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Global Conference on Signal and Information Processing (GlobalSIP), 2013 IEEE

Date 3-5 Dec. 2013

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Displaying Results 1 - 25 of 359
  • [Front cover]

    Publication Year: 2013 , Page(s): c1
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  • [Title page]

    Publication Year: 2013 , Page(s): i
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  • [Copyright notice]

    Publication Year: 2013 , Page(s): ii
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  • General chairs' welcome

    Publication Year: 2013 , Page(s): iii
    Save to Project icon | Request Permissions | Click to expandAbstract | PDF file iconPDF (115 KB) |  | HTML iconHTML  

    On behalf of the IEEE Global Conference on Signal and Information Processing (GlobalSIP) Organizing Committee, we would like to cordially welcome you to Austin, Texas. Austin is known as the Live Music Capital of the World. Indeed, Austin offers many opportunities to experience music just a stones throw away from the convention center, but it also has much to offer residents and visitors. Austin i... View full abstract»

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  • Technical program overview

    Publication Year: 2013 , Page(s): iv - v
    Save to Project icon | Request Permissions | Click to expandAbstract | PDF file iconPDF (146 KB) |  | HTML iconHTML  

    Welcome to Austin, Texas for the inaugural IEEE Global Conference on Signal and Information Processing. GlobalSIP is a new flagship IEEE Signal Processing Society conference that targets hot topics and up-and-coming themes in signal and information processing. GlobalSIP is organized differently from other IEEE SPS meetings to encourage new SPS research directions and to foster emerging areas. View full abstract»

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  • Organizing committee

    Publication Year: 2013 , Page(s): vi
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  • Reviewers

    Publication Year: 2013 , Page(s): vii - ix
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  • [Blank page]

    Publication Year: 2013 , Page(s): x
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  • Table of contents

    Publication Year: 2013 , Page(s): xi - xlii
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  • Hierarchical Event Descriptor (HED) tags for analysis of event-related EEG studies

    Publication Year: 2013 , Page(s): 1 - 4
    Cited by:  Papers (1)
    Save to Project icon | Request Permissions | Click to expandAbstract | PDF file iconPDF (318 KB) |  | HTML iconHTML  

    Data from well-designed EEG experiments should find uses beyond initial reports, even when study authors cannot anticipate how it may contribute to future analyses. Several ontologies have been proposed for describing events in cognitive experiments to make data available for re-use and meta-analysis, but none are widely used. One reason for this is that the tools needed to make use of these ontol... View full abstract»

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  • CTAGGER: Semi-structured community tagging for annotation and data-mining in event-rich contexts

    Publication Year: 2013 , Page(s): 5 - 8
    Cited by:  Papers (1)
    Save to Project icon | Request Permissions | Click to expandAbstract | PDF file iconPDF (608 KB) |  | HTML iconHTML  

    Analysis of dynamic brain imaging data from EEG, MEG or fMRI requires a common temporal context to enable meta-analysis and data mining across experiments. However, there is no standardized method of annotating events, even from laboratory experiments in controlled settings, and the event-rich environments of real-world brain imaging present a still greater annotation challenge. We have developed ... View full abstract»

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  • EEG and the human perception of video quality: Impact of channel selection on discrimination

    Publication Year: 2013 , Page(s): 9 - 12
    Save to Project icon | Request Permissions | Click to expandAbstract | PDF file iconPDF (773 KB) |  | HTML iconHTML  

    The ultimate goal of our research project is to quantify the human perception of video quality directly from brain responses. Specifically, subjects watch a set of video sequences whose qualities vary with time while their brain responses are monitored using a 128-channel electroencephalograph (EEG). We compare four potential sets of time-localized feature vectors-autoregressive coefficients, aver... View full abstract»

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  • A compressive sampling approach for brain-machine interfaces based on transcranial Doppler sonography: A case study of resting-state maximal cerebral blood velocity signals

