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Hybrid AOA-TDOA Localization of a Moving Source By Single Receiver

IEEE Transactions on Communications 2024年

作者： Pang, Dandan Wang, Gang Ho, K.C. Ningbo University Faculty of Electrical Engineering and Computer Science Ningbo315211 China University of Missouri Department of Electrical Engineering and Computer Science ColumbiaMO65211 United States

This paper addresses the passive source localization problem using hybrid angle-of-arrival (AOA) and time-difference-of-arrival (TDOA) measurements observed by single stationary receiver at several time intervals, when the source is moving with a constant velocity trajectory. We first show that the localization problem cannot be achieved using a sequence of one kind of measurements (i.e., AOAs only or TDOAs only), but can be accomplished by using both AOA and TDOA observations together. Then we develop a parametric model by integrating AOA and TDOA, and then formulate a non-convex constrained weighted least squares (CWLS) problem that is applicable to the 2-D and 3-D scenarios for locating the source. Benefiting from the integrated model, the global solution of the CWLS problem is guaranteed without having local convergence or divergence issues. Furthermore, we conduct the mean square error analysis to validate that the proposed method is able to reach the Cramer-Rao lower bound accuracy under small Gaussian noise for the 2-D and 3-D cases. Simulation results also confirm the good performance of the proposed method. © 1972-2012 IEEE.

关键词： Time of arrival

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Circumnavigation of UAVs integrating QP-Based CLF and CBF in a Cooperative Path-Following Framework 63

Circumnavigation of UAVs integrating QP-Based CLF and CBF in...

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63rd IEEE Conference on Decision and Control, CDC 2024

作者： Anand, Pallov Aguiar, A. Pedro Sujit, P.B. Universidade Do Porto SYSTEC-ARISE Research Center for Systems and Technologies Faculdade de Engenharia Porto Portugal Indian Institute of Science Education and Research Bhopal Department of Electrical Engineering and Computer Science Bhopal462060 India

ISBN: (纸本)9798350316339

Unmanned Aerial Vehicles (UAVs) are increasingly utilized in various applications often requiring coordinated operations of multiple UAVs. Circular formation control has garnered significant attention due to its diverse relevance in many situations. In this paper, we propose a Quadratic Programming (QP) based Control Lyapunov Function (CLF) and Control Barrier Function (CBF) framework for circumnavigating stationary and moving targets for single and multiple UAVs while avoiding collision with a known obstacle. By integrating CLF and CBF constraints in a cooperative path-following framework, our approach offers robustness and scalability for complex multi-agent systems, and ensures asymptotic tracking of the target while maintaining closed-loop stability of the error dynamics and safety margins around the obstacle. Through theoretical analysis and simulation studies, we demonstrate the effectiveness and versatility of our proposed framework in challenging environments. © 2024 IEEE.

关键词： Multi agent systems

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Trans-Mamba: The Cross-network of Transformer and Mamba for Traffic Flow Prediction 10

Trans-Mamba: The Cross-network of Transformer and Mamba for ...

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10th IEEE Smart World Congress, SWC 2024

作者： Liu, Qian Jia, Haonan Ye, Junchen Feng, Jinyan Lan, Fayang Du, Bowen Huang, Runhe Beihang University Ccse Lab Beijing China Beihang University School of Transportation Science and Engineering Beijing China Guizhou Qiantong Zhilian Technology Co. Ltd Guizhou China Beihang University and Zhongguancun Laboratory Transportation Science and Engineering Beijing China Hosei University Faculty of Computer and Information Sciences Department of Computer Science Metropolis Tokyo Japan

ISBN: (纸本)9798331520861

Traffic flow prediction is a crucial aspect of intelligent transportation systems. It holds significant importance in optimizing traffic management and enhancing road safety. However, the task of traffic flow forecasting remains a significant challenge due to its inherent complexity, dynamic nature, and the presence of long-range dependencies. Existing graph neural network methods exhibit two primary limitations. Firstly, when static graphs are employed to capture spatial dependencies, they fail to account for the dynamics in these dependencies. Secondly, these methods struggle to effectively capture long-range spatial dependencies. Furthermore, attention-based methods, while potentially addressing some of these issues, require substantial computational and memory resources, leading to inefficiencies. We design a cross-network model that combines Transformer and Mamba architectures for traffic flow prediction, which efficiently captures dynamic long-range spatio-temporal dependencies. The model architecture is composed of three main components: a learnable spatio-temporal embedding layer that adaptively captures the unique features of different nodes and time;stacked spatio-temporal blocks, which include two temporal blocks and one spatial block arranged between them to effectively capture spatio-temporal dependencies;and a fully connected output layer that generates the final predictions. Experiments on three public real-world datasets demonstrate the effectiveness of TransMamba. Specifically, the model improves the mean absolute error (MAE) and root mean square error (RMSE) by 2.59% and 4.14%, respectively, on the PeMS08 dataset. Additionally, ablation studies confirm the effectiveness and efficiency of each component of the model. © 2024 IEEE.

