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Synchronization and implementation of real-time traffic signal optimization regulator

Multimedia Tools and Applications 2025年第12期84卷 10725-10745页

作者： Varshney, Abhilasha Dakshayini, M. Gururaj, H.L. Hu, Yu-Chen Department of Information Science and Engineering B.M.S. College of Engineering Karnataka Bangalore India Department of Information Technology Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Manipal India Department of Computer Science Tunghai University No. 1727 Sec. 4 Boulevard Xitun District Taichung407224 Taiwan

In most cosmopolitan cities like Bengaluru, the major problems are the woes of congestion, such as increased vehicular density, travel time, stress, and vehicular carbon emissions are the major problems. These issues have led to quantifiable and non-quantifiable losses, such as high vehicle operation and maintenance costs, reduced human productivity and mobility, increased health hazards, etc. Sustainable transportation coupled with Technological interventions could alleviate these issues by efficiently managing the traffic flow. The major aims of this work are to reduce the overall commute time, traffic congestion, and air pollution, making the commutation of public stress-free and happy. So, we propose a comprehensive Real-time Traffic Signal Optimizer and Regulator (RTSOR) that identifies the right High-Density Corridors (HDC) and achieves these aims by successfully attaining the coordination among the Adaptive Traffic Signal Control System (ATSCS) fixed on the successive junctions of HDC considered in this work as a pilot initiative by upgrading the existing traffic signal in Bengaluru City to the latest ATSCS technology. Implementing this proposed RTSOR has effectively lessened the waiting time in traffic, travel time, and fuel consumption and improved air quality by reducing pollution and lowering stress levels, leading to improved quality of health and life of the public. In Bengaluru city, the current innovation would be implemented for the first time, and no prior model exists that could help optimize the travel time. The method that the study envisaged through RTSOR focuses on collecting real-time data on an HDC to elucidate our claim of originality in reducing the delay time on roads by signal synchronization. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Travel time

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Secure Energy Efficiency for ARIS Networks with Deep Learning: Active Beamforming and Position Optimization

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IEEE Transactions on Wireless Communications 2025年第6期24卷 5282-5296页

作者： Wang, Dawei Wang, Zijun Zhao, Hongbo Zhou, Fuhui Alfarraj, Osama Yang, Weichao Mumtaz, Shahid Leung, Victor C. M. Northwestern Polytechnical University School of Electronics and Information Shaanxi Xi'an710072 China Beihang University School of Electronic and Information Engineering Beijing100191 China Nanjing University of Aeronautics and Astronautics College of Electronic and Information Engineering Nanjing210000 China King Saud University Community College Computer Science Department Riyadh11437 Saudi Arabia Nottingham Trent University Department of Computer Science United Kingdom Shenzhen MSU-BIT University Artificial Intelligence Research Institute Shenzhen518172 China Shenzhen University College of Computer Science and Software Engineering Shenzhen528060 China The University of British Columbia Department of Electrical and Computer Engineering VancouverBCV6T 1Z4 Canada

Incorporating an active reconfigurable intelligent surface on an unmanned aerial vehicle (UAV), denoted as an aerial reconfigurable intelligent surface (ARIS), introduces a novel dimension for secure transmissions. Given the constraint of limited battery capacity in UAVs, energy management emerges as a key challenge within UAV networks. In response, we propose a secure energy efficiency (SEE) transmission scheme for ARIS networks, where active ARIS is strategically deployed to enhance information security. In addition, a SEE optimal problem is formulated by considering the imperfect wiretap channel state information to optimize the active beamforming vector and the ARIS position. For this non-convex problem, we first reformulate the fractional SEE objective into an equivalent form and subsequently decompose it into two distinct subproblems: optimizing the UAV's position and designing the active beamforming. For the UAV's position optimization, we propose a sophisticated deep deterministic policy gradient algorithm that enables the UAV to autonomously determine the optimal ARIS position through a self-learning strategy. Regarding beamforming design, we transform this aspect into a quadratic constrained quadratic programming problem and design an alternating direction multiplier method to optimize the reflection coefficient. Subsequently, an alternating optimization algorithm is proposed to synergistically solve these subproblems. Empirical simulations validate our proposed scheme, indicating an improvement in SEE of up to 47.2%. This significant improvement underscores the efficacy of the proposed ARIS-assisted secure transmission scheme in enhancing both security and energy efficiency in UAV networks. © 2002-2012 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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Offsets Reconstruction Method for Clock Skew Measurement Over High-Jitter Communication

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SN computer science 2025年第5期6卷 1-14页

