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IEEE International Symposium on Biomedical Imaging

作者： Aya A. Abd El-Khalek Hossam Magdy Balaha Ali Mahmoud Norah Saleh Alghamdi Mohammed Ghazal Abeer T. Khalil Mohy Eldin A. Abo-Elsoud Ayman El-Baz Communications and Electronics Engineering Department Nile Higher Institute for Engineering and Technology Mansoura Egypt Bioengineering Department J.B. Speed School of Engineering University of Louisville Louisville KY USA Department of Computer Sciences College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University Riyadh Saudi Arabia Electrical Computer and Biomedical Engineering Department Abu Dhabi University Abu Dhabi UAE Communications and Electronics Engineering Department Faculty of Engineering Mansoura University Egypt

ISBN: (数字)9798350313338

ISBN: (纸本)9798350313345

The increasing prevalence of eye disorders among the elderly has underscored the importance of early detection through routine eye examinations. Age-related Macular Degeneration (AMD), a common ailment affecting those over 45, is a leading cause of vision impairment in older individuals. This study introduces a comprehensive computer-Aided Diagnosis (CAD) system that leverages ML techniques to categorize fundus images into geographic atrophy (GA), intermediate AMD, normal, and wet AMD categories. We developed an innovative system that extracts both local and global appearance markers from fundus images. These markers are derived from the entire retina and iso-regions aligned with the optical disc, utilizing the fast marching level sets model. The features include markers from the Gray-Level Co-occurrence Matrix (GLCM) and Gray-Level Run-Length Matrix (GLRLM) models. The integration of weighted majority voting with the best classifiers enhances performance, resulting in an impressive overall accuracy of 96.85%, sensitivity of 93.72%, specificity of 97.89%, precision of 93.86%, F1 score of 93.72%, ROC of 95.85%, BAC of 95.81%, and WSM of 95.38%.

关键词： Macular degeneration Solid modeling Accuracy Sensitivity Feature extraction Retina Optical imaging

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Performance Evaluation of D2D Communications in of LTE-A Network

Performance Evaluation of D2D Communications in of LTE-A Net...

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Sciences and Techniques of Automatic Control and computer engineering MI-STA, IEEE International Maghreb Meeting of the Conference on

作者： Mona Bakri Hassan Rashid A. Saeed Othman O. Khalifa Elmustafa Sayed Ali Rania A. Mokhtar Department of Electronics Engineering Sudan University of Science and Technology Khartoum Sudan Department of Computer Engineering College of Computers and Information Technology Taif University Taif Saudi Arabia Department of Electrical and Computer Engineering International Islamic University Malaysia Gombak Malaysia Libyan Center for Engineering Resarch and Information Technology Bani Waleed Libya Department of Electrical and Electronics Engineering Faculty of Engineering Red Sea University Portsudan Sudan

In LTE-Advanced, device-to-device (D2D) communication is a technology enables to increase the coverage and capacity coverage in metropolitan areas. Improvements in energy efficiency, throughput, latency, spectrum efficiency, and interference reduction have all been proposed. It is used in many domains, including applications, network traffic offloading, and public safety, and is recognized as one of the viable ways for the 5G wireless communications system. LTE advanced might perform worse in D2D communications because of multipath impact and delay dispersion. It is an essential and important element for the design of receivers in mobile communication systems. In this study, the spectrum efficiency, throughput, data rate, signal-to-interference-pulse-noise ratio, and latency of LTE-based D2D communications will be examined (SINR).

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Dual Active Bridge Converter Output Current Ripple Prediction in EV Battery Chargers Through Multimodal Adaptive Spatio-Temporal Graph Neural Network

Dual Active Bridge Converter Output Current Ripple Predictio...

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Trends in Material Science and Inventive Materials (ICTMIM), International Conference on

作者： Abhay Chaturvedi Mudit Mittal Swapnil Uttamrao Deokar Rachit Adhvaryu G. Satish Natrayan L Department of Electronics & Communication Engineering GLA University Mathura Uttar Pradesh India Department of Computer Science & Engineering Graphic Era Deemed to be University Dehradun Departement of Mechanical Engineering Smt. Kashibai Navale College of Engineering Pune Maharashtra Computer Science and Engineering Faculty of Engineering and Technology Parul Institute of Engineering and Technology Parul University Post Limda Gujarat India Department of Electrical and Electronics Engineering Sree Dattha Institute of Engineering and Science Sheriguda Telangana India Department of Mechanical Engineering Saveetha School of Engineering SIMATS Chennai Tamil Nadu

