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A Free Placement Approach to Upper-Limb Tracking Using Inertial Sensors

A Free Placement Approach to Upper-Limb Tracking Using Inert...

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IEEE international conference on Green computing and Communications

作者： Xueyan Wu Qi Liu Mingxu Sun Xuqun Pei Haiping Mu Bin Ning Jingsu Coborive Innovion Cener of Aosheric Environen nd Equien Technoogy (CICAEET) Nnjing Universiy of Inforion Science TechnoogyShndong Beiing Medic Technoogy Co. Ld.Schoo of Eecric Engineering Universiy of Jinnde. Rehbiiion Jinn Cenr HosiThe Peoe's Hosi of Huiyinde. Sin surgery Jinn Cenr Hosi

ISBN: (数字)9781728176475

ISBN: (纸本)9781728176482

Currently, functional electrical stimulation is used as the most widely used rehabilitation therapy in intelligent medical treatment of stroke patients' limb rehabilitation. There are three methods for tracking optimization of FES rehabilitation therapy using MEMS inertial measurement units: based on Kalman filter algorithm, using quaternion fusion algorithm and establishing sensor information fusion algorithm, but there are existing, calibration needs to be performed in the early stage, inertial measurement unit Must be placed parallel to the arm, noise is prone to data drift and other issues. To solve the problem of the previous calibration of the sensor and the need to place the sensor according to the reference, and to improve the accuracy of the data, we propose a motion tracking model based on free placement to track the upper limb movement trajectory. The input data of the model is the sensor's triaxial acceleration data. The output data is angle change data, which avoids the process of pre-calibration and improves the accuracy of the data through the angle segmentation model. Four sets of comparative experiments show that this method effectively reduces the influence of the sensor position on the data, improves the data accuracy, and has higher accuracy and use value.

关键词： Task analysis Muscles Medical treatment Acceleration data models Robot sensing systems Iron data models Manipulator sensing systems task analysis MEDICAL TREATMENT data Accuracy acceleration inertia sensor muscles Sensors rehabilitation therapy Medicine Iron Measurement units sensor placement INERTIAL MEASUREMENT UNITS

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Way Forward to Digital Society – Digital Transformation of Msmes from Industry 4.0 to Industry 5.0

Way Forward to Digital Society – Digital Transformation of ...

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international conference on Emerging Research in Electronics, Computer Science and Technology (ICERECT)

作者： Kavita Bhatt S. Mohan Kumar School of Engineering and Technology CMR University Bangalore India

Digital Transformation is a new buzz word in industrial world which transforms the physical world into virtual space. It leverages the implementation of digital technologies to accelerate the growth of the industry/business. It refers to automation, digitalization of process, infrastructure, organization, management with the help of Industry 4.0 technologies like Cloud computing, 5G& 6G, analytics, Big data, Additive manufacturing, Artificial Intelligence, augmented reality, cyber-physical system, smart sensors, IoT Platforms, cloud computing. The field is continuously evolving and shaping the future of Medium, Small and micro industries (MSMEs). Digital Transformation (DX) provides opportunities and possibilities to MSMEs to accelerate their growth via recent disruption in technologies and its attached benefits. The industry is shifting towards virtual work from mechanized systems leveraging benefits of industry 4.0 practices like AI, big data, intelligent network system, machine learning, strong network connection and making effective use of digital transformation. Since end users' requirements and consumption of goods keep increasing product industry 5.0 is gaining much attention which refers to the integration of Artificial Intelligence matching to the level of human capabilities. This paper discusses the evolution of industry 4 and emerging I4.0 technology, major drivers and factors impacting the implementation of I4.0 practices. It also covers the paradigm shift in how next-generation industry is getting shifted with the help of I5.0. It highlights the prospects of the development of technologies that is contributing to the transition of Industry 4.0 to Industry 5.0. This study provides a high-level overview of industry 4.0, its emergences and emerging technologies and transition from Industry 4.0 to Industry 5.0. This paper also highlights the main components of Industry 5.0 and how digital transformation can be moved from machine to human-machine collaborati

关键词： Industries Productivity Cloud computing intelligent networks Computational modeling Transforms Big data

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Construction of Industrial Internet of Things Based on MQTT and OPC UA Protocols

Construction of Industrial Internet of Things Based on MQTT ...

