Pub Date : 2021-10-31DOI: 10.15866/irecap.v11i5.20926
Sushma Shankarappa, V. S. Rao
{"title":"S-Band Transceiver Antenna for Telemetry and Telecommand Applications","authors":"Sushma Shankarappa, V. S. Rao","doi":"10.15866/irecap.v11i5.20926","DOIUrl":"https://doi.org/10.15866/irecap.v11i5.20926","url":null,"abstract":"","PeriodicalId":38104,"journal":{"name":"International Journal on Communications Antenna and Propagation","volume":"51 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78545750","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 : 2021-10-31DOI: 10.15866/irecap.v11i5.20996
M. Malkawi, Ahmed S. Shatnawi, Khaldoon Al-Zoubi, L. Alawneh
{"title":"Improving Network Entry Procedure in Broadband Wi-Fi Networks","authors":"M. Malkawi, Ahmed S. Shatnawi, Khaldoon Al-Zoubi, L. Alawneh","doi":"10.15866/irecap.v11i5.20996","DOIUrl":"https://doi.org/10.15866/irecap.v11i5.20996","url":null,"abstract":"","PeriodicalId":38104,"journal":{"name":"International Journal on Communications Antenna and Propagation","volume":"66 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76670583","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 : 2021-08-31DOI: 10.15866/irecap.v11i4.19747
Maria Moussa, M. Madi, K. Kabalan
This paper represents several designs of microstrip 3D antenna arrays suitable for fifth generation mobile communication systems as well as mm-Wave imaging systems for cancer detection. Antennas presented show improved characteristics in terms of compact size and multi-directional radiation patterns associated with high gain values. This study implements array designs and fractal geometry for the purpose of miniaturizing the antennas. Triangle-fractal-shape microstrip antenna is designed and fabricated using a double-sided aluminum oxide ceramic substrate. The designed antenna has equal side length of 22 mm, a 29 mm base, operating frequency of 30 GHz frequency, a high gain operation, and multi-band resonance. A good agreement of the measured and simulated results of the reflection coefficient is obtained with acceptable discrepancies. Moreover, a measured gain above 8 dB is achieved. Multiple three-dimensional antenna designs are also discussed for broadband operation that can be used for 5G applications.
{"title":"High Gain 30 GHz Multi-Band Multi-Faceted Miniature Antenna for 5G and Cancer Detection","authors":"Maria Moussa, M. Madi, K. Kabalan","doi":"10.15866/irecap.v11i4.19747","DOIUrl":"https://doi.org/10.15866/irecap.v11i4.19747","url":null,"abstract":"This paper represents several designs of microstrip 3D antenna arrays suitable for fifth generation mobile communication systems as well as mm-Wave imaging systems for cancer detection. Antennas presented show improved characteristics in terms of compact size and multi-directional radiation patterns associated with high gain values. This study implements array designs and fractal geometry for the purpose of miniaturizing the antennas. Triangle-fractal-shape microstrip antenna is designed and fabricated using a double-sided aluminum oxide ceramic substrate. The designed antenna has equal side length of 22 mm, a 29 mm base, operating frequency of 30 GHz frequency, a high gain operation, and multi-band resonance. A good agreement of the measured and simulated results of the reflection coefficient is obtained with acceptable discrepancies. Moreover, a measured gain above 8 dB is achieved. Multiple three-dimensional antenna designs are also discussed for broadband operation that can be used for 5G applications.","PeriodicalId":38104,"journal":{"name":"International Journal on Communications Antenna and Propagation","volume":"49 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87898278","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 : 2021-08-31DOI: 10.15866/irecap.v11i4.19883
A. M. Aaref, Zuhair Shakor Mahmood
Speaker recognition systems use a model that learns a speaker's speech by inputting an audio recording and processing it. Time-varying signal, with frequencies that continuously change, is identified as a speech signal. There are many uncertain attributes to speech; thus traditional speech recognition techniques such as using zero crossings and the Fourier Transform are not up to the task. It aims to be accomplished with the aim of helping two causes. The first part is designed to address speaker identification technology that is resistant to noise. While most prior solutions have relied on changing mel frequency cepstrum coefficients, with a Fundamental frequency feature coefficient, this proposal integrates both of these modifications with a new cepstrum component. In order to construct the feature matrix, the system is fed with two-hundred and fifty speech imprints that are used to apply features extraction techniques. The matrix is used to teach the algorithm about features, and each one is then evaluated using incomplete data (thirty percent of total data in features matrix). Speaker recognition models with improved accuracy are developed by studying the algorithms invasively. These variables (metrics) are generated for each algorithm and applied to the algorithm for recognition accuracy and the time required to achieve that accuracy. When tested against previous research, the findings show that the Feed Forward Neural Network-based Particle Swarm Optimization method has been better. This model can accurately identify 96% of the input with less processing time. According to the findings, optimization utilizing advanced particle swarm optimization (a.k.a. Particle Swarm Optimization) is most likely responsible for the higher accuracy seen in speaker identification.
