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2nd International Conference on Intelligent Manufacturing and Robotics, ICIMR 2024

作者： Mahendren, Aniel P.P. Abdul Majeed, Anwar Ab Nasir, Ahmad Fakhri Luo, Yang Aslam, Saad Behjati, Mehran Jasser, Muhammed Basheer Mohd Khairuddin, Ismail School of Engineering and Technology Sunway University Bandar Sunway Selangor Subang Jaya47500 Malaysia Faculty of Computing University Malaysia Pahang Al-Sultan Abdullah Pahang Pekan26600 Malaysia School of Intelligent Manufacturing Ecosystem Xi’an Jiaotong-Liverpool University Suzhou Taicang215400 China Faculty of Manufacturing and Mechatronics Engineering Technology University Malaysia Pahang Al-Sultan Abdullah Pahang Pekan26600 Malaysia

ISBN: (纸本)9789819639489

White Blood Cells (WBCs) are vital components of the immune system, playing a crucial role in defending the body against infections and diseases. Traditionally, the classification of WBCs was performed manually by hematologists, a method fraught with limitations such as subjectivity and time consumption. To address these challenges, this study explores the efficacy of a transfer learning pipeline for WBC classification, leveraging pre-trained models like VGG16, VGG19, and InceptionV3 for feature extraction. The classification models employed include k-Nearest Neighbour (kNN), Support Vector Machine (SVM), and Logistic Regression (LR). The research utilizes Paul Mooney’s white blood cell dataset, which consists of 640 microscopic images of four different WBC types. The dataset is divided into training, validation, and testing sets in a 70:15:15 ratio. The results demonstrate that the VGG16-LR pipeline achieves superior classification accuracy compared to other model combinations tested. This study highlights the potential of integrating transfer learning with robust classifiers to improve the accuracy and efficiency of WBC classification, offering promising implications for medical diagnostics and treatment planning. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Contrastive Learning

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Deep Learning-Powered Edge Analytics for IoT-based Sensor Networks 2

Deep Learning-Powered Edge Analytics for IoT-based Sensor Ne...

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2nd International Conference on Machine Learning and Autonomous Systems, ICMLAS 2025

作者： Karthikeyan, M. Saravana Subramani, Ponnusamy Selvam, M. Panneer Sworna Kokila, M.L. Vijayalakshmi, R. Sundaravadivel, P. Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Avadi Department of Computer Science and Engineering Chennai India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Physics Chennai India Sona College of Technology Department of Electrical and Electronics Engineering Salem India School of Computing SRM Institute of Science and Technology Kattankulathur Faculty of Engineering and Technology Department of Computing Technologies Chennai India Velammal College of Engineering and Technology Department of Computer Science and Engineering Madurai India Saveetha Engineering College Department of Artificial Intelligence and Machine Learning Chennai India

ISBN: (纸本)9798331505745

The rapid expansion of loT-based sensor networks has necessitated the development of efficient edge analytics frameworks to process vast amounts of data in real time while minimizing computational overhead. Deep learning-powered edge computing has emerged as a transformative approach, enhancing data processing capabilities at the network edge while addressing constraints related to latency, bandwidth, and energy consumption. This study introduces a novel algorithm, Lightweight AI Model Compression for Energy-Efficient IoT Edge Devices (LiteEdgeAI), designed to optimize deep learning models for resource-constrained edge environments. The proposed method employs quantization, pruning, and knowledge distillation techniques to achieve significant reductions in model size and computational complexity while maintaining high inference accuracy. To validate the efficiency of LiteEdgeAI, a comparative simulation analysis was conducted against existing state-of-the-art algorithms using key performance metrics such prediction accuracy, energy efficiency and computational complexity. Experimental results demonstrated that LiteEdgeAI outperformed baseline models. The findings highlight the potential of LiteEdgeAI in enabling scalable, low-power, and real-time edge intelligence for IoT-based sensor networks. © 2025 IEEE.

