Entropy Constrained Exemplar-based Image Inpainting

Image inpainting is the process of filling the unwanted region in an image marked by the user. It is used for restoring old paintings and photographs, removal of red eyes from pictures, etc. In this paper, we propose an efficient inpainting algorithm which takes care of false edge propagation. We use the classical exemplar based technique to find out the priority term for each patch. To ensure that the edge content of the nearest neighbor patch found by minimizing L-2 distance between patches, we impose an additional constraint that the entropy of the patches be similar. Entropy of the patch acts as a good measure of edge content. Additionally, we fill the image by considering overlapping patches to ensure smoothness in the output. We use structural similarity index as the measure of similarity between ground truth and inpainted image. The results of the proposed approach on a number of examples on real and synthetic images show the effectiveness of our algorithm in removing objects and thin scratches or text written on image. It is also shown that the proposed approach is robust to the shape of the manually selected target. Our results compare favorably to those obtained by existing techniques

Hetero-atom Doped Carbon Nanotubes for Dye Degradation and Oxygen Reduction Reaction

We report the synthesis of nitrogen doped vertically aligned multi-walled (MWNCNTs) carbon nanotubes by pyrolysis and its catalytic performance for degradation of methylene blue (MB) dye & oxygen reduction reaction (ORR). The degradation of MB was monitored spectrophotometrically with time. Kinetic studies show the degradation of MB follows a first order kinetic with rate constant k=0.0178 min(-1). The present rate constant is better than that reported for various supported/non-supported semiconducting nanomaterials. Further ORR performance in alkaline media makes MWNCNTs a promising cost-effective, fuel crossover tolerance, metal-free, eco-friendly cathode catalyst for direct alcohol fuel cell.

Prior image based temporally constrained reconstruction algorithm for magnetic resonance guided high intensity focused ultrasound

Purpose: A prior image based temporally constrained reconstruction ( PITCR) algorithm was developed for obtaining accurate temperature maps having better volume coverage, and spatial, and temporal resolution than other algorithms for highly undersampled data in magnetic resonance (MR) thermometry. Methods: The proposed PITCR approach is an algorithm that gives weight to the prior image and performs accurate reconstruction in a dynamic imaging environment. The PITCR method is compared with the temporally constrained reconstruction (TCR) algorithm using pork muscle data. Results: The PITCR method provides superior performance compared to the TCR approach with highly undersampled data. The proposed approach is computationally expensive compared to the TCR approach, but this could be overcome by the advantage of reconstructing with fewer measurements. In the case of reconstruction of temperature maps from 16% of fully sampled data, the PITCR approach was 1.57x slower compared to the TCR approach, while the root mean square error using PITCR is 0.784 compared to 2.815 with the TCR scheme. Conclusions: The PITCR approach is able to perform more accurate reconstructions of temperature maps compared to the TCR approach with highly undersampled data in MR guided high intensity focused ultrasound. (C) 2015 American Association of Physicists in Medicine.

Organic-inorganic hybrid PtCo nanoparticle with high electrocatalytic activity and durability for oxygen reduction

In Pt-transition metal (TM) alloy catalysts, the electron transfer from the TM to Pt is retarded owing to the inevitable oxidation of the TM surface by oxygen. In addition, acidic electrolytes such as those employed in fuel cells accelerate the dissolution of the surface TM oxide, which leads to catalyst degradation. Herein, we propose a novel synthesis strategy that selectively modifies the electronic structure of surface Co atoms with N-containing polymers, resulting in highly active and durable PtCo nanoparticle catalysts useful for the oxygen reduction reaction (ORR). The polymer, which is functionalized on carbon black, selectively interacts with the Co precursor, resulting in Co-N bond formation on the PtCo nanoparticle surface. Electron transfer from Co to Pt in the PtCo nanoparticles modified by the polymer is enhanced by the increase in the difference in electronegativity between Pt and Co compared with that in bare PtCo nanoparticles with the TM surface oxides. In addition, the dissolution of Co and Pt is prevented by the selective passivation of surface Co atoms and the decrease in the O-binding energy of surface Pt atoms. As a result, the catalytic activity and durability of PtCo nanoparticles for the ORR are significantly improved by the electronic ensemble effects. The proposed organic/inorganic hybrid concept will provide new insights into the tuning of nanomaterials consisting of heterogeneous metallic elements for various electrochemical and chemical applications.

