محمدرضا احمدزاده

From within the feminist movement, a field of scientific-critical studies arose, which became known as "feminist criminology" among criminologists. This field of criminology provided the basis and context for the study of women's delinquency and its consequences in particular. Among various issues, the situation of women prisoners is of particular importance. Due to the importance of this issue, the international community and specifically the United Nations approved special rules about this group of criminals, known as the "Bangkok Rules".

The present research was carried out using a descriptive and analytical method.

One of the basic needs of women prisoners is to ensure their mental health during their stay in prison and after leaving prison. In this article, an attempt has been made to examine the rules governing the mental health of this group of prisoners in UN documents in accordance with domestic regulations.

In various UN documents about prisoners, especially the Bangkok rules, a set of practical solutions are provided to support the mental health of female prisoners. These solutions include providing appropriate housing for imprisoned women, maintaining family and social ties, psychological counseling, treatment programs, and similar measures. In comparing the rules stipulated in these documents with the internal regulations, it turns out that there are many inadequacies in the internal regulations in this field.

Puppet shows are a popular form of television. From these programs, popular puppet characters have been created that have remained in the minds of the viewers, and the audience of many children and adults has become interested in some of these puppets. Among them are the Kolah Ghermezi puppets and Jenab Khan. How to create this popularity and the factors affecting it is the question on which the present study is based. The purpose of this study is to examine the various factors that create popularity for television puppets among the public and viewers, the extent and priority of these factors in the eyes of the audience to be studied and analyzed. The method of this research consists of two stages. First, a survey of the audience about the reasons for the attractiveness of the puppets, and then a semi-structured interview with experts in this field, in order to examine the causes and motivations in popular opinion. Therefore, in order to achieve the desired goals, considering the continuity of production, distribution and communication with the audience in recent years, two Kolah Ghermezi and Jenab Khan were selected as the study sample. The data show that various factors, from technical and artistic features to the production team and broadcasting conditions, affect the popularity of TV puppets, but it is understood that most people preferred the content of the puppet show to its shape.so the importance people place on voice acting and storytelling in puppet shows is important to puppet show creators.

Lung’s air volume estimation is of great importance in lung disease diagnosis. In this paper a fully automatic algorithm, which we presented recently to estimate the lung’s air volume from CT-images, is more developed. In this algorithm, first a suitable cost function is introduced based on the long parenchyma physics to determine the voxels of lung’s air region. In this paper, a fully automatic framework is proposed to calculate the initial guess for the solution of the optimization problem. Moreover, a 3D model reconstruction technique is utilized to determine spatial localization of the lung’s air region in 3D CT-images. Furthermore, the performance of the whole-lung-volume-based methods and direct lung’s air volume measurment methods are compared and investigated. In order to evaluate the accuracy, porcine’s lung images and clinical human’s lung images from reliable databases are fed to the proposed algorithm. The significant accuracy and robust performance of the proposed algorithm is illustrated with respect to the resolution reduction of CT-images.

This study aims to investigate academic learning centers globally and provide a comprehensive plan to establish the universities. In this study, the components and features of educational content, human resources, physical environment, organization, media and equipment have been studied and analyzed. The research method is descriptive - analytical and sampling is also targeted. The population included all academic learning centers in the world-class design and is available on the website. In addition, academic learning centers and Allameh Tabataba’i University in Tehran are a statistical population. Also, all experts working in universities, administrators, professors, and experts are among the target population. The number of specimens in the world is 8 academic learning centers. A research-made checklist was used to collect data. The results show that the difference between academic learning centers worldwide and academic learning centers in Iranian educational systems is meaningful, in terms of the physical environment is meaningful, in terms of staffing, meaningful, and in terms of the media is significant, materials and equipment, is insignificant. The plan to set up a center of academic learning and the design was presented to the university.

