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Journal of Network Intelligence 2025年第1期10卷 559-572页

作者： Wang, Hong Wang, Yi-Gui Zhang, Jie Department of Computer and Software Engineering Shandong College of Electronic Technology Jinan250200 China School of Computer Science and Technology Shandong Jianzhu University Jinan250101 China College of Agricultural Science and Technology Yana Royal Polytechnic University Chiang Mai50000 Thailand

Traditional autonomous navigation methods for mobile robots mainly rely on geometric feature-based LiDAR scan-matching algorithms, but in complex environments, this method is often affected due to the presence of moving objects, occlusions, and other interfering factors, resulting in a decrease in positioning accuracy. With the growing demand for robotics applications in logistics, security, exploration and other fields, the need for robust autonomous navigation and high-precision mapping in highly dynamic and complex environments is becoming more and more urgent. To solve this problem, an improved front-end scan matching algorithm based on Frequency Modulated Continuous Wave (FMCW) LiDAR is proposed in this paper. Firstly, by using the target Doppler velocity information provided by the FMCW LiDAR, we design a novel point cloud segmentation algorithm based on velocity clustering, which is able to effectively distinguish between stationary and moving objects, and avoid the dynamic interference affecting the position estimation. Secondly, we introduce the Gaussian Mixture Model Sampling Consistency (GMMSC) algorithm, which is more robust to reject the mis-matched pairs in the scanning matching process and improve the alignment accuracy. Finally, based on the residual high-quality matched pairs, we combine the classical ICP algorithm with the robust kernel function to further enhance the stability of the position estimation in the case of occlusion and local mismatch. The experimental results show that the proposed improved algorithm significantly improves the mapping capability of mobile robots in complex environments compared with the existing techniques. The average relative error of the improved front-end scanning matching algorithm is reduced by 40.6 % compared with the pre-improved one. © 2025, Taiwan Ubiquitous Information CO LTD. All rights reserved.

关键词： Mobile robots

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Mango Pest Detection Using Entropy-ELM with Whale Optimization Algorithm

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Intelligent Automation & Soft Computing 2023年第3期35卷 3447-3458页

作者： U.Muthaiah S.Chitra Department of Computer Science and Engineering Sri Shanmugha College of Engineering and TechnologySankari637304India Department of Computer Science and Engineering Er.Perumal Manimekalai College of EngineeringHosur635117India

Image processing,agricultural production,andﬁeld monitoring are essential studies in the researchﬁ*** diseases have an impact on agricultural production and *** disease detection at a preliminary phase reduces economic losses and improves the quality of *** identifying the agricultural pests is usually evident in plants;also,it takes more time and is an expensive technique.A drone system has been developed to gather photographs over enormous regions such as farm areas and *** atmosphere generates vast amounts of data as it is monitored closely;the evaluation of this big data would increase the production of agricultural *** paper aims to identify pests in mango trees such as hoppers,mealybugs,inﬂorescence midges,fruitﬂies,and stem *** of the massive volumes of large-scale high-dimensional big data collected,it is necessary to reduce the dimensionality of the input for classify-ing *** community-based cumulative algorithm was used to classify the pests in the existing *** proposed method uses the Entropy-ELM method with Whale Optimization to improve the classiﬁcation in detecting pests in *** Entropy-ELM method with the Whale Optimization Algorithm(WOA)is used for feature selection,enhancing mango pests’classiﬁcation *** Vector Machines(SVMs)are especially effective for classifying while users get var-ious classes in which they are *** are created as suitable classiﬁers to categorize any dataset in Big Data *** proposed Entropy-ELM-WOA is more capable compared to the existing systems.

