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data Acquisition, Conditioning and processing System for a Wearable-based Bio stimulation

Data Acquisition, Conditioning and Processing System for a W...

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15th international Joint conference on Biomedical Engineering Systems and Technologies, BIOSTEC 2022

作者： Sestrem, Leonardo Kaizer, Raul Goncalves, João Leitao, Paulo Paulo Teixeira, João Lima, José Franco, Tiago Augusto Carvalho, José Instituto Politécnico de Bragança Campus de Santa Apolonia Braganca5300-253 Portugal

data acquisition by electromyography, as well as the muscle stimulation, has become more accessible with the new developments in the wearable technology and medicine. In fact, for treatments, games or sports, it is possible to find examples of the use of muscle signals to analyse specific aspects related, e.g., to disease, injuries or movement impulses. However, these systems are usually expensive, does not integrate data acquisition with the muscle stimulation and does not exhibit an adaptive control behaviour that consider the pathology and the patient response. This paper presents a wearable system that integrates the signal acquisition and the electro stimulation using dry thin-film titanium-based electrodes. The acquired data is transmitted to a mobile application running on a smartphone by using Bluetooth Low Energy (BLE) technology, where itis analysed by employing artificial intelligence algorithms to provide customised treatments for each patient profile and type of pathology, and taking into consideration the feedback of the acquired electromyography signal. The acquired patient’s data is also stored in a secure cloud database to support the physician to analyse and follow-up the clinical results from the rehabilitation process. © 2022 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved.

关键词： Electromyography

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international conference on Image processing and Capsule Networks, ICIPCN 2020

International Conference on Image Processing and Capsule Net...

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international conference on Image processing and Capsule Networks, ICIPCN 2020

ISBN: (纸本)9783030518585

The proceedings contain 72 papers. The special focus in this conference is on Image processing and Capsule Networks. The topics include: Developing a framework to identify potential firms for job assignment;developing a framework for trend prediction of stocks prices;machine learning based diagnosis of alzheimer’s disease;iot based vehicle (car) theft detection;emg signal classification with effective features for diagnosis;eye fatigue algorithm for driver drowsiness detection system;an analytical approach for recognizing the occupational gender bias in the data systems;development of an algorithm for vertebrae identification using speeded up robost features (surf) technique in scoliosis x-ray images;highly protective framework for medical identity theft by combining data hiding with cryptography;improving the prediction accuracy of asd using class imbalance mitigation technique;stock price prediction based on technical indicators with soft computing models;credit card fraud detection technique based on hybrid classification;improved fast block matching motion estimation using multiple frames;efficient framework for identification of soybean disease using machine learning algorithms;brain image visualization;a framework for degraded kannada character recognition;classification of soybean diseases using pre-trained deep convolutional neural networks;feedforward back propagation neural network (ffbpnn) based approach for the identification of handwritten math equations;comparison of machine learning algorithms for smart license number plate detection system;ritchie’s smart watch data analytics and visualization;sentiment analysis for product rating using classification;face recognition system using a hybrid scale invariant feature transform based on local binary pattern;sign language identification using image processing techniques;foreword;preface.

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Reliability Assessment of UAV Fleets 1

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IEEE lnternational conference on Advanced Trends in Radioelectronics, Telecommunications and Computer Engineering, TCSET 2022

作者： Zaitseva, Elena Levashenko, Vitaly Brinzei, Nicolae Kovalenko, Andriy Yelis, Marina Gopejenko, Viktors Mukhamediev, Ravil University of Zilina Žilina01026 Slovakia Centre de Recherche en Automatique de Nancy University of Lorraine Nancy France Kharkiv National University of Radio Electronics Nauky Avenue 14 Kharkiv61166 Ukraine Institute of Information and Computational Technologies Almaty050010 Kazakhstan Almaty050013 Kazakhstan Ventspils University of Applied Sciences International Radio Astronomy Centre LV-3601 Inzhenieru Street 101 Ventspils Latvia ISMA University of Applied Sciences Lomonosov Street 1 Riga1019 Latvia

