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CVTD: A Robust Car-Mounted Video Text Detector

computers, Materials & Continua 2024年第2期78卷 1821-1842页

作者： Di Zhou Jianxun Zhang Chao Li Yifan Guo Bowen Li Department of Computer Science and Engineering Chongqing University of TechnologyChongqingChina College of Information and Engineering Jingdezhen Ceramic UniversityJingdezhenChina

Text perception is crucial for understanding the semantics of outdoor scenes,making it a key requirement for building intelligent systems for driver assistance or autonomous *** information in car-mounted videos can assist drivers in making ***,Car-mounted video text images pose challenges such as complex backgrounds,small fonts,and the need for real-time *** proposed a robust Car-mounted Video Text Detector(CVTD).It is a lightweight text detection model based on ResNet18 for feature extraction,capable of detecting text in arbitrary *** model efficiently extracted global text positions through the Coordinate Attention Threshold Activation(CATA)and enhanced the representation capability through stacking two Feature Pyramid Enhancement Fusion Modules(FPEFM),strengthening feature representation,and integrating text local features and global position information,reinforcing the representation capability of the CVTD *** enhanced feature maps,when acted upon by Text Activation Maps(TAM),effectively distinguished text foreground from non-text ***,we collected and annotated a dataset containing 2200 images of Car-mounted Video Text(CVT)under various road conditions for training and evaluating our model’s *** further tested our model on four other challenging public natural scene text detection benchmark datasets,demonstrating its strong generalization ability and real-time detection *** model holds potential for practical applications in real-world scenarios.

关键词： Deep learning text detection Car-mounted video text detector intelligent driving assistance arbitrary shape text detector

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Artificial hummingbird algorithm with chaotic-opposition-based population initialization for solving real-world problems

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Neural Computing and Applications 2024年 1-44页

作者： Kaur, Sumandeep Kaur, Lakhwinder Lal, Madan Department of Computer Science & Engineering Punjabi University Punjab Patiala147001 India

The artificial hummingbird algorithm is a global search mechanism with many applications in engineering design, but it tends to stall in high-dimensional problems with locally optimal solutions. To address this issue, this paper enhances an artificial hummingbird algorithm by using a chaos map and opposition-based method for population initialization to combat the lack of population diversity, the imbalance between exploration and exploitation, and the algorithm’s premature convergence. The randomness of chaos maps has been leveraged to prevent solutions trapped in local optima and facilitate faster convergence. Moreover, an opposition-based population can serve as a better initial solution and accelerate convergence when compared to random initialization. Two numerical test suites are used to evaluate the efficacy of the proposed algorithm: 50 benchmark functions and the CEC 2018 benchmark test suite. The outcomes are compared to eight other cutting-edge metaheuristic algorithms. Wilcoxon rank sum test, Friedman test, and mean absolute error are used to conduct additional statistical analysis on the data. Moreover, experiments are conducted on the aforementioned 57 real-world optimization problems to demonstrate the efficacy of the proposed method. The outcomes are contrasted to the algorithms SASS, MAgES, EnMODE, and COLSHADE (which won the CEC2020 Competition on Real-World Single-Objective Constrained Optimization). All quantitative and qualitative results on benchmark functions, statistical tests, as well as real-world optimization problem results demonstrate that the proposed algorithm is competitive and preferable to the metaheuristics considered in the experiments. Hence it is concluded that the proposed algorithm balances exploration and exploitation more effectively and the population initialization technique is conducive to augmenting the search capabilities of the artificial hummingbird algorithm. © The Author(s), under exclusive licence to Springer-Verla

关键词： Constrained optimization

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Enhancing IoT intrusion detection through machine learning with AN-SFS: a novel approach to high performing adaptive feature selection

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Discover Internet of Things 2024年第1期4卷 16页

作者： Walling, Supongmen Lodh, Sibesh Department of Computer Science and Engineering National Institute of Technology Nagaland Dimapur India

The vast volume of redundant and irrelevant network traffic data poses significant hurdles for intrusion detection. Effective feature selection is crucial for eliminating irrelevant information. Presently, most filtering and embedded methods rely on fixed thresholds or ratios, necessitating prior knowledge. Conversely, wrapper methods are computationally intensive, and individual feature selection methods may introduce biases in evaluation. To address these challenges, this study introduces Adaptive Neighborhood based Feature Selection (AN-SFS), a dynamic feature selection approach that adapts to local statistical properties of the data. Unlike traditional methods, AN-SFS adjusts its threshold based on the characteristics of the current feature subset and incorporates statistical measures of neighboring features, capturing subtle relationships and dependencies. This adaptability enables AN-SFS to achieve robust and effective feature selection outcomes. Using NSL-KDD and UNSW-NB15 datasets, our model demonstrates superiority over conventional ML classifiers in detection rate, precision, and recall, achieving outstanding accuracy rates of 99.3% on NSL-KDD and 97.5% on UNSW-NB15, significantly outperforming contemporary methods. To further demonstrate the effectiveness of our feature selection approach, we conducted a series of comparisons with other statistical methods, including ANOVA, Pearson correlation, and the chi-square test. In each comparison, our approach consistently outperformed these alternatives, underscoring its superior efficacy. © The Author(s) 2024.

