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2024 systems of Signals Generating and Processing in the Field of on Board Communications, SOSG 2024

作者： Kryukovsky, A.S. Mikhaleva, E.V. Lukin, D.S. Rastyagaev, D.V. Institute of Information System and Engineer - Computer Technology Russian New University Moscow Russia

ISBN: (纸本)9798350383614

The ray and caustic features of the ricocheting propagation of decameter radio waves in the Earth's ionosphere have been studied. The rays and caustic structures of radio waves during trans-ionospheric channeled propagation of radiation from an elevated source are calculated using the bicharacteristics method. Ray oscillations arising during circumnavigation of the world have been studied. Using the extended bicharacteristic system method, the Jacobian of the ray flux divergence and, on its basis, the attenuation of the radio signal are calculated. ©2024 IEEE.

关键词： Radio waves

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Analysis of the Earth's Ionosphere Influence on the Parameters of High-Frequency Radio Signals Using the Bicharacteristics Method

Analysis of the Earth's Ionosphere Influence on the Paramete...

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2025 systems of Signals Generating and Processing in the Field of on Board Communications, SOSG 2025

作者： Kryukovsky, Andrew S. Kutuza, Boris G. Rastyagaev, Dmitry V. Institute of Information System and Engineer-Computer Technology Russian New University Moscow Russia Kotelnikov Institute of Radioengineering and Electronics Moscow Russia

ISBN: (纸本)9798331532635

The propagation of a radio signal emitted by a spacecraft through the Earth's ionosphere is considered. The influence of the ionosphere and traveling ionospheric disturbances on the propagation of a radio signal of the decimeter and meter ranges is studied. The following are calculated: deviations of rays from rectilinear propagation, changes in delay time, phase lag, and phase deviation. The total electron concentration and Faraday rotation are calculated in the presence of ionospheric inhomogeneities. The influence of the ionosphere on the absorption of a radio signal, its amplitude, and polarization is analyzed. © 2025 IEEE.

关键词： Ionosphere

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Robust tube-based MPC with smooth computation for dexterous robot manipulation

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Science China(information Sciences) 2024年第11期67卷 252-268页

作者： Yu LUO Tianying JI Fuchun SUN Qie SIMA Huaping LIU Mingxuan JING Jianwei ZHANG Department of Computer Science and Technology Tsinghua University Science & Technology on Integrated Information System Laboratory Institute of Software Chinese Academy of Sciences Department of Informatics University of Hamburg

Dexterous robot manipulation has shone in complex industrial scenarios, where multiple manipulators, or fingers, cooperate to grasp and manipulate objects. When encountering multi-objective optimization with system constraints in such scenarios, model predictive control(MPC) has demonstrated exceptional performance in complex multi-robot manipulation tasks involving multi-objective optimization with system constraints. However, in such scenarios, the substantial computational load required to solve the optimal control problem(OCP) at each triggering instant can lead to significant delays between state sampling and control application, hindering real-time performance. To address these challenges, this paper introduces a novel robust tube-based smooth MPC approach for two fundamental manipulation tasks: reaching a given target and tracking a reference trajectory. By predicting the successor state as the initial condition for imminent OCP solving, we can solve the forthcoming OCP ahead of time, alleviating delay effects. Additionally,we establish an upper bound for linearizing the original nonlinear system, reducing OCP complexity and enhancing response speed. Grounded in tube-based MPC theory, the recursive feasibility and closed-loop stability amidst constraints and disturbances are ensured. Empirical validation is provided through two numerical simulations and two real-world dexterous robot manipulation tasks, which shows that the seamless control input by our methods can effectively enhance the solving efficiency and control performance when compared to conventional time-triggered MPC strategies.

