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LiDAR-Based Climate Change Imaging in Geoscience Using Spatio Extreme Fuzzy Gradient Model

Remote Sensing in Earth systems Sciences 2025年第2期8卷 465-472页

作者： Krushnasamy, V.S. Al-Omari, Omaia Sundaram, Ajith Varghese, Issac K. Muniyandy, Elangovan Rao, M. V. A. L. Narasimha Dept. of EIE Dayananda Sagar College of Engineering Bangalore India Information Systems Department College of Computer and Information Sciences Prince Sultan University Riyadh Saudi Arabia Amrita School of Business Amrita Vishwa Vidyapeetham Kochi India T A Pai Management Institute Manipal Academy of Higher Education Manipal India Department of Biosciences Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai 602105 India Applied Science Research Center Applied Science Private University Amman Jordan Department of MBA Koneru Lakshmaiah Education Foundation Andhra Pradesh Vaddeswaram Guntur 522302 India

In order to estimate high-resolution air temperatures, computationally demanding procedures are required because climate models do not provide the resolution required for urban climate studies. Data-driven methods, on the other hand, provide air temperature downscaling that is quicker and more precise. Lidar technology has become a vital tool for researching these atmospheric components because of its capacity to measure backscattered signals and investigate the atmosphere at extremely high spatial and temporal resolution. However, machine learning has become a potent technique for sophisticated data processing and analysis because of the complexity and diversity of lidar technology. This research proposes a novel technique in urban–rural interface analysis in remote sensing by machine learning (ML) model. Here the input is collected as urban–rural landscape data and processed for noise removal as well as normalisation. Then these data features have been extracted and classified using a spatio extreme fuzzy gradient model with reinforcement Markov vector perceptron neural networks. Experimental analysis has been carried out for various geographical regions in terms of training accuracy, average precision, recall, RMSE, and F-1 score. The proposed model attained an F-1 score of 92%, training accuracy of 98%, average precision of 95%, recall of 93%, and RMSE of 55%. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025.

关键词： Lidar technology Machine learning model Reinforcement Markov vector Remote sensing Spatio extreme fuzzy gradient

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A Federated Learning Framework Based on Spatio-Temporal Agnostic Subsampling (STAS) for Forest Fire Prediction

A Federated Learning Framework Based on Spatio-Temporal Agno...

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IEEE Annual International computer Software and Applications Conference (COMPSAC)

作者： Abdul Mutakabbir Chung-Horng Lung Samuel A. Ajila Kshirasagar Naik Marzia Zaman Richard Purcell Srinivas Sampalli Thambirajah Ravichandran Department of Systems and Computer Engineering Carleton University Ottawa ON Canada Dept. Electrical and Computer Eng. University of Waterloo Waterloo ON Canada Research and Development Cistel Technology Ottawa ON Canada Faculty of Computer Science Dalhousie University Halifax NS Canada Research and Development Hegyi Geomatics International Inc. Ottawa ON Canada

ISBN: (数字)9798350376968

ISBN: (纸本)9798350376975

Prevention of forest fires increasingly impacted by climate change is essential to maintain ecological balance, preserve natural resources, prevent economic loss, and protect human and animal life. Data for forest fires is available from multiple sources and is huge. Federated learning can be implemented to distribute the computing across multiple edge devices by saving transmission costs, protecting data privacy, and maintaining security with no single point of failure as local models exist across multiple resources in different geographic regions. The proposed framework extends the Spatio-Temporal Agnostic Subsampling (STAS) technique by distributing the data into multiple computation nodes to leverage federated learning. It was found that the models trained using federated learning on weather data gained on average 0.3 in F1 for classifying the occurrence of fire. This study also demonstrates how to optimally choose the sources of data for either predicting the occurrence of fire or the severity of fire.

关键词： Climate change Big Data Federated learning Fires Forests Lightning Deep learning Remote sensing Ecology Environmental monitoring Spatial temporal resolution Sampling methods Predictive models

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Secure the 5G and Beyond Networks with Zero Trust and Access control systems for Cloud Native Architectures

Secure the 5G and Beyond Networks with Zero Trust and Access...