作者： Sastra, Nyoman Putra Saputra, Komang Oka Teng, Wei-Chung Study Program of Electrical Engineering Faculty of Engineering Udayana University Jalan Raya Kampus Bukit Jimbaran Bali Badung 80361 Indonesia Department of Computer Science and Information Engineering National Taiwan University of Science and Technology No. 43 Section 4 Keelung Rd Taipei 106 China

Measuring clock skew of devices over a network fully relies on the offsets, the differences between sending and receiving times. Offsets that shape a thick line are the most ideal one as their slope is directly the clock skew sought. When some outliers separate from the thick line due to higher delay jitter, current advanced approaches use the lower bound or the majority of offsets to derive accurate clock skew. However, outliers in offsets that shape parallel dotted lines, reduce the availability of offsets in the lower region, and scatter widely the offsets from their majority zone. These conditions drive the current skew estimation approaches to lose their accuracy. This paper then developed a new method, which reconstructs back the outliers into their origin positions, in order to provide ideal offsets for clock skew measurement. The experimental evaluations have demonstrated that the clock skew accuracy of dotted-lines offsets tampered by outliers was improved from 2.1 to 0.9 ppm by the proposed reconstruction method. For a broader purpose, the reconstruction method has been adjusted to be implemented also on non-dotted-lines offsets. The robustness of the proposed method for this case has also been exhibited by the very ideal reconstructed offsets that shape like a thick line. © The Author(s) 2025.

关键词： Clock skew Delay jitter Dotted lines Hough transform

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RedTops: real-time energy-aware dynamic task offloading via federated mountain gazelle optimisation in SDN-enhanced edge computing

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Neural Computing and Applications 2025年 1-39页

作者： Al Aghbari, Zaher Khedr, Ahmed M. Ahmed, Naveed Girija, Shini Baker, Thar Department of Computer Science University of Sharjah Sharjah United Arab Emirates School of Architecture Technology and Engineering University of Brighton BrightonBN2 4GJ United Kingdom

Offloading computational tasks is vital for real-time applications on mobile devices with limited resources. Mobile edge computing (MEC) is deemed a solution that puts computational resources closer to users. Nevertheless, there are many associated concerns during the offloading procedure (i.e., privacy, delay, and high energy consumption). Federated learning (FL) has been considered a solution to address MEC’s data privacy issues;however, it comes with its own resource consumption issues. To address these issues, this paper proposes a distributed learning paradigm inspired by FL. We propose an optimisation technique for offloading computational tasks that aims to reduce both total delay and energy consumption by using the mountain gazelle optimisation algorithm, which shows it can reduce both delay and energy consumption in dynamic situations. Additionally, an improved variant known as the improved mountain gazelle optimiser is integrated into a distributed SDN controller architecture to create an offloading policy model for optimal edge node selection. We also present a new SDN-enabled edge computing architecture that achieves the best task distribution through task offloading using federated mountain gazelle optimisation (RedTops). Energy usage, delay, and bandwidth are considered by RedTops, which successfully addresses high training costs, dependability issues, and privacy concerns in MEC. Based on the outcomes of five extensive simulations, RedTops is more energy-efficient and faster at completing tasks than four state-of-the-art offloading methods (DDLO, DROO, DRL without TL and SDN, and DTRL). © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Federated learning

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When Generative AI Meets Semantic Communication: Optimizing Radio Map Construction and Distribution in Future Mobile Networks

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IEEE Network 2025年第3期39卷 47-55页

作者： Zhou, Li Deng, Xinfeng Ning, Zhaolong Zhao, Haitao Wei, Jibo Leung, Victor C. M. National University of Defense Technology College of Electronic Science and Technology Changsha410073 China Chongqing University of Posts and Telecommunications School of Communications and Information Engineering Chongqing400065 China Shenzhen MSU-BIT University AI Research Institute Shenzhen518000 China Shenzhen University College of Computer Science and Software Engineering Shenzhen518000 China The University of British Columbia Department of Electrical and Computer Engineering VancouverBCV6T 1Z4 Canada

With the rapid development of the internet of things and smart cities, the demand for effective spectrum collaboration has grown *** maps play a crucial role in understanding the spatial radio environment, which is essential for wireless applications such as cell planning and radio resource management. However, generating radio maps is a resource-intensive task, as the construction process is complex and the distribution process consumes substantial bandwidth. This article proposes a method that combines semantic communication (SemCom) and generative artificial intelligence (GAI) to optimize the construction and distribution processes of radio maps. By incorporating SemCom, our method transmits only key semantic information during the distribution process, significantly reducing bandwidth usage while ensuring accurate and efficient information transfer. While the diverse generative capabilities of GAI introduce some instability, which could be a limitation in radio map construction, this article achieves precise content decoding through prompts based on multi-modal semantic information, enabling accurate construction and restoration of radio maps. By utilizing SemCom and GAI technologies, the cost of applications of radio maps can be significantly reduced, which is beneficial for stimulating the pace of intelligence evolution in future mobile networks. © 1986-2012 IEEE.

