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Enhancing lane-keeping technologies with optimised convolutional neural networks for steering angle prediction

International Journal of Vehicle Autonomous Systems 2025年第2期18卷 190-209页

作者： Haq, Ihtisham Ul Anas, Muhammad Husnain, Ghassan Ahmad, Waqas Department of Mechatronics Engineering University of Engineering and Technology Peshawar Khyber Pakhtunkhwa Peshawar Pakistan Department of Electrical Engineering University of Gujrat Hafiz Hayat Campus Gujrat Punjab Pakistan Department of Computer Science CECOS University of IT and Emerging Sciences Peshawar Khyber Pakhtunkhwa Peshawar Pakistan Department of Computer Science Iqra National University Khyber Pakhtunkhwa Peshawar Pakistan

Lane departure incidents, often caused by human negligence, contribute significantly to road fatalities. Lane-keeping systems can mitigate these risks by accurately predicting steering angles under varying road conditions and external disturbances. Traditional approaches struggle with feature extraction due to environmental complexities. This study proposes an enhanced Convolutional Neural Network (CNN) architecture, optimised for real-time steering angle prediction. A hybrid metaheuristic method, combining the Bat Algorithm and Particle Swarm Optimisation (PSO), is employed to fine-tune CNN hyper-parameters and architecture. The two-stage optimisation first tunes learning rate, batch size, and activation function via the Bat Algorithm, followed by PSO for optimising layer configurations. The model, trained on the Udacity dataset and 25,000 additional samples, uses Adagrad (learning rate 0.0052) and ELU activation. Real-world testing across diverse conditions showed improved generalisation and reduced Mean Squared Error (MSE) from 0.00329 to 0.00119, confirming the effectiveness of metaheuristic optimisation in enhancing CNN-based lane-keeping performance. Copyright © 2025 Inderscience Enterprises Ltd.

关键词： Convolutional neural networks

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Multiparty Random Phase Wrapping Secret-Sharing Systems for Visual Data Security

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IEEE Transactions on Systems, Man, and Cybernetics: Systems 2025年第5期55卷 3586-3600页

作者： Kim, Youhyun Jeong, Ongee Moon, Inkyu Javidi, Bahram Daegu Gyeongbuk Institute of Science and Technology Department of Robotics and Mechatronics Engineering Daegu42988 Korea Republic of AhnLab Inc. Gyeonggi Seongnam13493 Korea Republic of University of Connecticut Department of Electrical and Computer Engineering StorrsCT06269 United States

A secret sharing scheme is an important cryptographic procedure that enables the secure distribution of secret information, such as private images, in an untrusted network. However, in all secret sharing schemes, the sizes of the shares increase in proportion to the size of the secret information, because they involve computationally expensive polynomials of degree n-1 (n is total number of shares) and increasingly large modulus of modulo operations for security. Moreover, with large share sizes, it is not easy in practice to encrypt them quickly using block cipher algorithms. Therefore, ensuring the security of secret sharing for large-scale visual data with a reasonable share length and efficiently encrypting n shares properly represents formidable challenges. To overcome these challenges in secret sharing schemes, we propose new multiparty random phase wrapping secret sharing systems for visual datasets. Computational optical imaging enables the acquisition of wrapped phase information, ranging from–π to π, to be expressed in the form of a complex sinusoidal waveform. The proposed scheme allows for large-scale secret data—such as confidential digital images and visual data, including optical images—to be securely and efficiently shared and distributed to multiple parties or agencies utilizing a digital representation of a complex sinusoidal waveform of the secret information. The proposed scheme can be useful in cryptographic key escrow systems and in use with large-scale secret data. © 2013 IEEE.

