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International Conference on Electrical, computer and Communication Engineering (ECCE)

作者： Mir Mohibullah Sazid Syma Afsha Rupom Razzaque Kamruddin Nur EMJM in Intelligent Field Robotic Systems (IFRoS) Universitat de Girona Girona Spain School of Computer Science and Engineering Queen Mary University of London Department of Computer Science American International University-Bangladesh Dhaka Bangladesh

ISBN: (数字)9798350357509

ISBN: (纸本)9798350357516

This paper presents RoboPick, an advanced Vehicle-Manipulator System (VMS) designed to perform efficient and precise object manipulation tasks. The system integrates a TurtleBot 2 mobile base with a 4-DOF Swift Pro robotic arm, enabling the robot to autonomously navigate and interact with its environment. The system is equipped with an Intel RealSense D435i camera and ArUco markers for accurate object localization and positioning. The control system uses a task-priority algorithm to ensure that the robot performs pick-and-place operations efficiently, while adhering to safety constraints and maintaining high precision throughout. Behavior trees are implemented to manage the sequence of operations, ensuring that the robot executes tasks smoothly and effectively. The system's capabilities are validated through extensive simulation tests, demonstrating its high reliability and consistent efficiency in completing tasks. The robot's performance in terms of task completion time and success rate highlights its robustness in handling complex manipulation routines across multiple trials. This research emphasizes the importance of combining robust control architectures with precise localization techniques to enhance the performance of autonomous mobile manipulators in real-world object manipulation applications.

关键词： Location awareness Robust control Navigation Operating systems Robot vision systems Kinematics Manipulators Control systems Robustness Safety

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PSL-CFRT: A Benchmark Multi-View Dataset for Continuous Pakistani Sign Language Fingerspelling Recognition and Translation

PSL-CFRT: A Benchmark Multi-View Dataset for Continuous Paki...

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IT Innovation and Knowledge Discovery (ITIKD), International Conference on

作者： Muhammad Kamran Mubasher Baig Mian M. Awais El-Sayed M. El-Alfy Adnan Akhunzada Department of Computer Science National University of Computer and Emerging Sciences Lahore Pakistan Syed Babar Ali School of Science and Engineering Lahore University of Management Science (LUMS) Lahore Pakistan Information and Computer Science Department Computer Engineering Department IRC-Intelligent Secure Systems King Fahd University of Petroleum and Minerals (KFUPM) Dhahran Saudi Arabia Department of Cybersecurity University of Doha for Science and Technology Doha Qatar

ISBN: (数字)9798350355468

ISBN: (纸本)9798350355475

Fingerspelling, an essential component of sign language, utilizes distinct hand configurations for each letter of a written language to convey proper names, technical terms, addresses, numerical values, and other uncommon words. This intricate form of communication is challenging for the general community, creating a communication barrier between the deaf and hard-of-hearing (DHH) community and those unfamiliar with it. To bridge this gap, this paper introduces a novel multi-view dataset specifically designed for continuous fingerspelling recognition and translation in Pakistani Sign Language (PSL). This dataset is unique in its inclusion of diverse fingerspelling variations, enhancing its applicability to real-world scenarios. In addition to introducing the dataset, an object-detection-based method is proposed for PSL continuous fingerspelling recognition (PSL-CFR), marking a significant advancement in continuous PSL finger-spelling recognition technology, achieving a baseline accuracy of 63.16%.

关键词： YOLO Deep learning Sign language Technological innovation Translation Accuracy Computational modeling Streaming media Knowledge discovery Real-time systems

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Exploring Augmented Reality's Influence on Cognitive Load and Emotional Dynamics within UAV Training Environments

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IEEE Transactions on Learning Technologies 2025年

作者： Rahimi, Fatema Sadeghi-Niaraki, Abolghasem Song, Houbing Wang, Huihui Choi, Soo-Mi Sejong University Department of Computer Science and Engineering Convergence Engineering for Intelligent Drone XR Research Center Seoul Korea Republic of Department of Information Systems BaltimoreMD21250 United States St. Bonaventure University Cybersecurity Program St. BonaventureNY14778 United States

