Natural Color Image Enhancement based on Modified Multiscale Retinex Algorithm and Performance Evaluation usingWavelet Energy

This paper presents a new color image enhancement technique based on modified MultiScale Retinex(MSR) algorithm and visual quality of the enhanced images are evaluated using a new metric, namely, wavelet energy. The color image enhancement is achieved by down sampling the value component of HSV color space converted image into three scales (normal, medium and fine) following the contrast stretching operation. These down sampled value components are enhanced using the MSR algorithm. The value component is reconstructed by averaging each pixels of the lower scale image with that of the upper scale image subsequent to up sampling the lower scale image. This process replaces dark pixel by the average pixels of both the lower scale and upper scale, while retaining the bright pixels. The quality of the reconstructed images in the proposed method is found to be good and far better then the other researchers method. The performance of the proposed scheme is evaluated using new wavelet domain based assessment criterion, referred as wavelet energy. This scheme computes the energy of both original and enhanced image in wavelet domain. The number of edge details as well as wavelet energy is less in a poor quality image compared with naturally enhanced image. Experimental results presented confirms that the proposed wavelet energy based color image quality assessment technique efficiently characterizes both the local and global details of enhanced image.Comment: 10 pages, 3 figures, Recent Advances in Intelligent Informatics Advances in Intelligent Systems and Computing Volume 235, 2014, pp 83-9

Approximation Algorithms for Correlated Knapsacks and Non-Martingale Bandits

In the stochastic knapsack problem, we are given a knapsack of size B, and a set of jobs whose sizes and rewards are drawn from a known probability distribution. However, we know the actual size and reward only when the job completes. How should we schedule jobs to maximize the expected total reward? We know O(1)-approximations when we assume that (i) rewards and sizes are independent random variables, and (ii) we cannot prematurely cancel jobs. What can we say when either or both of these assumptions are changed? The stochastic knapsack problem is of interest in its own right, but techniques developed for it are applicable to other stochastic packing problems. Indeed, ideas for this problem have been useful for budgeted learning problems, where one is given several arms which evolve in a specified stochastic fashion with each pull, and the goal is to pull the arms a total of B times to maximize the reward obtained. Much recent work on this problem focus on the case when the evolution of the arms follows a martingale, i.e., when the expected reward from the future is the same as the reward at the current state. What can we say when the rewards do not form a martingale? In this paper, we give constant-factor approximation algorithms for the stochastic knapsack problem with correlations and/or cancellations, and also for budgeted learning problems where the martingale condition is not satisfied. Indeed, we can show that previously proposed LP relaxations have large integrality gaps. We propose new time-indexed LP relaxations, and convert the fractional solutions into distributions over strategies, and then use the LP values and the time ordering information from these strategies to devise a randomized adaptive scheduling algorithm. We hope our LP formulation and decomposition methods may provide a new way to address other correlated bandit problems with more general contexts

The Brownian Web: Characterization and Convergence

The Brownian Web (BW) is the random network formally consisting of the paths of coalescing one-dimensional Brownian motions starting from every space-time point in ${\mathbb R}\times{\mathbb R}$. We extend the earlier work of Arratia and of T\'oth and Werner by providing characterization and convergence results for the BW distribution, including convergence of the system of all coalescing random walkssktop/brownian web/finale/arXiv submits/bweb.tex to the BW under diffusive space-time scaling. We also provide characterization and convergence results for the Double Brownian Web, which combines the BW with its dual process of coalescing Brownian motions moving backwards in time, with forward and backward paths ``reflecting'' off each other. For the BW, deterministic space-time points are almost surely of ``type'' $(0,1)$ -- {\em zero} paths into the point from the past and exactly {\em one} path out of the point to the future; we determine the Hausdorff dimension for all types that actually occur: dimension 2 for type $(0,1)$, 3/2 for $(1,1)$ and $(0,2)$, 1 for $(1,2)$, and 0 for $(2,1)$ and $(0,3)$.Comment: 52 pages with 4 figure

Direct test of composite fermion model in quantum Hall systems

We show that neutron scattering and Raman scattering experiments can unambiguously determine a composite fermion parameter, viz., the effective number of Landau Levels filled by the composite fermions. For this purpose, one needs partially polarized or more preferably unpolarized quantum Hall states. We further find that spin correlation function acts as an order parameter in the spin transition.Comment: 18 page

Isolation and characterization of microsatellite markers in Garcinia gummi-gutta by next-generation sequencing and cross-species amplification

Garcinia gummi-gutta (L.) Roxb. (Clusiaceae) is an endemic, semidomesticated, fruit-yielding tree species distributed in the Western Ghats of India and Sri Lanka. Various bioactive phytochemicals, such as garcinol, benzophenones and xanthones are isolated from G. gummi-gutta and have shown antibacterial, antiviral and antioxidant activities. We sequenced the total genomic DNA using Illumina Hiseq 2000 platform and examined 241,141,804 bp high quality data, assembled into 773,889 contigs. In these contigs, 27,313 simple-sequence repeats (SSRs) were identified, among which mononucleotide repeats were predominant (44.98%) followed by dinucleotide and trinucleotide repeats. Primers were designed for 9964 microsatellites among which 32 randomly selected SSR primer pairs were standardized for amplification. Polymerase chain reaction (PCR) amplification of genomic DNA in 30 G. gummi-gutta genotypes revealed polymorphic information content (PIC) across all 32 loci ranging from 0.867 to 0.951, with a mean value of 0.917. The observed and expected heterozygosity ranged from 0.00 to 0.63 and 0.896 to 0.974, respectively. Alleles per locus ranged from 12 to 27. This is the first report on the development of genomic SSR markers in G. gummi-gutta using next-generation sequencing technology. The genomic SSR markers developed in this study will be useful in identification, mapping, diversity and breeding studies

Concurrence Vectors in Arbitrary Multipartite Quantum Systems

For a given pure state of multipartite system, the concurrence vector is defined by employing the defining representation of generators of the corresponding rotation groups. The norm of concurrence vector is considered as a measure of entanglement. For multipartite pure state, the concurrence vector is regarded as the direct sum of concurrence subvectors in the sense that each subvector is associated with a pair of particles. It is proposed to use the norm of each subvector as the contribution of the corresponding pair in entanglement of the system.Comment: 9 pages, v3, section 3 is revise