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

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Scene Classification: A Comprehensive Study Combining Local and Global Descriptors 场景分类:局部和全局描述符的综合研究
2019 27th Signal Processing and Communications Applications Conference (SIU) Pub Date : 2019-04-24 DOI: 10.1109/SIU.2019.8806590
Burak Fatih Cura, Elif Sürer
{"title":"Scene Classification: A Comprehensive Study Combining Local and Global Descriptors","authors":"Burak Fatih Cura, Elif Sürer","doi":"10.1109/SIU.2019.8806590","DOIUrl":"https://doi.org/10.1109/SIU.2019.8806590","url":null,"abstract":"In this paper, local region characteristics and overall structure of scene images are used for scene classification by combining different local and global descriptors. For this purpose, GIST, Histogram of Oriented Gradients (HOG), dense Scale-Invariant Feature Transform (SIFT), dense Speed-Up Robust Features (SURF), Daisy and Local Binary Patterns (LBP) features are classified individually and jointly with Support Vector Machine (SVM) by using different sizes of training sets. Evaluation tests were conducted on Places15, MIT indoor, SUN397 and Places365 datasets. Most used machine learning algorithms in scene classification literature -SVM with RBF and linear kernels, K-Nearest Neighbors and Random Forest- were evaluated on Places15 dataset for comparison. Besides accuracy, recall and precision, processing time for testing with SVM was measured individually and jointly for a deeper evaluation of the features.","PeriodicalId":326275,"journal":{"name":"2019 27th Signal Processing and Communications Applications Conference (SIU)","volume":"79 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126826142","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
Classification of FTIR Spectra of Olive Oil with Features of Variational Mode Decomposition and Wavelet Transform 基于变分模态分解和小波变换特征的橄榄油红外光谱分类
2019 27th Signal Processing and Communications Applications Conference (SIU) Pub Date : 2019-04-24 DOI: 10.1109/SIU.2019.8806409
Omer Faruk Karaaslan, G. Bilgin
{"title":"Classification of FTIR Spectra of Olive Oil with Features of Variational Mode Decomposition and Wavelet Transform","authors":"Omer Faruk Karaaslan, G. Bilgin","doi":"10.1109/SIU.2019.8806409","DOIUrl":"https://doi.org/10.1109/SIU.2019.8806409","url":null,"abstract":"Nowadays, it becomes important to determine the chemical structure without damaging the samples. As a result of the use of infrared, the spectras are obtained both quickly and without any special sample preparation process, and they contain specific characteristics. In this study, features of Fourier Transform Infrared spectra acquired from olive oil samples are extracted by Wavelet Transform (WT) and Variational Mode Decomposition (VMD) that does not require a main function.Afterwards, these attributes are classified in comparison by using the powerful classifiers, support vector machines (SVM) and random forests (RF). Experimental studies have shown that the features obtained by two proposed methods increase the classification performance.","PeriodicalId":326275,"journal":{"name":"2019 27th Signal Processing and Communications Applications Conference (SIU)","volume":"77 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121643194","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
Design of a Deep Face Detector by Mask R-CNN 基于R-CNN掩模的深度人脸检测器设计
2019 27th Signal Processing and Communications Applications Conference (SIU) Pub Date : 2019-04-24 DOI: 10.1109/SIU.2019.8806447
Ozan Cakiroglu, Caner Ozer, Bilge Günsel
{"title":"Design of a Deep Face Detector by Mask R-CNN","authors":"Ozan Cakiroglu, Caner Ozer, Bilge Günsel","doi":"10.1109/SIU.2019.8806447","DOIUrl":"https://doi.org/10.1109/SIU.2019.8806447","url":null,"abstract":"In this work an existing object detector, Mask RCNN, is trained for face detection and performance results are reported by using the learned model. Differing from the existing work, it is aimed to train the deep detector with a small number of training examples and also to perform instance segmentation along with an object bounding box detection. Training set includes 2695 face examples collected from PASCAL-VOC database. Performance has been reported on 159,000 test faces of WIDER FACE benchmarking database. Numerical results demonstrate that the trained Mask R-CNN provides higher detection rates with respect to the baseline detector [1], particularly 6%, 12%, and 3% higher face detection accuracy for the small, medium and large scale faces, respectively. It is also reported that our performance outperforms Viola & Jones face detector. We released the face segmentation ground-truth data that was used to train Mask R-CNN and training-test routines developed in TensorFlow platform to public usage at our GitHub repository.","PeriodicalId":326275,"journal":{"name":"2019 27th Signal Processing and Communications Applications Conference (SIU)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121483771","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: 12