关键词： Data Sharing

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L2H-NeRF: low- to high-frequency-guided NeRF for 3D reconstruction with a few input scenes

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Visual computer 2025年 1-12页

作者： Bao, Taoqi Ye, Jiangnan Bao, Zhankong Leow, Chee Siang Hu, Haoji Lu, Jianfeng Fujishiro, Issei Xu, Jiayi School of Computer Science and Technology Hangzhou Dianzi University Hangzhou China College of Information Science & Electronic Engineering Zhejiang University Hangzhou China Department of Mechatronics University of Yamanashi Kofu Japan Department of Information and Computer Science Faculty of Science and Technology Keio University Yokohama Japan

Nowadays, three-dimensional (3D) reconstruction techniques are becoming increasingly important in the fields of architecture, game development, movie production, and more. Due to common issues in the reconstruction process, such as perspective distortion and occlusion, traditional 3D reconstruction methods face significant challenges in achieving high-precision results, even when dense data are used as inputs. With the advent of neural radiance field (NeRF) technology, high-fidelity 3D reconstruction results are now possible. However, high computational resources are usually required for NeRF computations. Recently, a few data inputs are used to ensure the highest quality. In this paper, we propose an innovative low- to high-frequency-guided NeRF (L2H-NeRF) framework that decomposes scene reconstruction into coarse and fine stages. For the first stage, a low-frequency enhancement network based on a vision transformer is proposed, where the low-frequency-based globally coherent geometric structure is recovered, with the dense depth restored in a depth completion way. In the second stage, a high-frequency enhancement network is incorporated, where the high-frequency-related detail is compensated by robust feature alignment across adjacent views using a plug-and-play feature extraction and matching module. Experiments demonstrate that both the accuracy of the geometric structure and the feature detail of the proposed L2H-NeRF outperforms state-of-the-art methods. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025.

关键词： Three dimensional computer graphics

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Evaluating the Efficiency of Dual-Zone Registration System with Timer Mechanism in Wireless Networks 3

Evaluating the Efficiency of Dual-Zone Registration System w...

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3rd International Conference on Integrated Circuits and Communication Systems, ICICACS 2025

作者： Raman, Ramakrishnan Vinoth, T. Barve, Amit Tiwari, Vinita Pune India Chennai Institute of Technology Department of Mechatronics Engineering Kundrathur Chennai India Paruluniversity Faculty of Engineering and Technology Department of Computer Science and Engineering Vadodara India Pune India

ISBN: (纸本)9798331508456

The increasing demand for efficient communication in wireless networks necessitates optimized registration systems that ensure smooth connectivity and scalability. This paper introduces a novel Dual-Zone Registration System with a Dynamic Timer Mechanism designed to enhance registration efficiency in wireless networks. The system classifies devices into two zones: a primary zone for immediate registration and a secondary zone for devices requiring delayed registration. Leveraging a dynamic timer mechanism, the system regulates registration attempts to reduce congestion and optimize resource allocation. Simulations reveal that the proposed system significantly improves key performance metrics, including registration success rate, average registration time, packet loss, and network load. The dual-zone system achieved a 95% registration success rate, reducing packet loss to 3% compared to 12% in centralized systems and 8% in simple timer-based systems. Average registration time decreased to 6.3 seconds, outperforming centralized (12.5 seconds) and simple timer-based systems (9.8 seconds). Additionally, the system maintained a lower network load of 45 requests per second, showcasing its scalability and efficiency. By dynamically adjusting registration delays based on real-time network conditions, the proposed system ensures efficient resource utilization and balanced load distribution, making it a promising solution for large-scale wireless networks. These findings validate the effectiveness of the dual-zone approach in improving the scalability, reliability, and overall performance of modern wireless networks. © 2025 IEEE.

关键词： Resource allocation

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An Optimized Switching Scheme to Reduce the Leakage Current and Power Loss of Transformerless Inverter

An Optimized Switching Scheme to Reduce the Leakage Current ...

