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Weak deflection angle and shadow cast by the charged-Kiselev black hole with cloud of strings in plasma

Chinese Physics C 2023年第2期47卷 208-230页

作者： Farruh Atamurotov Ibrar Hussain Ghulam Mustafa AliÖvgün Inha University in Tashkent Ziyolilar 9Tashkent 100170Uzbekistan Akfa University Milliy Bog'Street 264Tashkent 111221Uzbekistan National University of Uzbekistan Tashkent 100174Uzbekistan Tashkent State Technical University Tashkent 100095Uzbekistan University of Public Safety of the Republic of Uzbekistan Tashkent Region 100109Uzbekistan School of Electrical Engineering and Computer Science National University of Sciences and TechnologyH-12IslamabadPakistan Department of Physics Zhejiang Normal UniversityJinhua 321004China Physics Department Eastern Mediterranean UniversityFamagusta99628 North Cyprus via Mersin 10Turkey

In this study,the gravitational deflection angle of photons in the weak field limit(or the weak deflection angle)and shadow cast by the electrically charged and spherically symmetric static Kiselev black hole(BH)in the string cloud background are *** influences of the BH charge Q,quintessence parameterγ,and string cloud parameter a on the weak deflection angle are studied using the Gauss-Bonnet *** addition to studying the influences on the radius of photon spheres and size of the BH shadow in the spacetime geometry of the chargedKiselev BH in string ***,we study the effects of plasma(uniform and non-uniform)on the weak deflection angle and shadow cast by the charged-Kiselev BH surrounded by the clouds of *** the presence of a uniform/nonuniform plasma medium,an increase in the string cloud parameter a increases the deflection angleα.In contrast,a decrease in the BH charge Q decreases the deflection ***,we observe that an increase in the BH charge Q causes a decrease in the size of the shadow of the *** notice that,with an increase in the values of the parametersγand a,the size of the BH shadow increases,and therefore,the intensity of the gravitational field around the charged-Kiselev BH in string clouds ***,the gravitational field of the charged-Kiselev BH in the string cloud background is stronger than the field produced by the pure Reissner-Nordstrom ***,we use the data released by the Event Horizon Telescope(EHT)collaboration,for the supermassive BHs M87*and Sgr A*,to obtain constraints on the values of the parametersγand a.

关键词： charged-Kiselev Black hole cloud of strings shadow weak deflection angle Gauss-Bonnet theorem plasma medium

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A Lightweight and Enhanced Semantic Segmentation Network for Mapping of Retrogressive Thaw Slumps from Sentinel-2 Images

A Lightweight and Enhanced Semantic Segmentation Network for...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Yi Yuan Guiyun Zhou Zhonghua Su Ziyin Liu Weiwei Sun Xiangchao Meng Jie Wen Zhiqiang Wu School of Resources and Environment University of Electronic Science and Technology of China Chengdu China School of Computer and Software Engineering Xihua University Chengdu China Department of Geography and Spatial Information Techniques Ningbo University Ningbo China Faculty of Electrical Engineering and Computer Science Ningbo University Ningbo China Sichuan Provincial Public 7 Security Department Communications and Information Technology Headquarters Chengdu China

ISBN: (数字)9798350360325

ISBN: (纸本)9798350360332

Fine mapping of retrogressive thaw slumps (RTSs) holds paramount significance in the study of permafrost degradation and carbon exchange. We propose a lightweight and enhanced semantic segmentation network (LessNet) for automatically mapping the RTSs from Sentinel-2 images. LessNet is constructed on the encoder-decoder framework with innovative incorporation of attention mechanism and dual-level semantic features fusion. The lightweight architecture of LessNet eliminates the need for pre-training, and the network hyperparameters are automatically updated based on the training dataset, which allows for fast convergence of supervised learning. Experiments conducted in the Beiluhe region of the Tibetan Plateau highlight the robustness and competitive performance of the model.

关键词： Training Semantic segmentation Supervised learning Transfer learning Semantics Robustness Permafrost

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Hardware Software Codesign Using an Open-Source Authentication Algorithm

Hardware Software Codesign Using an Open-Source Authenticati...

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Applied Computing (URC), Annual Undergraduate Research Conference on

作者： Sadoon L. Albader Haseeb Ur Rehman Ashraf A. Zaher Computer and Information Center Public Authority for Applied Education & Training Adailiya Kuwait Faculty of Engineering University of Ottawa Ottawa Canada Department of Electrical Engineering German International University Cairo Egypt

ISBN: (纸本)9798350346817

This paper explores the use of hardware/software codesign to implement algorithms for open-source message authentication code while investigating the required tools, the problems to be overcome, and satisfying the industry standards for synthesizing the hardware/software codesign. Technical issues related to using the DE10-Nano were carefully analysed, which include the very poor documentation, and the needed specific Linux kernel patches that only work with certain versions of the Linux kernel. This research builds the fundamental blocks for using the DE10-Nano for hardware/software codesign. The results are expected to pave the way to focus on promoting the current trends of hardware/software codesigns and to encourage the hardware manufacturers to offer more help for programmers and engineers.