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IEEE international conference on Artificial Intelligence and Computer Applications (ICAICA)

作者： Huichao Shi Li Niu Jinhao Sun College of Information Science and Technology Beijing University of Chemical Technology Beijing China Division for Digital Manufacturing and Flexible Assembly AVIC Manufacture Technology Institute Beijing China

ISBN: (数字)9781728170053

ISBN: (纸本)9781728170060

At present, the Internet of Things has become a hot area of global concern and is considered to be another major scientific and technological innovation after the Internet. The Internet of Things can be analyzed from two aspects, one is to access the Internet, and the other is the connection between things. The Industrial Internet of Things, as a new type of industrial ecosystem, collects resources and data during the industrial production process by using miniature low-cost controllers and high-bandwidth wireless networks to achieve flexible configuration of manufacturing raw materials and improve resource utilization. The Industrial Internet of Things not only includes traditional software elements, but also requires hardware controllers and sensors, as well as cloud service platforms, to ultimately achieve intelligent manufacturing. The Industrial Internet of Things based on MQTT adopts the method of upper computer (WeChat applet) + server (Alibaba Cloud Server) + lower computer (WiFi module ESP8266 NodeMcu) to realize data collection and control of industrial production process. Based on OPC UA protocol, The Industrial Internet of Things, by designing an intelligent node device based on embedded Linux system, uses Qt to build the OPC UA protocol. OPC UA is a C / S architecture real-time database framework. By building an OPC UA Client on a handheld terminal device, An OPC UA Serve is set up on the smart terminal device, and the information collected by the handheld terminal is sent to the smart terminal. At the same time, multiple smart terminals are connected through the OPC UA protocol to perform data interaction and information transfer. At the same time, an edge computing algorithm is embedded in the smart terminal, and a large amount of data collected is processed for analysis, screening, and calculation. At the same time, the processed information is transmitted or processed. Reduce data interactions between data sources and data centers, and reduce the am

关键词： Protocols Servers Internet of Things Tools Sensors Process control Cloud computing

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SemCom for Metaverse: Challenges, Opportunities and Future Trends

SemCom for Metaverse: Challenges, Opportunities and Future T...

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computing and Information Technology (ICCIT-1441), 2020 international conference on

作者： Saeed Hamood Alsamhi Ammar Hawbani Nabil Mohsen Santosh Kumar Lukasz Porwol Edward Curry Insight Centre for Data Analytics University of Galway Galway Ireland University of Science and Technology of China Heifa China Department of CSE IIIT Naya Raipur Chhattisgarh India

Metaverse is a new paradigm yet to come, gaining popularity in many applications and being developed using a combination of technologies, including Virtual Reality (VR), Augmented Reality (AR), blockchain, and other immersive technologies. One key technology critical to the success of the Metaverse is Semantic Communication (SemCom), which enables efficient and effective communication between humans and intelligent agents. This paper discusses the challenges and opportunities associated with SemCom in the Metaverse and potential future trends. We identify the critical challenges in SemCom, such as context awareness, scalability, and interoperability, and discuss potential solutions to address these challenges. Furthermore, we explore the various opportunities that SemCom can bring to the Metaverse, such as improved user experiences, interactions with virtual assistants, and enhanced data analysis capabilities. Moreover, we examine the emerging trends in SemCom for the Metaverse and highlight some key research directions that will be important in the future.

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A Generic stack frame work for Internet Of Things

A Generic stack frame work for Internet Of Things

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international conference on Communication, computing and Internet of Things (IC3IoT)

作者： Rakesh Kumar Ambhati Chaudhari Y Shashikant Valluri Sarimela Department of Electrical Engineering UEM Kolkata Department of Electronics and Communication Engineering IEM Kolkata School of Electric al and Electronic Engineering University of Manchester Manchester UK

ISBN: (纸本)9781538624609;9781538624593

Internet of things (IOT) does affect every aspect of human life severely, as it is envisioned to bridge physical world and virtual world. Automation and convenience are vital value addition to human life, by IOT phenomena. The growth of IoT technologies is primarily contributed by four factors. 1) Declining cost of sensors, 2) Increasing broadband penetration, 3) Cheap data storage and 4) Advanced software analytics, that can transform raw sensor data into vital knowledge. smart Home personal area networks(PANs), Industrial grids, Health care body area networks(BANs) and even Military vehicular networks will benefit from IOT concepts. However all the above mentioned areas have different IOT stack requirements. As different IOT networks do encounter issues like spectrum crunch, increased interference, Green energy, heterogeneity and coverage issues at varying degree of intensity. In this paper a broad classification of IOT networks is illustrated, form their stack requirements perspective. This paper also covers emerging Industry standardization efforts, to develop frameworks, API and platforms to realize IOT vision.