关键词： Graph neural networks

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Towards No Penalty Control Hazard Handling 66

Towards No Penalty Control Hazard Handling

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2023 IEEE 66th International Midwest Symposium on Circuits and systems, MWSCAS 2023

作者： Surya Balasubramanian, Linknath Rizkalla, Maher Lee, John J. Ytterdal, Trond Kumar, Mukesh Indiana University Purdue University Indianapolis Department of Electrical and Computer Engineering Indianapolis United States Norwegian University of Science and Technology Department of Electronic Systems Trondheim Norway Indian Institute of Technology Indore Department of Electrical Engineering Indore India

ISBN: (纸本)9798350302103

Achieving higher throughput is one of the most important requirements of a modern microcontroller. It is therefore not affordable for it to waste a considerable number of clock cycles in branch mispredictions. This paper proposes a novel mechanism that makes microcontrollers forgo branch predictors and thereby eliminate branch mispredictions. Further, we synthesized the proposed design with 7nm FinFET process and compared its latency with other designs to make sure that the microcontroller's operating frequency is not degraded by using this design. The proposed technique is implemented as five different modules that work together to forward required operands, resolve branches without prediction, and calculate the next instruction's address in the first stage of an in-order five stage pipelined micro-architecture. Since the address of successive instruction to a control transfer instruction is calculated in the first stage of pipeline, branch prediction is no longer necessary, thereby eliminating the clock cycle penalties occurred when using a branch predictor. The designed system was tested using test-bench with modifications to make the test-code compatible with the designed architecture. The designed architecture was able to successfully calculate the address of next correct instruction and fetch it without any wastage of clock cycles. Moreover, the latency of instruction fetch stage of pipeline integrated with the proposed architecture is 330 ps (3 GHz approx.) which is more than the operating frequencies of microcontrollers. Therefore, the proposed design can be integrated in high performance microcontrollers. © 2023 IEEE.

关键词： branch predictor control transfer instruction in-order five stage pipeline microcontroller mispredictions

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DIR-BHRNet: A Lightweight Network for Real-time Vision-based Multi-person Pose Estimation on Smartphones

arXiv

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arXiv 2024年

作者： Lan, Gongjin Wu, Yu Hao, Qi The Department of Computer Science and Engineering Southern University of Science and Technology China The Research Institute of Trustworthy Autonomous Systems The Department of Computer Science and Engineering Southern University of Science and Technology China

Human pose estimation (HPE), particularly multi-person pose estimation (MPPE), has been applied in many domains such as human-machine systems. However, the current MPPE methods generally run on powerful GPU systems and take a lot of computational costs. Real-time MPPE on mobile devices with low-performance computing is a challenging task. In this paper, we propose a lightweight neural network, DIR-BHRNet, for real-time MPPE on smartphones. In DIR-BHRNet, we design a novel lightweight convolutional module, Dense Inverted Residual (DIR), to improve accuracy by adding a depthwise convolution and a shortcut connection into the well-known Inverted Residual, and a novel efficient neural network structure, Balanced HRNet (BHRNet), to reduce computational costs by reconfiguring the proper number of convolutional blocks on each branch. We evaluate DIR-BHRNet on the well-known COCO and CrowdPose datasets. The results show that DIR-BHRNet outperforms the state-of-the-art methods in terms of accuracy with a real-time computational cost. Finally, we implement the DIR-BHRNet on the current mainstream Android smartphones, which perform more than 10 FPS. The free-used executable file (Android 10), source code, and a video description of this work are publicly available on the page 1 to facilitate the development of realtime MPPE on smartphones. © 2024, CC BY.

关键词： Smartphones

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Design and FPGA Implementation of the Henon Chaotic Map for Image Encryption 14

Design and FPGA Implementation of the Henon Chaotic Map for ...