ISBN: (数字)9798331501488

ISBN: (纸本)9798331501495

The prediction of converter output current ripple in Electric Vehicle (EV) battery chargers stands as a vital condition to maintain stable charging with safe battery operation by reducing voltage fluctuations and delivering steady currents. The prediction of converter output current ripple faces difficulties because the complex dynamics of converter behavior changes according to various load conditions and environmental factors. This measurement inaccuracy threatens the capability to make optimal power transfers and reduces loss management ability thereby affecting the life span and operational performance of the EV battery. This research presents a framework to predict the amount of current oscillation in chargers for EV battery systems powered by Dual Active Bridge (DAB) Converter. The proposed technique utilizes Multimodal Adaptive Spatio-Temporal Graph Neural Network (MASTGNN) as its method. The proposed method aims at both enhancing current ripple prediction accuracy and optimizing converter performance together with improving EV battery lifespan during charging. A MAST-GNN process predicts output current ripples in DAB converters used for EV battery charging through modeling which evaluates the behavior changes of voltage and current and power when operational parameters vary. Implementation of the proposed method in MATLAB will undergo comparison testing with CNN as well as BESO-ANFIS and ANN together with other existing methods. By achieving an accuracy of 97% in current ripple prediction and maintaining a high converter efficiency of 98.5%, the proposed MASTGNN method outperforms traditional approaches without compromising the stability and performance of the EV battery charging process.

关键词： Battery chargers Accuracy Adaptive systems Switched reluctance motors Electric vehicles Real-time systems Loss measurement Graph neural networks Batteries Vehicle dynamics

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Transformer-Based Deep Learning Detector for Dual-Mode Index Modulation 3D-OFDM

Transformer-Based Deep Learning Detector for Dual-Mode Index...

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Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA)

作者： Toan Gian Tien-Hoa Nguyen Trung Tan Nguyen Van-Cuong Pham Thien Van Luong School of Electrical and Electronics Engineering Hanoi University of Science and Technology Hanoi Vietnam Faculty of Radio-Electronics Le Quy Don Technical University Ha Noi Vietnam Faculty of Computer Science Phenikaa University Hanoi Vietnam

In this paper, we propose a deep learning-based signal detector called TransD3D-IM, which employs the Transformer framework for signal detection in the Dual-mode index modulation-aided three-dimensional (3D) orthogonal frequency division multiplexing (DM-IM-3D-OFDM) system. In this system, the data bits are conveyed using dual-mode 3D constellation symbols and active subcarrier indices. As a result, this method exhibits significantly higher transmission reliability than current IM-based models with traditional maximum likelihood (ML) detection. Nevertheless, the ML detector suffers from high computational complexity, particularly when the parameters of the system are large. Even the complexity of the Log-Likelihood Ratio algorithm, known as a low-complexity detector for signal detection in the DM-IM-3D-OFDM system, is also not impressive enough. To overcome this limitation, our proposal applies a deep neural network at the receiver, utilizing the Transformer framework for signal detection of DM-IM-3D-OFDM system in Rayleigh fading channel. Simulation results demonstrate that our detector attains to approach performance compared to the model-based receiver. Furthermore, TransD3D-IM exhibits more robustness than the existing deep learning-based detector while considerably reducing runtime complexity in comparison with the benchmarks.

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Leveraging Adaptive Algorithms for Real-Time Data Analysis

Leveraging Adaptive Algorithms for Real-Time Data Analysis

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International Conference on Computing and Networking technology (ICCNT)

作者： Girija Shankar Sahoo R Hannah Jessie Rani Manish Kumar Goyal Vazeer Ali Mohammed D Little Femilin Jana Namrata N. Wasatkar Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India Department of Electrical and Electronics Engineering Faculty of Engineering and Technology Jain (Deemed to be University) Bangalore Karnataka India Department of Computer Science & Application Vivekananda Global University Jaipur India Department of Computer & Information System Lewis University Illinois USA Department of Science and Humanities Prince Shri Venkateshwara Padmavathy Engineering College Chennai Department of Computer Engineering Vishwakarma Institute of Information Technology Pune INDIA

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

This paper proposes the use of adaptive algorithms for real-time data analysis. Adaptive algorithms are a set of practical methods used for acting information-driven optimization and studying. They have a wide variety of applications and may be implemented to a selection of facts analysis obligations. This paper offers an overview of existing adaptive algorithms and descriptions of several new programs in the area of actual-time facts analysis. In particular, the paper discusses the notion of adaptive thresholding, which allows for practical real-time evaluation of large datasets. The paper also provides a novel technique for making use of these algorithms in an online learning environment. Results from simulations illustrate the ability of the proposed method in real-international packages. Sooner or later, the paper presents numerous key lessons discovered and affords recommendations for future research and packages in this area.

