2018 41st International Conference on Telecommunications and Signal Processing (TSP)最新文献_第3页

Computer Vision Method for Grading of Health of a Fundus Image on Basis of Presence of Red Lesions 基于红色病灶的眼底图像健康度计算机视觉分级方法

2018 41st International Conference on Telecommunications and Signal Processing (TSP) Pub Date : 2018-07-01 DOI: 10.1109/TSP.2018.8441504

S. Bhattacharya, J. Sehgal, Ashish Issac, M. Dutta, Radim Burget, M. Kolarík

引用次数: 7

Video-based Pain Level Assessment: Feature Selection and Inter-Subject Variability Modeling 基于视频的疼痛程度评估:特征选择和主体间变异性建模

2018 41st International Conference on Telecommunications and Signal Processing (TSP) Pub Date : 2018-07-01 DOI: 10.1109/TSP.2018.8441252

Dimitra Bourou, A. Pampouchidou, M. Tsiknakis, K. Marias, P. Simos

{"title":"Video-based Pain Level Assessment: Feature Selection and Inter-Subject Variability Modeling","authors":"Dimitra Bourou, A. Pampouchidou, M. Tsiknakis, K. Marias, P. Simos","doi":"10.1109/TSP.2018.8441252","DOIUrl":"https://doi.org/10.1109/TSP.2018.8441252","url":null,"abstract":"Automatic pain level assessment, based on video features, may provide clinically-relevant, objective measures of pain intensity. In various clinical contexts accurate pain level estimation by health care personnel is challenging. This problem is compounded by considerable inter- and intra-individual variability of both perceived pain levels and of the associated facial expressions, especially at low pain levels. Thus, providing objective video-based indices for pain level assessment is a rather computationally challenging problem. In the present work both geometric and color-based features were extracted. The most informative features were identified with lasso regression, and subject variability was modeled through a generalized linear mixed effects probit model. Video recordings from the Biovid Heat Pain Database were used with the proposed methodology, aiming to classify video samples to five levels of pain. Performance of the proposed model was comparable to the state-of-the-art random forests algorithm despite its relative simplicity and more conservative cross-validation approach adopted.","PeriodicalId":383018,"journal":{"name":"2018 41st International Conference on Telecommunications and Signal Processing (TSP)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130810891","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

Current-Mode Square-Rooting Circuit Based on CMOS Translinear 基于CMOS跨线性电流型平方根电路

2018 41st International Conference on Telecommunications and Signal Processing (TSP) Pub Date : 2018-07-01 DOI: 10.1109/TSP.2018.8441503

Natapong Wongprommoon, P. Thongdit, P. Prommee

引用次数: 2

Depth Estimation and Ray Tracing Model Selection of Buried Utilities on Ground Penetrating Radar Data 基于探地雷达数据的埋地设施深度估算与射线追踪模型选择

2018 41st International Conference on Telecommunications and Signal Processing (TSP) Pub Date : 2018-07-01 DOI: 10.1109/TSP.2018.8441270

Uri Peer

引用次数: 0

System Performance of Interference Alignment in MIMO Cognitive Radio Networks Under Interference Leakage 干扰泄漏条件下MIMO认知无线网络干扰对准系统性能研究

2018 41st International Conference on Telecommunications and Signal Processing (TSP) Pub Date : 2018-07-01 DOI: 10.1109/TSP.2018.8441331

Hakan Alakoca, Seda Ustunbas, Mustafa Namdar, Arif Basgumus, Eylem Erdogan, L. Durak-Ata

引用次数: 1

A Novel Discrete Wavelet Transform based Coherent Optical OFDM System 基于离散小波变换的相干光OFDM系统

2018 41st International Conference on Telecommunications and Signal Processing (TSP) Pub Date : 2018-07-01 DOI: 10.1109/TSP.2018.8441430

A. Güner, A. Özen

引用次数: 1

A Real-Time System for Recognition of American Sign Language by using Deep Learning 基于深度学习的美国手语实时识别系统

