Al-Furat Journal of Innovations in Electronics and Computer Engineering

This paper has been studying two current control techniques for the two stages single-phase grid-tied photovoltaic (PV) inverter. These control techniques are Sinusoidal pulse width modulation (SPWM) and Hysteresis current. The whole structure of the grid-tied PV inverter has been studied in details for each of its parts. For comparison between the two techniques, the designed system has been tested under MATLAB/Simulink environment for different cases such as insolation and load variation. Results found that the Hysteresis current control method has a simple structure where it does not need PWM block, but it produces a higher total harmonic distortion (THD) as compared with the SPWM method.

With the rapid development of information technology, the EEG has become an increasingly popular method for analyzing brain signals. EEG signals greatly contribute to(BCI) brain-computer communication . BCI outputs are used to restore many functions in relation to individuals with a motor impairment, and these signals are complex random signals produced from hundreds of millions of neurons in EEG mixtures in the brain that contain many data about brain activity. its include artefacts which have a great influence on the diagnosis; Hence, these unwanted signals become the most important problem in EEG signal analysis. Therefore, used four blind source separation techniques (BSS), STONE, FICA, BEFICA and EFICA. The proposed system is using one of the new Antlion (ALO) optimization algorithms to improve the performance of the previous algorithms and find out which ones are the most responsive, by comparing them and choosing the best ones according to the (PSD) standard. With the use of real data, noticed through the results that the EFICA algorithm is the best response to the improvement and the most efficient, as the ratio of (PSD) was the least possible, as ALO worked to reduce the variance in the distribution of the frequency spectrum because it relied on solving constrained problems using various searches.

The skin is one of the first lines of defence against environmental influences such as sunlight, bacteria and germs, which cause various skin diseases. In addition to the bad psychological and physical impact caused by skin disease. So, in recent years, many artificial intelligence (AI) algorithms have appeared that can recognize Images, through which skin diseases can be diagnosed, avoiding traditional methods that rely on visual examination and self-evaluation based on experience. The paper aims to classify a group of skin diseases according to the type of disease, such as Atopic Dermatitis, Dyshidrotic Eczema and Nummular Dermatitis using a deep belief network algorithm (DBN) that was built to suit the work. A global dataset was also used, obtained from the Kaggle website, and after conducting experiments on it, the algorithm achieved high accuracy in diagnosing diseases, the percentage reached 98.773%. It is possible in the future to use the network to classify other types of diseases after providing it with a large number of images of affected people.

The workflow approach is a standard for displaying processes and their implementation process. With the advent of electrical sciences, more cumbersome workflows were created with more processing requirements. New distributed systems, such as grid systems and computing clouds, allow users to access heterogeneous resources that are geographically located at different points and execute their workflow tasks. Therefore, the simultaneous receipt and execution of several workflows is obvious. As a result of discussing scheduling algorithms, it is necessary to consider arrangements for implementing multiple workflows on a shared resource set. Improving the execution of multiple workflows can accelerate the process of obtaining results when sending processes to the cloud. In this paper, we first discuss the classification of multiple workflow scheduling algorithms, and then briefly describe the scheduling algorithms for the cloud environment, and finally, the algorithms of papers were compared with each other.

Colorization: Converting a grayscale image into an image with colors acceptable to the viewer and achieving the minimum coloration goal until it becomes closer to reality. The goal of the coloring process is to produce a convincing image and the result is close to the reality of the colored image. Converting grayscale images to color images is a very difficult process due to the specificity of grayscale images and how closely they match color in every detail of the image. Coloring methods have evolved to become less invasive and produce more acceptable results to the viewer's eye, but they have become more difficult. This research provides an overview of image colorization methods, their advantages and disadvantages, and compares the quality of each method according to specific criteria. Coloring methods have evolved from primitive methods based on the manual principle to using computing techniques such as adding scribbles within areas of the image, then moving to choosing a reference image that can be used to obtain colors, to using deep learning techniques that rely on training a model on colored images and then testing this model and showing Methods based on deep learning have better results than other methods, and the algorithm showed GAN outstanding results in coloring that outperform some deep learning coloring methods, and also outperform other methods. We hope that this study will be a scientific product that will benefit the research community and enable them to obtain a useful overview of the most important methods of coloring images.

There are lot of challenges when analyzing the brain signals that did not yet have a basic solution, as there are electrical activities between neurons in the brain, related to all activities in the body. This activity can be seen using a non-surgical technique that is called EEG (i.e. Electroencephalography) such as the appearance of artifacts through the registration process, which increases the difficulty of analyzing the signals of the brain, so the technique of blind source separation (BSS) has been used to overcome the problem of artifacts and to separate the main sources (Mixed) without making noise around the original sources.. Therefore, the system for rejecting all artifacts based on the algorithms for separating the blind source has been proposed, by making a comparison between four separation algorithms and choosing the best ones according to criteria. After passing a data set simulated through those criteria, the proposed system can remove the artifacts including Electrocardiogram (ECG), Electrooculogram (EOG) as well as a power line noise interference (LN)) and other EEG mixtures. The proposed method's influence is checked by two performance indexes Interference to signal ratio (ISR) and (SNR) signal to noise ratio. The results indicated that the BEFICA algorithm is the best and most efficient, as it achieved the highest ratio of VSNR among four separation algorithms due to its developmental advantages.

By huge advancements which are technological as well as digital assistance increasing popularity in daily life, also environments of work which goes along with this, technologies are required for evolving the areas of application to next class. IoT determines next stage in technology filed, bringing different alternations in development of medicine, industry, environmental care, urban. Networks of IoT attends to re-utilize the methods that are deployed for the Internet like routing protocols. Although, since the networks of IoT include devices with poor processing power, for suiting by the network modern kind, methods utilized Internet are needed modern methods, mechanisms, protocols, even adjustment, are offered. In this paper, the comprehensive and complete study of routing protocols in IoT-based healthcare systems is examined and presented.

Melanoma is considered to be a type of skin cancer that is characterised by symptoms of poor prognostic responses. In this paper, we propose a deep learning method using DCNNs, modifying the output layer and enhancing the features of skin scan images collected from Kaggle to be distinguished into two groups: melanoma and non-melanoma cells. The proposed modified three types of DCNN (MobilNet-v2, ResNet-18 and Squeeze Net) models have been tested in two experiments. In the first, the obtained values of training accuracy are (93, 95 and 91) % and the testing accuracy is (90.09, 90.54 and 90.4) %, using original datasets only. In the second experiment, the obtained values of training accuracy are (99.7, 96.3 and 92) % and the testing accuracy is (94.41, 94.14 and 91.43) %, The experimental findings show that the model utilized produces enhanced photos with more accuracy than original images.