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2016 24th Signal Processing and Communication Application Conference (SIU)最新文献

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Impedance matching of dielectric loaded T-junction in X-Ku band 介质负载t结在X-Ku波段的阻抗匹配
2016 24th Signal Processing and Communication Application Conference (SIU) Pub Date : 2016-05-16 DOI: 10.1109/SIU.2016.7495795
A. Genç, M. F. Çaglar
{"title":"Impedance matching of dielectric loaded T-junction in X-Ku band","authors":"A. Genç, M. F. Çaglar","doi":"10.1109/SIU.2016.7495795","DOIUrl":"https://doi.org/10.1109/SIU.2016.7495795","url":null,"abstract":"In this study, impedance matching of H-plane T-junction in the rectangular waveguide for both equal and unequal power division has been investigated. The matching has been made by a septum and two symmetric irises and the performances of power dividers have been viewed for both vacuum and dielectric loaded rectangular waveguides. Lossy Rogers RO4450B as dielectric material has been preferred. At 12GHz, the operating frequency, E-field distribution of T-junction, return loss of the input port and phase difference and power ratio between the output ports have been obtained. In addition, return loss better than 18dB was achieved in 10-14GHz frequency range according to the simulation results.","PeriodicalId":427250,"journal":{"name":"2016 24th Signal Processing and Communication Application Conference (SIU)","volume":"30 5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126077028","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}
引用次数: 3
Design, analysis and manufacturing of dielectric rod added conical horn antennas 介质杆加锥形喇叭天线的设计、分析与制造
2016 24th Signal Processing and Communication Application Conference (SIU) Pub Date : 2016-05-16 DOI: 10.1109/SIU.2016.7496044
Esra Avcioglu, Alper Ozaslan, Fatma Çalışkan
{"title":"Design, analysis and manufacturing of dielectric rod added conical horn antennas","authors":"Esra Avcioglu, Alper Ozaslan, Fatma Çalışkan","doi":"10.1109/SIU.2016.7496044","DOIUrl":"https://doi.org/10.1109/SIU.2016.7496044","url":null,"abstract":"In this study, ANSYS HFSS (High Frequency Structural Simulator) software had been used. By using this software, dielectric rod added conical horn antennas with various shapes and dimensions had been designed and analyzed. 5 (five) distinct antennas designed and their gains and return losses had been observed to evaluate the effects of the changes. From the results of these comparisons, the proper antenna type was selected and it was manufactured.","PeriodicalId":427250,"journal":{"name":"2016 24th Signal Processing and Communication Application Conference (SIU)","volume":"355 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114083716","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}
引用次数: 0
New approaches based on real and complex forms of ripplet-I transform for image analysis 基于真实和复杂形式的纹波变换的图像分析新方法
2016 24th Signal Processing and Communication Application Conference (SIU) Pub Date : 2016-05-16 DOI: 10.1109/SIU.2016.7495847
H. Yaşar, M. Ceylan
{"title":"New approaches based on real and complex forms of ripplet-I transform for image analysis","authors":"H. Yaşar, M. Ceylan","doi":"10.1109/SIU.2016.7495847","DOIUrl":"https://doi.org/10.1109/SIU.2016.7495847","url":null,"abstract":"The multi resolution analysis are important parts of image processing. Curvelet transform is analysis method which have been using wide variety of applications in multi resolution analysis. Ripplet-I transform is defined by recently generalising of the curvelet transform by adding parameters support (c) and degree (d). Even though this transform has been found out recently, it has been using wide variety of applications. Fast discrete and complex fast discrete versions of ripplet-I transform were examined by this study. In denoising application, better results were obtained with fast discrete and complex fast discrete versions of ripplet-I transform by discrete ripplet-I transform.","PeriodicalId":427250,"journal":{"name":"2016 24th Signal Processing and Communication Application Conference (SIU)","volume":"271 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115301133","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}
引用次数: 2
Effect of voxel selection on temporal mesh model for brain decoding 体素选择对大脑解码时间网格模型的影响
2016 24th Signal Processing and Communication Application Conference (SIU) Pub Date : 2016-05-16 DOI: 10.1109/SIU.2016.7496223
Arman Afrasiyabi, Itir Önal, F. Yarman-Vural
