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RSSI-Based 3D Wireless Sensor Node Localization Using Hybrid T Cell Immune and Lotus Optimization

computers, Materials & Continua 2024年第12期81卷 4833-4851页

作者： Weiwei Hu Kiran Sree Pokkuluri Rajesh Arunachalam Bander A.Jabr Yasser A.Ali Preethi Palanisamy School of Information Engineering Technology&MediaUniversity of HenanKaifeng475004China Department of Computer Science and Engineering Shri Vishnu Engineering College forWomenBhimavaram534202Andhra PradheshIndia Department of Electronics and Communication Engineering Saveetha School of EngineeringSaveetha Institute of Medical and Technical SciencesThandalam602105ChennaiIndia Department of Computer Engineering College of Computer and Information SciencesKing Saud UniversityRiyadh11543Saudi Arabia Department of Computer Science and Engineering Saveetha School of EngineeringSaveetha Institute of Medical and Technical SciencesThandalam602105ChennaiIndia

Wireless Sensor Network(WSNs)consists of a group of nodes that analyze the information from surrounding *** sensor nodes are responsible for accumulating and exchanging ***,node local-ization is the process of identifying the target node’s *** this research work,a Received Signal Strength Indicator(RSSI)-based optimal node localization approach is proposed to solve the complexities in the conventional node localization ***,the RSSI value is identified using the Deep Neural Network(DNN).The RSSI is conceded as the range-based method and it does not require special hardware for the node localization process,also it consumes a very minimal amount of cost for localizing the nodes in 3D *** position of the anchor nodes is fixed for detecting the location of the ***,the optimal position of the target node is identified using Hybrid T cell Immune with Lotus Effect Optimization algorithm(HTCI-LEO).During the node localization process,the average localization error is minimized,which is the objective of the optimal node *** the regular and irregular surfaces,this hybrid algorithm effectively performs the localization *** suggested hybrid algorithm converges very fast in the three-dimensional(3D)*** accuracy of the proposed node localization process is 94.25%.

关键词： Sensor node localization received signal strength indicator 3D wireless sensor network deep neural network average localization error and hybrid T cell immune with lotus effect optimization algorithm

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Enhancing Power Line Insulator Health Monitoring with a Hybrid Generative Adversarial Network and YOLO3 Solution

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Tsinghua Science and Technology 2024年第6期29卷 1796-1809页

作者： Ramakrishna Akella Sravan Kumar Gunturi Dipu Sarkar Department of Electronics and Communication Engineering Koneru Lakshmaiah Education FoundationHyderabad 500075India Department of Electrical and Electronics Engineering National Institute of Technology NagalandDimapur 797103India

In the critical field of electrical grid maintenance,ensuring the integrity of power line insulators is a primary *** study introduces an innovative approach for monitoring the condition of insulators using aerial surveillance via drone-mounted *** proposed method is a composite deep learning framework that integrates the“You Only Look Once”version 3(YOLO3)model with deep convolutional generative adversarial networks(DCGAN)and super-resolution generative adversarial networks(SRGAN).The YOLO3 model excels in rapidly and accurately detecting insulators,a vital step in assessing their *** effectiveness in distinguishing insulators against complex backgrounds enables prompt detection of defects,essential for proactive *** rapid detection is enhanced by DCGAN’s precise classification and SRGAN’s image quality improvement,addressing challenges posed by low-resolution drone *** framework’s performance was evaluated using metrics such as sensitivity,specificity,accuracy,localization accuracy,damage sensitivity,and false alarm *** show that the SRGAN+DCGAN+YOLO3 model significantly outperforms existing methods,with a sensitivity of 98%,specificity of 94%,an overall accuracy of 95.6%,localization accuracy of 90%,damage sensitivity of 92%,and a reduced false alarm rate of 8%.This advanced hybrid approach not only improves the detection and classification of insulator conditions but also contributes substantially to the maintenance and health of power line insulators,thus ensuring the reliability of the electrical power grid.

关键词： DCGAN generative adversarial networks insulators SRGAN YOLO

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Isomorphic Classification of First Degree Cellular Automata

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Complex Systems 2025年第1期34卷 59-90页

作者： Vikrant, Vicky Bhattacharjee, Kamalika Mukherjee, Sukanya Department of Computer Science and Engineering National Institute of Technology Tamil Nadu Tiruchirappalli620015 India Department of Computer Science and Engineering Institute of Engineering and Management West Bengal Kolkata700091 India

