Pub Date : 2022-05-15DOI: 10.1109/SIU55565.2022.9864770
Alper Endes, Baris Yuksekkaya
Communication systems to be delivered with the Fifth Generation (Fifth Generation, 5G) are expected to meet the requirements of high reliability, low delay, high security, high capacity, and high-speed. Mobile providers are looking for programmable solutions to provide numerous different services, and the 5G network structure provides a solution to this need using Network Slicing. In this study, artificial intelligence-based machine learning algorithms and methods of placing users in communication slices were examined by creating realistic user and base station data. Considered communication slices were selected as advanced mobile network (enhanced Mobile Broadband, eMBB), large-scale machine-type communication (mMTC), and ultra-low-latency data communication (Ultra-Reliable Low Latency Communications, URLLC). Two different machine learning models were created and tested in the proposed simulation environment, and their performances were compared.
{"title":"5G Network Slicing using Machine Learning Techniques","authors":"Alper Endes, Baris Yuksekkaya","doi":"10.1109/SIU55565.2022.9864770","DOIUrl":"https://doi.org/10.1109/SIU55565.2022.9864770","url":null,"abstract":"Communication systems to be delivered with the Fifth Generation (Fifth Generation, 5G) are expected to meet the requirements of high reliability, low delay, high security, high capacity, and high-speed. Mobile providers are looking for programmable solutions to provide numerous different services, and the 5G network structure provides a solution to this need using Network Slicing. In this study, artificial intelligence-based machine learning algorithms and methods of placing users in communication slices were examined by creating realistic user and base station data. Considered communication slices were selected as advanced mobile network (enhanced Mobile Broadband, eMBB), large-scale machine-type communication (mMTC), and ultra-low-latency data communication (Ultra-Reliable Low Latency Communications, URLLC). Two different machine learning models were created and tested in the proposed simulation environment, and their performances were compared.","PeriodicalId":115446,"journal":{"name":"2022 30th Signal Processing and Communications Applications Conference (SIU)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129106214","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 : 2022-05-15DOI: 10.1109/SIU55565.2022.9864893
Mehmet Yagan, E. Yilmaz, H. Özkan
Security cameras are widely used to detect and prevent crimes, but the number of surveillance videos has increased due to this prevalence. By processing these videos with the help of a suitable machine learning algorithm, unfavorable events can be brought to the attention of expert to manually monitor. Since these unfavorable events are of various types and few in number, this problem can be addressed in the anomaly detection structure. In this study, an anomaly detection algorithm has been developed using the UCF-Crime dataset consisting of 1900 surveillance videos of various lengths. First of all, features were extracted from these videos with the help of a pre-trained artificial neural network (ANN), the size of these features was reduced with another ANN, and the anomaly detection was performed using two different recurrent neural networks, one based on classification and the other based on future feature estimation by regression. Area under receiver operating characteristic (ROC) curve (AUC) was used as the evaluation criterion. At video level, regression method gives a better performance with 88.71% AUC than the classification method which only gives 85.82% AUC, while at video frame level, both methods perform similarly with 73.64% and 73.71%, but there are true positive rate ranges where they perform better than each other.
