Journal of Circuits, Data and Systems Analysis: 1 (1)

Open Access Article

1 - Infant Brain Image Segmentation using the Convolutional Neural Networks
Iran Sarafraz Hamed Agahi2 Azar Mahmoodzadeh

In this paper, a method based on convolutional neural networks for segmenting neonatal brain images is presented. One of the major challenges in neonatal brain image segmentation is the intensity distribution overlapping between gray matter and white matter tissues, whi More

In this paper, a method based on convolutional neural networks for segmenting neonatal brain images is presented. One of the major challenges in neonatal brain image segmentation is the intensity distribution overlapping between gray matter and white matter tissues, which reduces the segmentation accuracy of these areas. To increase the intensity differentiation between brain tissues, this paper presents a pre-processing method based on convolutional neural networks that effectively increases the segmentation accuracy. To obtain the final segmentation result, another convolutional neural network is proposed which performs segmentation based on T1-T2 images. To evaluate the performance of the proposed method, two databases are used, which include magnetic resonance imaging of infants' brains. The results show the appropriate efficiency of the proposed method in segmenting brain tissues. Manuscript profile

Open Access Article

2 - Copy-move forgery detection techniques based on traditional methods in digital images
maryam attaie Azar Mahmoodzadeh

Image forgery is one of the most widely used fields in image processing, which has been widely studied and studied by researchers. There are different types of digital image forgery, copy-move forgery is one of the common examples, and it is very important to recognize More

Image forgery is one of the most widely used fields in image processing, which has been widely studied and studied by researchers. There are different types of digital image forgery, copy-move forgery is one of the common examples, and it is very important to recognize this type of forgery. In this review article, while introducing the concepts of copy-move image forgery, the steps, classification of detection methods and research bias in this field have been discussed. This article can open the way for image processing researchers in the process of detecting copy- move forgery. The authors' effort has been to explore all aspects of this process. Manuscript profile

Open Access Article

3 - Optimal Classification of Brain Tumors in MRI Images Using Deep Learning Techniques
Zohreh Arabi Omid Mahdiyar Mehdi Taghizadeh

Medical and biological imaging technologies provide valuable image information of the structure and function of an organ from the level of molecules to the whole body. The brain is the most complex organ in the body and is attracting increasing research attention with t More

Medical and biological imaging technologies provide valuable image information of the structure and function of an organ from the level of molecules to the whole body. The brain is the most complex organ in the body and is attracting increasing research attention with the rapid development of medical and biological imaging technologies. One of the most common brain diseases is the creation of abnormal tissue in brain cells, which leads to the formation of brain tumors. Since brain tumors are associated with a significant risk of death and the accurate and rapid prediction of this disease has a direct impact on the treatment process, therefore, in this research, a large number of brain tumor MRI imaging data was used to identify brain cancers and find a method. Deep learning techniques were used. Several deep learning models were used for automatic diagnosis, and the classification of three types of brain tumors, consisting of glioma, meningioma, and pituitary, was also done with these algorithms. Based on the results of the conducted tests, the best accuracy of the results obtained in this research was 96%, which was obtained by considering the ratio of 60% for training data and 40% for test data. Manuscript profile

Open Access Article

4 - Well-being Model of Power System with Photovoltaic Unit
Amir Ghaedi Hamid Keyvani Ayoub Alipour

Photovoltaic units convert solar energy into electricity. The power of these plants is dependent on the amount of solar radiation, and because the solar radiation is variable, the production power also changes over time, and its effect on various issues, including the o More

Photovoltaic units convert solar energy into electricity. The power of these plants is dependent on the amount of solar radiation, and because the solar radiation is variable, the production power also changes over time, and its effect on various issues, including the operation of the power system, should be investigated. In the operation of the power system, to maintain the balance of production and consumption, some reserve is considered. In the past, the amount of reserve was considered as a percentage of load or power, and in probabilistic methods it is calculated based on risk. In the well-being model of power system, both the probabilistic model and the empirical rules are used to determine the indices. This model is based on risk and the amount of reserve is higher than the capacity of the largest unit. In this paper, the well-being model of the power system with the presence of photovoltaic plants is obtained. For this purpose, the reliability model of the photovoltaic plant is obtained by considering the failure of the components and changes in solar radiation. The simulation results are also presented to investigate the impact of photovoltaic plants on well-being model indices Manuscript profile

Open Access Article

5 - Deep Learning Algorithms in Super-Resolution Images
Bahar Ghaderi Hamid Azad

Image super-resolution is one of the important image processing processes to increase the resolution of images and videos. In recent years, methods based on deep neural networks for super-resolution have seen significant progress. The aim of this paper is to provide a c More

Image super-resolution is one of the important image processing processes to increase the resolution of images and videos. In recent years, methods based on deep neural networks for super-resolution have seen significant progress. The aim of this paper is to provide a comprehensive review on recent developments in super-resolution image using deep learning approaches. In this article, while introducing the concepts of image super-resolution, the common deep learning algorithms for super-resolution and the applications of super-resolution have been investigated. In addition, the set of databases and evaluation criteria are described. This article can open the way for image processing researchers in the super-resolution process. The authors’ effort has been to explore all aspects of this process. Manuscript profile

Open Access Article

6 - Improving stability of microgrids using adaptive sliding mode controller
Mehdi Motevasel

Today, microgrids are an important part of smart distribution networks, which include all kinds of renewable energy production sources and can operate either connected or disconnected from the main grid. Microgrid controllers play the most important role for the satisfa More

Today, microgrids are an important part of smart distribution networks, which include all kinds of renewable energy production sources and can operate either connected or disconnected from the main grid. Microgrid controllers play the most important role for the satisfactory automatic operation and control of the microgrid during operation in grid-connected and islanded mode. By adjusting the production capacities of scattered energy sources, the grid controller can increase the stability of the microgrid in the event of a short circuit error, switching and other disturbances.In this article, the stability of the microgrid in the grid-connected state is investigated when disturbances and additional harmonics are applied as a result of disconnection and connection moment or when the state changes from the island state to the grid-connected state. For this purpose, a control scheme based on adaptive sliding model has been proposed. Also, to show the resistance and efficiency of the control system, the Lyapunov stability analysis method has been used. The simulation results show that the interference caused by the interference in the microgrid inverter and the disturbance effect have been removed and the tracking has been obtained with high accuracy Manuscript profile

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