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2019 27th Signal Processing and Communications Applications Conference (SIU)最新文献

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Functional Classification of Web Pages with Deep Learning 基于深度学习的网页功能分类
2019 27th Signal Processing and Communications Applications Conference (SIU) Pub Date : 2019-04-01 DOI: 10.1109/SIU.2019.8806240
Caner Balim, Kemal Özkan
{"title":"Functional Classification of Web Pages with Deep Learning","authors":"Caner Balim, Kemal Özkan","doi":"10.1109/SIU.2019.8806240","DOIUrl":"https://doi.org/10.1109/SIU.2019.8806240","url":null,"abstract":"Automatic processing of websites is of great importance for applications such as search engine that extract information from web pages. Search engines use meta tag values when classifying pages of websites. Meta tag names can change for different languages. For example, for login page, entries such as login, login page or giris, giris sayfası may change from language to language. When the websites are examined, it can be seen that each of the pages created for the same purpose has similar designs. In this study, a deep learning based model was proposed for functional classification of web pages, regardless of language. Transfer learning was used to reduce the cost during the feature extraction process from recorded web page images. Finally, the results of two different experiments are presented for show the effectiveness of the proposed method in the classification of web pages according to their functions.","PeriodicalId":326275,"journal":{"name":"2019 27th Signal Processing and Communications Applications Conference (SIU)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121715922","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
Energy Efficient Sensor Design and Implementation on FPGA by Using Open Source Processors 基于开源处理器的FPGA节能传感器设计与实现
2019 27th Signal Processing and Communications Applications Conference (SIU) Pub Date : 2019-04-01 DOI: 10.1109/SIU.2019.8806264
Mehmet Onur Demirtürk, Latif Akçay, B. Yalçin
{"title":"Energy Efficient Sensor Design and Implementation on FPGA by Using Open Source Processors","authors":"Mehmet Onur Demirtürk, Latif Akçay, B. Yalçin","doi":"10.1109/SIU.2019.8806264","DOIUrl":"https://doi.org/10.1109/SIU.2019.8806264","url":null,"abstract":"IoT applications are becoming increasingly important. The most critical parameters for these applications are operating system support and low power consumption. In this study, open source processors that can be used for IoT applications that require low power consumption are examined. Leon3 and OpenRISC are two important platforms that can be used in this field and provide operating system support. The power consumption analyzes of the two systems were obtained and compared through different benchmark tests. Thus, a technical analysis support was provided to the designers from this point of view.","PeriodicalId":326275,"journal":{"name":"2019 27th Signal Processing and Communications Applications Conference (SIU)","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125137266","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
Performance Analysis of Color Shift Keying Systems in AWGN and Color Noise Environment AWGN和彩色噪声环境下的色移键控系统性能分析
2019 27th Signal Processing and Communications Applications Conference (SIU) Pub Date : 2019-04-01 DOI: 10.1109/SIU.2019.8806390
Furkan Durukan, Birhan Mert Güney, A. Özen
{"title":"Performance Analysis of Color Shift Keying Systems in AWGN and Color Noise Environment","authors":"Furkan Durukan, Birhan Mert Güney, A. Özen","doi":"10.1109/SIU.2019.8806390","DOIUrl":"https://doi.org/10.1109/SIU.2019.8806390","url":null,"abstract":"In this paper, bit error rate (BER) performances of the color shift keying (CSK) modulation which is selected as one of the modulation methods in visible light communication systems, are analyzed in AWGN and color noise environments. Red, blue, pink and violet noises are used as colored noise. In addition, the performance of visible light communication systems is investigated by using a single-path channel in indoor environments where the transmitter and the receiver see each other (Line of sight, LOS). Computer simulation studies are performed for 4-CSK, 8-CSK and 16-CSK modulation. It is seen that the best performances of computer simulation results are obtained in red noisy environments after AWGN.","PeriodicalId":326275,"journal":{"name":"2019 27th Signal Processing and Communications Applications Conference (SIU)","volume":"443 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122804718","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
Domain Adaptation with Nonparametric Projections 非参数投影的领域自适应
2019 27th Signal Processing and Communications Applications Conference (SIU) Pub Date : 2019-04-01 DOI: 10.1109/SIU.2019.8806543
Elif Vural
