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Integral Sliding Mode Control for Automated Vehicles under Coding-Decoding Mechanisms with Constrained Bit Rate

IEEE Internet of Things Journal 2025年第11期12卷 16214-16226页

作者： Zhang, Mingming Ding, Derui Shen, Bo Ge, Xiaohua University of Shanghai for Science and Technology Department of Control Science and Engineering Shanghai200093 China Donghua University College of Information Science and Technology Shanghai201620 China Swinburne University of Technology School of Science Computing and Engineering Technologies MelbourneVIC3122 Australia

In this paper, an integral sliding mode (ISM) control scheme is developed to tackle platooning control issues of vehicle systems under both an improved constant-Time headway (CTH) spacing strategy and a coding-decoding communication protocol (CDCP) with constrained bit rate. The utilization of CDCPs can effectively reduce communication burden and promote data security, while the improved CTH spacing policy based on relative velocities can ensure high traffic efficiency as well as platoon safety. First, an ISM surface is constructed to handle matched unmodeled dynamics by resorting to the improved CTH spacing strategy and the decoded information governed by CDCPs. Both an equivalent ISM law and a practical ISM controller are constructed in terms of such an ISM surface, reflecting the effect of both spacing strategies and decoded information. Then, a generally dynamical model of platoon tracking errors involving an unusual coupling with the adopted spacing strategy is derived by leveraging both the equivalent ISM law and a matrix transformation technique. Moreover, the reachability of the proposed ISM surface and the required platooning performance are profoundly discussed and sufficient conditions to determine the required gain parameters are deduced with essential inequality techniques. In terms of vehicle tracking errors, the upper boundary is determined by both the bit rate assigned and the spacing strategy employed. Finally, the effectiveness of the devised ISM control scheme is professionally evaluated through Carsim's simulation instances. © 2014 IEEE.

关键词： Decoding

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Synchronisation of solar PV-wind-battery-based water pumping system using brushless DC motor drive

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International Journal of Power Electronics 2023年第4期18卷 435-459页

作者： Gautam, Sunil Kumar Kumar, Rajan School of Computing and Electrical Engineering Indian Institute of Technology Mandi Mandi175005 India Department of Electrical Engineering National Institute of Technology Kurukshetra Kurukshetra136119 India

An environment-friendly, efficient and simple control method for solar photovoltaic (PV) and wind-based water pumping system using brushless DC (BLDC) motor drive and battery storage is proposed in this paper. This technology provides continuous water pumping under different climate conditions. By using perturb and observe (P&O) maximum power point tracking (MPPT), the solar PV and permanent magnet synchronous generator (PMSG)-based wind energy conversion system (WECS) generate maximum power, and feed it to a common DC bus. The power is delivered to a BLDC motor that operates the water pump. The motor speed and the battery charging are controlled, and the DC bus is always maintained at fixed voltage. Power generated by solar PV and WECS is delivered to charge the battery using a single-ended primary-inductor converter (SEPIC)-based charge controller if water pumping is not demanded. In MATLAB, the proposed model is developed and simulated, and demonstrated under various operating conditions. © 2023 Inderscience Publishers. All rights reserved.

关键词： Brushless DC motors

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Detecting a suitable path method for assisted localization techniques in transmission of data over wireless sensor networks

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International Journal of Information technology (Singapore) 2025年第4期17卷 2235-2242页

作者： Navaneethan, S. Arun, A. Department of Networking & Communications School of Computing SRM Institute of Science and Technology SRM Nagar Tamilnadu KattanKulathur 603203 India Department of Networking & Communications School of Computing Faculty of Engineering & Technology SRM Institute of Science and Technology Tamilnadu KattanKulathur 603203 India

