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Early Detection of Alzheimer’s Disease Based on Laplacian Re-Decomposition and XGBoosting

computer Systems Science & engineering 2023年第9期46卷 2773-2795页

作者： Hala Ahmed Hassan Soliman Shaker El-Sappagh Tamer Abuhmed Mohammed Elmogy Information Technology Department Faculty of Computers and InformationMansoura UniversityMansoura35511Egypt Faculty of Computer Science and Engineering Galala UniversitySuez435611Egypt Faculty of Computers and Artificial Intelligence Benha UniversityBanha13518Egypt College of Computing and Informatics Sungkyunkwan UniversitySuwonKorea

The precise diagnosis of Alzheimer’s disease is critical for patient treatment,especially at the early stage,because awareness of the severity and progression risks lets patients take preventative actions before irreversible brain damage *** is possible to gain a holistic view of Alzheimer’s disease staging by combining multiple data modalities,known as image *** this paper,the study proposes the early detection of Alzheimer’s disease using different modalities of Alzheimer’s disease brain ***,the preprocessing was performed on the ***,the data augmentation techniques are used to handle ***,the skull is removed to lead to good *** the second phase,two fusion stages are used:pixel level(early fusion)and feature level(late fusion).We fused magnetic resonance imaging and positron emission tomography images using early fusion(Laplacian Re-Decomposition)and late fusion(Canonical Correlation Analysis).The proposed system used magnetic resonance imaging and positron emission tomography to take advantage of *** resonance imaging system’s primary benefits are providing images with excellent spatial resolution and structural information for specific *** emission tomography images can provide functional information and the metabolisms of particular *** characteristic helps clinicians detect diseases and tumor progression at an early ***,the feature extraction of fused images is extracted using a convolutional neural *** the case of late fusion,the features are extracted first and then ***,the proposed system performs XGB to classify Alzheimer’s *** system’s performance was evaluated using accuracy,specificity,and *** medical data were retrieved in the 2D format of 256×256 *** classifiers were optimized to achieve the final results:for the decision tree,the maximum depth of a tree was *** best number of trees for the random forest was 60;for

关键词： Alzheimer’s disease(AD) machine learning(ML) image fusion Laplacian Re-decomposition(LRD) XGBoosting

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Guest Editorial:Special issue on recent developments in advanced mechatronics systems

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CAAI Transactions on Intelligence Technology 2022年第4期7卷 547-548页

作者： Lin Meng Mingcong Deng Hongnian Yu Shengjun Wen Department of Electronic and Computer Engineering Ritsumeikan UniversityKusatsuJapan Department of Electrical and Electronic Engineering Tokyo University of Agriculture and TechnologyFuchuJapan School of Engineering and the Built Environment Edinburgh Napier UniversityEdinburghUK Zhongyuan-Petersburg Aviation College Zhongyuan University of TechnologyZhengzhouChina

This Special Issue will accept contributions describing innovative research and developments in‘Advanced Mechatronic Systems’,and will cover a wide range of disciplines,including robotics,automation and control systems,and new energy *** particularly welcomes those emerging methodologies and techniques which bridge theoretical studies and applications in advanced mechatronic *** quantitative engineering and science studies may be considered as well.

关键词： mec innovative automation

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Retrieval-augmented Generation for GenAI-enabled Semantic Communications

arXiv

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arXiv 2024年

作者： Tang, Shunpu Zhang, Ruichen Yan, Yuxuan Yang, Qianqian Niyato, Dusit Wang, Xianbin Mao, Shiwen College of Information Science and Electronic Engineering Zhejiang University Hangzhou China College of Computing and Data Science Nanyang Technological University Singapore Department of Electrical and Computer Engineering Western University LondonONN6A 5B9 Canada Department of Electrical and Computer Engineering Auburn University AuburnAL36849 United States

