50 research outputs found
The mbsts package: Multivariate Bayesian Structural Time Series Models in R
The multivariate Bayesian structural time series (MBSTS) model
\citep{qiu2018multivariate,Jammalamadaka2019Predicting} as a generalized
version of many structural time series models, deals with inference and
prediction for multiple correlated time series, where one also has the choice
of using a different candidate pool of contemporaneous predictors for each
target series. The MBSTS model has wide applications and is ideal for feature
selection, time series forecasting, nowcasting, inferring causal impact, and
others. This paper demonstrates how to use the R package \pkg{mbsts} for MBSTS
modeling, establishing a bridge between user-friendly and developer-friendly
functions in package and the corresponding methodology. A simulated dataset and
object-oriented functions in the \pkg{mbsts} package are explained in the way
that enables users to flexibly add or deduct some components, as well as to
simplify or complicate some settings
Multi-sensor Image Data Fusion based on Pixel-Level Weights of Wavelet and the PCA Transform
Abstract -The goal of image fusion is to create new images that are more suitable for the purposes of human visual perception, object detection and target recognition. For Automatic Target Recognition (ATR), we can use multi-sensor data including visible and infrared images to increase the recognition rate. In this paper, we propose a new multiresolution data fusion scheme based on the principal component analysis (PCA) transform and the pixel-level weights wavelet transform including thermal weights and visual weights. In order to get a more ideal fusion result, a linear local mapping which based on the PCA is used to create a new "origin" image of the image fusion. We use multiresolution decompositions to represent the input images at different scales, present a multiresolution/ multimodal segmentation to partition the image domain at these scales. The crucial idea is to use this segmentation to guide the fusion process. Physical thermal weights and perceptive visual weights are used as segmentation multimodals. Daubechies Wavelet is choosen as the Wavelet Basis. Experimental results confirm that the proposed algorithm is the best image sharpening method and can best maintain the spectral information of the original infrared image. Also, the proposed technique performs better than the other ones in the literature, more robust and effective, from both subjective visual effects and objective statistical analysis results
The multiplexed light storage of Orbital Angular Momentum based on atomic ensembles
The improvement of the multi-mode capability of quantum memory can further
improve the utilization efficiency of the quantum memory and reduce the
requirement of quantum communication for storage units. In this letter, we
experimentally investigate the multi-mode light multiplexing storage of orbital
angular momentum (OAM) mode based on rubidium vapor, and demultiplexing by a
photonic OAM mode splitter which combines a Sagnac loop with two dove prisms.
Our results show a mode extinction ratio higher than 80 at 1 s of
storage time. Meanwhile, two OAM modes have been multiplexing stored and
demultiplexed in our experimental configuration. We believe the experimental
scheme may provide a possibility for high channel capacity and multi-mode
quantum multiplexed quantum storage based on atomic ensembles
Recommended from our members
Multivariate time series analysis from a Bayesian machine learning perspective
Multivariate Bayesian Structural Time Series Model
This paper deals with inference and prediction for multiple correlated time
series, where one has also the choice of using a candidate pool of
contemporaneous predictors for each target series. Starting with a structural
model for the time-series, Bayesian tools are used for model fitting,
prediction, and feature selection, thus extending some recent work along these
lines for the univariate case. The Bayesian paradigm in this multivariate
setting helps the model avoid overfitting as well as capture correlations among
the multiple time series with the various state components. The model provides
needed flexibility to choose a different set of components and available
predictors for each target series. The cyclical component in the model can
handle large variations in the short term, which may be caused by external
shocks. We run extensive simulations to investigate properties such as
estimation accuracy and performance in forecasting. We then run an empirical
study with one-step-ahead prediction on the max log return of a portfolio of
stocks that involve four leading financial institutions. Both the simulation
studies and the extensive empirical study confirm that this multivariate model
outperforms three other benchmark models, viz. a model that treats each target
series as independent, the autoregressive integrated moving average model with
regression (ARIMAX), and the multivariate ARIMAX (MARIMAX) model.Comment: 33 page
Remote Sensing Image Fusion Based on Average Gradient of Wavelet Transform
Abstract-Image fusion is one of the important techniques to enhance image information of remote sensing. In order to adequately make use of all kinds of remote sensing images information such as SPOT Panchromatic and three-band Landsat multi-spectral images, a novel remote sensing image fusion scheme based on average gradient of wavelet transform is proposed. In the fusion processing, the fused approximate coefficients are obtained with weighted average method. For the bigger average gradient of the each decomposed approximate coefficient, we choose a big power gene. The other approximate coefficient chooses a small one. The fused detailed coefficients are obtained by setting each coefficient equal to the corresponding input image wavelet coefficient that has the greatest average gradient. The information entropy and the image clarity are proposed as the quantitative evaluation criteria to assess this proposed method and some other methods. The visual and statistical analyses of experimental results show that the proposed fusion method is more effective than the other methods mentioned in this paper