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Number plate recognition smart parking management system using IoT

Measurement: Sensors 2025年 37卷

作者： Ditta, Allah Ahmed, Muhammad Maroof Mazhar, Tehseen Shahzad, Tariq Alahmed, Yazan Hamam, Habib Department of Information Sciences Division of Science and Technology University of Education Lahore54000 Pakistan School of Computer Science National College of Business Administration and Economics Lahore54000 Pakistan Department of Computer Science and Information Technology School Education Department Government of Punjab Layyah31200 Pakistan Department of Computer Science COMSATS University Islamabad Sahiwal Campus Sahiwal57000 Pakistan Software Engineering Al Ain University Al Ain University Abu Dhabi United Arab Emirates Faculty of Engineering University of de Moncton MonctonNBE1A3E9 Canada School of Electrical Engineering University of Johannesburg Johannesburg2006 South Africa Av. Grandes Ecoles LibrevilleBP 1989 Gabon Bridges for Academic Excellence - Spectrum Tunis Tunisia

This study aims to address the urban vehicle parking issues by proposing a solution using Automatic Number Plate Recognition (ANPR) through image processing and a sensor-based hardware system. Integrating these technologies forms a Smart Parking Management System (SPMS) to automate parking processes and enhance the parking experience. The study aims to create an efficient system that eliminates manual vehicle registration and optimizes space utilization. ANPR and IoT-based sensors help users identify the available slots and pay only for the actual parking duration, which will help to minimize the fixed billing rates. The proposed ANPR system processes vehicle number plates at entry, ensuring seamless identification and eliminating manual registration. IoT sensors monitor real-time slot occupancy, transmitting data to a web admin panel. This panel provides insights such as entry and exit times, total parking duration, and billing costs, facilitating efficient management and remote monitoring. The ANPR-based SPMS reduces reliance on manual processes, streamlining entry procedures. By dynamically assessing slot availability through IoT sensors, users can locate unoccupied spaces quickly, which enhances user convenience. The web admin panel allows administrators to monitor the system remotely, ensuring smooth operations and maintaining accurate records. This study introduces a comprehensive solution to urban parking challenges by integrating ANPR and IoT technologies. The SPMS improves efficiency, reduces human resource needs, and enhances user experience with flexible billing based on actual duration. The combination of hardware and software provides a foundation for effective urban parking management. © 2024 The Authors

关键词： Digital storage

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GPT-Based Automated Induction: Vulnerability Detection in Medical software

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IEEE Journal of Biomedical and Health Informatics 2025年 PP卷 PP页

作者： Deng, Liangjun Lei, Hang Khan, Fazlullah Srivastava, Gautam Chen, Jingxue Haque, Mainul University of Electronic Science and Technology of China School of Information and Software Engineering Chengdu610054 China University of Nottingham Ningbo China School of Computer Science and the Next-Generation Internet of Everything Laboratory Faculty of Science and Engineering Zhejiang Ningbo315104 China Lovely Professional University Division of Research and Development Phagwara India Chandigarh University University Centre of Research and Development Mohali India Brandon University Department of Math and Computer Science BrandonMBR7A 6A9 Canada China Medical University Research Centre for Interneural Computing Taichung40402 Taiwan Chitkara University Institute of Engineering and Technology Chitkara University Centre for Research Impact & Outcome Punjab Rajpura140401 India University of Nottingham Ningbo China Department of Mathematics Faculty of Science and Engineering Zhejiang Ningbo315104 China

Integrating Natural Language Processing (NLP) with Generative Pre-trained Transformer (GPT) models plays a pivotal role in enhancing the accuracy and efficiency of healthcare software, which is essential for patient safety and providing high-quality care. The precision of healthcare software is fundamental to protecting the well-being of the patient. In addition, it can ensure the delivery of superior care, maintain the integrity of healthcare systems, and promote trust and cost-effectiveness. It is necessary to emphasize the importance of software reliability in its development and deployment. Symbolic execution serves as a vital technology in automated vulnerability detection. However, symbolic execution often faces problems such as path explosion, which seriously affects efficiency. Although there have been several studies to reduce the number of computational paths in symbolic execution, this problem remains a major obstacle. Therefore, more efficient solutions are urgently needed to ensure the software security. This paper proposes a large-scale language model(LLM) induction method mitigating path explosion applied to symbolic execution engines. In contrast to traditional symbolic execution engines, which often result in timeout or out-of-memory detection, our approach achieves the task of detecting vulnerabilities in seconds. Furthermore, our proposal improves the scalability of symbolic execution, allowing more extensive and complex programs to be analyzed without significant increases in computational resources or time. This scalability is crucial to tackling modern software systems and improving the efficiency and effectiveness of automated defect verification in healthcare software. © 2025 IEEE. All rights reserved,