关键词： Whale optimization algorithm Entropy-ELM feature selection pests detection support vector machine mango trees classiﬁcation

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Data-driven output regulation control for constrained linear systems

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science China(Information sciences) 2025年第3期68卷 338-353页

作者： Chaoyu XIA Yi DONG Chaoli WANG Shengyuan XU Shanghai Research Institute for Intelligent Autonomous Systems Tongji University College of Electronic and Information Engineering Shanghai Research Institute for Intelligent Autonomous SystemsTongji University Department of Control Science and Engineering School of Optical-Electrical and Computer EngineeringUniversity of Shanghai for Science and Technology Department of Automation Nanjing University of Science and Technology

This study introduces a data-driven approach for state and output feedback control addressing the constrained output regulation problem in unknown linear discrete-time systems. Our method ensures effective tracking performance while satisfying the state and input constraints, even when system matrices are not available. We first establish a sufficient condition necessary for the existence of a solution pair to the regulator equation and propose a data-based approach to obtain the feedforward and feedback control gains for state feedback control using linear programming. Furthermore, we design a refined Luenberger observer to accurately estimate the system state, while keeping the estimation error within a predefined set. By combining output regulation theory, we develop an output feedback control strategy. The stability of the closed-loop system is rigorously proved to be asymptotically stable by further leveraging the concept of λ-contractive sets.

关键词： output regulation constrained system data-driven.

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E-voting system using cloud-based hybrid blockchain technology

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Journal of Safety science and Resilience 2024年第1期5卷 102-109页

作者： Beulah Jayakumari S Lilly Sheeba Maya Eapen Jani Anbarasi Vinayakumar Ravi A.Suganya Malathy Jawahar Department of Information Technology Tagore Engineering College Chennai India School of Computer Science and Engineering SRM Institute of Science and Technology Ramapuram ChennaiIndia Department of Information Technology Jerusalem College of Engineering ChennaiIndia School of Computer Science and Engineering Vellore Institute of Technology Chennai India Center for Artificial Intelligence Prince Mohammad Bin Fahd UniversityKhobarSaudi Arabia Leather Process Technology Division CSIR-Central Leather Research Institute ChennaiIndia

With the invention of Internet-enabled devices,cloud and blockchain-based technologies,an online voting system can smoothly carry out election *** pandemic situations,citizens tend to develop panic about mass gatherings,which may influence the decrease in the number of *** urges a reliable,flexible,transparent,secure,and cost-effective voting *** proposed online voting system using cloud-based hybrid blockchain technology eradicates the flaws that persist in the existing voting system,and it is carried out in three phases:the registration phase,vote casting phase and vote counting phase.A timestamp-based authentication protocol with digital signature validates voters and candidates during the registration and vote casting *** smart contracts,third-party interventions are eliminated,and the transactions are secured in the blockchain ***,to provide accurate voting results,the practical Byzantine fault tolerance(PBFT)consensus mechanism is adopted to ensure that the vote has not been modified or ***,the overall performance of the proposed system is significantly better than that of the existing *** performance was analyzed based on authentication delay,vote alteration,response time,and latency.

关键词： E-voting Cloud Blockchain Smart contract Consensus mechanism

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A Deep Learning and Powerful Computational Framework for Brain Cancer MRI Image Recognition

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Journal of The Institution of Engineers (India): Series B 2024年第1期105卷 1-18页

作者： Kumar, Ankit Shukla, Santosh Kumar Prakash, Navin Yadav, Rakesh Kumar Department of Information Technology Babu Banarasi Das Institute of Technology and Management Lucknow India Department of Computer Science and Engineering Babu Banarasi Das Engineering College Lucknow India Department of Computer Science and Engineering MSOET Maharishi University of Information Technology Lucknow India

Brain tumors are one of the deadliest diseases and require quick and accurate methods of detection. Finding the optimum image for research goals is the first step in optimizing MRI images for pre- and post-processing. As a result, the mean grey-level approach was used to segment the MRI images using a threshold. In the second stage of statistical feature analysis, the picture features were extracted using the spatial gray-level dependency matrix using Hara lick's feature equations. As a consequence, the best features were picked and the tumor was placed in the appropriate location. In the third stage, an automated tool for classifying the photographs under assessment as having a tumor or not was created utilizing supervised learning and an artificially intelligent methodology. An efficient test of the network's performance yielded 97% of the desired results. © 2023, The Institution of Engineers (India).