{"title":"Optimization the Accuracy of FFNN Based Speaker Recognition System Using PSO Algorithm","authors":"A. M. Aaref, Zuhair Shakor Mahmood","doi":"10.15866/irecap.v11i4.19883","DOIUrl":"https://doi.org/10.15866/irecap.v11i4.19883","url":null,"abstract":"Speaker recognition systems use a model that learns a speaker's speech by inputting an audio recording and processing it. Time-varying signal, with frequencies that continuously change, is identified as a speech signal. There are many uncertain attributes to speech; thus traditional speech recognition techniques such as using zero crossings and the Fourier Transform are not up to the task. It aims to be accomplished with the aim of helping two causes. The first part is designed to address speaker identification technology that is resistant to noise. While most prior solutions have relied on changing mel frequency cepstrum coefficients, with a Fundamental frequency feature coefficient, this proposal integrates both of these modifications with a new cepstrum component. In order to construct the feature matrix, the system is fed with two-hundred and fifty speech imprints that are used to apply features extraction techniques. The matrix is used to teach the algorithm about features, and each one is then evaluated using incomplete data (thirty percent of total data in features matrix). Speaker recognition models with improved accuracy are developed by studying the algorithms invasively. These variables (metrics) are generated for each algorithm and applied to the algorithm for recognition accuracy and the time required to achieve that accuracy. When tested against previous research, the findings show that the Feed Forward Neural Network-based Particle Swarm Optimization method has been better. This model can accurately identify 96% of the input with less processing time. According to the findings, optimization utilizing advanced particle swarm optimization (a.k.a. Particle Swarm Optimization) is most likely responsible for the higher accuracy seen in speaker identification.","PeriodicalId":38104,"journal":{"name":"International Journal on Communications Antenna and Propagation","volume":"163 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86406451","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 : 2021-08-31DOI: 10.15866/irecap.v11i4.20766
Kapil Kumar Gupta, Namrata Dhanda, U. Kumar
It is very important to consider the factors like blurred boundaries and external noise while analyzing the brain tumor structures. It is difficult to segment accurately the brain MRI. Many studies in both developing and developed countries indicate that an inactive diagnosis has led to the death of the majority of people who suffer from brain tumor. The proposed novel method is a filter mechanism based on texture Gabor filter, which is followed by a genetic algorithm proposed to improve segmentation accuracy. A texture-based Gabor filter has been used to detect irregularities and to extract statistical properties further used in segmentation and classification. In order to improve segmentation effectiveness, a better separation of different clusters of the features from Gabor filter is studied. An objective function has been also formalized to adjust filter parameters with gradient descent and genetic algorithm. This document has demonstrated the effects of the segmentation of both qualitative and quantitative productivity. The findings show that the novel proposed approach works better with respect to the segmentation accuracy.