关键词： dexterous robot manipulation model predictive control smooth computation

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Social Media-Based Surveillance systems for Health Informatics Using Machine and Deep Learning Techniques:A Comprehensive Review and Open Challenges

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computer Modeling in engineering & Sciences 2024年第5期139卷 1167-1202页

作者： Samina Amin Muhammad Ali Zeb Hani Alshahrani Mohammed Hamdi Mohammad Alsulami Asadullah Shaikh Institute of Computing Kohat University of Science and TechnologyKohat26000Pakistan Department of Computer Science College of Computer Science and Information SystemsNajran UniversityNajran61441Saudi Arabia Department of Information System College of Computer Science and Information SystemsNajran UniversityNajran61441Saudi Arabia

Social media(SM)based surveillance systems,combined with machine learning(ML)and deep learning(DL)techniques,have shown potential for early detection of epidemic *** review discusses the current state of SM-based surveillance methods for early epidemic outbreaks and the role of ML and DL in enhancing their ***,every year,a large amount of data related to epidemic outbreaks,particularly Twitter data is generated by *** paper outlines the theme of SM analysis for tracking health-related issues and detecting epidemic outbreaks in SM,along with the ML and DL techniques that have been configured for the detection of epidemic *** has emerged as a promising ML technique that adaptsmultiple layers of representations or features of the data and yields state-of-the-art extrapolation *** recent years,along with the success of ML and DL in many other application domains,both ML and DL are also popularly used in SM *** paper aims to provide an overview of epidemic outbreaks in SM and then outlines a comprehensive analysis of ML and DL approaches and their existing applications in SM ***,this review serves the purpose of offering suggestions,ideas,and proposals,along with highlighting the ongoing challenges in the field of early outbreak detection that still need to be addressed.

关键词： Social media epidemic machine learning deep learning health informatics pandemic

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Collaborative Service Provisioning for UAV-Assisted Mobile Edge Computing

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Chinese Journal of Electronics 2024年第6期33卷 1504-1514页

作者： Yuben QU Zhenhua WEI Zhen QIN Tao WU Jinghao MA Haipeng DAI Chao DONG The Key Laboratory of Dynamic Cognitive System of Electromagnetic Spectrum Space Ministry of Industry and Information Technology Colllege of Electronic and Information Engineering Nanjing University of Aeronautics and Astronautics Xi'an Research Institute of High Technology Department of Information and Communication Noncommissioned Officer Academy of PAP National University of Defense Technology Department of Computer Science and Technology Nanjing University

Unmanned aerial vehicle(UAV)-assisted mobile edge computing(MEC), as a way of coping with delaysensitive and computing-intensive tasks, is considered to be a key technology to solving the challenges of terrestrial MEC networks. In this work, we study the problem of collaborative service provisioning(CSP) for UAV-assisted MEC. Specifically, taking into account the task latency and other resource constraints, this paper investigates how to minimize the total energy consumption of all terrestrial user equipments, by jointly optimizing computing resource allocation, task offloading, UAV trajectory, and service placement. The CSP problem is a non-convex mixed integer nonlinear programming problem, owing to the complex coupling of mixed integral variables and non-convexity of CSP. To address the CSP problem, this paper proposes an alternating optimization-based solution with the convergence guarantee as follows. We iteratively deal with the joint service placement and task offloading subproblem, and UAV movement trajectory subproblem, by branch and bound and successive convex approximation, respectively,while the closed form of the optimal computation resource allocation can be efficiently obtained. Extensive simulations validate the effectiveness of the proposed algorithm compared to three baselines.

关键词： Energy consumption Multi-access edge computing Collaboration Programming Autonomous aerial vehicles Minimization Trajectory Resource management Optimization Convergence

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Deep Learning and Machine Learning Algorithms to Predict Lung Cancer 2

Deep Learning and Machine Learning Algorithms to Predict Lun...

