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2017 25th Signal Processing and Communications Applications Conference (SIU)最新文献

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Automated detection of citrus trees from a digital surface model 从数字表面模型自动检测柑橘树
2017 25th Signal Processing and Communications Applications Conference (SIU) Pub Date : 2017-05-15 DOI: 10.1109/SIU.2017.7960165
A. Ok, Asli Ozdarici-Ok
{"title":"Automated detection of citrus trees from a digital surface model","authors":"A. Ok, Asli Ozdarici-Ok","doi":"10.1109/SIU.2017.7960165","DOIUrl":"https://doi.org/10.1109/SIU.2017.7960165","url":null,"abstract":"In this paper, we present an approach to detect citrus trees from digitals surface models (DSMs) generated from Unmanned Aerial Vehicle (UAV). The symmetric characteristics of the citrus trees in a DSM are revealed by orientation-based radial symmetry transform. The method is tested on four UAV DSMs that have different planting characteristics of citrus orchards. The approach is compared with three previously developed approaches. Comparison to the state-of-the-art reveals that the proposed approach provides superior detection performances through supporting a nice balance between precision and recall measures.","PeriodicalId":217576,"journal":{"name":"2017 25th Signal Processing and Communications Applications Conference (SIU)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130682458","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
Ensemble learning with surrogate splits 具有代理分割的集成学习
2017 25th Signal Processing and Communications Applications Conference (SIU) Pub Date : 2017-05-15 DOI: 10.1109/SIU.2017.7960149
M. Amasyali
{"title":"Ensemble learning with surrogate splits","authors":"M. Amasyali","doi":"10.1109/SIU.2017.7960149","DOIUrl":"https://doi.org/10.1109/SIU.2017.7960149","url":null,"abstract":"Surrogate splits are used to classify test samples having missing values. In this work, they are used to produce different decisions from the same decision tree. In the popular ensemble algorithms, different sub-samples and sub-spaces are used to produce different decisions. But, in our approach, different versions of a test sample are generated by randomly deleting some features. For each version of the test sample, a different decision can be generated by using surrogate splits. 41 UCI datasets are used to compare original and surrogate split versions of the ensemble algorithms. Surrogate split versions have generally better performance than the original ones. The proposed method can be used within any ensemble algorithm using decision trees as its base learner.","PeriodicalId":217576,"journal":{"name":"2017 25th Signal Processing and Communications Applications Conference (SIU)","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133045015","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
Waveform design priorities in different wireless communications systems for 5G beyond 5G以上不同无线通信系统的波形设计优先级
2017 25th Signal Processing and Communications Applications Conference (SIU) Pub Date : 2017-05-15 DOI: 10.1109/SIU.2017.7960466
Ahmet Yazar, H. Arslan
{"title":"Waveform design priorities in different wireless communications systems for 5G beyond","authors":"Ahmet Yazar, H. Arslan","doi":"10.1109/SIU.2017.7960466","DOIUrl":"https://doi.org/10.1109/SIU.2017.7960466","url":null,"abstract":"It is thought that a huge inclusive system will be achieved which includes many different wireless communications systems while 5G and beyond networks are being developed. These communications systems which can be classified according to different system requirements show a characteristic converging to a generic but flexible structure. In this study, waveform design priorities of various wireless communications systems are analyzed which include aeronautical communications, millimeter wave communications, cognitive radio, machine-to-machine communications, device-to-device communications, and vehicle-to-vehicle communications under the inclusive structures of 5G and beyond. Besides, some considerations are presented about the current waveform designs which allow flexible operations and have hybrid structures with adaptive properties for the inclusive structures of 5G beyond.","PeriodicalId":217576,"journal":{"name":"2017 25th Signal Processing and Communications Applications Conference (SIU)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117348148","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
Music genre classification with word and document vectors 音乐流派分类与词和文件向量
2017 25th Signal Processing and Communications Applications Conference (SIU) Pub Date : 2017-05-15 DOI: 10.1109/SIU.2017.7960145
Onder Coban, Isil Karabey
{"title":"Music genre classification with word and document vectors","authors":"Onder Coban, Isil Karabey","doi":"10.1109/SIU.2017.7960145","DOIUrl":"https://doi.org/10.1109/SIU.2017.7960145","url":null,"abstract":"In these days, music genre classification (MGC) is a quite popular research field. The main goal of the MGC studies is automatically detecting music genre (eg., rap, rock). In literature, features are generally extracted from the music's melodic content or lyrics for this task. In this study, we have performed lyrics based MGC on a Turkish dataset. We have just used lyrics as feature source and considered the MGC as a classical text classification problem. However, we represented the features using word (word2vec) and document (doc2vec) vector methods which are quite popular recently. Also, we have compared these methods with traditional Bag of Words (BoW) feature model. In addition, we have investigated the impact of preprocessing steps and vector dimension on both word and document vectors. We have conducted experiments on Support Vector Machine algorithm. Our experimental results show that word vector can be employed for feature representation.","PeriodicalId":217576,"journal":{"name":"2017 25th Signal Processing and Communications Applications Conference (SIU)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132205524","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
