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检索条件"任意字段=International Conference on Signal and Data Processing, ICSDP 2019"

共 2690 条记录，以下是71-80 订阅

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MAKING DECISIONS WITH SHUFFLED BITS 44

MAKING DECISIONS WITH SHUFFLED BITS

引用

44th IEEE international conference on Acoustics, Speech and signal processing (ICASSP)

作者： Marano, Stefano Willett, Peter Univ Salerno DIEM Fisciano SA Italy Univ Connecticut ECE Dept Storrs CT USA

ISBN: (纸本)9781479981311

Unlabeled detection is an emerging paradigm for modern decentralized decision systems faced with big-data applications, and for all those applications in which data must be fused without exploiting their identity, due to the lack of provenance labels, or to uncontrolled data shuffling Our focus here is on binary alphabets, and we ask: If our data have been shuffled in an unknown way, can a reliable decision about the underlying state of nature be made? Should the decision be made after an attempt to estimate the lost labels? And do there exist easily implementable decision rules? In answering these questions, we gain much insight: We show that two greedy algorithms previously introduced in the literature are equivalent to the GLRT, whose performance can be quite poor, and the detector known as ULR is equivalent to a simple counting rule. A new detector based on the central limit theorem is simply implementable and offers close-to-optimal performance in many scenarios of practical interest.

关键词： Unlabeled Detection Shuffled data Unknown Permutation Big data Large Sensor Networks

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TV-DCT: METHOD TO IMPUTE GENE EXPRESSION data USING DCT BASED SPARSITY AND TOTAL VARIATION DENOISING 44

TV-DCT: METHOD TO IMPUTE GENE EXPRESSION DATA USING DCT BASE...

引用

44th IEEE international conference on Acoustics, Speech and signal processing (ICASSP)

作者： Farswan, Akanksha Gupta, Anubha IIIT Delhi Dept ECE Signal Proc & Biomed Imaging Lab SBILab New Delhi India

ISBN: (纸本)9781479981311

Most of the bioinformatics tools used in the analysis of gene expression data require complete data matrices. Missing values in data can adversely influence the downstream analysis for diagnostics and treatment. Several methods to impute missing values in gene data have been developed. However, most of these work at high levels of observability. In this paper, we have proposed a novel 2-stage method, namely, TV-DCT for imputing incomplete gene expression matrices using Total Variation denoising and Discrete Cosine Transform Domain Sparsity (TV-DCT) that achieves smaller imputation errors, consistently, at all levels of observability. The proposed method has been compared with three state-of-the-art matrix completion methods on three different cancer datasets and is observed to perform better. The validation of imputed data has been demonstrated on the application of classification.

关键词： Gene expression data matrix imputation sparse recovery machine learning cancer treatment

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SHIFT-INVARIANT SUBSPACE TRACKING WITH MISSING data 44

SHIFT-INVARIANT SUBSPACE TRACKING WITH MISSING DATA

引用

44th IEEE international conference on Acoustics, Speech and signal processing (ICASSP)

作者： Cho, Myung Chi, Yuejie Carnegie Mellon Univ Dept Elect & Comp Engn Pittsburgh PA 15213 USA

ISBN: (纸本)9781479981311

Subspace tracking is an important problem in signal processing that finds applications in wireless communications, video surveillance, and source localization in radar and sonar. In recent years, it is recognized that a low-dimensional subspace can be estimated and tracked reliably even when the data vectors are partially observed with many missing entries, which is greatly desirable when processing highdimensional and high-rate data to reduce the sampling requirement. This paper is motivated by the observation that the underlying lowdimensional subspace may possess additional structural properties induced by the physical model of data, which if harnessed properly, can greatly improve subspace tracking performance. As a case study, this paper investigates the problem of tracking direction-of-arrivals from subsampled observations in a unitary linear array, where the signals lie in a subspace spanned by columns of a Vandermonde matrix. We exploit the shift-invariant structure by mapping the data vector to a latent Hankel matrix, and then perform tracking over the Hankel matrices by exploiting their low-rank properties. Numerical simulations are conducted to validate the superiority of the proposed approach over existing subspace tracking methods that do not exploit the additional shift-invariant structure in terms of tracking speed and agility.

关键词： subspace tracking missing data shift-invariant subspace Hankel matrix

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ENHANCED STREAMING BASED SUBSPACE CLUSTERING APPLIED TO ACOUSTIC SCENE data CLUSTERING 44

ENHANCED STREAMING BASED SUBSPACE CLUSTERING APPLIED TO ACOU...