关键词： Bandwidth

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Interpretable CNN-Based Lithographic Hotspot Detection Through Error Marker Learning

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IEEE Transactions on computer-Aided Design of Integrated Circuits and Systems 2025年第3期44卷 1031-1044页

作者： Sun, Haoyang Jiang, Cong Ye, Xun Feng, Dan Tan, Benjamin Ma, Yuzhe Liu, Kang Huazhong University of Science and Technology School of Computer Science and Technology Wuhan430074 China Huazhong University of Science and Technology Wuhan National Laboratory for Optoelectronics Wuhan430074 China University of Calgary Department of Electrical and Software Engineering CalgaryABT2N 1N4 Canada With Microelectronics Thrust Guangzhou510530 China

As the technology node develops toward its physical limit, lithographic hotspot detection has become increasingly important and ever-challenging in the computer-aided design (CAD) flow. In recent years, convolutional neural networks (CNNs) have achieved great success in hotspot detection. However, the interpretability of their hotspot prediction has yet to be considered. Compared with conventional lithography simulation and pattern matching-based methods, the black-box nature of CNNs wavers their practical applications with confidence. In this article, we propose the first interpretable CNN-based hotspot detector capable of providing high-detection accuracy and reliable explanations for hotspot identification. Specifically, we augment the training dataset with expanded error markers obtained and preprocessed from lithography simulation, which are then learned by an encoder-decoder architecture as intermediate features. We additionally introduce coordinate attention in the encoder to facilitate better-feature extraction. By learning these error markers and part of their surrounding metals as root cause hotspot features, our architecture achieves the highest-hotspot accuracy of 99.78% and the lowest-false positive rate of 5.29% compared to all prior work. Moreover, our method demonstrates the best visual and quantitative interpretability results when applying CNN interpretation methods. © 2024 IEEE.

关键词： Pattern matching

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SkinRing: Ring-shaped Device Enabling Wear Direction-Independent Gesture Input on Side of Finger

SkinRing: Ring-shaped Device Enabling Wear Direction-Indepen...

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2025 IEEE/SICE International Symposium on System Integration, SII 2025

作者： Hanayama, Shogo Kitamura, Riku Yamamoto, Takumi Amesaka, Takashi Chan, Liwei Sugiura, Yuta Keio University Faculty of Science and Engineering 4-1-1 Hiyoshi Kohoku-ku Kanagawa Yokohama223-8521 Japan National Yang Ming Chiao Tung University Department of Computer Science Hsinchu Taiwan

ISBN: (纸本)9798331531614

Ring-shaped devices are small, socially acceptable wearable devices that are gaining attention as health tracking and input devices. Although various ring-shaped input devices have been developed, they are limited in the direction of rotation when worn, requiring the user to be aware of the wear direction while wearing or operating the device. We propose the SkinRing, a ring-shaped device that enables gesture input on the side of the finger, regardless of the wear direction. The SkinRing consists of eight photo-reflective sensors attached to a ring, which is put on the index finger and acquires skin deformation information for gesture identification when the thumb touches the side of the index finger. The initial calibration of the hand's opening and closing allows for input independent of the direction of rotation of the ring when worn. A user study, where the identification results of nine gestures were given, shows an average accuracy of 87.8%. © 2025 IEEE.

关键词： Gesture recognition

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Optimal Link Configuration for Covert Heterogeneous Wireless Networks with Power Constraints 59

Optimal Link Configuration for Covert Heterogeneous Wireless...

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59th Annual Conference on Information sciences and Systems, CISS 2025

作者： Gillani, Amna Lorenzo, Beatriz Ghaderi, Majid Dagefu, Fikadu Goeckel, Dennis University of Massachusetts Amherst Department of Electrical and Computer Engineering AmherstMA01003 United States University of Calgary Department of Computer Science CalgaryABT2N 1N4 Canada AdelphiMD20783 United States

ISBN: (纸本)9798331513269

Heterogeneous Networks (HetNets) leverage multiple communication interfaces (e.g., Wi-Fi, cellular, Bluetooth) seamlessly integrated to enhance connectivity and capacity across diverse environments. These capabilities can be advantageous for covert communication, where the objective is to ensure undetectable transmission in the presence of an adversary. This work investigates optimal power allocation strategies for covert communication within HetNets under maximum power constraints. We propose a polynomial-time (m2) algorithm for optimal link configuration by allocating power across multiple modes. Next, we address network-level covertness by allocating the end-to-end covertness constraint across links in a two-hop system that maximizes throughput. Leveraging the link configuration approach, we derive the optimal power allocation for each mode and link, ensuring the overall transmission remains undetectable. Numerical results demonstrate that the proposed method outperforms existing single-link and two-hop approaches, achieving higher efficiency and security in covert communication across HetNets. © 2025 IEEE.

