Browsing by Author "Khouas, Abdelhakim (supervisor)"

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    Algeria licence plate recognition system using faster-RCNN and YOLO models
    (2020) Boudissa, Mehieddine; Kissoum, Malik; Khouas, Abdelhakim (supervisor)
    In some institutions, of?ce buildings, or government facilities the ?ow of incoming and outgoing traf?c of people and cars needs to be monitored and recorded for security purposes as well as practicality and automation of entry pass for vehicles. Over the last years, many techniques have been proposed in an attempt to solve the Automatic License Plate Recognition System (ALPRS) problem. These techniques rely mainly on hand-crafted approaches and basic computer vision algorithms such as edge detection with Sobel ?lter. These approaches are not accurate enough for real-world applications, nor are they robust enough to changes in size, shape, and rotation of the license plates. Recently, deep learning techniques have been shown to be a strong tool for solving computer vision and object detection problems, such as ALPRS. In this project, we propose a solution based on convolutional neural networks (CNN). A data set containing 1000 car images has been collected, labeled, and then split into a training set and testing set. The size of this data set would allow for a transfer learning approach and ?ne-tuning of models. In the next step, various models belonging to the “You Only Look Once” (YOLO) CNN and “Faster Region-based CNN” (Faster RCNN) families are trained to perform plate detection task only. Once the models are trained and optimized, they are used to crop images of plates from the original car images. These cropped images are used to train models to perform the digit recognition task, similar to those trained for plate detection. The training process was repeated for different structures and parameters of the models to obtain the best performance possible. Evaluating these models relies on the use of the mean average precision (mAP) used in the original papers of YOLO and Faster-RCNN. The evaluation of the ?nal model (plate detection and digit recognition) relies on the accuracy of performing the identi?cation of the license plate numbers. The end result is an application that achieved an accuracy of 81.36% with real-time video processing capabilities and robust to changes in size, shape, color, and rotation of the license plates. This project provides users of the application with a reliable and practical security tool. It would also supply Algerian academics and software developers with a benchmark data set for further research on the topic and evaluation of future models.
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    Performance analysis of the routing protocol for low-ppwer and lossy networks in cc2538 based wireless sensor networks
    (2022) Rimouche, Wail; Merine Sassi, Saleh; Khouas, Abdelhakim (supervisor)
    Wireless Sensor Networks (WSN) are a collection of embedded devices responsible for receiving data from sensors then wirelessly transmitting and routing the data to a specified central device. They are a rapidly growing technology for data harvesting applications that necessitate the assessment of novel protocols more suitable for their distinctive network topology. Most Wireless Networks are based upon the prevalent TCP/IP protocol stack or similar parallel architectures. However, the novel “Routing protocol for Low Power and Lossy Networks”, abbreviated as “RPL”, is becoming ubiquitous in Wireless Sensor Networks where most packets are routed to a central sink or gateway node. Therefore, analysing the performance of RPL in isolation of the upper protocol layers present in most other implementations will potentially provide insight into the scalability and reliability of RPL independently of external factors. This project focuses on analysing the performance of a WSN based on the CC2538 module connected through the IPv6-based RPL utilizing the IEEE 802.15.4 physical layer. The analysis is conducted in two interconnected phases. The first phase of the project encompasses the identification of the performance characteristics of the CC2538 modules in peer-to-peer communication. The second phase utilizes the measurements derived from the previous phase in simulating the RPL protocol in a large-scale network with the help of the Contiki-Cooja simulator software. This project has resulted in identifying the necessary hardware and software for a practical CC2538 WSN node as well as identifying the simulated performance of RPL in a CC2538-based WSN.