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2025 IEEE International Conference on mechatronics, ICM 2025

作者： Mondal, Sudipto Rashid, Mamun Or Biswas, Shuvra Prokash Islam, Md. Rabiul Rajshahi University of Engineering & Technology Department of Electronics & Telecommunication Engineering Rajshahi6204 Bangladesh Rajshahi University of Engineering & Technology Department of Mechatronics Engineering Rajshahi6204 Bangladesh School of Electrical Computer & Telecommunications Engineering University of Wollongong NSW2522 Australia

ISBN: (纸本)9798331533892

Transformer-less grid-connected inverters gained its popularity in recent times due to their compact design, lower cost, lower weight and overall higher efficiency compared to transformer-based systems. However, among the other issues of the transformer-less solution, leakage current caused by common-mode (CM) voltage fluctuations remains a significant problem, impacting power output, system safety. This paper proposes an optimized switching scheme for a modified H7 transformerless photovoltaic (TLPV) inverter to address the issue by reducing the leakage current and increasing the power quality. Moreover, an optimized unipolar pulse width modulation (UPPWM) strategy is also presented to maintain fairly constant common mode voltage (CMV), effectively suppressing leakage current further. The modified H7 TLPV along with the optimized UPPWM strategy also ensures lower semiconductor power loss and overall better efficiency. The total harmonic distortion (THD) of the grid voltage and current with the optimized pulse width modulation (PWM) strategy found satisfactory which met the standards of IEEE-519. Simulation is conducted in the MATLAB/Simulink environment to validate the performance criteria over the other TLPV structures, feasibility of leakage current reduction and overall power loss. This novel strategy provides an efficient and minimal solution for the next generation TLPV. © 2025 IEEE.

关键词： Pulse width modulation

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A Deep Learning Approach to Stock Trend Forecasting using CNNs with Technical Indicators 3

A Deep Learning Approach to Stock Trend Forecasting using CN...

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3rd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Yuvaraj, S. Chenni Kumaran, J. Bharathiraja, M. Krishna Kumar, K. Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University TamilNadu Chennai India Department of Mechatronics Engineering Bannari Amman Institute of Technology Tamil Nadu Sathyamangalam India

ISBN: (数字)9798331527549

ISBN: (纸本)9798331527549

The approach encompassed under this model introduces stock trend forecasting using a new approach that involves Convolutional Neural Networks (CNNs) supplemented with technical indicators like the Exponential Moving Average (EMA), Moving Average Convergence Divergence (MACD), and Relative Strength Index (RSI). This study reacts to the limitations of traditional methods like ARIMA and GARCH due to their shortcomings with non-linear dependence on stock data. The robust solution for trend prediction closely follows the hierarchical learning of CNN. This model attains a remarkable accuracy of 99% in stock trend prediction using Convolutional Neural Networks, fine-tuning crucial but elaborate patterns for stock price movements. The forecast results categorize stock trends as Neutral, Bullish, and Bearish, with validation shown from high precision, recall, and F1-scores computed from complete classification reports and confusion matrices. The capability of forecasting provides tremendous assistance in making informed investment decisions. These results prove that CNNs more efficiently predict stocks if combined with technical analysis and give portions of insight to traders. This study shows the capacity to apply various deep learning techniques in finance to enhance stock trend predictions and shape on-the-ground investment strategies. The study fills an existing gap in research with the introduction of combining CNN with technical indicators in the prediction models for improvement with predictive power and accuracy. © 2025 IEEE.

关键词： Convolutional neural networks

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Interference Suppression and Jitter Elimination Ability-Based Adaption Tracking Guidance for Robotic Fishes

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IEEE/CAA Journal of Automatica Sinica 2025年第1期12卷 126-137页

作者： Dongfang Li Jie Huang Rob Law Xin Xu Limin Zhu Edmond Q.Wu IEEE the School of Electrical Engineering and Automation Fuzhou University the Key Laboratory of System Control and Information Processing Ministry of Education the School of Electrical Engineering and Automation Fuzhou University 5G+Industrial Internet Institute Fuzhou University the University of Macau the College of Mechatronics and Automation National University of Defense Technology the School of Mechanical Engineering Shanghai Jiao Tong University the Department of Computer Science and Engineering Shanghai Jiao Tong University