{"title":"Domain Adaptation with Nonparametric Projections","authors":"Elif Vural","doi":"10.1109/SIU.2019.8806543","DOIUrl":"https://doi.org/10.1109/SIU.2019.8806543","url":null,"abstract":"Domain adaptation algorithms focus on a setting where the training and test data are sampled from related but different distributions. Various domain adaptation methods aim to align the source and target domains in a new common domain by learning a transformation or projection. In this work, we learn a nonlinear and nonparametric projection of the source and target domains into a common domain along with a linear classifier in the new domain. Experiments on image data sets show that the proposed nonlinear approach outperforms baseline domain adaptation methods based on linear transformations","PeriodicalId":326275,"journal":{"name":"2019 27th Signal Processing and Communications Applications Conference (SIU)","volume":"19 9","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131574823","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
Jamming Performance Analysis of Chirp Jammers on DSSS Signals 啁啾干扰机对DSSS信号的干扰性能分析
2019 27th Signal Processing and Communications Applications Conference (SIU) Pub Date : 2019-04-01 DOI: 10.1109/SIU.2019.8806472
C. Toker, C. Sunu, Sevket Gögüsdere
{"title":"Jamming Performance Analysis of Chirp Jammers on DSSS Signals","authors":"C. Toker, C. Sunu, Sevket Gögüsdere","doi":"10.1109/SIU.2019.8806472","DOIUrl":"https://doi.org/10.1109/SIU.2019.8806472","url":null,"abstract":"Direct Sequence Spread Spectrum (DSSS) signals are commonly used for a variety of applications including radar, positioning and communications. Under certain circumstances, a jammer can used as a counter measure to DSSS systems. One of the several jamming mechanisms is the use of chirp signals. In this paper, we investigate the jamming performance of chirp signals which linearly sweep a certain bandwidth in a certain period on DSSS. We demonstrate that jamming performance of the chirp signal depends on both the parameters of the jammer and also to the period of the DSSS signal and the Pseudorandom Noise (PN) sequence which is used to generate the DSSS signal. Rules for designing a chirp jammer are also provided. Results of experimental trials are also discussed.","PeriodicalId":326275,"journal":{"name":"2019 27th Signal Processing and Communications Applications Conference (SIU)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132704047","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
MSVD-Turkish: A Large-Scale Dataset for Video Captioning in Turkish MSVD-Turkish:土耳其语视频字幕的大规模数据集
2019 27th Signal Processing and Communications Applications Conference (SIU) Pub Date : 2019-04-01 DOI: 10.1109/SIU.2019.8806555
Begum Citamak, Menekse Kuyu, Aykut Erdem, Erkut Erdem
{"title":"MSVD-Turkish: A Large-Scale Dataset for Video Captioning in Turkish","authors":"Begum Citamak, Menekse Kuyu, Aykut Erdem, Erkut Erdem","doi":"10.1109/SIU.2019.8806555","DOIUrl":"https://doi.org/10.1109/SIU.2019.8806555","url":null,"abstract":"Automatically generating natural language descriptions for videos, aka video captioning, has been recently introduced as a challenging integrated vision and language problem. Although researchers have demonstrated numerous solutions for English, to date there has been no study on Turkish language due to the lack of suitable datasets to train Turkish video captioning models. To tackle this, in this study we construct a largescale Turkish benchmark dataset by carefully translating English descriptions from MSVD dataset to Turkish. Moreover, we implement several neural models, including LSTM-based sequence-tosequence architectures with temporal attention mechanisms, and report the performances of these strong baselines on our dataset. We hope that our dataset will serve as a good resource for future efforts on Turkish video captioning.","PeriodicalId":326275,"journal":{"name":"2019 27th Signal Processing and Communications Applications Conference (SIU)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133125243","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: 5
Optimal Quantization in Decentralized Detection by Maximizing the Average Entropy of the Sensors 利用传感器平均熵最大化分散检测中的最优量化
2019 27th Signal Processing and Communications Applications Conference (SIU) Pub Date : 2019-04-01 DOI: 10.1109/SIU.2019.8806534
Muath A. Wahdan, M. Altınkaya
{"title":"Optimal Quantization in Decentralized Detection by Maximizing the Average Entropy of the Sensors","authors":"Muath A. Wahdan, M. Altınkaya","doi":"10.1109/SIU.2019.8806534","DOIUrl":"https://doi.org/10.1109/SIU.2019.8806534","url":null,"abstract":"In a wireless sensor network the sensor outputs are required to be quantized because of energy and bandwidth requirements. We propose such a distributed detection scheme for a point source which is based on Neyman-Pearson criterion where sensor outputs are quantized maximizing the average output entropy of the sensors under both hypotheses. The quantized local outputs are transmitted to a fusion center (FC) where they are used to make a global decision. The performance of the proposed maximum average entropy (MAE) method in quantizing sensor outputs was tested for binary, ternary and quarternary quantization. The effects of the channel from the sensors to the FC is also addressed by simplified channel models. The simulation studies show the success of the MAE method.","PeriodicalId":326275,"journal":{"name":"2019 27th Signal Processing and Communications Applications Conference (SIU)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115335123","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
Driver Classification Using K-Means Clustering of Within-Car Accelerometer Data 基于K-Means聚类的车内加速度计数据驾驶员分类
