Multiresolution models such as the wavelet-domain hidden Markov tree (HMT) model provide a powerful approach for image modeling and processing because it captures the key features of the wavelet coefficients of real-world data. It is observed that the Laplace distribution is peakier in the center and has heavier tails compared with the Gaussian distribution. Thus we propose a new HMT model base...

The linear mixture model (LMM) has recently been used for multi-channel representation of a blurred image. This enables use of multivariate data analysis methods such as independent component analysis (ICA) to solve blind image deconvolution as an instantaneous blind source separation (BSS) requiring no a priori knowledge about the size and origin of the blurring kernel. However, there remains ...

Abstract: This paper proposes a nonlinear model based predictive control (NMPC) algorithm for nonlinear systems by using multiple models approach. To have a less complexity model we expand each linear sub-model on an orthogonal Laguerre basis, the characteristic pole of which should be optimized. In this paper we propose a pole optimization algorithm based on the Gauss-Newton method and we use ...

BACKGROUND Ultrasound is frequently used to measure activity in the lumbar multifidus muscle (LMM). However previous reliability studies on diagnostic ultrasound and LMM have included a limited number of subjects and few have used Bland-Altman's Limits of Agreement (LOA). Further one does not know if activity affects the subjects' ability to contract the LMM. METHODS From January 2012 to Dece...

Motivation: Exploring the genetic basis of heritable traits remains one of the central challenges in biomedical research. In traits with simple mendelian architectures, single polymorphic loci explain a significant fraction of the phenotypic variability. However, many traits of interest appear to be subject to multifactorial control by groups of genetic loci. Accurate detection of such multivar...

The fraction of vegetation cover (FVC) is often estimated by unmixing a linear mixture model (LMM) to assess the horizontal spread of vegetation within a pixel based on a remotely sensed reflectance spectrum. The LMM-based algorithm produces results that can vary to a certain degree, depending on the model assumptions. For example, the robustness of the results depends on the presence of errors...

In this paper we will establish a generalized Swap Market Model (GSMM) by unifying the stochastic process of swap rates with constant tenors under a single swap measure. GSMM is a natural extension of Libor Market Model (LMM) for swap rates, and LMM can be considered as a special case of GSMM since Libors is a special swap rate with the constant tenor of one period. GSMM can be applied for pric...

Speech process has benefited a great deal from the wavelet transforms. Wavelet packets decompose signals in to broader components using linear spectral bisecting. In this paper, mixtures of speech signals are decomposed using wavelet packets, the phase difference between the two mixtures are investigated in wavelet domain. In our method Laplacian Mixture Model (LMM) is defined. An Expectation M...

BACKGROUND The summary measure approach (SMA) is sometimes the only applicable tool for the analysis of repeated measurements in medical research, especially when the number of measurements is relatively large. This study aimed to describe techniques based on summary measures for the analysis of linear trend repeated measures data and then to compare performances of SMA, linear mixed model (LMM...

It has been observed that heavy meromyosin (HMM) propels actin filaments to higher velocities than native myosin in the in vitro motility assay, yet the reason for this difference has remained unexplained. Since the major difference between these two proteins is the presence of the tail in native myosin, we tested the hypothesis that unknown interactions between actin and the tail (LMM) slow mo...