    Publication Year: 2013 , Page(s): 13 - 16
    Save to Project icon | Request Permissions | Click to expandAbstract | PDF file iconPDF (250 KB) |  | HTML iconHTML  

    Transcranial Doppler sonography was recently proposed as an approach for brain-machine interfaces. However, monitoring maximal cerebral blood flow velocity signals for extensive time periods can generate large volumes of data for processing. In this paper, a compressive sensing (CS) approach is proposed based on a time-frequency dictionary formed by modulated discrete prolate spheroidal sequences ... View full abstract»

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  • Content-based EEG database retrieval using a multiclass SVM classifier

    Publication Year: 2013 , Page(s): 17 - 20
    Save to Project icon | Request Permissions | Click to expandAbstract | PDF file iconPDF (273 KB) |  | HTML iconHTML  

    CBER (content-based-EEG-retrieval) systems present short data segments as query samples for similar segments in EEG databases. These systems have many applications in large-scale data-mining, but require effective and verifiable retrieval strategies. This paper introduces a new feature strategy based on class probabilities calculated by LIBSVM classification using low-order autoregressive (AR) mod... View full abstract»

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  • Using feedback in long term trajectory decoding from Local Field Potentials

    Publication Year: 2013 , Page(s): 21 - 24
    Save to Project icon | Request Permissions | Click to expandAbstract | PDF file iconPDF (668 KB) |  | HTML iconHTML  

    In this paper, we study a feedback mechanism in the design of a Local Field Potential (LFP) based Brain Computer Interface (BCI) that decodes arm movements. A major setback of using Local Field Potentials based BCI is their non-stationarity. In addition, many proposed BCI devices are usually trained and simulated in an open-loop environment, neglecting the effect of user adaptation in the loop. To... View full abstract»

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  • Towards an EEG search engine

    Publication Year: 2013 , Page(s): 25 - 28
    Save to Project icon | Request Permissions | Click to expandAbstract | PDF file iconPDF (815 KB) |  | HTML iconHTML  

    The current EEG analysis-publication workflow mostly documents qualitative descriptions of event-related EEG dynamics. This makes it difficult to look for comparable results in the literature since search options are limited to textual descriptions and/or similar-appearing results depicted in the paper figures. We demonstrate a method for quantitative comparison of source-resolved results (e.g., E... View full abstract»

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  • The Temple University Hospital EEG corpus

    Publication Year: 2013 , Page(s): 29 - 32
    Cited by:  Papers (2)
    Save to Project icon | Request Permissions | Click to expandAbstract | PDF file iconPDF (1766 KB) |  | HTML iconHTML  

    The recently established Neural Engineering Data Consortium (NEDC) is in the process of developing its first large-scale corpus. This corpus, known as the Temple University Hospital EEG Corpus, upon completion, will total over 20,000 EEG studies, and include patient information, medical histories and physician assessments, making it the largest and most comprehensive publicly released EEG corpus. ... View full abstract»

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  • A Deep Learning method for classification of images RSVP events with EEG data

    Publication Year: 2013 , Page(s): 33 - 36
    Save to Project icon | Request Permissions | Click to expandAbstract | PDF file iconPDF (308 KB) |  | HTML iconHTML  

    In this paper, we investigated Deep Learning (DL) for characterizing and detecting target images in an image rapid serial visual presentation (RSVP) task based on EEG data. We exploited DL technique with input feature clusters to handle high dimensional features related to time - frequency events. The method was applied to EEG recordings of a RSVP experiment with multiple sessions and subjects. Fo... View full abstract»

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  • Two-dimensional SVD for event detection in dynamic functional brain networks

    Publication Year: 2013 , Page(s): 37 - 40
    Cited by:  Papers (1)
    Save to Project icon | Request Permissions | Click to expandAbstract | PDF file iconPDF (392 KB) |  | HTML iconHTML  