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ACS/IEEE International Conference on computer systems and Applications

作者： Hisham A. Kholidy Keven Disen Andrew Karam Elhadj Benkhelifa Mohammad A. Rahman Atta-Ur Rahman Ibrahim Almazyad Ahmed F. Sayed Rakia Jaziri Dept. of Networks and Computer Security College of Engineering SUNY Polytechnic Institute Utica NY USA Dept. of Networks and Comp. Security SUNY Polytechnic Institute Utica NY USA The Air Force Research Laboratory (AFRL RIGB) Rome NY USA Smart Systems AI and Cybersecurity Research Centre Staffordshire Univ. UK Dept of Electrical and Computer Engineering Florida International University USA Dept. of Computer Science Imam Abdulrahman Bin Faisal University Dammam Saudi Arabia Dept. of Electrical and Computer Engineering Univ. of Arizona Tucson AZ USA Transmission Dept. Telecom Egypt Fayoum Egypt Paragraphe Research Lab. Univ. of Paris VIII France

5G networks are highly distributed, built on an open service-based architecture that requires multi-vendor hardware and software development environments, all of which create a high attack surface in the 5G networks than other proprietary fixed-function networks. Besides that, cloud-native architectures also present new security challenges. Cloud-native separates monolithic virtual machines into microservice pods, resulting in higher volumes of signaling and communication flowing through and between microservices. In addition, secure connections in monolithic applications have been replaced by untrusted communication between microservice pods, requiring additional cybersecurity capabilities. Access control systems were created to provide reliability and limit access to an organization’s assets. However, due to technology's constant evolution and dynamicity, these conventional security systems lack the security to protect an organization’s information because they were created to address access control for known users. For 5G based cloud native technology, these access controls need to be taken further by implementing a Zero Trust model to secure one’s essential assets for all users within the system. Zero Trust is implemented in an access control system under the concept "Never Trust, Always Verify". In this paper, we implement zero trust as a factor within access control systems by combining the principles of access control systems and zero-trust security by factoring in the user’s historical behavior and recommendations into the mix.

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Comparison of Whole Blood Coagulation Profiles in COVID-19 and Sepsis Patients Using a Handheld Dielectric Coagulometer

Comparison of Whole Blood Coagulation Profiles in COVID-19 a...

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IEEE Biomedical Circuits and systems (BIOCAS)

作者： Sina Pourang Michael A. Suster Asha Thomas Stephanie D. Lapping Lalitha V. Nayak Pedram Mohseni Dept. of Electrical Computer and Systems Engineering Case Western Reserve University Cleveland OH USA Division of Hematology/Oncology University Hospitals Cleveland Medical Center Cleveland OH USA

Rapid point-of-care (POC) assessment of thrombosis is clinically important in patients who develop significant blood coagulation abnormalities such as noted with sepsis or COVID-19. In this work, we compare the coagulation profiles of whole blood in sepsis and COVID-19 patients using a handheld dielectric coagulometer termed ClotChip ® . ClotChip ® is a three-dimensional, parallel-plate, capacitive sensor integrated into a single-use microfluidic channel with a total volume of <20 µL for sample analysis. The readout curve is defined as the temporal variation in the real part of dielectric permittivity of whole blood at 1 MHz. ClotChip ® is sensitized towards detecting altered coagulation states by adding recombinant thrombomodulin (rTM) or activated protein C (APC). Using a handheld ClotChip ® device, we measure the coagulation status in whole blood samples from hospitalized patients with sepsis and COVID-19 (both regular floor and intensive care unit) and compare it to samples from healthy donors. The coagulation profiles show a difference between COVID-19 and sepsis patients when samples are treated with rTM, as well as a difference between moderate and severe COVID-19 infections when samples are treated with APC. This study demonstrates that ClotChip ® measures a coagulation profile in COVID-19 that is different from that in sepsis, highlighting its future potential as a POC diagnostic/prognostic tool in COVID-19-associated coagulopathy.

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Simulation Model of a Pneumatic Drive for Compressing Electrodes of Stationary Contact Spot Welding Machine

Simulation Model of a Pneumatic Drive for Compressing Electr...