Semantic communication (SemCom) is an emerging paradigm aiming at transmitting only task-relevant semantic information to the receiver, which can significantly improve communication efficiency. Recent advancements in generative artificial intelligence (GenAI) have empowered GenAI-enabled SemCom (GenSemCom) to further expand its potential in various applications. However, current GenSemCom systems still face challenges such as semantic inconsistency, limited adaptability to diverse tasks and dynamic environments, and the inability to leverage insights from past transmission. Motivated by the success of retrieval-augmented generation (RAG) in the domain of GenAI, this paper explores the integration of RAG in GenSemCom systems. Specifically, we first provide a comprehensive review of existing GenSemCom systems and the fundamentals of RAG techniques. We then discuss how RAG can be integrated into GenSemCom. Following this, we conduct a case study on semantic image transmission using an RAG-enabled diffusion-based SemCom system, demonstrating the effectiveness of the proposed integration. Finally, we outline future directions for advancing RAG-enabled GenSemCom systems. © 2024, CC BY.

关键词： Latent semantic analysis

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BITLITE: Light Bit-wise Operative Vector Matrix Multiplication for Low-Resolution Platforms

BITLITE: Light Bit-wise Operative Vector Matrix Multiplicati...

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IEEE International Symposium on Circuits and Systems (ISCAS)

作者： Vince Tran Demeng Chen Roman Genov Mostafa Rahimi Azghadi Amirali Amirsoleimani Department of Electrical and Computer Engineering University of Toronto Toronto Canada College of Science and Engineering James Cook University Townsville Australia Department of Electrical Engineering and Computer Science York University Toronto Canada

ISBN: (数字)9798350330991

ISBN: (纸本)9798350331004

As machine learning (ML) algorithms, particularly neural networks (NN), expand in popularity and capacity, the quest for more efficient computation methods gains momentum. Memristor crossbar technology emerges as a promising alternative to traditional computing units, aiming to address traditional computing challenges. However, conventional matrix-vector multiplication (MVM) methods on these platforms are often plagued by device imperfections and drift. In this work, we introduce an innovative lightweight calculation approach leveraging bit-transformation for MVM, significantly enhancing operation precision and, consequently, the performance of ML algorithms on memristor crossbar platforms. We provide details of the core algorithm and its extensions, furnish digital validation, and simulate its efficacy using an autoencoder (AE) neural network with an extended VTEAM model. Our tests demonstrate an average reconstruction precision improvement of approximately 53.5%. This work’s applicability extends beyond NNs, offering a foundational method for conducting more precise analog MVM operations.

关键词： Performance evaluation Machine learning algorithms Circuits and systems Memristors Artificial neural networks Machine learning Approximation algorithms

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SpikeMotion: A Transformer Framework for High - Throughput Video Segmentation on FPGA

SpikeMotion: A Transformer Framework for High - Throughput V...

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Midwest Symposium on Circuits and Systems (MWSCAS)

作者： Uchechukwu Leo Udeji Martin Margala Electrical and Computer Engineering department University of Massachusetts Lowell Lowell USA Computing and Informatics department University of Louisiana at Lafayette Lafayette USA

ISBN: (数字)9798350387179

ISBN: (纸本)9798350387186

Transformers have emerged at the forefront in the training and inference of diverse machine learning tasks, encompassing video processing, image generation and classification, and natural language processing (NLP). Despite their increasing prevalence, a comprehensive framework to efficiently implement them has been lacking. This study introduces a transformer-based framework which accelerates image processing of UNETR (U-shaped neural network transformer) model for video segmentation task using the cityscapes dataset. Given the large size of images in the dataset we incorporate hyperattention and mixed precision in our design. Our model is trained on Google A1OO GPU accelerator and profiled. Finally, our design is implemented on FPGA to take advantage of the reconfigurable and high-throughput characteristics of system-on-chips (SoC) for image processing. Our results indicate improvements compared to existing research in this domain.

关键词： Training Image segmentation Neural networks Memory management Graphics processing units Machine learning Transformers

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Reactive Navigation of the Ackermann Steering Robot in Unknown Environments

Reactive Navigation of the Ackermann Steering Robot in Unkno...

