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AGV indoor localization: a high fidelity positioning and map building solution based on drawstring displacement sensors

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Journal of Ambient Intelligence and Humanized Computing 2024年第4期15卷 2277-2293页

作者： Wang, Shih-Yuan Li, Che-Ming Liong, Sze-Teng Sheng, Yu-Ting Huang, Yen-Chang Liong, Gen-Bing Gan, Yee-Siang Graduate Institute of Architecture National Yang Ming Chiao Tung University Hsinchu Taiwan Department of Electronic Engineering Feng Chia University Taichung40724 Taiwan School of Architecture Feng Chia University Taichung40724 Taiwan Department of Applied Mathematics National University of Tainan Tainan Taiwan Faculty of Computer Science & amp Information Technology University of Malaya Kuala Lumpur50603 Malaysia

The rapid evolution of artificial intelligence (AI) and robotics technologies are bringing drastic changes to society and industry in these recent years. The impressive progress in facilitating smart manufacturing in this era of Industry 4.0 has made our lives much more convenient than ever before. Despite the greater reliability and stability of the robotic system, there are several challenges to overcome such as the restrictions of scenes, obstacles, and hardware specifications. Since a high precision positioning algorithm is of paramount importance in devising a mobile robot, the research in developing simultaneous localization and mapping has been garnered immerse attention especially from domains of the computer vision and autonomous robots. In this paper, a novel method is presented to significantly enhance the positioning precision of indoor unmanned guided vehicles. The approach involves several steps, including setting up hardware configurations and collecting relevant data by installing necessary devices and system packages within the robot operating system (ROS). Trilateration is employed to determine the relative position of the mobile robot using distance measurements. Coordinate transformation is then conducted to convert the collected input data of relative distances and orientations. Trajectory paths are obtained, and occupancy maps are constructed to estimate the resulting trajectory and generate a 2D grid map. Indoor localization and mapping are achieved using three drawstring displacement sensors along with orientation information from an Inertial Measurement Unit (IMU). The proposed method is extensively evaluated through experimentation on predefined navigation paths, and its performance is compared to state-of-the-art methods such as RealSense T265, Hector SLAM, and wheel odometry. The results show that the proposed method exhibits compelling performance in both mean error and occupancy map construction. Ultimately, the findings reported herein

关键词： Mobile robots

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An Advanced IoT and GPS-GSM Based Real-Time Automated Data Monitoring Robot with Image Processing and Live Streaming for Human Life and Land Mine Detection Systems

An Advanced IoT and GPS-GSM Based Real-Time Automated Data M...

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2024 International Conference on computer, Electrical and Communication Engineering, ICCECE 2024

作者： Islam, Md. Muhaiminul Akter, Farzana Hossain, Md. Rakib Faruk Hossain, Md. Mahedy Hasan, S.M. Yakin Srizon, Md. Azmain Rajshahi University of Engineering & Technology Department of Electronics & Telecommunication Engineering Rajshahi6204 Bangladesh Department of Electrical & Electronic Engineering Rajshahi6204 Bangladesh Department of Computer Science & Engineering Rajshahi6204 Bangladesh

ISBN: (纸本)9798350386479

An era of automation is currently being experienced, where everything is becoming more automated day by day. Automation technology has been applied everywhere, from smaller to larger scales. Moreover, real-time communication between complicated human-machine interfaces is one of the difficulties of the fourth industrial revolution. That is why robots are essential in this field because they improve human lifestyles, lighten workloads, and carry out several dangerous tasks. This research presents a groundbreaking advancement in the form of an automated real-time IoT-based data monitoring robot in an era characterized by the proliferation of automation and the Internet of Things (IoT). This ground-breaking system combines GPS-GSM functionality, human life detection, land mine detection, image processing, and live streaming in an efficient manner. The robot utilizes its IoT framework and a variety of embedded sensors and technology to gather, process, and transmit realtime data from its surroundings. Notably, it supports disaster response, military operations, and safety measures by performing the vital tasks of detecting human life and land mines. The ability to categorize and process visual data is improved by the image processing feature, and situational awareness is provided by live streaming © 2024 IEEE.