This paper explores the concept of isomorphism in cellular automata (CAs), focusing on identifying and understanding isomorphic relationships between distinct CAs. A cellular automaton (CA) is said to be isomorphic to another CA if the configuration transition diagrams of the two CAs are identical. To illustrate how isomorphism entails the preservation of structural properties while allowing for variations in state representations, we exhaustively explore the isomorphic reversible rules in the first degree CAs (FDCAs), that is, the simple three-neighborhood CAs represented by eight parameters. The experimental study includes reversible FDCAs for states two to 10 with cell lengths of four to seven under the null boundary condition and states two to seven with cell lengths of four to seven under the periodic boundary condition. Given a CA, we also provide two synthesis methods for identifying the set of CAs that are isomorphic to it. The first method operates by permuting the states of a group of CA cell locations, whereas the second method involves exchanging cells at various points. We demonstrate that, depending on the range of exchanged cell positions, the isomorphic CAs produced in this way may have a bigger neighborhood than the input CA. Finally, we provide an exhaustive classification of the elementary CAs (ECAs) using the first synthesis method and compare it with existing results. © 2025, Complex Systems Publications, Inc. All rights reserved.

关键词： classification cycle structure FDCA first degree cellular automaton parameters reversibility synthesis schemes

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A Novel Separable Convolution Architecture for Image Processing Applications

Electronics

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electronics 2024年第1期28卷 12-21页

作者： Hassan, Jaiza Khurshid, Burhan Department of Electronics and Communication Engineering National Institute of Technology Srinagar India Department of Electronics and Communication Engineering National Institute of Technology Srinagar India

Two-dimensional (2D) Convolution is frequently used in many image processing applications like image smoothening, image sharpening, feature extraction, image enhancement, object recognition, etc. Although the operation of 2D Convolution is simple, its hardware implementation is quite challenging due to enormous computational and memory costs. Various 2D convolution implementations have been proposed in the literature. Among them Separable architectures stand out in terms of the reduction in the computational complexity. However, these have not been extensively explored in the literature, and more research needs to be done, especially for applications that are constrained in resources. This paper presents a novel separable Convolution architecture based on the folding transformation. The application of the folding transformation technique yields a resource-efficient architecture by time multiplexing the different functional units within the separable Convolution operation. The hardware implementation is done using Xilinx Artix-7 xc7a35tcpg236-1 FPGA device. The proposed architecture offers benefits over the existing architectures regarding on-chip resource utilization, power consumption, critical path delay, and external memory bandwidth (EMB). © (2023), (University of Banja Luka). All Rights Reserved.

关键词： Convolution

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Bio-inspired optimization based secure model for watermarking of medical data

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Multimedia Tools and Applications 2025年 1-31页

作者： Gupta, Akash Kumar Chakraborty, Chinmay Gupta, Bharat Department of Electronics and Communication Engineering Birla Institute of Technology Jharkhand Ranchi India Department of Electronics and Communication Engineering National Institute of Technology Patna Bihar India

In the field of digital watermarking significant research is going on for enhancing the medical data security. As we know, for enhancing the data security, authenticity and copy protection from intruders, digital watermarking can play a decisive role. In this paper, a robust watermarking methodology has been proposed using singular value decomposition in discrete wavelet transform domain. To begin with watermark embedding, medical data is decomposed first, using second level discrete wavelet transform. Further singular value decomposition is applied on both watermark and frequency sub-band of medical images. Artificial bee colony, bacterial foraging optimization, firefly algorithm, genetic algorithm and particle swarm optimization are few of the nature inspired optimization techniques that have been considered and analyzed here, for optimizing the strength factor of watermarking method using fitness function. Performance of this work is also analyzed against various attacks. Among all proposed optimization algorithm, artificial bee colony algorithm with discrete wavelet transforms and singular value decomposition provides the best result with PSNR 58.76 dB and NCC 0.99 against various attacks, whereas without attacks 61.01 dB and 1 respectively. The result shows adequacy of proposed algorithm and indicates balanced and higher value of robustness and imperceptibility. The proposed optimized watermarking method is compared with the recent research works and found acceptable and comparable performance. To measure the performance of proposed methodology in terms of imperceptibility and robustness, parameters like peak signal to noise ratio and normalized correlation coefficient are utilized. For applied input data set and watermark, artificial bee colony optimized watermarking technique presents the best result among all. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.

关键词： Wavelet decomposition

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Advancing mental health detection in texts via multi-task learning with soft-parameter sharing transformers

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Neural Computing and Applications 2025年第5期37卷 3077-3110页

作者： Kodati, Dheeraj Tene, Ramakrishnudu Department of Computer Science and Engineering National Institute of Technology Warangal India Department of Computer Science and Engineering Mahindra University Hyderabad India

