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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Diego Cerrai Brian Filipiak Aaron Spaulding David B. Wolff Ali Tokay Charles N. Helms Adrian M. Loftus Alexey V. Chibisov Carl Schirtzinger Larry Bliven Charanjit S. Pabla Mick J. Boulanger EunYeol Kim Hein Thant Francesc Junyent Venkatachalam Chandrasekar Branislav Notaros Gustavo B. H. De Azevedo Department of Civil and Environmental Engineering University of Connecticut Eversource Energy Center University of Connecticut Department of Civil and Environmental Engineering Princeton University NASA Goddard Space Flight Center Wallops Flight Facility Wallops Island Virginia Goddard Earth Sciences Technology and Research University of Maryland Baltimore County NASA Goddard Space Flight Center Greenbelt Maryland Earth System Science Interdisciplinary Center University of Maryland Science Systems and Applications Inc. Lanham Maryland Department of Electrical and Computer Engineering Colorado State University Department of Mechanical & Aero-Space Engineering Oklahoma State University

ISBN: (数字)9798350360325

ISBN: (纸本)9798350360332

During three consecutive winter seasons, between December 2021 and April 2024, several ground-based wintry precipitation measurement instruments were deployed at the University of Connecticut’s main campus. The instruments included an assortment of K-band and W-band profiling radars and Ka-Ku band scanning radars, weighing, and tipping bucket pluviometers, laser disdrometers, high-speed and high-resolution cameras for quantitative precipitation measurement, weather stations, and an unmanned aircraft system for environmental variables. The goal of this field campaign is to provide a dataset for validating NASA Global Precipitation Measurement (GPM) products, and to examine the error characteristics of co-located ground-based instruments. In this manuscript, we present the instrument suite and discuss possible uses of this unique set of measurements for remote sensing applications.

关键词： Weight measurement Meteorological radar Precipitation Laser radar Radar measurements Instruments Airborne radar

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Exceptional scattered sequences

arXiv

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arXiv 2022年

作者： Bartoli, Daniele Marino, Giuseppe Neri, Alessandro Vicino, Lara Department of Mathematics and Informatics University of Perugia Perugia Italy Department of Mathematics and Applications "R. Caccioppoli" University of Naples "Federico II" Napoli Italy Max-Planck-Institute for Mathematics in the Sciences Leipzig Germany Department of Applied Mathematics and Computer Science Technical University of Denmark Kgs. Lyngby Denmark

The concept of scattered polynomials is generalized to those of exceptional scattered sequences which are shown to be the natural algebraic counterpart of Fqn-linear MRD codes. The first infinite family in the first nontrivial case is also provided and equivalence issues are considered. As a byproduct, a new infinite family of MRD codes is *** Codes 94B05, 51E20, 94B27, 11T06 Copyright © 2022, The Authors. All rights reserved.

关键词： Polynomials

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Efficient Zero-Trust Data Transmission for Consumer Electronic Using Holographic Counterparts in Internet of Things: Ciphertext-Policy Attribute-Based Signcryption With Equality Test

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IEEE Transactions on Consumer Electronics 2025年

作者： Kuang, Ping Zhang, Xiyue Yang, Mengheng Xiong, Hu Feng, Chaosheng Wu, Deyan Wang, Weizheng Wahaballa, Abubaker University of Electronic Science and Technology of China Intelligent Information Technologies and Applications Lab Chengdu610054 China University of Electronic Science and Technology of China Network and Data Security Lab Chengdu610054 China Sichuan Normal University School of Computer Science Chengdu610101 China Sichuan Institute of Computer Sciences Chengdu610000 China City University of Hong Kong Department of Computer Science Hong Kong The University of Aizu Aizuwakamatsu Japan Arab East Colleges Department of Computer Science Riyadh53354 Saudi Arabia

Secure communication in consumer electronic devices, such as health monitoring systems, is essential as they process sensitive medical data. This paper proposes a Ciphertext-Policy Attribute-Based Signcryption with Equality Test (CP-ABSC-ET) scheme to address these security challenges. The scheme controls who can access data and ensures both encryption and signature verification are done together, keeping the data secure. The integration of equality test enables secure ciphertext verification without decryption, ensuring both data integrity and confidentiality. Compared to existing schemes, the proposed approach reduces the computational overhead. Comprehensive security analysis and performance evaluations demonstrate the scheme’s suitability for dynamic and resource-constrained environments. The CP-ABSC-ET scheme ensures strong data protection, better access control, and efficient communication, making it ideal for consumer electronics like health monitoring devices. © 1975-2011 IEEE.

