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TinyDL: Edge computing and deep learning based real-time hand gesture recognition using wearable sensor 22

TinyDL: Edge computing and deep learning based real-time han...

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22nd IEEE international conference on E-Health Networking, Application and Services, HEALTHCOM 2020

作者： Coffen, Brian Mahmud, Md.Shaad University of New Hampshire Electrical and Computer Engineering DurhamNH United States

ISBN: (纸本)9781728162676

Offloading data analysis to edge devices by decentralizing processing can be used to decrease bandwidth requirements, latency, and can decrease the total transmission time required in wireless devices. This can be especially useful for compact wearable devices used for health monitoring, human activity recognition, and gesture recognition, where sending raw data over wireless protocols such as Bluetooth can be both time and power consuming. By performing analysis on the wearable device, wireless radio usage can be greatly decreased, reducing a main power consumer on the device. Deep learning (DL) methods, specifically using Tensorflow (TF) and Keras were evaluated for their usage in such a case, in this example gesture recognition. A multilayer long short-term memory (LSTM) model was trained and evaluated off of data (10 gestures, 1000 trials total, balanced) from a finger-worn ring profile device that collected acceleration data, and was found to perform with accuracy from 75-95% per gesture. The attempted conversion of the model into a compressed TF Lite format, to allow for analysis on-device did not succeed, due to current incompatibilities between the different frameworks. Future work may improve the accuracy, and potentially expand the use of neural networks on wearables for health diagnostics or as input devices. © 2021 IEEE.

关键词： Long short-term memory

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Energy-efficient networks selection based deep reinforcement learning for heterogeneous health systems 22

Energy-efficient networks selection based deep reinforcement...

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22nd IEEE international conference on E-Health Networking, Application and Services, HEALTHCOM 2020

作者： Chkirbene, Zina Mohamed, Amr Erbad, Aiman Guizani, Mohsen College of Engineering Qatar University Qatar College of Science and Engineering Hamad Bin Khalifa University Qatar

ISBN: (纸本)9781728162676

Smart health systems improve the existing health services by integrating information and technology into health and medical practices. However, smart healthcare systems are facing major challenges including limited network resources, energy allocation, and latency. In this paper, we leverage the dense heterogeneous network (HetNet) architecture over 5G network to enhance network capacity and provide seamless connectivity for smart health systems. The network selection and energy allocation in HetNets are important factors in this regard due to their significant impact on system performance. Inspired by the success of Deep Reinforcement learning (DRL) in solving complicated control problems, we present a novel DRL model for energy-efficient network selection in heterogeneous health systems. The proposed model selects the set of networks to be used for data transmission with adaptive compression at the edge with an optimal energy allocation policy for all the network participants. Our experimental results show that the proposed DRL model has a good performance compared to the existing state of art techniques while meeting different users' demands in highly dynamic environments. © 2021 IEEE.

关键词： Reinforcement learning

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Visual-Inertial Odometry Priors for Bundle-Adjusting Neural Radiance Fields

Visual-Inertial Odometry Priors for Bundle-Adjusting Neural ...

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international conference on Control, Automation and Systems ( ICCAS)

作者： Hyunjin Kim Minkyeong Song Daekyeong Lee Pyojin Kim Department of Mechanical Systems Engineering Sookmyung Women’s University Seoul South Korea

We present bundle-adjusting Neural Radiance Fields (BARF) with motion priors. Neural Radiance Field (NeRF) has opened up tremendous potential for neural volume rendering and 3D scene representations in recognition of their ability to synthesize photo-realistic novel views. BARF mitigates NeRF’s reliance on accurate 6-DoF camera poses, enabling scene learning with inaccurate camera poses. However, initializing estimates far from an optimal solution, such as BARF, can easily fall into local minima. We utilize Visual-Inertial Odometry Motion Priors to the BARF, which jointly optimizes 3D scene representations and camera poses, providing higher accuracy in view synthesis and a more stable motion estimate. The proposed method achieves results that outperform original BARF in real-world data, demonstrating the effectiveness of motion priors to knowledge use.

关键词： Three-dimensional displays Automation Pose estimation Process control Rendering (computer graphics) Cameras Control systems

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Importance of Wearable Health Monitoring Systems Using IoMT;Requirements, Advantages, Disadvantages and Challenges

Importance of Wearable Health Monitoring Systems Using IoMT;...