In recent years, mental health issues have profoundly impacted individuals’ well-being, necessitating prompt identification and intervention. Existing approaches grapple with the complex nature of mental health, facing challenges like task interference, limited adaptability, and difficulty in capturing nuanced linguistic expressions indicative of various conditions. In response to these challenges, our research presents three novel models employing multi-task learning (MTL) to understand mental health behaviors comprehensively. These models encompass soft-parameter sharing-based long short-term memory with attention mechanism (SPS-LSTM-AM), SPS-based bidirectional gated neural networks with self-head attention mechanism (SPS-BiGRU-SAM), and SPS-based bidirectional neural network with multi-head attention mechanism (SPS-BNN-MHAM). Our models address diverse tasks, including detecting disorders such as bipolar disorder, insomnia, obsessive-compulsive disorder, and panic in psychiatric texts, alongside classifying suicide or non-suicide-related texts on social media as auxiliary tasks. Emotion detection in suicide notes, covering emotions of abuse, blame, and sorrow, serves as the main task. We observe significant performance enhancement in the primary task by incorporating auxiliary tasks. Advanced encoder-building techniques, including auto-regressive-based permutation and enhanced permutation language modeling, are recommended for effectively capturing mental health contexts’ subtleties, semantic nuances, and syntactic structures. We present the shared feature extractor called shared auto-regressive for language modeling (S-ARLM) to capture high-level representations that are useful across tasks. Additionally, we recommend soft-parameter sharing (SPS) subtypes-fully sharing, partial sharing, and independent layer-to minimize tight coupling and enhance adaptability. Our models exhibit outstanding performance across various datasets, achieving accuracies of 96.9%, 97.

关键词： Multi-task learning

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Two dimensional cuckoo search optimization algorithm based despeckling filter for the real ultrasound images

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Journal of Ambient Intelligence and Humanized Computing 2024年第1期15卷 921-942页

作者： Gupta, Pradeep K. Lal, Shyam Kiran, Mustafa Servet Husain, Farooq Department of Electronics and Communication Engineering Moradabad Institute of Technology Uttar Pradesh Moradabad244001 India Department of Electronics and Communication Engineering National Institute of Technology Karnataka Surathkal Karnataka Mangaluru575025 India Department of Computer Engineering Faculty of Engineering Konya Technical University Konya Turkey Department of Computer Engineering Faculty of Engineering Selcuk University Konya Turkey

A clinical ultrasound imaging plays a significant role in the proper diagnosis of patients because, it is a cost-effective and non-invasive technique in comparison with other methods. The speckle noise contamination caused by ultrasound images during the acquisition process degrades its visual quality, which makes the diagnosis task difficult for physicians. Hence, to improve their visual quality, despeckling filters are commonly used for processing of such images. However, several disadvantages of existing despeckling filters discourage the use of existing despeckling filters to reduce the effect of speckle noise. In this paper, two dimensional cuckoo search optimization algorithm based despeckling filter is proposed for avoiding limitations of various existing despeckling filters. Proposed despeckling filter is developed by combining fast non-local means filter and 2D finite impulse response (FIR) filter with cuckoo search optimization algorithm. In the proposed despeckling filter, the coefficients of 2D FIR filter are optimized by using the cuckoo search optimization algorithm. The quantitative results comparison between the proposed despeckling filter and other existing despeckling filters are analyzed by evaluating PSNR, MSE, MAE, and SSIM values for different real ultrasound images. Results reveal that the visual quality obtained by the proposed despeckling filter is better than other existing despeckling filters. The numerical results also reveal that the proposed despeckling filter is highly effective for despeckling the clinical ultrasound images. © Springer-Verlag GmbH Germany, part of Springer Nature 2018.

关键词： FIR filters

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Abstractive Summarization of Low resourced Nepali language using Multilingual Transformers 1

Abstractive Summarization of Low resourced Nepali language u...

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1st Workshop on Challenges in Processing South Asian Languages, CHiPSAL 2025, co-located with the 31st International Conference on Computational Linguistics, COLING 2025

作者： Dhakal, Prakash Baral, Daya Sagar Department of Electronics and Computer Engineering Institute of Engineering Tribhuvan University Pulchowk Campus Nepal

ISBN: (纸本)9798891762015

Nepali, one of the prominent languages of South Asia, remains underrepresented in natural language processing (NLP) research, particularly in the domain of abstractive summarization. While significant progress has been made in extractive summarization, the complexity of generating coherent, human-like summaries from low-resource languages like Nepali is still largely unexplored. This paper introduces the first comprehensive study on applying multilingual transformer-based models, specifically mBART and mT5, to the task of generating headlines for Nepali news articles through abstractive summarization. To address the absence of large-scale datasets for this task, we developed a Nepali news headline summarization corpus by aggregating data from multiple online news portals. Leveraging this dataset, we fine-tuned multilingual transformer models, mBART and mT5, using Low-Rank Adaptation (LoRA) and quantization techniques to optimize computational efficiency without sacrificing performance. Comprehensive evaluations were conducted using ROUGE scores to measure the models’ output quality, complemented by a detailed human evaluation to select the best summary overall based on relevance, fluency, conciseness, informativeness, factual accuracy, and coverage. Notably, the 4-bit quantized mBART model demonstrated superior performance, significantly reducing computational costs while maintaining high-quality results. This work not only underscores the feasibility of applying transformer-based approaches to Nepali abstractive summarization but also provides a scalable solution to advancing NLP capabilities for underrepresented South Asian languages. © 2025 International Committee on Computational Linguistics.