{"title":"A Novel Approach to Brain Tumor Detection Using Texture Based Gabor Filter Followed by Genetic Algorithm","authors":"Kapil Kumar Gupta, Namrata Dhanda, U. Kumar","doi":"10.15866/irecap.v11i4.20766","DOIUrl":"https://doi.org/10.15866/irecap.v11i4.20766","url":null,"abstract":"It is very important to consider the factors like blurred boundaries and external noise while analyzing the brain tumor structures. It is difficult to segment accurately the brain MRI. Many studies in both developing and developed countries indicate that an inactive diagnosis has led to the death of the majority of people who suffer from brain tumor. The proposed novel method is a filter mechanism based on texture Gabor filter, which is followed by a genetic algorithm proposed to improve segmentation accuracy. A texture-based Gabor filter has been used to detect irregularities and to extract statistical properties further used in segmentation and classification. In order to improve segmentation effectiveness, a better separation of different clusters of the features from Gabor filter is studied. An objective function has been also formalized to adjust filter parameters with gradient descent and genetic algorithm. This document has demonstrated the effects of the segmentation of both qualitative and quantitative productivity. The findings show that the novel proposed approach works better with respect to the segmentation accuracy.","PeriodicalId":38104,"journal":{"name":"International Journal on Communications Antenna and Propagation","volume":"8 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80924964","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 : 2021-08-31DOI: 10.15866/irecap.v11i4.20375
Mohammad Fawa’reh, Malik Qasaimeh, Ibrahim AbuArja, Mustafa A. Al-Fayoumi
Confidentiality and availability are the main concerns of website stakeholders, entailing service-security trade-offs. Many solutions seek to improve security. The most popular one is the completely automated public Turing test (CAPTCHA) security framework, which distinguishes between human and bot activity by asking the user to perform a certain task, such as solving a mathematical equation or retyping text written in an image. Many people believe that websites using CAPTCHAs are secure, but today’s attackers using innovative technology, such as deep learning, that can break them in milliseconds. Many solutions have been proposed to overcome this issue, mainly relying on increased CAPTCHA complexity or technical CAPTCHAs too difficult for normal users. In this case, many customers may go to websites that use simple CAPTCHAs, at the expense of websites with more complex ones. This paper proposes a robust and simple image-based CAPTCHA using an Arabic scheme and noise to make breaking this captcha very difficult, even for deep learning approaches. The proposed model is evaluated using two-factor, which focuses on the usability and the ability to beak the proposed CAPTCHA. The first test has been done via an empirical experiment involving 16 people from different sectors, and the second test has involved breaking the model using Convolutional Neural Network (CNN). The experimental results demonstrate the superiority of the proposed model in security and usability.
{"title":"Mitigating Deep learning Attacks Against Text Image CAPTCHA Using Arabic Scheme","authors":"Mohammad Fawa’reh, Malik Qasaimeh, Ibrahim AbuArja, Mustafa A. Al-Fayoumi","doi":"10.15866/irecap.v11i4.20375","DOIUrl":"https://doi.org/10.15866/irecap.v11i4.20375","url":null,"abstract":"Confidentiality and availability are the main concerns of website stakeholders, entailing service-security trade-offs. Many solutions seek to improve security. The most popular one is the completely automated public Turing test (CAPTCHA) security framework, which distinguishes between human and bot activity by asking the user to perform a certain task, such as solving a mathematical equation or retyping text written in an image. Many people believe that websites using CAPTCHAs are secure, but today’s attackers using innovative technology, such as deep learning, that can break them in milliseconds. Many solutions have been proposed to overcome this issue, mainly relying on increased CAPTCHA complexity or technical CAPTCHAs too difficult for normal users. In this case, many customers may go to websites that use simple CAPTCHAs, at the expense of websites with more complex ones. This paper proposes a robust and simple image-based CAPTCHA using an Arabic