Mushreq Abdulhussain Shuriji

Vehicle license plate recognition is essential due to the rising number of operational cars, which leads to an increasing difficulty of this task even for humans. Systems for car license recognition normally consist of two branch systems, namely, license plate recognition and license plate detection. The aim of the detection part is to pinpoint the car and the position of its license plate, while the objective of the recognition part is to recognize characters on that plate. In this work, the emphasis is on Arabic car license plates. In this category of plates, there are three lines containing numerals and characters. The information in these lines represents the Arabic license plate group, the English license plate group, and the type of the car, respectively. Unlike a single line license plate, in this research, we propose a two-phase training and recognition technique. First, the three lines are segmented for the purpose of training by means of deep learning (Inception-v3 and MobileNets). The second phase comprises segmenting Arabic letters and numerals in the corresponding line and then training. These trainings result in different models which are then deployed for the whole license plate testing. Training and testing were executed on the license plate image. These models are then used for license plate (LP) image deployment, which has diverse angle positioning. The accuracy rates of this technique for character recognition (CR) and line recognition (LR) were 90.81% and 95.42% respectively.

1. The backbone of all wireless communication systems is the antenna. It has the ability to establish communication between skilled devices wirelessly. As a result, the antenna has advanced signifi...

A reconfigurable patch loaded via a switching radio frequency (RF) diode has been designed, investigated, and fabricated. The fabricated microstrip is able to adjust among several frequencies. A spectrum analyzer is used to test and measure different modes of operations. The proposed antenna is offered a new solution of uncomplicated and incomplex design via eliminating the biasing circuit while maintaining the finest performance. Two fast-switching diodes have been inserted as a new electrical reconfiguration technique. Thus, the suggested antenna is skilled to function in different bands. An ultra-small size antenna of 31×21 mm2 has offered various operating frequencies such as S-band at 2.4 GHz, C-band at 4.97 GHz, 5.06 GHz, 5.18 GHz, 5.1 GHz, and 7.94 GHz, X-band at 10.73 GHz, 10.91 GHz, and 11.9 GHz, Ku-band at 13.52 GHz, 14.78 GHz, 14.97 GHz, 15.63 GHz, 16.4 GHz, 17.3 GHz, and 17.48 GHz, and K-band at 19 GHz. The manufactured reconfigurable patch antenna is proficient for several wireless technologies for instance industrial scientific medical band (ISM): Bluetooth, wireless-fidelity (Wi-Fi), ZigBee, internet of things (IoT), WiMAX, and smart power meters. Also, long term evolution (LTE), 5G applications, fixed wireless systems (FWS), and satellite communication applications.

A new frequency reconfigurable microstrip patch antenna based on implanting tuning capacitors

Robot swarming, increasingly find importance in the last decade. In these systems, multi mobile robots have to work cooperatively to perform specified tasks. One of the compelling problems is that the robots movements should be in such a way that they should follow a specific guide and at the same time they should have the ability of obstacle avoidance. Inspiriting such movement from biological swarms is a compelling problem. Fish schools, bird flocks and sheep herds are particular examples of biological systems swarming. In this paper, a robot swarming algorithm was developed based on swarming rules noticed in these biological systems, the combination between the swarm members and the leadership control also explained, an ad-hoc non-essential communication system was proposed for the purpose of use in case of collective takeoff and collective landing swarm-robots, in which activated automatically.