关键词： Wireless local area networks (WLAN)

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Exploiting the Potential of Self-Supervised Monocular Depth Estimation via Patch-Based Self-Distillation

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IEEE Internet of Things Journal 2025年第11期12卷 18042-18055页

作者： Yu, Ting Pan, Shan Chen, Wei Tian, Zijian Wang, Zehua Yu, F. Richard Leung, Victor C. M. Postdoctoral Research Station China RongTong Academy of Sciences Group Corporation Limited Beijing100022 China China RongTong Artificial Intelligence Research Center Beijing100022 China China University of Mining and Technology School of Artificial Intelligence Beijing100083 China China University of Mining and Technology School of Computer Science and Technology Xuzhou221116 China School of Artificial Intelligence Beijing100083 China The University of British Columbia Department of Electrical and Computer Engineering VancouverBCV6T 1Z4 Canada Carleton University Department of Systems and Computer Engineering OttawaONK1S 5B6 Canada Shenzhen University College of Computer Science and Software Engineering Shenzhen518060 China

Perceiving scene depth and 3D structure is one of the key tasks for Internet of Video Things (IoVT) devices to understand and interact with the environment. Self-supervised monocular depth estimation has demonstrated significant potential in leveraging large-scale unlabeled datasets to achieve competitive performance, thereby playing an increasingly important role in depth estimation. Despite recent methods providing additional supervisory signals through self-distillation strategies to improve depth estimation, an effective method for generating pseudo-depth labels suitable for addressing occlusion issues among elements far from the camera remains unexplored. To address this limitation, we propose a Patch-based Self-Distillation Learning Framework to exploit the potential of self-supervised monocular depth estimation in recovering fine-grained scene depth. In the proposed framework, elements far from the camera within the input image are enlarged by enlarging and cropping operations in the patch-based self-distillation branch. Guided by photometric consistency, the model learns the detailed occlusion relationships among elements from the enlarged patches, producing patch depth maps with fine structures. In the main branch, which takes full-scale images as input, patch depth maps serve as pseudo-depth labels through self-distillation loss to provide additional supervisory signals for regions where photometric consistency fails to offer effective supervision. This forces the depth estimation network to recover fine structures of elements far from the camera in full-scale input images. Regarding the architecture of the depth estimation network, we introduce a bin-center prediction. In this prediction, a global aggregator based on self-attention provides additional scene structure queries for adaptive scene depth discretization. Finally, to encourage the model to explore more general cues for depth inference beyond road plane cues, we propose a PatchMix data augmentati

关键词： Image enhancement

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On Computational Completeness of Semi-Conditional Matrix Grammars 50th

On Computational Completeness of Semi-Conditional Matrix Gr...

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50th International Conference on Current Trends in Theory and Practice of computer science, SOFSEM 2025

作者： Fernau, Henning Kuppusamy, Lakshmanan Raman, Indhumathi Fachbereich 4 – Abteilung Informatikwissenschaften Universität Trier Trier54286 Germany School of Computer Science and Engineering VIT University Vellore632014 India Department of Computing Technologies SRM Institute of Science and Technology Kattankulathur Chennai603203 India

ISBN: (纸本)9783031826696

Matrix grammars are one of the first approaches ever proposed in regulated rewriting, prescribing that rules have to be applied in a certain order. In regulated rewriting, the most interesting case shows up when all rules are context-free. Typical descriptional complexity measures incorporate the number of nonterminals or the matrix length, i.e., the number of rules per matrix. When viewing matrices as program fragments, it becomes natural to consider additional applicability conditions for such matrices. Here, we focus on attaching a permitting and a forbidden string to every matrix in a matrix grammar. The matrix is applicable to a sentential form w only if the permitting string is a subword in w and the forbidden string is not a subword in w. We call such a grammar, where the application of a matrix is conditioned as described, a semi-conditional matrix grammar. We consider (1) the maximal lengths of permitting and forbidden strings, (2) the number of nonterminals, (3) the number of conditional matrices, (4) the maximal length of any matrix and (5) the number of conditional matrices with nonempty permitting and forbidden strings, as the resources (descriptional complexity measures) of a semi-conditional matrix grammar. We show that certain semi-conditional matrix grammar families defined by restricting resources can generate all recursively enumerable languages. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Context free grammars

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