Pinned Repositories
NLP-Concepts-and-Transformers
My NLP notes and projects
Lane-Detection-and-Object-Detection-algorithms-for-Self-Driving-RC-Car
With the increase in numbers of car traffic collisions caused by the human driver, many car companies are now moving towards developing intelligent vision systems to help the car navigate itself safely. These systems are mainly concerned about two things, detecting objects around the car and keeping the car between the lanes. For detecting objects, most systems include sensor subsystems that surround the car, such as lidar, sonar, IMU, and odometry which can be costly and not efficient since these sensors alone cannot fully identify the objects and extract information from surroundings, such as colors in a traffic light, reading signs…etc. In this work, we addressed these issues by developing algorithms for detecting objects that surround the car using machine learning and Haar feature-based cascade classifier. Also, this work includes algorithms for lane detection using Hough line transform and Canny edge detection and improves these algorithms by using histogram method for identifying the lanes. Moreover, these algorithms are optimized to work on a Raspberry Pi 3 B+ as the master device which will be responsible for sending information to the Arduino UNO which will be responsible for controlling the motors of the RC car.
A-unique-32-bit-Pipelined-CPU
Designing a 32-bit in-order integer pipelined CPU with data forwarding, an instruction set architecture named NASH, and actively integrated a two-level memory hierarchy model using physical addressing
SIMULATION-OF-A-MANUFACTURING-FACILITY
A simulation of a manufacturing facility. Three products are being made, each product is produced by different workstation and requires certain components.
LQNanalysis
Using the LQN software to build a Layered Queuing Network to investigate the model's behavior, identify the model's Bottleneck, saturation points, apply sensitivity tests, trying different techniques and utilize multiple changes to the LQN model to alleviate the model's Bottleneck.
Azizkhaled
Azizkhaled.github.io
darknet
Convolutional Neural Networks
eclair-dataset
Home of the open source ECLAIR dataset
forcasting-labour-indicators
Azizkhaled's Repositories
Azizkhaled/TransNextCSI
Azizkhaled/Azizkhaled
Azizkhaled/forcasting-labour-indicators
Azizkhaled/NowCasting_Labour_Indicators
Azizkhaled/lag-llama
Lag-Llama: Towards Foundation Models for Probabilistic Time Series Forecasting
Azizkhaled/eclair-dataset
Home of the open source ECLAIR dataset
Azizkhaled/Azizkhaled.github.io
Azizkhaled/NLP-Concepts-and-Transformers
My NLP notes and projects
Azizkhaled/A-unique-32-bit-Pipelined-CPU
Designing a 32-bit in-order integer pipelined CPU with data forwarding, an instruction set architecture named NASH, and actively integrated a two-level memory hierarchy model using physical addressing
Azizkhaled/SIMULATION-OF-A-MANUFACTURING-FACILITY
A simulation of a manufacturing facility. Three products are being made, each product is produced by different workstation and requires certain components.
Azizkhaled/Lane-Detection-and-Object-Detection-algorithms-for-Self-Driving-RC-Car
With the increase in numbers of car traffic collisions caused by the human driver, many car companies are now moving towards developing intelligent vision systems to help the car navigate itself safely. These systems are mainly concerned about two things, detecting objects around the car and keeping the car between the lanes. For detecting objects, most systems include sensor subsystems that surround the car, such as lidar, sonar, IMU, and odometry which can be costly and not efficient since these sensors alone cannot fully identify the objects and extract information from surroundings, such as colors in a traffic light, reading signs…etc. In this work, we addressed these issues by developing algorithms for detecting objects that surround the car using machine learning and Haar feature-based cascade classifier. Also, this work includes algorithms for lane detection using Hough line transform and Canny edge detection and improves these algorithms by using histogram method for identifying the lanes. Moreover, these algorithms are optimized to work on a Raspberry Pi 3 B+ as the master device which will be responsible for sending information to the Arduino UNO which will be responsible for controlling the motors of the RC car.
Azizkhaled/PatternProject
Azizkhaled/LQNanalysis
Using the LQN software to build a Layered Queuing Network to investigate the model's behavior, identify the model's Bottleneck, saturation points, apply sensitivity tests, trying different techniques and utilize multiple changes to the LQN model to alleviate the model's Bottleneck.
Azizkhaled/darknet
Convolutional Neural Networks