Browsing by Author "Alkhedher,M."

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Citation Count: 0
Design and Study of an AI-Supported Autonomous Stair Climbing Robot
(TUBITAK, 2023) Ramadan,M.N.A.; Hilles,S.M.S.; Alkhedher,M.
Mobile robots are frequently utilized in the surveillance sector for both industrial and military purposes. The ability to navigate stairs is crucial for carrying out surveillance jobs like urban search and rescue operations. The research paper shows that the design methodology for a six-wheeled rover robot that can adapt to various stairs and maintain its stability based on the robot's specifications, kinematics restrictions, the maximum height, and the lowest step length needed to climb up and down the stairs is proposed. Based on a Raspberry Pi, camera, and LIDAR distance sensor, the suggested robot has the capacity to measure the stair height before starting to climb. A Convolutional Neural Networks (CNN) deep learning model is developed for the purpose of stair recognition. Additionally, stair alignment was estimated using statistical filtering on pictures and LIDAR distance reading. The robot can then decide whether it can climb the stairs or not based on its kinematics limitations and the height of the stairs as measured by our system. Result shows that our stair detection algorithm achieved an accuracy of 99.46% and a mean average precision of 99.64%. The proposed AI-supported Robot-based stair recognition system, according to final results, effectively climbed stairs with a height range between 13 and 23 cm. © 2023, TUBITAK. All rights reserved.
Citation Count: 0
Portable AI-powered spice recognition system using an eNose based on metal oxide gas sensors
(Institute of Electrical and Electronics Engineers Inc., 2023) Ramadan,M.N.A.; Alkhedher,M.; Tevfik Akgun,B.; Alp,S.
In our daily lives, we use spices and herbs. There are thousands of different sorts of spices that surround words. And occasionally it's difficult to distinguish between them. Furthermore, without specialized knowledge it is impossible to determine whether they are fresh or not. A challenging algorithm and highly sensitive sensors are needed to predict the labels and freshness of spices and herbs based primarily on their smell. In this paper, we present AI-powered spice recognition system (AISRS), which is made up of an array of 8 inexpensive BME688 digital tiny sensors are exploited to classify four different types of herbs and spices: clove, cinnamon, anise, and chamomile. The proposed eNose measures temperature, humidity, pressure, and gas concentrations for various types of spices and condiments. For every sort of class, we keep track of more than 10,000 readings. Through the use of assessment indexes at each level, we were able to determine whether or not algorithms such as k-NN, Random Forest, SVM, MLP, DT, and AdaBoost were successful. The Random Forest instantaneous classification algorithm performed the best among others where the success rate for predicting and differentiating between the four classes was better than 97 percent according to the validation data. These validation findings plus the eNose's low power consumption (0.05 W) make it possible for it to be improved and used in portable and battery-operated applications in the future. © 2023 IEEE.
Citation Count: 3
Real-time Automated License Plate Recognition and Tracking of Runaway Vehicles
(Institute of Electrical and Electronics Engineers Inc., 2022) Ramadan,M.N.A.; Hilles,S.M.S.; Alkhedher,M.; Ghazal,M.
We propose real-time vehicle plate number recognition in this study, which aids law enforcement in detecting and stopping wanted vehicles. The system is made up of smart devices that are dispersed throughout the county via major thoroughfares. A camera, an industrial computer, and a wireless RF module are included in each device. The system detects vehicle activity, then recognizes the plate number and matches it to the wanted list plat numbers uploaded to the server by the cops. When the system detects a wanted plate number, it sends an automatic signal to the next police checkpoint via a radio frequency RF module, allowing the officers to intervene and halt the car. Furthermore, without using GPS, the system can be used to track vehicles throughout the country. Each smart unit detects a vehicle license plate number and sends the information to the server, which records the information within the specified time range. Tracking all vehicles in the county and knowing each vehicle when it passes each road by verifying its history would be efficient. The system will assist police in instantly locating and tracking any desired vehicles around the county, saving time and effort by eliminating the need to study the history of video recording devices. The proposed method uses an 80-frame-per-second camera system to evaluate license plates from moving cars traveling at 100 km/h. In scenarios where more than a thousand car images are gathered and examined, our findings demonstrate that the system has a 98 percent recognition accuracy. © 2022 IEEE.