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Usage of Blockchain technology for Improving computer Security

Usage of Blockchain Technology for Improving Computer Securi...

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information technology (ACIT)

作者： Adai Almomani Al Refai Mohammed N Ahmad Aburomman Omar Khair Alla Alidmat Qotadeh Saber Fuad Alshariedeh Mohammed Khouj Department of Computer Science Zarqa University Zarqa Jordan Software Engineering Dep. Zarqa University Zarqa Jordan Department of Data Science and A Zarqa University Zarqa Jordan Department of Electronic Enginnering Univeristy of Business and Technology Jeddah Saudi Arabia

ISBN: (数字)9798331540012

ISBN: (纸本)9798331540029

Cyber threats have grown increasingly sophisticated, surpassing the capabilities of traditional security measures. Blockchain technology, with its decentralized and immutable structure, offers a potential solution to these challenges. This study examines the role of blockchain tools such as smart contracts, decentralized identity management, and secure data sharing in enhancing cybersecurity. A review of key literature and analysis of survey data from cybersecurity professionals demonstrate blockchain's effectiveness in improving data integrity, access control, and secure communications. The results show significant correlations between the adoption of blockchain technologies and the mitigation of threats like data breaches and unauthorized access. While blockchain presents promising advancements, scalability, regulatory barriers, and energy consumption remain challenges. The study provides valuable insights for researchers, practitioners, and policymakers on how blockchain can be integrated into modern cybersecurity strategies to create more secure digital environments.

关键词： Access control Industries Proof of stake Scalability Smart contracts Data protection Blockchains computer security Identity management systems Business

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The Realization Factors of Taiwan's Low Altitude Airspace Economy and UAM: Analysis and Evaluation

The Realization Factors of Taiwan's Low Altitude Airspace Ec...

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IEEE International Conference on e-Business engineering (ICEBE)

作者： Tung-Ling Wu Chih-Ming Kung Guan-Ting Lin Kuen-Yu Tsai Guang-Yun Meng Yu-Che Huang Wan-Shu Cheng Institute of Transportation at Ministry of Transportation and Communications Taiwan R.O.C. Dep. Of Information Technology&Communication Shin Chin Univeristy Kaohsiung Campus Taiwan R.O.C. Dep. of Computer Science and Information Management Providence University Taichung Taiwan R.O.C. Department of Electrical Engineering National Taiwan Univeristy Taipei Taiwan R.O.C. Executive Officer Center for Research on High-Performance Remote Sensing and Urban Informatics NTU Taiwan R.O.C.

ISBN: (数字)9798350365856

ISBN: (纸本)9798350365863

Low altitude airspace economy applications will be of highly effective in a wide range of civil activities and will be a critical element in the smart cities of the future. In view of the main features of unmanned aerial vehicle (UAV), such as automatic piloting system, access to uninhabited areas, low cost compared to road and manned aircraft, shorter delivery time, and the ability to carry heavy payloads, it is important to recognize that UAVs can be used for a wide range of civil activities. With the development of these applications in full swing around the world, in addition to advanced communications technology and other related infrastructure, business demand and a favorable investment climate, clarity, definiteness and feasible regulations in the Taiwanese environment are also essential. Based on the premise of local economic strength, the technical level of the telecommunication industry, and commercial demands, Taiwan should establish a rational standard for the UAV regulatory system as soon as possible, so as to facilitate the development of UAM and to ensure the international competitiveness of the related technologies for market development,

关键词： Industries Technological innovation Transportation Autonomous aerial vehicles Regulation Communications technology Aircraft Standards Drones Investment

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Predictive Maintenance of Power Transformers in Distribution Network with Energy Management Using Deep Learning

Predictive Maintenance of Power Transformers in Distribution...

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Intelligent Communication Technologies and Virtual Mobile Networks (ICICV), International Conference on

作者： Pooja Pande K Hussain B Pravallika Mohammed Saleh Al Ansari R.M. Tharsanee Srinivasa Acharya Department of Information Technology Yeshwantrao Chavan College of Engineering Nagpur Maharashtra India Department of EE Sharad Institute of Technology College of Engineering Ichalkaranji Kolhapur Maharashtra Institute of Aeronautical Engineering Hyderabad College of Engineering Dep artment of Chemical Engineering University of Bahrain Bahrain Department of Computer science and engineering Bannari Amman Institute of Technology Sathyamangalam Erode Electrical and Electronics Engineering Department Aditya Institute of Technology and Managemnt Tekkali Andhra Pradesh India