{"title":"Anomaly Detection in Surveillance Videos Using Regression With Recurrent Neural Networks","authors":"Mehmet Yagan, E. Yilmaz, H. Özkan","doi":"10.1109/SIU55565.2022.9864893","DOIUrl":"https://doi.org/10.1109/SIU55565.2022.9864893","url":null,"abstract":"Security cameras are widely used to detect and prevent crimes, but the number of surveillance videos has increased due to this prevalence. By processing these videos with the help of a suitable machine learning algorithm, unfavorable events can be brought to the attention of expert to manually monitor. Since these unfavorable events are of various types and few in number, this problem can be addressed in the anomaly detection structure. In this study, an anomaly detection algorithm has been developed using the UCF-Crime dataset consisting of 1900 surveillance videos of various lengths. First of all, features were extracted from these videos with the help of a pre-trained artificial neural network (ANN), the size of these features was reduced with another ANN, and the anomaly detection was performed using two different recurrent neural networks, one based on classification and the other based on future feature estimation by regression. Area under receiver operating characteristic (ROC) curve (AUC) was used as the evaluation criterion. At video level, regression method gives a better performance with 88.71% AUC than the classification method which only gives 85.82% AUC, while at video frame level, both methods perform similarly with 73.64% and 73.71%, but there are true positive rate ranges where they perform better than each other.","PeriodicalId":115446,"journal":{"name":"2022 30th Signal Processing and Communications Applications Conference (SIU)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134003202","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 : 2022-05-15DOI: 10.1109/SIU55565.2022.9864825
E. G. Sahin
This paper presents a novel microstrip dual-band bandstop filter design by using inductively coupled Coplanar Waveguide fed (CPW) dual-mode resonators. Two nested dual-mode square loop resonators with perturbation elements on microstrip layer are constructed to produce first and second band. In the proposed filter design method, inductively coupled CPW rectangular rings are used to excite the degenerate modes of the resonators to obtain dual band bandstop filtering structure. Two perturbation elements are used to control the reflection zeros of the first and second band. So, two different filter responses are obtained by repositioning the reflection zeros. Charge distributions of the reflection zeros and poles of the structure are investigated for each stopband to exhibit the mode characteristic. Two filters are simulated with a full-wave EM simulator. Two filters are implemented and measured. Despite the production losses are high as a result of the CPW-fed structure, the measurement results are in good agreement with the simulation results.
{"title":"Coplanar-Waveguide Fed Microstrip Dual- Band Bandstop Filters with Inductively Coupled Dual-Mode Ring Resonators","authors":"E. G. Sahin","doi":"10.1109/SIU55565.2022.9864825","DOIUrl":"https://doi.org/10.1109/SIU55565.2022.9864825","url":null,"abstract":"This paper presents a novel microstrip dual-band bandstop filter design by using inductively coupled Coplanar Waveguide fed (CPW) dual-mode resonators. Two nested dual-mode square loop resonators with perturbation elements on microstrip layer are constructed to produce first and second band. In the proposed filter design method, inductively coupled CPW rectangular rings are used to excite the degenerate modes of the resonators to obtain dual band bandstop filtering structure. Two perturbation elements are used to control the reflection zeros of the first and second band. So, two different filter responses are obtained by repositioning the reflection zeros. Charge distributions of the reflection zeros and poles of the structure are investigated for each stopband to exhibit the mode characteristic. Two filters are simulated with a full-wave EM simulator. Two filters are implemented and measured. Despite the production losses are high as a result of the CPW-fed structure, the measurement results are in good agreement with the simulation results.","PeriodicalId":115446,"journal":{"name":"2022 30th Signal Processing and Communications Applications Conference (SIU)","volume":"53 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130497416","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 : 2022-05-15DOI: 10.1109/SIU55565.2022.9864846
Cemal Efe Tezcan, Berk Kiras, G. Bilgin
It is very important to have a high accuracy rate in detecting cancerous cells in histopathological images. Thanks to high-accuracy images, cancerous cells will be detected more sensitively, and there will be a chance for more accurate and early diagnosis. Thus, a very important preliminary step will be taken in the treatment of cancerous cells. In this study, classification performances were comparatively analyzed by applying various methods to four different cancer cell types (benign, normal, carcinoma in situ and invasive carcinoma). By using BACH and Bioimaging as datasets, the desired parts are tried to be obtained primarily by several image processing methods (pyramid mean shifting, line detection, spreading). After obtaining images of different sizes, their performances are examined by using VGG16, DenseNet121, ResNet50, MobileNetV2, InceptionResNetV2, CNN deep transfer learning methods.