Tracking of flying objects via passive sensors is one of the basic requirementsfor surveillance and control.The uses of radar systems are expensive.In this paper the image is used as reference for locates the flying object.Proposed system consists of an infrared camera and an eye safe laser range finder which are mounted on a pan/tilt to track a flying object.The tracking data are sent to the trajectory analysis system which calculate the target's trajectory in real time.Tracking algorithm which is used in the proposed system improves the errors of other methods and can be implemented easily and has a good speed Automatic landing system is necessary for safer landing of a UAV.A more events of crash happen at the landing phase because of inexperience of pilots or sudden changes in the weather such as wind,rain.Automatic landing system requires continuous and precise positioning information.So, positioning device is one of the key factors of this system.Proposed system use image processing techniques for tracking of the UAVs in an automatic landing system.This system are inexpensive,passive and robust to jamming In this paper we propose an automatic landing system with accuracy of less than one meter which has covered many of the disadvantages of other methods and has a very lowcost compared to other methods Experiments show that the algorithm in the worst case lose about5% of the frames which does not harm auto landing system.These experiments were performed on real data and algorithm achieved accurate performance in the presence of cloud and background clutter.

Logistic regression models in classification problems by assuming the linear effects of covariates is a modeling for class membership posterior probabilities. The main problem that includes nonlinear combinations of covariates is maximum likelihood estimation (MLE) of the model parameters. In recent investigations، an approach of solving this problem is combination of neural networks، evolutionary algorithms and MLE methods. In this paper، another type of radial basis functions، namely inverse multiquadratic functions and hybrid method، are considered for estimating the parameters of these models. The experimental results of comparing the proposed models show that the inverse multiquadratic functions compared to the Gaussian functions have better precision in classification problems.

Locally Linear Model Tree (LOLIMOT) algorithm proposed by Nelles deals with local linear nearo-fuzzy models that is based on divides-and-conquer strategy that a complex modeling problem is divided to a number of smaller and thus simpler sub problems. So the characteristic of such a neuro-fuzzy model depends on division strategy for the original complex problem. For finding the best output the algorithm divides the problem to a number of local linear models (LLMs), then continues with finding the worst LLM and dividing it. LOLIMOT splits the local linear models into two equal halves with an axis-orthogonal decomposition strategy. In this paper a new approach based on extremeal optimization (EO) is used to optimize the structure of LOLIMOT. Simulation results show the effectiveness of the enhanced LOLIMOT to have a higher precision with optimal number of neurons.

This paper presents an algorithm for the segmentation of Blood Vessels from x-ray angiography of coronary artery images using Matched filters and Otsu thresholding. Identifying these arteries is a difficult process because of the vessel overlapping, and superimposition with various anatomical structures such as the ribs, the spine and the heart chambers. Firstly, the 3D Fourier and 2D Wavelet transforms are first used to eliminate the background and reduce noise in the images. Afterwards, a set of matched filters was applied to enhance the coronary arteries in the images. Finally with Otsu thresholding and 8-connected neighborhood pixels the segmented vessel was obtained.

In this paper a very intelligent tool with low computational complexity is presented for Electroencephalogram (ECG) signal classification. The proposed classifier is based on Discrete Wavelet Transform (DWT) and Probabilistic Neural Network (PNN). The novelty of this approach is that signal statistics, morphological analysis and DWT of the histogram of signal (density estimation) altogether have been used to achieve a higher recognition rate. ECG signals and their density estimation are decomposed into sub-classes using DWT. A PNN is used to classify ECG signals using statistical discriminating features which are extracted from ECG and its sub-classes. Experimental results on five classes of ECG signals from MIT-BIH arrhythmia database show that the proposed method learns very fast, low computational complexity, and a very high performance compared to the previous methods.

Breast cancer is one of the leading causes of deaths among women. Mammography is currently the best method for early detection. Due to the breast tissue type and different kinds of lesions, by using low dose x-ray in mammography, the detection of lesions in mammograms becomes very ambiguous and a tedious work. Early detection is the most effective ways to reduce the mortality rate. Our main aim in this paper is detection and recognition of tumors in digital mammograms. Mammograms usually have a large size so the processing of the entire mammogram takes a lot of time. To reduce the size and therefore the processing time and also to decrease the False Positive Rate, a two-step algorithm is used. At the first step some unimportant regions such as background and pectoral muscle are eliminated and at the second step an ROI detection algorithm is proposed which extracts the most likely regions to tumors. To recognize the tumors in the detected regions, some features are extracted from each region. To find the most effective features for tumor detection, several data mining feature extraction and feature selection methods are used and then compared. To increase the performance and reduce the number of features a GA based algorithm is proposed. Finally, SVM is used as our classifier, because it has the best results in comparison with other tools in our application. Experimental results show that the performance of proposed methods is better than other previous methods. The True Positive Rate using SVM is 94.59% and the False Positive Rate is 22.95%.