关键词： Authentication

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Deep Hashing with Attribute Guidance for Image Retrieval 4

Deep Hashing with Attribute Guidance for Image Retrieval

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4th International Conference on Artificial Intelligence, Automation and Control Technologies, AIACT 2020

作者： Lu, Yao Huang, Heyan Beijing Eng. Res. Ctr. of High Vol. Language Information Processing and Cloud Computing Applications Department of Computer Science and Technology Beijing Institute of Technology China

Similarity-preserving hashing is an important method to solve approximate nearest neighbour search problem for image retrieval. Lots of works have been proposed for supervised hashing and image category labels are utilized as supervised information. However, current works ignore a fact that images can be described by a set of attributes. Intuitively, images of different categories with close visual features can be separated by their attribute features because attributes keep more detailed information. In this paper, we propose a novel supervised deep hashing method with image attribute guidance. Specifically, hash codes are learnt through image visual features and guided by image attributes by maintaining pair wise similarities between images as well as the corresponding attribute descriptions. Extensive experimental results on two benchmark datasets show that our proposed method achieves better performance compared with the state of the art hashing methods. © Published under licence by IOP Publishing Ltd.

关键词： Image retrieval

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Machine Learning Technique to Detect and Classify Mental Illness on Social Media Using Lexicon-Based Recommender System

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Computational Intelligence and Neuroscience 2022年第0期2022卷 5906797页

作者： Sumathy, B. Kumar, Anand Sungeetha, D. Hashmi, Arshad Saxena, Ankur Kumar Shukla, Piyush Nuagah, Stephen Jeswinde Department of Instrumentation and Control Engineering Sri Sairam Engineering College Chennai India School of Computer Science and Applications REVA University Bangalore India Department Electronics and Communication Engineering Saveetha School of Engineering SIMATS Tamil Nadu Chennai India King Abdulaziz University Jeddah Saudi Arabia Indus Institute of Information and Communication Technology Indus University Gujarat Ahmedabad India Computer Science and Engineering Department University Institute of Technology Rajiv Gandhi Proudyogiki Vishwavidyalaya Technological University of Madhya Pradesh Bhopal462033 India Department of Electrical Engineering Tamale Technical University Tamale Ghana

The emergence of social media has allowed people to express their feelings on products, services, films, and so on. The feeling is the user's view or attitude towards any topic, object, event, or service. Overall, feelings have always influenced people's decision-making. In recent years, emotions have been analyzed intensively in natural language, but many problems still have to be watched. One of the most important problems is the lack of precise classification resources. Most of the research into feeling gradation is concerned with the issue of polarity grading, although, in many practical applications, this relatively grounded feeling measure is insufficient. Design methods are therefore essential, which can accurately classify feelings into a natural language. The principal goal of the research is to develop an overflow of grammatical rules-based classification of Indian language tweets. In this work, three main challenges are identified to classify feelings in Indian language tweets and possible methods for tackling such issues. Firstly, it has been found that the informal nature of tweets is crucial for the classification of feelings. Based on the tweets, the mental illness of the person has been classified. Therefore, to categorize Indian language tweets, a combination of grammar rules based on adjectives and negations is proposed. Secondly, people often express their feelings with slang words, abbreviations, and mixed words. A technique called field tags is used to include nongrammatical arguments such as slang words and diverse words. Thirdly, if a tweet is more complex, the morphological richness of the Indian language results in a loss of performance. The grammar rules are embedded in N-gram techniques and machine learning methods. These methods are grouped into three approaches, which functionally predict Indian language tweets with syntactic words. © 2022 B. Sumathy et al.

关键词： Decision making

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The Gm System for the use of AGMR System

The Gm System for the use of AGMR System

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Advance Computing and Innovative Technologies in Engineering (ICACITE), International Conference on

作者： Aditya Verma Hassan M. Al-Jawahry H. A. Alsailawi P. Kirubanantham SherinEliyas Osama Nazim Saadoun Manalmoradkarim Doaa Talib Zaidan Department of CSE Graphic Era Hill University Research Scholar Graphic Era Deemed to be University Dehradun Uttarakhand Department of computers Techniques engineering College of technical engineering The Islamic university Najaf Iraq Department of Chemistry and Biochemistry College of Medicine University of Kerbala Kerbala Iraq Department of Computing Technologies School of Computing College of Engineering and Technology Faculty of Engineering and Technology SRM Institute of Science and TechnologySRM Nagar Kattankulathur Chennai India Department of computer applications Hindustan institute of technology and science Law Department Al-kunooze University College Basra Iraq College of Pharmacy National University of Science and Technology DhiQar Iraq Department of Laser and Optical Electronics Engineering Kut University College Al Kut Iraq