{"title":"Classification of Breast Cancer Histopathological Images with Deep Transfer Learning Methods","authors":"Cemal Efe Tezcan, Berk Kiras, G. Bilgin","doi":"10.1109/SIU55565.2022.9864846","DOIUrl":"https://doi.org/10.1109/SIU55565.2022.9864846","url":null,"abstract":"It is very important to have a high accuracy rate in detecting cancerous cells in histopathological images. Thanks to high-accuracy images, cancerous cells will be detected more sensitively, and there will be a chance for more accurate and early diagnosis. Thus, a very important preliminary step will be taken in the treatment of cancerous cells. In this study, classification performances were comparatively analyzed by applying various methods to four different cancer cell types (benign, normal, carcinoma in situ and invasive carcinoma). By using BACH and Bioimaging as datasets, the desired parts are tried to be obtained primarily by several image processing methods (pyramid mean shifting, line detection, spreading). After obtaining images of different sizes, their performances are examined by using VGG16, DenseNet121, ResNet50, MobileNetV2, InceptionResNetV2, CNN deep transfer learning methods.","PeriodicalId":115446,"journal":{"name":"2022 30th Signal Processing and Communications Applications Conference (SIU)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130688859","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 : 2022-05-15DOI: 10.1109/SIU55565.2022.9864861
Semih Gencay, Caner Özcan
Due to the imaging mechanism of Synthetic Aperture Radar (SAR) and the noise in the images, visual identification of objects in the scene is not as easy as in optical images. SAR images have limited color information and cannot reflect the spectral information of objects. Optical images, on the other hand, have rich spectral information. SAR-Optical image fusion is an important area of study so that SAR data can be easily evaluated by anyone, but it is difficult to find a matching SAR and optical image of the same scene. In order to overcome this difficulty, Sentinel-1 and Sentinel-2 datasets have been published and image fusion studies have been carried out with various methods. However, it has been observed that the effect of SAR noise removal before merging on image fusion methods has not been investigated. In the studies conducted to investigate this effect, five different fusion algorithms used in the literature were tested with twenty different image groups using different noise reduction ratios. The success of the fusion results obtained was compared with five different metrics that are widely used in the literature. The images and metric results obtained as a result of the tests showed that the removal of speckle noise in the SAR data has a positive effect on the fusion results.
{"title":"The Effect of SAR Speckle Removal in SAR-Optical Image Fusion","authors":"Semih Gencay, Caner Özcan","doi":"10.1109/SIU55565.2022.9864861","DOIUrl":"https://doi.org/10.1109/SIU55565.2022.9864861","url":null,"abstract":"Due to the imaging mechanism of Synthetic Aperture Radar (SAR) and the noise in the images, visual identification of objects in the scene is not as easy as in optical images. SAR images have limited color information and cannot reflect the spectral information of objects. Optical images, on the other hand, have rich spectral information. SAR-Optical image fusion is an important area of study so that SAR data can be easily evaluated by anyone, but it is difficult to find a matching SAR and optical image of the same scene. In order to overcome this difficulty, Sentinel-1 and Sentinel-2 datasets have been published and image fusion studies have been carried out with various methods. However, it has been observed that the effect of SAR noise removal before merging on image fusion methods has not been investigated. In the studies conducted to investigate this effect, five different fusion algorithms used in the literature were tested with twenty different image groups using different noise reduction ratios. The success of the fusion results obtained was compared with five different metrics that are widely used in the literature. The images and metric results obtained as a result of the tests showed that the removal of speckle noise in the SAR data has a positive effect on the fusion results.","PeriodicalId":115446,"journal":{"name":"2022 30th Signal Processing and Communications Applications Conference (SIU)","volume":"116 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117235190","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 : 2022-05-15DOI: 10.1109/SIU55565.2022.9864873
Saadet Aytaç Arpaci, Songül Varlı
The mixup data augmentation method is a method that creates new images via a linear function from multiple images. In this paper, it is examined whether the mixup data augmentation method improves the U-Net model’s segmentation capability. In this study, artifact segmentation was performed with histopathological images. The dataset used was examined into three different groups: (1) images that are produced through traditional data augmentation methods like flipping and rotation; (2) images that are produced through only the mixup method; and (3) images that are produced through both the traditional and mixup methods. According to the findings, the use of the mixup method in combination with the traditional data augmentation methods improved the model’s average Dice coefficient value for artifact segmentation of histopathological images.
