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3rd international symposium on Signal and Image Processing, ISSIP 2020

ISBN: (纸本)9789813369658

The proceedings contain 16 papers. The special focus in this conference is on Signal and Image Processing. The topics include: Deep Convolutional Neural Network-Based Diagnosis of Invasive Ductal Carcinoma;speaker Identification in Spoken Language Mismatch Condition: An Experimental Study;Ultrasound Image Classification Using ACGAN with Small Training Dataset;preface;Chaotic Ions Motion Optimization (CIMO) for Biological Sequences Local Alignment: COVID-19 as a Case Study;assessment of Eyeball Movement and Head Movement Detection Based on Reading;using Hadoop Ecosystem and Python to Explore Climate Change;a Brief Review of Intelligent Rule Extraction Techniques;the Effect of Different Feature Selection Methods for Classification of Melanoma;intelligent Hybrid Technique to Secure Bluetooth Communications;parallel Algorithm to find Integer k where a given Well-distributed Graph is k-Metric Dimensional;a Fog-Based Retrieval of Real-Time Data for Health applications;differential Evolution-Based Shot Boundary Detection Algorithm for Content-Based Video Retrieval;qutrit-Based Genetic Algorithm for Hyperspectral Image Thresholding.

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Physics-Infused Framework for Strengthening Decentralized Security and Data Integrity in distributed Blockchain Networks

Physics-Infused Framework for Strengthening Decentralized Se...

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international symposium on Advanced Computing and Communication (ISACC)

作者： Gangidi Srikanth Reddy Sreeram Gutha Brahmanapally Satya Gaayathri Gottapu Shravni Kotha Suma Reddy P Hanumantha Rao Dept of CSE Vignana Bharathi Institute of Technology Ghatkesar Telangana India

ISBN: (数字)9798331523893

ISBN: (纸本)9798331523909

Blockchain is one of the most promising technologies which supports the concepts of decentralization and provides secure solutions across the fields. However, implementing reliable and efficient security and data consistency in real-time, high-risk domains is still an active research issue due to the ever-growing number of cyber threats and complex characteristics of large-scale distributed systems. Some of the problems that are prevalent in current blockchain environment include; fault tolerance, scalability and anomaly detection. Encouraged by these gaps, this research presents PhyBb-WBS: Physics-Infused Framework for Enhancing Decentralized Security and Data Credibility of Blockchain-Based distributed Apps. This framework implements the Fault Tolerant Consensus via Weighted Byzantine Strategy to achieve resilient and robust consensus, the Physics Infused Deep Neural Networks for anomaly detection and the Multimodal Bowerbird Optimization for dynamic parameters optimization providing a security solution robust in the face of such challenges. Specifically, the principal goal is the development of a reliable and, at the same time, practically oriented blockchain structure capable of meeting real-time requirements for data protection and storage. Originally, the framework is tailored for the applications like energy trading and transactions that require high quality of network. Performance evaluations demonstrate significant advancements, achieving over 99.8% anomaly detection accuracy and fault tolerance rates surpassing 99.5%. Additionally, it enhances throughput by 99.25% compared to traditional optimization methods. In conclusion, this paper presents the PhyBb-WBS framework that helps to improve the security and viability of blockchain. Solving important problems by leveraging sophisticated consensus protocols, effective anomaly detection, and adaptive optimization, this research proposes a significant advancement in utilizing decentralized systems in high-stake

关键词： Fault tolerance Accuracy Data integrity Fault tolerant systems Telecommunication traffic Throughput Real-time systems Blockchains Security Anomaly detection

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K2rdF: A distributed rdF Data Management System on Kudu and Impala 3rd

K2RDF: A Distributed RDF Data Management System on Kudu and ...