关键词： Global positioning system

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Software design and FPGA implementation of optimized medical image fusion techniques

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Multimedia Tools and Applications 2025年 1-31页

作者： Elshazly, E.A. El-Shafai, Walid El-Hoseny, Heba El-Rabaie, El-Sayed M Zahran, O. Abdelwahab, Safey A. S. El-Halawany, M.M. Fikry, R.M. Elaraby, S.M. Faragallah, Osama S. Mohamed, Wael A. Mahmoud, Korany R. Abd El-Samie, Fathi E. Cairo Egypt Computer Science Department Prince Sultan University Riyadh11586 Saudi Arabia Department of Electronics and Electrical Communications Engineering Faculty of Electronic Engineering Menoufia University Menouf32952 Egypt Faculty of Computer Studies Arab Open University 11681 Riyadh Saudi Arabia Department of Information Technology College of Computers and Information Technology Taif University 11099 Taif21944 Saudi Arabia Department of Electrical Engineering Faculty of Engineering Benha University Benha Egypt Electronics and Communications Engineering Department Faculty of Engineering Helwan University Helwan Egypt Giza Egypt Department of Information Technology College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University P.O. Box 84428 Riyadh11671 Saudi Arabia

Nowadays, medical image fusion plays a crucial role in enhancing the diagnosis accuracy and the clinical decision-making process in various healthcare applications. This research work presents a comprehensive study of the design and implementation of optimized medical image fusion techniques using a combination of software and Field-Programmable Gate Array (FPGA) technologies. The proposed medical image fusion strategy is based on the utilization of Discrete Wavelet Transform (DWT) and Modified Central Force Optimization (MCFO). The implementation of the proposed technique as well as the traditional medical image fusion techniques is considered using an appropriate software design and FPGA. The presented techniques aim to overcome the limitations of traditional fusion techniques by integrating advanced image processing algorithms, optimization algorithms, and parallel computing capabilities offered by FPGA platforms. The first step in the proposed framework is to match the histogram of one of the images with that of the other, so that both images will have the same dynamic range. After that, the DWT is used to decompose the images that should be fused together. Based on some constraints, the MCFO optimization algorithm is used to evaluate the optimum level of decomposition and the optimum parameters for the best fusion quality. Finally, to improve the obtained visual quality and reinforce the information in the fusion result, an additional contrast enhancement step using adaptive histogram equalization is applied to the fusion result. Comparative study between the proposed optimized DWT-based fusion framework, the traditional Principal Component Analysis (PCA), Additive Wavelet Transform (AWT), and DWT-based fusion techniques is presented. Various metrics of fusion quality are considered, including average gradient, standard deviation, local contrast, entropy, edge strength, Peak Signal-to-Noise Ratio (PSNR), Qab/f, and processing time. The proposed optimized DWT-ba

关键词： MATLAB

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Near-Field Communications: Shape and Structure Design for Uniform Planar Array

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IEEE Internet of Things Journal 2025年第11期12卷 15151-15165页

作者： Chen, Siyu Gu, Juping Duan, Wei Zhang, Lei Dang, Shuping Wen, Miaowen Ding, Zhiguo Ho, Pin-Han Nantong University School of Information Science and Technology Nantong226019 China Suzhou University of Science and Technology China Hubei Polytechnic University Department of Electronics and Information Engineering Huangshi435003 China University of Bristol School of Electrical Electronic and Mechanical Engineering BristolBS8 1UB United Kingdom South China University of Technology School of Electronic and Information Engineering Guangzhou510640 China Khalifa University Department of Electrical Engineering and Computer Science Abu Dhabi United Arab Emirates University of Electronic Science and Technology of China Shenzhen Institute for Advanced Study Shenzhen518000 China University of Waterloo Department of Electrical and Computer Engineering WaterlooONN2L 3G1 Canada

