Pub Date : 2021-09-21DOI: 10.1109/ICRAMI52622.2021.9585907
Abderrahmane Abbes, A. Ouannas, N. Shawagfeh
This work studies the synchronization of the fractional order discrete neural networks based on h-fractional difference operator. In addition, using simple linear control, it has been confirmed that two chaotic fractional discrete neural network achieve synchronized dynamics. Finally, numerical simulations are given in order to illustrate the results.
{"title":"Synchronization in Fractional Discrete Neural Networks Using Linear Control Laws","authors":"Abderrahmane Abbes, A. Ouannas, N. Shawagfeh","doi":"10.1109/ICRAMI52622.2021.9585907","DOIUrl":"https://doi.org/10.1109/ICRAMI52622.2021.9585907","url":null,"abstract":"This work studies the synchronization of the fractional order discrete neural networks based on h-fractional difference operator. In addition, using simple linear control, it has been confirmed that two chaotic fractional discrete neural network achieve synchronized dynamics. Finally, numerical simulations are given in order to illustrate the results.","PeriodicalId":440750,"journal":{"name":"2021 International Conference on Recent Advances in Mathematics and Informatics (ICRAMI)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123201205","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-09-21DOI: 10.1109/ICRAMI52622.2021.9585977
S. Bensaoucha
This paper presents an investigation study of seven Machine Learning Algorithms (MLAs) for Breast Cancer (BC) diagnosis. These algorithms are: Decision Tree (DT), Discriminated Analysis (DA), Naive Bayes (NB), Support Vector Machine (SVM), K Nearest Neighbor (KNN), Ensemble Methods (EMs) and Multi-Layer Perceptron (MLP) classifier. All of these algorithms are applied to the Wisconsin Diagnostic Breast Cancer (Diagnostic) (WDBC) dataset.The main objective of the study is to optimize the hyperparameters of each MLA in order to achieve the best BC classification. This process can also help to reduce the effort and time required for classification. For this reason, Bayesian optimization method is used in MATLAB software to select the hyperparameters values of the six first algorithms. In Python language, Grid search method is used to optimize the MLP hyperparameters. To demonstrate the effect of the optimization process, several predefined models with a corresponding optimized model are evaluated for each algorithm to diagnose the category of BC, whether benign or malignant. The maximum accuracy reported in this study is 96.52%, offered by SVM and MLP algorithms.
{"title":"Breast Cancer Diagnosis Using Optimized Machine Learning Algorithms","authors":"S. Bensaoucha","doi":"10.1109/ICRAMI52622.2021.9585977","DOIUrl":"https://doi.org/10.1109/ICRAMI52622.2021.9585977","url":null,"abstract":"This paper presents an investigation study of seven Machine Learning Algorithms (MLAs) for Breast Cancer (BC) diagnosis. These algorithms are: Decision Tree (DT), Discriminated Analysis (DA), Naive Bayes (NB), Support Vector Machine (SVM), K Nearest Neighbor (KNN), Ensemble Methods (EMs) and Multi-Layer Perceptron (MLP) classifier. All of these algorithms are applied to the Wisconsin Diagnostic Breast Cancer (Diagnostic) (WDBC) dataset.The main objective of the study is to optimize the hyperparameters of each MLA in order to achieve the best BC classification. This process can also help to reduce the effort and time required for classification. For this reason, Bayesian optimization method is used in MATLAB software to select the hyperparameters values of the six first algorithms. In Python language, Grid search method is used to optimize the MLP hyperparameters. To demonstrate the effect of the optimization process, several predefined models with a corresponding optimized model are evaluated for each algorithm to diagnose the category of BC, whether benign or malignant. The maximum accuracy reported in this study is 96.52%, offered by SVM and MLP algorithms.","PeriodicalId":440750,"journal":{"name":"2021 International Conference on Recent Advances in Mathematics and Informatics (ICRAMI)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128889181","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-09-21DOI: 10.1109/ICRAMI52622.2021.9585900
Boucetta Aldjia, Boussaad Leila
In this paper, a new multi-modal biometric identification system is proposed using a Convolutional neural network (CNN), in which we make an early fusion (sensor level fusion) of face, palmprint, and iris by stacking the three biometric like RGB channels of an image, then used as input to CNN. This approach uses four popular pretrained deep-convolutional neural network (CNN) models, which are Inceptionv3, GoogleNet, ResNet18, and SqueezeNet, to make a robust and fast classification. Also, it avoids training a new model from scratch that needs lots of data and calculations. So, we explore the pretrained deep-convolutional neural network by two strategies: feature extraction and fine-tuning. In the first strategy, the pre-trained deep-convolutional neural network (CNN) models are used as feature extractors, and in the second one, the pretrained SqueezeNet model is adopted to our task with 152 classes instead of the ImagenNet classification with 1000 classes. The experimental results of the proposed multi-modal biometric system achieve promising accuracy.