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2nd International Conference on Emerging Trends in information technology and engineering, ic-ETITE 2024

作者： Gowthami, K. Kamalakannan, J. School of Computer Science Engineering and Information System Vellore Institute of Technology Vellore India

ISBN: (纸本)9798350328202

Lung cancer is the most deadly illness for patients, and it is an incurable condition. Patient survival rates are greatly increased by early cancer prognosis and identification. The Computed Tomography (CT) scan is an important imaging tool for a lung cancer diagnosis. On the other hand, a manual CT scan inspection is laborious and prone to mistakes. Lung cancer can be impulsively recognized by utilizing an image processing technique of computer-Aided Diagnosis (CAD). In recent years, various researchers identified various ways for accurate prediction by Using techniques for deep learning (DL) and machine learning (ML). This research represents a number of methodologies like Lung cancer disease prediction analysis makes use of Artificial Neural Network (ANN), Logistic Regression (LR), Support Vector Machine (SVM), K-means clustering, Convolutional Neural Network (CNN) Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM). © 2024 IEEE.

关键词： Diseases

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Image Enhancement via Associated Perturbation Removal and Texture Reconstruction Learning

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IEEE/CAA Journal of Automatica Sinica 2024年第11期11卷 2253-2269页

作者： Kui Jiang Ruoxi Wang Yi Xiao Junjun Jiang Xin Xu Tao Lu IEEE the School of Computer Science and Technology Harbin Institute of Technology Zhengzhou Research Institute Harbin Institute of Technology the School of Artificial Intelligence Jianghan University the School of Geodesy and Geomatics Wuhan University the Hubei Province Key Laboratory of Intelligent Information Processing and Real-Time Industrial System Wuhan University of Science and Technology the School of Computer Science and Engineering and also with the Hubei Province Key Laboratory of Intelligent Robot Wuhan Institute of Technology

Degradation under challenging conditions such as rain, haze, and low light not only diminishes content visibility, but also results in additional degradation side effects, including detail occlusion and color distortion. However, current technologies have barely explored the correlation between perturbation removal and background restoration, consequently struggling to generate high-naturalness content in challenging scenarios. In this paper, we rethink the image enhancement task from the perspective of joint optimization: Perturbation removal and texture reconstruction. To this end, we advise an efficient yet effective image enhancement model, termed the perturbation-guided texture reconstruction network(PerTeRNet). It contains two subnetworks designed for the perturbation elimination and texture reconstruction tasks, respectively. To facilitate texture recovery,we develop a novel perturbation-guided texture enhancement module(PerTEM) to connect these two tasks, where informative background features are extracted from the input with the guidance of predicted perturbation priors. To alleviate the learning burden and computational cost, we suggest performing perturbation removal in a sub-space and exploiting super-resolution to infer high-frequency background details. Our PerTeRNet has demonstrated significant superiority over typical methods in both quantitative and qualitative measures, as evidenced by extensive experimental results on popular image enhancement and joint detection tasks. The source code is available at https://***/kuijiang94/PerTeRNet.

关键词： Association learning attention mechanism image enhancement perturbation modeling

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Research on Network Security Situational Awareness Prediction Method Based on Intelligent Learning Algorithm

Research on Network Security Situational Awareness Predictio...

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2023 Asia-Pacific Conference on Image Processing, Electronics and computers, IPEC 2023

作者： Wu, Yue Tao, Jun He, Hao Anhui Institute of Information Technology Computer and Soft Engineer Department Wuhu China

ISBN: (纸本)9781665457033

With the rapid development of the Internet, the network has become an indispensable part of people's lives and has been widely used in daily life and work, but at the same time, computer technology is also facing great challenges. Hacker attacks, virus attacks and other problems have emerged one after another, and these security risks have brought us a series of serious impacts and hazards. The study of network security posture is important because it can improve network monitoring capabilities, emergency response capabilities and predict the development trend of network security. In order to do a good job in network security defence, based on improving the existing static defence mechanism, network security situational awareness enables efficient analysis of various network security-related data and threat intelligence, while traditional assessment and prediction methods have problems such as being highly subjective, requiring access to a priori knowledge as well as being difficult to process in the face of massive amounts of data and relying too much on human resources when facing a dynamically changing network environment. Therefore, the combination of intelligent learning algorithms in this study allows network security situational awareness to be developed in the direction of intelligence and automation as well. A Bayesian decision model and Class-Attribute Interdependency Maximization (CAIM) discretization algorithm are used for the study, which further improves the network security situational assessment method to a certain extent. © 2023 IEEE.