关键词： Scalability

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Efficient Batch Verification and Privacy-Preserving Data Aggregation Scheme in V2G Networks

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IEEE Transactions on Vehicular Technology 2025年

作者： Wang, Congcong Wang, Chen Shen, Jian Vasilakos, Athanasios V. Wang, Bin Wang, Weizheng Department of Software Engineering Nanjing University of Information Science and Technology Nanjing210044 China the Zhejiang Key Laboratory of Digital Fashion and Data Governance the Zhejiang Provincial International Cooperation Base for Science and Technology on Cloud Computing Security and Data Aggregation Zhejiang Sci-Tech University Hangzhou310018 China Zhejiang Provincial Innovation Center of Advanced Textile Technology Shaoxing312000 China College of Computer Science and Information Technology IAU Saudi Arabia Grimstad4879 Norway Network and Data Security Hangzhou310053 China Computer Science Department City University of Hong Kong Hong Kong the Division of Computer Science University of Aizu Fukushima965–8580 Japan

In recent years, against the backdrop of smart grids, vehicle-to-grid (V2G) interaction has become a hot topic. With the increasing number of new energy vehicles, handling massive amounts of electricity transaction data has become an urgent problem in need of resolution. Ensuring the security of the vast quantities of electricity transaction data has emerged as a significant challenge. Therefore, two data aggregation schemes are proposed in this paper, both supporting batch verification and privacy protection in centralized and distributed scenarios, respectively. Firstly, an improved Paillier cryptosystem is utilized to achieve lightweight encryption in the proposed schemes. In addition, a novel distributed data aggregation structure that combines a large number of geographically distributed fog nodes with centralized cloud servers is introduced. This structure not only meets the requirements of lightweight encryption and resistance against single point failures but also reduces the communication complexity of distributed data aggregation to O(n). According to the security proofs and experimental analysis, it can be demonstrated that both proposed schemes are effective and secure, meeting the lightweight requirements of modern smart grid systems. © 2025 IEEE. All rights reserved,

关键词： Data aggregation

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Accuracy of computer-simulated mandibular autorotation following single-jaw maxillary repositioning surgery using customized surgical cutting guides and fixation plates

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International Journal of Oral and Maxillofacial Surgery 2025年

作者： Vu, C. Hartsfield, J.K. Mian, A. Allan, B. Gebauer, D. Goonewardene, M. Orthodontic Department School of Dentistry The University of Western Australia Nedlands WA Australia Division of Orthodontics Department of Oral Health Science College of Dentistry University of Kentucky Kentucky United States School of Computer Science and Software Engineering The University of Western Australia Crawley WA Australia Oral and Maxillofacial Surgery Department Royal Perth Hospital Perth WA Australia

The literature on computer-aided surgical simulation (CASS) has focused extensively on the precision and accuracy of maxillary and mandibular repositioning surgery. However, the predictability of mandibular autorotation remains understudied. The aim of this study was to evaluate the accuracy of CASS-planned mandibular autorotation in 50 consecutive patients undergoing maxillary repositioning surgery with customized titanium surgical cutting guides and fixation plates. The study patients (32 male and 18 female, mean age 25 years for both sexes) underwent Le Fort I osteotomy, with or without simultaneous genioplasty were performed by one experienced maxillofacial surgeon. Preoperative and postoperative multi-slice computed tomography scans were superimposed and contrasted with the surgical plan to evaluate any discrepancies between the planned and observed mandibular positions after surgery. The translational movements of the maxilla were a median 0.38 mm transversely, mean −5.29 mm anteroposteriorly, and median −1.00 mm vertically. Using a computed mandibular centroid, the absolute linear and angular discrepancies between the planned and observed mandibular autorotation movements were found to be within the clinically significant thresholds of 1 mm and 2°, respectively. It is concluded that CASS-planned mandibular autorotation is predictable when maxillary down-grafting and impaction does not exceed 5 mm and 4 mm, respectively. © 2025 The Authors

关键词： computer-assisted surgery Mandible Maxillofacial surgery Orthognathic surgery Reproducibility of results

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Comparison of Missing Data Imputation Methods in Time Series Forecasting

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computers, Materials & Continua 2022年第1期70卷 767-779页

