI), and skeletal pose, with a comparison between their performance. The GEI descriptor extracted from RGB imagery is introduced for exercise recognition and evaluation tasks achieving remarkable results. The GEI descriptor is further utilized for subject recognition and verification from the performed exercise showing the ability of subject discrimination from the way of exercising. Consequently, we introduce new kinds of biometrics based on indoor exercising inspired by the notion that individuals are recognizable from the fingerprint motion dynamics in exercise regimes. To the best of our knowledge, our work is the first to use GEI in such directions and show its remarkable effectiveness.

关键词： Exercise Recognition Subject Recognition and Verification Exercise Evaluation Multi-Task Learning

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Probabilistic Health Monitoring and Maintenance Scheduling for Nuclear Power systems

Probabilistic Health Monitoring and Maintenance Scheduling f...

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AIAA science and Technology Forum and Exposition, AIAA SciTech Forum 2025

作者： Amer, Ahmad Tsilifis, Panagiotis Saxena, Abhinav Baglietto, Emilio Khan, Genghis Probabilistic Design & Material Informatics GE Aerospace Research Niskayuna NY12309 United States Robotics AI & Computer Vision GE Vernova Advanced Research Niskayuna NY12309 United States AI & Computer Vision GE Aerospace Research San RamonCA94583 United States Department of Nuclear Science & Engineering Massachusetts Institute of Technology CambridgeMA02139 United States Sustainable Systems GE Aerospace Research Niskayuna NY12309 United States

ISBN: (数字)9781624107238

ISBN: (纸本)9781624107238

Operations and Maintenance (O&M) cost optimization in the nuclear energy industry is an imperative task for developing sustainable systems and efficient renewable technologies. We present a modular probabilistic framework for addressing maintenance scheduling challenges for small modular nuclear reactors using a Dynamic Bayesian Network (DBN) approach that monitors fatigue and performance in multiple subsystems of the reactors and provides a probabilistic assessment for potential failure events. The DBN framework fuses information from potential digital twins that assimilate real time data from several sensors with different time granularities in order to monitor and update thecurrentstate of knowledge aboutthestatus of several fatigue or performance criteria. Comprised of multi-modal digital twins (empirical, data- and physics-based twins), this DBN serves as a predictive framework forecasting the future state of specific reactor subsystems and enhancing decision-making capabilities associated with preventing system failure and minimizing the time and cost of maintenance events. The work presented in this paper is a holistic approach for monitoring and safeguarding complex, multi-component engineering systems where maintenance scheduling is critical and does not restrict itself to nuclear environments. However, our goal is to illustrate how such a framework can be adjusted to small modular water reactors. © 2025, American Institute of Aeronautics and Astronautics Inc, AIAA. All rights reserved.

关键词： Nuclear energy

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Novel Approach for Enhancing Mental Well-Being Through Machine Learning Techniques

Novel Approach for Enhancing Mental Well-Being Through Machi...

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Advancements in Computing (ICAC), International Conference on

作者： O.V.D.E Roshani H.M.K.J.J Ayuwardhana P.H.M.S Rodrigo R.U Hewageegana Harinda Fernando Dilshan I. de Silva Department of Computer Science and Software Engineering Faculty of Computing Sri Lanka Institute of Information Technology Malabe Sri Lanka Department of Computer Systems Engineering Faculty of Computing Sri Lanka Institute of Information Technology Malabe Sri Lanka

Frequently, individuals undergo specific episodes of mental health challenges throughout their lifetime. But the COVID pandemic has triggered a surge in mental health disorders arising from isolation, monotonous routines, demanding workloads, financial disparities, and disruptions to daily schedules. Furthermore, the global pandemic has induced constant anxiety and stress. Beyond the pandemic, the competition and intense pressure of the modern world impact mental health. Access to advanced mental health solutions and the necessary familiarity remain limited for most of the population. Given the integration of technology into daily life, diverse remedies, including mobile and web applications, have emerged to tackle the escalating challenge of mental health disorders. This study proposes an accessible and cost-effective approach that employs machine learning to detect stress levels and discern user emotions from journal entries and facial expressions while integrating self-journaling, video recommendations, and visual content generation to stimulate positive emotions and relieve stress.

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Modelling in-device inference and classification of binary digits using nonlinear dynamics of spin Hall oscillator

Modelling in-device inference and classification of binary d...

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2023 IEEE International Magnetic Conference - Short Papers, INTERMAG Short Papers 2023

作者： Mohan, John Rex Yamanaka, Chisato Feng, Ryoyan Mathew, Arun Jacob Nakamura, Yoji Medwal, Rohit Gupta, Surbhi Rawat, Rajdeep Singh Fukuma, Yasuhiro Kyushu Institute of Technology Faculty of Computer Science and Systems Engineering Department of Physics and Information Technology Iizuka820-8592 Japan Indian Institute of Technology Kanpur Department of Physics Kanpur208016 India National Institute of Education Nanyang Technological University Natural Sciences and Science Education 637616 Singapore Kyushu Institute of Technology Research Center for Neuromorphic AI Hardware Kitakyushu808-0196 Japan

ISBN: (纸本)9798350338362

In this work, we employ micromagnetic modelling of a spin Hall oscillator for a direct inference and classification of binary digit inputs. The spectral characteristics of the oscillation is utilized for the classification. We observed a direct inference of binary digit inputs up to a sequence of four binary digits. Subsequently, handwritten digit image recognition is tested with the Modified National Institute of Standards and Testing (MNIST) handwritten digit database and acquired an accuracy of 88.6% with a linear classifier network. © 2023 IEEE.