{"title":"Sensor Level Fusion for Multi-modal Biometric Identification using Deep Learning","authors":"Boucetta Aldjia, Boussaad Leila","doi":"10.1109/ICRAMI52622.2021.9585900","DOIUrl":"https://doi.org/10.1109/ICRAMI52622.2021.9585900","url":null,"abstract":"In this paper, a new multi-modal biometric identification system is proposed using a Convolutional neural network (CNN), in which we make an early fusion (sensor level fusion) of face, palmprint, and iris by stacking the three biometric like RGB channels of an image, then used as input to CNN. This approach uses four popular pretrained deep-convolutional neural network (CNN) models, which are Inceptionv3, GoogleNet, ResNet18, and SqueezeNet, to make a robust and fast classification. Also, it avoids training a new model from scratch that needs lots of data and calculations. So, we explore the pretrained deep-convolutional neural network by two strategies: feature extraction and fine-tuning. In the first strategy, the pre-trained deep-convolutional neural network (CNN) models are used as feature extractors, and in the second one, the pretrained SqueezeNet model is adopted to our task with 152 classes instead of the ImagenNet classification with 1000 classes. The experimental results of the proposed multi-modal biometric system achieve promising accuracy.","PeriodicalId":440750,"journal":{"name":"2021 International Conference on Recent Advances in Mathematics and Informatics (ICRAMI)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121832750","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-09-21DOI: 10.1109/ICRAMI52622.2021.9585996
Sourour Maalem
Wireless sensor networks (WSNs) are a new type of technology that aspires to provide new capabilities and solutions. Their use is continuing to increase in numerous areas. However, the scarce resources of the sensor nodes ought to be considered, mainly in terms of energy efficiency. As a result, one of the most pressing concerns in WSNs is the development of an energy-efficient routing system to extend network lifetime. One way to achieve energy efficiency would be using a clustering technique. In this work, we propose an approach based on computational intelligence to deal with the problem of sensor nodes clustering in a WSN. Thus, the ultimate goal of reducing energy costs is to extend the network lifetime. In this context, a data routing protocol within a WSN is developed using a computational technique. The performance analysis shows that our proposed protocol GA-LEACHPEGASIS (Genetic Algorithm LEACH PEGASIS) is able to optimize the network lifetime by minimizing energy consumption compared to the well-known LEACH protocol.
{"title":"A Collective Intelligence-Based System to Improve Cluster Formation in Wireless Sensor Networks","authors":"Sourour Maalem","doi":"10.1109/ICRAMI52622.2021.9585996","DOIUrl":"https://doi.org/10.1109/ICRAMI52622.2021.9585996","url":null,"abstract":"Wireless sensor networks (WSNs) are a new type of technology that aspires to provide new capabilities and solutions. Their use is continuing to increase in numerous areas. However, the scarce resources of the sensor nodes ought to be considered, mainly in terms of energy efficiency. As a result, one of the most pressing concerns in WSNs is the development of an energy-efficient routing system to extend network lifetime. One way to achieve energy efficiency would be using a clustering technique. In this work, we propose an approach based on computational intelligence to deal with the problem of sensor nodes clustering in a WSN. Thus, the ultimate goal of reducing energy costs is to extend the network lifetime. In this context, a data routing protocol within a WSN is developed using a computational technique. The performance analysis shows that our proposed protocol GA-LEACHPEGASIS (Genetic Algorithm LEACH PEGASIS) is able to optimize the network lifetime by minimizing energy consumption compared to the well-known LEACH protocol.","PeriodicalId":440750,"journal":{"name":"2021 International Conference on Recent Advances in Mathematics and Informatics (ICRAMI)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122409811","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-09-21DOI: 10.1109/ICRAMI52622.2021.9585929
Nouioua Tarek, Belbachir Ahmed Hafid
Many researchers and laboratories have been engaged for years in a competition for a single objective, which is the quantum computer. Recently, Google has announced that it has achieved quantum supremacy with its quantum computer "Sycamore". According to Google, such a machine can make an astronomical quantity of calculations considerably faster than any conventional computer. A technology which could be a real revolution in several domains, Computing, Experimentation Technics, Artificial Intelligence, Medical, Chemical, Banking...etc. But, the main question is: when can it be a reality? In the present paper, we will explore quantum phenomena and explain principles of quantum computer especially the qubit (quantum bit), to finally explain whether or not the quantum computer is a reality and could it be a danger for the information systems? We will explore the subject by presenting existing works, and by defining the framework of laws to achieve a quantum computer that may solves all our possible problems.