The reliability and efficiency of the electrical grid network are obtained through proper maintenance of the power transformers. The advanced maintenance of the electrical grid is obtained through modern strategies to enhance the performance of the system. Power transformers are an important part of the electrical distribution systems. Failure of transformers leads to various constraints regarding service interferences with costly outrages. Traditional forms of maintenance rely on manual inspections on fixed timelines which is not able to detect the transformer degradation at early stages. Advanced predictive maintenance with energy management is obtained through deep learning techniques with hybrid optimization techniques. This includes Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN). This is done through various sensors and monitoring equipment placed in the distribution network. The extraction of spatial and temporal data from images is obtained through the CNN component. The hidden defects and abnormal functioning of various parts in the transformer are detected through CNN. Oil leakages, overheating and insulation failure are the early signs of transformer failures. The RNN component concentrates on the data generated by sensors and meters in real-time environment. This helps to predict the future demand for power transformers. The sequential patterns are obtained through the LS TM component. They provide accurate details regarding the performance and life of transformers. The integration of energy management with predictive maintenance helps optimise power utility while maintaining the reliable operation of transformers.

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FVMM: Fast VM Migration for Virtualization-based Fault Tolerance Using Templates

FVMM: Fast VM Migration for Virtualization-based Fault Toler...

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IEEE International Conference on Cloud Computing technology and Science (CloudCom)

作者： Wen-Hsiu Tsai Po-Jui Tsao Che-Rung Lee Dep. of Computer Science National Tsing Hua University Hsinchu Taiwan Information and Communications Research Lab. Industrial Technology Research Institute Hsinchu Taiwan

ISBN: (纸本)9781665463683

In the era of cloud computing, virtualization based fault tolerance that utilizes the continuous virtual machine (VM) migration to synchronize a VM and its remote replica is a common technique to achieve high availability. However, traditional live VM migration, whose goal is to minimize the system downtime, has a long duration owing to the expense of the pre-copy for machine status and memory content, which increases the period of failover when failures occur. In this paper, we proposed a new VM migration method, called Fast VM Migration (FVMM), which utilizes the templating technique to accelerate the VM migration by reducing the cost of pre-copy. The templating technique that creates VMs from a master copy, called a template, is a usually used to dep.oy many similar VMs in a large virtual environment. FVMM employs VM templating to mitigate the cost of pre-copy. Its implementation is optimized with six new techniques: SFVMM and LFVMM, COW templating, continuous templating, asynchronous transmission buffering, VM recovery from continuous templating, and templating regularly. We also applied FVMM for virtualization based fault tolerance to accelerate the failover process. Experimental results show that FVMM is more than 56 times faster than pre-copy live migration for a VM with 16GB, and can reduce upto 32% of the system downtime and the fault tolerance resynchronization time comparing to the methods using pre-copy live migration.

关键词： computers Fault tolerance Cloud computing Costs Fault tolerant systems Cows Virtual machining

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EMG-Based Volitional Torque Estimation in Functional Electrical Stimulation Control

EMG-Based Volitional Torque Estimation in Functional Electri...

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IEEE EMBS Conference on Biomedical engineering and Sciences (IECBES)

作者： Hossein Kavianirad Satoshi Endo Thierry Keller Sandra Hirche Dep. of Electrical and Computer Engineering Technical University of Munich Munich Germany TECNALIA Basque Research and Technology Alliance (BRTA) San Sebastian Spain

Functiona1 electrical stimulation (FES) applies electrical pulses to muscle fibers through the skin for assisting functional movements in patients with motor disability. Muscle activity feedback such as volitional Electromyography (vEMG) can optimize the performance of the FES system in both rehabilitation or activity of daily living (ADL), however, artifacts caused by simultaneous use of FES and EMG on the same muscles contaminate the EMG signal. This paper, using an adaptive filter, aims to investigate the estimation of the volitional torque from filtered vEMG. Based on this estimation, the usability and performance of the adaptive filter for estimating volitional torque are studied on 5 healthy participants and we show that this filter can be used for volitional torque estimation. In the next step, it is shown how this map can be used in closed-loop FES control for estimating volitional torque.

