No-reference image quality assessment using modified extreme learning machine classifier

In this paper, we present a machine learning approach to measure the visual quality of JPEG-coded images. The features for predicting the perceived image quality are extracted by considering key human visual sensitivity (HVS) factors such as edge amplitude, edge length, background activity and background luminance. Image quality assessment involves estimating the functional relationship between HVS features and subjective test scores. The quality of the compressed images are obtained without referring to their original images ('No Reference' metric). Here, the problem of quality estimation is transformed to a classification problem and solved using extreme learning machine (ELM) algorithm. In ELM, the input weights and the bias values are randomly chosen and the output weights are analytically calculated. The generalization performance of the ELM algorithm for classification problems with imbalance in the number of samples per quality class depends critically on the input weights and the bias values. Hence, we propose two schemes, namely the k-fold selection scheme (KS-ELM) and the real-coded genetic algorithm (RCGA-ELM) to select the input weights and the bias values such that the generalization performance of the classifier is a maximum. Results indicate that the proposed schemes significantly improve the performance of ELM classifier under imbalance condition for image quality assessment. The experimental results prove that the estimated visual quality of the proposed RCGA-ELM emulates the mean opinion score very well. The experimental results are compared with the existing JPEG no-reference image quality metric and full-reference structural similarity image quality metric.

Risk minimization in water quality control problems of a river system

Methodologies are presented for minimization of risk in a river water quality management problem. A risk minimization model is developed to minimize the risk of low water quality along a river in the face of conflict among various stake holders. The model consists of three parts: a water quality simulation model, a risk evaluation model with uncertainty analysis and an optimization model. Sensitivity analysis, First Order Reliability Analysis (FORA) and Monte-Carlo simulations are performed to evaluate the fuzzy risk of low water quality. Fuzzy multiobjective programming is used to formulate the multiobjective model. Probabilistic Global Search Laussane (PGSL), a global search algorithm developed recently, is used for solving the resulting non-linear optimization problem. The algorithm is based on the assumption that better sets of points are more likely to be found in the neighborhood of good sets of points, therefore intensifying the search in the regions that contain good solutions. Another model is developed for risk minimization, which deals with only the moments of the generated probability density functions of the water quality indicators. Suitable skewness values of water quality indicators, which lead to low fuzzy risk are identified. Results of the models are compared with the results of a deterministic fuzzy waste load allocation model (FWLAM), when methodologies are applied to the case study of Tunga-Bhadra river system in southern India, with a steady state BOD-DO model. The fractional removal levels resulting from the risk minimization model are slightly higher, but result in a significant reduction in risk of low water quality. (c) 2005 Elsevier Ltd. All rights reserved.

High-quality annotation of promoter regions for 913 bacterial genomes

Motivation: The number of bacterial genomes being sequenced is increasing very rapidly and hence, it is crucial to have procedures for rapid and reliable annotation of their functional elements such as promoter regions, which control the expression of each gene or each transcription unit of the genome. The present work addresses this requirement and presents a generic method applicable across organisms. Results: Relative stability of the DNA double helical sequences has been used to discriminate promoter regions from non-promoter regions. Based on the difference in stability between neighboring regions, an algorithm has been implemented to predict promoter regions on a large scale over 913 microbial genome sequences. The average free energy values for the promoter regions as well as their downstream regions are found to differ, depending on their GC content. Threshold values to identify promoter regions have been derived using sequences flanking a subset of translation start sites from all microbial genomes and then used to predict promoters over the complete genome sequences. An average recall value of 72% (which indicates the percentage of protein and RNA coding genes with predicted promoter regions assigned to them) and precision of 56% is achieved over the 913 microbial genome dataset.

QUAD: quality assessment of documents

We propose a set of metrics that evaluate the uniformity, sharpness, continuity, noise, stroke width variance,pulse width ratio, transient pixels density, entropy and variance of components to quantify the quality of a document image. The measures are intended to be used in any optical character recognition (OCR) engine to a priori estimate the expected performance of the OCR. The suggested measures have been evaluated on many document images, which have different scripts. The quality of a document image is manually annotated by users to create a ground truth. The idea is to correlate the values of the measures with the user annotated data. If the measure calculated matches the annotated description,then the metric is accepted; else it is rejected. In the set of metrics proposed, some of them are accepted and the rest are rejected. We have defined metrics that are easily estimatable. The metrics proposed in this paper are based on the feedback of homely grown OCR engines for Indic (Tamil and Kannada) languages. The metrics are independent of the scripts, and depend only on the quality and age of the paper and the printing. Experiments and results for each proposed metric are discussed. Actual recognition of the printed text is not performed to evaluate the proposed metrics. Sometimes, a document image containing broken characters results in good document image as per the evaluated metrics, which is part of the unsolved challenges. The proposed measures work on gray scale document images and fail to provide reliable information on binarized document image.

Biogeochemical facsimile of the organic matter quality and trophic status of a micro-tidal tropical estuary

Seasonal studies were carried out from 21 stations, comprising of three zones, of Cochin Estuary, to assess the organic matter quality and trophic status. The hydographical parameters showed significant seasonal variations and nutrients and chlorophylls were generally higher during the monsoon season. However, chemical contamination along with the seasonal limitations of light and nitrogen imposed restrictions on the primary production and as a result, mesotrophic conditions generally prevailed in the water column. The nutrient stoichometries and delta C-13 values of surficial sediments indicated significant allochthonous contribution of organic matter. Irrespective of the higher content of total organic matter, the labile organic matter was very low. Dominance of carbohydrates over lipids and proteins indicated the lower nutritive aspect of the organic matter, and their aged and refractory nature. This, along with higher amount of phytodetritus and the low algal contribution to the biopolymeric carbon corroborated the dominance of allochthonous organic matter and the heterotrophic nature. The spatial and seasonal variations of labile organic components could effectively substantiate the observed shift in the productivity pattern. An alternative ratio, lipids to tannins and lignins, was proposed to ascertain the relative contribution of allochthonous organic matter in the estuary. This study confirmed the efficiency of an integrated biogeochemical approach to establish zones with distinct benthic trophic status associated with different degrees of natural and anthropogenic input. Nevertheless, our results also suggest that the biochemical composition alone could lead to erroneous conclusions in the case of regions that receive enormous amounts of anthropogenic inputs.

Curvature Management in Buffer Layer for Device Quality GaN Growth on Si (111)

An efficient buffer layer scheme has been designed to address the issue of curvature management during metalorganic chemical vapour deposition growth of GaN on Si (111) substrate. This is necessary to prevent cracking of the grown layer during post-growth cooling down from growth temperature to room temperature and to achieve an allowable bow (<40 m) in the wafer for carrying out lithographic processes. To meet both these ends simultaneously, the stress evolution in the buffer layers was observed carefully. The reduction in precursor flow during the buffer layer growth provided better control over curvature evolution in the growing buffer layers. This has enabled the growth of a suitable high electron mobility transistor (HEMT) stack on 2'' Si (111) substrate of 300 m thickness with a bow as low as 11.4 m, having a two-dimensional electron gas (2DEG) of mobility, carrier concentration, and sheet resistance values 1510 cm(2)/V-s, 0.96 x 10(13)/cm(2), and 444 /, respectively. Another variation of similar technique resulted in a bow of 23.4 m with 2DEG mobility, carrier concentration, and sheet resistance values 1960 cm(2)/V-s, 0.98 x 10(13)/cm(2), and 325 /, respectively.