Majlesi Journal of Telecommunication Devices
Volume:12 Issue: 4, Dec 2023

Nowadays, in modern and developing countries, it is very difficult to live without continuous and reliable electric energy. After realizing the high importance of energy resources and power generation in power plants, its transmission and distribution in a safe, sustainable and high-quality manner became very important. In the past, most of the electrical energy was carried out at the low voltage level through aerial networks with copper wires, but in recent years due to problems such as the greater importance of accessibility, the importance of improving power quality, theft of copper wires due to the increasing price of copper, electricity theft, etc., the implementation of aerial wire networks is prohibited except in special cases in distribution companies, and the use of aerial bundled cables has been replaced instead. In this article, first by introducing the merits and demerits of the implementation of aerial bundled cables, economic study (profit and loss) and also the return period of the capital in a system have been investigated and at the end its efficiency or inefficiency for distribution companies has been studied.

In this paper, an improved decision tree classification method based on wild horse optimization algorithm is proposed and then the application in customer behavior analysis is evaluated. Customer behavior is modeled in the form of time series. The proposed method includes two general steps. First, the customers are classified into clusters based on the features extracted from the time series, and then the customers’ behavior is estimated based on an efficient predictive algorithm in the second step. In this paper, an improved decision tree classification based on wild horse optimization algorithm is used to predict customer behavior. The proposed method is implemented in the MATLAB software environment and its efficiency is evaluated in the Symmetric Mean Absolute Percentage Error (SMAPE) index. The experimental results show that variance, spikiness, lumpiness and entropy have a high impact intensity among the extracted features. The overall evaluation indicate that this proposed method obtains the lowest prediction error in compared to other evaluated methods.

The critical current density (J ) c of a superconductor with a high transition temperature is a fundamental quantity that determines the scope of the application of new superconductors in practice. Reports show that the critical transport current density of thin films of yttrium-based superconductors grown by different methods can range from the value106A/cm2` in temperature77k∘ to a value of the 107A/cm2order of4 ko. These values of current density provide the use of superconductors on a small scale in the electronic industry In this work, the dependence of the critical transfer current density of type II flat superconductor with a rectangular cross-section that is mixed in three magnetic fields that are applied perpendicular to the surface of the superconducting strip is investigated. The results of these calculations clearly show that (a)- as the thickness of the superconducting sample increases, the critical current density decreases (b)- the comparison of the results of the calculations of the application of three different fields indicates that with the increase of the field, it decreases.

Face recognition is one of the most important and challenging issues in computer vision and image processing. About half a century ago, since the first face recognition system was introduced, facial recognition has become one of the most important issues in industry and academia. In recent years, with the developing of computers throughput and developments of a new generation of hierarchical learning algorithms called deep learning, much attention has been devoted to solving learning problems by deep learning algorithms. Deep neural networks perform feature learning instead of feature extraction which by this strategy they are much useful for image processing and computer vision problems. Deep neural network through feature learning perform data representation well and have gained many successes in learning and complex problems, many studies have been done on the application of deep neural networks to face recognition and many successes has been achieved. In this study we examine the neural network based methods used for face recognition such as multilayer perceptrons, restricted Boltzmann machine and auto encoders. Most of our study devoted to convolutional neural network as one of the most successful deep learning algorithms. At the end we have examined the results of the encountered methods on ORL, AR, YALE, FERET datasets and show deep neural network has gained high recognition rate in comparing with benchmark methods.

This article compares the quality and complexity of LPC, CELP, and MELP standard audio encoders. These standards are based on linear predictive and are used in sound (speech) processing. These standards are powerful high-quality speech coding methods that provide highly accurate estimates of audio parameters and are widely used in the commercial (mobile) and military (NATO) communications industries. To compare LPC, CELP, and MELP audio encoders in two male and female voice modes and four voice models: quiet, Audio recorded without sound by the microphone, MCE, office, and two noise models 1% and 05% were used. The simulation results show the complexity of MELP is higher than LPC and CELP in terms of both processor and memory requirements. The MELP analyzer requires 72% of its total processing time. This additional memory is, of course, due to the vector quantization tables that MELP uses for the linear spectral frequencies (LSFs) and the Fourier magnitude. Also, according to the quality comparison test using the MOS index, MELP has the highest score, followed by CELP and LPC.

Breast cancer is the most common type of cancer among women worldwide. If diagnosed by a doctor in the early stages, it can save the patient's life. Ultrasound imaging is one of the most widely used diagnostic tools for diagnosing and classifying breast abnormalities. However, accurate segmentation of the ultrasound image is a challenging problem due to the artifacts created on the ultrasound image. Although deep learning-based methods have been able to overcome some of these challenges, the accuracy of tumor region detection in this image is still low. In this paper, we have proposed approaches for breast ultrasound image segmentation based on auto-encoder deep neural network. The proposed method has two parts. The classification section to determine the image with cancerous tissue and the tumor segmentation section to segment the desired area. which will be shown in the network output of the encoder itself. The proposed method has been evaluated qualitatively and quantitatively. The superiority of the proposed method with accuracy and dice criteria is 89 and 90 percent, respectively which shows the effectiveness of this method in diagnosis.