Parking information system with artificial neural network

In view of the intelligent demand of modern large-scale parking lot and based on the enormous application advantages of fog computing on the Internet of things and the unique characteristics of NB-IoT technology, the design and application of NB-IoT technology which is based on fog computing in parking system model architecture is proposed, to promote parking systems to be more intelligent, increase efficiency, reduce expenses, and increase efficiency. The system model design adopts four-layer structure: Perception Layer, Network Layer, Cloud-Fog Platform Layer and User Layer, and uses the technical characteristics and advantages such as Fog Computing and NB-IoT to make data analysis, storage, calculation, exchange and communication in the system more integrated, fluent, and real-time. Meanwhile, the model is built into a low-latency, wide-coverage, highly intelligent parking system, and its research results provide totally new ideas and solutions to the drawbacks of the existing parking systems.

With the development of urbanization, the number of car holdings is growing dramatically and the problem of parking difficulties is becoming more and more prominent. The irrational distribution of vehicles and parking measures in today's cities not only wastes limited road resources, but also brings great challenges to urban traffic management. Therefore, the demand for a smarter parking system is increasing to provide more convenient services and reduce the loss of human and material resources. The IoT-based intelligent parking system adds the functions of parking reservation and real-time detection to the original parking system, which realises the visualisation of parking information and makes it more obvious to see the real-time situation of the car park, thus making parking more convenient and quicker.

This paper explains the design and fabrication of an intelligent, user-friendly parking system developed in the United Arab Emirates. The need for a smart parking system was assessed by conducting a survey on the current parking issues. This paper elaborates on the hardware and software realization of the proposed parking system developed for motorists to locate vacant parking using mobile application. The various types of vehicle detection sensors available in the market have been evaluated for the implementation of the system. The main objective of this paper is to build a prototype intelligent parking system with maximum accuracy. The goal of the project was to control the detection modules wirelessly through a customized mobile application, allowing ease of operation and maintenance. While the users can enjoy better comfort and safety, the focus is to create a self-reliable, and ecologically sustainable system while reducing searching time, fuel wastage resulting in lower carbon footprint. The mobile application has been developed using Android Studio, and the results are presented in this paper.

BACKGROUND: Currently, artificial intelligence in the form of artificial neural networks is being actively implemented in a number of areas of our lives, including medicine. In particular, in otorhinolaryngology, artificial neural networks are used to analyze images obtained during endoscopic examinations of patients (e.g., videolaryngoscopy) [1–3]. The interpretation of laryngoscopic images often presents significant difficulties for practicing physicians, which reduces the frequency of detection of precancerous laryngeal diseases and contributes to the increase in the number of patients with stage III–IV laryngeal cancer [4, 5]. This underscores the significance of prompt performance and accurate interpretation of the findings of endoscopic examinations of patients with laryngeal disorders. Artificial neural networks can be employed to analyze the results of videolaryngoscopy, furnishing the physician with supplementary information that can enhance diagnostic accuracy and diminish the probability of error [6, 7]. AIM: The study aims to develop and train an artificial neural network for recognizing characteristic features of laryngeal neoplasms and variants of laryngeal normality. MATERIALS AND METHODS: The study was conducted under the grant of the Moscow Center for Innovative Technologies in Healthcare (grant No. 2112-1/22) entitled “Using Neural Networks (Artificial Intelligence Algorithms) for Control and Improving the Quality of Diagnosis and Treatment of Diseases of Laryngeal and Ear Structures through Digital Technologies”.The following methods were used during the course of the study: data collection for the creation of a photobank (dataset) of medical images obtained during videolaryngoscopy; data partitioning for the formation of datasets for individual nosologies and groups of diseases; the method of consilium; analysis of the accuracy of recognition and classification of digital endoscopic images; and training of classification neural networks. Consequently, a dataset comprising 1,471 laryngeal images in digital formats (JPEG, BMP) was assembled, labelled, and uploaded for the purpose of training the artificial neural network. Of the total number of images, 410 were classified as pertaining to laryngeal formation, while 1061 were classified as variants of normality. Subsequently, the neural network was trained and tested to identify the signs of normal and laryngeal masses. RESULTS: The results of the testing of the artificial neural network indicated the formation of an inaccuracy matrix, the calculation of the value of recognition accuracy, the calculation of the quality indicators of the model performance, and the construction of the ROC curve. The developed and trained artificial neural network demonstrated an accuracy of 86% in recognizing the signs of laryngeal masses and norms. CONCLUSIONS: This study demonstrates that a trained artificial neural network can successfully distinguish between signs of normal and laryngeal masses in endoscopic photographs. With further training of the neural network and achievement of high accuracy, this technology can be used in clinical practice as an assistant in the interpretation of laryngoscopic images and early diagnosis of laryngeal masses. It can also be employed to control and improve the quality of diagnosis and treatment of diseases of the throat, nose, and ears by primary care physicians.