    In recent years, there has been a growing interest in analyzing functional connectivity networks estimated from neuroimaging technologies using graph theory. Previous studies of the functional brain networks have focused on extracting static or time-independent networks to describe the long-term behavior of brain activity. In this paper, we propose a dynamic functional brain network tracking and s... View full abstract»

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  • Differential analysis of rna methylation sequencing data

    Publication Year: 2013 , Page(s): 41 - 42
    Save to Project icon | Request Permissions | Click to expandAbstract | PDF file iconPDF (183 KB) |  | HTML iconHTML  

    Recently, a new technique, combining Methylated RNA Immunoprecipatation with RNA sequencing (MeRIP-Seq), was developed and applied to survey the global mRNA N6-methyladenosine (m6A) methylation in mammalian cells. MeRIP-Seq has the potential to survey, for the first time, the transcriptome-wide distribution of different types of post-transcriptional RNA modifications. Yet, this new tech... View full abstract»

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  • MetaPar: Metagenomic sequence assembly via iterative reclassification

    Publication Year: 2013 , Page(s): 43 - 46
    Save to Project icon | Request Permissions | Click to expandAbstract | PDF file iconPDF (271 KB) |  | HTML iconHTML  

    We introduce a parallel algorithmic architecture for metagenomic sequence assembly, termed MetaPar, which allows for significant reductions in assembly time and consequently enables the processing of large genomic datasets on computers with low memory usage. The gist of the approach is to iteratively perform read (re)classification based on phylogenetic marker genes and assembler outputs generated... View full abstract»

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  • Integrated genotyping of structural variation

    Publication Year: 2013 , Page(s): 47 - 48
    Save to Project icon | Request Permissions | Click to expandAbstract | PDF file iconPDF (245 KB) |  | HTML iconHTML  

    Discovering genotype of structural variations (SV) is a new and challenging topic. To the best of our knowledge, estimation of variant allele frequency (VAF) of an SV from both read depth and read alignment has not been done. In this study, we propose BreakDown, a new statistical model that integrates read depth, discordant and split paired-end read alignment to accurately estimate SVs' genotypes ... View full abstract»

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  • Cloud processing of 1000 genomes sequencing data using Amazon Web Service

    Publication Year: 2013 , Page(s): 49 - 52
    Save to Project icon | Request Permissions | Click to expandAbstract | PDF file iconPDF (931 KB) |  | HTML iconHTML  

    We deployed the genetic variant pipeline SNPTools in the cloud utilizing the Amazon Web Service (AWS). With the cloud SNPTools pipeline, we performed the SNP calling and genotype imputation on the 1000 Genomes Project Phase 3 data and assessed the quality of SNPs. We also explored different strategies of exploiting Amazon Elastic Cloud Compute instances and the Amazon Simple Storage Service in ord... View full abstract»

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  • Analyzing T cell repertoire diversity by high-throughput sequencing

    Publication Year: 2013 , Page(s): 53 - 56
    Save to Project icon | Request Permissions | Click to expandAbstract | PDF file iconPDF (1060 KB) |  | HTML iconHTML  

    Diversity on a large scale is one of the most striking and powerful features utilized by the mammalian immune system to fight off a vast universe of pathogens. The T-cell driven immune response is characterized by a multitude of distinct receptors capable of antigen recognition with high specificity. Using high-throughput sequencing we are able to investigate the T cell receptor (TCR) repertoire a... View full abstract»

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  • Application of double asymptotics and random matrix theory in error estimation of regularized linear discriminant analysis

    Publication Year: 2013 , Page(s): 57 - 59
    Save to Project icon | Request Permissions | Click to expandAbstract | PDF file iconPDF (659 KB) |  | HTML iconHTML  

    The theory of double asymptotics and random matrices has been employed to construct a nearly unbiased estimator of true error rate of linear discriminant analysis with ridge estimator of inverse covariance matrix in the multivariate Gaussian model. In such a scenario, the performance of the constructed estimator, as measured by Root-Mean-Square (RMS) error, shows improvement over well-known estima... View full abstract»

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