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14th International Conference on Information and Communication systems, ICICS 2023

作者： Taqieddin, Eyad Mustafa, Dheya Shatnawi, Ahmed Al-Khaleel, Osama Papachristou, Christos Jordan University of Science and Tech. Network Eng. and Security Department Irbid22110 Jordan The Hashemite University Faculty of Engineering Dept. of Computer Engineering Zarqa13133 Jordan Jordan University of Science and Tech. Software Engineering Department Irbid22110 Jordan Jordan University of Science and Tech. Computer Engineering Department Irbid22110 Jordan Case Western Reserve University Ecse Department ClevelandOH44106 United States

ISBN: (纸本)9798350307870

Due to the ever increasing usage of multimedia content sharing and the inherent nature of the insecure links used for transmission, it is quite necessary to provide a fast and secure encryption algorithm. Current symmetric ciphers possess properties that may reveal some content of the plaintext images, especially when a block cipher is used without any chaining. Chaotic encryption has been proposed to allow for hiding inherent properties of the images and to generate almost uniform histograms of the cipher image (i.e.;hiding any visual properties). To that end, we propose the use of hardware to implement chaos-based ciphers for faster execution time. The results of this work show that such an implementation is feasible, and even favorable, when compared to software-based image encryption. © 2023 IEEE.

关键词： Field programmable gate arrays (FPGA)

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Utilizing Machine Learning Algorithms to Optimize Real-Time Data Analysis 3

Utilizing Machine Learning Algorithms to Optimize Real-Time ...

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3rd International Conference on Smart Generation Computing, Communication and Networking, SMART GENCON 2023

作者： Singh, Vaishali Gupta, Eesha Jana, D. Little Femilin Deshmukh, Gopal B. Singla, Abhishek Krishna, K. Gopala Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India Vivekananda Global University Department of Computer Science & Engineering Jaipur India Prince Shri Venkateshwara Padmavathy Engineering College Department of Science and Humanities Chennai127 India Vishwakarma Institute of Information Technology Department of Computer Engineering Pune India Chitkara University Institute of Engineering and Technology Chitkara University Centre of Interdisciplinary Research in Business and Technology Punjab India Faculty of Engineering and Technology Department of Electronics and Communication Engineering Karnataka Bangalore India

ISBN: (纸本)9798350319125

This paper offers a way for optimizing actual-time records analysis the usage of a variety of machine gaining knowledge of algorithms. The algorithms paintings by creating predictive fashions which extract useful information from datasets, follow appropriate preprocessing techniques to make sure the high-quality of the records, and model the information to deduce meaningful capabilities from the data. The predictive models are then used to discover applicable developments, find patterns, and summarize the data for clean analysis. specifically, this paper outlines the technique of using supervised and unsupervised learning algorithms, consisting of Random wooded area, Naïve Bayes, and k-approach clustering, to extract meaningful styles and relationships from facts points in real-time. Additionally, the paper discusses wonderful capacity packages of the proposed technique, including fraud detection and prediction of stock costs. Such capability programs demonstrate the fee of actual-time records analysis for organizations and organizations. © 2023 IEEE.

关键词： Clustering algorithms

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SSABA:Search Step Adjustment Based Algorithm

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computers, Materials & Continua 2022年第6期71卷 4237-4256页

作者： Fatemeh Ahmadi Zeidabadi Ali Dehghani Mohammad Dehghani Zeinab Montazeri Stepán Hubálovsky Pavel Trojovsky Gaurav Dhiman Department of Mathematics and Computer Sciences Sirjan University of TechnologySirjanIran Department of Civil Engineering Islamic Azad UniversityEstahban BranchEstahbanIran Department of Mathematics Faculty of ScienceUniversity of Hradec KrálovéHradec Králové50003Czech Republic Department of Electrical and Electronics Engineering Shiraz University of TechnologyShirazIran Department of Applied Cybernetics Faculty of ScienceUniversity of Hradec KrálovéHradec Králové50003Czech Republic Department of Computer Science Government Bikram College of CommercePatialaPunjabIndia

Finding the suitable solution to optimization problems is a fundamental challenge in various *** algorithms are one of the effective stochastic methods in solving optimization *** this paper,a new stochastic optimization algorithm called Search StepAdjustment Based Algorithm(SSABA)is presented to provide quasi-optimal solutions to various optimization *** the initial iterations of the algorithm,the step index is set to the highest value for a comprehensive search of the search ***,with increasing repetitions in order to focus the search of the algorithm in achieving the optimal solution closer to the global optimal,the step index is reduced to reach the minimum value at the end of the algorithm *** is mathematically modeled and its performance in optimization is evaluated on twenty-three different standard objective functions of unimodal and multimodal *** results of optimization of unimodal functions show that the proposed algorithm SSABA has high exploitation power and the results of optimization of multimodal functions show the appropriate exploration power of the proposed *** addition,the performance of the proposed SSABA is compared with the performance of eight well-known algorithms,including Particle Swarm Optimization(PSO),Genetic Algorithm(GA),Teaching-Learning Based Optimization(TLBO),Gravitational Search Algorithm(GSA),Grey Wolf Optimization(GWO),Whale Optimization Algorithm(WOA),Marine Predators Algorithm(MPA),and Tunicate Swarm Algorithm(TSA).The simulation results show that the proposed SSABA is better and more competitive than the eight compared algorithms with better performance.