引用

44th IEEE international conference on Acoustics, Speech and signal processing (ICASSP)

作者： Li, Shuoyang Gu, Yuantao Luo, Yuhui Chambers, Jonathon Wang, Wenwu Univ Surrey Dept Elect & Elect Engn Guildford Surrey England Tsinghua Univ Dept Elect Engn Beijing Peoples R China Natl Phys Lab Data Sci Dept Teddington Middx England Univ Leicester Dept Engn Leicester Leics England

ISBN: (纸本)9781479981311

Labelled data are often required to train an acoustic scene classification system. However, it is time-consuming and expensive to label the data manually. An unsupervised clustering algorithm can be used to facilitate the labelling process by dividing the acoustic data into different categories. Nevertheless, it can be problematic to run a clustering algorithm with growing data volume and dimension due to the sharp increase in the computational and memory costs. We propose a new streaming based subspace clustering algorithm which allows the data to be clustered on the fly, and also resolves data points in the overlapping regions of two subspaces by augmenting the learned low-rank representation with the original data samples. Experimental results show that our method can achieve the clustering objective for overwhelmingly high-volume data in an online fashion, while retaining good accuracy and reducing the memory cost significantly.

关键词： Clustering algorithms Acoustics Convergence Reliability Symmetric matrices Labeling Clustering methods

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AGGREGATION GRAPH NEURAL NETWORKS 44

AGGREGATION GRAPH NEURAL NETWORKS

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44th IEEE international conference on Acoustics, Speech and signal processing (ICASSP)

作者： Gama, Fernando Marques, Antonio G. Ribeiro, Alejandro Leus, Geert Univ Penn Dept Elect & Syst Engn Philadelphia PA 19104 USA King Juan Carlos Univ Dept Signal Theory & Commun Madrid Spain Delft Univ Technol Dept Microelect Delft Netherlands

ISBN: (纸本)9781479981311

Graph neural networks (GNNs) regularize classical neural networks by exploiting the underlying irregular structure supporting graph data, extending its application to broader data domains. The aggregation GNN presented here is a novel GNN that exploits the fact that the data collected at a single node by means of successive local exchanges with neighbors exhibits a regular structure. Thus, regular convolution and regular pooling yield an appropriately regularized GNN. To address some scalability issues that arise when collecting all the information at a single node, we propose a multi-node aggregation GNN that constructs regional features that are later aggregated into more global features and so on. We show superior performance in a source localization problem on synthetic graphs and on the authorship attribution problem.

关键词： graph neural networks convolutional neural networks network data graph signal processing

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SPARSE SUBSPACE CLUSTERING FOR EVOLVING data STREAMS 44

SPARSE SUBSPACE CLUSTERING FOR EVOLVING DATA STREAMS

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44th IEEE international conference on Acoustics, Speech and signal processing (ICASSP)

作者： Sui, Jinping Liu, Zhen Liu, Li Jung, Alexander Liu, Tianpeng Peng, Bo Li, Xiang Natl Univ Def Technol Coll Elect Sci Changsha 410073 Hunan Peoples R China Aalto Univ Dept Comp Sci Espoo 02150 Finland Natl Univ Def Technol Coll Syst Engn Changsha 410073 Hunan Peoples R China Univ Oulu Ctr Machine Vis & Signal Anal Oulu Finland

ISBN: (纸本)9781479981311

The data streams arising in many applications can be modeled as a union of low-dimensional subspaces known as multi-subspace data streams (MSDSs). Clustering MSDSs according to their underlying low-dimensional subspaces is a challenging problem which has not been resolved satisfactorily by existing data stream clustering (DSC) algorithms. In this paper, we propose a sparse-based DSC algorithm, which we refer to as dynamic sparse subspace clustering (D-SSC). This algorithm recovers the low-dimensional subspaces (structures) of high-dimensional data streams and finds an explicit assignment of points to subspaces in an online manner. Moreover, as an online algorithm, D-SSC is able to cope with the time-varying structure of MSDSs. The effectiveness of D-SSC is evaluated using numerical experiments.

关键词： data stream clustering high-dimensional data stream subspace clustering online clustering

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PREDICTION-ERROR-ORDERING FOR HIGH-FIDELITY REVERSIBLE data HIDING 44

PREDICTION-ERROR-ORDERING FOR HIGH-FIDELITY REVERSIBLE DATA ...