Anomalous reduction in domain wall displacement at the morphotropic phase boundary of the piezoelectric alloy system PbTiO3-BiScO3

A comparative study of field-induced domain switching and lattice strain was carried out by in situ electric-field-dependent high-energy synchrotron x-ray diffraction on a morphotropic phase boundary (MPB) and a near-MPB rhombohedral/pseudomonoclinic composition of a high-performance piezoelectric alloy (1-x) PbTiO3-(x)BiScO3. It is demonstrated that the MPB composition showing large d(33) similar to 425 pC/N exhibits significantly reduced propensity of field-induced domain switching as compared to the non-MPB rhombohedral composition (d(33) similar to 260 pC/N). These experimental observations contradict the basic premise of the martensitic-theory-based explanation which emphasizes on enhanced domain wall motion as the primary factor for the anomalous piezoelectric response in MPB piezoelectrics. Our results favor field-induced structural transformation to be the primary mechanism contributing to the large piezoresponse of the critical MPB composition of this system.

Thermal co-reduction approach to vary size of silver nanoparticle: its microbial and cellular toxicology

In recent years, silver nanoparticles (AgNPs) have attracted considerable interest in the field of food, agriculture and pharmaceuticals mainly due to its antibacterial activity. AgNPs have also been reported to possess toxic behavior. The toxicological behavior of nanomaterials largely depends on its size and shape which ultimately depend on synthetic protocol. A systematic and detailed analysis for size variation of AgNP by thermal co-reduction approach and its efficacy toward microbial and cellular toxicological behavior is presented here. With the focus to explore the size-dependent toxicological variation, two different-sized NPs have been synthesized, i.e., 60 nm (Ag60) and 85 nm (Ag85). A detailed microbial toxicological evaluation has been performed by analyzing minimum inhibitory concentration (MIC), minimum bactericidal concentration (MBC), diameter of inhibition zone (DIZ), growth kinetics (GrK), and death kinetics (DeK). Comparative cytotoxicological behavior was analyzed by 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) assay. It has been concluded by this study that the size of AgNPs can be varied, by varying the concentration of reactants and temperature called as ``thermal co-reduction'' approach, which is one of the suitable approaches to meet the same. Also, the smaller AgNP has shown more microbial and cellular toxicity.

A Novel Approach for Multispectral Satellite Image Classification Based on the Bat Algorithm

Among the multiple advantages and applications of remote sensing, one of the most important uses is to solve the problem of crop classification, i.e., differentiating between various crop types. Satellite images are a reliable source for investigating the temporal changes in crop cultivated areas. In this letter, we propose a novel bat algorithm (BA)-based clustering approach for solving crop type classification problems using a multispectral satellite image. The proposed partitional clustering algorithm is used to extract information in the form of optimal cluster centers from training samples. The extracted cluster centers are then validated on test samples. A real-time multispectral satellite image and one benchmark data set from the University of California, Irvine (UCI) repository are used to demonstrate the robustness of the proposed algorithm. The performance of the BA is compared with two other nature-inspired metaheuristic techniques, namely, genetic algorithm and particle swarm optimization. The performance is also compared with the existing hybrid approach such as the BA with K-means. From the results obtained, it can be concluded that the BA can be successfully applied to solve crop type classification problems.

On the Geometrically Dependent Quasi-Saturation and g(m) Reduction in Advanced DeMOS Transistors

This paper reveals an early quasi-saturation (QS) effect attributed to the geometrical parameters in shallow trench isolation-type drain-extended MOS (STI-DeMOS) transistors in advanced CMOS technologies. The quasi-saturation effect leads to serious g(m) reduction in STI-DeMOS. This paper investigates the nonlinear resistive behavior of the drain-extended region and its impact on the particular behavior of the STI-DeMOS transistor. In difference to vertical DMOS or lateral DMOS structures, STI-DeMOS exhibits three distinct regions of the drain extension. A complete understanding of the physics in these regions and their impact on the QS behavior are developed in this paper. An optimization strategy is shown for an improved g(m) device in a state-of-the-art 28-nm CMOS technology node.

