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All Journal International Journal of Electrical and Computer Engineering
Ajel, Ahmed R.
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Computer Science & IT Electrical & Electronics Engineering

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Journal : International Journal of Electrical and Computer Engineering

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Position and speed optimization of servo motor control through FPGA Ajel, Ahmed R.; Abdul Abbas, Huda M.; Mnati, Mohannad Jabbar
International Journal of Electrical and Computer Engineering (IJECE) Vol 11, No 1: February 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v11i1.pp319-327

Abstract

We have put our model in this paper in which we will be controlling the speed and direction of the servomotor through FPGA. So, as to guarantee the precision from the check control procedure, we have made a project in which the document provides the control plane associated with servo motor depending on Altera DE1 board gentle primary processor as program controller. The system utilizes FPGA since the primary gadget, as well as within Quartus II 10.0 program atmosphere. The associated control components aremade to type a good executable control program in which speed and direction will be controlled the servo motor performance. The particular handle signs from your handle method are usually separated and amplified which results in the push to appreciate the particular handle with the servo motor. Based on the features associated with Altera, it is expounded through 2 facets of equipment’s hardware as well as a software program that supplies an answer for that style associated with the servo control system. This particular document utilizes the actual PID control formula to manage the actual common screening device to attain versatile as well as precise control reasons. The actual equipment execution from the PID control formula is put in place through FPGA; precise as well as effective control program is built to enhance the speed and performance of the servomotor through FPGA.
Skin cancer classifier based on convolution residual neural network Ajel, Ahmed R.; Al-Dujaili, Ayad Qasim; Hadi, Zaid G.; Humaidi, Amjad Jaleel
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 6: December 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i6.pp6240-6248

Abstract

Accurate automatic classification of skin lesion images is a great challenge as the image features are very close in these images. Convolution neural networks (CNN) promise to provide a potential classifier for skin lesions. This work will present dermatologist-level classification of skin cancer by using residual network (ResNet-50) as a deep learning convolutional neural network (DLCNN) that maps images to class labels. It presents a classifier with a single CNN to automatically recognize benign and malignant skin images. The network inputs are only disease labels and image pixels. About 320 clinical images of the different diseases have been used to train CNN. The model performance has been tested with untrained images from the two labels. This model identifies the most common skin cancers and can be updated with a new unlimited number of images. The DLCNN trained by the ResNet-50 model showed good classification of the benign and malignant skin categories. The ResNet-50 as a DLCNN has verified a significant recognition rate of more than 97% on the testing images, which proves that the benign and malignant lesion skin images are properly classified.
Co-Authors Abdul Abbas, Huda M. Al-Dujaili, Ayad Qasim Hadi, Zaid G. Humaidi, Amjad Jaleel Mnati, Mohannad Jabbar