关键词： Computational linguistics

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Enhanced Far-Field Localization Scheme Using Multi-RIS and Efficient Beam Sweeping

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Progress In Electromagnetics Research C 2024年 140卷 163-175页

作者： Alhafid, Abdulrahaman Kh. Younis, Sedki Ali, Y. E. Mohammed Electrical Engineering Department College of Engineering University of Mosul Mosul Iraq Computer and Information Engineering Department College of Electronics Engineering Ninevah University Mosul Iraq Computer and Communications Engineering Department College of Engineering Nawroz University KRG Duhok Iraq

Future 6G networks are anticipated to use reconfigurable intelligent surfaces (RISs) because of their capability to expand coverage, provide a customizable wireless environment, increase localization accuracy, etc. In this paper, RIS-aided localization is considered with orthogonal frequency division multiplexing (OFDM) and single-input single-output (SISO) downlink system in millimeter-wave (mmWave). An efficient beam sweeping (EBS) scheme is proposed accomplished by an RIS to scan the area of interest and estimate the direction of the user equipment (UE), i.e., the signal’s angle of departure (AoD). The AoD with the measured signal time of arrival (ToA), from the RIS to the UE, is used to estimate the UE position. The ToA measurements can be obtained by exploiting the OFDM signal, while the beam sweeping can be obtained by carefully designing the RIS phase profile. The first step of the proposed EBS scheme is to scan the whole area of interest with equally spaced beam angles for coarse estimation of AoD. Then, based on this estimation, the RIS is reconfigured to sweep a slight angle’s range by narrow beams to refine the AoD estimation. Besides, a multi-RIS scenario is proposed, and leveraging the EBS and the consensus fusion method is used to obtain accurate position estimation. Simulation results demonstrate that the proposed EBS in single and multi-RIS scenarios enhances positioning accuracy compared to linear beam sweeping (LBS) methods. Also, the impact of increasing the number of RIS elements and number of sweeping beams, as well as the number of RISs, is investigated thoroughly via numerical simulations. Furthermore, the achievable localization accuracy is assessed using the positioning error bound (PEB). © 2024, Electromagnetics Academy. All rights reserved.

关键词： Millimeter waves

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Cepstral and acoustic ternary pattern based hybrid feature extraction approach for end-to-end bangla speech recognition

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Journal of Ambient Intelligence and Humanized Computing 2023年第12期14卷 16903-16919页

作者： Dua, Mohit Akanksha Dua, Shelza Department of Computer Engineering National Institute of Technology Kurukshetra India Department of Electronics Communication and Engineering Punjab Engineering College Chandigarh Chandigarh India

In the last three decades, a lot of work has been done for building Automatic Speech Recognition (ASR) systems for well-established languages such as English, Chinese, etc. However, for implementing a Large Vocabulary Continuous Speech Recognition (LVCSR) system for low resource languages, the research work is also growing rapidly in corpus-focused areas. Hence, there is a requirement of benchmarking large corpus in case of low-resource language like Bangla such that prejudice results can be avoided and limitations can be handled. In the proposed work, an openly available large-scale Bangla speech corpus provided by Google has been used. The work in this paper proposes a combination of image inspired features with well explored cepstral features to build front end feature extraction phase. It uses integration of Convolutional Neural Network (CNN) and bi-directional Long-short Term Memory (bi-LSTM) with Connectionist Temporal Classification (CTC) loss function to implement backend acoustic model. The experiments employ static and dynamic features of Mel-frequency Cepstral Coefficients (MFCC), Constant Q Cepstral Coefficients (CQCC), and Gammatone Cepstral Coefficients (GTCC) techniques, one by one, with Acoustic Ternary Patterns (ATP) features. The proposed work investigates the effect of these various hybrid front-end approaches with CNN, bi-LSTM and integration of these two models. The novelty of this paper lies in the fact that fusion of ATP with cepstral features improves performance of the proposed low resource language ASR system, where the proposed combination of ATP-dynamic CQCC features with integrated backend acoustic model shows a relative improvement of 10–15% in Word Error Rate (WER) over all other experimented combinations. Further to exploit the noise robust nature of GTCC features, the ATP-dynamic GTCC features with integrated CNN-bi-LSTM back-end model are evaluated in noisy scenario, also. © 2023, The Author(s), under exclusive licence to Springer-V

关键词： Long short-term memory

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