Primary and rechargeable zinc-air batteries using ceramic and highly stable TiCN as an oxygen reduction reaction electrocatalyst

Primary and secondary zinc-air batteries based on ceramic, stable, one dimensional titanium carbonitride (TiCN) nanostructures are reported. The optimized titanium carbonitride composition by density functional theory reveals their good activity towards the oxygen reduction reaction (ORR). Electrochemical measurements show their superior performance for the ORR in alkaline media coupled with favourable kinetics. The nanostructured TiCN lends itself amenable to be used as an air cathode material in primary and rechargeable zinc-air batteries. The battery performance and cyclability are found to be good. Further, we have demonstrated a gel-based electrolyte for rechargeable zinc-air batteries based on a TiCN cathode under ambient, atmospheric conditions without any oxygen supply from a cylinder. The present cell can work at current densities of 10-20 mA cm(2) (app. 10 000 mA g(-1) of TiCN) for several hours (63 h in the case of 10 mA cm(-2)) with a charge retention of 98%. The low cost, noble metal-free, mechanically stable and corrosion resistant TiCN is a very good alternative to Pt for metal-air battery chemistry.

FIVDL: Fingerprint Image Verification using Dictionary Learning

Fingerprints are used for identification in forensics and are classified into Manual and Automatic. Automatic fingerprint identification system is classified into Latent and Exemplar. A novel Exemplar technique of Fingerprint Image Verification using Dictionary Learning (FIVDL) is proposed to improve the performance of low quality fingerprints, where Dictionary learning method reduces the time complexity by using block processing instead of pixel processing. The dynamic range of an image is adjusted by using Successive Mean Quantization Transform (SMQT) technique and the frequency domain noise is reduced using spectral frequency Histogram Equalization. Then, an adaptive nonlinear dynamic range adjustment technique is utilized to determine the local spectral features on corresponding fingerprint ridge frequency and orientation. The dictionary is constructed using spatial fundamental frequency that is determined from the spectral features. These dictionaries help in removing the spurious noise present in fingerprints and reduce the time complexity by using block processing instead of pixel processing. Further, dictionaries are used to reconstruct the image for matching. The proposed FIVDL is verified on FVC database sets and Experimental result shows an improvement over the state-of-the-art techniques. (C) 2015 The Authors. Published by Elsevier B.V.

IMAGE DENOISING USING OPTIMALLY WEIGHTED BILATERAL FILTERS: A SURE AND FAST APPROACH

The bilateral filter is known to be quite effective in denoising images corrupted with small dosages of additive Gaussian noise. The denoising performance of the filter, however, is known to degrade quickly with the increase in noise level. Several adaptations of the filter have been proposed in the literature to address this shortcoming, but often at a substantial computational overhead. In this paper, we report a simple pre-processing step that can substantially improve the denoising performance of the bilateral filter, at almost no additional cost. The modified filter is designed to be robust at large noise levels, and often tends to perform poorly below a certain noise threshold. To get the best of the original and the modified filter, we propose to combine them in a weighted fashion, where the weights are chosen to minimize (a surrogate of) the oracle mean-squared-error (MSE). The optimally-weighted filter is thus guaranteed to perform better than either of the component filters in terms of the MSE, at all noise levels. We also provide a fast algorithm for the weighted filtering. Visual and quantitative denoising results on standard test images are reported which demonstrate that the improvement over the original filter is significant both visually and in terms of PSNR. Moreover, the denoising performance of the optimally-weighted bilateral filter is competitive with the computation-intensive non-local means filter.

IMAGE DENOISING IN MULTIPLICATIVE NOISE

We address the problem of denoising images corrupted by multiplicative noise. The noise is assumed to follow a Gamma distribution. Compared with additive noise distortion, the effect of multiplicative noise on the visual quality of images is quite severe. We consider the mean-square error (MSE) cost function and derive an expression for an unbiased estimate of the MSE. The resulting multiplicative noise unbiased risk estimator is referred to as MURE. The denoising operation is performed in the wavelet domain by considering the image-domain MURE. The parameters of the denoising function (typically, a shrinkage of wavelet coefficients) are optimized for by minimizing MURE. We show that MURE is accurate and close to the oracle MSE. This makes MURE-based image denoising reliable and on par with oracle-MSE-based estimates. Analogous to the other popular risk estimation approaches developed for additive, Poisson, and chi-squared noise degradations, the proposed approach does not assume any prior on the underlying noise-free image. We report denoising results for various noise levels and show that the quality of denoising obtained is on par with the oracle result and better than that obtained using some state-of-the-art denoisers.