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International Colloquium on Signal Processing & Its Applications, CSPA

作者： Saddaf Rubab Fida Hussain Dahri Ghulam E Mustafa Abro Nooritawati Md Tahir Muhammad Javed Department of Computer Engineering College of Computing and Informatics University of Sharjah Sharjah United Arab Emirates School of Computer Science and Engineering Southeast University Nanjing China Interdisciplinary Research Centre for Aviation and Space Exploration King Fahd University of Petroleum and Minerals (KFUPM) Saudi Arabia School of Electrical Engineering College of Engineering Universiti Teknologi MARA Shah Alam Selangor Malaysia Department of Cybersecurity International information Technology University Alamty Kazakhstan Department of Computer Science and Technology College of Computer Science Donghua University Shanghai China

ISBN: (数字)9798331522193

ISBN: (纸本)9798331522209

Urban traffic congestion necessitates innovative monitoring and control solutions. This study leverages UAV footage and deep learning techniques to classify and analyze traffic density levels autonomously. By employing a pre-trained ResN et50 model enhanced with additional layers, the research optimizes traffic density classification using UAV-captured data. A custom dataset of traffic scenarios in Dammam, Saudi Arabia, was developed, incorporating diverse conditions through advanced normalization and data augmentation techniques to improve resilience and robustness. The modified ResN et50 model demonstrated high classification accuracy across varied traffic scenarios, validating its reliability and scalability for real-world applications. Key findings include the superior adaptability of UAV-based monitoring for real-time urban traffic assessment and the enhanced classification precision achieved through model optimization. This research underscores the potential of integrating UAV technology with deep learning to advance urban traffic management and foster more efficient urban planning strategies.

关键词： Deep learning Scalability Heuristic algorithms Urban planning Autonomous aerial vehicles Real-time systems Monitoring Traffic congestion Resilience Residual neural networks

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Edge AI Robot Technology Application with YOLOv8, MQTT and MQ-5 Sensors for Realtime Fire and Smoke Detection 14th

Edge AI Robot Technology Application with YOLOv8, MQTT and...

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14th International Conference on Frontier Computing, FC 2024

作者： Andriyadi, Anggi Wijaya, Chandra Chen, Shih-Yen Huang, Ding-Hsiang Yang, Chao-Tung Department of Industrial Engineering and Enterprise Information Tunghai University Taichung City Taiwan Department of Computer Science Tunghai University Taichung City Taiwan Department of Information System Institut Informatika dan Bisnis Darmajaya Bandar Lampung Indonesia Department of Informatics Universitas Katolik Parahyangan Bandung Indonesia Research Center for Smart Sustainable Circular Economy Tunghai University Taichung City Taiwan

ISBN: (纸本)9789819623945

Fire incidents pose a significant security threat that requires attention from all stakeholders. For example, Taipei reported 893 fire incidents in 2023 alone. This study aims to enhance security surveillance by promptly detecting fire incidents through the development of a patrol robot. The robot captures sensor signals from the MQ-5 and transmits them via MQTT to the Raspberry Pi control center, delivering real-time reports of smoke and fire to fire officials. Additionally, equipped with a camera, the robot streams footage using RTSP to Jetson for fire and smoke object recognition using the You Look Only Once (YOLO) algorithm. Our robot effectively detected smoke and fire using the MQ5-Sensor and transmitted the signals via MQTT. Furthermore, it analyzed the visual stream to identify fire and smoke patterns with YOLOv8, resulting in a significant increase in accuracy. The evaluation of our model yielded impressive performance metrics, including a precision of 93%, a recall of 97%, a precision-recall of 97.90%, and an F1-Score of 97%. In the future studies we will integrate a mini camera and Large Language Model (LLM) to enable the robot to communicate with humans to warn of early fire incidents. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Smoke detectors

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Benchmarking HEp-2 cell segmentation methods in indirect immunofluorescence images - standard models to deep learning

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computers in Biology and Medicine 2025年 192卷 110150-110150页