This study investigates the cognitive and emotional processes involved in augmented reality (AR)-based learning. The study looks at learning outcomes, emotional responses, meditation, and attention using a comprehensive approach that includes self-assessment, EEG data gathering, and post-experiment questionnaires. Twelve participants, selected based on their English proficiency and lack of prior knowledge of the course material, engaged in AR-based learning, while a baseline reading condition was included to contextualize cognitive and emotional engagement. The study findings indicate that the AR group's participants demonstrated notably elevated attention and meditation levels, indicating heightened engagement and focus that is advantageous for efficient assimilation and retention of knowledge. Furthermore, AR learners reported feeling less tired and exhausted, which may have mitigated the negative emotional states that are frequently connected to learning activities. However, no significant differences in negative emotions were observed between the reading and AR groups. These results emphasize the value of customized AR environments for education goals and the need for more study to maximize learning outcomes and affective experiences in AR learning contexts. © 2008-2011 IEEE.

关键词： Engineering education

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Human Fall Detection Using Transfer Learning-Based 3D CNN 1st

Human Fall Detection Using Transfer Learning-Based 3D CNN

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1st International Conference on Computational Technologies and Electronics, ICCTE 2023

作者： Alam, Ekram Sufian, Abu Dutta, Paramartha Leo, Marco Department of Computer Science Gour Mahavidyalaya Old Malda India Department of Computer Science University of Gour Banga English Bazar India Department of Computer and System Sciences Visva-Bharati University Santiniketan India National Research Council of Italy Institute of Applied Sciences and Intelligent Systems Lecce Italy

ISBN: (纸本)9783031819346

Unintentional or accidental falls are one of the significant health issues in senior persons. The population of senior persons is increasing steadily. So, there is a need for an automated fall detection monitoring system. This paper introduces a vision-based fall detection system using a pre-trained 3D CNN. Unlike 2D CNN, 3D CNN extracts not only spatial but also temporal features. Proposed model leverages the original learned weights of a 3D CNN model pre-trained on the Sports1M dataset to extract the spatio-temporal features. Only the SVM classifier was trained, which saves the time required to train the 3D CNN. Stratified shuffle five split cross-validation has been used to split the dataset into training and testing data. Extracted features from the proposed 3D CNN model were fed to an SVM classifier to classify the activity as fall or ADL. Two datasets, GMDCSA and CAUCAFall, were utilized to conduct the experiment. The source code for this work can be accessed via the following link: https://***/ekramalam/HFD_3DCNN. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Fall detection

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Self-Supervised Reflective Learning Through Self-Distillation and Online Clustering for Speaker Representation Learning

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IEEE Transactions on Audio, Speech and Language Processing 2025年 33卷 1535-1550页

作者： Danwei Cai Zexin Cai Ze Li Ming Li Department of Electrical and Computer Engineering Duke University Durham NC USA Suzhou Municipal Key Laboratory of Multimodal Intelligent Systems Digital Innovation Research Center Duke Kunshan University Kunshan China School of Computer Science Wuhan University Wuhan China

Speaker representation learning is crucial for voice recognition systems, with recent advances in self-supervised approaches reducing dependency on labeled data. Current two-stage iterative frameworks, while effective, suffer from significant computational overhead due to repeated rounds of clustering and training. They also struggle with noisy pseudo labels that can impair model learning. This paper introduces self-supervised reflective learning (SSRL), an improved framework that addresses these limitations by enabling continuous refinement of pseudo labels during training. Through a teacher-student architecture and online clustering mechanism, SSRL eliminates the need for iterative training rounds. To handle label noise, we incorporate noisy label modeling and pseudo label queues that maintain temporal consistency. Experiments on VoxCeleb show SSRL's superiority over current two-stage iterative approaches, surpassing the performance of a 5-round method in just a single training round. Ablation studies validate the contributions of key components like noisy label modeling and pseudo label queues. Moreover, consistent improvements in pseudo labeling and the convergence of cluster counts demonstrate SSRL's effectiveness in deciphering unlabeled data. This work marks an important advancement in efficient and accurate self-supervised speaker representation learning through the novel reflective learning paradigm.

