Computer

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Hardware/software codesign of real time obstacle detection system using stereovision
(2022) Bendahmane, Meryem; Khouas, Abdelhakim (Supervisor)
Obstacle detection and localization are crucial abilities for a mobile robot to navigate in an environment. Therefore, a highly developed environment perception system is necessary. Systems based on stereo vision are strongly recommended due to their high resolution, relatively wide range of measurement, and low cost. Such systems generate a disparity map from two stereo images (left and right), the generation process and disparity map quality depend on the stereo matching technique. In this project, we built a software system for a real-time obstacle detection using stereovision. For the stereo vision module, we used the RANK non-parametric transformation and the Sum of Absolute Differences (SAD) correlation matric to estimate the disparity between the two images, the obstacle detection phase was carried forward using the U-V disparity approach. This approach makes use of the disparity map to build the V-disparity and U-disparity maps, from which obstacles can be easily localized after extracting vertical and horizontal lines from the maps respectively. The software design processes 240×320 pixels images at a rate of 1/3 fps, with a single thread Central Processing Unit (CPU) rate of 667 MHz and a Double Data Rate (DDR) memory running at a frequency of 533 MHz, this result reveals the inefficiency of our system for real-time applications. To overcome this limitation, we involved the hardware/software codesign methodology, where the system is split into hardware and software partitions. The choice of the best partitioning depends on the characteristics required to be present at the final system implementation. For our project, the speed was the main factor to meet the real-time application requirements. In order to get a better partitioning, we used profiling tools to determine the most time-consuming functions in our software system. The profiling shows that the stereo vision module consumes 90% of the total execution time, as a result, we replaced the stereo vision module in our application by a hardware stereo vision module. The software/hardware codesign solution built with the same software characteristics and a hardware stereo vision module running at 7.8 MHz led to a huge improvement in the system’s speed, for 240×320 pixels images the processing rate reaches 78 fps achieving a 234 times acceleration from the original software design.
FPGA implementation of stereo matching algorithm for depth estimation.
(2022) Yahia, Karim; Khouas, Abdelhakim (Supervisor)
Computer vision is an artificial intelligence branch developed for machines to perceive image and videos. It interprets visual data (pictures or videos) to extract information. One of its fundamental concepts is defined as stereo vision; used to estimate 3-D information of the scene. During depth estimation, a process denoted stereo matching is considered as the complex part; it requires a considerable amount of time to execute on a processor which prevents the systemto reach its minimum speed (30 frames per second). The proposed solution is moving the complex part of the system to hardware, since the latter is faster than software. To implement stereo matching, different
FPGA implementation of stereo matching algorithm for depth estimation.
(2022) Yahia, Karim; Khouas, Abdelhakim (Supervisor)
Computer vision is an artificial intelligence branch developed for machines to perceive image and videos. It interprets visual data (pictures or videos) to extract information. One of its fundamental concepts is defined as stereo vision; used to estimate 3-D information of the scene. During depth estimation, a process denoted stereo matching is considered as the complex part; it requires a considerable amount of time to execute on a processor which prevents the systemto reach its minimum speed (30 frames per second). The proposed solution is moving the complex part of the system to hardware, since the latter is faster than software. To implement stereo matching, different methods and algorithms are proposed, however, only few are suitable for hardware implementation. In this project, a correlation-based on Rank Transform and Sum of Absolute Differences algorithm is implemented on a FPGA; the minimum speed reached by this module is 301 frames per second for the VGA format images.
IoT Asset tracking based on GPS and LoRa wireless technology
(2021) Hamdi, Abdelkhalek; Khouas, Abdelhakim (Supervisor)
Asset tracking is the process of collecting location data regarding valuable items within an organization. The Internet of Things (IoT) stands for connecting physical objects wirelessly, for the purpose of exchanging data over the internet. IoT asset tracking defines the use of IoT enabling technologies in order to collect, store, and visualize location data of assets in real- time, which allows better and more reliable decision making. Technologies such as Wi-Fi and cellular are widely used to enable IoT wireless connection of physical objects; however, when dealing with use cases in large industrial environments where assets are spread randomly indoor and outdoor, the network coverage problem arises. This report proposes an IoT solution to the asset tracking use case in which Global Positioning System (GPS) is used to acquire real-time location data and LoRa communication technology is used to allow wireless data transmission over long ranges. A tracker and a gateway embedded systems were designed and implemented based on microcontrollers and System-on-Chip (SoC) modules. The tracker was designed to acquire and send location data wirelessly over long ranges; the gateway on the other hand receives data then forwards it to a web application for storing and visualization. Different software drivers were written using C programming language to allow interfacing the GPS and LoRa modules. By the end of this report, we were able to visualize real-time locations of a moving vehicle with 1-3 meters accuracy in localizing the asset, and a wireless signal reach of 293 meters. Our solution is used to track outdoor moving assets within industrial environments.

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