作者： Iyer, Balaji Deoghare, Smruti Ranjan, Krish Aronow, Bruce J. Prasath, V.B. Surya Division of Biomedical Informatics Cincinnati Children's Hospital Medical Center CincinnatiOH45229 United States Department of Pediatrics University of Cincinnati United States Department of Biomedical Informatics College of Medicine University of Cincinnati OH45267 United States Department of Electrical Engineering and Computer Science University of Cincinnati OH45221 United States

Indirect Immunofluorescence (IIF) stained Human Epithelial (HEp-2) cells are considered the gold standard for detecting autoimmune diseases. Accurate cell segmentation, though often viewed as an intermediary step to downstream tasks like classification, significantly enhances overall performance when executed with precision. In this study, we conduct a systematic literature review of HEp-2 cell segmentation techniques, identifying 28 key papers utilizing traditional image processing, machine learning classifiers, deep convolutional neural networks (CNNs), and generative adversarial network (GAN) frameworks. Building on these insights, we benchmark 17 CNN models without pretraining and 8 CNN models pretrained on ImageNet using both Frozen Encoder and Tunable Encoder strategies on the I3A dataset. Cross-validation (CV) and Benjamini–Hochberg (BH) significance correction were employed to ensure statistical rigor in model comparisons. Domain-Specific Pretraining (DSPT) experiments demonstrated performance improvements, particularly for underrepresented classes, while Data Augmentation strategies (DA-1 and DA-2) revealed distinct impacts across model categories. GAN-based segmentation experiments using the top-performing CNN architectures as generators within a Pix2Pix framework revealed performance degradation due to data limitations and adversarial training instabilities. Nonetheless, GANs displayed class-specific improvements in visual alignment of segmentation masks. Results were evaluated comprehensively across eight performance metrics, including Dice, IOU, Accuracy, Precision, Sensitivity, Specificity, AU-ROC and AU-PR. This work offers a robust benchmarking of state-of-the-art CNN, GAN, and Transformer-based models for HEp-2 cell segmentation, providing valuable insights for future research directions, including ensemble approaches, dynamic patch sampling, and diffusion models. © 2025 The Authors

关键词： Generative adversarial networks

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Hybrid BiGRU-CNN Model for Load Forecasting in Smart Grids with High Renewable Energy Integration

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IET Generation, Transmission and Distribution 2025年第1期19卷

作者： Ullah, Kaleem Shakir, Daniyal Abid, Usama Alahmari, Saad Aslam, Sheraz Ullah, Zahid US-Pakistan Centre for Advanced Studies in Energy University of Engineering and Technology Peshawar Peshawar Pakistan Sr. Business Analyst S&P Global Islamabad Islamabad Pakistan Department of Computer Science and Information Technology School of Computing Engineering and Mathematical Sciences La Trobe University MelbourneVIC Australia Department of Computer Science Applied College Northern Border University Arar Saudi Arabia Department of Electrical Engineering Computer Engineering and Informatics Cyprus University of Technology Limassol Cyprus Department of Computer Science CTL eurocolege Limassol Cyprus Dipartimento di Elettronica Informazione e Bioingegneria Politecnico di Milano Lombardia Italy

Integrating renewable energy sources into smart grids increases supply and demand management because renewable energy sources are intermittent and variable. To overcome this type of challenge, short-term load forecasting (STLF) is essential for managing energy, demand-side flexibility, and the stability of smart grids with renewable energy integration. This paper presents a new model called BiGRU-CNN to improve the operation of STLF in smart grids. The BiGRU-CNN model integrates bidirectional gated recurrent units (BiGRUs) to model temporal dependencies and convolutional neural networks (CNNs) to extract spatial patterns from energy consumption data. The newly developed BiGRU captures past and future contexts through bidirectional processing, and the CNN component extracts high-level features to enhance the accuracy of load demand prediction. The BiGRU-CNN model is compared with two other hybrid models, CNN-LSTM and CNN-GRU, on real-world data from American electric power (AEP) and ISONE datasets. Simulation results show that the proposed BiGRU-CNN outperforms other models, with single-step forecasting yielding root mean square error (RMSE) of 121.43 (AEP) and 123.57 (ISONE), mean absolute error (MAE) of 90.95 (AEP) and 62.97 (ISONE), and mean absolute percentage error (MAPE) of 0.61% (AEP) and 0.41% (ISONE). For multi-step forecasting, the model yields RMSE of 680.02 (AEP) and 581.12 (ISONE), MAE of 481.12 (AEP) and 411.20 (ISONE), and MAPE of 3.27% (AEP) and 2.91% (ISONE). The results show that the BiGRU-CNN model can generate accurate and reliable STLF, which is useful in managing massive renewable energy-integrated smart grids. © 2025 The Author(s). IET Generation, Transmission & Distribution published by John Wiley & Sons Ltd on behalf of The Institution of engineering and Technology.