关键词： Adaptation models Data analysis Real-time systems Optimization

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VGG16-Based Deep Learning Approach for Accurate Detection of Fruit Diseases in Precision Agriculture

VGG16-Based Deep Learning Approach for Accurate Detection of...

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Intelligent Data Communication Technologies and Internet of Things (IDCIoT), International Conference on

作者： S. N. Sheela Evangelin Prasad Nitin Rakesh Elavel Visuvanathan G N. Mohankumar R. Vinodha S. Murugan Department of Computer Science and Engineering Sri Krishna Engineering College Chennai Tamil Nadu India Symbiosis Institute of Technology Nagpur Campus Symbiosis International (Deemed University) Pune India Department of Electronics and Communication Engineering Faculty of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Tamil Nadu India Department of Electronics and Communication Engineering ULTRA College of Engineering and Technology Madurai Tamil Nadu India Department of Biomedical Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamil Nadu India

ISBN: (数字)9798331527549

ISBN: (纸本)9798331527556

Early and accurate disease detection in fruit is crucial for precision agriculture to improve yields and provide healthy crops. This research proposes a deep learning (DL) method based on VGG16 to detect fruit illnesses from high-resolution images. The method uses VGG16 architecture, which has been fine-tuned using massive image datasets, to detect fruit illnesses. The proposed technique takes images of fruits that have several prevalent illnesses and uses those images to extract deep characteristics that allow for accurate classification. This system accurately identifies illnesses, including apple scab, citrus greening, and bacterial spots, by combining a strong convolutional neural network (CNN). The dataset used to train and test the model to simulate real-world situations contains images of healthy and sick apples taken under different lighting circumstances. The evaluation of performance indicators, including recall, accuracy, precision, and F1-score, shows the model's efficiency and accuracy in illness detection. Reduced pesticide consumption and optimized resource allocation result from this automated detection system's ability to allow farmers to monitor crop health and make prompt choices continually. It provides a useful and scalable tool for managing diseases in modern precision agriculture. A DL model based on VGG16 was created for accurate fruit disease diagnosis in precision agriculture, with an accuracy of 96.5%, allowing early intervention to improve crop health and production sustainability.

关键词： Precision agriculture Deep learning Accuracy Crops Production Convolutional neural networks Resource management Sustainable development Monitoring Diseases

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Agile Enterprise Geographic Information System (AEGIS) from Design and Development Perspective 8

Agile Enterprise Geographic Information System (AEGIS) from ...

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8th International Conference on Mechatronics engineering, ICOM 2022

作者： Taha Ali, Taha Alfadul Saeed, Rashid A. Khalifa, Othman O. Ali, Elmustafa Sayed Odeh, Nidhal Hayder, Gasim Hashim, Aisha A. Geo-informatics Centre Alzaiem Alazhari University Sudan Sudan Department of Computer Engineering College of Computers and Information Technology Taif University P.O. Box 11099 Taif21944 Saudi Arabia Department of Electrical and Electronics Engineering Faculty of Engineering International Islamic University Malaysia Malaysia The Libyan Center for Engineering Research and Information Technology Bani Walid Libya Sudan School of electrical computer and telecommunications engineering University of Wollongong Australia Kajang43000 Malaysia

ISBN: (数字)9781839538063

ISBN: (纸本)9781839538063

Agile Enterprise GIS Development (AEGISD) is an environment with a high collaborative feature, needs to be achieved by active communications between the stakeholders. Recently, a number of researchers and users have been interested in Enterprise GIS due to its capabilities in providing a number of services related to co-locate Business. The benefit of AEGISD is that it enables the distribution of GIS components and the possibility of integrating them with other technologies that achieve the concept of smart cities for smart vehicles automation and transportation. This paper reviews the concepts about developing the GIS systems from the prospective of "Agile", where it focus on five keys: development, migration, integration, adaptation, and training. It also explains the methodology used at several levels related to storage, design, implementation, security and integration capabilities. © 2022 IET Conference Proceedings. All rights reserved.

关键词： Geographic information systems

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Through-Wall Human Sensing Using mmWave FMCW Radar

Through-Wall Human Sensing Using mmWave FMCW Radar

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Research, Invention, and Innovation Congress (RI2C)

作者： Kritthee Phaisiri Akkarat Boonpoonja Chainarong Kittiyanpunya Kamol Kaemarungsi Nonchanutt Chudpooti Department of Electrical and Computer Engineering Faculty of Engineering King Mongkut's University of Technology North Bangkok Bangkok Thailand Department of Mechatronics Engineering Faculty of Engineering Rajamangala University of Technology Rattanakosin Nakhon Pathom Thailand National Electronics and Computer Technology Center National Science and Technology Development Agency Pathumthani Thailand Department of Industrail Physics and Medical Instrumentation Faculty of Applied Science King Mongkut's University of Technology North Bangkok Bangkok Thailand