关键词： Internet of Things intelligent sensors Wireless sensor networks Monitoring Protocols Software

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IoT and Cloud computing based smart Water Metering System

IoT and Cloud Computing based Smart Water Metering System

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Power Electronics & IoT Applications in Renewable Energy and its Control (PARC), 2020 international conference on

作者： Aritra Ray Shreemoyee Goswami A. K. C. School of Information Technology University of Calcutta Kolkata India

ISBN: (数字)9781728165752

ISBN: (纸本)9781728165769

This paper focuses on the developmental and implementation methodology of smart water meter based on Internet of Things (IoT) and Cloud computing equipped with machine learning algorithms, to differentiate between normal and excessive water usage at industrial, domestic and all other sectors having an abundance of water usage, both for Indian and worldwide context. Recognizing that intelligent metering of water has the potential to alter customer engagement of water usage in urban and rural water supplies, this paper fosters for sustainable water management, a need of the present. With shrinking reserves of clean water resources worldwide, it is becoming cumbersome to cater for this resource to masses in the coming years on a consistent basis. Using our smart water meter, water resources can be managed efficiently and an optimum use could save water for the future generations. Sensors will provide for real time monitoring of hydraulic data, automated control and alarming from Cloud platform in case of events such as water leakages, excessive usage, etc. Analysis of the same will help in taking meaningful actions. Thus we do propose for a smart water metering technology that can be utilized by Indian citizens, and worldwide, to curb wastage of water. With an ease of monitoring and visualization of the data through the Cloud platform combined with machine learning based tools to detect excess water consumption, the server-less architecture we propose can be easily adopted and implemented in a large scale.

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An Integrated Blockchain Approach to Model Secure Internet of Things based Systems

An Integrated Blockchain Approach to Model Secure Internet o...

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data analytics for Business and Industry: Way Towards a Sustainable Economy (ICDABI), international conference on

作者： Pramod Mathew Jacob Prasanna Mani Santhosh Simon Renju Rachel Varghese Kuruvilla John Nisha Aniyan Providence College of Engineering Chengannur Kerala India SITE Vellore Institute of Technology Vellore TN India

ISBN: (数字)9781728196756

ISBN: (纸本)9781728196763

Internet of Things (IoT) made a tremendous change in the field of automation technologies. Objects or Things in the real world can be converted into a `smart object' by attaching some sensors or actuators. These devices are capable of communicating each other using internet. Light weight protocols make the IoT system more user friendly. But one of the critical challenges faced by any IoT designer is to provide the data security for IoT applications. Though the `things' are connected to a network, there is a high chance of data stealing or data manipulation. Block chain can be used to ensure data security, scalability and reliability of IoT applications. We propose a secure blockchain based IoT architecture to assure data security. Blockchain is utilized for tracking and coordinating millions of connected components. Blockchain uses decentralized approach which can prevent from single points of failures thereby creates a stable interoperable secure system. Cryptographic algorithms can be used for securing the sensor data. Thus, our model enhances not only security but also the privacy concerns of an end user.

关键词： Blockchain Internet of Things Security Computer architecture Peer-to-peer computing data models Business

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Harnessing Low-Fidelity data to Accelerate Bayesian Optimization via Posterior Regularization

Harnessing Low-Fidelity Data to Accelerate Bayesian Optimiza...

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international conference on Big data and smart computing (BIGCOMP)

作者： Bin Liu School of Computer Science Jiangsu Key Lab of Big Data Security & Intelligent Processing Nanjing University of Posts and Telecommunications Nanjing China

ISBN: (数字)9781728160344

ISBN: (纸本)9781728160351

Bayesian optimization (BO) is a powerful paradigm for derivative-free global optimization of a black-box objective function (BOF) that is expensive to evaluate. However, the overhead of BO can still be prohibitive for problems with highly expensive function evaluations. In this paper, we investigate how to reduce the required number of function evaluations for BO without compromise in solution quality. We explore the idea of posterior regularization to harness low fidelity (LF) data within the Gaussian process upper confidence bound (GP-UCB) framework. The LF data can arise from previous evaluations of an LF approximation of the BOF or a related optimization task. An extra GP model called LF-GP is trained to fit the LF data. We develop an operator termed dynamic weighted product of experts (DW-POE) fusion. The regularization is induced by this operator on the posterior of the BOF. The impact of the LF GP model on the resulting regularized posterior is adaptively adjusted via Bayesian formalism. Extensive experimental results on benchmark BOF optimization tasks demonstrate the superior performance of the proposed algorithm over state-of-the-art.

