About the journal
Established in 2012, Majlesi Journal of Telecommunication Devices (MJTD) has been publishing original papers in all aspects of Telecommunication Engineering from the theoretical modeling and design in telecommunications and high-frequency electronics to practical relevant devices and systems. The journal is devoted primarily to research papers, but high-quality survey and tutorial papers are also published. All papers are checked for plagiarism by iThenticae or turnitin and are peer-reviewed by our international editorial board and reviewers. At MJTD we maintain the highest ethical standards in compliance with the Committee on Publication Ethics (COPE). To learn more you may refer to the MJTD publication ethics.
Recent Articles
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Open Access Article
1 - Technical and Economic Investigation of the use of Aerial Bundled Cables in the Electricity Distribution Network
Mehrdad MollaNorooziIssue 4 , Vol. 12 , Autumn 2023Nowadays, 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, sustain MoreNowadays, 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. Manuscript profile -
Open Access Article
2 - An Improved Decision Tree Classification Method based on Wild Horse Optimization Algorithm
raheleh sharifi Mohammadreza RamezanpourIssue 4 , Vol. 12 , Autumn 2023In 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 includ MoreIn 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. Manuscript profile -
Open Access Article
3 - The effect of sample thickness on the critical current density of the superconducting strip
Rasool GhanbariIssue 4 , Vol. 12 , Autumn 2023The critical current density 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 yttri MoreThe critical current density 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 value in temperature to a value of the order 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. Manuscript profile -
Open Access Article
4 - A Survey on Face Recognition Based on Deep Neural Networks
mohsen Norouzi Ali ArshaghiIssue 4 , Vol. 12 , Autumn 2023Face 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 MoreFace 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. Manuscript profile -
Open Access Article
5 - Comparison of Standards Digital Audio Encoders LPC, CELP, and MELP based on the Quality and Complexity of the Content in the Transmitted
Saeed Talati Pouriya Etezadifar Mohammad Reza Hassani Ahangar Mahdi MolazadeIssue 4 , Vol. 12 , Autumn 2023This 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 MoreThis 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. Manuscript profile -
Open Access Article
6 - Detection and Segmentation of Breast Cancer Using Auto Encoder Deep Neural Networks
Ageel Abed Mehran EmadiIssue 4 , Vol. 12 , Autumn 2023Breast 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. H MoreBreast 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. Manuscript profile
Most Viewed Articles
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Open Access Article
1 - Comparison of Standards Digital Audio Encoders LPC, CELP, and MELP based on the Quality and Complexity of the Content in the Transmitted
Saeed Talati Pouriya Etezadifar Mohammad Reza Hassani Ahangar Mahdi MolazadeIssue 4 , Vol. 12 , Autumn 2023This 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 MoreThis 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. Manuscript profile -
Open Access Article
2 - Road Detection with Deep Learning in Satellite Images
Zohreh DorraniIssue 1 , Vol. 12 , Winter 2023Road detection from high-resolution satellite images using deep learning is proposed in this article. The VGG19 architecture, which is one of the deep convolutional neural network architectures, is used in the proposed method. To detect the road, two steps are implement MoreRoad detection from high-resolution satellite images using deep learning is proposed in this article. The VGG19 architecture, which is one of the deep convolutional neural network architectures, is used in the proposed method. To detect the road, two steps are implemented. To achieve high accuracy, image segmentation is done in the first step. At this stage, based on the semantic division, the objects whose area is small are removed. In the second stage, edge detection of images combines two techniques of segmentation and edge detection to improve road detection. Considering the good accuracy of the VGG19 architecture and the need for few parameters, the obtained results are favorable. To check the performance of the proposed method, the IoU criterion was used. The values obtained for this criterion show an improvement of more than 80%. While this criterion is less than 80% for the compared methods. The obtained results can be used for the purposes of digital mapping, transportation management and many other applications. Manuscript profile -
Open Access Article
3 - Technical and Economic Investigation of the use of Aerial Bundled Cables in the Electricity Distribution Network
Mehrdad MollaNorooziIssue 4 , Vol. 12 , Autumn 2023Nowadays, 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, sustain MoreNowadays, 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. Manuscript profile -
Open Access Article
4 - Honeypot Intrusion Detection System using an Adversarial Reinforcement Learning for Industrial Control Networks
Abbasgholi Pashaei Mohammad Esmaeil Akbari Mina Zolfy Lighvan Asghar CharminIssue 1 , Vol. 12 , Winter 2023Distributed Denial of Service (DDoS) attacks are a significant threat, especially for the Internet of Things (IoT). One approach that is practically used to protect the network against DDoS attacks is the honeypot. This study proposes a new adversarial Deep Reinforcemen MoreDistributed Denial of Service (DDoS) attacks are a significant threat, especially for the Internet of Things (IoT). One approach that is practically used to protect the network against DDoS attacks is the honeypot. This study proposes a new adversarial Deep Reinforcement Learning (DRL) model that can deliver better performance using experiences gained from the environment. Further regulation of the agent's behavior is made with an adversarial goal. In such an environment, an attempt is made to increase the difficulty level of predictions deliberately. In this technique, the simulated environment acts as a second agent against the primary environment. To evaluate the performance of the proposed method, we compare it with two well-known types of DDoS attacks, including NetBIOS and LDAP. Our modeling overcomes the previous models in terms of weight accuracy criteria (> 0.98) and F-score (> 0.97). The proposed adversarial RL model can be especially suitable for highly unbalanced datasets. Another advantage of our modeling is that there is no need to segregate the reward function. Manuscript profile -