关键词： Classification (of information)

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Machine Learning-Based Cybersecurity Framework for IoT Devices

Machine Learning-Based Cybersecurity Framework for IoT Devic...

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Advances in Computing, Communication and Applied Informatics (ACCAI), International Conference on

作者： Rajeshwarrao Arabelli Mrunalini Buradkar Kotla Lakshmaji Anand Prakash Dube C Mary Shiba B. T. Geetha Department of ECE School of Engineering SR University Warangal Telangana India Electronic & Telecommunication Engineering St. Vincent Pallotti College of Engineering & Technology Shri Vishnu engineering college for women Bhimavaram Computer Science School of Management Sciences Varanasi Uttar Pradesh Department of computer science and business systems R.M.K Engineering college Thiruvallur Department of ECE Saveetha School of Engineering SIMATS Saveetha University Tamil Nadu India

ISBN: (数字)9798350389449

ISBN: (纸本)9798350389456

Due to their low processing power, disparate operating systems, and frequently reticent security mechanisms, Internet of Things gadgets create unique security difficulties. These elements come together to provide special security challenges for the Internet of Things. This is specifically because they are becoming less common in artificial and consumer activities. The architecture that has been provided uses sophisticated machine literacy techniques to identify and eliminate a variety of cybersecurity issues, including malware attacks, unauthorised access, and data breaches. The framework can be used to recognise and remove these hazards. The frame detects anomalies and hidden dangers in real time by continuously monitoring network activity and device gesture. This is a big plus since it makes it possible to design creative protection mechanisms. Experiments have shown that the technique is useful for improving the security of networks linked to the Internet of Things (IoT) without appreciably impacting device performance. The trials’ outcomes served as evidence for this. The investigation’s conclusions provide insight into the potential for fusing cybersecurity tactics with machine literacy to solve issues that are spreading throughout the Internet of Things’ geographic domain. This would offer a dependable solution that would protect confidential information and ensure that connected systems would carry on as usual until further notice.

关键词： Performance evaluation Technological innovation Scalability Operating systems Real-time systems Rigidity Internet of Things

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Defeating Proactive Jammers Using Deep Reinforcement Learning for Resource-Constrained IoT Networks

arXiv

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arXiv 2023年

作者： Ali, Abubakar Sani Naser, Shimaa Muhaidat, Sami Department of Electrical Engineering and Computer Science Khalifa University Abu Dhabi127788 United Arab Emirates Department of Systems and Computer Engineering Carleton University OttawaONK1S 5B6 Canada

Traditional anti-jamming techniques like spread spectrum, adaptive power/rate control, and cognitive radio, have demonstrated effectiveness in mitigating jamming attacks. However, their robustness against the growing complexity of internet-of-thing (IoT) networks and diverse jamming attacks is still limited. To address these challenges, machine learning (ML)based techniques have emerged as promising solutions. By offering adaptive and intelligent anti-jamming capabilities, ML-based approaches can effectively adapt to dynamic attack scenarios and overcome the limitations of traditional methods. In this paper, we propose a deep reinforcement learning (DRL)-based approach that utilizes state input from realistic wireless network interface cards. We train five different variants of deep Q-network (DQN) agents to mitigate the effects of jamming with the aim of identifying the most sample-efficient, lightweight, robust, and least complex agent that is tailored for power-constrained devices. The simulation results demonstrate the effectiveness of the proposed DRL-based anti-jamming approach against proactive jammers, regardless of their jamming strategy which eliminates the need for a pattern recognition or jamming strategy detection step. Our findings present a promising solution for securing IoT networks against jamming attacks and highlights substantial opportunities for continued investigation and advancement within this field. © 2023, CC BY.

关键词： Reinforcement learning

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Melon(Cucumis melo)fruit‑specific monoterpene synthase

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Molecular Horticulture 2023年第1期3卷 357-360页

作者： Kathrine H.Davidson Syamkumar Sivasankara Pillai Yukihiro Nagashima Jashbir Singh Rita Metrani Kevin MCrosby John Jifon Bhimanagouda Patil Seyednami Niyakan Xiaoning Qian Hisashi Koiwa Department of Horticultural Sciences Vegetable and Fruit Improvement CenterTexas A&M UniversityTX 77843 College StationUSA Texas A&M AgriLife Research and Extension Center 2415 E Business 83WeslacoTX 78596USA Department of Food Science and Technology Texas A&M UniversityCollege StationTX 77843USA Department of Electrical and Computer Engineering Texas A&M UniversityCollege StationTX 77843USA TEES‑AgriLife Center for Bioinformatics&Genomic Systems Engineering Texas A&M UniversityCollege StationTX 77843USA Department of Computer Science and Engineering Texas A&M UniversityCollege StationTX 77843USA Molecular and Environmental Plant Sciences Texas A&M UniversityCollege StationTX 77843USA

Muskmelon(Cucumis melo L.)is one of the important horticultural crops of the Cucurbitaceae *** production of melon fruits was approximately 27 million tons,with the United States production yielding 616,050 tons(FAO 2018).Melon is diploid(2n=24)and has an approximate genome size of 450 Mbp(Arumuganathan and Earle 1991).A high-quality reference genome of melon(DHL92 v4.0)covers 358 Mbp pseudomolecules(Castanera et al.2019).

关键词： crops yielding Melon

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