关键词： Network intrusion

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Microgrids with day-ahead energy forecasting for efficient energy management in smart grids: hybrid CS-RERNN

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Australian Journal of Electrical and Electronics engineering 2024年第3期21卷 213-227页

作者： Shirley, C.P. Pattar, Jagannath Kavitha Rani, P. Saini, Sumit Ranga, Jarabala Elangovan, D. Venkatakrishna Reddy, Ch. Department of Computer Science and Engineering Karunya Institute of Technology and Sciences Coimbatore India Department of Mechanical Engineering Madanapalle Institute of Technology & Science Madanapalle India Department of M.Tech Computer Science and Engineering Sri Krishna College of Engineering and Technology Coimbatore India Department of Electrical Engineering School of Engineering & Technology Central University of Haryana Mahendergarh India Detartment of Electrical and Electronics Engineering Ramachandra College of Engineering Eluru India Department of Computer Science and Engineering Panimalar Engineering College Chennai India Department of Electrical and Electronics Engineering Chaitanya Bharathi institute of technology Hyderabad India

By integrating smart grid technology with home energy management systems, households can monitor and optimise their energy consumption. This allows for more efficient use of energy resources, reducing waste and lowering energy bills. In this manuscript, a hybrid approach is proposed for smart grid home energy management with microgrids and day-ahead energy forecasts. The proposed control approach combines the Circle Search (CS) algorithm and Recalling-Enhanced Recurrent Neural Network (RERNN). Commonly it is named as CS-RERNN technique. The novelty of this paper is to optimise energy consumption and production within microgrids, thereby contributing to the overall efficiency of the smart grid system. The proposed method is used to reduce the electricity cost, peak-to-average ratio (PAR), and maximising consumer comfort. Energy management is performed based on the CS algorithm. A smart home connected to the external power grid (PG) is managed by the proposed method. Here, load demand is predicted by using RERNN. By then, the performance of the proposed method is implemented in MATLAB platform. The proposed method shows a high efficiency of 96%, and 22 $ of low electricity bill cost compared with other existing methods such as Particle swarm optimisation (PSO), Cuckoo search algorithm (CSA, and Border collie Optimisation (BCO). ©, Engineers Australia.

关键词： Energy utilization

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Quantum-Inspired Differential Evolution for Freshness-aware Caching-aided Offloading in Digital Twin-enabled Internet of Vehicles

IEEE Transactions on Intelligent Vehicles

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IEEE Transactions on Intelligent Vehicles 2024年 1-15页

作者： Bandyopadhyay, Biswadip Kuila, Pratyay Bey, Marlom Govil, Mahesh Chandra Department of Computer Science and Engineering National Institute of Technology Sikkim Ravangla India

With the development of the sixth-generation network, Digital Twin (DT) is driving the explosive growth of Internet-of-Vehicles (IoVs). The rapid proliferation of highly mobile IoVs, coupled with advanced applications, resulted in rigorous demands for quality of experience (QoE) and intricate task caching. The diverse requirements of on-vehicle applications, as well as the freshness of dynamic cached information, provide significant challenges for edge servers in efficiently fulfilling energy and latency demands. This work studies a freshness-aware caching-aided offloading-based task allocation problem (FCAOP) in DT-enabled IoV (DTIoV) with Intelligent Reflective Surfaces (IRS) and edge computing. DT is used to accumulate real-time data and digitally depict the physical objects of the IoV to enhance decision-making. A quantum-inspired differential evolution (QDE) algorithm is proposed to reduce the overall delay and energy consumption in DTIoV (QDE-DTIoV). The quantum vector (QV) is encoded to represent a complete solution to the FCAOP. The decoding of the QVs is done using a one-time hashing algorithm. The fitness function is derived by considering delay, energy consumption, and freshness of the tasks. Extensive simulations demonstrate the superiority of QDE-DTIoV over other benchmark algorithms, showing an average latency improvement of 23%-26% and a reduction in energy consumption ranging from 22% to 33%. IEEE

关键词： Energy utilization

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A Graph-Assisted Digital-Twin-Driven Multiagent Shared Offloading for Internet of Vehicles

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IEEE Internet of Things Journal 2025年第11期12卷 17349-17363页

作者： Zahangir Alam, Md Rahman, Suryaia Asif Bin Khaled, Md Islam, Ashraful Jamalipour, Abbas Independent University Bangladesh Department of Computer Science and Engineering Center for Computational and Data Sciences Dhaka1229 Bangladesh The University of Sydney School of Electrical and Computer Engineering SydneyNSW2006 Australia

