Pub Date : 2020-10-05DOI: 10.1109/SIU49456.2020.9302216
Ş. Altay, G. Ulutaş
Digital watermarking is a method used to protect intellectual property rights and copyrights. This study represents a blind method based on Discrete Wavelet Transform (DWT) and QR decomposition. In this method, the cover image is firstly divided into non-overlapping blocks, and then the blocks with low standard deviation are selected to embed the watermark. After each of the selected blocks is decomposed by DWT, low frequency sub-bands are subjected to QR decomposition. The first row of the R matrix resulting from QR decomposition is used to embed the watermark bits. Firefly Algorithm (FA) is utilized to optimize the gain factor of embedding, which is effective in determining the robustness and imperceptibility of the watermarking scheme. To appraise the performance of the study, Peak Signal to Noise Ratio (PSNR) is used to measure the perceptual quality and Normalized Correlation (NC) is used to evaluate robustness. According to experimental results, the perceptual quality of the proposed method is found to be higher than that of similar studies in the literature. In addition, it is established that the robustness of the method against attacks such as noise, cropping, compression, and filtering is better.
{"title":"DWT-QR based blind image watermarking method using firefly algorithm","authors":"Ş. Altay, G. Ulutaş","doi":"10.1109/SIU49456.2020.9302216","DOIUrl":"https://doi.org/10.1109/SIU49456.2020.9302216","url":null,"abstract":"Digital watermarking is a method used to protect intellectual property rights and copyrights. This study represents a blind method based on Discrete Wavelet Transform (DWT) and QR decomposition. In this method, the cover image is firstly divided into non-overlapping blocks, and then the blocks with low standard deviation are selected to embed the watermark. After each of the selected blocks is decomposed by DWT, low frequency sub-bands are subjected to QR decomposition. The first row of the R matrix resulting from QR decomposition is used to embed the watermark bits. Firefly Algorithm (FA) is utilized to optimize the gain factor of embedding, which is effective in determining the robustness and imperceptibility of the watermarking scheme. To appraise the performance of the study, Peak Signal to Noise Ratio (PSNR) is used to measure the perceptual quality and Normalized Correlation (NC) is used to evaluate robustness. According to experimental results, the perceptual quality of the proposed method is found to be higher than that of similar studies in the literature. In addition, it is established that the robustness of the method against attacks such as noise, cropping, compression, and filtering is better.","PeriodicalId":312627,"journal":{"name":"2020 28th Signal Processing and Communications Applications Conference (SIU)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133083025","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 : 2020-10-05DOI: 10.1109/SIU49456.2020.9302430
Gülhan Ustabas Kaya
In this study, it is aimed to image the transparent objects by applying the phase shifting technique to lateral shearing digital holographic microscopy. In order to extract the phase information from interference patterns obtained by phase shifting technique, a hologram with 4 different phase values is created using quarter wave and half wave plates. In conventional lateral shearing digital holographic microscopy, duplicate images are formed due to the interference between two sheared object beams and information overlap occurs. The aim is to eliminate this problem by shifting the phase. Thus, the noise-free image is reconstructed without the need for any special signal processing algorithm or filter design. In this study, the images obtained with the conventional method and the proposed system are presented in 3 dimension. By comparing these two images, it is proved that the result is successfully achieved with the proposed system.
{"title":"Imaging of Transparent Objects with Phase Shifting-Lateral Shearing Digital Holographic Microscopy","authors":"Gülhan Ustabas Kaya","doi":"10.1109/SIU49456.2020.9302430","DOIUrl":"https://doi.org/10.1109/SIU49456.2020.9302430","url":null,"abstract":"In this study, it is aimed to image the transparent objects by applying the phase shifting technique to lateral shearing digital holographic microscopy. In order to extract the phase information from interference patterns obtained by phase shifting technique, a hologram with 4 different phase values is created using quarter wave and half wave plates. In conventional lateral shearing digital holographic microscopy, duplicate images are formed due to the interference between two sheared object beams and information overlap occurs. The aim is to eliminate this problem by shifting the phase. Thus, the noise-free image is reconstructed without the need for any special signal processing algorithm or filter design. In this study, the images obtained with the conventional method and the proposed system are presented in 3 dimension. By comparing these two images, it is proved that the result is successfully achieved with the proposed system.","PeriodicalId":312627,"journal":{"name":"2020 28th Signal Processing and Communications Applications Conference (SIU)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131233035","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 : 2020-10-05DOI: 10.1109/SIU49456.2020.9302480
Mustafa Atahan Nuhoglu, A. Bayri, H. A. Çırpan
This paper focuses on Doppler positioning of stationary narrowband radars for multiple Low Earth Orbit satellites, single pass scenario. In literature, methods utilize either frequency measurements and/or frequency change of rate (FCR) measurements for geolocation. Methods utilizing frequency measurements use grid search and they solve Least Squares (LS) at each search step in order to estimate center frequency of received signal; hence, they are computationally expensive. Methods based on FCR are highly sensitive to FCR measurement error. In order to overcome these problems, we propose a new geolocation method which uses only frequency measurements without estimating center frequency of received signal and without solving LS. We compare the proposed method to other methods in literature and simulation results show that the proposed method is able to produce effective initial points for an iterative Maximum Likelihood method while it is computationally less expensive than the compared methods.