{"title":"Semantic Segmentation with the Mixup Data Augmentation Method","authors":"Saadet Aytaç Arpaci, Songül Varlı","doi":"10.1109/SIU55565.2022.9864873","DOIUrl":"https://doi.org/10.1109/SIU55565.2022.9864873","url":null,"abstract":"The mixup data augmentation method is a method that creates new images via a linear function from multiple images. In this paper, it is examined whether the mixup data augmentation method improves the U-Net model’s segmentation capability. In this study, artifact segmentation was performed with histopathological images. The dataset used was examined into three different groups: (1) images that are produced through traditional data augmentation methods like flipping and rotation; (2) images that are produced through only the mixup method; and (3) images that are produced through both the traditional and mixup methods. According to the findings, the use of the mixup method in combination with the traditional data augmentation methods improved the model’s average Dice coefficient value for artifact segmentation of histopathological images.","PeriodicalId":115446,"journal":{"name":"2022 30th Signal Processing and Communications Applications Conference (SIU)","volume":"118 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131278129","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 : 2022-05-15DOI: 10.1109/SIU55565.2022.9864968
Ezgi Ekiz, Erol Sahin, F. Vural
This study proposes a method to distinguish fake documents from the originals using the textural structures of the papers they are printed on. The study is based on observations showing that paper textures are different and unique, just like fingerprint and iris tissue. This method, which captures the visually distinctive features of paper textures, can detect whether the documents of which the origin is suspected are fake or not. The proposed method can measure Type-2 error by training a Siamese network and thresholding the similarity results between two papers. Experimental results show that the proposed method has better distinguishing features than classical methods.
{"title":"Texture Analysis by Deep Twin Networks for Paper Fraud Detection","authors":"Ezgi Ekiz, Erol Sahin, F. Vural","doi":"10.1109/SIU55565.2022.9864968","DOIUrl":"https://doi.org/10.1109/SIU55565.2022.9864968","url":null,"abstract":"This study proposes a method to distinguish fake documents from the originals using the textural structures of the papers they are printed on. The study is based on observations showing that paper textures are different and unique, just like fingerprint and iris tissue. This method, which captures the visually distinctive features of paper textures, can detect whether the documents of which the origin is suspected are fake or not. The proposed method can measure Type-2 error by training a Siamese network and thresholding the similarity results between two papers. Experimental results show that the proposed method has better distinguishing features than classical methods.","PeriodicalId":115446,"journal":{"name":"2022 30th Signal Processing and Communications Applications Conference (SIU)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132856838","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 : 2022-05-15DOI: 10.1109/SIU55565.2022.9864736
M. Elamassie, M. Uysal
In this paper, we investigate the outage performance of vertical stratified underwater optical links in the presence of moderate/strong turbulence conditions. Specifically, we consider the cascaded Gamma-Gamma turbulence channel model and derive a closed-form expression for outage probability. We then use our derived expression to investigate the achievable diversity order (DO) and asymptotic diversity order (ADO). We further confirm our derivations through Monte Carlo simulations.
{"title":"Outage Performance Analysis of Vertical Underwater VLC Links","authors":"M. Elamassie, M. Uysal","doi":"10.1109/SIU55565.2022.9864736","DOIUrl":"https://doi.org/10.1109/SIU55565.2022.9864736","url":null,"abstract":"In this paper, we investigate the outage performance of vertical stratified underwater optical links in the presence of moderate/strong turbulence conditions. Specifically, we consider the cascaded Gamma-Gamma turbulence channel model and derive a closed-form expression for outage probability. We then use our derived expression to investigate the achievable diversity order (DO) and asymptotic diversity order (ADO). We further confirm our derivations through Monte Carlo simulations.","PeriodicalId":115446,"journal":{"name":"2022 30th Signal Processing and Communications Applications Conference (SIU)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131622520","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 : 2022-05-15DOI: 10.1109/SIU55565.2022.9864730
Ensar Emirali, M. Karsligil
Developing systems for automatically detection of date, time, duration and set expressions containing time information in texts is within the scope of Natural Language Processing research field. When studies for Turkish in the literature are reviewed, it is observed that only date and time expressions are included in the expressions detected by the models developed within the scope of Named Entity Recognition. There are studies to develop only rule-based systems on the subject of detection of temporal expressions in Turkish. Within the scope of this study, first Artificial Neural Networks based model for the detection of temporal expressions in Turkish texts is developed. The input of the developed model is word embeddings. In this study, the developed model success with using word embeddings built by different methods is measured on a dataset consisting of Turkish complaint texts collected from internet websites. By comparing the success of word embeddings on the detection of temporal expressions with the coverage percentages of word embeddings on the dataset, it is concluded that there is no correlation between them.