scheme and noise to make breaking this captcha very difficult, even for deep learning approaches. The proposed model is evaluated using two-factor, which focuses on the usability and the ability to beak the proposed CAPTCHA. The first test has been done via an empirical experiment involving 16 people from different sectors, and the second test has involved breaking the model using Convolutional Neural Network (CNN). The experimental results demonstrate the superiority of the proposed model in security and usability.","PeriodicalId":38104,"journal":{"name":"International Journal on Communications Antenna and Propagation","volume":"244 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80558811","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 : 2021-08-31DOI: 10.15866/irecap.v11i4.20366
M. B. Yassein, Ahmad Alrawashdeh, Omar Alzoubi, M. Shatnawi
{"title":"Adjusted and Robust Routing Update Algorithms for Internet of Things","authors":"M. B. Yassein, Ahmad Alrawashdeh, Omar Alzoubi, M. Shatnawi","doi":"10.15866/irecap.v11i4.20366","DOIUrl":"https://doi.org/10.15866/irecap.v11i4.20366","url":null,"abstract":"","PeriodicalId":38104,"journal":{"name":"International Journal on Communications Antenna and Propagation","volume":"15 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89160338","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 : 2021-08-31DOI: 10.15866/irecap.v11i4.20861
B. K. A. Shaji, A. Pradeep, P. Mohanan
In this paper, a fractal inspired metamaterial superstrate is proposed for the gain enhancement of microstrip patch antenna resonating at 2.4 GHz. Fractal structure is used as the metamaterial unit cell, which is designed on a FR4 substrate with a thickness of 1.6 mm. The metamaterial unit cell exhibits near-zero refractive index at 2.4 GHz. The superstrate consists of a 2×2 array of metamaterial unit cells which acts as a partial reflective surface of Fabry-Perot (FP) cavity. The distance between the antenna and the superstrate is optimized by the FP resonance condition. The antenna has a directional radiation pattern more than the microstrip patch antenna. The antenna gives a peak gain of 7 dBi with a gain enhancement of 4 dB and a beamwidth reduction of 23° and 2° along H plane and E plane respectively. The aperture efficiency of the antenna is 80.47%. The whole structure is simulated and analyzed using finite element method. The simulated and the measured results show good agreement.
{"title":"Fractal Inspired Metamaterial Superstrate for Gain Enhancement","authors":"B. K. A. Shaji, A. Pradeep, P. Mohanan","doi":"10.15866/irecap.v11i4.20861","DOIUrl":"https://doi.org/10.15866/irecap.v11i4.20861","url":null,"abstract":"In this paper, a fractal inspired metamaterial superstrate is proposed for the gain enhancement of microstrip patch antenna resonating at 2.4 GHz. Fractal structure is used as the metamaterial unit cell, which is designed on a FR4 substrate with a thickness of 1.6 mm. The metamaterial unit cell exhibits near-zero refractive index at 2.4 GHz. The superstrate consists of a 2×2 array of metamaterial unit cells which acts as a partial reflective surface of Fabry-Perot (FP) cavity. The distance between the antenna and the superstrate is optimized by the FP resonance condition. The antenna has a directional radiation pattern more than the microstrip patch antenna. The antenna gives a peak gain of 7 dBi with a gain enhancement of 4 dB and a beamwidth reduction of 23° and 2° along H plane and E plane respectively. The aperture efficiency of the antenna is 80.47%. The whole structure is simulated and analyzed using finite element method. The simulated and the measured results show good agreement.","PeriodicalId":38104,"journal":{"name":"International Journal on Communications Antenna and Propagation","volume":"65 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84436680","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 : 2021-08-31DOI: 10.15866/irecap.v11i4.20665
U. Nissanov, G. Singh, Akinola Segun
{"title":"Sixth-Generation (6G) Microstrip Antenna with High-Gain","authors":"U. Nissanov, G. Singh, Akinola Segun","doi":"10.15866/irecap.v11i4.20665","DOIUrl":"https://doi.org/10.15866/irecap.v11i4.20665","url":null,"abstract":"","PeriodicalId":38104,"journal":{"name":"International Journal on Communications Antenna and Propagation","volume":"44 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74262247","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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