In many Wireless Sensor Networks (WSNs) applications, the relevant sensor node’s location information is essential in determining where the event or situation occurs. Therefore, localization is one of the critical challenges in WSNs. Mobile Anchor Node Assisted Localization (MANAL) is one of the promising solutions for the localization of statically deployed sensors. The main problem in MANAL is that the path planning of the Mobile Anchor Node (MAN) should be done in a way that the localization error in the network will be minimal and that all unknown nodes in the network are covered. In this paper, a new path planning approach called Nested Hexagons Curves (NH-exCurves) is proposed for MANAL. NH-exCurves guarantees that it will receive messages from at least three non-collinear anchors to locate all unknown nodes in the network. The proposed model is compared to six different path planning schemes using Accuracy Priority Trilateration (APT) under different evaluation criteria. In these comparisons, first of all, localization errors of the models are compared using some statistical concepts. Secondly, the variation of the localization error according to parameters such as resolution (R) and the standard deviation of noise (σ) are observed. The simulation results present the superiority of the proposed work over existing path planning models used in this study. © Bharati Vidyapeeth's Institute of Computer Applications and Management 2025.

关键词： Accuracy priority trilateration (APT) Localization Mobile anchor node assisted localization (MANAL) Static path planning Wireless sensor networks (WSNs)

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Robust Multi-Dialect End-to-End ASR Model Jointly with Beam Search Threshold Pruning and LLM

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SN Computer Science 2025年第4期6卷 1-12页

作者： Shunmuga Priya, M.C. Karthika Renuka, D. Ashok Kumar, L. Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Coimbatore India Department of Information Technology PSG College of Technology Coimbatore India Department of Electrical and Electronics Engineering Thiagarajar College of Engineering Madurai India

This paper aims to develop a novel robust multi-dialect end-to-end ASR system with beam search threshold pruning. The efficacy of our proposed model is evaluated using word error rate (WER). Our key contributions are: (1) To develop an end-to-end ASR system using attention-based neural network architecture and analyze the effectiveness of two features such as MFCC and log mel filter bank energies on multiple speech dialect corpora including American, Britain, and Indian accents;(2) To integrate beam search threshold pruning as a decoding mechanism to reduce the decoding time (3) To conduct an experimental analysis to test the model performance and compare the results against baseline system. (4) Post processing analysis are carried out using Llama2-7B based large language model(LLM) for enhancing the performance of proposed ASR system. The proposed model significantly improves performance by 1.91% and 4.29% over clean and noisy speech in librispeech corpus. Similarly, for the Indian accented speech, the model attains an average WER of about 6.6%. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： Automatic speech recognition Beam search Decoding LLM Log Mel filter bank energies

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Detecting the symptoms of COVID-19 during pandemic environment using smart spectacle thermal images and deep capsule networks

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Multimedia Tools and Applications 2025年第8期84卷 4475-4501页

作者： Dwarakanath, B. Pandimurugan, V. Mohandas, R. Sambath, M. Baiju, B.V. Chinnasamy, A. SRM Institute of Science and Technology Ramapuram Chennai India Networking and Communications School of Computing SRMIST kattankulathur Chennai India Computational Intelligence School of Computing SRMIST kattankulathur Chennai India Computer Science and Engineering Hindustan Institute of Technology and Science Chennai India School of Computer Science and Engineering Vellore Institute of Technology Vellore India Department of Data Science and Business Systems School of Computing SRMIST kattankulathur Chennai India

The pandemic creates a more complicated providence of medical assistance and diagnosis procedures. In the world, Covid-19, Severe Acute Respiratory Syndrome Coronavirus-2 (SARS Cov-2), and plague are widely known pandemic disease desperations. Due to the recent COVID-19 pandemic tragedies, various medical diagnosis models and intelligent computing solutions are proposed for medical applications. In this era of computer-based medical environment, conventional clinical solutions are surpassed by many Machine Learning and Deep Learning-based COVID-19 diagnosis models. Anyhow, many existing models are developing lab-based diagnosis environments. Notably, the Gated Recurrent Unit-based Respiratory Data Analysis (GRU-RE), Intelligent Unmanned Aerial Vehicle-based Covid Data Analysis (Thermal Images) (I-UVAC), and Convolutional Neural Network-based Computer Tomography Image Analysis (CNN-CT) are enriched with lightweight image data analysis techniques for obtaining mass pandemic data at real-time conditions. However, the existing models directly deal with bulk images (thermal data and respiratory data) to diagnose the symptoms of COVID-19. Against these works, the proposed spectacle thermal image data analysis model creates an easy and effective way of disease diagnosis deployment strategies. Particularly, the mass detection of disease symptoms needs a more lightweight equipment setup. In this proposed model, each patient's thermal data is collected via the spectacles of medical staff, and the data are analyzed with the help of a complex set of capsule network functions. Comparatively, the conventional capsule network functions are enriched in this proposed model using adequate sampling and data reduction solutions. In this way, the proposed model works effectively for mass thermal data diagnosis applications. In the experimental platform, the proposed and existing models are analyzed in various dimensions (metrics). The comparative results obtained in the experiments just