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international Symposium on Computational Intelligence and Informatics

作者： Fahime Khozeimeh Mohamad Roshanzamir Afshin Shoeibi Mohammad Tayarani Darbandy Roohallah Alizadehsani Hamid Alinejad-Rokny Davood Ahmadian Abbas Khosravi Saeid Nahavandi Institute for Intelligent Systems Research and Innovation (IISRI) Deakin University Victoria Australia Department of Computer Engineering Fasa University Fasa Iran Internship in Health Data Analytics Program AI-enabled Processes (AIP) Research Centre Macquarie University Sydney Australia School of Architecture Islamic Azad University Taft Taft Iran BioMedical Machine Learning Lab The Graduate School of Biomedical Engineering UNSW Sydney Sydney NSW Australia Faculty of Mathematics Statistics and Computer Sciences University of Tabriz Tabriz Iran

ISBN: (纸本)9798350398830

Providing better quality of service is one of the most important goals in medical/healthcare systems. The services provided should have features such as low latency, appropriate geographic distribution, and real-time interaction. Internet of Medical Things (IoMT) technology is one of the solutions to achieve high quality of services with these features. The wearable health monitoring systems which have become popular in the last decade are one of the foundations of achievement to these features. In this paper, the importance of wearable devices under IoMT technology is investigated. There are several reasons for using wearable devices under IoMT technology, such as reducing healthcare costs and long waiting times. Therefore, anyone working on wearable devices needs to have a general understanding of how they are designed and function. In fact, the integration of new devices to monitor and alert patients about their physical health is a promising option for improving health care systems around the world. Overcrowding in medical centers will be prevented by upgrading the facilities for the virtual presence of patients. It can also ensure real-time monitoring and reduce operating costs. In addition, it can accelerate treatment of patients and reduce the cost of investment and increase its coverage.

关键词： Costs Medical devices Wearable computers Quality of service Organizations Real-time systems Security

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Cu-T-Pi Revised: An Updated Model Supercomputer for Parallel Computing Pedagogy 2nd

Cu-T-Pi Revised: An Updated Model Supercomputer for Parallel...

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22nd international conference on Interactive Collaborative learning (ICL) / 48th IGIP international conference on engineering Pedagogy - Impact of the 4th Industrial Revolution on engineering Education

作者： Wolfer, James Indiana Univ South Bend IN 46615 USA

ISBN: (纸本)9783030402716;9783030402709

Cu-T-Pi, named for the CUDA, Nvidia TK1, and Raspberry N technology included, is a heterogeneous model supercomputer. Used as a pedagogic tool for teaching high-performance parallel computing, this model supports the major programming paradigms used in modern supercomputing. This work describes a complete remake of the original computer as a hardware and performance refresh, along with augmentation to support embedded Deep learning.

关键词： High-Performance Computing Parallel computing Hardware model Deep-learning inference Pedagogy

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Unsupervised Representation learning: Target Regularization for Cross-Domain Sentiment Classification 1

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22nd international conference on Artificial Intelligence and Soft Computing, ICAISC 2023

作者： Perelkiewicz, Michal Poświata, Rafal Kierzkowski, Jakub National Information Processing Institute Al. Niepodleglości 188B Warsaw Poland

ISBN: (数字)9783031425059

ISBN: (纸本)9783031425042

This article proposes an autoencoder-based domain invariant feature representation learning approach to domain adaptation and the cross-domain text classification problem. Finding domain invariant feature representations is a transfer learning method for transmitting knowledge between source and target domain data. Our method aims to avoid the overfitting of an autoencoder model on source domain training data in a trained embedded feature space using a target regularization technique. We hypothesize that when forcing the semantic similarity of target domain representation to source domain representation by adding the source domain similarity penalty to reconstruction loss during autoencoder training, the penalty is greater when the the domain’s representations separability is. In this work, we contribute to domain adaptation by demonstrating that a regularization technique based on an auxiliary pretrained domain classification model can be used to build robust, shared domain feature representations. Our model achieves a classification accuracy improvement in standard cross-domain sentiment classification tasks over the baseline model in most cases. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Semantics

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Development of Management System for Bushing UV Imaging Detection 22

Development of Management System for Bushing UV Imaging Dete...

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22nd international Symposium on High Voltage engineering, ISH 2021

作者： Chen, Yufeng Gao, Zhaoli Xv, Mingkai Lu, Xianfei Niu, Leilei State Grid Shandong Electric Power Research Institute Jinan China State Grid Jinan Supply Company Jinan China School of Electrical and Electronic Engineering North China Electric Power University Beijing China

ISBN: (纸本)9781839536052

The insulation state of electrical bushing is related to the safety of power grid. To diagnose the insulation state of the in-field bushing, an intelligent diagnosis framework based on artificial intelligence is designed;the B/S development mode manages the UV images and videos of bushing discharge, and develops a set of image and video management data According to the database system, the database management system of UV imaging samples is constructed, which integrates the function of bushing recognition and discharge severity assessment based on UV image. The developed software is applied to the inspection of transmission lines and substations with good application result. © 2021 The Institution of engineering and Technology.

关键词： Bushings

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Towards a Common Testing Terminology for Software engineering and data Science Experts 22nd

Towards a Common Testing Terminology for Software Engineerin...