{"title":"A Novel Doppler Frequency Geolocation Method for Multiple Satellites","authors":"Mustafa Atahan Nuhoglu, A. Bayri, H. A. Çırpan","doi":"10.1109/SIU49456.2020.9302480","DOIUrl":"https://doi.org/10.1109/SIU49456.2020.9302480","url":null,"abstract":"This paper focuses on Doppler positioning of stationary narrowband radars for multiple Low Earth Orbit satellites, single pass scenario. In literature, methods utilize either frequency measurements and/or frequency change of rate (FCR) measurements for geolocation. Methods utilizing frequency measurements use grid search and they solve Least Squares (LS) at each search step in order to estimate center frequency of received signal; hence, they are computationally expensive. Methods based on FCR are highly sensitive to FCR measurement error. In order to overcome these problems, we propose a new geolocation method which uses only frequency measurements without estimating center frequency of received signal and without solving LS. We compare the proposed method to other methods in literature and simulation results show that the proposed method is able to produce effective initial points for an iterative Maximum Likelihood method while it is computationally less expensive than the compared methods.","PeriodicalId":312627,"journal":{"name":"2020 28th Signal Processing and Communications Applications Conference (SIU)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131085898","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 : 2020-10-05DOI: 10.1109/SIU49456.2020.9302257
Efendi Fidan, O. Kucur
In this work, performance analysis of multi-hop networks over independent but non-identically distributed (i.n.i.d.) Nakagami-m fading channels for half-duplex (HD) and decodeand- forward (DF) transmission protocols is revised. The closed form expressions of outage probability (OP), moment generating function (MGF), symbol error rate (SER), and ergodic capacity (achievable rate) are derived. The obtained expressions are simpler than the existing ones. The validity of OP, SER, and ergodic capacity expressions is shown via Monte Carlo simulation technique.
{"title":"Performance of Decode-and-Forward Multihop Networks over i.n.i.d. Nakagami-m Fading Channels","authors":"Efendi Fidan, O. Kucur","doi":"10.1109/SIU49456.2020.9302257","DOIUrl":"https://doi.org/10.1109/SIU49456.2020.9302257","url":null,"abstract":"In this work, performance analysis of multi-hop networks over independent but non-identically distributed (i.n.i.d.) Nakagami-m fading channels for half-duplex (HD) and decodeand- forward (DF) transmission protocols is revised. The closed form expressions of outage probability (OP), moment generating function (MGF), symbol error rate (SER), and ergodic capacity (achievable rate) are derived. The obtained expressions are simpler than the existing ones. The validity of OP, SER, and ergodic capacity expressions is shown via Monte Carlo simulation technique.","PeriodicalId":312627,"journal":{"name":"2020 28th Signal Processing and Communications Applications Conference (SIU)","volume":"671 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133269113","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 : 2020-10-05DOI: 10.1109/SIU49456.2020.9302516
Durmuş Özkan Şahin, Sercan Demirci
In this study, it is aimed to filter spam e-mails by using machine learning and text mining techniques. K-Nearest Neighbor (KNN) algorithm which is one of the techniques of machine learning is used. KNN algorithm is an easy to use and high performance classification algorithm. But the main problem of this algorithm is what will be the k value at the beginning. The performance of the algorithm changes according to the selected k value. In this study, three different data sets are discussed. These are Enron, Ling-Spam and SMSSpam-Collection data sets. Firstly, basic text mining techniques and term frequency–inverse document frequency (TF-IDF) term weighting method are applied to all data sets. By, according to the Chi-Square feature selection method, the best 500 attributes are selected and given to KNN algorithm. Finally, extensive experiments are carried out by giving the values of 1, 3, 5, 7 and 9 to the k value of the algorithm. In all three data sets, the most successful result is obtained when k is 1. The most successful results obtained from Ling-Spam, Enron and SMSSpam-Collection data sets according to F-measure are 0:9324, 0:9215 and 0:9196 respectively.