{"title":"The Quantum Computer and the Security of Information Systems","authors":"Nouioua Tarek, Belbachir Ahmed Hafid","doi":"10.1109/ICRAMI52622.2021.9585929","DOIUrl":"https://doi.org/10.1109/ICRAMI52622.2021.9585929","url":null,"abstract":"Many researchers and laboratories have been engaged for years in a competition for a single objective, which is the quantum computer. Recently, Google has announced that it has achieved quantum supremacy with its quantum computer \"Sycamore\". According to Google, such a machine can make an astronomical quantity of calculations considerably faster than any conventional computer. A technology which could be a real revolution in several domains, Computing, Experimentation Technics, Artificial Intelligence, Medical, Chemical, Banking...etc. But, the main question is: when can it be a reality? In the present paper, we will explore quantum phenomena and explain principles of quantum computer especially the qubit (quantum bit), to finally explain whether or not the quantum computer is a reality and could it be a danger for the information systems? We will explore the subject by presenting existing works, and by defining the framework of laws to achieve a quantum computer that may solves all our possible problems.","PeriodicalId":440750,"journal":{"name":"2021 International Conference on Recent Advances in Mathematics and Informatics (ICRAMI)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128764857","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-09-21DOI: 10.1109/ICRAMI52622.2021.9585932
Boucetta Aldjia, E. Melkemi Kamal
This paper proposes a new approach of multi-spectral image compression based on the combination of the particle swarm optimization (PSO) and the discrete wavelet transforms (DWT). In the first stage, the PSO is used to reduce the redundancies in the spectral domain. In fact, the PSO transforms a given multispectral image to optimize the energy in the first band. Despite to the complexity of this kind of approach, the transformed multispectral image is easily computed by multiplying a de-correlation matrix and the input multispectral image. The de-correlation matrix is estimated via a PSO evolution derived by a defined fitness function. In the second stage, the compressed data, related to the input multispectral image, is computed from the transformed multispectral image using an efficient 2D-DWT based algorithm. In addition to this compression approach, the original multispectral image can be recovered using a decompression algorithm. Experimental results show the validity of our proposed approach. These significant results are evaluated according to Peak signal-to-noise ratio (PSNR), compression ratio (CR) and bits per pixel (bpp) metrics.