{"title":"Effect of voxel selection on temporal mesh model for brain decoding","authors":"Arman Afrasiyabi, Itir Önal, F. Yarman-Vural","doi":"10.1109/SIU.2016.7496223","DOIUrl":"https://doi.org/10.1109/SIU.2016.7496223","url":null,"abstract":"In this study, we combine a voxel selection method with temporal mesh model to decode the discriminative information distributed in functional Magnetic Resonance Imaging (fMRI) data. We first employ one way Analysis of Variance (ANOVA) feature selection to select the most informative voxels. Then, we form meshes around selected voxels with their spatial and functional neighbors by employing the Mesh Model with Temporal Measurements (MM-TM). We estimate the arc weights of meshes, which represent the relationships among voxels within the selected neighborhood. In order to get rid of the redundant relationships, we prune the estimated mesh weights using ANOVA. By doing so, we obtain a sparse representation of discriminative information in the brain. Finally, we train k-Nearest Neighbor (kNN) and Support Vector Machine (SVM) classifiers using the sparse mesh arc weights. We used fMRI recordings from a visual object recognition experiment. Our results show that employing the selected voxels in classification performs better than employing all voxels in the brain. Moreover, mesh arc weights formed around selected voxels outperform the intensity values of selected voxels. Finally, pruning the mesh arc weights leads to a slight increase in the classification performance.","PeriodicalId":427250,"journal":{"name":"2016 24th Signal Processing and Communication Application Conference (SIU)","volume":"49 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121555171","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}
引用次数: 2
Comparison of compressed sensing based algorithms for sparse signal reconstruction 基于压缩感知的稀疏信号重构算法比较
2016 24th Signal Processing and Communication Application Conference (SIU) Pub Date : 2016-05-16 DOI: 10.1109/SIU.2016.7496021
Safa Çelik, M. Başaran, Serhat Erküçük, H. A. Çırpan
{"title":"Comparison of compressed sensing based algorithms for sparse signal reconstruction","authors":"Safa Çelik, M. Başaran, Serhat Erküçük, H. A. Çırpan","doi":"10.1109/SIU.2016.7496021","DOIUrl":"https://doi.org/10.1109/SIU.2016.7496021","url":null,"abstract":"Compressed sensing theory shows that any signal which is defined as sparse in a given domain can be reconstructed using fewer linear projections instead of using all Nyquist-rate samples. In this paper, we investigate basis pursuit, matching pursuit, orthogonal matching pursuit and compressive sampling matching pursuit algorithms, which are basic compressed sensing based algorithms, and present performance curves in terms of mean squared error for various parameters including signal-to-noise ratio, sparsity and number of measurements with regard to mean squared error. In addition, accuracy of estimation performances has been supported with theoretical lower bounds (Cramer-Rao lower bound and deterministic lower mean squared error). Considering estimation performances, compressive sampling matching pursuit yields the best results unless the signal has a non-sparse structure.","PeriodicalId":427250,"journal":{"name":"2016 24th Signal Processing and Communication Application Conference (SIU)","volume":"258 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114299811","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}
引用次数: 4
A frequency domain channel equalizer for discrete Wavelet Transform based OFDM systems 基于离散小波变换的OFDM系统的频域信道均衡器
2016 24th Signal Processing and Communication Application Conference (SIU) Pub Date : 2016-05-16 DOI: 10.1109/SIU.2016.7495949
Engin Oksuz, Ahmet Altun, A. Özen
{"title":"A frequency domain channel equalizer for discrete Wavelet Transform based OFDM systems","authors":"Engin Oksuz, Ahmet Altun, A. Özen","doi":"10.1109/SIU.2016.7495949","DOIUrl":"https://doi.org/10.1109/SIU.2016.7495949","url":null,"abstract":"A novel frequency domain channel equalizer, inspired by [1], based on discrete wavelet transform (DWT) based OFDM has been proposed to improve the frequency domain channel equalizer employed OFDM systems in this paper. In this study, the performance of DWT based OFDM frequency domain channel equalizer (DWT-OFDM-FDE) has been compared with the conventional FFT based OFDM (FFT-OFDM-FDE) systems. Computer simulations have been performed to verify the performance of the proposed method in frequency selective Rayleigh fading channels. The obtained simulation results using HIPERLAN/2 standard have demonstrated that the proposed DWT-OFDM-FDE system has considerably better performance than the conventional FFT-OFDM-FDE system in all modulation types and also provides high SNR improvement of approximately 8 dB for a BER value of 1E-3.","PeriodicalId":427250,"journal":{"name":"2016 24th Signal Processing and Communication Application Conference (SIU)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125285263","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}
引用次数: 3
Super-resolution using multiple structured dictionaries based on the gradient operator and bicubic interpolation 基于梯度算子和双三次插值的多结构字典超分辨率
2016 24th Signal Processing and Communication Application Conference (SIU) Pub Date : 2016-05-16 DOI: 10.1109/SIU.2016.7495896
F. Yeganli, M. Nazzal, Hüseyin Özkaramanli