关键词： Deep learning

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Optimizing Household Energy Consumption: A Web-Based Tool for Real-Time Carbon Footprint Calculation and Sustainable Benchmarking

Optimizing Household Energy Consumption: A Web-Based Tool fo...

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Advances in Computer Science, Electrical, Electronics, and Communication Technologies (CE2CT), International Conference on

作者： Harsh Raja Jayaditya Kishore J. Kalaivani V. Angayarkanni P. Rama Department Of Computing Technologies Faculty Of Engineering And Technology Srm Institute Of Science And Technology Chengalpattu District Tamil Nadu India

ISBN: (数字)9798331518578

ISBN: (纸本)9798331518585

This is an innovative project from an energy-saving perspective. This project ensures that users become in control of their energy consumption and able to take an actively reducing their carbon footprints. It provides users with an interface to input details about household appliances and usage patterns, through which a correct calculation of the carbon footprint could be done. Compared against standard norms, it would help the users identify certain areas where improvement is possible and adopt more sustainable use of energy. Apart from personalized energy assessments, it has now become the central source that connects users to local schemes and initiatives on sustainable energy, including information on resources and programs into waste energy and carbon emission reduction. This integration, therefore, promotes more engagement and involvement within the community as users are nudged to join in the efforts that are being made for the goal of energy conservation. The platform educates its users about energy consumption and aids them in making green choices in data; however, with a holistic approach in carbon footprint management and education pertaining to sustainable long-term behavior, the process takes place through integrated accurate energy tracking, fine recommendations, and community-driven action.

关键词： Energy consumption Energy conservation Green products Real-time systems Sustainable development Carbon footprint Standards Research and development Meteorology Resilience

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Power Line Following Based on Measurements of the Magnetic Field

Power Line Following Based on Measurements of the Magnetic F...

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International Conference on Unmanned Aircraft Systems (ICUAS)

作者： Goran Vasiljević Dean Martinović Stjepan Bogdan Faculty of Electrical Engineering and Computing Laboratory for Robotics and Intelligent Control Systems Unska 3 University of Zagreb Zagreb Croatia

ISBN: (数字)9798331513283

ISBN: (纸本)9798331513290

In this paper, we present a method to control the UAV to follow the power line with a specific position and orientation based only on the measurement of the magnetic field generated by the current flow in the power line. In this way, it is possible to localize the UAV with respect to the power line without the need for additional sensors, even in poor visibility conditions. The measurements from four magnetometers attached to the UAV are used to solve an optimization problem that involves determining the relative pose of the UAV with respect to the power line. Based on the relative pose, the UAV is controlled to follow the power line in a predefined position and orientation. Experiments in a test setup have confirmed that the method is applicable in a realistic environment.

关键词： Location awareness Visualization Power measurement Magnetic field measurement Magnetometers Current measurement Position measurement Autonomous aerial vehicles Magnetic fields Optimization

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Optimal Control of Sensor-Induced Illusions on Robotic Agents

arXiv

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arXiv 2025年

作者： Medici, Lorenzo LaValle, Steven M. Sakçak, Başak Faculty of Information Technology and Electrical Engineering University of Oulu Finland Department of Advanced Computing Sciences Maastricht University Netherlands

This paper presents a novel problem of creating and regulating localization and navigation illusions considering two agents: a receiver and a producer. A receiver is moving on a plane localizing itself using the intensity of signals from three known towers observed at its position. Based on this position estimate, it follows a simple policy to reach its goal. The key idea is that a producer alters the signal intensities to alter the position estimate of the receiver while ensuring it reaches a different destination with the belief that it reached its goal. We provide a precise mathematical formulation of this problem and show that it allows standard techniques from control theory to be applied to generate localization and navigation illusions that result in a desired receiver behavior. © 2025, CC BY.