Optimizing Parameters of Signal Temporal Logic Formulas with Local Search 基于局部搜索的信号时序逻辑公式参数优化
2019 27th Signal Processing and Communications Applications Conference (SIU) Pub Date : 2019-04-24 DOI: 10.1109/SIU.2019.8806568
Sertaç Kagan Aydin, E. A. Göl
{"title":"Optimizing Parameters of Signal Temporal Logic Formulas with Local Search","authors":"Sertaç Kagan Aydin, E. A. Göl","doi":"10.1109/SIU.2019.8806568","DOIUrl":"https://doi.org/10.1109/SIU.2019.8806568","url":null,"abstract":"Signal temporal logic (STL) is a formal language for expressing temporal and real-time properties of real valued signals. In this paper, we study the problem of generating an STL formula from a labeled dataset. We propose a local search algorithm to synthesize parameters of a template formula. Starting from a random initial point, the parameter space is explored in the directions improving the formula evaluation. In addition, the local search method is integrated to the genetic algorithms developed for formula synthesis as the adaptation step. The findings of the paper are shown on a case study and compared with the previous results, which shows that the adaptation step improves the convergence.","PeriodicalId":326275,"journal":{"name":"2019 27th Signal Processing and Communications Applications Conference (SIU)","volume":"74 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128914894","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
Contribution of the Resampling Stage to the Execution Time of Particle Filter 重采样阶段对粒子滤波器执行时间的贡献
2019 27th Signal Processing and Communications Applications Conference (SIU) Pub Date : 2019-04-24 DOI: 10.1109/SIU.2019.8806527
Özcan Dülger, Halit Oğuztüzün
{"title":"Contribution of the Resampling Stage to the Execution Time of Particle Filter","authors":"Özcan Dülger, Halit Oğuztüzün","doi":"10.1109/SIU.2019.8806527","DOIUrl":"https://doi.org/10.1109/SIU.2019.8806527","url":null,"abstract":"Particle filter is a serial Monte Carlo estimation algorithm. It represents the posterior probability density function with particles and their weights. As time progresses, the normalized weight of one of particles becomes nearly one, while the normalized weights of the remaining ones get close to zero. A common way to solve this problem, known as the degeneracy problem, is resampling. In resampling, the particles with larger weights are replicated, and the particles with smaller weights are eliminated. To tackle the numerical instability problem that is encountered by some of the resampling methods, the Metropolis resampling method is proposed by Murray and his co-workers. Unfortunately, Metropolis is liable to non-coalesced global memory access patterns on the GPU. In this work, we point to the Metropolis-C1 and Metropolis-C2 resampling methods which are proposed earlier. Then we examine the contribution of the stages of the particle filter to the total execution time by increasing the number of particles on a tracking application on the GPU. We use the Sampling Importance Resampling (SIR) method, which is a common particle filter. In the experiments, Metropolis resampling consumes the biggest portion of the execution time of the SIR particle filter. The share of Metropolis increases as the number of particles grows. It can be argued that this is because of non-coalesced global memory access patterns. To reach this conclusion it is sufficient to i) Compare the results of Metropolis, which has non-coalesced access patterns, with Metropolis-C1 and Metropolis-C2, which have confined non-coalesced access patterns, ii) See that the previous stages of the SIR particle filter are not subject to non-coalesced access patterns.","PeriodicalId":326275,"journal":{"name":"2019 27th Signal Processing and Communications Applications Conference (SIU)","volume":"117 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126796696","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