ISBN: (数字)9783031249631

ISBN: (纸本)9783031249624

UAVs have a great potential of application for monitoring, search, detection, communication, delivery and transportation of cargo in various sectors of economy. In spite of this, the existing software and hardware, as well as legal limitations, prevent the wide application of UAVs. There is intensive research related to automation and optimization of missions of one or more UAVs in various application areas. However, the execution of missions of both individual vehicles and their homogeneous or heterogeneous groups depends on the reliability issues of these technical devices, UAVs fleets, and control systems. In this paper, we present models for assessing fleet reliability of UAVs that are managed centralized or decentralized. The method is based on the representation of the fleet as a Binary-State System. The following topologies are considered: (a) a homogenous irredundant drone fleet, (b) a homogenous hot stable redundant drone fleet, (c) a heterogeneous irredundant drone fleet, and (d) a heterogeneous hot stable redundant drone fleet. For the listed topologies, reliability estimates were obtained as a function of the number of primary and redundant UAVs. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Application programs

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Human-Machine Interaction for Monitoring COVID-19 Internet data in Russia and the World 15th

Human-Machine Interaction for Monitoring COVID-19 Internet D...

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15th APCA international conference on Automatic Control and Soft Computing (CONTROLO)

作者： Levashkin, Sergei Zakharova, Oksana Ivanov, Konstantin Povolzhskiy State Univ Telecommun & Informat Artificial Intelligence Lab Samara 443090 Russia

ISBN: (纸本)9783031100475;9783031100468

We develop a human-machine interaction via dashboard for COVID-19 data visualization in the regions of Russia and the world. In particular, it includes an adaptive-compartmental multi-parametric model of the epidemic spread, which is a generalization of the classical SEIR models;and a module for visualizing and setting the parameters of this model according to epidemiological data, implemented in a dashboard. data for testing have been collected since March 2020 on a daily basis from open Internet sources and placed on a "data farm" (an automated system for collecting, storing and pre-processing data from heterogeneous sources) hosted on a remote server. The combination of the proposed approach and its implementation in the form of a dashboard with the ability to conduct visual numerical experiments and compare them with real data allows most accurately tune the model parameters thus turning it into an intelligent system to support a decision-making. That is a small step towards Industry 5.0.

关键词： Human-machine interaction Industry 5.0 Big data algorithm data visualization Optimization of the decision-making process Dashboard COVID-19 spread mathematical model

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Research on spatio-temporal fusion algorithm of remote sensing image based on GF-1 WFV and Sentinel-2 satellite data 3

Research on spatio-temporal fusion algorithm of remote sensi...

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3rd international conference on Geology, Mapping and Remote Sensing, ICGMRS 2022

作者： Wang, Shengbo Yang, Xiufeng Li, Guohong Jin, YongTao Tian, Chuanzhao College of Remote Sensing and Information Engineering North China Institute of Aerospace Engineering Langfang065000 China Collab. Innov. Ctr. of Aerosp. Remote Sensing Info. Processing and Application of Hebei Province Langfang065000 China Natl. Loc. Jt. Eng. Research Center for Aerospace Remote Sensing Information Application Technology Langfang065000 China Hebei Aerospace Remote Sensing Information Technology Innovation Center Langfang China

ISBN: (数字)9781665485951

ISBN: (纸本)9781665485951

The remote sensing data set with high spatial and temporal resolution is of great significance for monitoring surface change. However, the earth observation satellites at domestic and international cannot obtain high spatial resolution and high temporal resolution images at the same time. Remote sensing data spatio-temporal fusion technology is an effective means to solve this problem. In this paper, the multi-period GaoFen-1 WideField-View (GF-1 WFV) satellite images and Sentinel-2 satellite images of Beijing-Tianjin-Hebei region in 2020 are used to perform fusion simulation for the three spatio-temporal fusion algorithms of Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM), Enhanced-STARFM (ESTARFM) and Enhanced Flexible Spatiotemporal data Fusion Model (EFSDAF) and these simulation degree of the results are quantitatively evaluated. The results show that ESTARFM algorithm is more suitable for the construction of high spatio-temporal fusion data of GF-1 WFV and Sentinel-2 satellites in Beijing-Tianjin-Hebei region. © 2022 IEEE.