关键词： Tumors

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Gene Variant Analysis for the Detection of Hemophilia: A Literature Review

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Recent Patents on engineering 2024年第9期18卷 48-62页

作者： Sumathi, N. Anitha Kumari, K. Department of Computer Science and Engineering Dr. Mahalingam College of Engineering and Technology Tamil Nadu Pollachi India Department of Information Technology PSG College of Technology Tamil Nadu Coimbatore641004 India

Background: Proteins act as clotting factors to stop bleeding at the lesion site. This implies that people with hemophilia tend to bleed longer after an injury and are more prone to internal bleeding. Depending on the type of hemophilia, individuals with hemophilia will have lesser amounts of factor VIII or factor IX than people without it. Objective: By analyzing the gene variant of hemophilia affected patient we can predict the severity of disease at earlier stage which helps to avoid further complications. Methods: Predicting hemophilia can be achieved through potential technologies like machine learning. Using these technologies, one can detect and predict the severity of hemophilia, such as mild, moderate, or severe. Results: By comparing the methods used in protein structure analysis, the advantages and limitations of methods used in protein structure analysis are discussed. Conclusion: The best practices in predicting hemophilia are highlighted in this patent study and particularly aim at the basic understanding of applying the potential technologies in the prediction of hemophilia and its severity. This study represents recent research on hemophilia and the use of different machine learning techniques (MLT) in this area. © 2024 Bentham science Publishers.

关键词： Proteins

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mLBOA-DML: modified butterfly optimized deep metric learning for enhancing accuracy in intrusion detection system

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Journal of Reliable Intelligent Environments 2023年第3期9卷 333-347页

作者： Prabhakaran, Varun Kulandasamy, Ashokkumar Department of Computer Science and Engineering St.Joseph’s College of Engineering Chennai India Department of Computer Science and Engineering Sathyabama Institute of Science and Technology Chennai India

Intrusion detection is a prominent factor in the cybersecurity domain that prevents the network from malicious attacks. Cloud security is not satisfactory for securing the user’s information because it is based on standard protocols. Hence, cloud users cannot fully trust the security offered by cloud service providers. The state-of-the-art techniques create clusters for classes and manually label the unknown classes to detect novel attacks. This notion binds the network traffic associated with each attack together and drifts the similarity between the same attacks. These techniques are often prone to errors and degrade performance. To overcome this drawback, various researchers have developed different intrusion detection system which relies on specific attack patterns to distinguish between normal and abnormal behavior. This paper presents a modified Lagrange interpolated Butterfly optimization algorithm-based deep metric learning (mLBOA-DML) architecture for intrusion detection to detect both host-based and network attacks. DML architecture parameters are optimized utilizing mLBOA algorithm via its global optimization capability for increasing the accuracy of attack prediction. DML algorithm does both feature extraction and classification processes. When evaluated using the UNSW-NB15 and NSL-KDD datasets, the proposed model offers improved accuracy near 99% for both datasets. © 2023, The Author(s), under exclusive licence to Springer Nature Switzerland AG.

关键词： Intrusion detection

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Efficiency-optimized 6G:A virtual network resource orchestration strategy by enhanced particle swarm optimization

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Digital Communications and Networks 2024年第5期10卷 1221-1233页

作者： Sai Zou Junrui Wu Haisheng Yu Wenyong Wang Lisheng Huang Wei Ni Yan Liu College of Big Data and Information Engineering Guizhou UniversityGuiyangChina Prospective Technology Lab of the China Future Internet Engineering Center BeijingChina College of Computer Science and Engineering University of Electronic Science and TechnologyChengduChina International Institute for Next Generation Internet Macao University of Science and TechnologyMacaoChina Data61 Business Unit CSIROAustralia