关键词： Industries Linux Software algorithms Documentation Market research Hardware Software

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A general survey on medical image super-resolution via deep learning

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computers in Biology and Medicine 2025年 193卷 110345-110345页

作者： Yu, Miao Xu, Zhenghua Lukasiewicz, Thomas Public Health Sciences and Engineering College Tianjin University of Traditional Chinese Medicine Tianjin China State Key Laboratory of Reliability and Intelligence of Electrical Equipment School of Health Sciences and Biomedical Engineering Hebei University of Technology Tianjin China Tianjin Key Laboratory of Bioelectromagnetic Technology and Intelligent Health School of Health Sciences and Biomedical Engineering Hebei University of Technology China Institute of Logic and Computation TU Wien Vienna Austria Department of Computer Science University of Oxford Oxford United Kingdom

Medical image super-resolution (SR) is a classic regression task in low-level vision. Limited by hardware limitations, acquisition time, low radiation dose, and other factors, the spatial resolution of some medical images is not sufficient. To address this problem, many different SR methods have been proposed. Especially in recent years, medical image SR networks based on deep learning have been vigorously developed. This survey provides a modular and detailed introduction to the key components of medical image SR technology based on deep learning. In this paper, we first introduce the background concepts of deep learning and medical image SR task. Subsequently, we present a comprehensive analysis of the key components from the perspectives of effective architecture, upsampling module, learning strategy, and image quality assessment of medical image SR networks. Furthermore, we focus on the urgent problems that need to be addressed in the medical image SR task based on deep learning. And finally we summarize the trends and challenges of future development. © 2025 Elsevier Ltd

关键词： Damage detection

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Electrocoagulation-based AZO DYE (P4R) Removal Rate Prediction Model using Deep Learning

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Procedia computer Science 2024年 236卷 51-58页

作者： Meryem Akoulih Smail Tigani Fouzia Byoud Meryem El Rharib Rachid Saadane Samuel Pierre Abdellah Chehri Sanae El Ghachtouli IME Lab Faculty of Science Hassan 2 University 20250 Casablanca Morocco Research and Development Unit Accsellium LLC Fez Morocco Electrical Engineering Department SIRC-LaGeS Hassania School of Public Labors 20250 Casablanca Morocco Department of Computer and Software Engineering Polytechnique Montréal Montréal Québec Canada Department of Mathematics and Computer Science Royal Military College of Canada Kingston ON 11 K7K 7B4 Canada

This article presents a study on the effectiveness of electrocoagulation (EC) for the removal of azo dyes from wastewater. The analysis was performed using a combination of statistical methods, including density estimation, correlation analysis, and deep learning for electrocoagulation performance prediction. The results showed that electrocoagulation was able to effectively remove azo dyes from the wastewater, considering the energy consumption and the mass of flocs being important factors in the process. Deep Learning (DL) is used to build our predictive model using the datasets collected during the experimentation stage. Overall, the findings suggest that electrocoagulation is a promising technique for the treatment of wastewater containing azo dyes, and that the use of statistical and machine learning methods can aid in the optimization of the process.

关键词： Electrocoagulation Wastewater Azo dye Deep learning

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Poster: Design of a Music Intervention System Using Social Robotics for Cognitive Enhancement 23

Poster: Design of a Music Intervention System Using Social R...

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Proceedings of the 8th ACM/IEEE International Conference on Connected Health: Applications, Systems and engineering Technologies

作者： Tyler Morris Darina V. Petrovsky Sai Swaminathan Xiaopeng Zhao Department of Mechanical Aerospace and Biomedical Engineering The University of Tennessee Knoxville TN USA Rutgers School of Nursing New Brunswick NJ USA Institute for Health Heath Care Policy and Aging Research New Brunswick NJ USA Min H. Kao Department of Electrical Engineering and Computer Science The University of Tennessee Knoxville TN USA

ISBN: (纸本)9798400701023

To aid in the care of older adults living with Alzheimer's disease and related dementias (ADRD), many forms of therapies and interventions have been created. One such approach, music intervention, uses song and dance to engage users cognitively, physically, and emotionally. This form of intervention has been proven effective for persons living with ADRD, yet it has not been widely implemented due to a low number of professionals (caregivers, music intervention specialists, etc.) performing the interventions. Social robots can provide relief to caregivers by engaging older adults living with ADRD in music. Therefore, we suggest using social robots to use song and dance to interact with older adults living with ADRD through the Music intervention Using Socially Engaging robotics (MUSE) app. This app will have older adults with ADRD participate in a variety of music-based activities that promote positive cognitive, physical, and emotional well-being.

关键词： social robots

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Dynamic Charge Scheduling of Solar PV-Storage Hybrid Resources Based on Solar-Load Correlation

Dynamic Charge Scheduling of Solar PV-Storage Hybrid Resourc...

