Recent progress in microscopy technologies, biological markers, and automated processing methods is making possible the development of gene expression atlases at cellular-level resolution over whole embryos. Raw data on gene expression is usually very noisy. This noise comes from both experimental (technical/methodological) and true biological sources (from stochastic biochemical processes). In...

Simultaneous-source acquisition has been recognized as an economic and efficient acquisition method, but the direct imaging of the simultaneous-source data produces migration artifacts because of the interference of adjacent sources. To overcome this problem, we propose the regularized least-squares reverse time migration method (RLSRTM) using the singular spectrum analysis technique that impos...

How to analyze security alarms automatically and find useful information form them has attract a lot of interests. Although many alarm correlation approaches and risk assessment methods have been proposed, most of them were implemented with high computational complexity and time consuming, and they can not deal well with huge number of security alarms. This work focus on performing an real-time...

As hyperspectral imagery (HSI) contains rich spectral and spatial information, a novel principal component analysis (PCA) segmented-PCA (SPCA)-based multiscale 2-D-singular spectrum (2-D-SSA) fusion method is proposed for joint spectral–spatial HSI feature extraction classification. Considering the overall spectra adjacent band correlations of objects, PCA SPCA methods are utilized first dimens...

New filtering and spectral interpretations of Singular Spectrum Analysis (SSA) are provided. It is shown that the variables reconstructed from diagonal averaging of reduced-rank approximations to the trajectory matrix can be obtained from a noncausal convolution filter with zero-phase characteristics. The reconstructed variables are readily constructed using a two-pass filtering algorithm that ...

Prompt γ-ray emissions from gamma-ray bursts (GRBs) exhibit a vast range of extremely complex temporal structures with a typical variability time-scale significantly short - as fast as milliseconds. This work aims to investigate the apparent randomness of the GRB time profiles making extensive use of nonlinear techniques combining the advanced spectral method of the Singular Spectrum Analysis (...

This study employs NASA’s recent satellite measurements of sea-surface temperatures (SSTs) and sea-level winds (SLWs) with missing data filled-in by Singular Spectrum Analysis (SSA), to construct empirical models that capture both intrinsic and SST-dependent aspects of SLW variability. The model construction methodology uses a number of algorithmic innovations that are essential in providing st...

The record of human brain neural activities, namely electroencephalogram (EEG), is known to be nonstationary in general. In addition, the human head is a non-linear medium for such signals. In many applications, it is useful to divide the EEGs into segments in which the signals can be considered stationary. Here, Hilbert-Huang Transform (HHT), as an effective tool in signal processing is applie...