作者： Hyun Ahn Kyunghee Sun Kwanghoon Pio Kim Division of Software Convergence Hanshin UniversityGyeonggi18101Korea Contents Convergence Software Research Institute Kyonggi UniversityGyeonggi16227Korea Division of AI Computer Science and Engineering Kyonggi UniversityGyeonggi16227Korea

Time series forecasting has become an important aspect of data analysis and has many real-world ***,undesirable missing values are often encountered,which may adversely affect many forecasting *** this study,we evaluate and compare the effects of imputationmethods for estimating missing values in a time *** approach does not include a simulation to generate pseudo-missing data,but instead perform imputation on actual missing data and measure the performance of the forecasting model created *** an experiment,therefore,several time series forecasting models are trained using different training datasets prepared using each imputation ***,the performance of the imputation methods is evaluated by comparing the accuracy of the forecasting *** results obtained from a total of four experimental cases show that the k-nearest neighbor technique is the most effective in reconstructing missing data and contributes positively to time series forecasting compared with other imputation methods.

关键词： Missing data imputation method time series forecasting LSTM

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Scenario pattern matching in large sensor recordings with simulation models for cyber-physical systems

Scenario pattern matching in large sensor recordings with si...

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Summer computer Simulation Conference, SCSC 2014, Part of the 2014 Summer Simulation Multiconference, SummerSim 2014

作者： Berger, Christian Department of Computer Science and Engineering Division for Software Engineering University of Gothenburg Sweden

ISBN: (纸本)9781632667335

Today's cyber-physical systems (CPS) like advanced driver assistance systems (ADAS) in modern vehicles use a large variety of sensors to process data from their surroundings. Thereby, systems like lane departure warning and adaptive cruise control support the driver in tedious or critical traffic situations. During the development of such systems, engineers also use recorded sensor data in offline validations to complement simulations that exhibit optimal environmental conditions. Such recordings are an ever-growing data source, and thus, effective methods are needed to find proper recordings in databases to support the system validation. Textual annotations for these sensor recordings require a well-defined taxonomy and continuous maintenance. Instead of relying on such a manually maintained taxonomy, an automated method for identifying relevant scenarios from real world sensor recordings by using simulation models is described. The outlined approach is evaluated with real world data sets used by lane-detection algorithms from nine different projects. Results from these data sets of more than 2.3 GB show that finding relevant traffic scenarios is possible in less than 0.15s.

关键词： Big data

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Fast block mode decision algorithm in H.264/AVC video coding

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JOURNAL OF VISUAL COMMUNICATION AND IMAGE REPRESENTATION 2008年第3期19卷 175-183页

作者： Kim, Jong-Ho Kim, Byung-Gyu [a]Real-time Multimedia Research Lab Embedded Software Division Electronics and Telecommunications Research Institute Republic of Korea [b]Computer Software & Engineering Department University of Science and Technology Republic of Korea

The recent video coding standard H.264/AVC show extremely higher coding efficiency compare to any other previous standards. H.264/AVC can achieve over 50% of bit rate saving with same quality using the rate-distortion process, but it brings high computational complexity. In this paper, we propose an algorithm that can reduce the complexity of the codec by reducing the block mode decision process adaptively. Block mode decision process in H.264/AVC consists of inter mode decision process and intra mode decision process. We deal with reduction method for inter and intra mode decision. In this paper an efficient method is proposed to reduce the inter mode decision complexity using the direct prediction methods based on block correlation and adaptive rate distortion cost threshold for early stopping. The fast intra mode reduction algorithm based on inter mode information is also proposed to reduce the computational complexity. The experimental results show that the proposed algorithm can achieve up to 63.34-77.39% speed up ratio with a little PSNR loss. Increment in bit requirement is also not much noticeable. (c) 2007 Elsevier Inc. All rights reserved.

关键词： video coding image compression image coding prediction methods video processing

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MangoLeafXNet: An Explainable Deep Learning Model for Accurate Mango Leaf Disease Classification

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IEEE Access 2025年

作者： Rayed, Md Eshmam Jim, Jamin Rahman Islam, Md Juniadul Mridha, M.F. Kabir, Md Mohsin Hossen, Md. Jakir American International University-Bangladesh Department of Computer Science and Engineering Dhaka1229 Bangladesh Mälardalens University Division of Computer Science and Software Engineering Västerås722 20 Sweden Multimedia University Faculty of Engineering and Technology Department of Robotics and Automation Melaka Malaysia