关键词： Convolutional neural networks

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A STUDY ON SOME CLASSES OF PANTOGRAPH LANGEVIN FRACTIONAL DIFFERENTIAL PROBLEMS WITH NONLOCAL CONDITIONS

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Journal of Mathematical Sciences (United States) 2025年 1-18页

作者： Bouzid, Houari Benali, Abdelkader Salim, Abdelkrim Erhan, İnci M. Department of Mathematics Faculty of Exact Science and Informatics Laboratory of Mathematics and Applications (LMA) Hassiba Benbouali University of Chlef Ouled Fares Chlef 02000 Algeria Faculty of Technology Hassiba Benbouali University of Chlef Chlef 02000 Algeria Laboratory of Mathematics Djillali Liabes University of Sidi Bel-Abbes Sidi Bel-Abbes 22000 Algeria Department of Computer Engineering Aydın Adnan Menderes University Aydın 09010 Turkey

This paper concentrates on the examination of a class of nonlocal initial value problems for Langevin fractional differential equations featuring the ψ-Caputo fractional derivative within Banach spaces. By utilizing Banach’s fixed-point theorem, we establish the uniqueness of the solutions to these problems. The existence of solutions is then confirmed using Schaefer’s and Darbo’s fixed-point theorems. Finally, we provide examples to illustrate our findings. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025.

关键词： Darbo theorem Fractional differential equations (FDE) Measure of noncompactness ψ-fractional integral

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Scaling laws in wearable human activity recognition

arXiv

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arXiv 2025年

作者： Hoddes, Tom Bijamov, Alex Joshi, Saket Roggen, Daniel Etemad, Ali Harle, Robert Racz, David Google DeepMind United Kingdom Google LLC United States School of Engineering & Informatics University of Sussex United Kingdom Queen’s University Canada Computer Laboratory University of Cambridge United Kingdom

Many deep architectures and self-supervised pre-training techniques have been proposed for human activity recognition (HAR) from wearable multimodal sensors. Scaling laws have the potential to help move towards more principled design by linking model capacity with pre-training data volume. Yet, scaling laws have not been established for HAR to the same extent as in language and vision. By conducting an exhaustive grid search on both amount of pre-training data and Transformer architectures, we establish the first known scaling laws for HAR. We show that pre-training loss scales with a power law relationship to amount of data and parameter count and that increasing the number of users in a dataset results in a steeper improvement in performance than increasing data per user, indicating that diversity of pre-training data is important, which contrasts to some previously reported findings in self-supervised HAR. We show that these scaling laws translate to downstream performance improvements on three HAR benchmark datasets of postures, modes of locomotion and activities of daily living: UCI HAR and WISDM Phone and WISDM Watch. Finally, we suggest some previously published works should be revisited in light of these scaling laws with more adequate model capacities. © 2025, CC BY.

关键词： Scaling laws

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Multi-Agent Actor-Critic with Harmonic Annealing Pruning for Dynamic Spectrum Access Systems

arXiv

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arXiv 2025年

作者： Stamatelis, George Kanatas, Angelos-Nikolaos Alexandropoulos, George C. Department of Informatics and Telecommunications National and Kapodistrian University of Athens Panepistimiopolis Ilissia Athens16122 Greece School of Electrical and Computer Engineering National Technical University of Athens Zografou Campus Athens15772 Greece

Multi-Agent Deep Reinforcement Learning (MADRL) has emerged as a powerful tool for optimizing decentralized decision-making systems in complex settings, such as Dynamic Spectrum Access (DSA). However, deploying deep learning models on resource-constrained edge devices remains challenging due to their high computational cost. To address this challenge, in this paper, we present a novel sparse recurrent MARL framework integrating gradual neural network pruning into the independent actor global critic paradigm. Additionally, we introduce a harmonic annealing sparsity scheduler, which achieves comparable, and in certain cases superior, performance to standard linear and polynomial pruning schedulers at large sparsities. Our experimental investigation demonstrates that the proposed DSA framework can discover superior policies, under diverse training conditions, outperforming conventional DSA, MADRL baselines, and state-of-the-art pruning techniques. © 2025, CC BY.

