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Semantic Segmentation Based Real-Time Traffic Monitoring via Res-UNet Classifier and Kalman Filter

SN computer science 2025年第1期6卷 1-17页

作者： Qureshi, Asifa Mehmood Algarni, Asaad Aljuaid, Hanan Alatiyyah, Mohammed Hamad Alnowaiser, Khaled Jalal, Ahmad Department of Creative Technologies Air University E-9 Islamabad 44000 Pakistan Department of Computer Sciences Faculty of Computing and Information Technology Northern Border University Rafha 91911 Saudi Arabia Department of Computer Sciences College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University P.O. Box 84428 Riyadh 11671 Saudi Arabia Department of Computer Science College of Sciences and Humanities in Aflaj Prince Sattam Bin Abdulaziz University Al-Kharj Saudi Arabia Department of Computer Engineering College of Computer Engineering and Sciences Prince Sattam Bin Abdulaziz University Al-Kharj 11942 Saudi Arabia Department of Computer Science Air University E-9 Islamabad 44000 Pakistan

In computer vision, accurate vehicle recognition and tracking is a challenging research topic. Manual surveillance systems are cumbersome, labor-intensive, and inefficient in today's congested traffic environment. Automated traffic monitoring systems are needed in today's world because they provide potential solutions to these issues. However, there are still some challenges in automating these systems which need to be addressed. One of the challenges is the extraction of foreground from a complex environment that only contains vehicles along with spatial and temporal information. In this paper, a semantic segmentation-based foreground extraction model is proposed to overcome these challenges. After segmentation, a blob detection technique is applied to detect each vehicle. To overcome the issue of vehicle-to-vehicle occlusion we extracted Harris corner points and computed the separation line based on the vehicle geometry coordinates. Furthermore, we introduce an ID assignment mechanism based on VGG-16 extracted features. Based on the detections, the number of cars is also counted for each frame. For tracking, we first applied Farneback optical flow algorithm to detect motion in vehicles. Only those vehicles were passed to the tracking algorithm in which motion was detected. To estimate each vehicle's location in succeeding image frames Kalman filter was implemented. A vehicle reidentification and ID recovery module are also proposed to enhance the tracking outcome. Vehicle trajectories based on vehicle location midpoint were also estimated. Extensive experiments are used to validate the proposed system and achieved a precision score of 0.965 and 0.931 over Roundabout and AU-AIR datasets for vehicle detection. However, for vehicle tracking the precision is 0.948 over the Roundabout dataset and 0.839 for the AU-AIR dataset. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Blob detection Detection and tracking Kalman filter Occlusion handling Res-Unet Semantic segmentation

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A Hybrid Reinforcement Learning Framework for Optimizing Node Deployment in WSNs 14

A Hybrid Reinforcement Learning Framework for Optimizing Nod...

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14th IEEE International Conference on Communication Systems and Network Technologies, CSNT 2025

作者： Priyadarshi, Rahul Rout, Jayant Kumar Gupta, Akash Kumar Ranjan, Rakesh Verma, Amit Teja, Panduranga Ravi Faculty of Engineering and Technology ITER SOA Bhubaneswar India School of Computer Application KIIT Bhubaneswar India Dept of ECE BIT Mesra Deoghar India School of Computer Science UPES Dehradun India

ISBN: (纸本)9798331531935

Efficient node deployment in Wireless Sensor Networks (WSNs) is critical for maximizing network coverage, enhancing energy efficiency, and prolonging network lifetime. However, achieving optimal node placement is challenging due to the limited energy resources and dynamic environmental conditions in WSNs. This paper presents a novel hybrid approach that combines Reinforcement Learning (RL) with the Differential Evolution (DE) algorithm to optimize node deployment in WSNs. In this method, RL dynamically adapts node positions based on local network states, while DE performs global optimization to refine these placements and improve coverage. Simulation results demonstrate that the proposed RL-DE hybrid approach significantly outperforms traditional deployment methods, standalone RL, and DE in terms of coverage, energy conservation, and network longevity. The results suggest that the RL-DE approach provides an adaptive and robust solution for node deployment in WSNs, with potential applications in real-world sensor network deployments. © 2025 IEEE.