With the flourishing development of 6G wireless networks, the demand of spectrum efficiency rapidly increases, in order to support the demands of high quality data transmission and connections of massive users. Among various promising technologies, the technology of near-field communications provides a great potential to address such an issue due to the unique spherical-wave channels for electromagnetic (EM) propagation. In this paper, multiple-input single-output (MISO) near-field communications is comprehensively studied to clarify the influences of the shape and structure of uniform planar array (UPA) on the system performance, considering three cases with uniform linear array (ULA), rectangular UPA, and circular UPA. In particular, we reveal the properties of rapid deterioration for signal-to-noise ratio (SNR) from the reduced projection aperture in near-field communications and investigate the single spherical crown antenna design and spherical crown antenna array design in order to address this issue. Moreover, we also characterize the role of antenna projection aperture in details, and theoretically analyze the shape of UPA, yielding the corresponding exact closed-form expressions for SNR and outage probability (OP). Based on these above analytical results, we find out that adjusting the spacing between adjacent antennas to control the relative angle between user and selected antennas is an efficient way to improve the projection aperture of antenna and SNR. Simulation results are shown to well match analytical results, which validate the correctness of our analysis, clarifying that our proposed designs outperform the conventional works and illustrating a better stability for angle variations. © 2025 IEEE.

关键词： Spheres

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UAMP-Based Delay-Doppler Channel Estimation for OTFS Systems

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China Communications 2024年第10期21卷 1-15页

作者： Li Zhongjie Yuan Weijie Guo Qinghua Wu Nan Zhang Ji Department of Electronic and Electrical Engineering Southern University of Science and TechonologyShenzhen 518055China School of Electrical Computer and Telecommunications EngineeringUniversity of WollongongWollongongNSW 2522Australia School of Integrated Circuits and Electronics Beijing Institute of TechnologyBeijing 100081China School of Mathematics and Statistics Henan University of Science and TechnologyLuoyang 471000China

Orthogonal time frequency space(OTFS)technique, which modulates data symbols in the delayDoppler(DD) domain, presents a potential solution for supporting reliable information transmission in highmobility vehicular networks. In this paper, we study the issues of DD channel estimation for OTFS in the presence of fractional Doppler. We first propose a channel estimation algorithm with both low complexity and high accuracy based on the unitary approximate message passing(UAMP), which exploits the structured sparsity of the effective DD domain channel using hidden Markov model(HMM). The empirical state evolution(SE) analysis is then leveraged to predict the performance of our proposed algorithm. To refine the hyperparameters in the proposed algorithm,we derive the update criterion for the hyperparameters through the expectation-maximization(EM) algorithm. Finally, Our simulation results demonstrate that our proposed algorithm can achieve a significant gain over various baseline schemes.

关键词： channel estimation hidden Markov model(HMM) orthogonal time frequency space(OTFS) unitary approximate message passing(UAMP)

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Review of dehazing techniques: challenges and future trends

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Multimedia Tools and Applications 2025年第3期84卷 1103-1131页

作者： Ayoub, Abeer El-Shafai, Walid El-Samie, Fathi E. Abd Hamad, Ehab K. I. EL-Rabaie, El-Sayed M. Department of Electronics and Electrical Communication Engineering Faculty of Electronic Engineering Menoufia University Menouf32952 Egypt Security Engineering Lab Computer Science Department Prince Sultan University Riyadh11586 Saudi Arabia Department of Information Technology College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University Riyadh11671 Saudi Arabia Electrical Engineering Department Faculty of Engineering Aswan University Aswan81542 Egypt