ISBN: (数字)9798350360165

ISBN: (纸本)9798350360172

Due to its ability to navigate crop fields with ease, mobile robots have become more important in the agricultural industry. This study discusses a number of important topics related to agricultural robot navigation, including mapping, localization, route planning, obstacle detection, and controlled guidance. With laser-based weeding tools installed, the suggested Guiding Manager aims to maximize tracking performance of mobile autonomous robots. Integrating spiral, lateral, and linear speed controls, the system can accommodate a variety of trajectories, including curves and straight lines, that are seen in agricultural settings. To confirm the performance of the controllers under various conditions, such as loose soil, stones, and humidity, a thorough field test is conducted on a tracked mobile platform. With the Lateral Controller, the average angular error is around -0.005 rad, and the average absolute lateral error is approximately 0.09 m. In contrast, the average absolute lateral error and angular inaccuracy displayed by the Spiral Controller are 0.15 m and 0.013 rad, respectively. The system guarantes the safe and accurate implementation of robotic tasks in agriculture, as shown by its ±0.009 rad angular precision and ±0.015 m horizontal accuracy.

关键词： Spirals Accuracy Navigation Service robots Velocity control Soil Planning

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Predicting blood glucose level using salivary glucose and other associated factors: A machine learning model selection and evaluation study

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Informatics in Medicine Unlocked 2024年 48卷

作者： Chopra, Aditi Rao, Rohini R. Kamath, Shobha U. Akhila Arun, Sanjana Shettigar, Laasya Department of Periodontology Manipal College of Dental Sciences Manipal Academy of Higher Education Manipal Manipal Pin: 576 104 Karnataka India Department of Data Science and Computer Applications Manipal Institute of Technology Manipal Academy of Higher Education Manipal Manipal Pin: 576 104 Karnataka India Department of Biochemistry Kasturba Medical College and Hospital Manipal Academy of Higher Education Manipal Manipal Pin: 576 104 Karnataka India

Introduction: There is a need for designing non-invasive methods to predict blood glucose levels to ensure timely diagnosis of Diabetes Mellitus. Needle anxiety and bleeding disorders preclude many from undertaking blood tests. Objectives: The primary objective of this study was to assess if biomarkers like saliva can be used to estimate blood glucose levels. The second objective was to develop and evaluate Machine Learning (ML) models to predict blood glucose levels based on salivary glucose and associated features. An insight into the patient's features, which was important for predicting blood glucose levels, was also required. Methods: A cross-sectional study was conducted, and blood and saliva samples, along with patient-related data, were collected from healthy and diabetic patients. ML techniques were applied to the data to develop a tool for predicting blood glucose levels using patient features. The prediction intervals were computed, clinical accuracy was assessed, and important features for the prediction were identified. Results: The Random Forest Regressor Model, with features identified using the wrapper method, was selected as the best, with an average RMSE of 43.28. The prediction intervals were computed for point estimate, MAE = 23.821, and coverage was 100 percent, the clinical accuracy was compared with that of glucometers and continuous monitoring systems. All predicted values are in Zones A and B of the Clarke error grid, and the bias was 6.41. The most important feature for predicting blood glucose level is salivary glucose level, followed by known risk factors like Family History, BMI, etc. The study found that salivary glucose levels are insufficient to classify blood glucose levels as high or normal. Conclusion: The study concluded that salivary glucose with associated patient features could be a potential non-invasive biomarker for predicting blood glucose levels in patients. The developed ML model could be deployed in a device that inputs

关键词： Blood glucose level Confirmatory prediction Diabetes mellitus Feature selection Machine learning model Non-invasive method Random forest regressor Salivary glucose

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Brain MRI Synthesis Using Stylegan2-ADA

Brain MRI Synthesis Using Stylegan2-ADA

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IEEE International Symposium on Biomedical Imaging

作者： Matteo Lai Chiara Marzi Mario Mascalchi Stefano Diciotti Department of Electrical Electronic and Information Engineering "Guglielmo Marconi" - DEI University of Bologna Cesena Italy Alma Mater Research Institute for Human-Centered Artificial Intelligence University of Bologna Bologna Italy Department of Statistics Computer Science Applications "G. Parenti" University of Firenze Firenze Italy Experimental and Clinical Biomedical Sciences "Mario Serio" University of Florence Firenze Italy Clinical Epidemiology Unit Institute for Cancer Research Prevention and Clinical Network (ISPRO) Florence Italy

ISBN: (数字)9798350313338

ISBN: (纸本)9798350313345

Deep learning (DL) applications in the medical field often face challenges related to limited data availability, resulting in issues like overfitting and imbalanced datasets. Synthetic data offers a promising solution to these problems by enabling data augmentation and enhancing the performance of DL models. In this study, we trained the state-of-the-art generative model StyleGAN2-ADA on 1412 images from the Alzheimer’s disease neuroimaging initiative (ADNI) dataset to generate synthetic slices of T1-weighted brain MRI of healthy subjects. The quality of the synthetic images has been evaluated through quantitative and qualitative assessments, including a visual Turing test conducted by an expert observer with 2000 images. The observer achieved an accuracy of 52.95%, indicative of a performance level comparable to random guessing. These results demonstrate the capability of StyleGAN2-ADA to generate anatomically relevant synthetic brain MRI data.

