Raspberry Pi 4 and Python Based on Speed and Direction of DC Motor

Habil, Hayder Jasim; Al-Jarwany, Q. A.; Hawas, Majli Nema; Mnati, Mohannad Jabbar

doi:10.1109/gpecom55404.2022.9815716

Cited by 3 publications

(1 citation statement)

References 16 publications

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“…The extraction of a vehicle's license plate number from an initial image will be carried out in this phase utilizing a Raspberry Pi 4 and a camera with a spatial resolution of 2,592×1,944 pixels. Figure 2 shows the hardware utilized in this study [13], [14]. Any ordinary image contains a lot of unnecessary details, such as noise and background texture.…”

Section: Hardware Descriptionmentioning

confidence: 99%

License plate recognition in slow motion vehicles

Aljelawy

Salman

2023

Bulletin EEI

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The recognition of license plate numbers represents one of the most efficient techniques to identify any individual vehicle. The principle of the system is that the detection of the license plate will be done with two techniques first you only look once (YOLO) and cascade classifier. Then after achive correct detection, the system will send the result (the image of the license plate) to Easy optical character recognition (OCR) library to read it and transform the image into text. In this paper, an analytical study of the surveillance system which affects by parallax due to camera movement has been done, by merging the OCR technique with the attached camera using python aided Raspberry Pi. The hardware system has been designed and implemented.

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Section: Hardware Descriptionmentioning

confidence: 99%

License plate recognition in slow motion vehicles

Aljelawy

Salman

2023

Bulletin EEI

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Speed Control of BLDC Motor: Design and Simulation Based on Neural Network

Hamad,

Al-Jarwany,

Sabbar

et al. 2023

2023 2nd International Engineering Conference on Electrical, Energy, and Artificial Intelligence (EICEEAI)

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Skill Acquisition and Controller Design of Desktop Robot Manipulator Based on Audio–Visual Information Fusion

Chen

et al. 2022

Machines

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The development of AI and robotics has led to an explosion of research and the number of implementations in automated systems. However, whilst commonplace in manufacturing, these approaches have not impacted chemistry due to difficulty in developing robot systems that are dexterous enough for experimental operation. In this paper, a control system for desktop experimental manipulators based on an audio–visual information fusion algorithm was designed. The robot could replace the operator to complete some tedious and dangerous experimental work by teaching it the arm movement skills. The system is divided into two parts: skill acquisition and movement control. For the former, the visual signal was obtained through two algorithms of motion detection, which were realized by an improved two-stream convolutional network; the audio signal was extracted by Voice AI with regular expressions. Then, we combined the audio and visual information to obtain high coincidence motor skills. The accuracy of skill acquisition can reach more than 81%. The latter employed motor control and grasping pose recognition, which achieved precise controlling and grasping. The system can be used for the teaching and control work of chemical experiments with specific processes. It can replace the operator to complete the chemical experiment work while greatly reducing the programming threshold and improving the efficiency.

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Raspberry Pi 4 and Python Based on Speed and Direction of DC Motor

Cited by 3 publications

References 16 publications

License plate recognition in slow motion vehicles

License plate recognition in slow motion vehicles

Speed Control of BLDC Motor: Design and Simulation Based on Neural Network

Skill Acquisition and Controller Design of Desktop Robot Manipulator Based on Audio–Visual Information Fusion

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