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Studying of an Adaptive Unscented Kalman Filter as a Target Tracking Algorithm with Matlab Simulation 2

Studying of an Adaptive Unscented Kalman Filter as a Target ...

2nd IEEE International Maghreb Meeting of the Conference on Sciences and Techniques of Automatic Control and computer engineering, MI-STA 2022

作者： Ammar, Abdussalam Masaud Ellafi, Amira Youssef Bozed, Kenz Amhmed Zerek, Amer R. Faculty of Electronic Technology Tripoli Libya Benghazi University Faculty of Information Technology Department of Computer System Design Libya Zawia University Faculty of Engineering EE Department Zawia Libya

ISBN: (数字)9781665479189

ISBN: (纸本)9781665479189

The radar is considered as one of tracking application which we need to tracking unknown targets. This application is using a kalman filter. Also the extended and unscented kalman filter has been created to deal with nonlinear systems such as multidimensional radar tracking. If wideband radar is used, multiple return signals may be obtained by scattering on a single target in a single pulse. It is important to monitor multiple dispersions on an undefined number of artifacts in the specific problem considered, with a standard wideband radar model, whose motion involves a nonlinear rotational velocity component. The measurements used are scattered range, range-rate (in analogy to Doppler), nonlinear functions of the scattered position and velocity, with parameters in the state-space model. In order to evaluate the scene and observe its progression over time, the tracker design uses a Kalman Filter type to model the nonlinear dynamics and measurement items and a multi-pronged association scheme. © 2022 IEEE.

关键词： Mean square error

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A Review Paper on Face Recognition Techniques 9

A Review Paper on Face Recognition Techniques

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9th International Conference on Advanced Computing and Communication Systems, ICACCS 2023

作者： Rashid Al-Bakri, Ibrahim Mahmood Imran Ahmad, Muhammad Md Isa, Mohd Nazrin Zuhaer Nayef Al-Dabagh, Mustafa Universiti Malaysia Perlis Faculty of Electronic Engineering Technology Perlis Malaysia Telafer University Collage of Agriculture Telafer Iraq Knowledge University Department of Computer Science Erbil Iraq

ISBN: (纸本)9798350397376

The study of computer vision and pattern recognition is growing because of the various commercial and practical applications of these disciplines. Identification of individuals in a multitude, access control, forensics, and human-computer interactions are only among the topics studied by these areas. However, analyzing unconstrained face recognition poses ethical issues and privacy concerns. Many recent proposals employ Holistic Methods, Geometric Approach and Local-Texture Approach, methods and databases like ORL, FERET and AR Dataset to study constrained face recognition. At least some understanding of 2D perspective was achieved. This occurred in highly controlled environments where parameters such as camera angles, lighting and distance were strictly regulated. However, significant degradation in recognition performance occurred if the environment changed or the subject smiled or frowned. This critique discusses the technology utilized in face recognition, as well as the databases of methods that utilize this technology. To help guide future research, this article discusses current knowledge and suggests future directions for study in the field of facial recognition. © 2023 IEEE.

关键词： Face recognition

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Centralized RSU Deployment Strategy for Effective Communication in Multi-hop Vehicular Adhoc Networks (VANETs) 6th

Centralized RSU Deployment Strategy for Effective Communicat...

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6th International Conference on Innovative Computing and Communication, ICICC 2023

作者： Rashid, Sami Abduljabbar Audah, Lukman Hamdi, Mustafa Maad Abdulsattar, Nejood Faisal Mutar, Mohammed Hasan Alkhafaji, Mohamed Ayad Faculty of Electrical and Electronic Engineering Universiti Tun Hussein Onn Malaysia Batu Pahat Johor Parit Raja86400 Malaysia Department of Computer Engineering Technology Al-Maarif University College Al-Anbar Iraq Department of Computer Technical Engineering College of Information Technology Imam Ja’afar Al-Sadiq University Al-Muthanna Baghdad66002 Iraq National University of Science and Technology DhiQar Iraq

ISBN: (纸本)9789819940707

Due to the advent of smart cities, vehicular technologies are high focus to perform intelligent transmission. Road traffic control and accident control becomes a primary tasks in Vehicular Adhoc Networks (VANETs). In VANETs communication is ineffective without presence of Road Side Units (RSUs). In some places due to lack of RSUs data transmission is improve as well as end to end delay and routing overhead is increased during the time of communication so effective RSU deployment is very essential to provide effective communication in multi-hop VANETs. In this paper, Centralized RSU Deployment Strategy is proposed to control and monitor the traffic density of the vehicle. The parameters which are considered for the process of centralized RSU deployment strategy are centralization scale, past transmission rate and location identification. To analysis the performance of the network the simulation is carried out in NS2.35 and SUMO. The parameters which are taken for the performance analysis are packet delivery ratio, end to end delay, routing overhead and packet loss. In order to execute comparative analysis the recent research works are considered which are AVRCV and RDSTDV. From the outcome it is understood that the proposed CRDSMV approach achieves 70–200 ms lower end to end delay, 7–14% improved packet delivery ratio, and 50–300 packets lower routing overhead and 50–240 Kbps improved throughputs when compared with the earlier methods AVRCV and RDSTDV. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2024.