2018 41st International Conference on Telecommunications and Signal Processing (TSP) Pub Date : 2018-07-01 DOI: 10.1109/TSP.2018.8441304

M. Taskiran, Mehmet Killioglu, N. Kahraman

引用次数: 45

Synchrosqueezing Transform Based Methodology for Radiometric Identification 基于同步压缩变换的辐射识别方法

2018 41st International Conference on Telecommunications and Signal Processing (TSP) Pub Date : 2018-07-01 DOI: 10.1109/TSP.2018.8441378

G. Baldini, G. Steri, Raimondo Giuliani

{"title":"Synchrosqueezing Transform Based Methodology for Radiometric Identification","authors":"G. Baldini, G. Steri, Raimondo Giuliani","doi":"10.1109/TSP.2018.8441378","DOIUrl":"https://doi.org/10.1109/TSP.2018.8441378","url":null,"abstract":"This paper describes the application of the SynchroSqueezing Transform (SST) to the problem of radiometric identification, which means that wireless devices can be identified and authenticated through their radio frequency emissions. Radiometric identification has been applied to enhance the security of wireless networks based on WiFi or cellular communication standards. In literature, radiometric identification has been performed by feature extraction in the 1D time domain, 1D frequency domain or also in the 2D time-frequency domain. This paper describes the novel application of the 2D SST to the problem of radiometric identification. An experimental data set of Radio Frequency (RF) emissions from 12 wireless devices is used to evaluate the performance of the SST transform in terms of identification accuracy. This paper shows that the identification accuracy obtained using 2D SST is superior to conventional techniques based in the 1D time domain or 1D frequency domain especially in presence of gaussian noise. 1 This work has been partially supported by the European Commission through project SerIoT funded by the European Union H2020 Programme under Grant Agreement No. 780139. The opinions expressed in this paper are those of the authors and do not necessarily reflect the views of the European Commission.","PeriodicalId":383018,"journal":{"name":"2018 41st International Conference on Telecommunications and Signal Processing (TSP)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130015039","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

Machine Learning Techniques for Automatic Depression Assessment 自动抑郁评估的机器学习技术

2018 41st International Conference on Telecommunications and Signal Processing (TSP) Pub Date : 2018-07-01 DOI: 10.1109/TSP.2018.8441422

A. Maridaki, A. Pampouchidou, K. Marias, M. Tsiknakis

{"title":"Machine Learning Techniques for Automatic Depression Assessment","authors":"A. Maridaki, A. Pampouchidou, K. Marias, M. Tsiknakis","doi":"10.1109/TSP.2018.8441422","DOIUrl":"https://doi.org/10.1109/TSP.2018.8441422","url":null,"abstract":"Depression is one of the most common mood disorder that is inherently related to emotions, involving bad mood, low self-esteem and loss of interest in normal pleasurable activities. The aim of this work is to develop a framework based on the dataset provided by AVEC'14 for depression assessment. The proposed work presents two different motion representation methods: a) Gabor Motion History Image (GMHI), and b) Motion History Image (MHI). Several combinations of appearance-based low level features are extracted from both motion representations. These features were further combined with statistically derived features, and used for training and testing with several machine learning techniques. The proposed approach reached an F1 score of 81.93%, both for MHI and GMHI, with SVM classifier. The achieved performance is comparable to state-of-the-art approaches, while manages to outperform several others. Apart from accomplishing a competitive performance, the proposed work provides an exhaustive exploration of different combinations of the investigated motion representations, descriptors, and classifiers.","PeriodicalId":383018,"journal":{"name":"2018 41st International Conference on Telecommunications and Signal Processing (TSP)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130733923","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

Modification of Tracking Algorithm Struck by the Application of the Scale Estimation Method 尺度估计法对跟踪算法的改进

2018 41st International Conference on Telecommunications and Signal Processing (TSP) Pub Date : 2018-07-01 DOI: 10.1109/TSP.2018.8441458

V. Pavlov, S. Zavjalov, S. Volvenko, M. A. Zanina

引用次数: 4