关键词： Optimization Hafnium Adaptation models Bayes methods Heuristic algorithms data models Acceleration

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Detection and Tracking Contagion using IoT-Edge Technologies: Confronting COVID-19 Pandemic

Detection and Tracking Contagion using IoT-Edge Technologies...

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international conference on Electrical, Communication, and Computer Engineering (ICECCE)

作者： Muhammad Usman Ashraf Abdul Hannan Sehrish Munawar Cheema Zahra Ali Kamal m. Jambi Abdulraheem Alofi University of management and Technology (Sialkot) Sialkot Pakistan King Abdulaziz Univesity Jeddah Saudi Arabia Taibah University Madinah Saudi Arabia

ISBN: (数字)9781728171166

ISBN: (纸本)9781728171173

The ongoing pandemic of Corona-Virus (COVID-19) induced by the coming forth category of SARS-CoV-2, has terrified the worldwide human health. Primarily, COVID-19 challenges can be categorized into (a) way of epidemic prevention and blocking transmission, (b) live monitoring of infected / suspected persons (c) FDA approved vaccine. Leading to said COVID-19 (a), (b) challenges, digit technologies such Artificial Intelligence, Big data analytics and Internet of Things (IoT), can play a vital role in epidemic prevention and blocking COVID-19 transmission. In this study, we have proposed a smart edge surveillance system that is effective in remote monitoring, advance warning and detection of a person's fever, heart beat rate, cardiac conditions and some of the radiological features to detect the infected (suspicious) person using wearable smart gadgets. The proposed framework provides a continually updated map/pattern of communication chain of COVID-19 infected persons that may span around in our national community. The health and societal impact of suggested research is to help public health authorities, researchers and clinicians contain and manage this disease through smart edge surveillance systems. The proposed model will help to detect and track the contagious person. Moreover, it will also keep the patient's data record for analysis and decision making using edge computing.

关键词： Cloud computing Monitoring Internet of Things Artificial intelligence Diseases Edge computing Computer science

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Harnessing Deep Learning for Fault Detection in Industry 4.0: A Multimodal Approach

Harnessing Deep Learning for Fault Detection in Industry 4.0...

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IEEE international conference on Cognitive Machine Intelligence (CogMI)

作者： Jialie Shen Marie Morrison Haiyan Miao Fengshou Gu Department of Computer Science City St Georgia’s University of London London United Kingdom School of Engineering Mathematics and Technology University of Bristol Bristol United Kingdom School of Computing and Engineering University of Huddersfield Huddersfield United Kingdom

ISBN: (数字)9798350386721

ISBN: (纸本)9798350386738

The advent of Industry 4.0 has reshaped the modern industries (e.g., Manufacturing, Automotive, Aerospace and Defense), driven by the rapid development of artificial intelligence, smart sensing technologies, and interconnected cyber-physical systems. One of the most important goal for Industry 4.0 is the improvement and enhancement in productivity, reduction of production losses, and operational efficiency. High quality fault detection within different environments is the foundation to achieve these goals. Industry 4.0 emphasizes the use of interconnected systems, smart sensors, and advanced analytics, which generate large volumes of data from production environments. The data complexity and scale, product by the widespread deployment of sensors and IoT devices, demands sophisticated Machine Learning (ML) and data-driven methods. In this paper, we argue that the future of accurate and reliable fault detection lies in the seamless fusion of edge-cloud computing, explainable AI, and adaptive machine learning algorithms capable of processing high-frequency, multimodal data streams in real time. Different kinds of advantages for multimondal learning and core enabling techniques are also discussed and revise. This vision paper outlines the future trajectory of multimodal learning based fault detection, via identifying critical research gaps and promising directions for future study.

关键词： Productivity Machine learning algorithms Fault detection Real-time systems Fourth Industrial Revolution Trajectory Internet of Things intelligent sensors Streams Machine intelligence

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