2019 IEEE Global Conference on Signal and Information Processing (GlobalSIP)最新文献_第6页

Where Am I Looking: Localizing Gaze In Reconstructed 3D Space 我在看哪里:在重建的3D空间中定位凝视

2019 IEEE Global Conference on Signal and Information Processing (GlobalSIP) Pub Date : 2019-11-01 DOI: 10.1109/GlobalSIP45357.2019.8969158

Devarth Parikh, Yawen Lu, Yuan Xin, Di Wu, J. Pelz, G. Lu

引用次数: 4

A Geometric Convolutional Neural Network for 3D Object Detection 三维物体检测的几何卷积神经网络

2019 IEEE Global Conference on Signal and Information Processing (GlobalSIP) Pub Date : 2019-11-01 DOI: 10.1109/GlobalSIP45357.2019.8969077

Yawen Lu, Qianyu Guo, G. Lu

引用次数: 1

Low-Complexity Adaptive Switched Prediction-Based Lossless Compression of Time-Lapse Hyperspectral Image Data 基于低复杂度自适应切换预测的时移高光谱图像数据无损压缩

2019 IEEE Global Conference on Signal and Information Processing (GlobalSIP) Pub Date : 2019-11-01 DOI: 10.1109/GlobalSIP45357.2019.8969499

T. Shinde, A. Tiwari, Weiyao Lin

引用次数: 0

GSP Analysis of Brain Imaging Data from Athletes with History of Multiple Concussions 多发脑震荡运动员脑成像数据的GSP分析

2019 IEEE Global Conference on Signal and Information Processing (GlobalSIP) Pub Date : 2019-11-01 DOI: 10.1109/GlobalSIP45357.2019.8969487

Saurabh Sihag, S. Naze, F. Taghdiri, M. Tartaglia, J. Kozloski

{"title":"GSP Analysis of Brain Imaging Data from Athletes with History of Multiple Concussions","authors":"Saurabh Sihag, S. Naze, F. Taghdiri, M. Tartaglia, J. Kozloski","doi":"10.1109/GlobalSIP45357.2019.8969487","DOIUrl":"https://doi.org/10.1109/GlobalSIP45357.2019.8969487","url":null,"abstract":"Study of neurological disorders affecting the structure-function relationships in the brain has been an ongoing challenge in neuroscience. Joint analysis of structure and function of the brain may disentangle a number of mechanisms and operations that can help interpret the interdependence between white matter degeneration and degradation of cognitive abilities. In this scenario, graph signal processing analysis of different signals generated within the physical structure of the brain may provide new insights and corroborate existing clinical findings. This paper illustrates the utility of graph signal processing tools in the joint analysis of diffusion and functional magnetic resonance imaging (i.e. dMRI and fMRI) data collected from a population of former athletes with a history of multiple concussions, and healthy subjects. Specifically, the distributions of the energy of low-graph-frequency components of the functional networks (derived from fMRI) are observed to be significantly different for fronto-temporal regions of the brain in athletes and healthy subjects. Furthermore, for the two groups of subjects, we observe significantly different associations between the ages of subjects and the energies of high graph frequency components in lingual region. While the effect on fronto-temporal regions for former athletes is in line with the existing clinical studies on concussion, significantly different associations between age and features extracted using GSP for the two groups of subjects could inform future clinical applications and medical diagnosis.","PeriodicalId":221378,"journal":{"name":"2019 IEEE Global Conference on Signal and Information Processing (GlobalSIP)","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133568369","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

Modeling and Recovery of Graph Signals and Difference-Based Signals 图信号和差分信号的建模与恢复

2019 IEEE Global Conference on Signal and Information Processing (GlobalSIP) Pub Date : 2019-11-01 DOI: 10.1109/GlobalSIP45357.2019.8969536

Ariel Kroizer, Yonina C. Eldar, T. Routtenberg

{"title":"Modeling and Recovery of Graph Signals and Difference-Based Signals","authors":"Ariel Kroizer, Yonina C. Eldar, T. Routtenberg","doi":"10.1109/GlobalSIP45357.2019.8969536","DOIUrl":"https://doi.org/10.1109/GlobalSIP45357.2019.8969536","url":null,"abstract":"In this paper, we consider the problem of representing and recovering graph signals with a nonlinear measurement model. We propose a two-stage graph signal processing (GSP) framework. First, a GSP representation is obtained by finding the graph filter that best approximates the known measurement function. The new GSP representation enables performing tractable operations over graphs, as well as gaining insights into the signal graph-frequency contents. Then, we formulate the signal recovery problem under the smoothness constraint and derive a regularized least-squares (LS) estimator, which is obtained by applying the inverse of the approximated graph filter on the nonlinear measurements. In the second part of this paper, we investigate the proposed recovery and representation approach for the special case of graph signals that are influenced by the differences between vertex values only. Difference-based graph signals arise, for example, when modeling power signals as a function of the voltages in electrical networks. We show that any difference-based graph signal corresponds to a filter that lacks the zero-order filter coefficient, and thus, these signals can be recovered up to a constant by the regularized LS estimator. In our simulations, we show that for the special case of state estimation in power systems the proposed GSP approach outperforms the state-of-the-art estimator in terms of total variation.","PeriodicalId":221378,"journal":{"name":"2019 IEEE Global Conference on Signal and Information Processing (GlobalSIP)","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122236590","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: 11