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International Conference on Autonomous Robots and Agents, ICARA

作者： ANA ŠELEK IVAN HRABAR SAŠA ŠTEKOVIĆ Department of Control and Computer Engineering Faculty of Electrical Engineering and Computing University of Zagreb Zagreb Croatia Autegra D.O.O. Zagreb Croatia

ISBN: (数字)9798331509231

ISBN: (纸本)9798331509248

Mobile robots are now widely used in numerous real-world applications that have complex navigation requirements, especially in environments used by humans. This requires highly accurate navigation that can be performed in realtime. In this paper, a method for generating a smooth motion of nonholonomic mobile robots is proposed. It enables robots to move optimally toward the desired goal and allows fast path replanning when encountering unknown or dynamic obstacles. The method generates smooth, collision-free trajectories based on clothoids, ensuring high computational efficiency and suitability for realtime path planning. By applying a smoothing algorithm, the proposed method improves the robot's efficiency in terms of travel time and trajectory length from start to goal, as demonstrated by a comparison with model predictive control.

关键词： Smoothing methods Navigation Dynamics Real-time systems Trajectory Computational efficiency Mobile robots Collision avoidance Testing Predictive control

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Enhancing Facility Safety for Autonomous Gas Inspection Drones Leveraging Convolutional Neural Networks and loT Technology 11

Enhancing Facility Safety for Autonomous Gas Inspection Dron...

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11th International Conference on Reliability, Infocom Technologies and Optimization, ICRITO 2024

作者： Latha, S. Asha, P. Srinivasan, V. Prasanna Elangovan, K. Thamizhamuthu, R. Sujatha, S. Department of Biotechnology Tamil Nadu Salem India Sathyabama Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu Chennai India R.M.D. Engineering College Department of Information Technology Tamil Nadu Chennai India Dhanalakshmi Srinivasan Engineering College Department of Mechanical Engineering Tamil Nadu Chennai India Srm Institute of Science and Technology Department of Computing Technologies Tamil Nadu Chennai India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Department of Biomedical Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350350357

To increase facility safety by integrating autonomous gas inspection drones with Convolutional Neural Networks (CNNs) and Internet of Things (loT) technologies. Due to their ability to reach difficult situations, unmanned aerial vehicles are increasingly used for gas inspection in industry. However, drone safety and gas detection accuracy remain major issues. It offers a comprehensive method that uses CNN s for real-time image analysis and classification to help the drone locate gas leaks with high accuracy. CNN is trained on a broad dataset of gas leak situations and environmental conditions. loT technology also allows the drone and centralized monitoring system to communicate in real-time for data sharing and decision-making. Our method can adjust to changing lighting and weather and discriminate between innocuous abnormalities and gas leaks. Advanced navigation algorithms let the drone traverse complicated industrial sites while avoiding obstacles and maximizing coverage. Experimental findings show the suggested technology improves facility safety by lowering gas leak reaction times and operator risk. CNNs with loT technologies increase gas leak detection and allow proactive maintenance, improving industrial facility safety and efficiency. It advances autonomous systems for industrial applications by tackling gas inspection safety problems. The suggested framework lays the groundwork for integrating innovative technology to improve autonomous gas inspection drone capabilities and safety. © 2024 IEEE.

关键词： Gases

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Design of a Smart Energy Meter and Monitoring System for Industrial and Residential Use Based on the Internet of Things

Design of a Smart Energy Meter and Monitoring System for Ind...

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2023 International Conference on System, Computation, Automation and Networking, ICSCAN 2023

作者： Moorthy, Vijai Meyyappan Muthukumaran Mansingh, Benson Manoj, Britto Sateesh Kumar Reddy, C. Elango, Arul Vignan Foundation of Science Research and Technology Department of Advanced Computer Science and Engineering Andhra Pradesh Guntur India M. I. E. T. Engineering College Department of Electrical and Electronics Engineering Tamilnadu Trichy India

ISBN: (纸本)9798350315127

Electronic meters have become increasingly important for efficient energy use, and the proposed research aims to develop an IoT-based architecture for metering and monitoring. This smart monitoring and control system (SMACS) for home appliances is suggested to enhance household electricity control. The technology uses a wireless control module with an ESP8266 microcontroller and Wi-Fi channel to monitor current consumption and send measured amounts over Wi-Fi. The system calculates current consumption, counts units, and displays results on the user's website and smartphone. It avoids using traditional analog and digital meter reading techniques and allows remote access to electronic meters. The proposed work offers lower total costs, greater energy savings, and reduced time and labour requirements. © 2023 IEEE.