关键词： Training Representation learning Iterative methods Noise Adaptation models Noise measurement Predictive models Speech processing Robustness Convergence

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Matched Topological Subspace Detector

arXiv

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arXiv 2025年

作者： Liu, Chengen Tenorio, Victor M. Marques, Antonio G. Isufi, Elvin Faculty of Electrical Engineering Mathematics and Computer Science Netherlands Department of Intelligent Systems Delft University of Technology Netherlands Department of Signal Theory and Communications King Juan Carlos University Spain

—Topological spaces, represented by simplicial complexes, capture richer relationships than graphs by modeling interactions not only between nodes but also among higher-order entities, such as edges or triangles. This motivates the representation of information defined in irregular domains as topological signals. By leveraging the spectral dualities of Hodge and Dirac theory, practical topological signals often concentrate in specific spectral subspaces (e.g., gradient or curl). For instance, in a foreign currency exchange network, the exchange flow signals typically satisfy the arbitrage-free condition and hence are curl-free. However, the presence of anomalies can disrupt these conditions, causing the signals to deviate from such subspaces. In this work, we formulate a hypothesis testing framework to detect whether simplicial complex signals lie in specific subspaces in a principled and tractable manner. Concretely, we propose Neyman-Pearson matched topological subspace detectors for signals defined at a single simplicial level (such as edges) or jointly across all levels of a simplicial complex. The (energy-based projection) proposed detectors handle missing values, provide closed-form performance analysis, and effectively capture the unique topological properties of the data. We demonstrate the effectiveness of the proposed topological detectors on various real-world data, including foreign currency exchange networks. © 2025, CC BY.

关键词： Network coding

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Retinal disorder diagnosis based on hybrid deep learning models

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Multimedia Tools and Applications 2025年 1-25页

作者： Sedik, Ahmed El-Shafai, Walid El-Hag, Noha A. El-Banby, Ghada M. Abd El-Samie, Fathi E. Smart Systems Engineering Laboratory College of Engineering Prince Sultan University Riyadh11586 Saudi Arabia Department of the Robotics and Intelligent Machines Faculty of Artificial Intelligence Kafrelsheikh University Kafr el-Sheikh Egypt Computer Science Department Prince Sultan University Riyadh11586 Saudi Arabia Department of Electronics and Electrical Communications Engineering Faculty of Electronic Engineering Menoufia University Menouf32952 Egypt The Higher Institute of Commercial Sciences Algarbia Al Mahalla Al Kubra31951 Egypt Department of Industrial Electronics and Control Engineering Faculty of Electronic Engineering Menoufia University Menouf32952 Egypt Department of Information Technology College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University P.O. Box 84428 Riyadh11671 Saudi Arabia

Anomaly detection from medical images is badly needed for automated diagnosis. For example, medical images obtained with several modalities, such as magnetic resonance (MR) and confocal microscopy, need to be classified for anomaly detection. The MR images may contain tumors that need to be detected with anomaly detection techniques. In MR imaging, brain tumors are represented as areas composed of abnormal cells that need to be detected. On the other hand, confocal microscopy can be used for retinal imaging. Hence, there is a need to apply classification techniques for diagnosing diseases of the eye, such as Diabetic Retinopathy (DR), which leads to exudates in different regions of the retina. The detection of exudates is necessary for the diagnosis of DR. In this paper, two models are presented for the classification of MR and retinal images. Much research has been done in this area, but it did not achieve high accuracy of classification in an acceptable time. In addition, the previous models did not consider both types of image modalities considered in this paper. The suggested models are Convolutional Neural Network (CNN) and Convolutional Long Short-Term Memory (Conv-LSTM). They are used to classify normal and abnormal cases of both brain tumor and DR diseases. The CNN model is composed of six convolutional layers and six max-pooling layers. A soft-max activation function takes the classification decision. The proposed Conv-LSTM model is composed of three stages: pre-processing, feature extraction, and classification. The deep learning model performance is better than those of traditional networks. The CNN gives an accuracy of 94.26% for DR classification and 95.8% for brain tumor classification, while the Conv-LSTM gives the maximum accuracy of 96.41% for DR classification and 97.4% for brain tumor classification. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Confocal microscopy

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Scaling Off-Policy Reinforcement Learning with Batch and Weight Normalization

arXiv

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arXiv 2025年

作者： Palenicek, Daniel Vogt, Florian Peters, Jan Intelligent Autonomous Systems Group Technical University of Darmstadt Germany hessian.AI Darmstadt Germany Department of Computer Science University of Freiburg Germany Germany Germany

Reinforcement learning has achieved significant milestones, but sample efficiency remains a bottleneck for real-world applications. Recently, CrossQ has demonstrated state-of-the-art sample efficiency with a low update-to-data (UTD) ratio of 1. In this work, we explore CrossQ’s scaling behavior with higher UTD ratios. We identify challenges in the training dynamics, which are emphasized by higher UTD ratios. To address these, we integrate weight normalization into the CrossQ framework, a solution that stabilizes training, has been shown to prevent potential loss of plasticity and keeps the effective learning rate constant. Our proposed approach reliably scales with increasing UTD ratios, achieving competitive performance across 25 challenging continuous control tasks on the DeepMind Control Suite and Myosuite benchmarks, notably the complex dog and humanoid environments. This work eliminates the need for drastic interventions, such as network resets, and offers a simple yet robust pathway for improving sample efficiency and scalability in model-free reinforcement learning. © 2025, CC BY.

