2017 25th Signal Processing and Communications Applications Conference (SIU)最新文献_第2页

A fast and robust automatic object detection algorithm to detect small objects in infrared images 一种快速、鲁棒的红外图像小目标自动检测算法

2017 25th Signal Processing and Communications Applications Conference (SIU) Pub Date : 2017-05-15 DOI: 10.1109/SIU.2017.7960456

Muhammet Ozbay, M. C. Sahingil

引用次数: 10

Performance evaluation of schedulers in MIMO-OFDMA based cellular networks 基于MIMO-OFDMA的蜂窝网络调度器性能评价

2017 25th Signal Processing and Communications Applications Conference (SIU) Pub Date : 2017-05-15 DOI: 10.1109/SIU.2017.7960166

Omer Narmanlioglu, E. Zeydan

引用次数: 1

Comparison of convolutional neural network models for document image classification 卷积神经网络模型在文档图像分类中的比较

2017 25th Signal Processing and Communications Applications Conference (SIU) Pub Date : 2017-05-15 DOI: 10.1109/SIU.2017.7960562

Doggucan Yaman, Fevziye Irem Eyiokur, H. K. Ekenel

引用次数: 1

Face recognition classifier based on dimension reduction in deep learning properties 基于深度学习特性降维的人脸识别分类器

2017 25th Signal Processing and Communications Applications Conference (SIU) Pub Date : 2017-05-15 DOI: 10.1109/SIU.2017.7960368

Ahmet Bilgic, Onur Can Kurban, T. Yıldırım

{"title":"Face recognition classifier based on dimension reduction in deep learning properties","authors":"Ahmet Bilgic, Onur Can Kurban, T. Yıldırım","doi":"10.1109/SIU.2017.7960368","DOIUrl":"https://doi.org/10.1109/SIU.2017.7960368","url":null,"abstract":"Nowadays, with the increasing use of biometric data, it is expected that systems can give successful results against difficult situations and work robustly. Especially, in face recognition systems, variables such as direction of light, facial expression and reflection are making difficult to identify. Thus, in recent years, Convolutional Neural Network (CNN) models, which are deep learning models as an alternative to traditional feature extraction and artificial neural network methods, have begun to be developed. In this work, for face recognition, VGG Face deep learning model is compared with our proposed model which uses Multi Layer Perceptron (MLP) classifier and reduced deep features by principal component analysis. The Kinect RGB image dataset belonging to 40 people with different facial expressions and lighting conditions has been tested with 4-fold cross validation method. While 97.18% classification ratio was achieved with the first model, 100% recognition accuracy has been obtained by the second model. The results show that deep learning achieves a high performance in face recognition under different light and expression conditions, however, the proposed classification method based on dimension reduction in deep learning properties achieves better performance.","PeriodicalId":217576,"journal":{"name":"2017 25th Signal Processing and Communications Applications Conference (SIU)","volume":"12 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":"125672805","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

Efficient incremental checkpoint algorithm for primary-backup replication 高效的主备份复制增量检查点算法

2017 25th Signal Processing and Communications Applications Conference (SIU) Pub Date : 2017-05-15 DOI: 10.1109/SIU.2017.7960709

Berkin Guler, Öznur Özkasap

引用次数: 2

Turkish tweet sentiment analysis with word embedding and machine learning 土耳其推特情绪分析与词嵌入和机器学习

2017 25th Signal Processing and Communications Applications Conference (SIU) Pub Date : 2017-05-15 DOI: 10.1109/SIU.2017.7960195

Değer Ayata, M. Saraçlar, Arzucan Özgür

引用次数: 23

A secure wireless home automation system 一个安全的无线家庭自动化系统

2017 25th Signal Processing and Communications Applications Conference (SIU) Pub Date : 2017-05-15 DOI: 10.1109/SIU.2017.7960389

Muhammed Onur Gungor, Yusuf Gungor, G. Ince

引用次数: 1

Dynamic decision-based spectrum sharing framework for next-generation (5G) systems 下一代(5G)系统的动态决策频谱共享框架

2017 25th Signal Processing and Communications Applications Conference (SIU) Pub Date : 2017-05-15 DOI: 10.1109/SIU.2017.7960375

Z. Sadreddini, Pavel Mašek, Tugrul Çavdar, Jiri Hosek, Erkan Guler

{"title":"Dynamic decision-based spectrum sharing framework for next-generation (5G) systems","authors":"Z. Sadreddini, Pavel Mašek, Tugrul Çavdar, Jiri Hosek, Erkan Guler","doi":"10.1109/SIU.2017.7960375","DOIUrl":"https://doi.org/10.1109/SIU.2017.7960375","url":null,"abstract":"Looking into the concept of next-generation (5G) cellular systems, it is necessary to do a revision of existing radio spectrum management techniques and come up with more flexible solutions. A new wave of spectrum policy reforms can be envisaged with a direction shift from static to dynamic optimization. According to the peak hours, the number of served users in mobile networks is increasing. Since the radio spectrum is limited, cognitive radio (CR) technology provides an opportunity to recognize under-utilized cellular spectrum (licensed band) resources. To this end, efficient spectrum management techniques based on CR technology should be implemented to share the spectrum between different types of users in order to maximize spectrum utilization and spectral efficiency. In this work, we present dynamic decision-based spectrum sharing model among multiple classes of users in CR network (CRN) in order to increase network utilization and the quality of experience (QoE) by increasing the users' satisfaction. Obtained simulation results from created toolkit in Matlab tool (calibrated by data set from real 3GGP LTE-Advanced system) show the performance of the developed model and appropriate user selection among multiple users' types.","PeriodicalId":217576,"journal":{"name":"2017 25th Signal Processing and Communications Applications Conference (SIU)","volume":"26 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":"132639651","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

Spatial modulation for multi-user massive MIMO systems 多用户大规模MIMO系统的空间调制

2017 25th Signal Processing and Communications Applications Conference (SIU) Pub Date : 2017-05-15 DOI: 10.1109/SIU.2017.7960432

Seyfettin Uluocak, E. Başar

引用次数: 1

Optimal power allocation for DL NOMA systems DL - NOMA系统的最佳功率分配

2017 25th Signal Processing and Communications Applications Conference (SIU) Pub Date : 2017-05-15 DOI: 10.1109/SIU.2017.7960497

F. Kara, O. F. Gemici, Ibrahim Hökelek, H. A. Çırpan

引用次数: 3