关键词： Image enhancement

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Multi-objective Particle Swarm Optimization Based Enhanced Fuzzy C-Means Algorithm for the Segmentation of MRI data 3rd

Multi-objective Particle Swarm Optimization Based Enhanced F...

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3rd international conference on VLSI, Communication and Signal processing, VCAS 2020

作者： Singh, Munendra Asha, C.S. Sharma, Neeraj Department of Mechatronics Manipal Institute of Technology Manipal Academy of Higher Education Manipal India Varanasi India

ISBN: (纸本)9789811627606

Fuzzy c-means algorithm and its variants are popular for the segmentation of magnetic resonance imaging (MRI) data. The enhanced fuzzy c-means approach is one among them that comprises weighted local spatial data. However, the quantity of spatial data added with input MRI image differs and that depends on the noise content and sequence of MRI. Hence, the value of weight factor needs to be chosen appropriately and automatically to attain the accurate segmentation results. In this perspective, the current work focuses to generate optimum weight values and presents an optimized enhanced fuzzy c-means algorithm for MRI data. The proposed method utilizes the multi-objective particle swarm optimization to control the weight parameter that leads to maximum segmentation accuracy. The new approach is tested and validated on a standard simulated BrainWeb MRI dataset. The outcome shows that the proposed approach is flexible and robust to noise content as compared to the conventional algorithms. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Magnetic resonance imaging

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MFENet: Multi-Feature Extraction Net for Remote Sensing Semantic Segmentation 7

MFENet: Multi-Feature Extraction Net for Remote Sensing Sema...

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7th international conference on Intelligent Computing and Signal processing, ICSP 2022

作者： Zhang, Chao Lu, Xingshun Ye, Qihong Wang, Chao Yang, Chuansheng Wang, Quanqing Zhejiang Ocean University School of Information Engineering Zhoushan China Zhejiang Ocean University Key Laboratory of Oceanographic Big Data Mining and Application of Zhejiang Province Zhoushan China

ISBN: (纸本)9781665478571

In this paper, we tackle the remote sensing semantic segmentation task by capturing feature information across multiple scales, all channels, and global locations. Different from previous works that simply use U-net to extract multi-scale features, we further improve U-net and propose a Multi-Feature Extraction Network (MFE-Unet). Specifically, we propose the MFE module, which uses both dilated convolution module and two attention modules. Dilated convolution is used to enhance U-net's ability to represent multi-scale information. The two attention modules refer to the channel attention module and the pixel attention module. Channel attention maps all channels centrally, assigns weights uniformly, and adaptively adjusts the importance of each channel's information. Pixel attention treats features at each location as the same individual, and similar features will be associated together to further improve feature representation. We conducted multiple sets of experiments on the "AI+"remote sensing image dataset. Experiments show that our network is sufficient against several advanced models. © 2022 IEEE.

关键词： Convolution

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New Energy Station Monitoring Equipment Rule Verification Model Based on data Envelopment Analysis

New Energy Station Monitoring Equipment Rule Verification Mo...

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2020 international conference on Smart Grid and Energy Engineering, SGEE 2020

作者： Zhu, Dongge Ma, Yiming Li, Yongliang Yan, Zhenhua Liu, Jia Ma, Rui Electric Power Research Institute of State Grid Ningxia Electric Power Co. Ltd. Ningxia750002 China

According to the functional and performance requirements of the new energy station monitoring equipment, an operating rule verification model based on data envelopment analysis is proposed, and the online automatic discovery of equipment and safe operation rules for key links are designed. The equipment adopts data envelopment analysis method to read real-time data of new energy station monitoring equipment, optimizes the automatic discovery function of key sections, and tests the operating parameters of wind turbines and wind farms online through remote testers, formulates monitoring rules, and forms data requirements, Use algorithms to acquire and process data, and build models. Realize the discrete processing and rule mining of key links and safety operating procedures to ensure the safe operation of new energy station monitoring equipment. Finally, the experimental results show that the equipment can quickly and automatically generate new energy station monitoring equipment safe operation knowledge and improve new energy Safety of station monitoring equipment. © Published under licence by IOP Publishing Ltd.

