Saudi Summer Internship Program (SSI) 2021

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  • Poster

    Streaming Opengl On Hololens2

    (2021-08-19) Altamimi, Shorouq
  • Poster

    Trace Compression

    (2021-08-19) Hommadi, Tariq

    Problem statementMethodologyReferencesFurther researchConclusionExperimental setupExperimental evaluationHow is it implementedObjective• To study and analyze the performance of a CPU in the most proper way, long memory address traces are needed to give the most accurate result. However, more time and memory space will be required to have the analysis• We need to enhance the performance of the applied methodology and reduce the size of the trace compared with the traditional gzip tool• Loop Detection and Reduction:•Dinero FormatA popular format used in memory addresses00.10.20.30.40.50.60.70.80.91Trace 1 Trace 2 Trace 3Original gzip LDR gzip on LDR• The study resulted in a ratio of up to 22 between gzip and gzip on LDR• The Technique will show more compression ratio if it is applied on a trace contains a considerable number of loops• Since the size of the outputted trace depends on its content, the documented addresses (fetch and data addresses) could be not fully written, instead, using smaller data set to reference to the information that most be needed to decompress the outputted trace fileFigure 1. Categories of loop addresses 45.4951.37 121.1940.4884.33 337.3310.7452.57245.335KAddresses• Elnozahy, E. Address Trace Compression Through Loop Detection and Reduction• Stack Overflow. Retrieved from https://stackoverflow.com/• Java67. Retrieved from https://www.java67.com/2015/01/how-to-sorthashmap-in-java-based-on.htmlTrace Compression Tariq HommadiSupervised by Elmootazbellah N. ElnozahyComputer, Electrical and Mathematical Sciences and Engineering Division (CEMSE)King Fahd University of Petroleum & Minerals (KFUPM), Dhahran, Saudi ArabiaKing Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia for(i=0; i<n; i++)ai = 100bi = ci*4 Figure 2. Activity diagram of the program 100KAddresses300KAddresses

  • Poster

    Automated Landform Detection On Mars Using Convolutional Neural Networks

    (2021-08-19) Aljabr, Rana

    As the neighbor of Earth in the Solar System, Mars has been recognized as an important reference for investigating the evolution of history and future of Earth due to the similar rocky structure, water, thin atmosphere, earth-like elements and small molecule organic matter all been detected on Mars, which makes it a perfect candidate for our first interstellar settlement. Utilizing the large volume of public, high-resolution images of its surfaces we develop fully automated Deep Learning algorithms that serve exploring the planet. Convolutional Neural Networks(ConvNets) simulates human nerves. Through training, it operates as a feature extractor that detects and investigates various geological landforms on the surface of Mars; Its history and its resource reservoirs.

  • Poster

    Simultaneous Multi-Camera Localization And Mapping With Apriltags (Tagslam) And Trajectory Planning For Underwater Rov

    (2021-08-19) Andigani, Razan

    Over 70% of the Earth’s surface is covered by oceans, yet less than 5% has been explored due to the dangerous and inaccessible marine environment.

    Remotely operated vehicles (ROVs) allow marine scientists to explore the ocean without having to be in the ocean.

    The goal of this project is to allow the robot to localize itself within its environment, map its surroundings, and follow a desired path (trajectory planning).

    Vision System:

    In order to process images and record captures: 1.An image of Ubuntu MATE 18.04 was flashed to the ROV’s Raspberry Pi 3. 2.Ethernet connection between the topside computer (Ubuntu 18.04) and the Raspberry Pi was established.

    3.ROS and usb_cam packages were installed.

    TagSLAM:

    To map the robot’s surrounding environment and determine its position: 1.tagslam_root packages were installed. 2.Extrinsic camera calibration was performed. 3.april_tag_detector and tagslam nodes were launched. 4.Apriltags of Tag family 36h11 were printed.

    5.Odometry and camera images were published into TagSLAM.

    ROV Thrusters:

    In order to control the robot under the water and test TagSLAM: 1.Connection between the autopilot (Pixhawk) and the companion computer (Raspberry Pi 3) was established. 2.MAVROS was installed and run in the companion computer. 3.Vehicle’s mode was set to MANUAL and Failsafes in QGC were disabled.

    4.Parameters were sent to actuate the thrusters using the overrideRCIn topic.

    Pose Estimation:

    To get the distance between the camera frame and an apriltag: 1.A python script that reads sensor data was written.

    2.The node was run to output the ROV’s position and orientation in x, y, z.

    To install ROS on the companion computer, different versions of Ubuntu were flashed with the Pi image. Ubuntu MATE 18.04 was found to be the most compatible.

    The CSI camera on the ROV was replaced with a fisheye USB camera, which was successfully calibrated. Data of captured images and recorded videos are saved in a rosbag for later use.

    Apriltags were detected by the ROV and mapped on Rviz as a body_rig. Localization was achieved using the camera perception frame view.

    ROV’s six thrusters were successfully controlled using MAVROS nodes and topics. ROV is able to move and dive underneath the water surface.

    Implement PID controller for the ROV to keep a desired distance and overcome pose offset error.

    Apply a fractional-order control algorithm to run the robot more robustly.

    Automate the ROV to perform trajectory planning.