关键词： Neuroimaging Visualization Magnetic resonance imaging Observers Brain modeling Data augmentation Data models

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Evaluating Topological Insulator-Based FETs in Circuit-Level Performance with Advanced Device Simulations

Evaluating Topological Insulator-Based FETs in Circuit-Level...

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International Conference on Power Energy, Environment and Intelligent Control (PEEIC)

作者： A Karthik Asha V T Bhargava Ramu Rupa Rani Sharma Ginni Nijhawan Zahraa Salem Mohsen Institute of Aeronautical Engineering Hyderabad Department of Computer Applications New Horizon College of Engineering Bangalore Department of Electrical and Electronics Engineering MLR Institute of Technology Hyderabad Telangana Dept. of ASH GL Bajaj Institute of Technology & Management Greater Noida U.P. India Lovely Professional University Phagwara College of Engineering Technology National University of Science and Technology Dhi Qar Iraq

In recent years., topological insulators (TIs) have emerged as a promising material class in the realm of advanced electronic devices due to their unique electronic properties. This paper presents a comprehensive evaluation of Field-Effect Transistors (FETs) based on topological insulators through advanced device simulations to understand their potential in circuit-level applications. We have employed state-of-the-art simulation tools to model the behavior of TI-based FETs under various operating conditions and benchmarked their performance against conventional semiconductor-based FETs. Our results indicate that TI-based FETs exhibit superior switching characteristics., reduced leakage currents., and enhanced thermal stability. Furthermore., the inherent spin-momentum locking property of TIs introduces a new dimension in device design., paving the way for spintronic applications. The study also highlights the challenges associated with integrating TI-based FETs in conventional circuits., such as interface quality and material purity. In conclusion., while topological insulator-based FETs hold significant promise for next-generation electronics., meticulous material engineering and device optimization are crucial for their successful integration into mainstream circuitry.

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Predicting the Impacts of the Environment on the Probability and Severity of Electrical Injuries by AIML

Predicting the Impacts of the Environment on the Probability...

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New Frontiers in Communication, Automation, Management and Security (ICCAMS), International Conference on

作者： Araddhana Arvind Deshmukh K Parish Venkata Kumar B.Anjanee Kumar Pramoda Patro Pavitar Parkash Singh N. Ashokkumar Department of Artificial Intelligence and Data science Marathwada Mitra Mandal College of Engineering Pune India Department of Computer Applications Velagapudi Ramakrishna Siddhartha Engineering College (Autonomous) Vijayawada India Electronics and Communication Engineering Dadi institute of engineering and technology Visakhapatnam India Department of Mathematics Koneru Lakshmaiah Education Foundation Hyderabad India Department of Management Lovely Professional University Phagwara India Department of ECE Mohan Babu University (Erstwhile Sree Vidyanikethan Engineering College) Tirupati India

ISBN: (数字)9798350317060

ISBN: (纸本)9798350317077

The acceleration of global warming and environmental degradation has been attributed to industrial growth and fossil fuel utilization. Consequently, this phenomenon has resulted in a rise in the frequency and intensity of natural disasters. Due to their predominantly outdoor installation, generators are subject to significant impacts. The occurrence of natural disasters has led to an increase in accidents associated with electrical equipment. This paper analyses statistical data on electrical fires, electric shock accidents, and electricity-related accidents caused by climate change in domestic settings to determine the associated risk *** this study, we propose a risk assessment model for electrical fires, explicitly focusing on every month. Our approach involves analysing data on electrical hazards in different regional areas, particularly metropolitan cities, considering climatic conditions such as temperature and humidity. Additionally, we aim to evaluate the accident risk rating associated with natural disasters, specifically those related to low and high-voltage equipment. In this study, a risk analysis model has been developed to assess electrical hazards in different regions and equipment types. The model presented herein serves as a foundational predictive tool. Hence, offering electrical security amenities in the forthcoming period is feasible by giving a risk estimation map delineating potential dangers specific to each geographical area and wiring category via web platforms or mobile applications utilizing the investigation data provided. Additionally, it is imperative to enhance the resilience and dependability of electrical components as a proactive measure against electrocute ions resulting from climate change-induced emergencies.

关键词： Wiring Temperature distribution Electric potential Analytical models Disasters Urban areas Humidity

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