{"title":"Spam Filtering with KNN: Investigation of the Effect of k Value on Classification Performance","authors":"Durmuş Özkan Şahin, Sercan Demirci","doi":"10.1109/SIU49456.2020.9302516","DOIUrl":"https://doi.org/10.1109/SIU49456.2020.9302516","url":null,"abstract":"In this study, it is aimed to filter spam e-mails by using machine learning and text mining techniques. K-Nearest Neighbor (KNN) algorithm which is one of the techniques of machine learning is used. KNN algorithm is an easy to use and high performance classification algorithm. But the main problem of this algorithm is what will be the k value at the beginning. The performance of the algorithm changes according to the selected k value. In this study, three different data sets are discussed. These are Enron, Ling-Spam and SMSSpam-Collection data sets. Firstly, basic text mining techniques and term frequency–inverse document frequency (TF-IDF) term weighting method are applied to all data sets. By, according to the Chi-Square feature selection method, the best 500 attributes are selected and given to KNN algorithm. Finally, extensive experiments are carried out by giving the values of 1, 3, 5, 7 and 9 to the k value of the algorithm. In all three data sets, the most successful result is obtained when k is 1. The most successful results obtained from Ling-Spam, Enron and SMSSpam-Collection data sets according to F-measure are 0:9324, 0:9215 and 0:9196 respectively.","PeriodicalId":312627,"journal":{"name":"2020 28th Signal Processing and Communications Applications Conference (SIU)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133314028","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 : 2020-10-05DOI: 10.1109/SIU49456.2020.9302039
Abdullah Emin Gürel, A. Orduyilmaz, G. Soysal
In this paper, the effect of the distance information error between phase centers on the accuracy of monopulse phase comparison direction finding methods used in electronic support systems is analyzed. If the path difference causing the phase difference is greater than half the wavelength of the signal, ambiguity occurs in the phase comparison method . The ambiguity in the phase comparison method can be solved by SODA method using artificial aperture or hybrid method using the amplitude comparison. In this study, the robustness of phase comparison methods accuracy to distance information error between phase centers was analyzed with errors having different standard deviation values at 180 degrees viewpoint.
{"title":"Analysis of Distance Information Error Between Phase Centers in Phase Comparison Direction Finding Methods","authors":"Abdullah Emin Gürel, A. Orduyilmaz, G. Soysal","doi":"10.1109/SIU49456.2020.9302039","DOIUrl":"https://doi.org/10.1109/SIU49456.2020.9302039","url":null,"abstract":"In this paper, the effect of the distance information error between phase centers on the accuracy of monopulse phase comparison direction finding methods used in electronic support systems is analyzed. If the path difference causing the phase difference is greater than half the wavelength of the signal, ambiguity occurs in the phase comparison method . The ambiguity in the phase comparison method can be solved by SODA method using artificial aperture or hybrid method using the amplitude comparison. In this study, the robustness of phase comparison methods accuracy to distance information error between phase centers was analyzed with errors having different standard deviation values at 180 degrees viewpoint.","PeriodicalId":312627,"journal":{"name":"2020 28th Signal Processing and Communications Applications Conference (SIU)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127858945","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 : 2020-10-05DOI: 10.1109/SIU49456.2020.9302379
Meryem Taşkesen, B. Ergen
In recent years, deep learning methods have achieved high success as solution to problems in the computer vision. Especially, CNN algorithms that extract information from the image is widely applied in logo detection. In this case, the recognition of trademark in trademark applications or infringement has been one of the major problems in the literature in terms of companies. In this paper, the dataset containing the logos of banks acquired from public domain images was collected in order to perform logo recognition, by using Faster R-CNN, an approach for the recognition of the bank logo in the video have been developed and as a result average accuracy of %98 was obtained.
