Pub Date : 2022-05-15DOI: 10.1109/SIU55565.2022.9864838
Abdulsamet Dagasan, Mustafa Akur, Mehmet Umut Demircin
Fiber Optic Distributed Acoustic Sensing (DAS) Systems use standard telecommunication fibers to detect acoustic vibrations up to 50 kms along the cable. In this paper we propose algorithms to detect fiber optic cable termination points and optical signal losses using DAS data. Proposed algorithms add traditional Optical Time-Domain Reflectometer (OTDR) measurement functionality to the DAS systems. Cable termination detection algorithm models the noise data in DAS signal that consists of electronic noise [e.g. Analog-to-Digital Converter (ADC) noises] and optical laser reflection noise. The cable termination detection algorithm analyzes noise statistics of the sensor data and finds the location where optic noise is no longer present. Signal loss detection algorithm first eliminates the environmental acoustic noise from the DAS signal; then, change point detection algorithm is applied to detect locations where significant signal loss occurs. Proposed algorithms are tested in various DAS installations in Turkey. Predicted cable termination and signal loss locations agree with OTDR measurements.
{"title":"Fiber Optic Cable Termination and Signal Loss Detection in DAS Systems","authors":"Abdulsamet Dagasan, Mustafa Akur, Mehmet Umut Demircin","doi":"10.1109/SIU55565.2022.9864838","DOIUrl":"https://doi.org/10.1109/SIU55565.2022.9864838","url":null,"abstract":"Fiber Optic Distributed Acoustic Sensing (DAS) Systems use standard telecommunication fibers to detect acoustic vibrations up to 50 kms along the cable. In this paper we propose algorithms to detect fiber optic cable termination points and optical signal losses using DAS data. Proposed algorithms add traditional Optical Time-Domain Reflectometer (OTDR) measurement functionality to the DAS systems. Cable termination detection algorithm models the noise data in DAS signal that consists of electronic noise [e.g. Analog-to-Digital Converter (ADC) noises] and optical laser reflection noise. The cable termination detection algorithm analyzes noise statistics of the sensor data and finds the location where optic noise is no longer present. Signal loss detection algorithm first eliminates the environmental acoustic noise from the DAS signal; then, change point detection algorithm is applied to detect locations where significant signal loss occurs. Proposed algorithms are tested in various DAS installations in Turkey. Predicted cable termination and signal loss locations agree with OTDR measurements.","PeriodicalId":115446,"journal":{"name":"2022 30th Signal Processing and Communications Applications Conference (SIU)","volume":"74 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":"133388668","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/SIU55565.2022.9864703
Erdem Bayhan, Cenk Berkan Deligoz, Feride Seymen, Mustafa Namdar, Arif Basgumus
In this study, the detection of the objects that they may encounter with deep learning models and the methods of the tactile paving surface tracking with Hough’s theorem are presented so that visually impaired individuals can easily walk outdoors. In the proposed approach, the training is primarily realized for machine learning of the deep learning models. The Faster R-CNN model and the SSD MobileNetV2 model are used in the training, and the accuracy performances of these two models are compared. During the training phase of the two models, a data set is generated using real-time and internet-based photographs. The training is completed by making use of 3653 photographs for 11 different objects that visually impaired individuals may encounter. In the detection of the objects, the accuracy rate of Faster R-CNN model is approximately 91%, and the SSD MobileNetV2 model achieved approximately 93% success. In addition, with the help of Hough’s theorem, it is observed that the edge surface lines are followed correctly in the tracking of the tactile paving surfaces.
{"title":"Unimpeded Walking with Deep Learning","authors":"Erdem Bayhan, Cenk Berkan Deligoz, Feride Seymen, Mustafa Namdar, Arif Basgumus","doi":"10.1109/SIU55565.2022.9864703","DOIUrl":"https://doi.org/10.1109/SIU55565.2022.9864703","url":null,"abstract":"In this study, the detection of the objects that they may encounter with deep learning models and the methods of the tactile paving surface tracking with Hough’s theorem are presented so that visually impaired individuals can easily walk outdoors. In the proposed approach, the training is primarily realized for machine learning of the deep learning models. The Faster R-CNN model and the SSD MobileNetV2 model are used in the training, and the accuracy performances of these two models are compared. During the training phase of the two models, a data set is generated using real-time and internet-based photographs. The training is completed by making use of 3653 photographs for 11 different objects that visually impaired individuals may encounter. In the detection of the objects, the accuracy rate of Faster R-CNN model is approximately 91%, and the SSD MobileNetV2 model achieved approximately 93% success. In addition, with the help of Hough’s theorem, it is observed that the edge surface lines are followed correctly in the tracking of the tactile paving surfaces.","PeriodicalId":115446,"journal":{"name":"2022 30th Signal Processing and Communications Applications Conference (SIU)","volume":"153 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":"133795602","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/SIU55565.2022.9864770
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/SIU55565.2022.9864679
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}
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/SIU55565.2022.9864893
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/SIU55565.2022.9864758
Tolga Tüfekçi, Oguz Ülgen, Serhat Erküçük, T. Baykaş
In order to satisfy the need for high data rate and high number of users, new generation communication techniques are developed. One of the techniques that may be used in future generation communication networks is Sparse Code Multiple Access (SCMA). With this new technique, the aim is to allocate users frequency resources in a non-orthogonal way by using code books. For this new technique, which is has a potential to be used in 5G and beyond communication networks, most researches have focused on flat fading channels and related results have been provided. In this work, different from earlier studies, fast fading channels have been considered for channels varying at different rates, and bit-error performance results have been provided with computer simulations.