Vehicular edge computing (VEC) allows vehicles to process part of the tasks locally at the network edge while offloading the rest of the tasks to a centralized cloud server for processing. A massive volume of tasks generated by the Internet of Vehicles (IoV) leads to buffer overflow that causes higher latency. Elevating latency, in turn, can increase network energy consumption. Both higher latency and energy consumption lead to a degradation of network performance. Therefore, VEC design requires a balance between latency and energy consumption tradeoff. To reduce overwhelming amount of offloading to edge servers, a cooperative cluster-based shared offloading strategy has been proposed in this work. We use digital twin technology in VEC for managing and adapting to environmental dynamic changes. Then, we leverage Lyapunov (Ly) optimization to transform the stochastic offloading problem into a more manageable deterministic form. Finally, we present a decentralized coordination graph (CG)-driven Ly-based multiagent deep deterministic policy gradient (CG-LyMADDPG) algorithm that trains agents toward energy efficient optimal offloading policy while maintaining queue stability at a maximum delay constraint. The experimental result shows that the proposed learning significantly outperforms the baseline algorithms for energy savings while maintain queue stability. © 2014 IEEE.

关键词： Stochastic systems

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Congestion aware optimal routing path selection using hybrid meta heuristic algorithms in sparse wireless sensor networks

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International Journal of Mobile Network Design and Innovation 2024年第2期11卷 59-75页

作者： Ram, G. Mohan Ilavarasan, E. Department of Computer Science and Engineering Puducherry Technological University Puducherry605014 India

A novel energy-efficient clustering-based congestion-awareness routing mechanism has been developed for wireless sensor network (WSN). In the first stage, some set of sensor nodes are initialised in the WSN environment. The cluster is formed from the group of nodes by the advanced k-means clustering (A-KDC) model. The cluster head (CH) is selected based on some of the objective functions like distance, energy and so on using an exaggerated manta ray optimization algorithm (EXMro). The optimal routing selection has been formed to transmit the packet from source to destination. The packets are transferred from the CHs to the base station using hybrid Multi-objective May Honey Badger optimisation (MHB), which selects the optimal path without congestion. The performance of the proposed approach is examined using the NS2 platform. The proposed approach has an average throughput of 99.06% and an average packet delivery ratio (PDR) of 98.45%, indicating higher efficiency than other existing systems. Copyright © 2024 Inderscience Enterprises Ltd.

关键词： Heuristic algorithms

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Quantum-MUSIC: Multiple Signal Classification for Quantum Wireless Sensing

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IEEE Wireless Communications Letters 2025年第6期14卷 1623-1627页

作者： Kim, Hanvit Park, Hyunwoo Kim, Sunwoo The Department of Electronics and Computer Engineering Hanyang University Seoul04763 Korea Republic of

This letter proposes a Quantum-MUSIC, the first signal processing algorithm for quantum wireless sensing of multi-user with atomic receiver. Although the atomic receiver highly enhances sensing accuracy via extremely sensitive Rydberg atoms, leveraging its potential is challenging due to its magnitude-only measurement. To fill in this gap, the proposed algorithm first retrieves the channel matrix and incorporates the multiple signal classification (MUSIC) algorithm, enabling multi-user quantum wireless sensing. Simulation results showed that the proposed algorithm outperforms the classical MUSIC algorithm, validating the theoretical potential of quantum wireless sensing. © 2025 IEEE. All rights reserved.

关键词： Quantum communication

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Consistent Action for Stable Training in Reinforcement Learning–based Gain Tuning of Linear Feedback Controller

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Journal of Institute of Control, Robotics and Systems 2024年第9期30卷 965-972页

作者： Byun, Hyungjo ASRI Department of Electrical and Computer Engineering Seoul National University Korea Republic of

Controlling nonlinear systems with linear feedback controller after linearization is a widely used method. This paper proposes a new method to efficiently train a reinforcement learning agent to select the control gain of the linear feedback controller. The proposed method involves the agent selecting a consistent gain by choosing between a new and an old gain. Since the linear feedback controller can operate with this consistent gain, the agent does not need to select the optimal gain, thus simplifying the training process. Numerical simulations were performed by applying the proposed method to a longitudinal model of a nonlinear missile with a 3-loop autopilot. The proposed method enables training with a wide range of gains and enhances the reference tracking performance compared to classical gain scheduling. © ICROS 2024.

关键词： Reinforcement learning

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Motion Planning in Topologically Complex Environments Via Hybrid Feedback

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IEEE Transactions on Automatic Control 2024年第6期70卷 3742-3754页

作者： Kanakis, George S. Rovithakis, George A. Aristotle University of Thessaloniki Department of Electrical & Computer Engineering Thessaloniki54124 Greece

In this paper we consider the motion planning problem in an n-dimensional Euclidean space, n ≥q 2, containing finitely many obstacles with boundaries possessing a smooth structure. Obstacle boundaries are assumed to be compact and connected embedded submanifolds of the ambient Euclidean space of codimension 1. The proposed solution guarantees both global asymptotic stability of a target configuration and avoidance of the obstacles. The controller is based on a switching scheme between two modes of operation: a free space mode and an avoidance mode. A simulation study illustrates and clarifies the theoretical results in a 3-dimensional workspace. © 1963-2012 IEEE.

关键词： Asymptotic stability

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