{"title":"Multispectral Images Compression using PSO-based De-correlation Matrix and DWT Transform","authors":"Boucetta Aldjia, E. Melkemi Kamal","doi":"10.1109/ICRAMI52622.2021.9585932","DOIUrl":"https://doi.org/10.1109/ICRAMI52622.2021.9585932","url":null,"abstract":"This paper proposes a new approach of multi-spectral image compression based on the combination of the particle swarm optimization (PSO) and the discrete wavelet transforms (DWT). In the first stage, the PSO is used to reduce the redundancies in the spectral domain. In fact, the PSO transforms a given multispectral image to optimize the energy in the first band. Despite to the complexity of this kind of approach, the transformed multispectral image is easily computed by multiplying a de-correlation matrix and the input multispectral image. The de-correlation matrix is estimated via a PSO evolution derived by a defined fitness function. In the second stage, the compressed data, related to the input multispectral image, is computed from the transformed multispectral image using an efficient 2D-DWT based algorithm. In addition to this compression approach, the original multispectral image can be recovered using a decompression algorithm. Experimental results show the validity of our proposed approach. These significant results are evaluated according to Peak signal-to-noise ratio (PSNR), compression ratio (CR) and bits per pixel (bpp) metrics.","PeriodicalId":440750,"journal":{"name":"2021 International Conference on Recent Advances in Mathematics and Informatics (ICRAMI)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122358365","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-09-21DOI: 10.1109/ICRAMI52622.2021.9585937
M. S. Rao, O. Pavan Kalyan, N. N. Kumar, Md. Tasleem Tabassum, B. Srihari
Classifying various music into its genre has a lot of applications in the real world. It plays an important role in several online music streaming services such as Gaana, Spotify etc. Most of the music recommender systems implement such feature. Over the past two decades music coming from various sources has been increasing at a high speed. Several musical communities are emerged based on the music genre. Therefore, in order to satisfy their requirements, the need for an automatic music genre classifier became evident. In the process of determining the genre of a music, accuracy of the prediction must be well maintained. In our project we are automatically classifying an unknown music into its genre with an effective accuracy. We are separating the linguistic content from the noise while extracting features from the set of audio files. This helps in obtaining a good accuracy of prediction. We are implementing various Machine Learning Algorithms to build our project. We considered the GTZAN dataset [4], which contains 1000 music files of 10 different genres with each file having a duration of 30 sec.
{"title":"Automatic Music Genre Classification Based on Linguistic Frequencies Using Machine Learning","authors":"M. S. Rao, O. Pavan Kalyan, N. N. Kumar, Md. Tasleem Tabassum, B. Srihari","doi":"10.1109/ICRAMI52622.2021.9585937","DOIUrl":"https://doi.org/10.1109/ICRAMI52622.2021.9585937","url":null,"abstract":"Classifying various music into its genre has a lot of applications in the real world. It plays an important role in several online music streaming services such as Gaana, Spotify etc. Most of the music recommender systems implement such feature. Over the past two decades music coming from various sources has been increasing at a high speed. Several musical communities are emerged based on the music genre. Therefore, in order to satisfy their requirements, the need for an automatic music genre classifier became evident. In the process of determining the genre of a music, accuracy of the prediction must be well maintained. In our project we are automatically classifying an unknown music into its genre with an effective accuracy. We are separating the linguistic content from the noise while extracting features from the set of audio files. This helps in obtaining a good accuracy of prediction. We are implementing various Machine Learning Algorithms to build our project. We considered the GTZAN dataset [4], which contains 1000 music files of 10 different genres with each file having a duration of 30 sec.","PeriodicalId":440750,"journal":{"name":"2021 International Conference on Recent Advances in Mathematics and Informatics (ICRAMI)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133633950","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-09-21DOI: 10.1109/ICRAMI52622.2021.9585909
Djamila Mohdeb, Meriem Laifa, Miloud Naidja
In 2020, we have witnessed a universal health crisis that affected the lives of many people around the world. Covid-19 outbreak has been accompanied with an unprecedented wave of misinformation shared on the web and social media leading to confusion and inappropriate public reactions. In this paper, we investigate the fake news spread in Arabic content during the pandemic crisis. We have collected a dataset for the aim of detecting fake news that are related to the coronavirus subject. The dataset includes Arabic fake and true news extracted from reliable sources. To the best of our knowledge, it is the first fake news dataset on Covid-19 Arabic misinformation. The collected data have been explored then exploited for fake news detection task using the classification baseline methods. Results indicated comparable high performance of baseline models with a relative superiority of SVM classifier.