关键词： Torque Adaptive filters Estimation Nonlinear filters Muscles Iron Electromyography

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Big Data from Space for Precision Agriculture Applications 11

Big Data from Space for Precision Agriculture Applications

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11th International Symposium on Digital Earth, ISDE 2019

作者： Bovolo, F. Bruzzone, L. Fernández-Prieto, D. Paris, C. Solano-Correa, Y.T. Volden, E. Zanetti, M. Fondazione Bruno Kessler Center for Information and Communication Technology Trento Italy Dep. of Information Engineering and Computer Science University of Trento Trento Italy ESRIN Frascati Italy

This paper presents an approach for precision agriculture large scale applications based on the analysis of big data consisting in Satellite Image Time Series (SITS) acquired by ESA Sentinel-2 (S2) satellite constellation. The approach has been developed in the framework of the ESA SEOM - Scientific Exploitation of Operational Missions - S2-4Sci Land and Water project [1]. To focus only on agricultural areas, images are first filtered based on a land cover (LC) map that is generated by updating available old maps by means of recent images. Then S2 SITS are used to analyse agricultural areas. Two macro challenges are therefore considered: (i) automatic update of LC maps and generation of agricultural areas mask;and (ii) unsupervised multi-temporal (MT) fine characterization of land plots. © 2020 Published under licence by IOP Publishing Ltd.

关键词： Precision agriculture

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Image Captioning Using Inception V3 Transfer Learning Model 6

Image Captioning Using Inception V3 Transfer Learning Model

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6th IEEE International Conference on Communication and Electronics Systems, ICCES 2021

作者： Degadwala, Sheshang Vyas, Dhairya Biswas, Haimanti Chakraborty, Utsho Saha, Sowrav Sigma Institute of Engineering Head of Comp. Eng. Dep. Gujarat Vadodara India Shree Drashti Infotech LLP Nizampura Gujarat Vadodara India Gujarat Technological University Information Technology India Gujarat Technological University Computer Engineering India Gujarat Technological University and Civil Infrastructure Engineering India

ISBN: (纸本)9781665435871

As artificial intelligence has grown rapidly in recent years, picture captioning has attracted the interest of numerous experts, which has become a fascinating and challenging challenge. A critical component of scene analysis which combines machine vision and the natural languages of language processing capabilities is visual subtitles which automatically generate natural language interpretations based on image details. This paper utilizes different NLP strategies for perceiving and clarifying an image meaning in a natural language such as English. The proposed Inception V3 image caption generator model uses CNN (Coevolutionary Neural Networks) and LSTM (Long Short-Term Memory) units. The InceptionV3 model has been educated in 1000 different classes on an ImageNet dataset. The model was imported directly from the Keras module of applications. Remove from the InceptionV3model the last classification layer for the dimension (1343, ) vector. The embedded matrix is used for vocabulary connections. A building matrix is the linear transformation of the original space with important relations into a real-life space. Image captions are commonly used and, for example, important in implementing interaction between humans and the computer. © 2021 IEEE.

关键词： Long short-term memory

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Rate-Splitting Multiple Access Networks Assisted by Aerial Intelligent Reflecting Surfaces

Rate-Splitting Multiple Access Networks Assisted by Aerial I...

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IEEE Latin-American Conference on Communications, LATINCOM

作者： Brena Kelly S. Lima Rui Dinis Daniel Benevides Da Costa Marko Beko Rodolfo Oliveira Rui Vigelis Merouane Debbah COPELABS Lusofona University Lisbon Portugal Dep. of Electrical Engineering NOVA University of Lisbon Caparica Portugal Technology Innovation Institute Abu Dhabi United Arab Emirates Instituto de Telecomunicações Instituto Superior Técnico Lisbon Portugal Dep. of Electrical and Computer Eng NOVA University of Lisbon Caparica Portugal Dep. of Computer Engineering Federal University of Ceará Sobral Brazil

This paper investigates rate-splitting multiple access (RSMA) networks assisted by aerial intelligent reflecting surfaces (AIRS) and assuming a downlink multiple-input single-output (MISO) scenario (AIRS-RSMA) with imperfect successive interference cancelation (SIC). An optimization problem is formulated in order to maximize the total achievable rate by optimizing the transmit beamforming and common achievable rate of the users. By using approximation and transformation techniques, we convert the optimization problem into a semi-definite program (SDP) problem. To solve this problem, an algorithm based on alternating optimization (AO) is proposed to iteratively solve the transmit beamforming problem. Simulation results are provided to demonstrate the efficiency of the proposed method, in which it is revealed that the performance gains in terms of sum-rate of AIRS-RSMA networks with robust beamforming are significantly greater than the non-optimized AIRS-RSMA and conventional non-orthogonal multiple access (NOMA) schemes.