关键词： data envelopment analysis

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ASTC: An Adaptive Gradient Compression Scheme for Communication-Efficient Edge Computing 23

ASTC: An Adaptive Gradient Compression Scheme for Communicat...

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23rd IEEE international conference on High Performance Computing and Communications, 7th IEEE international conference on data Science and Systems, 19th IEEE international conference on Smart City and 7th IEEE international conference on Dependability in Sensor, Cloud and Big data Systems and Applications, HPCC-DSS-SmartCity-DependSys 2021

作者： Wu, Jun Mao, Yingchi Xu, Xuesong School of Computer and Information Hohai University Nanjing China Hohai University Key Laboratory of Water Big Data Technology of Ministry of Water Resources Nanjing China

ISBN: (纸本)9781665494571

In edge computing, though distributed training of Deep Neural Network (DNN) is expected to exchange massive gradients between the parameter servers and workers, the high communication cost constrains the training speed. To break this limitation, the gradient compression algorithms employ the ultimate compression ratio at the expense of the accuracy of the trained model. Therefore, new gradient compression techniques are necessary to ensure both communication efficiency and model accuracy. This paper introduced a novel technique - an Adaptive Sparse Ternary Gradient Compression (ASTC) scheme to enhance the communication efficiency and training accuracy during the DNN training. Specifically, ASTC firstly establishes the model compression selection criterion by the amount of gradients to compress the network layer that meets the criterion. Secondly, ASTC evaluates the gradients' importance based on entropy to adaptively perform sparse compression. Finally, ASTC performs ternary quantization compression and a lossless code scheme to further reduce the communication cost. Numerical results demonstrate that the average iteration time of ASTC can be reduced largely compared to the state-of-art benchmark schemes. Furthermore, ASTC improves an average of about 1.9% in training accuracy. © 2021 IEEE.

关键词： Training Adaptation models Costs Quantization (signal) Smart cities Computational modeling Benchmark testing

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Formalization and processing of data Requirements for the Development of Next Generation Railway Traffic Management Systems 4th

Formalization and Processing of Data Requirements for the De...

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4th international conference on Reliability, Safety, and Security of Railway Systems (RSSRail)

作者： Magnien, Airy Cecchetti, Gabriele Ruscelli, Anna Lina Hyde, Paul Liu, Jin Wegele, Stefan SNCF Paris France UIC Paris France Scuola Super Sant Anna Pisa Italy Newcastle Univ Sch Engn Newcastle Upon Tyne Tyne & Wear England Siemens Munich Germany

ISBN: (纸本)9783031058134;9783031058141

Railway Traffic Management Systems (TMSs) handle data from multiple railway subsystems, including Rail Business Services (such as interlocking, RBC, maintenance service, etc.) and external services (such as passenger information systems, weather forecast, etc.). In turn, the data from these subsystems are described in several models or ontologies contributed by various organizations or projects which are in a process of converging or federation. The challenge of the Shift2Rail OPTIMA project, which is implementing a communication platform for virtual testing of new applications for railway TMS, is to allow the exchange of data between different services or users and to support new traffic management applications, enabling access to a large number of disparate data sources. In this paper, the core activities of the OPTIMA project related to the formulation and standardization of a common data model are described. A new Common data Model is developed based on standardized data structures to enable the seamless exchange of large amounts of data between different and heterogeneous sources and consumers of data, that contributes to the building of next generation of a more effective and efficient railway TMS suitable to offer precise and real-time traffic information to railway operators and other end users.

关键词： Railway Traffic Management System Common data Model Model transformation Context-free grammar

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