Electrical Engineering
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- ItemTelecommunication Engineering(2024) Eman AlawnahThe Vehicle Tracking Based on GSM&GPS project brings together innovative approaches combining both GPS &GSM technology to create a vehicle tracking system that is both efficient and practical. Its functioning relies on a smartphone and the Arduino UNO, offering cost-effective usage in numerous industries. By using GPS tech, our Vehicle Tracking System tracks the vehicle's movement—accurately determining longitude and latitude data. This information is then transmitted through the GSM network which uses cellular towers- sending & receiving data in text message form; this includes identifying visualizations of each respective location on a map. Adding an ignition sensor provides a level of security. It sends a text message warning whenever someone tries to steal the vehicle acting as a deterrent, against theft. This aligns with the goal of reducing vehicle theft as it gives users a way to monitor, control and protect their vehicles. The incorporation of a control system, for a DC motor into the framework of an Arduino Uno, GPS GSM module and an ignition sensor signifies an approach to managing vehicles. This system allows users to have control over the DC motor by sending SMS commands. The simplicity and ease of use of this mechanism is demonstrated by instructions like "turn vehicle on" which activates the DC motor. The data is sent in as a text message, which includes the longitude, latitude, and location on the map. The message can be received on a mobile phone or any device capable of receiving text Messages. This project holds particular significance in detecting stolen vehicles, as it enables swift and precise recovery of the vehicle. Overall, Vehicle tracking systems have many applications today and will continue to develop in the future due to the value of the service they provide in terms of tracking vehicles and knowing their location at any time.
- ItemEnhanced Control Strategy for Single-Phase Grid-Tie Inverter with Repetitive Learning Controller(2024) Yahya Naser; Hassan BargouthPhotovoltaic (PV) power being supplied to the utility grid is becoming increasingly popular as the world’s power demand continues to rise. Solid-state inverters are the key technology that enables the integration of PV systems into the grid. Inverters are used to convert the DC voltage obtained from the PV system to AC voltage, which is then fed to the grid. Hysteresis control is typically used to control the inverter to regulate the real and reactive power injected from the PV into the grid. However, in this project, the real and reactive power will be controlled using a different technique, namely the PI controller in addition to the repetitive controller. The repetitive controller will compensate for any errors introduced by the PI controller in tracking the sinusoidal reference, as shown in Figure 1. Figure 1: PV multi-level-based Grid The project will first be simulated in MATLAB Simulink. The hardware components include a microcontroller, current, voltage sensors, H-bridge, and transformer to implement both the PI and the repetitive controllers, in addition to generating a PWM signal to the H-bridge to control the injection of real and reactive power into the grid.
- ItemAIEN Artificial Intelligence Electrical Nose(2024) Ameer Abo liel; Malath Ghazalnspired by the high drug smuggling crime rates all over the world, and due to the fact that current mechanisms of drug detection in airports cause discomfort to many travelers worldwide, the need for new reformed detection mechanisms is constantly growing. Using trained K9 dogs could cause many fearful scenarios that could cause trauma to innocent patients. The new mechanism we are presenting to you shows instantaneous results about the material detected. This project presents an artificial intelligence electronic nose (AIEN) for detecting and identifying various odors and substances. The AIEN utilizes MQ gas sensors and machine learning algorithms to analyze and classify different volatile organic compounds. A filtration system with ethanol is implemented to ensure accurate results between samples. The device also incorporates DC and AC fans and motors controlled by a microcontroller to automate the sampling process. Extensive testing produced consistent characteristic odor profiles and plots for different substances like perfumes and alcoholic beverages. The fusion of gas sensor technology with artificial intelligence offers an innovative approach to processing complex olfactory data. AIEN provides a proof of concept for the capabilities of intelligent odor detection systems in fields ranging from quality control to law enforcement
- ItemOnline Signature Recognition Using CNN &RNN(2022) Ekram Othman; Maram Bani jabberSignature is widely used in human daily lives, and serves as a supplementary characteristic for verifying human identity. However, there is rare work of verifying signature. In this paper, we propose a few deep learning architectures to tackle this task, ranging from CNN, RNN to CNN-RNN compact model. We also improve Path Signature Features by encoding temporal information in order to enlarge the discrepancy between genuine and forgery signatures. Our numerical experiments demonstrate the effectiveness of our constructed models and features representations, also the experimental results indicate significant error reduction and accuracy enhancement in comparison with state of the art counterparts.
- ItemOffline Signature Recognition Using ANN(2021) Ekram Othman; Maram Bani jabber [11611935]A person’s signature is an important vital feature for a person that can be used to verify human identity, so we used the artificial neural network method to recognize the signature, and it consists of simple elements that work in parallel, these elements are inspired by the biological nervous system. The principle of its work is that the signature is captured and presented to the user in a form picture. Signature verification can be classified into online signature verification and offline signature verification. Online verification is based on dynamic capturing of signatures when they are made whereas Offline verification generally uses a scanned image of signatures. The objective of this project is to focus on the offline model of verification where several signatures are put through various processes before finally verifying it to be true or forged through Artificial Neural Networks (ANN). . To perform verification or identification of a signature, several steps must be performed. These steps are: * Image pre-processing * Feature extraction * Neural Network Training From many algorithms and methods with different accuracy percentages In this project we propose a human signature recognition system based canny edge detection and pattern averaging and back propagation neural network system.