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High-fidelity EEG feature-engineered taxonomy for bruxism and PLMS prognostication through pioneering and avant-garde ML frameworks

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Measurement: Sensors 2025年 39卷

作者： Tiwari, Shivam Arora, Deepak Bhardwaj, Barkha Department of Computer Science and Engineering Sharda University Greater Noida India Faculty of Engineering and Computing Liwa College Abu Dhabi United Arab Emirates NIET Greater Noida India

Periodic Leg Movement during Sleep (PLMS) and Bruxism are linked with changes in EEG signal characteristics. This work applies machine learning and data mining approaches to examine these changes. Patients with PLMS and bruxism had nighttime EEG recordings to examine changes in brain activity. The findings revealed constant variations in brain hemodynamics even in the absence of clearly observable arousals in the EEG. Wavelet decomposition was used to improve classification precision. Using the N3 sleep stage, accuracy varied from 92 % to 96 %, with an AUC of 0.85–0.89, in diagnosing binary sleep disorders. Still, adding wavelet-based elements greatly enhanced performance, obtaining an AUC of 0.99 with classification accuracy ranging from 94 % to 98 %. This emphasizes how strongly discriminative power wavelet-extracted EEG characteristics possess. Using K-Nearest Neighbors (KNN), Artificial Neural Networks (ANN), and Support Vector Machines (SVM) with Radial Basis Function (RBF), Bruxism categorization was accomplished. These models attained respectively 82 %, 90 %, and 93 % percent classification accuracy. This work is the first to show a direct connection among differences in brain activity based on PLMS, Bruxism, and EEG-based technologies. The results show how well machine learning methods and EEG feature extraction might diagnose sleep problems. Although the therapeutic relevance of these findings is yet unknown, the results imply that enhanced EEG-based classification techniques could produce more reliable and automated diagnostic instruments for Bruxism and PLMS. © 2025 The Authors

关键词： Radial basis function networks

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Application Deployment: The Game-Changing Impact of Containerization

Application Deployment: The Game-Changing Impact of Containe...

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Power, Control and computing Technologies (ICPC2T), Conference on

作者： Parth Galhotra Kumar Aniruddh Singh Zafarul Hasan M Safa Department of Networking and Communications School of Computing Faculty of Engineering and Technology SRM IST SRM Nagar Kattankulathur Chennai TN

ISBN: (数字)9798331530402

ISBN: (纸本)9798331530419

This study will introduce the use of Docker for simple application packaging to investigate the impact of packaging on application deployment and management. Containerization is a technological method for packaging and consuming applications in a self-contained environment that is more efficient than virtualization. Docker is a well-known containerization platform that enables efficient creation, deployment, and management of applications by focusing and efficiently utilizing resources across multiple locations. Delivering programs and investigating their similarities in terms of consistency, efficiency, and effectiveness. It also solves security issues and multi-container issues. The approach will be to package a simple web application, deploy it via Docker, and then measure how performance and manageability are improved compared to traditional deployments. Using Docker for application deployment helps you better understand how containerization can improve and simplify deployment and management in today’s software development.

关键词： Power demand Memory management Power control Green products Focusing Packaging Containers Security Virtualization System analysis and design

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Pose-independent 3D Anthropometry from Sparse Data

arXiv

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arXiv 2025年

作者： Bojanić, David Wuhrer, Stefanie Petković, Tomislav Pribanić, Tomislav University of Zagreb Faculty of Electrical Engineering and Computing Zagreb Croatia Inria centre University Grenoble Alpes Grenoble France

3D digital anthropometry is the study of estimating human body measurements from 3D scans. Precise body measurements are important health indicators in the medical industry, and guiding factors in the fashion, ergonomic and entertainment industries. The measuring protocol consists of scanning the whole subject in the static A-pose, which is maintained without breathing or movement during the scanning process. However, the A-pose is not easy to maintain during the whole scanning process, which can last even up to a couple of minutes. This constraint affects the final quality of the scan, which in turn affects the accuracy of the estimated body measurements obtained from methods that rely on dense geometric data. Additionally, this constraint makes it impossible to develop a digital anthropometry method for subjects unable to assume the A-pose, such as those with injuries or disabilities. We propose a method that can obtain body measurements from sparse landmarks acquired in any pose. We make use of the sparse landmarks of the posed subject to create pose-independent features, and train a network to predict the body measurements as taken from the standard A-pose. We show that our method achieves comparable results to competing methods that use dense geometry in the standard A-pose, but has the capability of estimating the body measurements from any pose using sparse landmarks only. Finally, we address the lack of open-source 3D anthropometry methods by making our method available to the research community at ***/DavidBoja/pose-independent-anthropometry. Copyright © 2025, The Authors. All rights reserved.