{"title":"Super-resolution using multiple structured dictionaries based on the gradient operator and bicubic interpolation","authors":"F. Yeganli, M. Nazzal, Hüseyin Özkaramanli","doi":"10.1109/SIU.2016.7495896","DOIUrl":"https://doi.org/10.1109/SIU.2016.7495896","url":null,"abstract":"In this paper we present an extension to the algorithm of super-resolution via selective sparse representation over a set of coupled low and high resolution cluster dictionary pairs. Patch clustering and sparse model selection are carried out using the magnitude and phase of the patch gradient operator. A compact dictionary pair is learned for each cluster. A low resolution patch is classified into one of the clusters using the two criteria. A high resolution patch is reconstructed using the high resolution cluster dictionary, and the spare representation coefficients of its low resolution counterpart over the low resolution cluster dictionary. This extension aims at super-resolving patches of low sharpness or poor directionality with bicubic interpolation. Accordingly, the computationally expensive sparse representation framework will only be applied on a limited portion of image patches. As a result, the super-resolution reconstruction computational complexity is significantly reduced without sacrificing the performance. Experiments conducted over natural images validate this result.","PeriodicalId":427250,"journal":{"name":"2016 24th Signal Processing and Communication Application Conference (SIU)","volume":"48 6","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114017440","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}
引用次数: 6
Regression based stereo Palm Vein extraction and Identification system 基于回归的立体掌纹提取与识别系统
2016 24th Signal Processing and Communication Application Conference (SIU) Pub Date : 2016-05-16 DOI: 10.1109/SIU.2016.7496043
O. F. Ozdemir, Serkan Colak, Y. S. Akgul
{"title":"Regression based stereo Palm Vein extraction and Identification system","authors":"O. F. Ozdemir, Serkan Colak, Y. S. Akgul","doi":"10.1109/SIU.2016.7496043","DOIUrl":"https://doi.org/10.1109/SIU.2016.7496043","url":null,"abstract":"Palm Vein Identification(PVI) systems have been attracting interests from academia, industry, and governments for their advantages such as identification accuracy and relative low costs. However, low cost Infrared (IR) camera sensors produce noisy images which degrades the robustness of these systems. This paper proposes a new PVI system that uses a mirror based stereo camera setup to increase the PVI robustness. The two images from the stereo setup are analyzed with a new vein extraction method that uses Support Vector Regressors (SVR). The junction points of these images are compared to find junction disparities for an added 3D biometric feature. We collected a dataset of PVI images from volunteers to validate the system and we also compared parts of the proposed system on standard datasets. The overall results are promising and we will continue testing new stereo PVI image analysis methods in the future.","PeriodicalId":427250,"journal":{"name":"2016 24th Signal Processing and Communication Application Conference (SIU)","volume":"57 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122634321","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}
引用次数: 1
More efficient implementations of CASCADE information reconciliation protocol 更有效地实现CASCADE信息协调协议
2016 24th Signal Processing and Communication Application Conference (SIU) Pub Date : 2016-05-16 DOI: 10.1109/SIU.2016.7495702
M. Toyran
{"title":"More efficient implementations of CASCADE information reconciliation protocol","authors":"M. Toyran","doi":"10.1109/SIU.2016.7495702","DOIUrl":"https://doi.org/10.1109/SIU.2016.7495702","url":null,"abstract":"In this paper, we present more efficient implementations of CASCADE information reconciliation (IR) protocol, using some inherent information already available in the protocol, exactly known bits and already known parities. Our experiments have shown that our presented protocols are of higher efficiency than both all the previous CASCADE versions and several other more recently proposed IR methods.","PeriodicalId":427250,"journal":{"name":"2016 24th Signal Processing and Communication Application Conference (SIU)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131214590","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}
引用次数: 6
Hyperspectral data classification using deep convolutional neural networks 基于深度卷积神经网络的高光谱数据分类
2016 24th Signal Processing and Communication Application Conference (SIU) Pub Date : 2016-05-16 DOI: 10.1109/SIU.2016.7496193
Mesut Salman, S. E. Yüksel
{"title":"Hyperspectral data classification using deep convolutional neural networks","authors":"Mesut Salman, S. E. Yüksel","doi":"10.1109/SIU.2016.7496193","DOIUrl":"https://doi.org/10.1109/SIU.2016.7496193","url":null,"abstract":"In the last five years, deep learning has been gaining a large amount of interest in the computer vision community due to its capability to perform feature learning and classification at the same time. However, the studies using deep learning for hyperspectral imaging are still very few. In this paper, a deep convolutional neural network structure to classify hyperspectral data is proposed. The results are compared to the support vector machine and K-nearest neighbourhood algorithms and it has been shown that deep learning with the proposed architecture is much more successful in hyperspectral data classification.","PeriodicalId":427250,"journal":{"name":"2016 24th Signal Processing and Communication Application Conference (SIU)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121281319","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}
引用次数: 7
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