{"title":"An Arabic Corpus for Covid-19 related Fake News","authors":"Djamila Mohdeb, Meriem Laifa, Miloud Naidja","doi":"10.1109/ICRAMI52622.2021.9585909","DOIUrl":"https://doi.org/10.1109/ICRAMI52622.2021.9585909","url":null,"abstract":"In 2020, we have witnessed a universal health crisis that affected the lives of many people around the world. Covid-19 outbreak has been accompanied with an unprecedented wave of misinformation shared on the web and social media leading to confusion and inappropriate public reactions. In this paper, we investigate the fake news spread in Arabic content during the pandemic crisis. We have collected a dataset for the aim of detecting fake news that are related to the coronavirus subject. The dataset includes Arabic fake and true news extracted from reliable sources. To the best of our knowledge, it is the first fake news dataset on Covid-19 Arabic misinformation. The collected data have been explored then exploited for fake news detection task using the classification baseline methods. Results indicated comparable high performance of baseline models with a relative superiority of SVM classifier.","PeriodicalId":440750,"journal":{"name":"2021 International Conference on Recent Advances in Mathematics and Informatics (ICRAMI)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132940081","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-09-21DOI: 10.1109/ICRAMI52622.2021.9585925
Amira Bendaoud, F. Hachouf
In this paper, an attention is given to edge-stop function (ESF) in active contour models which are based on a gradient calculus. Usually this kind of algorithms uses the gradient of a smoothed image by a Gaussian kernel. In this work, a fractional calculus is introduced to the edge-stop function formulation. The regular gradient in the ESF formulation has been substituted by a fractional one. The Grunwald-Letnikov definition has been used. The proposed method has been tested on MRI database. Obtained results are good enough compared to existing methods in literature.
{"title":"Fractional Calculus for improving Edge-Based Active Contour Models.","authors":"Amira Bendaoud, F. Hachouf","doi":"10.1109/ICRAMI52622.2021.9585925","DOIUrl":"https://doi.org/10.1109/ICRAMI52622.2021.9585925","url":null,"abstract":"In this paper, an attention is given to edge-stop function (ESF) in active contour models which are based on a gradient calculus. Usually this kind of algorithms uses the gradient of a smoothed image by a Gaussian kernel. In this work, a fractional calculus is introduced to the edge-stop function formulation. The regular gradient in the ESF formulation has been substituted by a fractional one. The Grunwald-Letnikov definition has been used. The proposed method has been tested on MRI database. Obtained results are good enough compared to existing methods in literature.","PeriodicalId":440750,"journal":{"name":"2021 International Conference on Recent Advances in Mathematics and Informatics (ICRAMI)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133319870","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-09-21DOI: 10.1109/ICRAMI52622.2021.9585971
Abdelmalik Mekaoussi, M. Titaouine
In this work, we studied an L-shaped frequency selective surface (FSS) by a method called Wave Concept Iterative Procedure (WCIP), this method developed from the Modal Fast Transformation (FMT) is based on the cross- formulation. wave and the solution obtained by an iterative procedure does not use the matrix to ensure convergence and the procedure is stopped when it arrives at convergence, for this geometry the results of a single resonance obtained by the WCIP method have a resonant frequency of 5.35 GHz with a band bandwidth of 2.3 GHz, when the structure is excited in the X direction, a frequency at 10.35 GHz with a bandwidth of 0.44 GHz when the structure is excited in the Y direction. The simulation of the results obtained by the WCIP method is compared with the results of the software HFSS 13.0 (High Frequency Structure Simulator), we find a good agreement.
{"title":"Simulation Of The Structure FSS Using The WCIP Method For Dual Polarization Applications","authors":"Abdelmalik Mekaoussi, M. Titaouine","doi":"10.1109/ICRAMI52622.2021.9585971","DOIUrl":"https://doi.org/10.1109/ICRAMI52622.2021.9585971","url":null,"abstract":"In this work, we studied an L-shaped frequency selective surface (FSS) by a method called Wave Concept Iterative Procedure (WCIP), this method developed from the Modal Fast Transformation (FMT) is based on the cross- formulation. wave and the solution obtained by an iterative procedure does not use the matrix to ensure convergence and the procedure is stopped when it arrives at convergence, for this geometry the results of a single resonance obtained by the WCIP method have a resonant frequency of 5.35 GHz with a band bandwidth of 2.3 GHz, when the structure is excited in the X direction, a frequency at 10.35 GHz with a bandwidth of 0.44 GHz when the structure is excited in the Y direction. The simulation of the results obtained by the WCIP method is compared with the results of the software HFSS 13.0 (High Frequency Structure Simulator), we find a good agreement.","PeriodicalId":440750,"journal":{"name":"2021 International Conference on Recent Advances in Mathematics and Informatics (ICRAMI)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114034802","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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