Open Access Article
5 - A Survey on Face Recognition Based on Deep Neural Networks
mohsen Norouzi Ali ArshaghiIssue 4 , Vol. 12 , Autumn 2023Face 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 MoreFace 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. Manuscript profile -
Open Access Article
6 - An Iterative Method for ASC Hybrid Precoding Structure for mmWave Ma-MIMO Systems
Amirreza Moradi Kamal Mohamed-pour Nasim Jafari FarsaniIssue 3 , Vol. 11 , Summer 2022The use of millimeter wave (mmWave) massive multiple input multiple output (Ma-MIMO) systems makes it possible to meet the essential needs of future generation wireless systems and solve the impending wireless network crisis. The mmWave Ma-MIMO Technique offers higher n MoreThe use of millimeter wave (mmWave) massive multiple input multiple output (Ma-MIMO) systems makes it possible to meet the essential needs of future generation wireless systems and solve the impending wireless network crisis. The mmWave Ma-MIMO Technique offers higher numbers of antennas and carrier frequencies. Hybrid precoding is considered as a key technique for the practical deployment of mmWave Ma-MIMO systems, since it significantly decreases the implementation costs, energy consumption, and hardware complexity. The large using of mmWave Ma-MIMO technologies in future generation wireless systems, causes imperative develop cost-effective hybrid precoding solutions that match the various application cases of these systems. The fully-connected structure can offer spectral efficiency (SE) close to the fully-digital precoding but, unfortunately with high energy consumption. Furthermore, the sub-connected structure with reduced power consumption, provides poor SE. Therefore, the trade-off between SE and energy efficiency (EE), can be made, and in this paper, we consider an adaptive sub-connected (ASC) hybrid precoding structure, where a switch network is able to provide dynamic connections from phase shifters to radio frequency (RF)chains. The simulation results indicate that in terms of SE, the proposed algorithm with ASC structure obtains higher performance than the sub-connected structure. As a result, since the ASC structure reduces the number of phase shifters, it can offer a better EE compared to the sub-connected structure. Manuscript profile -
Open Access Article
7 - Investigation of Steganography Methods in Audio Standard Coders: LPC, CELP, MELP
Saeed Talati Pouria EtezadiFar Mohammad Reza Hassani Ahangar Mahdi MolazadeIssue 1 , Vol. 12 , Winter 2023Information security is currently one of the most important issues that have been considered by many researchers. The purpose of Steganography is to hide hidden messages in a non-secret file. In general, information Steganography is a method of secure communication that MoreInformation security is currently one of the most important issues that have been considered by many researchers. The purpose of Steganography is to hide hidden messages in a non-secret file. In general, information Steganography is a method of secure communication that aims to hide data so that no data appears to be hidden. The principle of Steganography is to use spaces from the information carrier that do not harm the identity of the carrier. By Steganography information from unauthorized recipients, the information is hidden and hidden inside it without harming the signals. This information may be transmitted around us and wherever any file is sent. It may contain very dangerous content for the security of the space in which we live. Audio signals are very used for steganography because Digital audio signals have higher redundancy and higher data transfer speeds, making them suitable for use as a cover. The LPC10, CELP, and MELP audio standards are widely used in audio and speech processing and are powerful high-quality speech coding methods that provide highly accurate estimates of audio parameters and are widely used in communications. Therefore, since considering that these audio standards are used in commercial and military telecommunication systems, they can be considered a suitable platform for sending the following message of audio content. We try to carefully examine these standards and the audio Steganography done in these standards. Manuscript profile -
Open Access Article
8 - Detection and Segmentation of Breast Cancer Using Auto Encoder Deep Neural Networks
Ageel Abed Mehran EmadiIssue 4 , Vol. 12 , Autumn 2023Breast 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. H MoreBreast 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. Manuscript profile -
Open Access Article
9 - An Improved Decision Tree Classification Method based on Wild Horse Optimization Algorithm
raheleh sharifi Mohammadreza RamezanpourIssue 4 , Vol. 12 , Autumn 2023In 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 includ MoreIn 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. Manuscript profile -
Open Access Article
10 - Improving the speed and accuracy of arrhythmia classification based on morphological features of ECG signal
Kamran Dehgany habib abadi Mohammad YousefiIssue 4 , Vol. 9 , Autumn 2020ECG cardiac signals play a crucial role in determining heart disease. Somehow, by knowing the heart rate on the ECG, one can distinguish the type of arrhythmia and the type of disease. Arrhythmias are a type of heart disease that affects the normal functioning of the he MoreECG cardiac signals play a crucial role in determining heart disease. Somehow, by knowing the heart rate on the ECG, one can distinguish the type of arrhythmia and the type of disease. Arrhythmias are a type of heart disease that affects the normal functioning of the heart. The electrical activity of the heart is shown at the peaks of P, QRS, T, and the ST and PR sections. In this study, an effective method for identifying cardiac arrhythmias based on morphological features is presented. The extracted features are classified using SVM and KNN classification and random forest RF. Accuracy, sensitivity, positive predictive rate, negative predictive rate as well as execution time were used to evaluate the proposed method. The results show the superiority of the proposed method. Manuscript profile