This work presents an adaptive tracking guidance method for robotic fishes. The scheme enables robots to suppress external interference and eliminate motion jitter. An adaptive integral surge line-of-sight guidance rule is designed to eliminate dynamics interference and sideslip issues. Limited-time yaw and surge speed observers are reported to fit disturbance variables in the model. The approximation values can compensate for the system's control input and improve the robots' tracking ***, this work develops a terminal sliding mode controller and third-order differential processor to determine the rotational torque and reduce the robots' run jitter. Then, Lyapunov's theory proves the uniform ultimate boundedness of the proposed method. Simulation and physical experiments confirm that the technology improves the tracking error convergence speed and stability of robotic fishes.

关键词： Adaptive integral surge line-of-sight approximation robotic fish speed observer

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Applying Radio Frequency Attenuators to Reduce the Impact of U-NII-4 Interference on DSRC Channel 180

Applying Radio Frequency Attenuators to Reduce the Impact of...

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2025 IEEE SoutheastCon, SoutheastCon 2025

作者： Hatcher, Grayson Tello, Marco Kirkwood, Nathan Kihei, Billy Kennesaw State University Department of Electrical and Computer Engineering MariettaGA United States Kennesaw State University Department of Robotics and Mechatronics Engineering MariettaGA United States

ISBN: (纸本)9798331504847

In recent years, competition within Radio Frequency (RF) communication bands has led to an overlap between the U-NII-4 band used by low-cost consumer devices, and the Dedicated Short Communication Band (DSRC) used by the Georgia department of Transportation. Past research confirmed that this interference leads to drastic reduction in Packet Reception Rate (PRR) for Roadside Units (RSUs) and On-Board Units (OBUs) throughout the country, resulting in a reduction of stability between different components of the Intelligent Transportation System (ITS). Our research team set a goal to prove that a low-cost and simple modification can be made to RSUs currently in use that could increase PRR, by developing and testing RF technologies, primarily attenuators available on the market currently. These solutions could be easily adapted to infrastructure systems, and only require they run in-line with the stock antennas already in use. We were able to accomplish this goal, as well as made findings that may be beneficial within the world of RF communications for RSU devices. © 2025 IEEE.

关键词： Advanced driver assistance systems

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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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Optimizing Multi-Agent System Swarm Performance Through Explainable AI 11

Optimizing Multi-Agent System Swarm Performance Through Expl...

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11th International Conference on mechatronics and Robotics engineering, ICMRE 2025

作者： Mahmud, Shekhar Kutlu, Mustafa Alan, Alper Turan Military Technological College System Engineering Department Muscat Oman Sakarya University of Applied Sciences Mechatronics Engineering Department Sakarya Turkey International University of Kuwait Computer Science & Cybersecurity Department Kuwait City Kuwait

ISBN: (纸本)9798331509293

Advancements in multi-agent systems (MAS) have enabled swarm-based systems to perform decentralized decision-making and autonomous tasks. However, optimizing their performance while ensuring transparency and interpretability remains a challenge. This paper introduces a framework that combines Bayesian optimization with Explainable Artificial Intelligence (XAI) techniques to enhance both the efficiency and transparency of MAS swarms. The Bayesian optimization framework fine-tunes agent parameters to improve swarm metrics such as energy efficiency, task completion time, and coordination success. The experimental results show significant improvements: a 25% increase in the coordination success rate, a 15% increase in energy efficiency, and a 20 % reduction in task completion time. XAI techniques, including SHAP values, provide interpretable explanations for optimization decisions, improving user trust and understanding. This study demonstrates the efficacy of integrating Bayesian optimization with XAI to create transparent, efficient, and reliable MAS swarms. Future work should address scalability and implications in dynamic environments. © 2025 IEEE.

关键词： Multi agent systems

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