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3rd BenchCouncil international symposium on Benchmarking, Measuring, and Optimizing, Bench 2020

作者： Chen, Xu Qiu, Boyu Xu, Jungang Liu, Renfeng School of Computer Science and Technology University of Chinese Academy of Sciences Beijing China

ISBN: (纸本)9783030710576

The Resource Description Framework (rdF) has been widely used in various applications or services as a model for displaying, sharing and connecting data. With the increase of rdF data scale, distributed rdF data management system becomes popular, but there are still many problems to be solved. To solve these problems, we proposed a distributed rdF data management system K2rdF based on the Porperty Chain model on the Kudu and Impala platforms. Kudu is a data storage engine that combines OLAP and OLTP scenario and Impala can process SQL queries in real time. The combination of these two platforms provides new options for processing rdF data, making storage more efficient and queries faster. The Property Chain model is derived from the rdF data content. The rdF data is divided into different parts stored in the corresponding attribute table by the class information extracted from rdF schema. In the attribute table, In the attribute table, each column corresponds to a property in the rdF class. This model can increase the data storage density and improve the query processing speed by reducing the number of the join operation. By comparing with the current popular distributed rdF data management systems in some experiments, our system has lower query latency and faster query speed. © 2021, Springer Nature Switzerland AG.

关键词： Query processing

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Development of theoretical foundations for the creation of intelligent technology based on a unified artificial immune system for complex objects control of the oil and gas industry 3

Development of theoretical foundations for the creation of i...

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3rd international Scientific Conference on Applied Physics, Information Technologies and Engineering, APITECH-III 2021

作者： Samigulina, G.A. Samigulina, Z.I. RSE Institute of Information and Computational Technologies MES RK CS Almaty050010 Kazakhstan Kazakh-British Technical University Almaty050000 Kazakhstan

The article is devoted to the current and promising direction of the implementation of the Industry 4.0 program based on the introduction of artificial intelligence (AI) approaches in the industrial production of the oil and gas industry. A review of the application of modern bioinspired AI approaches for solving problems of control and diagnostics of industrial equipment is carried out. The application of the approach of artificial immune systems (AIS) and various modified AIS algorithms is substantiated in the creation of innovative and competitive applications for solving various specific engineering problems and creating a modern highly efficient technology for automating production processes at oil refineries. The theoretical foundations for the creation of an intelligent innovative technology based on a unified artificial immune system (UAIS) for distributed systems for complex objects control using modern computer technology have been developed. Definitions of UAIS and immunological homeostasis are given. Unification is carried out with the aim of reducing the variety of modified AIS algorithms and their optimal use for solving the assigned tasks for the technological processes control. The classification and assessment of the effectiveness of the application of modified AIS algorithms is carried out. The block diagram of an intelligent control system based on the developed UAIS is presented. © 2021 Institute of Physics Publishing. All rights reserved.

关键词： Process control

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Multi-Agent Systems for Autonomous IoT Network Management Using distributed Reinforcement Learning

Multi-Agent Systems for Autonomous IoT Network Management Us...

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international symposium on Advanced Computing and Communication (ISACC)

作者： Neelamegam G Rajaram Venkatesan Ramya SR R.S. Ramya Akshya. J M. Sundarrajan Mani Deepak Choudhry Department of Computer Science and Engineering VSB College of Engineering and Technology Coimbatore Tamil Nadu India Department of Artificial Intelligence and Data Science Builders Engineering College Tirupur Tamil Nadu India Department of Computer Science and Engineering Dr NGP Institute of Technology Coimbatore Tamil Nadu India Department of Computer Science and Engineering Sri Ramakrishna Engineering College Coimbatore Tamil Nadu India Department of Computational Intelligence SRM Institute of Science and Technology Chennai Tamil Nadu India Department of Networking and Communications SRM Institute of Science and Technology Chennai Tamil Nadu India Department of Computing Technologies SRM Institute of Science and Technology Chennai Tamil Nadu India