关键词： Domestic appliances

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SAR-MemPipe: A Hybrid Pipeline-SAR Memristive ADC for Analog Resistive Arrays

SAR-MemPipe: A Hybrid Pipeline-SAR Memristive ADC for Analog...

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IEEE International Symposium on Circuits and Systems (ISCAS)

作者： Hao You Jianxiong Xu Amirali Amirsoleimani Mostafa Rahimi Azghadi Roman Genov Department of Electrical and Computer Engineering University of Toronto Toronto Canada Department of Electrical Engineering and Computer Science York University Toronto Canada College of Science and Engineering James Cook University Townsville Australia

ISBN: (数字)9798350330991

ISBN: (纸本)9798350331004

This paper presents a hybrid pipeline SAR ADC with a loop-unrolled structure to reduce the crossbar’s ADC power while maintaining high speed. The 1 st stage memristive SAR ADC can fully utilize the TIA originally in the crossbar and avoid the extra MDAC in pipeline ADC. Further, memristive weight calibration and a new resistive alternated binary search are implemented on the 1 st stage to maintain the TIA gain and ADC’s accuracy. Both stage’s ADC are loop-unroll to eliminate the delay brought by SAR logic for high speed. Through multiple simulations, the design is demonstrated to be robust to the frequency and mismatch variations with the highest sampling frequency reaching 300MHz and SNDR up to 65.1dB in 9MHz input. The power consumption is designed to be as low as 6.7mW, which helps the ADC to achieve a 15.2fJ/conv FoM. Not limited to the crossbar, the presented ADC also shows promising potential for applications in various fields (biomedical, IoT etc.) for general purposes.

关键词： Time-frequency analysis Accuracy Power demand Pipelines computer architecture Robustness Delays

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Empowering the Solar Street Lighting Revolution: Developing a Smart-Relay-Controlled Photovoltaic System for Enhanced Efficiency, Sustainability, and Operational Control 6

Empowering the Solar Street Lighting Revolution: Developing ...

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6th International Congress on Human-computer Interaction, Optimization and Robotic Applications, HORA 2024

作者： Ahmed, Saadaldeen Rashid Mejbel, Basim Galeb Khamil, Talib Hussein Radhi, Ahmed Dheyaa AL-Ani, Azhar R. Hussain, Abadal-Salam T. Majeed, Duaa A. Tawfeq, Jamal Fadhil Sekhar, Ravi Computer Science Department Bayan University Kurdistan Erbil Iraq Artificial Intelligence Engineering Department College of Engineering Al-Ayen University Thi-Qar Iraq Al Hikma University College Baghdad Iraq Electrical Power Techniques Al-Ma’moon University College Al-Washash / Baghdad Iraq College of Pharmacy University of Al-Ameed Karbala PO Box 198 Iraq Computer technical engineerning Al Hikma University College Baghdad Iraq Department of Medical Instrumentation Techniques Engineering Technical Engineering College Al-Kitab University Altun Kupri Kirkuk Iraq Aeronautical Engineering Department Baghdad University Baghdad Iraq College of Technical Engineering National University of Science and Technology Dhi Qar Iraq Department of Medical Instrumentation Technical Engineering Medical Technical College Al-Farahidi University Baghdad Iraq Maharashtra Pune412115 India

ISBN: (纸本)9798350394634

This paper focuses on the development of a standalone photovoltaic street lighting system controlled by a smart relay. The system incorporates key components such as a photovoltaic module, solar charger controller, light-dependent resistor, battery, relay, and direct current lamp. Operating on the principle of photovoltaic cells, the solar street lighting harnesses solar energy during the day, converting it into electrical energy stored in a battery. As night falls, the lamps activate automatically, drawing power from the stored energy. Thus, the operation is continuous and energy saving capital is ensured. The project's purpose is to promote the effective use of lights in urban regions, thereby achieving energy efficiency, decreasing maintenance costs, and extending product life. To summarize, this unique solar street lighting option is the best way to solve the urban lighting problem sustainably. © 2024 IEEE.

关键词： Energy efficiency

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