This paper experimentally demonstrates the through wall sensing to detect human movement using a mmwave FMCW radar operating in the 60 GHz to 64 GHz band. The mmWave radar data processing framework focuses on the generation of point cloud data. Each human movement is illustrated with point cloud data, whose represents the position and orientation of pose, usually in 3D-cartesian coordinate system. In experiments, point cloud data from sensing through two different walls were collected for three activities into a 3 m by 3 m room. The data was analyzed through a comparison between the position of point cloud data and the position of the actual posture movement. The results prove that a mmWave radar placed behind the wall of the room is suitable for human detection and tracking in indoor environments, in terms of consistency with the actual posture.

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Topological Signal Processing and Learning: Recent Advances and Future Challenges

arXiv

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arXiv 2024年

作者： Isufi, Elvin Leus, Geert Beferull-Lozano, Baltasar Barbarossa, Sergio Lorenzo, Paolo Di Faculty of Electrical Engineering Mathematics and computer Science Delft University of Technology Delft Netherlands SIGIPRO Department Simula Metropolitan Center for Digital Engineering Oslo Norway Department of Information Engineering Electronics and Telecommunications Sapienza University of Rome Rome Italy

Developing methods to process irregularly structured data is crucial in applications like gene-regulatory, brain, power, and socioeconomic networks. Graphs have been the go-to algebraic tool for modeling the structure via nodes and edges capturing their interactions, leading to the establishment of the fields of graph signal processing (GSP) and graph machine learning (GML). Key graph-aware methods include Fourier transform, filtering, sampling, as well as topology identification and spatiotemporal processing. Although versatile, graphs can model only pairwise dependencies in the data. To this end, topological structures such as simplicial and cell complexes have emerged as algebraic representations for more intricate structure modeling in data-driven systems, fueling the rapid development of novel topological-based processing and learning methods. This paper first presents the core principles of topological signal processing through the Hodge theory, a framework instrumental in propelling the field forward thanks to principled connections with GSP-GML. It then outlines advances in topological signal representation, filtering, and sampling, as well as inferring topological structures from data, processing spatiotemporal topological signals, and connections with topological machine learning. The impact of topological signal processing and learning is finally highlighted in applications dealing with flow data over networks, geometric processing, statistical ranking, biology, and semantic communication. © 2024, CC BY.

关键词： Network theory (graphs)

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Machine Learning based Mobile Applications for Autonomous Fertilizer Suggestion 4

Machine Learning based Mobile Applications for Autonomous Fe...

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4th International Conference on Inventive Research in Computing Applications, ICIRCA 2022

作者： Raviraja, S. Raghavender, K.V. Sunagar, Prashant Ragavapriya, R.K. Kumar, M Jogendra Bharath, V.G. University of Garden City Faculty of Computer Studies Khartoum Sudan Karnataka Mysuru India Department of Computer Science and Engineering Telangana Hyderabad India M S Ramaiah Institute of Technology Department of Civil Engineering Karnataka Bengaluru India Sri Ramakrishna Engineering College Department of Electrical and Electronics Engineering Tamil Nadu Coimbatore India KoneruLakshmaiah Education Foundation Department of Computer Science and Engineering Andhra Pradesh Vaddeswaram India Brindavan College of Engineering Department of Mechanical Engineering Karnataka Bengaluru560063 India

ISBN: (数字)9781665497077

ISBN: (纸本)9781665497077

To ensure food security, advanced agriculture and the Green Movement haveincreased overall output, but with a yield gap. However, the use of agrochemicals such as fertilizers and pesticides, as well as modern cultivars, irrigation, and other inputs, continues to suffer agriculture in some locations. The agriculture-based on traditional agrochemicals may result in soil and environmental degradation, acidification, and a reduction in soil fertility. Only by utilizing appropriate fertilizers and limiting the number of soil resources that are exploited, can help to maintain soil quality and crop yield. As a result, fertilizer recommendations in agriculture are crucial to meeting productivity, and financial needs, and minimizing nutrient losses to the environment. In this research, the smartphone app will be used to construct an autonomous fertilizer suggestion system for farmers. The dataset was gathered from the website Kaggle. The encoding technique is done to make the data entries numeric. Three Machine Learning (ML) models like K-Nearest Neighbour (KNN), Random Forest (RF), and Decision Tree (DT) are employed to predict fertilizer based on environmental and soil characteristics. The best model is identified using performance metrics and deployed on the mobile app. From the performance analysis, the RF gives high accuracy of 90%. © 2022 IEEE.

关键词： Fertilizers

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