Development a Machine Vision System For Marble Classification 开发一种用于大理石分类的机器视觉系统
2019 27th Signal Processing and Communications Applications Conference (SIU) Pub Date : 2019-04-24 DOI: 10.1109/SIU.2019.8806419
Y. Torun, M. Akbas, M. Çelik, O. Kaynar
{"title":"Development a Machine Vision System For Marble Classification","authors":"Y. Torun, M. Akbas, M. Çelik, O. Kaynar","doi":"10.1109/SIU.2019.8806419","DOIUrl":"https://doi.org/10.1109/SIU.2019.8806419","url":null,"abstract":"In marble sector, marble quality varies depending on vessel pattern and color. These patterns and colors are the most important factors affecting the quality and possible class of marble. The marble tiles in the marble palette ordered by the marble palette and the difference between the pattern and quality of the product causes the return of the product. Therefore, many firms suffer economic damage. In order to prevent this damage, it has become an important issue to automatically process the classification process with image processing and deep learning methods. In this study, it is aimed to make classification by adding new data to pre-trained network by AlexNet model. Fimar Marble Mine Co. Inc. operating in Sivas. In 3 different classes, 600 marble samples were trained by AlexNet model and Local Binary Pattern method and the pattern information was obtained. Local Binary Pattern method was used to classify the characteristic by creating color and pattern.","PeriodicalId":326275,"journal":{"name":"2019 27th Signal Processing and Communications Applications Conference (SIU)","volume":"344 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133992390","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
Automatic Fraud Detection In Call Center Conversations 呼叫中心会话中的自动欺诈检测
2019 27th Signal Processing and Communications Applications Conference (SIU) Pub Date : 2019-04-24 DOI: 10.1109/SIU.2019.8806262
Berk Özlan, Ali Haznedaroglu, L. Arslan
{"title":"Automatic Fraud Detection In Call Center Conversations","authors":"Berk Özlan, Ali Haznedaroglu, L. Arslan","doi":"10.1109/SIU.2019.8806262","DOIUrl":"https://doi.org/10.1109/SIU.2019.8806262","url":null,"abstract":"In this paper, a machine learning system that automatically detects fraudulent call center conversations is presented. The system first transcribes the call center telephone conversations into text using a speech recognition engine and then it automatically detects the fraudulent conversations by a text-categorization algorithm using the transcribed texts. Several classifiers that use different document vectorizers are trained, tested and their performances are compared. The best results are obtained by using deep convolutional neural networks that use word embedding vectors as their inputs. With these networks, 43% of fraudulent calls can be automatically detected with 62% precision.","PeriodicalId":326275,"journal":{"name":"2019 27th Signal Processing and Communications Applications Conference (SIU)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134603948","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
Using Morpheme-Level Attention Mechanism for Turkish Sequence Labelling 基于语素水平注意机制的土耳其语序列标注
2019 27th Signal Processing and Communications Applications Conference (SIU) Pub Date : 2019-04-24 DOI: 10.1109/SIU.2019.8806530
Yasin Esref, Burcu Can
{"title":"Using Morpheme-Level Attention Mechanism for Turkish Sequence Labelling","authors":"Yasin Esref, Burcu Can","doi":"10.1109/SIU.2019.8806530","DOIUrl":"https://doi.org/10.1109/SIU.2019.8806530","url":null,"abstract":"With deep learning being used in natural language processing problems, there have been serious improvements in the solution of many problems in this area. Sequence labeling is one of these problems. In this study, we examine the effects of character, morpheme, and word representations on sequence labelling problems by proposing a model for the Turkish language by using deep neural networks. Modeling the word as a whole in agglutinative languages such as Turkish causes sparsity problem. Therefore, rather than handling the word as a whole, expressing a word through its characters or considering the morpheme and morpheme label information gives more detailed information about the word and mitigates the sparsity problem. In this study, we applied the existing deep learning models using different word or sub-word representations for Named Entity Recognition (NER) and Part-of-Speech Tagging (POS Tagging) in Turkish. The results show that using morpheme information of words improves the Turkish sequence labelling.","PeriodicalId":326275,"journal":{"name":"2019 27th Signal Processing and Communications Applications Conference (SIU)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123977513","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