Signature recognition application based on deep learning 基于深度学习的签名识别应用
2017 25th Signal Processing and Communications Applications Conference (SIU) Pub Date : 2017-05-15 DOI: 10.1109/SIU.2017.7960454
Nurullah Çalık, Onur Can Kurban, Ali Riza Yilmaz, L. Durak-Ata, T. Yıldırım
{"title":"Signature recognition application based on deep learning","authors":"Nurullah Çalık, Onur Can Kurban, Ali Riza Yilmaz, L. Durak-Ata, T. Yıldırım","doi":"10.1109/SIU.2017.7960454","DOIUrl":"https://doi.org/10.1109/SIU.2017.7960454","url":null,"abstract":"Nowadays, with the increase of biometric studies, the diversity of biometric data increases and new methods are used in evaluation methods. Traditional biometrics, such as face, fingerprints, handpieces, now leave their place to a variety of biometrics, which contain characteristic information about more people and include movement information. In this study, the performance of the deep learning method based on convolutional neural network (CNN) is demonstrated on a nonlinear signature recognition problem. In this non-real-time signature recognition application, it has been tried to reduce the process load and memory requirement by using deep learning method. Two data sets with different participant numbers were created in the study. The performance and reliability of the system are examined by various ratios of training and testing data on these data sets.","PeriodicalId":217576,"journal":{"name":"2017 25th Signal Processing and Communications Applications Conference (SIU)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132188475","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
Realtime object detection in IoT (Internet of Things) devices IoT(物联网)设备中的实时对象检测
2017 25th Signal Processing and Communications Applications Conference (SIU) Pub Date : 2017-05-15 DOI: 10.1109/SIU.2017.7960690
E. Aribas, Evren Daglarli
{"title":"Realtime object detection in IoT (Internet of Things) devices","authors":"E. Aribas, Evren Daglarli","doi":"10.1109/SIU.2017.7960690","DOIUrl":"https://doi.org/10.1109/SIU.2017.7960690","url":null,"abstract":"IoT (Internet of Things) is acommunication network that connects physical or things to each other or with a group all together. The use is widely popular nowadays and its usage has expanded into interesting subjects. Especially, it is getting more popular to research in cross subjects such as mixing smart systems with computer sciences and engineering applications together. Object detection is one of these subjects. Realtime object detection is one of the foremost interesting subjects because of its compute costs. Gaps in methodology, unknown concepts and insufficiency in mathematical modeling makes it harder for designing these computing algorithms. Algortihms in these applications can be developed with in machine learning and/or numerical methods that are available in scientific literature. These operations are possible only if communication of objects within theirselves in physical space and awareness of the objects nearby. Artificial Neural Networks may help in these studies. In this study, yolo algorithm which is seen as a key element for real-time object detection in IoT is researched. It is realized and shown in results that optimization of computing and analyzation of system aside this research which takes Yolo algorithm as a foundation point [10]. As a result, it is seen that our model approach has an interesting potential and novelty.","PeriodicalId":217576,"journal":{"name":"2017 25th Signal Processing and Communications Applications Conference (SIU)","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123303145","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
Classification of band-specific regional hemispheric connectivity in obsessive compulsive disorder 强迫症的波段特异性区域半球连通性分类
2017 25th Signal Processing and Communications Applications Conference (SIU) Pub Date : 2017-05-15 DOI: 10.1109/SIU.2017.7960143
S. Aydın, O. Tan
{"title":"Classification of band-specific regional hemispheric connectivity in obsessive compulsive disorder","authors":"S. Aydın, O. Tan","doi":"10.1109/SIU.2017.7960143","DOIUrl":"https://doi.org/10.1109/SIU.2017.7960143","url":null,"abstract":"In the present study, inter-electrode hemispheric dependency has been estimated by using frequency, time and phase domain methods (Fourier Correlation, Wavelet Correlation (WC), Hilbert Correlation) for eight individual brain lobes (pre-frontal, anterio-frontal, central, occipital, parietal, posterio-frontal, anterio-temporal, posterio-temporal) in five frequency band activities (Delta (0.5–4 Hz), Theta (4–8 Hz), Alpha (8–16 Hz), Beta (16–32 Hz) and, Gamma (32–64 Hz)) for detection of obsessive compulsive disorder (OCD). For this purpose, patients and controls are classified by using non-linear Least-Squares Support-Vector-Machine with 10-fold cross validation for both eight features in each sub-band and single ban-specific feature at each lobe. The best classification performance (87,15% and 96, 65% in Beta and Gamma) is obtained for eight features estimated by using WC. In particular, single feature through WC has provided the relatively lower but useful classification performance in Beta (72, 34% at prefrontal, (72, 59% at occipital, 76, 39% at posterio-frontal, 70, 89% at anterio-temporal, 71,14% at posterio-temporal) and Gamma (71, 84% at prefrontal, 76, 39% at occipital, 76, 39% at posterio-frontal, 70, 89% at anterio-temporal, 71, 77% at posterio-temporal). In detail, OCD is found to be characterized by low hemispheric dependency in Gamma over cortex. In conclusion, OCD causes abnormalities at almost every hemispheric lobe. WC provides the best estimations to compute band specific asymmetry levels due to non-linear and non-stationary nature of EEG.","PeriodicalId":217576,"journal":{"name":"2017 25th Signal Processing and Communications Applications Conference (SIU)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123560034","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