关键词： NOMA Array signal processing Simulation MISO communication Performance gain Autonomous aerial vehicles Numerical simulation

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Convolutional Neural Network Based Deep Neural Network Model for Electrocardiogram Records Classification 23

Convolutional Neural Network Based Deep Neural Network Model...

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Proceedings of the 7th International Conference on Future Networks and Distributed Systems

作者： Ahmed Mahmood Khudhur Mohammed Hasan Alwan Qayssar Al Omairi Omar Abdulkareem Mahmood Yousif I Hammadi Mohammed Saleh Ali Muthanna Ahmed Aziz Ammar Muthanna Information Technology Department Kirkuk University Iraq Department of Communications Engineering University of Diyala Iraq Department of Computer Engineering University of Diyala Iraq Dep. of Medical Instruments Engineering Techniques Bilad Alrafidain University College Iraq Institute of Computer Technologies and Information Security Southern Federal University Russia Department of computer science Benha university Egypt and International Business Management Department Tashkent State University of Economics Uzbekistan Department of Telecommunication Networks and Data Transmission The Bonch Bruevich Saint Petersburg State University of Telecommunications Russia and Department of Applied Probability and Informatics Peoples Friendship University of Russia Russia

ISBN: (纸本)9798400709036

Сardiovascular diseases, also known as CVDs, currently rank as the primary incidence of mortality. The present approach for identifying illnesses involves the analysis of the Electrocardiogram (ECG), an electronic diagnostic device utilized to capture the rhythm of the heart. Regrettably, the process of seeking out specialists to conduct analysis on a substantial volume of electrocardiogram (ECG) data results in a significant dep.etion of medical capabilities. Hence, the utilization of deep learning algorithms for the identification of ECG characteristics has progressively gained prominence. Nevertheless, there exist certain limitations associated with these conventional approaches, which necessitate the need for manual characteristic identification, intricate models, and extensive training duration. This study presents a novel and effective approach for categorizing the five different types of heartbeats in the MIT-BIH Arrhythmia database. The proposed method involves the utilization of a 16-layer deep one-dimensional convolutional neural network, which exhibits both robustness and efficiency in its classification performance. Thus, the designed model is consisting of five-groups, first to fourth groups are the main feature extraction and mapping blocks, while the fifth group is fully connected and classification layers. The findings indicate that the model presented in this study has superior performance in terms of accuracy, precision, and F1-score. The proper classification of medical cases has a significant impact on the conservation of medical resources, hence positively influencing clinical practice. Additionally, the model training phase is characterized by its efficiency, since it does not significantly dep.ete resources.

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Impedance measurements for demineralized tooth lesions assessment

Impedance measurements for demineralized tooth lesions asses...

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IEEE International Workshop on Medical Measurement and Applications (MEMEA)

作者： Isabella Sannino Leonardo Iannucci Luca Lombardo Marco Parvis Allegra Comba Pasquale Arpaia Emma Angelini Sabrina Grassini Department of Applied Science and Technology Politecnico di Torino Turin Italy Department of Electronics and Telecommunication Politecnico di Torino Turin Italy Department of Surgical Science Università degli Studi di Torino Turin Italy Dep. of Electrical Engineering and Information Technology Università degli Studi di Napoli Federico II Naples Italy Dep. of Applied Science and Technology Politecnico di Torino Turin Italy

This work deals with the design and development of a non invasive though rather effective solution to detect carious lesions. The main aim of the study is to develop a simple automatic approach for the assessment of the tooth demineralization, which represents the earlier stage of the dynamic carious process, by means of impedance spectroscopy. In particular, impedance measurements were carried out on 50 extracted human teeth. Teeth were demineralized in-vitro according to a validated protocol; then, a morphological analysis of the tooth surface was performed by scanning electron microscopy to confirm enamel demineralization. The proposed approach tries to take advantage of the change of impedance phase due to the demineralization process. Data analysis confirmed that the best frequency for discriminating between demineralized and non-demineralized teeth is about 15 Hz, and this parameter can be used for building up an automatic classifier based on Multilayer Perceptron (MLP) topology. Impedance data were processed by using a modified single neuron, which allows classifying demineralized and sound teeth with an error rate of about 7%, estimating therefore the presence of carious lesions in progress. The presented work can be considered as a feasibility study with a final goal to conceive a simple and low-cost measurement system to identify caries at an early stage

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