Minimizing The Number of Effective Channel Taps by Energy Optimization in Communications Systems 通信系统中能量优化的有效通道接入数最小化
2019 27th Signal Processing and Communications Applications Conference (SIU) Pub Date : 2019-04-24 DOI: 10.1109/SIU.2019.8806378
Ünzüle Senol Kizilkaya, S. Çolak, H. Arslan
{"title":"Minimizing The Number of Effective Channel Taps by Energy Optimization in Communications Systems","authors":"Ünzüle Senol Kizilkaya, S. Çolak, H. Arslan","doi":"10.1109/SIU.2019.8806378","DOIUrl":"https://doi.org/10.1109/SIU.2019.8806378","url":null,"abstract":"Channel delays and mobility cause the transmitted signal to propagate in both time and frequency regions, resulting in inter-symbol and inter-carrier interference, respectively. The filter structures used in the transmitter and receiver structures for impact shaping affect the size of the interference between these symbols and carriers. In this study, the composite effects of the wireless environment and the filters used were examined on the equalization of multi-carrier systems and the determination of the appropriate filter structure was studied. The number of effective taps of the composite channel effect was obtained as a function of the channel, the transmitter/receiver filters and the signal to noise ratio (SNR). For this purpose, the composite channel was modeled as an symbol-spaced finite impulse response (FIR) filter, then the AIC (Akaike Information Criterion) method was used to determine the number of taps. Subsequently, the transmitter/receiver filter types were determined which minimized the effective taps of the composite channel model by energy optimization in the equalization process.","PeriodicalId":326275,"journal":{"name":"2019 27th Signal Processing and Communications Applications Conference (SIU)","volume":"83 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129244579","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
Visual Place Recognition by DTW-based sequence alignment 基于dtw序列比对的视觉位置识别
2019 27th Signal Processing and Communications Applications Conference (SIU) Pub Date : 2019-04-24 DOI: 10.1109/SIU.2019.8806363
A. Hafez, Ammar Tello, Saed Alqaraleh
{"title":"Visual Place Recognition by DTW-based sequence alignment","authors":"A. Hafez, Ammar Tello, Saed Alqaraleh","doi":"10.1109/SIU.2019.8806363","DOIUrl":"https://doi.org/10.1109/SIU.2019.8806363","url":null,"abstract":"Place recognition, also called visual localization, facilitates the autonomous navigation capabilities of the future of driverless cars. This paper proposes a new place recognition algorithm that considers the appearancebased methodology to localize the vehicle by utilizing visual route map, i.e. a sequence of images, or sets of features extracted from these images, that were recorded over different times and dates for the route environments. These reference sequences are accurately labeled and annotated using GPS tags or manually using odometry information. The dynamic time warping (DTW) algorithm is used to achieve image sequence alignment and find the best match for each frame from the test sequence. The proposed algorithm considered hand-crafted features like SIFT, HOG, and LDB. Experiments, using common challenging and benchmark datasets, i.e. “UQ St Lucia” and “Nordland”, have been conducted, and it has been observed that the proposed technique has significantly improved the performance of well-known appearancebased descriptors SIFT, HOG, and LDB as compared to its individual performance and to some of the state-of-the-art localization and mapping methods such as ABLE (Binary-appearance Loop-closure).","PeriodicalId":326275,"journal":{"name":"2019 27th Signal Processing and Communications Applications Conference (SIU)","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115632259","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
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