关键词： Deep reinforcement learning

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Light-EvoOPT: A Lightweight Evolutionary Optimization Framework for Ultra-Large-Scale Mixed Integer Linear programs

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IEEE Transactions on Evolutionary Computation 2025年

作者： Ye, Huigen Xu, Hua Coello, Carlos A. Coello Tsinghua University State Key Laboratory of Intelligent Technology and Systems Department of Computer Science and Technology Beijing100084 China Department of Computer Science D.F México07300 Mexico Tecnologico de Monterrey Faculty of Excellence The School of Engineering and Sciences N.L Monterrey Mexico

Machine Learning (ML)-based optimization frameworks emerge as a promising technique for solving large-scale Mixed Integer Linear programs (MILPs), as they can capture the mapping between problem structures and optimal solutions to expedite their solution process. However, existing solution frameworks often suffer from high model computation costs, incomplete problem reduction, and reliance on large-scale solvers, leading to performance bottlenecks in ultra-large-scale problems with complex constraints. To address these issues, this paper proposes Light-EvoOPT, a Lightweight Evolutionary Optimization Framework for Ultra-Large-Scale Mixed Integer Linear programs, which can be divided into four stages: (1) Problem Formulation for problem division to reduce model computational costs, (2) Model-based Initial Solution Prediction for predicting and constructing the initial solution using a small-scale training dataset, (3) Problem Reduction for both variable and constraint reduction, and (4) Evolutionary Optimization for current solution improvement employing a lightweight optimizer. Experiments on four benchmark datasets with tens of millions of variables and constraints and a real-world problem show that the proposed framework based on the sole use of a lightweight optimizer, trained on only one-thousandth of the scale of ultra-large-scale problems, is able to outperform state-of-the-art ML-based frameworks and advanced solvers (e.g. Gurobi) within a specified computational time, validating the feasibility and effectiveness of our proposed ML-based evolutionary optimization framework for ultra-large-scale MILPs. © 2025 IEEE. All rights reserved.

关键词： Mixed-integer linear programming

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Data-Driven vs Traditional Approaches to Power Transformer’s Top-Oil Temperature Estimation

arXiv

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arXiv 2025年

作者： Tembo, Francis Bragone, Federica Laneryd, Tor Barreau, Matthieu Morozovska, Kateryna Division of Decision and Control Systems Department of Intelligent Systems KTH Royal Institute of Technology Stockholm Sweden Division of Computational Science and Technology Department of Computer Science KTH Royal Institute of Technology Stockholm Sweden Hitachi Energy Research Västerås Sweden

Power transformers are subjected to electrical currents and temperature fluctuations that, if not properly controlled, can lead to major deterioration of their insulation system. Therefore, monitoring the temperature of a power transformer is fundamental to ensure a long-term operational life. Models presented in the IEC 60076-7 and IEEE standards, for example, monitor the temperature by calculating the top-oil and the hot-spot temperatures. However, these models are not very accurate and rely on the power transformers’ properties. This paper focuses on finding an alternative method to predict the top-oil temperatures given previous measurements. Given the large quantities of data available, machine learning methods for time series forecasting are analyzed and compared to the real measurements and the corresponding prediction of the IEC standard. The methods tested are Artificial Neural Networks (ANNs), Time-series Dense Encoder (TiDE), and Temporal Convolutional Networks (TCN) using different combinations of historical measurements. Each of these methods outperformed the IEC 60076-7 model and they are extended to estimate the temperature rise over ambient. To enhance prediction reliability, we explore the application of quantile regression to construct prediction intervals for the expected top-oil temperature ranges. The best-performing model successfully estimates conditional quantiles that provide sufficient coverage. Copyright © 2025, The Authors. All rights reserved.

关键词： Distribution transformers

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