关键词： Energy efficiency

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List Edge Colorings of Planar Graphs without Non-induced 7-cycles

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Acta Mathematica Sinica,English Series 2025年第3期41卷 1037-1054页

作者： Li Zhang Hajo Broersma You Lu Shenggui Zhang School of Mathematics and Computer Science Yan’an UniversityYan’an716000P.R.China School of Mathematics and Statistics Northwestern Polytechnical UniversityXi’an710129P.R.China Faculty of Electrical Engineering Mathematics and Computer ScienceUniversity of TwenteP.O.Box 2177500 AEEnschedeNetherlands Xi’an-Budapest Joint Research Center for Combinatorics Northwestern Polytechnical UniversityXi’an710129P.R.China

A graph G is edge-k-choosable if,for any assignment of lists L(e)of at least k colors to all edges e∈E(G),there exists a proper edge coloring such that the color of e belongs to L(e)for all e∈E(G).One of Vizing’s classic conjectures asserts that every graph is edge-(Δ+1)-*** is known since 1999 that this conjecture is true for general graphs withΔ≤*** recently,in 2015,Bonamy confirmed the conjecture for planar graph withΔ≥8,but the conjecture is still open for planar graphs with 5≤Δ≤*** confirm the conjecture for planar graphs withΔ≥6 in which every 7-cycle(if any)induces a C_(7)(so,without chords),thereby extending a result due to Dong,Liu and Li.

关键词： Planar graph list edge coloring edge-k-choosability Combinatorial Nullstellensatz discharging

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The Impact of 5G on the Transformation and Communication of Smart Entities

The Impact of 5G on the Transformation and Communication of ...

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International Conference on Smart Business and technology, ICSBT 2024

作者： Talbi, Chaymae Rahmouni, M’hamed Tahiri Alaoui, Moulay Lakbir Belhiah, Meryam Lagmiri, Souad Najoua Ziti, Soumia IPSS Intelligent Processing and Security of Systems Faculty of Science Mohammed V University Rabat1014 RP Morocco Mohammed V University Rabat Morocco IRSM Computer Science Networks Security and Management Higher Institute of Management Administration and Computer Engineering Rabat10040 Morocco

ISBN: (纸本)9783031866975

The advent of 5G technology marks a significant milestone in the evolution of smart businesses and technologies. This chapter explores the transformative effects of 5G on the communication and operational paradigms of intelligent entities. By providing unprecedented speed, low latency, and high connectivity, 5G facilitates real-time data exchange and enhances the integration of Internet of Things (IoT) devices. This, in turn, empowers smart cities, industries, and enterprises to achieve greater efficiency, innovation, and responsiveness. The chapter delves into specific case studies across various sectors, including healthcare, transportation, and manufacturing, demonstrating how 5G drives digital transformation. In healthcare, for example, 5G enables remote surgeries and real-time patient monitoring. In transportation, it supports autonomous vehicles and smart traffic management systems. In manufacturing, it enhances automation and predictive maintenance. The theoretical analysis presented highlights the critical role of 5G in supporting advanced applications such as augmented reality (AR), virtual reality (VR), and artificial intelligence (AI). These technologies benefit from the high bandwidth and low latency provided by 5G, enabling new levels of interaction and data processing. The findings underscore the potential of 5G to revolutionize communication infrastructure and operational methodologies, paving the way for future advancements in smart technologies. This chapter concludes by discussing the challenges and future prospects of 5G deployment, including issues related to security, infrastructure investment, and regulatory frameworks. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： 5G mobile communication systems

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A Hybrid Blockchain Framework for Secure, Scalable, and Energy-Efficient Digital Identity Management with Token-based Selective Disclosure 3

A Hybrid Blockchain Framework for Secure, Scalable, and Ener...

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3rd International Conference on Electronics and Renewable Systems, ICEARS 2025

作者： Velmurugan, M. Shinzeer, C.K. Maya, G. Ramya, R. Kanimozhi, R. Lathif, Syed Ismail Abdul Vel Tech Rangarajan Dr Sagunthala R&d Institute of Science And Technology Department Of Computer Science & Engineering Chennai600 062 India Lovely Professional University Department of School of Computer Applications Punjab Phagwara144411 India Srm Institute of Science and Technology Faculty of Management Kattankulathur603 203 India K.Ramakrishnan College of Engineering Department of Electronics and Communication Engineering Samayapuram621112 India Muthayammal Engineering College Computer Science and Engineering Rasipuram637408 India Srm Institute of Science and Technology Department of Data Science and Business Systems Chennai603203 India

ISBN: (纸本)9798331509675

Data breaches, identity theft, and the lack of user control within traditional digital identity management systems, it begs a more secure and decentralized alternative. In this study we propose a blockchain based digital identity management framework that implements privacy, security and transparency by employing distributed ledger technology. To provide tamper proof identity storage and automated verification, the proposed model uses cryptographic hashing and smart contracts. The use of a novel token-based access mechanism enables users to safely share their identity data with third parties, while keeping sensitive information out of sight. Security metrics are enhanced by 30% for data integrity with 45% reduction in unauthorized access attempts all through simulations. The framework also achieves improved efficiency reducing identity verification time by 40 percent compared to traditional systems. It offers a promising approach to address important vulnerabilities in centralized identity management in line with the development of marketable and user-friendly digital identity solutions. © 2025 IEEE.