Parking management systems often need to improve due to outdated infrastructure, inefficient manual processes, and the lack of automation, resulting in increased congestion and poor user experience.Traditional parking management systems, reliant on manual license plate entry and outdated payment methods, need help to meet modern demands.In response, this research introduces a novel Intelligent Parking System (IPS) that leverages automatic License Plate Recognition (LPR) with a YOLO model for real-time detection and recognition of vehicle license plates.By eliminating the need for sensors and manual data entry, our system enhances accuracy, reduces maintenance costs, and optimizes parking operations.Furthermore, integrating QR code-based payment simplifies and accelerates the payment process, reducing wait times and improving user experience.This approach addresses the growing need for scalable and adaptable parking solutions in smart city initiatives.Through this research, the IPS provides a scalable, intelligent alternative to conventional parking systems, contributing to smarter urban environments.

Artificial neural networks (ANN) have been widely used in the field of data classification. Normally, training of neural network is applied with the traditional back propagation technique. As, this approach has various drawbacks, training of neural network is done with Particle Swarm Optimization (PSO). PSO has been widely used to solve the diverse kind of optimization problems. Population initialization performs a significant role in meta-heuristic algorithms. This paper describes a new initialization population approach Log Logistic termed as PSOLL-NN to create the initialization of the swarm. The proposed algorithm has been tested for weight optimization of feed forward neural network; and compared with back propagation Algorithm (BPA), standard PSO (PSO-NN), PSO initialized with Halton Sequence (PSOH-NN), Torus sequence (PSOT-NN) and Sobol sequence (PSOS-NN). The experimental results show that the proposed technique performed exceptionally better than the other traditional techniques. Moreover, the outcome of our work presents a foresight that how the proposed initialization technique can be used as an efficient alternative to standard training approaches for the data classification problems.

Car parking is one of the most significant issues in today's world. Parking cars on surrounding roads and pathways can cause unfair traffic jams and thus hampers people's daily activity. To avoid these problems, the development of a smart car parking system is a major concern for several developed countries. At present, most of the previous studies on car parking systems suffer from several limitations such as lack of security, wastage of time, huge money expenses, and lack of user interest-aware car parking system. To overcome existing challenges, this paper presents a user interest and payment-aware automated car parking system using Internet-of-things (IoT) technology. In this paper, an android application for smart car parking is developed for Bangladeshi people that allow users to choose emergency or non-emergency parking slots based on their interest and payment verification. For anti-theft purposes, this system offers an early alert and notification feature. The experimental test results by investigating several use cases depict the suitability of the proposed system.

This paper proposes a robust design of Hybrid Fuzzy Controller for speed and steering angle control in an Intelligent Autonomous Parking System (IAPS). The Hybrid Fuzzy Controller consists of a Base Fuzzy Controller (BFC) and a Supervisory Fuzzy Decision Tree Controller (SFDTC). The BFC evolves from previous work on fuzzy logic control for unmanned parking and it ensures that optimal parking trajectory is achieved with minimal computational cost. SFDTC further increases the system robustness when there is noise in the operating environment. The design of SFDTC combines Decision Tree theory and fuzzy inference mechanism. A data training process is also formulated to achieve better control performance. As a result, IAPS equipped with the new Hybrid Fuzzy Controller with Fuzzy Decision Tree (HFC-FDT) demonstrates optimal performance with faster convergence and minimal deviation from optimal parking trajectory. The detailed design of Supervisory Fuzzy Decision Tree Controller is presented in this paper with a MATLAB simulated experiment which concludes the superior performance of the new design.