关键词： Data fusion

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Unsupervised Meteorological Downscaling Based on Dual Learning and Subgrid-scale Auxiliary information

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Advances in Atmospheric Sciences 2025年第1期42卷 53-66页

作者： Jing HU Jialing MU Xiaomeng HUANG Xi WU Department of Computer Science Chengdu University of Information TechnologyChengdu 610225China Department of Earth System Science Ministry of Education Key Laboratory for Earth System ModelingInstitute for Global Change StudiesTsinghua UniversityBeijing 100084China

Climate downscaling is used to transform large-scale meteorological data into small-scale data with enhanced detail,which finds wide applications in climate modeling,numerical weather forecasting,and renewable *** deeplearning-based downscaling methods effectively capture the complex nonlinear mapping between meteorological data of varying scales,the supervised deep-learning-based downscaling methods suffer from insufficient high-resolution data in practice,and unsupervised methods struggle with accurately inferring small-scale specifics from limited large-scale inputs due to small-scale *** article presents DualDS,a dual-learning framework utilizing a Generative Adversarial Network–based neural network and subgrid-scale auxiliary information for climate *** a learning method is unified in a two-stream framework through up-and downsamplers,where the downsampler is used to simulate the information loss process during the upscaling,and the upsampler is used to reconstruct lost details and correct errors incurred during the *** dual learning strategy can eliminate the dependence on high-resolution ground truth data in the training process and refine the downscaling results by constraining the mapping *** findings demonstrate that DualDS is comparable to several state-of-the-art deep learning downscaling approaches,both qualitatively and ***,for a single surface-temperature data downscaling task,our method is comparable with other unsupervised algorithms with the same dataset,and we can achieve a 0.469 dB higher peak signal-to-noise ratio,0.017 higher structural similarity,0.08 lower RMSE,and the best correlation *** summary,this paper presents a novel approach to addressing small-scale uncertainty issues in unsupervised downscaling processes.

关键词： downscaling unsupervised deep learning dual learning auxiliary information

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A Fruit and Vegetable Detection Based on Pytorch Machine Learning Frame Work Model 2

A Fruit and Vegetable Detection Based on Pytorch Machine Lea...

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2nd International Conference on Emerging Trends in information technology and engineering, ic-ETITE 2024

作者： Priyadharshini, U. Vijayan, R. School of Advanced Science Vellore Institute of Information Technology Vellore India School of Computer Science Engineering and Information System Vellore Institute of Information Technology Vellore India

ISBN: (纸本)9798350328202

Today, Agricultures has become a significant element of people's life. Among them fruits and vegetables are a great thing for healthy lives. Classification of fruits and vegetable are difficult for normal person because of some same similarities like shape and color. Also, Fruits and Vegetables classification is an important process in industries applications as well as local market. Lots of similarities available between fruits like apples, pears and peaches, and that kinds of fruits are generally used in daily routine. As well as some vegetables are same in shape, so it will be difficult to differentiate in this type of situation for old persons. The previous research for solution of this type of problem for classification of fruits CNN is used but the problem in this research is when they use the self-made datasets then their accuracy decreases as compare to public datasets accuracy increases. To solve this type of problem, here produce a kind of algorithms in the paper to classify fruits and vegetable, using deep leaning technique such as VGG16 and DenseNet169 model have capability to extract the feature of images very deeply using various small filters so it provide a better result. On this premise, it was used to classify fruit kinds against a complex background and integrated with data improvement technology for future usage. It might be helpful for human for automatic differentiate the both categories. This project is implemented in the python programming language and the dataset used Fruits and Vegetables Image Recognition Dataset for the classification. Which will help to differentiate between Fruits and Vegetables with high accuracy rate. For the implementation of this project used Python programming and this implementation using the tool like Jupiter and Kaggle Notebook and used GPU for highspeed execution. © 2024 IEEE.

关键词： Fruits

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