Automated nuclei detection in serous effusion cytology based on machine learning 浆液细胞学中基于机器学习的细胞核自动检测
2017 25th Signal Processing and Communications Applications Conference (SIU) Pub Date : 2017-05-15 DOI: 10.1109/SIU.2017.7960323
Elif Baykal, Hulya Dogan, M. Ekinci, M. Erçin, S. Ersoz
{"title":"Automated nuclei detection in serous effusion cytology based on machine learning","authors":"Elif Baykal, Hulya Dogan, M. Ekinci, M. Erçin, S. Ersoz","doi":"10.1109/SIU.2017.7960323","DOIUrl":"https://doi.org/10.1109/SIU.2017.7960323","url":null,"abstract":"Serous effusions are common in clinical practice and they are frequently encountered specimen type in cytopathological assessment. Since this assessment is subjective, time-consuming and cause intra- and inter-observer variability, the need for an automated system is arised. Identification of the cancer cells in serous effusion cytology allows for the early diagnosis of the cancer and also the staging, prognosis and monitoring these cells. The detection of cell nuclei is seen as the corner stone for diagnostic purposes in automatic analysis of cytopathological images. Nuclei detection also yield the following automated microscopy applications, such as cell counting, segmentation and classification. In this paper, machine learning based Viola-Jones object detection approach is used to detect the cell nuclei locations in serous cytology images. When the method has been tested on number of serous cytology images, the obtained results show that this method has high nuclei detection performance.","PeriodicalId":217576,"journal":{"name":"2017 25th Signal Processing and Communications Applications Conference (SIU)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127312733","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
On identifying leaves: A comparison of CNN with classical ML methods 关于叶子的识别:CNN与经典ML方法的比较
2017 25th Signal Processing and Communications Applications Conference (SIU) Pub Date : 2017-05-15 DOI: 10.1109/SIU.2017.7960257
Mohamed Abbas Hedjazi, Ikram Kourbane, Yakup Genç
{"title":"On identifying leaves: A comparison of CNN with classical ML methods","authors":"Mohamed Abbas Hedjazi, Ikram Kourbane, Yakup Genç","doi":"10.1109/SIU.2017.7960257","DOIUrl":"https://doi.org/10.1109/SIU.2017.7960257","url":null,"abstract":"Convolution neural networks (CNNs) eliminate the need for feature extraction which is one of the most important and time-consuming part of traditional machine learning (ML) methods. However, the challenge of training a deep CNN model with a limited amount of training data still remains. Transfer learning and parameter fine-tuning have emerged as solutions to this problem. Following the recent trends, we address the task of visual identification of leaves in images by modifying a trained model on a similar problem. In particular, we show that a pre-trained CNN model on a large dataset (ImageNet) can be used to train a model from a small training set (ImageCLEF2013 Plant Identification). The resulting model outperforms the classical machine learning methods using local binary patterns (LBPs), a well utilized feature in the field.","PeriodicalId":217576,"journal":{"name":"2017 25th Signal Processing and Communications Applications Conference (SIU)","volume":"111 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115110016","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: 33
Detection and segmentation of masses in mammograms by the rule based elimination approach 基于规则消除法的乳房x光片肿块检测与分割
2017 25th Signal Processing and Communications Applications Conference (SIU) Pub Date : 2017-05-01 DOI: 10.1109/SIU.2017.7960440
H. Ture, T. Kayikçioglu
{"title":"Detection and segmentation of masses in mammograms by the rule based elimination approach","authors":"H. Ture, T. Kayikçioglu","doi":"10.1109/SIU.2017.7960440","DOIUrl":"https://doi.org/10.1109/SIU.2017.7960440","url":null,"abstract":"In this study, a method was proposed that eliminated the non-suspicious salient regions for the detection and segmentation of masses in mammograms. Since suspicious regions are generally salient dense regions, the method firstly extracts the maximum regions of interest (ROIs) that have the optimum lifetime. Subsequently, these ROIs are segmented with the rule-based elimination using morphological and intensity properties. The texture features taken from the suspicious regions are classified by Rus Boost method for detection of masses. The developed method has been tested on all mammograms, which includes mass, taken from the MIAS database. Experimental results demonstrate that the method achieves a satisfactory performance during the detection and segmentation of suspicious regions.","PeriodicalId":217576,"journal":{"name":"2017 25th Signal Processing and Communications Applications Conference (SIU)","volume":"202 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115816158","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
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