引用

44th IEEE international conference on Acoustics, Speech and signal processing (ICASSP)

作者： Dragoi, Ioan Catalin Coltuc, Dinu Valahia Univ Targoviste Fac Elect Engn Elect & Informat Technol Targoviste Romania

ISBN: (纸本)9781479981311

Pixel-value-ordering (PVO) appears as an efficient solution for high-fidelity reversible data hiding. State-of-the-art PVO schemes split the host image into blocks, sort pixels within the blocks based on their graylevel and, finally, embed data into some differences between the sorted values. This paper investigates the use of the prediction error instead of the pixel graylevel both for ordering and embedding. The proposed prediction-error-ordering (PEO) scheme also introduces a two-stage procedure by splitting each block in two sets following a chessboard pattern and by processing set by set each block. The pixels of one set are used to classify and predict the ones of the other set and vice-versa. The proposed PEO approach outperforms the state-of-the-art PVO schemes.

关键词： Reversible data hiding pixel-value-ordering pairwise embedding

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Heterogeneous Multi-sensor data Fusion in Radar signal processing 4

Heterogeneous Multi-sensor Data Fusion in Radar Signal Proce...

引用

4th international conference on Electromechanical Control Technology and Transportation (ICECTT)

作者： Liu, Qiyue Zhang, Qi Chinese Peoples Liberat Army Troop 91245 Huludao Peoples R China Naval Univ Engn Sch Elect Engn Wuhan Hubei Peoples R China

ISBN: (纸本)9781728100425

The complexity of the spatial environment and the ambiguity of the radar signal processing lower the reliability of the single radar in the precision tracking system. It has become a trend of using the radar in conjunction with other measurement equipment to improve the measurement accuracy, and this involves the comprehensive and multi-dimensional data fusion processing of the multi-sensor data. This paper summarized and analyzed the existing mechanisms and application methods of multi-sensor data fusion, and categorized the traditional and the new fusion methods according to their application scope, advantages, and disadvantages. These mainly include radar target detection, tracking, and recognition, as well as various heterogeneous sensors in the radar data fusion. Moreover, this paper also compared the data fusion algorithms and methods of different multi-sensors.

关键词： multi-sensor precision tracking system data fusion radar signal processing

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INFORMED EGO-NOISE SUPPRESSION USING MOTOR data-DRIVEN DICTIONARIES 44

INFORMED EGO-NOISE SUPPRESSION USING MOTOR DATA-DRIVEN DICTI...

引用

44th IEEE international conference on Acoustics, Speech and signal processing (ICASSP)

作者： Schmidt, Alexander Kellermann, Walter Friedrich Alexander Univ Erlangen Nurnberg Multimedia Commun & Signal Proc Cauerstr 7 D-91058 Erlangen Germany

ISBN: (纸本)9781479981311

The suppression of ego-noise for (humanoid) robots is typically addressed by learning-based techniques. In this paper, we propose a novel approach which models significant parts of ego-noise spectrograms based on motor data and does not require a prior training step. Accordingly, the intrinsic harmonic structure of ego noise is taken into account by introducing a nonnegative matrix factorization (NMF) framework with motor data-driven dictionaries. Limited improvement was observed by employing an additional pre-trained small-sized dictionary accounting for the residual ego-noise. The presented approach exhibits comparable suppression performance to an audio only-based approach trained specifically to the scenario, while the number of dictionary elements which require prior learning can be reduced by a factor of two. For ego-noise resulting from previously unseen movements, the proposed method shows consistently superior suppression results while the audio only-based approach degrades drastically.

关键词： Ego-noise suppression robot motor data dictionary learning nonnegative matrix factorization

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USING 3D RESIDUAL NETWORK FOR SPATIO-TEMPORAL ANALYSIS OF REMOTE SENSING data 44

USING 3D RESIDUAL NETWORK FOR SPATIO-TEMPORAL ANALYSIS OF RE...

引用

44th IEEE international conference on Acoustics, Speech and signal processing (ICASSP)

作者： Bhimra, Muhammad Ahmed Nazir, Usman Taj, Murtaza LUMS Syed Babar Ali Sch Sci & Engn Dept Comp Sci Lahore Pakistan

ISBN: (纸本)9781479981311

In this paper, we propose an approach to recognize spatio-temporal changes from remote sensing data. Instead of performing independent analysis on each instance of satellite imagery, we proposed a 3D Convolutional Neural Network (CNN) based on the ResNet architecture. Our approach takes as input a 3D spatio-temporal block comprising of spatial as well as temporal data from multiple years. We predict four key transition classes namely construction, destruction, cultivation and decultivation. In our proposed architecture, we introduced Leaky ReLU instead of ReLU which improves the overall performance as it solves the dying ReLU problem. We also provided dataset and annotations1 for these four classes and have evaluated the efficacy of our approach on data from three different cities.

关键词： Spatial-Temporal Convolutional Neural Network (CNN) ResNet Satellite Imagery Remote Sensing data

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