The phenomenon of atmospheric haze arises due to the scattering of light by minute particles suspended in the atmosphere. This optical effect gives rise to visual degradation in images and videos. The degradation is primarily influenced by two key factors: atmospheric attenuation and scattered light. Scattered light causes an image to be veiled in a whitish veil, while attenuation diminishes the image inherent contrast. Efforts to enhance image and video quality necessitate the development of dehazing techniques capable of mitigating the adverse impact of haze. This scholarly endeavor presents a comprehensive survey of recent advancements in the domain of dehazing techniques, encompassing both conventional methodologies and those founded on machine learning principles. Traditional dehazing techniques leverage a haze model to deduce a dehazed rendition of an image or frame. In contrast, learning-based techniques employ sophisticated mechanisms such as Convolutional Neural Networks (CNNs) and different deep Generative Adversarial Networks (GANs) to create models that can discern dehazed representations by learning intricate parameters like transmission maps, atmospheric light conditions, or their combined effects. Furthermore, some learning-based approaches facilitate the direct generation of dehazed outputs from hazy inputs by assimilating the non-linear mapping between the two. This review study delves into a comprehensive examination of datasets utilized within learning-based dehazing methodologies, elucidating their characteristics and relevance. Furthermore, a systematic exposition of the merits and demerits inherent in distinct dehazing techniques is presented. The discourse culminates in the synthesis of the primary quandaries and challenges confronted by prevailing dehazing techniques. The assessment of dehazed image and frame quality is facilitated through the application of rigorous evaluation metrics, a discussion of which is incorporated. To provide empiri

关键词： Generative adversarial networks

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An Efficient Intrusion Detection Framework for Industrial Internet of Things Security

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computer Systems Science & Engineering 2023年第7期46卷 819-834页

作者： Samah Alshathri Ayman El-Sayed Walid El-Shafai Ezz El-Din Hemdan Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Science and Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Security Engineering Lab Computer Science DepartmentPrince Sultan UniversityRiyadh11586Saudi Arabia Department of Electronics and Electrical Communications Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt

Recently,the Internet of Things(IoT)has been used in various applications such as manufacturing,transportation,agriculture,and healthcare that can enhance efficiency and productivity via an intelligent management console *** the increased use of Industrial IoT(IIoT)applications,the risk of brutal cyber-attacks also *** leads researchers worldwide to work on developing effective Intrusion Detection Systems(IDS)for IoT infrastructure against any malicious ***,this paper provides effective IDS to detect and classify unpredicted and unpredictable severe attacks in contradiction to the IoT infrastructure.A comprehensive evaluation examined on a new available benchmark TON_IoT dataset is *** data-driven IoT/IIoT dataset incorporates a label feature indicating classes of normal and attack-targeting IoT/IIoT ***,this data involves IoT/IIoT services-based telemetry data that involves operating systems logs and IoT-based traffic networks collected from a realistic medium-scale IoT *** is to classify and recognize the intrusion activity and provide the intrusion detection objectives in IoT environments in an efficient ***,several machine learning algorithms such as Logistic Regression(LR),Linear Discriminant Analysis(LDA),K-Nearest Neighbors(KNN),Gaussian Naive Bayes(NB),Classification and Regression Tree(CART),Random Forest(RF),and AdaBoost(AB)are used for the detection intent on thirteen different intrusion *** performance metrics like accuracy,precision,recall,and F1-score are used to estimate the proposed *** experimental results show that the CART surpasses the other algorithms with the highest accuracy values like 0.97,1.00,0.99,0.99,1.00,1.00,and 1.00 for effectively detecting the intrusion activities on the IoT/IIoT infrastructure on most of the employed *** addition,the proposed work accomplishes high performance compared to other recent rela

关键词： Attacks intrusion detection machine learning deep learning industrial IoT TON_IoT dataset

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Design and Realization of Broadband Active Inductor Based Band Pass Filter

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Chinese Journal of electronics 2023年第4期32卷 731-735页