There are many transportation equipment such as a car, a motorbike in Indonesia. Increasing the volume of vehicle users will increase parking activities in every public place. Parking is a condition of a vehicle that is not temporary. The current parking system still uses a conventional parking system that only uses parking lots and parking attendants who control each vehicle that enters, and often do not pay attention to the capacity of parking space that has been provided and often drivers go around first to find an empty parking lot. Therefore, detection of empty slots in the parking system is needed. To obtain this information, this study using a neural network method. The neural network is one of the information processing systems designed by imitating the workings of the human brain in problem-solving. The purpose of this research is to determine which car parking slots filled or empty parking slots. The results of detection of car parking slots using a neural network method were carried out experiments on 20 objects with as many as 12 slots and with three slots that are rectangles, squares, and traingles. In the square slot with an area of 784 cm2 get accuracy results 100%, rectangular slots with an area of 3.608 cm2 obtained accuracy 95% and a traingle slot with an area of 1.804 cm2 obtained an accuracy 90%.

Increased use of cars that are not balanced with the provision of adequate parking areas and many drivers who park in any place. The existing parking system only utilizes the parking area and the parking attendant who controls it. Motorists also have to find a place that is free of parking officers or drivers do not pay attention to the parking area, the vehicle owner will look for must get out if you do not get a parking space. To overcome this, make a parking system by using a microcontroller system that facilitates vehicle users to understand the number and arrange an empty parking space. The design of this parking system discusses the available parking system which will be notified via LCD (Liquid Crystal Display) and LED (Light Emitting Diode) at the entrance of the parking lot, and also so that it can be used for the total vehicles in the parking lot with LED indicators ( Light Emitting Diode). This design uses the Arduino Nano as a processor to process data. The servo that translates the bar will be driven. When the parking lot is full, the LCD (Liquid Crystal Display) gives information that there is no vacant land, so the parking user goes up to the next floor. From the Arduino test results, Nano can adjust the circuit well, and Ultrasonic sensors can connect vehicles in each parking area, and the servo bars can be opened and closed again.

Joint A-SNN: Joint training of artificial and spiking neural networks via self-Distillation and weight factorization

Advances in technology today require humans to increase their effectiveness and efficiency in various ways. Basically the purpose of creating a tool is to help human work to be more effective and efficient. Thus, humans are expected to function in a relatively short time, especially when it comes to safety. The parking lot is a medium that is used as a way of getting in and out of vehicles. Anticipating some problems in the parking lot, for example: losing parking papers, losing cars in parking lots, and preventing illegal parking at high levels. Then we need an automatic parking door system. This final project aims to build a prototype of an IoT-based automated parking system.
 This project is a simple parking simulator with a microcontroller device programmed to use the Arduino idea. Hardware integrated with mysql database and run by simple web. The automated parking system is designed using nodemcu lolin, expansion board, 16x2 + i2c LCD adapter, mini mp3 dfplayer, 0.5 watt ohm earbuds, infrared sensor, rfid rc522 and SG90 servo motor. Infrared sensor detects vehicle, rfid receives input by tapping card and then gives output to i2c and web. Furthermore, the output from the LCD goes to the service motor which opens and closes the parking door, and the dfmp3 player, as well as the speaker which makes a sound when the card is tapped. This prototype automatic parking system can provide access and close automatic parking doors which saves time and effort. It can be concluded that the prototype of this IoT automatic parking system can be used as a guide for the development of a better technology.

Nowadays the concept of smart cities has gained grate popularity. and thanks to the IoT of things technology for the concept and the idea of smart city in this paper, we present an IoT based smart parking system. A Smart parking system that helps drivers to find vacant spot. Driver Problems such as limited car parking, traffic problems and road safety are being addressed by IoT. In this paper, I present an IoT based smart parking system. The Smart car Parking system will help user to solve the problem for finding a parking space and to reduce the time that they have spent in searching for the nearest available car parking. A mobile application is also provided that allows an end user to check the availability of parking space and book a parking slot according to their time convenience.

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