Compressive multi-spectral imaging using self-correlations of images based on hierarchical joint sparsity models

We propose a novel multi-spectral imaging method based on compressive sensing (CS). In CS theory, the enhancement of signal sparsity is important for accurate signal reconstruction. The main novelty of the proposed method is the employment of a self-correlation of an image, that is a local intensity similarity and multi-spectral correlation, to enhance the sparsity of the multi-spectral image to be recovered. Local intensity similarity, which is based on the concept that spatial changes in intensity are likely to be similar within local regions, contributes to sparsity enhancement. Furthermore, we exploit multi-spectral correlation to improve the sparsity of the multi-spectral components to be recovered. In order to simultaneously exploit different types of characteristics (i.e., local intensity similarity and multi-spectral correlation) for representing a signal as sufficiently sparse, we introduce a hierarchical joint sparsity model in the CS image recovery process. Our experiments show that the use of a self-correlation significantly improves the performance of multi-spectral image reconstruction.     

A heuristic approach to discovering user correlations from organized social stream data

Multimedia Tools and Applications - Recently, with the widespread popularity of SNS (Social Network Service), such as Twitter, Facebook, people are increasingly accustomed to sharing feeling,...     

Unravel four hairpins!

DNA machines consisting of consecutive hairpins, which we have previously described, have various potential applications in DNA computation. In the present study, a 288-base DNA machine containing four consecutive hairpins was successfully constructed by ligation and PCR. PAGE and fluorescence spectroscopy experiments verified that all four hairpins were successfully opened by four opener oligomers, and that hairpin opening was dependent on the proper openers added in the correct order. Quantitative analysis of the final results by fluorescence spectroscopy indicated that all four hairpins were open in about 1/4 to 1/3 of the DNA machines.     

Autonomic control of heart rate during physical exercise and fractal dimension of heart rate variability

The objectives of the present study were to investigate autonomic nervous system influence on heart rate during physical exercise and to examine the relationship between the fractal component in heart rate variability (HRV) and the system's response. Ten subjects performed incremental exercise on a cycle ergometer, consisting of a 5-min warm-up period followed by a ramp protocol, with work rate increasing at a rate of 2.0 W/min until exhaustion. During exercise, alveolar gas exchange, plasma norepinephrine (NE) and epinephrine (E) responses, and beat-to-beat HRV were monitored. HRV data were analyzed by "coarse-graining spectral analysis" (Y. Yamamoto and R. L. Hughson. J. Appl. Physiol. 71: 1143-1150, 1991) to break down their total power (Pt) into harmonic and nonharmonic (fractal) components. The harmonic component was further divided into low-frequency (0.0-0.15 Hz) and high-frequency (0.15-0.8 Hz) components, from which low-frequency and high-frequency power (Pl and Ph, respectively) were calculated. Parasympathetic (PNS) and sympathetic (SNS) nervous system activity indicators were evaluated by Ph/Pt and Pl/Ph, respectively. From the fractal component, the fractal dimension (DF) and the spectral exponent (beta) were calculated. The PNS indicator decreased significantly (P < 0.05) when exercise intensity exceeded 50% of peak oxygen uptake (VO2 peak). Conversely, the SNS indicator initially increased at 50-60% VO2peak (P < 0.05) and further increased significantly (P < 0.05) at > 60% VO2peak when there were also more pronounced increases in NE and E.(ABSTRACT TRUNCATED AT 250 WORDS)     

On-line Algorithm for Blind Signal Extraction of Arbitrarily Distributed,but Temporally Correlated Sources Using Second Order Statistics

Most of the algorithms for blind separation/extraction and independent component analysis (ICA) can not separate mixtures of sources with extremely low kurtosis or colored Gaussian sources. Moreover, to separate mixtures of super- and sub-Gaussian signals, it is necessary to use adaptive (time-variable) or switching nonlinearities which are controlled via computationally intensive measures, such as estimation of the sign of kurtosis of extracted signals. In this paper, we develop a very simple neural network model and an efficient on-line adaptive algorithm that sequentially extract temporally correlated sources with arbitrary distributions, including colored Gaussian sources and sources with extremely low values (or even zero) of kurtosis. The validity and performance of the algorithm have been confirmed by extensive computer simulation experiments.     

Logichart — Intelligible Program Diagram for Prolog and its Processing System

An intelligible program diagram called Logichart has been developed for Prolog visualization. Its syntax rules and layout rules are formalized in terms of an attribute graph grammar. This grammar is underlain by a context–free graph grammar whose productions are defined to formalize the graph–syntax rules of Logichart diagrams. Semantic rules attached to the productions are defined in such a way that they can extract the layout information needed to display a Logichart diagram as attributes. Our Prolog visualization system, which has been implemented based on the attribute graph grammar, can draw a Logichart diagram for any correct Prolog program (completeness), and any Logichart diagram displayed by the system is always valid for the grammar (soundness). The system can also display the execution of a Prolog program in real time; that is, the Logichart diagram is dynamically updated as the program runs.     

Role of frustration in quasi 1D conductor — Charge ordering and/or CDW in (R1,R2-DCNQI)2M (M=Ag, Li, Cu) system —

Synchrotron X-ray high-resolution measurements have been performed to investigate the nature of the insulating state of a quarter-filled quasi-one-dimensional conductor (DI-DCNQI)₂Ag and the π-d interacted conductor (DBr-DCNQI)₂Cu. The two-fold structure in the ground state of (DI-DCNQI)₂Ag system consists of not only charge ordering columns but also monotonic charge dimerized columns caused by frustration among DCNQI columns. On the other hand, Cu salt in the insulating phase realizes a commensurate structure where the Cu charge ordering is coupled with the 3c-CDW in the insulator phase. The frustration among charged columns is restrained by the charge ordering of the Cu ions.     

On a generalised plane strain crack problem for inhomogeneous anisotropic elastic materials

A generalised plane strain crack problem is considered for a class of inhomogeneous anisotropic elastic materials. The problem is reduced to a boundary integral equation involving hypersingular integrals. The boundary integral equation may be solved numerically using standard procedures. Some crack problems for a particular inhomogeneous material are considered in detail and the stress intensity factors are obtained in order to assess the effect of the anisotropy and inhomogeneity on the stress field near the crack tips.