{"title":"Performance of SCMA Systems in Fast-Fading Channels","authors":"Tolga Tüfekçi, Oguz Ülgen, Serhat Erküçük, T. Baykaş","doi":"10.1109/SIU55565.2022.9864758","DOIUrl":"https://doi.org/10.1109/SIU55565.2022.9864758","url":null,"abstract":"In order to satisfy the need for high data rate and high number of users, new generation communication techniques are developed. One of the techniques that may be used in future generation communication networks is Sparse Code Multiple Access (SCMA). With this new technique, the aim is to allocate users frequency resources in a non-orthogonal way by using code books. For this new technique, which is has a potential to be used in 5G and beyond communication networks, most researches have focused on flat fading channels and related results have been provided. In this work, different from earlier studies, fast fading channels have been considered for channels varying at different rates, and bit-error performance results have been provided with computer simulations.","PeriodicalId":115446,"journal":{"name":"2022 30th Signal Processing and Communications Applications Conference (SIU)","volume":"3 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":"121072790","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/SIU55565.2022.9864775
Ismail Can Büyüktepe, A. K. Hocaoglu
In this study, an algorithm that can classify human and car has been developed by using vibration signals obtained from a three-axis accelerometer sensor station placed on three different floors. Data were collected over soil, asphalt and concrete ground. As classifiers, k-Nearest Neighbor classifier (k-NN) and Support Vector Machine (SVM) classifiers are used. Using classifiers alone limits classification performance. A two-stage classifier model has been proposed to improve the classification performance. The classifier model, which is proposed in two stages, detects the presence of motion in the first stage. In the second stage, it performs the classification of moving targets. As a result of the experimental studies, it has been shown that the proposed two-stage classifier model improves the performance in solving the problem.
{"title":"Classification of Moving Ground Targets Using Measurement from Accelerometer on Road Surface","authors":"Ismail Can Büyüktepe, A. K. Hocaoglu","doi":"10.1109/SIU55565.2022.9864775","DOIUrl":"https://doi.org/10.1109/SIU55565.2022.9864775","url":null,"abstract":"In this study, an algorithm that can classify human and car has been developed by using vibration signals obtained from a three-axis accelerometer sensor station placed on three different floors. Data were collected over soil, asphalt and concrete ground. As classifiers, k-Nearest Neighbor classifier (k-NN) and Support Vector Machine (SVM) classifiers are used. Using classifiers alone limits classification performance. A two-stage classifier model has been proposed to improve the classification performance. The classifier model, which is proposed in two stages, detects the presence of motion in the first stage. In the second stage, it performs the classification of moving targets. As a result of the experimental studies, it has been shown that the proposed two-stage classifier model improves the performance in solving the problem.","PeriodicalId":115446,"journal":{"name":"2022 30th Signal Processing and Communications Applications Conference (SIU)","volume":"166 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":"121480785","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/SIU55565.2022.9864960
Latif Akçay, Bartu Sürer, B. Yalçin
In this study, it is aimed to implement the low-RISC system-on-chip, which is based on the Rocket processor created with the RISC-V instruction set architecture developed by Berkeley University, on FPGA and to run image processing algorithms on this system. While making this implementation, the main target is a system that is very simple, consumes low power, and can be quickly redirected to other purposes. Therefore, it is based on the effective evaluation of the existing system without using any extra customized accelerators. Thus, a free, open source, and powerful enough platform for many embedded system applications is proposed to the designers. For this purpose, a lane detection application designed with standard C libraries such as Gaussian blur filter, Sobel operation filter and other elements, which are widely used in image processing applications, is run with embedded Linux operating system and the results are shared.
{"title":"Implementation of a SoC by Using lowRISC Architecture on an FPGA for Image Filtering Applications","authors":"Latif Akçay, Bartu Sürer, B. Yalçin","doi":"10.1109/SIU55565.2022.9864960","DOIUrl":"https://doi.org/10.1109/SIU55565.2022.9864960","url":null,"abstract":"In this study, it is aimed to implement the low-RISC system-on-chip, which is based on the Rocket processor created with the RISC-V instruction set architecture developed by Berkeley University, on FPGA and to run image processing algorithms on this system. While making this implementation, the main target is a system that is very simple, consumes low power, and can be quickly redirected to other purposes. Therefore, it is based on the effective evaluation of the existing system without using any extra customized accelerators. Thus, a free, open source, and powerful enough platform for many embedded system applications is proposed to the designers. For this purpose, a lane detection application designed with standard C libraries such as Gaussian blur filter, Sobel operation filter and other elements, which are widely used in image processing applications, is run with embedded Linux operating system and the results are shared.","PeriodicalId":115446,"journal":{"name":"2022 30th Signal Processing and Communications Applications Conference (SIU)","volume":"74 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":"115961969","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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