The future Sixth-Generation (6G) wireless systems are expected to encounter emerging services with diverserequirements. In this paper, 6G network resource orchestration is optimized to support customized networkslicing of services, and place network functions generated by heterogeneous devices into available *** is a combinatorial optimization problem that is solved by developing a Particle Swarm Optimization (PSO)based scheduling strategy with enhanced inertia weight, particle variation, and nonlinear learning factor, therebybalancing the local and global solutions and improving the convergence speed to globally near-optimal *** show that the method improves the convergence speed and the utilization of network resourcescompared with other variants of PSO.

关键词： Virtualization Network function orchestration Network resource virtualized orchestration (NRVO) Particle swarm optimization(PSO)

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Impact of the chaotic semiconductor laser output power on time-delay-signature of chaos and random bit generation

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Chinese Physics B 2025年第3期34卷 325-330页

作者： Chenpeng Xue Xu Wang Likai Zheng Haoyu Zhang Yanhua Hong Zuxing Zhang College of Electronic and Optical Engineering and College of Flexible Electronics(Future Technology) Nanjing University of Posts and TelecommunicationsNanjing 210023China School of Computer Science and Engineering Bangor UniversityBangor LL571UTUK

We experimentally analyze the effect of the optical power on the time delay signature identification and the random bit generation in chaotic semiconductor laser with optical *** to the inevitable noise during the photoelectric detection and analog-digital conversion,the varying of output optical power would change the signal to noise ratio,then impact time delay signature identification and the random bit *** results show that,when the optical power is less than-14 dBm,with the decreasing of the optical power,the actual identified time delay signature degrades and the entropy of the chaotic signal ***,the extracted random bit sequence with lower optical power is more easily pass through the randomness testing.

关键词： chaos time delay signature entropy estimation random bit generation

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E-SWAN:Efficient Sliding Window Analysis Network for Real-Time Speech Steganography Detection

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computers, Materials & Continua 2025年第3期82卷 4797-4820页

作者： Kening Wang Feipeng Gao Jie Yang Hao Zhang School of Engineering and Technology Jiyang College of Zhejiang A & F UniversityZhuji311800China College of Mathematics and Computer Science Zhejiang A & F UniversityHangzhou311300China

With the rapid advancement of Voice over Internet Protocol(VoIP)technology,speech steganography techniques such as Quantization Index Modulation(QIM)and Pitch Modulation Steganography(PMS)have emerged as significant challenges to information *** techniques embed hidden information into speech streams,making detection increasingly difficult,particularly under conditions of low embedding rates and short speech *** steganalysis methods often struggle to balance detection accuracy and computational efficiency due to their limited ability to effectively capture both temporal and spatial features of speech *** address these challenges,this paper proposes an Efficient Sliding Window Analysis Network(E-SWAN),a novel deep learning model specifically designed for real-time speech steganalysis.E-SWAN integrates two core modules:the LSTM Temporal Feature Miner(LTFM)and the Convolutional Key Feature Miner(CKFM).LTFM captures long-range temporal dependencies using Long Short-Term Memory networks,while CKFM identifies local spatial variations caused by steganographic embedding through convolutional *** modules operate within a sliding window framework,enabling efficient extraction of temporal and spatial *** results on the Chinese CNV and PMS datasets demonstrate the superior performance of *** conditions of a ten-second sample duration and an embedding rate of 10%,E-SWAN achieves a detection accuracy of 62.09%on the PMS dataset,surpassing existing methods by 4.57%,and an accuracy of 82.28%on the CNV dataset,outperforming state-of-the-art methods by 7.29%.These findings validate the robustness and efficiency of E-SWAN under low embedding rates and short durations,offering a promising solution for real-time VoIP *** work provides significant contributions to enhancing information security in digital communications.

关键词： Steganalysis speech convolutional sliding window deep learning

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