ISBN: (数字)9798331523893

ISBN: (纸本)9798331523909

The ever-growing complexity of IoT networks ignited by their wide scale adoption in applications such as smart cities, the industrial automation, and health care, compelled to develop sophisticated yet resource efficient and scalable network management solution. Centralized systems are traditional in architecture, which leads to higher latency and bottlenecks, while decentralized systems can distribute workloads however the lack of adaptability to real time, along with the difficulty of extracting maximum resource usage, are limitations. In recent years, reinforcement learning (RL) based approaches have taken shape as a promising alternative to facilitate the automated learning and optimisation of task management. However, there are two challenges for existing RL based methods: slow convergence, inefficient priority assignment of tasks, and poor coordination amongst agents in the case of dynamic, large scale IoT environment. To solve these challenges, in this research we propose a distributed reinforcement learning based multi agent framework for managing IoT network. The framework also integrates a task prioritization mechanism to dynamically choose an optimal task allocation solution. Further validation was performed in a simulated IoT environment where real world inspired datasets were used. The proposed framework demonstrated 30% more task throughput than centralized, 25% less latency than decentralized, and 40% more energy efficiency than current RL based systems. The system was further validated using advanced visualizations such as anomaly detection maps and resource allocation efficiency graphs, demonstrating its capacity to manage dynamic load of tasks and resource allocation. The proposed framework addresses the critical limitations of current methods, through scalable, adaptive, and energy efficient framework for managing IoT network, and accordingly helps advancing the intelligent and autonomous IoT systems.

关键词： Smart cities Reinforcement learning Medical services Throughput Dynamic scheduling Energy efficiency Real-time systems Resource management Optimization Multi-agent systems

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The Technique of Operational Processing of Heterogeneous Surveillance Data in Assessing Situation in Geographic Information Systems

The Technique of Operational Processing of Heterogeneous Sur...

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IEEE international Conference on Advanced Trends in Information Theory (ATIT)

作者： Mykhailo Rakushev Oleksandr Matsko Oleksandr Permiakov Oleksandr Lavrinchuk Oleksandr Koshlan Ihor Varlamov National Defence University of Ukraine Named After Ivan Cherniakhovskyi Kyiv Ukraine

ISBN: (纸本)9781665406673

The paper deals with the analysis of observational data in the assessment of the situation developed technique of heterogeneous data operational processing in assessing situation with the help of Geographic Information Systems, which allow to store raw data about surveillance objects, structured heterogeneous surveillance data due to the principle "Data Lake".The basis of the technique is the Model of Storage of Heterogeneous Data, which differs from the known ones by the presence of templates of objects of observation and templates of parameters of objects of observation, which allow distributed storage of both unstructured heterogeneous data and structured data according to a certain scheme, to access data.

关键词： Decision support systems Surveillance distributed databases Memory Big Data applications Market research Data models

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Squeeze-and-Excitation network-Based Radar Object Detection With Weighted Location Fusion 21

Squeeze-and-Excitation network-Based Radar Object Detection ...

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11th international Conference on Multimedia Retrieval (ICMR)

作者： Sun, Pengliang Niu, Xuetong Sun, Pengfei Xu, Kele Chinese Univ Hong Kong Hong Kong Peoples R China Kings Coll London London England Univ Zurich Inst Neuroinformat Zurich Switzerland Swiss Fed Inst Technol Zurich Switzerland Natl Key Lab Parallel & Distributed Proc Changsha Peoples R China

ISBN: (纸本)9781450384636

Radar object detection refers to identify objects from radar data, and the topic has received increasing interest during the last years, due to the appealing property of radar imaging and evident applications. However, the detection performance heavily relied on semantic information extraction, which is a great challenge in practical settings. Moreover, although remarkable progress has been made, most previous attempts are restrained from the essentially limited property of the employed single modality. Inspired by the recent success of cross-modality deep learning, we propose a novel cross-modality deep learning framework for the radar object detection task using the Squeeze-and-Excitation network, aiming to provide more powerful feature representation. Moreover, a novel noisy detection approach is also explored in our study, to increase the model's ability to handle with noise. Finally, a novel weighted location fusion strategy is introduced in our framework, to improve the detection performance further. To empirically investigate the effectiveness of the proposed framework, we conduct extensive experiments on the 2021 ICMR ROD challenge. The obtained results suggest that our framework outperforms related approaches. Our method ranks as the 3rd place on the final leaderboard, with an average precision (AP) percentage of 76.1. Models and codes are available at https://***/sunpengliang/modelConfusion.