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22nd international conference on Product-Focused Software Process Improvement (PROFES)

作者： Joeckel, Lisa Bauer, Thomas Klaes, Michael Hauer, Marc P. Gross, Janek Fraunhofer Inst Expt Software Engn IESE Fraunhofer Pl 1 D-67663 Kaiserslautern Germany TU Kaiserslautern Algorithm Accountabil Lab Gottlieb Daimler Str 48 D-67663 Kaiserslautern Germany

ISBN: (纸本)9783030914523;9783030914516

Analytical quality assurance, especially testing, is an integral part of software-intensive system development. With the increased usage of Artificial Intelligence (AI) and Machine learning (ML) as part of such systems, this becomes more difficult as well-understood software testing approaches cannot be applied directly to the AI-enabled parts of the system. The required adaptation of classical testing approaches and the development of new concepts for AI would benefit from a deeper understanding and exchange between AI and software engineering experts. We see the different terminologies used in the two communities as a major obstacle on this way. As we consider a mutual understanding of the testing terminology a key, this paper contributes a mapping between the most important concepts from classical software testing and AI testing. In the mapping, we highlight differences in the relevance and naming of the mapped concepts.

关键词： Analytical quality assurance Machine learning evaluation data-driven model Quality characteristics Artificial intelligence testing Definitions Concept mapping Target application scope

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RCA-IDS: A Novel Real-time Cloud-based Adversarial IDS for Connected Vehicles

RCA-IDS: A Novel Real-time Cloud-based Adversarial IDS for C...

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IEEE international conference on Trust, Security and Privacy in Computing and Communications (TrustCom)

作者： Zahra Pooranian Mohammad Shojafar Pedram Asef Matthew Robinson Harry Lees Mark Longden Department of Computer Science University of Reading UK 5G/6GIC Institute for Communication Systems (ICS) University of Surrey Guildford UK Dept. of Mechanical Engineering University College London London UK Dept. of Engineering and Technology University of Hertfordshire Hatfield UK R&D Group RL Auto Ltd Basingstoke UK

ISBN: (数字)9798350381993

ISBN: (纸本)9798350382006

This paper focuses on the requirement for creating novel frameworks to monitor and identify cyberattacks in Connected Vehicles (CVs). The health of the sensors in CVs becomes crucial when performance predictions and communication-related errors can compromise the resilience of the sensory network. To meet the evolving demands of connected vehicle (CV) systems, Intrusion Detection Systems (IDS) must be regularly updated and tailored as powerful monitoring entities. To equip cloud-tied operators with the ability to comprehend unusual sensor data originating from vehicles at the cloud level, we designed an innovative Real-time Cloud-based Adversarial IDS called RCA-IDS. This system exclusively focuses on detecting and explaining instances of sensor data manipulation caused by poisoning attacks. Two attack mechanisms were created utilizing random-based and silhouette-based clustering methods. Subsequently, two defence mechanisms based on multi-layer neural network-type deep learning were proposed to counter these attacks. The newly introduced RCA-IDS demonstrates a minimum accuracy of 90% in detecting cyberattacks.

关键词： Deep learning Cloud computing Connected vehicles Clustering algorithms Real-time systems Liquid crystal displays Classification algorithms

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A data-Driven Driving Under the Influence (DUI) Detection, Notification and Prevention System Using Artificial Intelligence and Internet-Of-Things (IoT) 25th

A Data-Driven Driving Under the Influence (DUI) Detection, N...

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25th international conference on Internet Computing and IoT, ICOMP 2024, and 22nd international conference on Embedded Systems, Cyber-physical Systems, and Applications, ESCS 2024, held as part of the World Congress in Computer Science, Computer engineering and Applied Computing, CSCE 2024

作者： Li, Aaron Sun, Yu Francis Parker School San Diego United States Computer Science Department California State Polytechnic University Pomona Pomona United States

ISBN: (纸本)9783031859229

Drunk driving represents a significant public health crisis not only in the United States but also globally [16]. Sober Guardian is developed to mitigate this issue by leveraging advanced technologies including Machine learning, Computer Vision, and potentially chemical sensors in the future. This system employs facial recognition and machine learning algorithms to swiftly determine if a driver is impaired, with the aim of preventing them from starting the vehicle [17]. We used an IoT System to control the blocker and successfully prevent vehicle start with 100% rate. Additionally, Sober Guardian plans to incorporate hardware solutions such as sensors that can detect alcohol levels directly from the driver’s breath or skin, enhancing the accuracy and reliability of impairment detection. Throughout its development, the project has faced challenges such as image clarity, limited datasets, and ensuring user-friendliness. Preliminary experiments with both image and video inputs have demonstrated the system’s ability to predict sobriety with an accuracy between 86% and 87.5% [18]. However, video analysis for impaired individuals was hindered by the scarcity of appropriate datasets. Despite these challenges, Sober Guardian, in its nascent stages, has proven to be a promising solution for detecting driver impairment and preventing drunk driving, with ongoing enhancements expected to improve its functionality and deployment readiness. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Drunk Driving Facial Recognition Internet-Of-Things Machine learning

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