关键词： COVID-19

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An Assessment Towards 2D and 3D Human Pose Estimation and its Applications to Activity Recognition: A Review

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SN Computer Science 2025年第2期6卷 1-24页

作者： Verma, Pratishtha Srivastava, Rajeev Tripathy, Santosh Kumar Department of Computer Engineering National Institute of Technology Haryana Kurukshetra India Computer Science and Engineering IIT BHU Varanasi India Department of Computing Science and Engineering National Institute of Technology Patna Bihar Patna India

Human pose estimation (HPE) from images or video is not only a major issue of computer vision, but also it plays a vital role in many real-world applications. The most challenging problems of human pose estimation are the disparity of poses, disturbing background clutter, different camera views, changes in scene lighting conditions, and occlusion. To solve these issues, a significant amount of research effort has been considered in the past. In this paper, we explore various vision-based activity recognition using 2D and 3D human pose estimations. We explore the conventional system, which includes both statistical and deep learning-based approaches for HPE, and then explore the current progress of each one in comparison to existing ones. At last, we also present various current state-of-the-art human activity recognition (HAR) techniques that use HPE for recognition. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： Human activity recognition (HAR) Human body model (HBM) Human pose estimation (HPE)

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A Review of RF Based Drone Detection, Direction of Arrival and Identification Techniques 14

A Review of RF Based Drone Detection, Direction of Arrival a...

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14th IEEE International Conference on Control System, computing and engineering, ICCSCE 2024

作者： Jurn, Yaseen N. Ibraheem, Zeyid T. Zaki, Nashwan Dheyaa University ofInformation Technology and Communications Engineering College Mobile Communications and Computing Engineering Department Baghdad Iraq

ISBN: (纸本)9798350364507

In this review, different drone detection methodologies based-RF techniques are presented and discussed. Where, the drone RF-techniques are depended on communications signals between drone and its controller for controlling of flying or for video transmission signals. On the bases of these detection methodologies, a lot of researcher proposed different methods to determine the direction of arrival (DOA) of the presence drone in the protected area. Some of these methods have been discussed and compared in this paper. Also, the identification techniques based on drone fingerprint are discussed and evaluated. Finally, a universal comparisons for different methods and techniques are presented for the purpose of present an efficient summary useful for next researchers working in this field, as well as propose a new methodology for both detection and identification of drone.. © 2024 IEEE.

关键词： Drones

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Multi-Controller Architecture Design and Features in Software - Defined Networking (SDN) 6

Multi-Controller Architecture Design and Features in Softwar...

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6th International Conference on Mobile computing and Sustainable Informatics, ICMCSI 2025

作者： Prabhu, V. Balamurugan, P. Department of Computer Science and Engineering School of Computing College of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Tamilnadu Chengalpattu603 203 India Department of Networking and Communications School of Computing College of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Tamilnadu Chengalpattu603 203 India

ISBN: (纸本)9798331522667

Networking paradigm known as "Software-Defined Networking" (SDN) offers more flexibility with network management and is fast gaining popularity. Separating the control plane from the data plane is largely responsible for this success. In SDN-enabled networks, a centralized controller is used to translate high-level network application requirements into low-level directives that are issued to the forwarding devices. Such the controller requires limited ability to react to the request for flow processing generated by forwarding devices. Whenever an SDN-enabled network expands in terms of size or as a network traffic rises, flow processing demands can’t be able to fulfill by a single centralized controller. This causes a controller's performance to decline, which eventually results in a malfunctioning SDN-enabled network. Implementing a multi-controller architecture, in which a number of controllers cooperate to manage a sizable amount of network traffic and flow processing requirements, would be a successful strategy. The introduction of a multi-controller architecture and a consideration of the problems with its scalability, coherence as well as accessibility make up the core contribution of this study. Additionally, it identifies areas where study in this area could still be done. © 2025 IEEE.