作者： Aysu Belen Mehmet A.Belen Merih Paland?ken Peyman Mahouti ?zlem Tari Hybrid and Electric Vehicle Technology Iskenderun Technical University Department of Electrical Electronic Engineering ?skenderun Technical University Department of Electrical Electronic Engineering ?zmir Katip ?elebi Universtiy Department of Aviation Electrics and Electronics Engineering Y?ld?z Technical University Davutpa?a Department of Mathematics and Computer Science ?stanbul Arel University

With the latest developments in the wireless communication systems, the alternative design methodologies are required for the broadband design of microwave components. In this paper, a compact broad band pass filter(BPF) design is introduced through the microwave design technique based on the active inductor(AIN) with the numerical computation and experimental measurement studies. The proposed AIN based BPF has operating frequency band extending from 0.8 GHz to 2.7GHz in compact size with high selectivity in comparison to conventional LC based BPF. The experimental measurement results agree well with the numerical computation results. The proposed AIN based BPF design has technical capability to be conveniently tuned to operate at different frequency bands.

关键词： Band-pass filters Microwave measurement Wireless communication Radio frequency Active inductors Insertion loss Microwave communication

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Federated learning-based lightweight selective feature fusion and irregular-aware network for crack detection

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Optoelectronics Letters 2025年

作者： LIU Hui HE Tian CHENG Xu SHI Fan NIKOLI? Sa?a V. ZHANG Jianhua School of Computer Science and Engineering， Tianjin University of Technology Department of Electronics， Faculty of Electronic Engineering， University of Nis

Employing deep learning for pavement crack detection can significantly enhance accuracy， and federated learning can help to overcome the challenges of data silos and data security. This paper proposes a lightweight method to detect cracks. We use lightweight encoder modules to extract multi-scale features， and further feature fusion and modelling by Selective Fusion Blocks and Irregular-aware Blocks. Moreover， this method is the first to combine federated learning with crack detection， effectively resolving the tension between data privacy and data sharing in decentralised devices. Experiments were conducted to compare our proposed method with other crack detection methods on two publicly available datasets， including the original method without federated learning and five state-of-the-art methods combined with the same federated learning framework. The experiment proves that our model obtains comparable F1 score with minimal parameters and computational effort. For example， compared to the original method， the number of parameters and computation are reduced by 67.1% and 73.6%， respectively.

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PC-DeepNet: A GNSS Positioning Error Minimization Framework Using Permutation-Invariant Deep Neural Network

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IEEE Sensors Journal 2025年第11期25卷 20764-20777页

作者： Humayun Kabir, M. Hasan, Md. Ali Islam, Md. Shafiqul Ko, Kyeongjun Shin, Wonjae Islamic University Department of Electrical and Electronic Engineering Kushtia7003 Bangladesh Korea University School of Electrical Engineering Seoul02841 Korea Republic of Bangladesh University of Business and Technology Department of Computer Science and Engineering Dhaka1216 Bangladesh Hongik University School of Electronics and Electrical Engineering Seoul04066 Korea Republic of

Global navigation satellite systems (GNSS) face significant challenges in urban and sub-urban areas due to non-line-of-sight (NLOS) propagation, multipath effects, and low received power levels, resulting in highly non-linear and non-Gaussian measurement error distributions. In light of this, conventional model-based positioning approaches, which rely on Gaussian error approximations, struggle to achieve precise localization under these conditions. To overcome these challenges, we put forth a novel learning-based framework, PC-DeepNet, that employs a permutation-invariant (PI) deep neural network (DNN) to estimate position corrections (PC). This approach is designed to ensure robustness against changes in the number and/or order of visible satellite measurements, a common issue in GNSS systems, while leveraging NLOS and multipath indicators as features to enhance positioning accuracy in challenging urban and sub-urban environments. To validate the performance of the proposed framework, we compare the positioning error with state-of-the-art model-based and learning-based positioning methods using two publicly available datasets. The results confirm that proposed PC-DeepNet achieves superior accuracy than existing model-based and learning-based methods while exhibiting lower computational complexity compared to previous learning-based approaches. © 2025 IEEE.

关键词： Global positioning system

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