关键词： Roads and streets

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Comparison between Machine Learning Classifier Based On Face Recognition

Comparison between Machine Learning Classifier Based On Face...

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2023 IEEE Renewable Energy and Sustainable E-Mobility Conference, RESEM 2023

ISBN: (纸本)9798350311327

With face recognition, machine learning is one of the computer sciences fields that is getting bigger the quickest. The goal of this study is to give a basic overview of machine learning and the algorithmic paradigms it provides. The study gives a detailed explanation of the basic ideas behind machine learning and the math that turns these ideas into methodologies that can be used in the real world, and discusses and compares the performance of various face recognition methods. Machine learning, a field of AI, has emerged as an important part of the digitizing approaches that have attracted a lot of interest. The purpose of this work is to provide a high-level overview of several of the most widely utilized and commonly used algorithmic techniques for machine learning currently available. The goal of this work is to help readers make educated decisions about the best algorithm for machine learning they should employ for a given task by highlighting the benefits and drawbacks of each method from an implementation point of view. © 2023 IEEE.

关键词： Decision trees

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Leaf Guard: Detecting the Diseases in Banana Leaf 5

Leaf Guard: Detecting the Diseases in Banana Leaf

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5th International Conference on Advancements in Computing, ICAC 2023

作者： Yathurshan, K. Barathy, S. Karini, G. Sajitha, P. Abeygunawardhna, Pradeep Rajapakse, Kaushalya Sri Lanka Institute of Information Technology Department of Information Technology Malabe Sri Lanka Sri Lanka Institute of Information Technology Faculty of Engineering Electrical & Electronic Engineering Malabe Sri Lanka Sri Lanka Institute of Information Technology Department of Computer Science and Software Engineering Malabe Sri Lanka

ISBN: (纸本)9798350358131

This research paper introduces 'Leaf Guard,' a mobile app designed for disease detection and management in banana trees, specifically tailored for Sri Lanka's agricultural landscape. Through the amalgamation of image processing, deep learning, and customized chemical recommendations, this app proficiently identifies early-stage diseases, offering tailored remedies. Catering to farmers and agricultural experts, 'Leaf Guard' provides an intuitive tool, revolutionizing disease management and bolstering sustainable banana cultivation practices. By seamlessly integrating these cutting-edge technologies, this research significantly advances disease detection methods, paving the way for environmentally friendly and economical interventions. The study's findings not only enrich the realm of disease detection technology but also hold promise for future innovations, fostering sustainable approaches to enhance banana cultivation. © 2023 IEEE.

关键词： Cultivation

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Modifying Quasi Z-Source Network for an Innovative Extendable Switched-Capacitor Boost Multilevel Inverter Topology

Modifying Quasi Z-Source Network for an Innovative Extendabl...

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2024 International Conference on Green Energy, Computing and Sustainable technology, GECOST 2024

作者： Sia, Yew Wei Law, Kah Haw Hann Lim, Garenth King Sim, Sy Yi Kiam Yeo, Kenneth Siok Curtin University Malaysia Department of Electrical and Computer Engineering Sarawak Malaysia Universiti Teknologi Brunei Electrical and Electronic Engineering Programme Area Bandar Seri Begawan Brunei University Tun Hussein Onn Malaysia Faculty of Engineering Technology Johor Batu Pahat86400 Malaysia

ISBN: (纸本)9798350357905

The emergence of Hybrid Multilevel Inverters (HMLIs) represents a significant advancement in addressing the limitations of traditional multilevel inverter (MLI) topologies. In recent years, various concepts and topologies have been proposed to enhance HMLIs, with a primary focus on generating a greater number of AC voltage levels while minimizing component count and utilizing isolated DC sources. Traditionally, these HMLI topologies required two-stage configurations involving DC-to-DC converters, leading to increased costs and complexity. This paper highlights the transition to single-stage DC-AC inverters, which offers substantial benefits, including high boost gain, superior conversion efficiency, and a reduced reliance on passive components. Furthermore, the paper introduces a groundbreaking solution - a 57-level switched-capacitor boost multilevel inverter (SCBMLI), derived from the quasi z-source inverter (qZSI). The proposed SCBMLI offers a multitude of advantages, including the generation of high voltage levels, balancing the voltage of capacitor in the circuit without the need for any control system, and achieving low Total Harmonic Distortion (THD) in high voltage applications. © 2024 IEEE.