{"title":"Detection of Bank Logos on Video using Faster R-CNN Method","authors":"Meryem Taşkesen, B. Ergen","doi":"10.1109/SIU49456.2020.9302379","DOIUrl":"https://doi.org/10.1109/SIU49456.2020.9302379","url":null,"abstract":"In recent years, deep learning methods have achieved high success as solution to problems in the computer vision. Especially, CNN algorithms that extract information from the image is widely applied in logo detection. In this case, the recognition of trademark in trademark applications or infringement has been one of the major problems in the literature in terms of companies. In this paper, the dataset containing the logos of banks acquired from public domain images was collected in order to perform logo recognition, by using Faster R-CNN, an approach for the recognition of the bank logo in the video have been developed and as a result average accuracy of %98 was obtained.","PeriodicalId":312627,"journal":{"name":"2020 28th Signal Processing and Communications Applications Conference (SIU)","volume":"53 8","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131688035","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 : 2020-10-05DOI: 10.1109/SIU49456.2020.9302131
Burak Tiğli, E. Erdem, Eylem Erdogan
In communication systems, cognitive radio networks have a important to meet the demand for free frequency band. The most important issue in cognitive radio networks is to accurately detect the empty spectrum and to prevent the interference between the licensed user and the unlicensed user. With the new communication systems that will be used in the coming years, the use of UAVs in communication systems will gain great importance. In this study, we propose and analyze a cognitive multi UAV communication model. In this model, we first determine the empty spectrums by using spectrum sensing methods. Afterwards, with the aid of dynamic spectrum access, the empty spectrums will be shared with the secondary users without causing any interfering to the primary users. Finally, fast and reliable communication is provided with UAVs and the outage probability performance of the overall system is obtained.
{"title":"Outage Probability Performance of Cognitive Radio Enabled UAV Relaying","authors":"Burak Tiğli, E. Erdem, Eylem Erdogan","doi":"10.1109/SIU49456.2020.9302131","DOIUrl":"https://doi.org/10.1109/SIU49456.2020.9302131","url":null,"abstract":"In communication systems, cognitive radio networks have a important to meet the demand for free frequency band. The most important issue in cognitive radio networks is to accurately detect the empty spectrum and to prevent the interference between the licensed user and the unlicensed user. With the new communication systems that will be used in the coming years, the use of UAVs in communication systems will gain great importance. In this study, we propose and analyze a cognitive multi UAV communication model. In this model, we first determine the empty spectrums by using spectrum sensing methods. Afterwards, with the aid of dynamic spectrum access, the empty spectrums will be shared with the secondary users without causing any interfering to the primary users. Finally, fast and reliable communication is provided with UAVs and the outage probability performance of the overall system is obtained.","PeriodicalId":312627,"journal":{"name":"2020 28th Signal Processing and Communications Applications Conference (SIU)","volume":"245 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133783529","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 : 2020-10-05DOI: 10.1109/SIU49456.2020.9302322
O. O. Karakilinc, Erdem Tokabas
{"title":"Degenerate Mode Split in Two Dimensional Photonic Crystal Indirect Coupling Structure","authors":"O. O. Karakilinc, Erdem Tokabas","doi":"10.1109/SIU49456.2020.9302322","DOIUrl":"https://doi.org/10.1109/SIU49456.2020.9302322","url":null,"abstract":"","PeriodicalId":312627,"journal":{"name":"2020 28th Signal Processing and Communications Applications Conference (SIU)","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115526092","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 : 2020-10-05DOI: 10.1109/SIU49456.2020.9302116
Onur Acun, Ayhan Küçükmanísa, Yakup Genç, O. Urhan
Nowadays, many methods are developed on autonomous vehicles and driver assistance systems to prevent traffic accidents and support drivers. In this work, a drivable area detection method based on CNN and regression is proposed. In the proposed method, Cityscapes dataset, which is open to sharing on the Internet is used as dataset. The images in the dataset are cut into slices to obtain new input images. With those images, a CNN based deep learning network is trained. By applying linear regression on the characteristics of the output of the network, the road boundary points in the relevant slice are tried to be determined. Experimental results have shown that the developed method has real-time operating performance and the results can be improved.
{"title":"Drivable Road Area Detection with Regression Output CNN","authors":"Onur Acun, Ayhan Küçükmanísa, Yakup Genç, O. Urhan","doi":"10.1109/SIU49456.2020.9302116","DOIUrl":"https://doi.org/10.1109/SIU49456.2020.9302116","url":null,"abstract":"Nowadays, many methods are developed on autonomous vehicles and driver assistance systems to prevent traffic accidents and support drivers. In this work, a drivable area detection method based on CNN and regression is proposed. In the proposed method, Cityscapes dataset, which is open to sharing on the Internet is used as dataset. The images in the dataset are cut into slices to obtain new input images. With those images, a CNN based deep learning network is trained. By applying linear regression on the characteristics of the output of the network, the road boundary points in the relevant slice are tried to be determined. Experimental results have shown that the developed method has real-time operating performance and the results can be improved.","PeriodicalId":312627,"journal":{"name":"2020 28th Signal Processing and Communications Applications Conference (SIU)","volume":"66 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115666318","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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