An Accurate Evaluation of MSD Log-likelihood and its Application in Human Action Recognition MSD对数似然的准确评价及其在人体动作识别中的应用

2019 IEEE Global Conference on Signal and Information Processing (GlobalSIP) Pub Date : 2019-11-01 DOI: 10.1109/GlobalSIP45357.2019.8969324

Nuha Zamzami, N. Bouguila

引用次数: 4

Continuous Parkinsonian Tremor Estimation Using Motion Data 用运动数据估计连续帕金森震颤

2019 IEEE Global Conference on Signal and Information Processing (GlobalSIP) Pub Date : 2019-11-01 DOI: 10.1109/GlobalSIP45357.2019.8969093

Murtadha D. Hssayeni, J. Jimenez-shahed, M. Burack, B. Ghoraani

引用次数: 0

A QoE-Based Alarm Model for Terminal Video Quality 基于qos的终端视频质量报警模型

2019 IEEE Global Conference on Signal and Information Processing (GlobalSIP) Pub Date : 2019-11-01 DOI: 10.1109/GlobalSIP45357.2019.8969366

X. Peng, Yiping Duan, Bingrui Geng, Xiwen Liu, Xiaoming Tao, N. Ge

{"title":"A QoE-Based Alarm Model for Terminal Video Quality","authors":"X. Peng, Yiping Duan, Bingrui Geng, Xiwen Liu, Xiaoming Tao, N. Ge","doi":"10.1109/GlobalSIP45357.2019.8969366","DOIUrl":"https://doi.org/10.1109/GlobalSIP45357.2019.8969366","url":null,"abstract":"With the advent of the 5G era, quality of experience (QoE) has become one of the most important indicators to measure mobile video quality. If QoE degradation events can be accurately predicted, it will be beneficial for operators to provide better services. However, many factors could affect QoE, including users’ subjective factors, network and users’ terminal parameters. This paper proposes a model for QoE degradation alarm in the mobile video stream. Specifically, a large-scale dataset is collected in the practical environment by a self-developed application. The dataset is cleaned to reduce the influence of redundancy and outliers firstly. Then, a QoE degradation alarm model is proposed based on data-driven approaches. The random forest is chosen as the core of this model which integrates the advantages of several classification tree and can deal with the high-dimensional data. In order to have a better prediction performance, Synthetic Minority Oversampling Technique (SMOTE) is used for solving the imbalance problem of the train set. As is shown by the experimental results, this alarm model can strike a balance between precision and false alarm rate and performance outperforms the state-of-the-art methods. This result means the proposed model can save the operational cost while increasing users’ satisfaction with the mobile video quality.","PeriodicalId":221378,"journal":{"name":"2019 IEEE Global Conference on Signal and Information Processing (GlobalSIP)","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133197205","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

Deep Neural Hybrid Beamforming for Multi-User mmWave Massive MIMO System 多用户毫米波大规模MIMO系统的深度神经混合波束形成

2019 IEEE Global Conference on Signal and Information Processing (GlobalSIP) Pub Date : 2019-11-01 DOI: 10.1109/GlobalSIP45357.2019.8969154

Jiyun Tao, Jing Xing, Jienan Chen, Chuan Zhang, Shengli Fu

引用次数: 12

Pain Detection from Facial Videos Using Two-Stage Deep Learning 基于两阶段深度学习的面部视频疼痛检测

2019 IEEE Global Conference on Signal and Information Processing (GlobalSIP) Pub Date : 2019-11-01 DOI: 10.1109/GlobalSIP45357.2019.8969274

Guglielmo Menchetti, Zhanli Chen, Diana J. Wilkie, R. Ansari, Y. Yardimci, A. Enis Cetin

{"title":"Pain Detection from Facial Videos Using Two-Stage Deep Learning","authors":"Guglielmo Menchetti, Zhanli Chen, Diana J. Wilkie, R. Ansari, Y. Yardimci, A. Enis Cetin","doi":"10.1109/GlobalSIP45357.2019.8969274","DOIUrl":"https://doi.org/10.1109/GlobalSIP45357.2019.8969274","url":null,"abstract":"A new method to objectively measure pain using computer vision and machine learning technologies is presented. Our method seeks to capture facial expressions of pain to detect pain, especially when a patients cannot communicate pain verbally. This approach relies on using Facial muscle-based Action Units (AUs), defined by the Facial Action Coding System (FACS), that are associated with pain. It is impractical to use human FACS coding experts in clinical settings to perform this task as it is too labor-intensive and recent research has sought computer-based solutions to the problem. An effective automated system for performing the task is proposed here in which we develop an end-to-end deep learning-based Automated Facial Expression Recognition (AFER) that jointly detects the complete set of pain-related AUs. The facial video clip is processed frame by frame to estimate a vector of AU likelihood values for each frame using a deep convolutional neural network. The AU vectors are concatenated to form a table of AU values for a given video clip. Our results show significantly improved performance compared with those obtained with other known methods.","PeriodicalId":221378,"journal":{"name":"2019 IEEE Global Conference on Signal and Information Processing (GlobalSIP)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133805224","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: 10