关键词： Data integrity

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Enhancing single-lead electrocardiogram arrhythmia detection with empirical mode decomposition

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Neural Computing and Applications 2025年 1-23页

作者： Issa, Mohamed F. Yousry, Ahmed Tuboly, Gergely Wang, Zeyu Zoltan, Juhasz Selim, Mazen M. AbuEl-Atta, Ahmed H. Department of Scientific Computing Faculty of Computers and Artificial Intelligence Benha University Benha13511 Egypt Department of Computer Science Faculty of Computers and Artificial Intelligence Benha University Benha13511 Egypt Department of Electrical Engineering and Information Systems University of Pannonia Veszprem8200 Hungary Department of Mechatronics Delta University for Science and Technology Gamasa35712 Egypt

Cardiac arrhythmias pose a significant challenge to health care, requiring accurate and reliable detection methods to enable early diagnosis and treatment. However, traditional ECG beat classification methods often lack robustness and fail to generalize effectively to diverse clinical scenarios, particularly when using single-lead-I recordings, which are less studied compared to lead II. To address this gap, this paper proposes a novel method that combines deep learning and empirical mode decomposition (EMD) for arrhythmia detection using single-lead-I ECG recordings obtained with electrodes on each hand. The hybrid model comprises 149 layers of one-dimensional convolution, max pooling, and residual blocks. By leveraging EMD, it classifies ECG beats into normal beats, premature ventricular contractions, and atrial premature beats with high sensitivity (99.86%, 99.72%, and 99.84%, respectively) using the first four intrinsic mode functions (IMFs). The proposed method is applicable in clinical and out-of-hospital monitoring scenarios, and an accompanying web application provides real-time analysis and diagnostic results. This approach addresses a critical gap in lead-I ECG analysis, enhancing diagnostic precision and expanding the scope of ECG applications in health care. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Empirical mode decomposition

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Water Quality Classification Using an Artificial Neural Network 1st

Water Quality Classification Using an Artificial Neural Netw...

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1st International Conference on Intelligent Systems, Blockchain, and Communication Technologies, ISBCom 2024

作者： Mohsen, Saeed Abdel-Rehim, Wael M. F. Abdel-Aziz, M. Electronics and Communications Engineering Department Al-Madinah Higher Institute for Engineering and Technology Giza12947 Egypt South Sinai46511 Egypt Department of Computer Science Faculty of Computers and Information Suez University Suez43512 Egypt Department of Basic Sciences Faculty of Computer and Information Sciences Ain Shams University Cairo11566 Egypt

ISBN: (纸本)9783031823763

Water quality is vital for public health and ecosystem sustainability. However, approximately 2 billion people worldwide do not have access to drinking water that is appropriately managed, contributing to significant health risks and environmental issues. This paper proposes an efficient approach to classifying water quality as potable or impotable water using artificial neural networks (ANNs). This work highlights the perspective of ANNs in reducing water pollution and ensuring safe water access. The ANN is implemented with a dataset consisting of 2 water classes based on 3,276 water samples. The proposed ANN is tested via the Keras framework with a tuning technique to obtain the most significant water quality classification (WQC) precision. An error matrix is used to evaluate the ANN efficiency. The results show that the ANN is highly efficient in the classification of water quality. Moreover, The ANN provides a test accuracy of 68.38%, a mean precision of 69.64%, and the average area under the ROC curve for the implemented ANN is 51%. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Artificial Neural Network Classification Deep Learning Water Quality

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Comparative Analysis and Evaluation of Well-being Activity-Infused Fine-Tuned Language Models with Benchmark Models

Comparative Analysis and Evaluation of Well-being Activity-I...