关键词： Deep reinforcement learning

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Water Stress Detection in Indoor Farming using Thermal Infrared Imaging 24

Water Stress Detection in Indoor Farming using Thermal Infra...

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Proceedings of the 28th Pan-Hellenic Conference on Progress in Computing and informatics

作者： Georgios Fevgas Thomas Lagkas Vasileios Argyriou Panagiotis Sarigiannidis Department of Informatics Democritus University of Thrace Kavala Greece Department of Network and Digital Media Kingston University Surrey United Kingdom Department of Electrical and Computer Engineering University of Western Macedonia Kozani Greece

ISBN: (纸本)9798400713170

Early water stress detection is crucial for increasing crop yield, optimizing agricultural practices, and improving water management. This research proposes a novel method for detecting early water stress in lettuces using Thermal Infrared (TIR) and RGB images in a controlled environment. The method initially removes the background of the lettuce canopy to eliminate the noise, then clusters the canopy's thermal data to determine temperature variation and applies a yellow pseudo-color to annotate the stressed areas in the RGB images. The logic behind the proposed method is that during water stress, the transpiration of the leaves' stomata is reduced, and the temperature of the leaves increases. The results show that the proposed method finds water stress sooner than visual symptoms expression, providing helpful information for improved crop management and better water use in agriculture.

关键词： Indoor Farming

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Cyber-Physical Steganography in Robotic Motion Control

arXiv

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arXiv 2025年

作者： Chang, Ching-Chun Lin, Yijie Echizen, Isao The Information and Society Research Division National Institute of Informatics Japan The Department of Information Engineering and Computer Science Feng Chia University Taiwan The Graduate School of Information Science and Technology University of Tokyo Japan

Steganography, the art of information hiding, has continually evolved across visual, auditory and linguistic domains, adapting to the ceaseless interplay between steganographic concealment and steganalytic revelation. This study seeks to extend the horizons of what constitutes a viable steganographic medium by introducing a steganographic paradigm in robotic motion control. Based on the observation of the robot's inherent sensitivity to changes in its environment, we propose a methodology to encode messages as environmental stimuli influencing the motions of the robotic agent and to decode messages from the resulting motion trajectory. The constraints of maximal robot integrity and minimal motion deviation are established as fundamental principles underlying secrecy. As a proof of concept, we conduct experiments in simulated environments across various manipulation tasks, incorporating robotic embodiments equipped with generalist multimodal policies. © 2025, CC BY.

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Improved Data Encoding for Emerging Computing Paradigms: From Stochastic to Hyperdimensional Computing

arXiv

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arXiv 2025年

作者： Moghadam, Mehran Shoushtari Aygun, Sercan Najafi, M. Hassan Electrical Computer and Systems Engineering Department Case Western Reserve University ClevelandOH44106 United States School of Computing and Informatics University of Louisiana at Lafayette LafayetteLA70503 United States

Data encoding is a fundamental step in emerging computing paradigms, particularly in stochastic computing (SC) and hyperdimensional computing (HDC), where it plays a crucial role in determining the overall system performance and hardware cost efficiency. This study presents an advanced encoding strategy that leverages a hardware-friendly class of low-discrepancy (LD) sequences, specifically powers-of-2 bases of Van der Corput (VDC) sequences (VDC-2n), as sources for random number generation. Our approach significantly enhances the accuracy and efficiency of SC and HDC systems by addressing challenges associated with randomness. By employing LD sequences, we improve correlation properties and reduce hardware complexity. Experimental results demonstrate significant improvements in accuracy and energy savings for SC and HDC systems. Our solution provides a robust framework for integrating SC and HDC in resource-constrained environments, paving the way for efficient and scalable AI implementations. © 2025, CC BY.

关键词： Encoding (symbols)

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