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International Conference on control systems, Mathematical Modelling, Automation and Energy Efficiency (SUMMA)

作者： Ilya L Sandler Dmitriy V Ivanov Michail A Terekhin Irina N Bragina Ilya V Lebakin Ruzil A Zaripov Dept. Digital Technologies Samara State University of Railway Transport Samara Russia Dept. Automation and Control in Technical Systems Samara State Technical University Samara Russia Dept. Information Systems Security Samara National Research University Samara Russia Dept. Digital Technologies Institute of Innovative Development FSBEI HE SamSMU MOH Russia Samara State University of Transport Higher School of Medical Engineering Samara Russia

The article presents a simulation model of a pneumatic drive for electrodes of a stationary contact spot welding machine. Thanks to the developed model, it becomes possible to analyze the system, as well as visually study its operation. The result of modeling the system is the obtained transients of the elements of the pneumatic system. The developed simulation model adequately describes the state and behavior of a real system of pneumatic compression of electrodes. The work is of interest to pneumatic engineers, designers and other specialists working in the field of pneumatics and can be used for more accurate and efficient design, analysis and optimization of not only a specific system of pneumatic compression of electrodes, but also pneumatic systems in general.

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Multi-label transfer learning for identifying lung diseases using chest X-rays 2

Multi-label transfer learning for identifying lung diseases ...

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2nd IEEE International Conference on Electronic engineering, ICEEM 2021

作者： El-Fiky, Azza Shouman, Marwa Ahmed Hamada, Salwa El-Sayed, Ayman Karar, Mohamed Esmail Electronics Research Institute Department of Informatics Cairo Egypt Menoufia University Faculty of Electronic Engineering Department of Computer Science and Engineering Minuf Egypt Menoufia University Faculty of Electronic Engineering Dept. Industrial Electronics and Control Engineering Minuf Egypt

ISBN: (纸本)9781665418423

Chest radiography presents one of the main medical imaging modalities for diagnosing lung diseases. To assist radiologists during interventional procedures, this paper aims at proposing a transfer learning-based classifier to automatically identify 14 different thoracic diseases in Chest X-ray (CXR) images. The proposed method is relied on deep residual neural networks with 50 layers (ResNet-50) to accomplish the diagnostic task of many chest diseases. In this study, a public dataset of 112, 120 frontal radiograph images for Chest X-ray has been used for validating the proposed deep learning classifier. It achieved the best performance of multi-label classification of normal and 14 different lung diseases with an average area under curve (AUC) of 0.911 and F1-score of 0.66. This study demonstrated that the proposed ResNet-50 classifier as a transfer learning model outperforms other relevant methods in the previous studies for automatic multi-label classification of chest X-rays. © 2021 IEEE.

关键词： computer aided diagnosis

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WIP: Impulsive Noise Source Recognition with OFDM-WiFi Signals Based on Channel State Information Using Machine Learning

WIP: Impulsive Noise Source Recognition with OFDM-WiFi Signa...

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IEEE International Symposium on World of Wireless Mobile and Multimedia Networks (WoWMoM)

作者： Iratxe Landa Guillermo Dí az Iker Sobró n Iñ aki Eizmendi Manuel Vé lez Dept. Communications Engineering University of the Basque Country (UPV/EHU) Bilbao Spain Dept. of Computer Languages and Systems University of the Basque Country (UPV/EHU) Bilbao Spain

ISBN: (数字)9781665408769

ISBN: (纸本)9781665408776

This study presents contributions on the detection of impulsive noise sources using OFDM-Wifi signals and Machine Learning models. The influence of impulsive noise sources on WiFi signals is used to obtain the features for supervised Machine learning models. A measurement campaign has been carried out at two indoor locations, using the Atheros CSI tool to obtain the channel state information. Feature extraction for impulsive noise detection has been performed by processing the amplitude of the channel state information of each subcarrier. These features have fed two supervised Machine Learning models, a Random Forest algorithm, and a higher level algorithm such as a Deep Neural Network. The results obtained indicate that Wifi-OFDM signals can be used for impulsive noise source recognition. The main contributions of this work focus on the extraction of suitable features for the identification of impulsive noise sources through machine learning models. The accuracy greater than 0.9 in source identification validates the proposed model, which serves as a precedent for future studies in this area.

关键词： Wireless communication Machine learning algorithms Electric variables measurement Predictive models Feature extraction Noise measurement Security

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Optimal MmWave Sensor Selection for Bearing-Only Localization in Smart Environments

Optimal MmWave Sensor Selection for Bearing-Only Localizatio...