{"title":"Using Word Embeddings in Detection of Temporal Expressions in Turkish Texts","authors":"Ensar Emirali, M. Karsligil","doi":"10.1109/SIU55565.2022.9864730","DOIUrl":"https://doi.org/10.1109/SIU55565.2022.9864730","url":null,"abstract":"Developing systems for automatically detection of date, time, duration and set expressions containing time information in texts is within the scope of Natural Language Processing research field. When studies for Turkish in the literature are reviewed, it is observed that only date and time expressions are included in the expressions detected by the models developed within the scope of Named Entity Recognition. There are studies to develop only rule-based systems on the subject of detection of temporal expressions in Turkish. Within the scope of this study, first Artificial Neural Networks based model for the detection of temporal expressions in Turkish texts is developed. The input of the developed model is word embeddings. In this study, the developed model success with using word embeddings built by different methods is measured on a dataset consisting of Turkish complaint texts collected from internet websites. By comparing the success of word embeddings on the detection of temporal expressions with the coverage percentages of word embeddings on the dataset, it is concluded that there is no correlation between them.","PeriodicalId":115446,"journal":{"name":"2022 30th Signal Processing and Communications Applications Conference (SIU)","volume":"54 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131654819","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 : 2022-05-15DOI: 10.1109/SIU55565.2022.9864679
Ada Irem Pekdemir, Ö. Özdemir, G. Kurt
Terahertz (THz) communication is one of the remarkable topics in the field of communication. Terahertz communication, which is one of the promising topics in meeting the rapidly increasing number of devices and the need for data speed and channel capacity, aims to increase the wireless communication frequency band up to terahertz level. When the terahertz frequency is used the wavelength decreases significantly, which causes electromagnetic waves to be more affected by the channel effects. There must be a clear line of sight (LOS) between the transmitter and the receiver in THz communication systems and the electromagnetic wave sent must be highly directive. The use of intelligent reflective surfaces in the terahertz band can provide advantages in the reflected wave being more directive. In this study, different Intelligent Reflective Surface designs in the literature are implemented and a performance comparison is presented in terms of beam characteristics.
{"title":"Comparison of Different Intelligent Reflective Surface Designs in terms of Beam Properties at Sub-Terahertz Frequencies","authors":"Ada Irem Pekdemir, Ö. Özdemir, G. Kurt","doi":"10.1109/SIU55565.2022.9864679","DOIUrl":"https://doi.org/10.1109/SIU55565.2022.9864679","url":null,"abstract":"Terahertz (THz) communication is one of the remarkable topics in the field of communication. Terahertz communication, which is one of the promising topics in meeting the rapidly increasing number of devices and the need for data speed and channel capacity, aims to increase the wireless communication frequency band up to terahertz level. When the terahertz frequency is used the wavelength decreases significantly, which causes electromagnetic waves to be more affected by the channel effects. There must be a clear line of sight (LOS) between the transmitter and the receiver in THz communication systems and the electromagnetic wave sent must be highly directive. The use of intelligent reflective surfaces in the terahertz band can provide advantages in the reflected wave being more directive. In this study, different Intelligent Reflective Surface designs in the literature are implemented and a performance comparison is presented in terms of beam characteristics.","PeriodicalId":115446,"journal":{"name":"2022 30th Signal Processing and Communications Applications Conference (SIU)","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132034882","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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