Comparison of Matching Pursuit Algorithm with Other Signal Processing Techniques for Computation of the Time-Frequency Power Spectrum of Brain Signals

Signals recorded from the brain often show rhythmic patterns at different frequencies, which are tightly coupled to the external stimuli as well as the internal state of the subject. In addition, these signals have very transient structures related to spiking or sudden onset of a stimulus, which have durations not exceeding tens of milliseconds. Further, brain signals are highly nonstationary because both behavioral state and external stimuli can change on a short time scale. It is therefore essential to study brain signals using techniques that can represent both rhythmic and transient components of the signal, something not always possible using standard signal processing techniques such as short time fourier transform, multitaper method, wavelet transform, or Hilbert transform. In this review, we describe a multiscale decomposition technique based on an over-complete dictionary called matching pursuit (MP), and show that it is able to capture both a sharp stimulus-onset transient and a sustained gamma rhythm in local field potential recorded from the primary visual cortex. We compare the performance of MP with other techniques and discuss its advantages and limitations. Data and codes for generating all time-frequency power spectra are provided.

Three-Dimensional Modeling of Heat Transfer and Fluid Flow in Laminar-Plasma Material Re-Melting Processing

Modeling study is performed concerning the heat transfer and fluid flow for a laminar argon plasma jet impinging normally upon a flat workpiece exposed to the ambient air. The diffusion of the air into the plasma jet is handled by using the combined-diffusion-coefficient approach. The heat flux density and jet shear stress distributions at the workpiece surface obtained from the plasma jet modeling are then used to study the re-melting process of a carbon steel workpiece. Besides the heat conduction within the workpiece, the effects of the plasma-jet inlet parameters (temperature and velocity), workpiece moving speed, Marangoni convection, natural convection etc. on the re-melting process are considered. The modeling results demonstrate that the shapes and sizes of the molten pool in the workpiece are influenced appreciably by the plasma-jet inlet parameters, workpiece moving speed and Marangoni convection. The jet shear stress manifests its effect at higher plasma-jet inlet velocities, while the natural convection effect can be ignored. The modeling results of the molten pool sizes agree reasonably with available experimental data.

Independent component analysis of microarray data in the study of endometrial cancer.

Gene microarray technology is highly effective in screening for differential gene expression and has hence become a popular tool in the molecular investigation of cancer. When applied to tumours, molecular characteristics may be correlated with clinical features such as response to chemotherapy. Exploitation of the huge amount of data generated by microarrays is difficult, however, and constitutes a major challenge in the advancement of this methodology. Independent component analysis (ICA), a modern statistical method, allows us to better understand data in such complex and noisy measurement environments. The technique has the potential to significantly increase the quality of the resulting data and improve the biological validity of subsequent analysis. We performed microarray experiments on 31 postmenopausal endometrial biopsies, comprising 11 benign and 20 malignant samples. We compared ICA to the established methods of principal component analysis (PCA), Cyber-T, and SAM. We show that ICA generated patterns that clearly characterized the malignant samples studied, in contrast to PCA. Moreover, ICA improved the biological validity of the genes identified as differentially expressed in endometrial carcinoma, compared to those found by Cyber-T and SAM. In particular, several genes involved in lipid metabolism that are differentially expressed in endometrial carcinoma were only found using this method. This report highlights the potential of ICA in the analysis of microarray data.