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Annual North-American Power Symposium

作者： Sarangan Rajendran Esther Liu Mary Peterson Visvakumar Aravinthan Al Tamimi Charles R. Yokley Electrical and Computer Engineering Department Wichita State University Wichita KS USA Wichita East High School Wichita KS USA Transmission Planning and Policy Sunflower Electric Power Corporation Wichita KS USA

Solar PV-storage hybrid resources are an attractive way of reducing grid dependency for project developers when collectively serving small communities. A well-structured charging schedule of the battery storage can improve the effectiveness of the hybrid system by reducing the grid dependency at the peak demand hours and improving the utilization of the battery. Studying the correlation between load and solar curves helps to identify periods at which short-term charging or discharging of the battery can aid to those benefits. In this paper, a dynamic charging schedule is proposed, where a nominal schedule is created based on full day forecasting of load and solar output, and further adjustments are made to the schedule based on the hourly expected correlation between load and solar curves. The proposed method is then tested on a test system based on actual data for a period of one year. The results show that the proposed approach can reduce the maximum power import from the grid, overall cost of imports and improve the battery utilization.

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Explaining Deep Learning Embeddings for Speech Emotion Recognition by Predicting Interpretable Acoustic Features

arXiv

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arXiv 2024年

作者： Dixit, Satvik Low, Daniel M. Elbanna, Gasser Catania, Fabio Ghosh, Satrajit S. Dept. of Electrical and Computer Engineering Carnegie Mellon University Pittsburgh United States Harvard Chan School of Public Health Harvard University Boston United States Harvard Medical School Harvard University Boston United States Cambridge United States

Pre-trained deep learning embeddings have consistently shown superior performance over handcrafted acoustic features in speech emotion recognition (SER). However, unlike acoustic features with clear physical meaning, these embeddings lack clear interpretability. Explaining these embeddings is crucial for building trust in healthcare and security applications and advancing the scientific understanding of the acoustic information that is encoded in them. This paper proposes a modified probing approach to explain deep learning embeddings in the SER space. We predict interpretable acoustic features (e.g., f0, loudness) from (i) the complete set of embeddings and (ii) a subset of the embedding dimensions identified as most important for predicting each emotion. If the subset of the most important dimensions better predicts a given emotion than all dimensions and also predicts specific acoustic features more accurately, we infer those acoustic features are important for the embedding model for the given task. We conducted experiments using the WavLM embeddings and eGeMAPS acoustic features as audio representations, applying our method to the RAVDESS and SAVEE emotional speech datasets. Based on this evaluation, we demonstrate that Energy, Frequency, Spectral, and Temporal categories of acoustic features provide diminishing information to SER in that order, demonstrating the utility of the probing classifier method to relate embeddings to interpretable acoustic features. © 2024, CC BY.

关键词： Self-supervised learning

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Deep Learning based Currency Trend Classification Trained on Technical Indicators based Generated Dataset

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Procedia computer Science 2023年 225卷 4364-4370页

作者： Smail Tigani Amal Makrane Rachid Saadane Abdellah Chehri AAIR Lab Digital Engineering and Artificial Intelligence Systems High Private School 20100 Casablanca Moroccoo Electrical Engineering Department SIRC-LaGeS Hassania School of Public Labors 20250 Casablanca Morocco Department of Mathematics and Computer Science Royal Military College of Canada Kingston ON 11 K7K 7B4 Canada

This research paper presents a deep learning-based predictive model for classifying currency trends using technical indicators. The model is trained on a dataset generated from three technical indicators: relative strength index (RSI), moving average convergence divergence (MACD), and stochastic. The dataset consists of historical currency data along with the corresponding values of the technical indicators. The deep learning model can accurately classify the trends of a given currency based on the importance of these indicators. The model's performance is evaluated using standard metrics, and the results demonstrate its effectiveness in classifying currency trends. The proposed model provides a valuable tool for traders and investors in the foreign exchange market by helping them make informed decisions about the direction of currency prices.

关键词： Deep Learning Algorithmic Trading Predictive Analytics

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Toward Ethical Robotic Behavior in Human-Robot Interaction Scenarios

arXiv

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arXiv 2022年

作者： Chen, Shengkang Surendran, Vidullan Wagner, Alan R. Borenstein, Jason Arkin, Ronald C. Electrical and Computer Engineering Georgia Tech AtlantaGA United States Aerospace Engineering Penn State University University ParkPA United States School of Public Policy Georgia Tech AtlantaGA United States Interactive Computing Georgia Tech AtlantaGA United States

This paper describes current progress on developing an ethical architecture for robots that are designed to follow human ethical decision-making processes. We surveyed both regular adults (folks) and ethics experts (experts) on what they consider to be ethical behavior in two specific scenarios: pill-sorting with an older adult and game playing with a child. A key goal of the surveys is to better understand human ethical decision-making. In the first survey, folk responses were based on the subject’s ethical choices ("folk morality");in the second survey, expert responses were based on the expert’s application of different formal ethical frameworks to each scenario. We observed that most of the formal ethical frameworks we included in the survey (Utilitarianism, Kantian Ethics, Ethics of Care and Virtue Ethics) and "folk morality" were conservative toward deception in the high-risk task with an older adult when both the adult and the child had significant performance deficiencies. Copyright © 2022, The Authors. All rights reserved.

关键词： Human robot interaction

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