关键词： Entertainment industry

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mmWave Radar-Based Non-Invasive Vehicle Occupancy Detection with Insights from Vision, RF, and Physical Sensors: A Review

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IEEE Sensors Journal 2025年第11期25卷 18643-18661页

作者： Yousaf, Jawad Palmer, Alyssa Shams, Maitha Wasma, Zayna Qasymeh, Montasir Hassan, Taimur Arora, Deepak Ghazal, Mohammed Abu Dhabi University Electrical Computer and Biomedical Engineering Department Abu Dhabi United Arab Emirates Liwa College Faculty of Engineering and Computing Abu Dhabi United Arab Emirates

The potential uses of human occupancy detection (HOD) in vehicles are crucial for handling resources, passenger safety, and privacy-preserving technology. In this study, the usage of various sensor systems for non-invasive vehicle occupant detection is examined, including mmWave radar, vision-based, and physical sensors. These technologies undergo a thorough review analysis that looks at methods, performance indicators, and use cases before being assessed for robustness, accuracy, real-time capabilities, scalability, and cost-effectiveness. The impact of clutter, scalability, and privacy in car interiors on HOD is also thoroughly discussed. Based on its ability to balance cost, accuracy, and adaptability, frequency-modulated continuous wave (FMCW) radar is termed the best option for vehicle occupant identification. The study discussed the possible challenges and future direction, as well as the conclusion provides research opportunities in hybrid sensing systems and advanced machine learning integration to overcome existing constraints. © 2001-2012 IEEE.

关键词： Frequency shift keying

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Performance Analysis and Power Allocation for Massive MIMO ISAC Systems

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IEEE Transactions on Signal Processing 2025年 73卷 1691-1707页

作者： Nguyen, Nhan Thanh Nguyen, Van-Dinh Nguyen, Hieu V. Ngo, Hien Quoc Swindlehurst, A. Lee Juntti, Markku University of Oulu Centre for Wireless Communications Oulu90014 Finland VinUniversity College of Engineering and Computer Science Hanoi100000 Viet Nam University of Science and Technology The University of Danang Faculty of Electronics and Telecommunication Engineering Da Nang50000 Viet Nam Queen’s University Belfast School of Electronics Electrical Engineering and Computer Science BelfastBT7 1NN United Kingdom Kyung Hee University Department of Electronic Engineering Yongin-si17104 Korea Republic of University of California Center for Pervasive Communications and Computing IrvineCA92697 United States

Integrated sensing and communications (ISAC) is envisioned as a key feature in future wireless communications networks. Its integration with massive multiple-input-multiple-output (MIMO) techniques promises to leverage substantial spatial beamforming gains for both functionalities. In this work, we consider a massive MIMO-ISAC system employing a uniform planar array with zero-forcing and maximum-ratio downlink transmission schemes combined with monostatic radar-type sensing. Our focus lies on deriving closed form expressions for the achievable communications rate and the Cramér–Rao lower bound (CRLB), which serve as performance metrics for communications and sensing operations, respectively. The expressions enable us to investigate important operational characteristics of massive MIMO-ISAC, including the mutual effects of communications and sensing as well as the advantages stemming from using a very large antenna array for each functionality. Furthermore, we devise a power allocation strategy based on successive convex approximation to maximize the communications rate while guaranteeing the CRLB constraints and transmit power budget. Extensive numerical results are presented to validate our theoretical analyses and demonstrate the efficiency of the proposed power allocation approach. © 1991-2012 IEEE.

关键词： Budget control

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