جامعة بلاد الرافدين - Bilad Alrafidain University - Noor aldeen

م.د نور الدين عباس خالد

Noor aldeen

تدريسي : هندسة تقنيات الاجهزة الطبية

Teaching : DEPARTMENT OF MEDICAL INSTRUMENTS ENGINEERING TECHNIQUES

دكتوراه

PhD

dr.nooraldeen@bauc14.edu.iq

nooraldeen4561@gmail.com

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Research

	2024	Bulletin of Electrical Engineering and Informatics

	2024	Journal of Advanced Research in Applied Sciences and Engineering Technology
"ABSTRACT In agricultural research, a fruit's appearance, which affects its market value, is the first and most crucial sensory evaluation. Based on external criteria like characteristics of shape and size, they can be categorized and rated during the post-harvest management. However, the mango processing industry still faces significant challenges due to the largely manual post-harvest processing of mangos. Manual grading can be inconsistent, erroneous, and labor-intensive. To address this issue, researchers have explored the use of image processing and machine learning techniques to automate the grading and classification process. This paper implements a proposed system that uses fruit shape, uniformity, and size as feature parameters for evaluating Harumanis mango quality. This system was able to identify the irregularity of the mango shape and its estimated mass. A morphological analysis, median filter, and multilevel threshold-based image processing technique were created to assess the geometric shape of the mango image, including its length, width, and area. These attributes are utilized to assess the mass and categorize its size into four classifications: small (S), medium (M), large (L), and extra-large (XL). Fourier descriptors and shape parameters were employed to characterize the mango's morphology. Stepwise discriminant analysis identified variables that effectively"

	2024	Online and Biomedical Engineering
"Object detection and recognition play a crucial role in computer vision applications, ranging from security systems to autonomous vehicles. Deep learning algorithms have shown remarkable performance in these tasks, but they often require large, annotated datasets for training. However, collecting such datasets can be time-consuming and costly. Data augmentation techniques provide a solution to this problem by artificially expanding the training dataset. In this study, we propose a deep learning approach for object detection and recognition that leverages data augmentation techniques. We use deep convolutional neural networks (CNNs) as the underlying architecture, specifically focusing on popular models such as You Only Look Once version 3 (YOLOv3). By augmenting the training data with various trans-formations, such as rotation, scaling, and flipping, we can effectively increase the diversity and size of the dataset. Our approach not only improves the robustness and generalization of the models but also reduces the risk of overfitting. By training on augmented data, the models can learn to recognize objects from different viewpoints, scales, and orientations, leading to improved accuracy and performance. We conduct extensive experiments on benchmark datasets and evaluate the performance of our approach using standard metrics such as pre-cision, recall, and mean average precision (mAP). The experimental results demonstrate that our data augmentation-based deep learning approach achieves superior object detection and recognition accuracy compared to traditional training methods without data augmentation. We compare the average accuracy of the YOLOv3-SPP model with two other variants of the YOLOv3 algorithm: one with a feature extraction network consisting of 53 convolutional layers and the other with 13 convolutional layers. The average accuracy of the proposed model (YOLOv3-SPP) is reported as accuracy of 97%, F1-score of 96%, precision of 94%, and average Intersection over Union (IoU) of 78.04%."

	2024	Electrical Systems
There are around 285 million people worldwide who have a visual impairment or are blind. This has a significant impact on their abilities to interact with their surroundings, reducing their ability to work and produce and making their daily lives difficult. There are many technologies working to provide solutions to these problems, one of which is the "blind protection system from surrounding obstacles", a device developed to help them interact more efficiently with the environment by avoiding obstacles and traveling. By wearing it on the body parts, it will alert the user by audible sounds due to ultrasound wave sensors connected to modern microchip technology called Arduino Pro Mini and piezoelectric buzzers that convert electric signals to sound waves.

	2023	Ieee
In recent years, there have been many proposals to improve the performance of traditional linear discriminant analysis (LDA). One of these is subset-improving linear discriminant analysis (S-LDA), which is based on clustering the whole set of classes into subsets and performing the LDA locally on these subsets. However, this algorithm suffers from an improper mapping of the classes to the corresponding subset during the testing procedure due to the inevitable discrepancy between the images used for training and those for testing. This discrepancy is caused either by spatial distortion or noise. The wrong mapping is severe when the number of data samples is small which is a common scenario in biometric datasets. In this paper, overlapping clustering is proposed for class clustering, to overcome the aforementioned problem. The proposed algorithm outperforms the S-LDA by 35.4% in mapping accuracy when using the PolyU palmprint database, 36.96%, resp. 33.92% when using left resp. Right palm images from the IIT Delhi Touchless Palmprint Database

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