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2025 IEEE Symposium on Computational Intelligence in Health and Medicine, CIHM 2025

作者： Mohammed, Hamza Haruna Kiss, Gabriel Serrano, J. Artur Lindseth, Frank Department of Computer Science Faculty of Information Technology and Electrical Engineering Norwegian University of Science and Technology Trondheim Norway Department of Neuromedicine and Movement Science Faculty of Medicine and Health Sciences Norwegian University of Science and Technology Trondheim Norway

ISBN: (纸本)9798331508333

Large language models (LLMs) have significantly impacted natural language processing (NLP), healthcare, and beyond fields. LLMs have demonstrated unprecedented abilities to understand and generate human-like text, making them highly valuable for various tasks, including information retrieval, question-answering, and conversational AI. However, as these models are integrated into real-world applications, especially in sensitive domains such as healthcare, it becomes essential to evaluate their performance comprehensively. We present a process for fine-tuning LLMs to incorporate cardiac exercise and corpus of well-being activity, aiming to promote activity health support through NLP-driven applications. Using QLoRA, you can ensure that the model size remains small and reduce overhead while maintaining the high performance of the fine-tuned model. Subsequently, this paper presents a comparative analysis benchmark pipeline to evaluate fine-tuned models with base models on standard benchmark datasets from the healthcare domain. The aim is to evaluate the performance, robustness and accuracy of these models, showing that the fine-tuning process does not degrade the model performance, but improves the performance to some extent in some benchmarks. © 2025 IEEE.

关键词： Nutrition

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Dual-step optimization for binary sequences with high merit factors

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Digital Signal Processing: A Review Journal 2025年 165卷

作者： Pšeničnik, Blaž Mlinarič, Rene Brest, Janez Bošković, Borko Computer Architecture and Languages Laboratory Institute of Computer Science Faculty of Electrical Engineering and Computer Science University of Maribor Koroška c. 46 Maribor2000 Slovenia

The problem of finding aperiodic low auto-correlation binary sequences (LABS) presents a significant computational challenge, particularly as the sequence length increases. Such sequences have important applications in communication engineering, physics, chemistry, and cryptography. This paper introduces a dual-step algorithm for long binary sequences with high merit factors. The first step employs a parallel algorithm utilizing skew-symmetry and restriction classes to generate sequence candidates with merit factors above a predefined threshold. The second step uses a priority queue algorithm to refine these candidates further, searching the entire search space unrestrictedly. By combining GPU-based parallel computing and dual-step optimization, our approach has successfully identified best-known binary sequences for all lengths ranging from 450 to 527, with the exception of length 518, where the previous best-known merit factor value was matched with a different sequence. This hybrid method significantly outperforms traditional exhaustive and stochastic search methods, offering an efficient solution for finding long sequences with good merit factors. © 2025 The Author(s)

关键词： Digital arithmetic

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Single-And Dual-Beam Pattern Reconfigurable Patch Antenna With Dual Switchable CP for Internet of Vehicles

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IEEE Internet of Things Journal 2025年

作者： Wang, Shi-Tong Zhu, Lei University of Macau Faculty of Science and Technology Department of Electrical and Computer Engineering China

In this article, a novel method is proposed to facilitate the design of compact, low-profile, pattern reconfigurable antennas with fixed or switchable circular polarization (CP) for Internet of Vehicles (IoV) applications. The proposed method eliminates the need for a complex beamforming or phase-tuning network by innovatively exploiting four resonant modes of a square patch antenna. Specifically, the TM01 and TM10 modes are utilized to accomplish the single-beam CP pattern, while, the dualbeam CP pattern is achieved by combining the TM11 and TM02 modes. An electric current model is developed to investigate the far-field radiation, revealing that the radiation pattern of TM11 mode can be effectively reshaped from quad-beam to dual-beam by manipulating the surface current distribution. The complete design method is verified by an antenna prototype working at 3.5 GHz, which consists of a radiative patch, a pair of open-ended stubs, a pair of slots, and four equivalent switchable pins. Different from the conventional pin-loading method, the proposed equivalent switchable pins attain excellent CP performance during switching by compensating for the detrimental electric coupling. The prototype is fabricated and measured. Its radiation can be switched between single-beam (7.8 dBic) and dual-beam CP pattern (3.5 dBic), exhibiting an overlapped CP coverage from -123° to 73°. Furthermore, another prototype with the extended switchable CP capability is realized. Without increasing the number of p-i-n diodes, this prototype achieves four operating states with independent pattern (single-and dual-beam) and polarization (right-and left-handed CP) reconfiguration. The merits of compact size, low profile, and flexible reconfigurability make the proposed method and design cases excellent candidates for IoV applications and intelligent transportation systems (ITSs). © 2014 IEEE.

关键词： Circular polarization

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