关键词： Topology

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CNN-Based Deep Learning Model for Early Identification and Categorization of Melanoma Skin Cancer Using Medical Imaging

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SN computer Science 2024年第7期5卷 911页

作者： Pandimurugan, V. Ahmad, Sultan Prabu, A.V. Rahmani, Mohammad Khalid Imam Abdeljaber, Hikmat A. M. Eswaran, Malathi Nazeer, Jabeen Computer Science and Engineering Acharya Institute of Technology Bangalore India Department of Computer Science College of Computer Engineering and Sciences Prince Sattam Bin Abdulaziz University Alkharj 11942 Saudi Arabia University Center for Research and Development (UCRD) Department of Computer Science and Engineering Chandigarh University Gharuan Punjab Mohali 140413 India Department of Electronics and Communication Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram AP Guntur 522502 India College of Computing and Informatics Saudi Electronic University Riyadh Saudi Arabia Department of Computer Science Faculty of Information Technology Applied Science Private University Amman Jordan MEU Research Unit Middle East University Amman 11831 Jordan Department of Computer Technology Kongu Engineering College Perundurai Tamil Nadu Erode India

In human life, skin cancer is a curse. If not appropriately diagnosed, it spreads around all body parts in the earlier stage. The melanoma skin cancer death rate is 75% all over the world. There is an urgent need for a cure to be in place. Swarm and evolutionary computation for healthcare deals with real-world healthcare applications. One of the best ways to do this is to find the genes that need to be turned off such that only cancer cells can die. The current therapies used to cure cancer focus on turning off cancer cells but, at times, affect healthy tissues. Using the power of Artificial Intelligence, one can easily mark out the cancer-affected area and provide treatment accordingly. Segmentation techniques can be used to mark cancer lesions, and advanced deep learning algorithms in computer vision can help classify which class of cancer lesions. Automatic detection of melanoma by using a dermoscopic sample is tricky to find the stage and percentage of lesions affected by using deep convolutional neural networks with the help of machine vision tools. To predict skin lesions, we developed a model with three layers, each having output channels of 16, 32, and 64, respectively. For this research, different samples were collected from the international skin imaging collaboration database (ISIC2019, ISIC2020, ISIC2021). The study computed the most important parameters for classifying and identifying the lesion with accuracy, precision, recall, and specificity values. The proposed DCNN model classifier attained an accuracy of 88.82%, 93.45%, and 95.15% with the respective datasets of ISIC2019, ISIC2020, and ISIC2021, indicating high performance compared with the other state-of-the-art networks. The proposed model is a better framework for automating early detection and classification of melanoma skin cancer to protect many lives and avoid mishaps. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Classification Convolution neural network Deep learning Medical imaging Melanoma Skin cancer Swarm and evolutionary computation

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Design and Application of Programmable Analog Computing Circuit for Kalman Filter Algorithm Based on Memristive Array

IEEE Transactions on Circuits and Systems for Artificial Int...

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IEEE Transactions on Circuits and Systems for Artificial Intelligence 2024年第2期1卷 272-284页

作者： Pingdan Xiao Sichun Du Zhengmiao Wei Qinghui Hong Shiping Wen College of Computer Science and Electronic Engineering Hunan University Changsha China Australian AI Institute Faculty of Engineering and Information Technology University of Technology Sydney Sydney NSW Australia