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Mediterranean Conference on control and Automation (MED)

作者： Evangelos Vlachos Evangelos D. Spyrou Chrysostomos Stylios Kostas Berberidis Industrial Systems Institute Athena Research and Innovation Centre Greece Dept. of Informatics and Telecommunications University of Ioannina Greece Dept. of Computer Engineering and Informatics University of Patras Greece

ISBN: (数字)9781665406734

ISBN: (纸本)9781665406741

Nowdays, millimeter wave (mmWave) direction sensors are being used increasingly as general-purpose radars, since they can provide high-level of accuracy for a variety of situations at low-cost. Via mutliple mmWave sensors, bearing estimation can be derived to track the position of a target, while in smart environments several sensors can be deployed. In this work, we provide an optimal sensor selection technique, for choosing which sensors to activate for bearing estimation and which not. The proposed approach is decomposed into training phase, where sensor selection is performed, and operational phase, where bearing estimation is obtained. Via simulation results we evaluate the proposed approach compared with the conventional methodology of utilizing all available data streams.

关键词： Training Sensor placement Direction-of-arrival estimation Target tracking Simulation Optimization methods Radar tracking

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Unmanned Aerial Vehicle Solutions for Weed Detection in Precision Agriculture

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IEEE Access 2025年

作者： Borah, Shekhar Anand, Aryan Sundaravadivel, Prabha Fletcher, Reginald Reddy, Krishna The University of Texas at Tyler Center for Robotics and Intelligent Systems Dept. of Electrical and Computer Engineering Tyler 75701 TX United States USDA-Agricultural Research Service (ARS) Crop Production Systems Research Unit Stoneville 38776 MS United States

This study presents a novel Unmanned Aerial Vehicle (UAV)-based approach for detecting pigweed in soybean (Glycine max) fields using a combination of deep learning and advanced image processing techniques. A custom, high-resolution dataset comprising RGB and multispectral images was collected from USDA operated fields and manually annotated to target pigweed detection. Beyond incorporating YOLOv8 variants for real-time weed classification, this research integrates a comprehensive image processing pipeline, incorporating global thresholding, k-means clustering, 3D surface mapping, and spectral signature analysis to enhance interpretability and detection accuracy. A comparative evaluation of YOLOv8 nano, small, medium, and large models was performed to identify the most practical model for deployment in precision agriculture. The YOLOv8 nano model emerged as the most balanced in terms of precision (75.6%), recall (81.7%), and mAP@0.5 (81.6%), demonstrating effective weed detection performance under real field conditions. Also, quadrant-level weed coverage and spatial heatmaps were generated to support targeted intervention. This work advances the current state of UAV based weed detection by providing a field-ready, explainable, and resource-efficient solution, contributing to sustainable farming and data-driven weed management practices. © 2013 IEEE.

关键词： Pigweed Detection Soybean Unmanned Aerial Vehicle (UAV) YOLOv8

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Ant Colony Algorithm for Placing Electronic Components on a Printed Circuit Board, Taking into Account the Thermal Compatibility Criterion*

Ant Colony Algorithm for Placing Electronic Components on a ...

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International Conference on Actual Problems of Electronic Instrument engineering (APEIE)

作者： Ilya V. Suzdaltsev Vladimir A. Suzdaltsev Nelli Y. Bogula Dept. of Computer-Aided Design System Kazan National Research Technical University Named After A.N. Tupolev Kazan Russia Dept. of Automated Information Processing and Control Systems Kazan National Research Technical University Named After A.N. Tupolev Kazan Russia

The problem of electronic components placing on a printed circuit board of an electronic device is formulated in this paper. The choice of constraints and criteria for the optimization problem is validated. We propose to take into account the thermal compatibility criterion to ensure normal thermal conditions of the printed circuit board. An indicator of the problem solution quality is the maximum temperature of a component among all components placed on a printed circuit board. The temperature of the components is determined based on the solution of the heat balance equation. The ant algorithm for placement problem solving is described. The sequence of vertices of the decision graph in the path formed by the ant colony determines the sequence of elements placement on the mounting plane of the printed circuit board and thus corresponds to the placement problem solution. The decrease in the maximum temperature of the printed circuit board temperature field was assessed depending on the number of the stage of constructing the best path by a ant colony, with different thermal conductivity between the components and the surrounding air environment. The results of computational experiments for effectiveness study of the ant algorithm are presented. The experiments carried out showed the effectiveness of the applied ant algorithm and that rational placement of the electronic components on the PCB makes it possible to reduce the maximum temperature of the components.

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