Blind Compressed Sensing Over a Structured Union of Subspaces

This paper addresses the problem of simultaneous signal recovery and dictionary learning based on compressive measurements. Multiple signals are analyzed jointly, with multiple sensing matrices, under the assumption that the unknown signals come from a union of a small number of disjoint subspaces. This problem is important, for instance, in image inpainting applications, in which the multiple signals are constituted by (incomplete) image patches taken from the overall image. This work extends standard dictionary learning and block-sparse dictionary optimization, by considering compressive measurements, e.g., incomplete data). Previous work on blind compressed sensing is also generalized by using multiple sensing matrices and relaxing some of the restrictions on the learned dictionary. Drawing on results developed in the context of matrix completion, it is proven that both the dictionary and signals can be recovered with high probability from compressed measurements. The solution is unique up to block permutations and invertible linear transformations of the dictionary atoms. The recovery is contingent on the number of measurements per signal and the number of signals being sufficiently large; bounds are derived for these quantities. In addition, this paper presents a computationally practical algorithm that performs dictionary learning and signal recovery, and establishes conditions for its convergence to a local optimum. Experimental results for image inpainting demonstrate the capabilities of the method

Electrodeposition of Diamond-like Carbon Films

Electrodeposition of diamond-like carbon (DLC) films was studied on different substrates using two different electrochemical methods. The first electrochemical method using a three-electrode system was studied to successfully deposit hydrogenated DLC films on Nickel, Copper and Brass substrates. The as-deposited films were characterized by scanning electron microscopy (SEM), Raman spectroscopy, X-ray photoelectron spectroscopy (XPS), fourier transform infrared spectroscopy (FTIR) and cyclic voltammetry (CV). A variety of experimental parameters were shown to affect the deposition process. The second electrochemical method was developed for the first time to deposit hydrogen free DLC films on Ni substrates through a two-electrode system. The as-deposited films were characterized by Raman spectroscopy and FTIR. According to Raman spectra, a high fraction of diamond nanocrystals were found to form in the films. Several possible mechanisms were discussed for each deposition method. An electrochemical method was proposed to deposit boron-doped diamond films for future work

Minimum superlattice thermal conductivity from molecular dynamics

The dependence of superlattice thermal conductivity on period length is investigated by molecular dynamics simulation. For perfectly lattice matched superlattices, a minimum is observed when the period length is of the order of the effective phonon mean free path. As temperature decreases and interatomic potential strength increases, the position of the minimum shifts to larger period lengths. The depth of the minimum is strongly enhanced as mass and interatomic potential ratios of the constituent materials increase. The simulation results are consistent with phonon transmission coefficient calculations, which indicate increased stop bandwidth and thus strongly enhanced Bragg scattering for the same conditions under which strong reductions in thermal conductivity are found. When nonideal interfaces are created by introducing a 4% lattice mismatch, the minimum disappears and thermal conductivity increases monotonically with period length. This result may explain why minimum thermal conductivity has not been observed in a large number of experimental studies

Communications-Inspired Projection Design with Application to Compressive Sensing

We consider the recovery of an underlying signal x \in C^m based on projection measurements of the form y=Mx+w, where y \in C^l and w is measurement noise; we are interested in the case l < m. It is assumed that the signal model p(x) is known, and w CN(w;0,S_w), for known S_W. The objective is to design a projection matrix M \in C^(l x m) to maximize key information-theoretic quantities with operational significance, including the mutual information between the signal and the projections I(x;y) or the Renyi entropy of the projections h_a(y) (Shannon entropy is a special case). By capitalizing on explicit characterizations of the gradients of the information measures with respect to the projections matrix, where we also partially extend the well-known results of Palomar and Verdu from the mutual information to the Renyi entropy domain, we unveil the key operations carried out by the optimal projections designs: mode exposure and mode alignment. Experiments are considered for the case of compressive sensing (CS) applied to imagery. In this context, we provide a demonstration of the performance improvement possible through the application of the novel projection designs in relation to conventional ones, as well as justification for a fast online projections design method with which state-of-the-art adaptive CS signal recovery is achieved.Comment: 25 pages, 7 figures, parts of material published in IEEE ICASSP 2012, submitted to SIIM

Proteomic Analysis of Serum in Lung Cancer Induced by 3-Methylcholanthrene

Lung cancer remains the leading cause of cancer-related mortality worldwide. Early detection of lung cancer is problematic due to the lack of a marker with high diagnosis sensitivity and specificity. To determine the differently expressed proteins in the serum of lung cancer and figure out the function of the proteins, two-dimensional electrophoresis (2DE) and matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF-MS) were used to screen the serum proteins of lung cancer model induced by 3-methylcholanthrene (MCA). From optimized 2DE image, 455 spots in the normal sera and 716 spots in the lung cancers sera were detected. Among them, 141 protein spots were differentially expressed when comparing the serum from normal rat and serum from lung cancer model, including 82 overexpressed proteins and 59 underexpressed proteins. Changes of haptoglobin, transthyretin, and TNF superfamily member 8 (TNFRS8) were confirmed in sera from lung cancer by MALDI-TOF-MS. Proteomics technology leads to identify changes of haptoglobin, transthyretin, and TNFRS8 in serum of rat lung cancer model and represents a powerful tool in searching for candidate proteins as biomarkers