Addressing the global challenge of ensuring a consistent and abundant supply of fresh fruit, particularly in the context of fruit crops, is hindered by the prevalence of plant diseases. These diseases directly impact the quality of fruits, leading to a decline in overall agricultural production. Mango leaf diseases pose significant threats to global mango production, necessitating accurate and efficient classification techniques for timely disease management. Our study focuses on introducing MangoLeafXNet, a customized Convolutional Neural Network (CNN) architecture specifically tailored for the classification of mango leaf diseases, along with a healthy class. Our proposed model comprises six layers optimized to capture intricate disease patterns, demonstrating superior performance compared with prevalent pre-trained models. The model is trained and evaluated on three publicly available datasets: MangoLeafBD (4000 images across 8 classes), MangoPest (16 pest classes including healthy leaves), and MLDID (3000 high-resolution images across 5 classes). Our model demonstrated exceptional classification performance, attaining 99.8% accuracy, 99.62% recall, 99.5% precision, and an F1-score of 99.56%. Further validation on the MangoPest dataset and the Mango Leaf Disease Identification Dataset (MLDID) resulted in accuracies of 96.31% and 96.33%, respectively, confirming the robustness and adaptability of MangoLeafXNet across different datasets. Additionally, we incorporate Explainable AI techniques, including GRAD-CAM, Saliency Map, and LIME to enhance the interpretability of our model. We deployed Gradio web interface to create an interactive interface that allows users to upload images of mango leaves and get real-time classification and validation results along with confidence scores. This contribution not only advances the state-of-the-art in mango leaf disease classification but also offers promising prospects for real-time disease diagnosis and precision agriculture

关键词： Fruits

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A light-weight defect classification scheme for embedded automotive software and its initial evaluation

A light-weight defect classification scheme for embedded aut...

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2012 IEEE 23rd International Symposium on software Reliability engineering, ISSRE 2012

作者： Mellegård, Niklas Staron, Miroslaw Törner, Fredrik Software Engineering Division Department of Computer Science and Engineering Chalmers University of Technology Sweden Software Engineering Division Department of Computer Science and Engineering University of Gothenburg Sweden Volvo Car Corporation Sweden

ISBN: (纸本)9780769548883

Objective: Defect classification is an essential part of software development process models as a means of early identification of patterns in defect inflow profiles. Such classification, however, may often be a tedious task requiring analysis work in addition to what is necessary to resolve the issue. To increase classification efficiency, adapted schemes are needed. In this paper a light-weight defect classification scheme adapted for minimal process footprint - in terms of learning and classification effort - is proposed and initially evaluated. Method: A case study was conducted at Volvo Car Corporation to adapt the IEEE Std. 1044 for automotive embedded software. An initial evaluation was conducted by applying the adapted scheme to defects from an existing software product with industry professionals as subjects. Results: The results showed that the classification scheme was quick to learn and understand - required classification time stabilized around 5-10 minutes already after practicing on 3-5 defects. The results also showed that the patterns in the classified defects were interesting for the professionals, although in order to apply statistical methods more data was needed. Conclusions: We conclude that the adapted classification scheme captures what is currently tacit knowledge and has the potential of revealing patterns in the defects detected in different project phases. Furthermore, we were, in the initial evaluation, able to contribute with new information about the development process. As a result we are currently in the process of incorporating the classification scheme into the company's defect reporting system. © 2012 IEEE.

关键词： software reliability

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Improving scenario selection for simulations by run-time control-flow analysis

Improving scenario selection for simulations by run-time con...

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Summer computer Simulation Conference, SCSC 2013 and Work in Progress, WIP 2013, Part of the 2013 Summer Simulation Multiconference, SummerSim 2013

作者： Berger, Christian Chalmers / University of Gothenburg Department of Computer Science and Engineering Division for Software Engineering Sweden

ISBN: (纸本)9781627482769

Cyber-physical systems like active safety systems in recent vehicles are significantly driven by software and rely predominantly on data that is perceived by cameras, laser scanners, and the like from the system's environment. For example, these sensor-based systems realize pedestrian protection functionalities, which cannot be tested under simplified conditions on proving grounds only or by arbitrary test-runs on public roads anymore. Instead, simulative environments are used nowadays, which provide the virtual surroundings for such a system where its real input sources are replaced with simplified sensor models. Thus, interactive and hazard-free system tests and automated system evaluations can be carried out easily. However, the simple strategy to run all available modeled traffic scenarios in the simulation on any change of the implementation would consume too much computation time to provide effective and fast feedback for developers. In this article, an improved strategy for selecting scenarios that shall be run in a simulation based on run-time control-flow analysis is proposed, which resulted from the in-depth analysis of the revision history of the source code and their accompanying simulations for two self-driving vehicles. The outlined strategy is evaluated on a self-driving miniature vehicle.

关键词： Cyber Physical System

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