2019 27th Signal Processing and Communications Applications Conference (SIU) Pub Date : 2019-04-01 DOI: 10.1109/SIU.2019.8806602
Tuba Nur Serttas, Ö. N. Gerek, F. Hocaoglu
{"title":"Driver Classification Using K-Means Clustering of Within-Car Accelerometer Data","authors":"Tuba Nur Serttas, Ö. N. Gerek, F. Hocaoglu","doi":"10.1109/SIU.2019.8806602","DOIUrl":"https://doi.org/10.1109/SIU.2019.8806602","url":null,"abstract":"In this study, driving characteristics of 13 different people on a predetermined route have been analyzed by using the driving characteristics of the drivers and the drivers are classified into 3 groups: calm, normal and aggressive. The data recorded by the acceleration meter sensor and the global positioning (GPS) receiver of a smart phone were analyzed using signal processing methods in the computer environment. Based on the connections between the data, the basic data that reveal the driving characteristics are determined. In the current phase of the study, K-means method was used as the classification method. The classification accuracy was investigated by changing the K value. For experimental data, the most accurate results were obtained as 93.3% for K = 5. This result shows that simple 3-axis accelerometers installed in the cars are sufficient for providing necessary features for classifying driving characteristics using very simple classifiers.","PeriodicalId":326275,"journal":{"name":"2019 27th Signal Processing and Communications Applications Conference (SIU)","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122987424","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
Compressive Focal Plane Array Imager Reconstruction Using Learning Based Regularization 基于学习正则化的压缩焦平面阵列成像仪重建
2019 27th Signal Processing and Communications Applications Conference (SIU) Pub Date : 2019-04-01 DOI: 10.1109/SIU.2019.8806369
Oğuzhan Fatih Kar, A. Güngör, H. E. Güven
{"title":"Compressive Focal Plane Array Imager Reconstruction Using Learning Based Regularization","authors":"Oğuzhan Fatih Kar, A. Güngör, H. E. Güven","doi":"10.1109/SIU.2019.8806369","DOIUrl":"https://doi.org/10.1109/SIU.2019.8806369","url":null,"abstract":"In this paper, we develop a learning based regularization method for reconstructing compressive focal plane array imager (CFPAI). While many optimization algorithms employ proximal operators for regularization purposes such as total variation minimization, they are often inadequate to fully capture the likelihood of complex natural images. Recently, deep learning based approaches obtain promising results in different imaging problems, creating the possibility to use them as a regularizer in an optimization framework. Here, we utilize this approach in CFPAI obtaining spatially modulated and downsampled measurements of the incoming light intensity. We first formulate the problem of finding original high resolution image from its measurements as an optimization problem. Then, we solve the resulting problem using alternating direction method of multipliers (ADMM). In ADMM, we replace the proximal operator corresponding to the regularization function with a deep convolutional denoising network. Results show successful reconstruction performance in terms of reconstruction pSNR and visual quality even under significant noise levels.","PeriodicalId":326275,"journal":{"name":"2019 27th Signal Processing and Communications Applications Conference (SIU)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122635878","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
Kernel Density Estimation for Optimal Detection in All-Bit-Line MLC Flash Memories 全位行MLC闪存中最优检测的核密度估计
2019 27th Signal Processing and Communications Applications Conference (SIU) Pub Date : 2019-04-01 DOI: 10.1109/SIU.2019.8806517
Reza A. Ashrafi, A. E. Pusane, Suayb S. Arslan
{"title":"Kernel Density Estimation for Optimal Detection in All-Bit-Line MLC Flash Memories","authors":"Reza A. Ashrafi, A. E. Pusane, Suayb S. Arslan","doi":"10.1109/SIU.2019.8806517","DOIUrl":"https://doi.org/10.1109/SIU.2019.8806517","url":null,"abstract":"NAND flash memories have recently become the main component of large-scale non-volatile storage systems. Recent studies have shown that various error sources degrade the Multi-level cell (MLC) memory performance, including intercell interference, retention error, and random telegraph noise. Accurate integration of these error sources into the analytical model to optimally derive the governing probability distributions and consequently the detection thresholds to minimize error rates lie at the heart of MLC research. Utilizing static derivations will not address the detection problem, as aforementioned error sources exhibit a strong non-stationary behavior. In this paper, a novel low-complexity implementation of a non-parametric learning mechanism, kernel density estimation, shall be used to periodically estimate the underlying probability distributions and hence approximate the optimal detection performance for time-varying all-bit-line MLC flash channel.","PeriodicalId":326275,"journal":{"name":"2019 27th Signal Processing and Communications Applications Conference (SIU)","volume":"86 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125904326","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
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