关键词： Optimization population-based optimization problem search step optimization algorithm minimization maximization

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GastroFuse-Net: an ensemble deep learning framework designed for gastrointestinal abnormality detection in endoscopic images

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Mathematical Biosciences and engineering 2024年第8期21卷 6847-6869页

作者： Aggarwal, Sonam Gupta, Isha Kumar, Ashok Kautish, Sandeep Almazyad, Abdulaziz S. Mohamed, Ali Wagdy Werner, Frank Shokouhifar, Mohammad Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Model Institute of Engineering and Technology J&K Jammu India Chandigarh University Punjab Mohali140413 India Department of Computer Engineering College of Computer and Information Sciences King Saud University P.O. Box 51178 Riyadh11543 Saudi Arabia Operations Research Department Faculty of Graduate Studies for Statistical Research Cairo University Giza12613 Egypt Applied Science Research Center Applied Science Private University Amman11931 Jordan Faculty of Mathematics Otto-von-Guericke University Magdeburg39016 Germany Institute of Research and Development Duy Tan University Da Nang550000 Viet Nam

Convolutional Neural Networks (CNNs) have received substantial attention as a highly effective tool for analyzing medical images, notably in interpreting endoscopic images, due to their capacity to provide results equivalent to or exceeding those of medical specialists. This capability is particularly crucial in the realm of gastrointestinal disorders, where even experienced gastroenterologists find the automatic diagnosis of such conditions using endoscopic pictures to be a challenging endeavor. Currently, gastrointestinal findings in medical diagnosis are primarily determined by manual inspection by competent gastrointestinal endoscopists. This evaluation procedure is labor-intensive, time-consuming, and frequently results in high variability between laboratories. To address these challenges, we introduced a specialized CNN-based architecture called GastroFuse-Net, designed to recognize human gastrointestinal diseases from endoscopic images. GastroFuse-Net was developed by combining features extracted from two different CNN models with different numbers of layers, integrating shallow and deep representations to capture diverse aspects of the abnormalities. The Kvasir dataset was used to thoroughly test the proposed deep learning model. This dataset contained images that were classified according to structures (cecum, z-line, pylorus), diseases (ulcerative colitis, esophagitis, polyps), or surgical operations (dyed resection margins, dyed lifted polyps). The proposed model was evaluated using various measures, including specificity, recall, precision, F1-score, Mathew’s Correlation Coefficient (MCC), and accuracy. The proposed model GastroFuse-Net exhibited exceptional performance, achieving a precision of 0.985, recall of 0.985, specificity of 0.984, F1-score of 0.997, MCC of 0.982, and an accuracy of 98.5%. ©2024 the Author(s), licensee AIMS Press.

关键词： Endoscopy

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Design of Automated Opinion Mining Model Using Optimized Fuzzy Neural Network

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computers, Materials & Continua 2022年第5期71卷 2543-2557页

作者： Ala’A.Eshmawi Hesham Alhumyani Sayed Abdel Khalek Rashid A.Saeed Mahmoud Ragab Romany F.Mansour Department of Cybersecurity College of Computer Science and EngineeringUniversity of JeddahJeddahSaudi Arabia Department of Computer Engineering College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia Department of Mathematics Faculty of SciencesTaif UniversityTaif21944Saudi Arabia Centre for Artificial Intelligence in Precision Medicine King Abdulaziz UniversityJeddah21589Saudi Arabia Department of Mathematics Faculty of ScienceNew Valley UniversityEl-Kharga72511Egypt

Sentiment analysis or Opinion Mining (OM) has gained significant interest among research communities and entrepreneurs in the recentyears. Likewise, Machine Learning (ML) approaches is one of the interestingresearch domains that are highly helpful and are increasingly applied in severalbusiness domains. In this background, the current research paper focuses onthe design of automated opinion mining model using Deer Hunting Optimization Algorithm (DHOA) with Fuzzy Neural Network (FNN) abbreviatedas DHOA-FNN model. The proposed DHOA-FNN technique involves fourdifferent stages namely, preprocessing, feature extraction, classification, andparameter tuning. In addition to the above, the proposed DHOA-FNN modelhas two stages of feature extraction namely, Glove and N-gram ***, FNN model is utilized as a classification model whereas GTOA isused for the optimization of parameters. The novelty of current work is thatthe GTOA is designed to tune the parameters of FNN model. An extensiverange of simulations was carried out on the benchmark dataset and the resultswere examined under diverse measures. The experimental results highlightedthe promising performance of DHOA-FNN model over recent state-of-the-arttechniques with a maximum accuracy of 0.9928.

关键词： Opinion mining sentiment analysis fuzzy neural network metaheuristics feature extraction classification

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