关键词： Cross-modal machine learning Ensemble learning Radar object detection

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A U-Band PLL Using Implicit distributed Resonators for Sub-THz Wireless Transceivers in 40 nm CMOS

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS II-EXPRESS BRIEFS 2020年第9期67卷 1574-1578页

作者： Chen, Zipeng Deng, Wei Jia, Haikun Liu, Yibo Wu, Jianxi Guan, Pingda Zhu, Jinyu Mao, Luhong Wang, Zhihua Chi, Baoyong Tsinghua Univ Inst Microelect Beijing 100084 Peoples R China Tianjin Univ Sch Elect & Informat Engn Tianjin 300072 Peoples R China

A U-band phase-locked loop (PLL) with implicit distributed resonators in the voltage-controlled oscillator (VCO) and injection-locked frequency divider (ILFD) using 40 nm CMOS is investigated in this brief. While LC-based, transformer-based, and distributed resonators are typically adopted for mm-wave and sub-terahertz applications, implicit distributed resonators are proposed to enhance the oscillation frequency of the VCO and expand the locking range of the ILFD. The operating principles and the characteristics of the proposed implicit distributed resonators are discussed for intuitive insight. The presented PLL occupies 0.53 mm(2) core area and has 81.8 mW power consumption. The PLL achieves a measured frequency range of 8.15 GHz (16%) from 46.75 GHz to 54.9 GHz. The measured phase noise is -91.8 dBc/Hz at 1 MHz offset frequency from a carrier of 46.75 GHz. The proposed U-band PLL can provide a clear and stable local oscillation signal for the transceivers operating at mm-wave and sub-terahertz (sub-THz) frequency band.

关键词： CMOS distributed resonator injection-locked frequency divider (ILFD) millimeter-wave (mm-wave) phase-locked loop (PLL) sub-terahertz (sub-THz) voltage-controlled oscillator (VCO)

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Spatio-temporal variation analysis of snow cover in China-Pakistan Economic Corridor based on remote sensing technology 3

Spatio-temporal variation analysis of snow cover in China-Pa...

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3rd international symposium on Architecture Research Frontiers and Ecological Environment, ARFEE 2020

作者： Zhang, Fan Zhang, Zhihua Faculty of Geomatics Lanzhou Jiaotong University Lanzhou730070 China Natl.-Loc. Jt. Eng. Res. Ctr. of Technol. and Applications for National Geographic State Monitoring Lanzhou730070 China Gansu Provincial Engineering Laboratory for National Geographic State Monitoring Lanzhou730070 China

Based on the MODIS10A2 snow product data from 2001 to 2019, the characteristics of annual variation, interannual variation and spatial distribution of snow cover in China Pakistan Economic Corridor from 2001 to 2019 are analyzed by using remote sensing technology. The result shows that: the snow-cover in a year generally starts from the middle of October, and the snow cover area reaches the maximum value in January of the next year, and reaches the minimum value in July. From 2001 to 2019, the snow area of China-Pakistan Economic Corridor generally showed a decreasing trend. The distribution of snow in the China-Pakistan Economic Corridor is extremely uneven. The northern area is obviously more than that in the south. The mountainous and plateau areas are with high frequency of snow cover, and the plains are areas with low frequency of snow-cover. Permanent snow-cover is relatively low. Few, mainly concentrated in the Karakoram Mountains, in terms of distribution range, mainly distributed in 4452-8378 m. © The Authors, published by EDP Sciences, 2021.

关键词： Snow

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A distributed Digital Object Architecture to Support Secure IoT Ecosystems 3rd

A Distributed Digital Object Architecture to Support Secure ...

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3rd international Conference on Optimization and Learning (OLA)

作者： Ruiz-Zafra, Angel Magan-Carrion, Roberto Univ Cadiz Cadiz Spain

ISBN: (纸本)9783030419134;9783030419127

Security is one of the most challenging issues facing the Internet of Things. One of the most usual architecture for IoT ecosystems has three layers (Acquisition, Networks and applications), and provides the security to the different elements of the IoT ecosystems through specific technology or techniques, available in the different layers. However, the deployment of security technology at each layer complicates the management and maintainability of the security credentials increasing the risk of information leak, greater manual intervention and complicates the maintainability of consistency of the sensitive data. In this paper we propose a new architecture model, where a fourth security layer has been added, containing all the security technology traditionally delegated to the other layers, removing them from other layers. This new model is supported by the widespread use of Digital objects, covering all aspects including physical components, processes and sensed data.

关键词： Internet of Things (IoT) Ecosystems Digital object architecture Security distributed

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