Kalman Filter algorithm is a crucial tool for estimating the state of dynamic systems in the presence of numerous uncertainties, which plays a pivotal role in various application scenarios such as information fusion and deep learning. However, current accelerators and hardware face limitations in terms of both latency and performance overhead when executing the Kalman Filter algorithm. Aiming at the above problem, we firstly propose a programmable circuits based on memristors for full process of Kalman Filter algorithm, which can efficiently run Kalman Filter algorithm with the advantage of analog circuits in parallel and fast computing, which achieve computing speed increasing in order of magnitude. Moreover, the empowerment of programmability from memristors avoid intensive memory access. For the problem how to linearly adjust weight for the memristor, we propose a new memristor-based programmable unit, the corresponding crossbar and method in order to achieve linear weight adjustment. The evaluation shows that the proposed circuits not only has over 95 $\%$ accuracy for 32-th order parameter matrix in Kalman Filter algorithm, but also has good robustness against some non-ideal factors it can still achieve 90 $\%$ accuracy under 10 $\%$ noise interference. The evaluating results of performance also show that it reduces energy consumption by 3.1 $\times$ and 8.9 $\times$ , compared with corresponding VLSI and FPGA accelerator, respectively. Moreover, an improved image fusion algorithm and circuit based on the proposed Kalman Filter circuits is proposed, which achieve good image fusion and speedup in computing time by 82.6 $\times$ and 121.3 $\times$ , compared with corresponding ASIC and FPGA, respectively.

关键词： Memristors Circuits Kalman filters Covariance matrices Artificial intelligence Analog circuits Noise Power demand Image fusion Field programmable gate arrays

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Self-Supervise Reinforcement Learning Method for Vacant Parking Space Detection Based on Task Consistency and Corrupted Rewards

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IEEE Transactions on Intelligent Transportation Systems 2024年第2期25卷 1346-1363页

作者： Nguyen, Manh-Hung Chao, Tzu-Yin Hsiao, Ching-Chun Li, Yung-Hui Huang, Ching-Chun Hcm University of Technology and Education Faculty of Electrical Electronic Engineering Ho Chi Minh City700000 Viet Nam National Yang Ming Chiao Tung University Department of Computer Science Hsinchu300093 Taiwan Hon Hai Research Institute Ai Research Center New Taipei City114699 Taiwan

This paper proposes a novel task-consistency learning method that enables us to train a vacant space detection network (target task) based on the logic consistency with the semantic outcomes from a flow-based motion behavior classifier (source task) in a parking lot. Note that the source task can introduce false detection during task-consistency learning, which implies noisy rewards or supervision. The target network can be trained in a reinforcement learning setting by appropriately designing the reward mechanism upon semantic consistency. We also introduce a novel symmetric constraint to detect corrupted samples and reduce the effect of noisy rewards. Unlike conventional corrupted learning methods that use only training losses to identify corrupted samples, our symmetric constraint also explores the relationship among training samples to improve performance. Compared with conventional supervised detection methods, the main contribution of our work is the ability to learn a vacant space detector via semantic consistency rather than supervised labels. The dynamic learning property allows the proposed detector to be easily deployed and updated in various lots without heavy human loads. Experiments demonstrate that our noisy task consistency mechanism can be successfully applied to train a vacant space detector from scratch. © 2000-2011 IEEE.

关键词： Semantics

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Accelerating Point-Voxel Representation of 3-D Object Detection for Automatic Driving

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第1期5卷 254-266页

作者： Cao, JieCheng Tao, Chongben Zhang, Zufeng Gao, Zhen Luo, Xizhao Zheng, Sifa Zhu, Yuan Suzhou University of Science and Technology School of Electronic and Information Engineering Suzhou215009 China Tsinghua University Suzhou Automobile Research Institute Suzhou215134 China College of Mechanical Engineering Tongji University Shanghai200092 China Tsinghua University Department of Automation Beijing100084 China Wuhan Electronic Information Institute Wuhan430019 China McMaster University Faculty of Engineering HamiltonONL8S 0A Canada Soochow University School of Computer Science and Technology Suzhou215009 China Tongji University College of Automotive Studies Shanghai200092 China

Current point-voxel fusion methods for 3-D object detection could not make full use of complementary information in the field of autonomous driving. Therefore, a novel two-stage 3-D object detection method, called accelerating point-voxel representation (APVR), is proposed. The advantages of point-based feature and voxel-based feature can be integrated into a single 3-D representation. Thereby, the proposed method retains more fine-grained information of an object while maintaining high efficiency. Specifically, the computational cost is reduced by adding offsets to query neighboring voxels of key-points. More fine-grained information can be obtained by calculating the matching probability between neighboring voxels and key-points. During the optimization of the prediction boxes, virtual grid points are set to capture the spatial information between key-points. The constraint of minimum enclosing rectangle is also added to optimize the directions of the prediction boxes. A large number of experiments on the KITTI, NuScenes, and Waymo datasets demonstrate great generalizability and portability of the proposed approach. The effectiveness and efficiency of APVR have been proved by comparisons with the state-of-the-art methods. APVR makes the real-time processing frame rate reach 40.4 Hz while ensuring high prediction accuracy. © 2020 IEEE.

关键词： Computational efficiency

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