关键词： Scalability

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Enhancing waste sorting and recycling efficiency: robust deep learning-based approach for classification and detection

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Neural computing and Applications 2025年第6期37卷 4567-4583页

作者： Sayem, Faizul Rakib Islam, Md. Sakib Bin Naznine, Mansura Nashbat, Mohammad Hasan-Zia, Mazhar Kunju, Ali K Ansaruddin Khandakar, Amith Ashraf, Azad Majid, Molla Ehsanul Kashem, Saad Bin Abul Chowdhury, Muhammad E. H. Department of Electrical and Electronic Engineering University of Dhaka Dhaka Bangladesh Department of Biomedical Engineering Military Institute of Science and Technology Dhaka Bangladesh Department of Computer Science & Engineering Rajshahi University of Engineering & Technology Rajshahi6204 Bangladesh Chemical Engineering Department University of Doha for Science and Technology Doha Qatar Department of Electrical Engineering Qatar University Doha2713 Qatar Computer Applications Department Academic Bridge Program Qatar Foundation Doha Qatar Department of Computing Science AFG College With the University of Aberdeen Doha Qatar

Given the severity of waste pollution as a major environmental concern, intelligent and sustainable waste management is becoming increasingly crucial in both developed and developing countries. The material composition and volume of urban solid waste are key considerations in processing, managing, and utilizing city waste. Deep learning technologies have emerged as viable solutions to address waste management issues by reducing labor costs and automating complex tasks. However, the limited number of trash image categories and the inadequacy of existing datasets have constrained the proper evaluation of machine learning model performance across a large number of waste classes. In this paper, we present robust waste image classification and object detection studies using deep learning models, utilizing 28 distinct recyclable categories of waste images comprising a total of 10,406 images. For the waste classification task, we proposed a novel dual-stream network that outperformed several state-of-the-art models, achieving an overall classification accuracy of 83.11%. Additionally, we introduced the GELAN-E (generalized efficient layer aggregation network) model for waste object detection tasks, obtaining a mean average precision (mAP50) of 63%, surpassing other state-of-the-art detection models. These advancements demonstrate significant progress in the field of intelligent waste management, paving the way for more efficient and effective solutions. © The Author(s) 2024.

关键词： Deep learning for waste management Intelligent waste management Object detection in waste management Sustainable waste processing Urban solid waste Waste image classification

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An advanced security model for fog computing to tackle various attacks on the data link layer using the QQL-FHES scheme

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International Journal of Information technology (Singapore) 2024年第5期16卷 3093-3100页

作者： Balaji, V. Selvaraj, P. Department of Computing Technologies College of Engineering and Technology Faculty of Engineering and Technology SRM Institute of Science and Technology SRM Nagar Kattankulathur Chengalpattu Dt TN Chennai 603203 India

Fog computing provides maximum flexibility and low latency by divesting the usage of cloud resources and amenities to the end users. In rendering the services to the cloud environment, Fog computing encounters critical challenges over Privacy and security while utilizing the network edges. Mainly, attacks like impersonation attacks, eavesdropping attacks, and jamming attacks are common threats in the fog environment. To overcome these challenges, an intelligent defense scheme is essential for improved datalink layer security (DLS). With the proposed intelligent defense scheme, the fog layer could take up the challenge of finding all the possible attacks. This work has proposed a Modified Homomorphic Encryption Scheme (MHES). With the hybrid Fully Homomorphic Encryption Scheme (FHES) and a Q-Learning algorithm, the proposed encryption scheme is the most effective method to resist malicious attacks in Fog computing. Here a quadruple Q-learning method (QQL) is proposed for confining the motive of attacks and generating an optimal security mechanism against clever attackers. They were illegitimate to the datalink layer (DL). The combination of the FHES and QQL in Fog computing provides better security to the DL against various intelligent malicious attacks. © Bharati Vidyapeeth's Institute of Computer Applications and Management 2024.

关键词： Datalink layer Eavesdropping attack Fog computing Impersonation attack Quadruple Q-learning algorithm

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