Optimal weighed Holoentropy for video compression: an advanced HEVC

ABSTRACT

In recent times, the applications of multimedia are rising in a greedy mode and hence the amount of video transactions is also increasing exponentially. This has shouted great demands on effective models on video encoding and also for reducing the transmission channel congestion. This research work introduces a managing technique termed weighted encoding for High-Efficiency Video Coding (HEVC). HEVC, also termed as MPEG-H Part 2 and H.265 is a video compression standard that is widely utilized AVC (H.264 or MPEG-4 Part 10). When compared to AVC, HEVC grants double the ratio of data compression at a similar level of quality of the video or considerably enhanced video quality at a similar bit rate. This work intends to optimize the weight that adopted in HEVC for encoding. For this, this paper proposes a new Iterative based propagation update in the water wave Optimization Algorithm (IPU-WWO), which is the improved form of Water wave Optimization (WWO). The performance of proposed IPU-WWO is compared over other conventional methods like Artificial Bee Colony (ABC), Firefly (FF), Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) with respect to Peak Signal to Noise Ratio (PSNR). By doing the encoding process, it minimizes the video size with perceptually better quality video or PSNR.     

Performance of a magnesium–lithium alloy as an anode for magnesium batteries

In this work, we describe an evaluation of an Mg–Li alloy (Li: 13 wt %) for possible use in magnesium primary reserve batteries. Higher OCP for the Mg–Li alloy have been observed in 2 M MgCl₂ and MgBr₂ electrolyte. The corrosion rate of the Mg–Li alloy is found to be in the order: MgCl₂ < Mg(COOCH₃)₂ < MgSO₄ < MgBr₂ < Mg(ClO₄)₂. Mg–Li alloys exhibit higher (81%) anodic efficiencies even when the current density is increased to 8.6 mA cm ^–2. It has been observed that Mg–Li/MgCl₂/CuO cells offer higher operating voltage and capacity than those with the conventionally used Mg–Al alloy.     

Water quality assessment of a tropical coastal lake system using multivariate cluster,principal component and factor analysis

Statistical techniques represent a reliable tool for classifying, modelling and interpreting surface water quality monitoring data, particularly for lakes. The complexity associated with the analysis of a large number of measured variables, however, is a major problem in water quality assessments. Multivariate analysis, such as cluster analysis and factor analysis (FA), was utilized in this study for the analysis of water quality data (including water discharges and 28 water quality parameters) for Akkulam–Veli Lake, a tropical coastal lake system in Kerala, India. This lake is partially divided into two sub‐systems, namely Veli Lake and Akkulam Lake. Akkulam Lake exhibits freshwater characteristics, in contrast to Veli Lake, which exhibits saline water characteristics because of its close proximity to the sea. Thus, studying this lake provides insights into water quality variations in both a freshwater and saline water lake in a tropical region. Water quality patterns and variations in Akkulam–Vela Lake over three seasons, including pre‐monsoon (PRM), monsoon (MON) and post‐monsoon (POM), also were studied, utilizing multivariate techniques. The organic pollution factor played a significant role on lake water quality during PRM. The influence of organic pollution tends to decrease during MON and POM, a particular situation faced by urban lakes in tropical regions. Polluted stretches in a lake system during different seasons can easily be ascertained by hierarchical cluster analysis. Further, the factors affecting a lake system as a whole, as well as for a particular sampling site, can easily be identified by FA. Improved water quality can be observed during POM. Akkulam and Vela lakes exhibit a wide variation in water quality during all seasons, a finding that corroborates a water flow obstruction from Akkulam Lake to Veli Lake because of the bund existing between the two lakes. The location of the bund is identified as the major reason for different hydrochemical processes in A–V Lake.     

Effect of EGR on the exhaust gas temperature and exhaust opacity in compression ignition engines

In diesel engines, NOx formation is a highly temperature-dependent phenomenon and takes place when the temperature in the combustion chamber exceeds 2000 K. Therefore, in order to reduce NOx emissions in the exhaust, it is necessary to keep peak combustion temperatures under control. One simple way of reducing the NOx emission of a diesel engine is by late injection of fuel into the combustion chamber. This technique is effective but increases fuel consumption by 10–15%, which necessitates the use of more effective NOx reduction techniques like exhaust gas recirculation (EGR). Re-circulating part of the exhaust gas helps in reducing NOx, but appreciable paniculate emissions are observed at high loads, hence there is a trade-off between NOx and smoke emission. To get maximum benefit from this trade-off, a paniculate trap may be used to reduce the amount of unburnt particulates in EGR, which in turn reduce the paniculate emission also. An experimental investigation was conducted to observe the effect of exhaust gas re-circulation on the exhaust gas temperatures and exhaust opacity. The experimental setup for the proposed experiments was developed on a two-cylinder, direct injection, air-cooled, compression ignition engine. A matrix of experiments was conducted for observing the effect of different quantities of EGR on exhaust gas temperatures and opacity     

Performance analysis of neural networks for classification of medical images with wavelets as a feature extractor

In this article, we analyze the performance of artificial neural network, in classification of medical images using wavelets as feature extractor. This work classifies the mammographic image, MRI images, CT images, and ultrasound images as either normal or abnormal. We have tested the proposed approach using 50 mammogram images (13 normal and 37 abnormal), 24 MRI brain images (9 normal and 15 abnormal), 33 CT images (11 normal and 22 abnormal), and 20 ultrasound images (6 normal and 14 abnormal). Four kind of neural network models such as BPN (Back Propagation Network), Hopfield, RBF (Radial Basis Function), and PNN (Probabilistic neural network) were chosen for study. To improve diagnostic accuracy, the feature extracted using wavelets such as Harr, Daubechies (db2, db4, and db8), Biorthogonal and Coiflet wavelets are given as input to the neural network models. Good classification percentage of 96% was achieved using the RBF when Daubechies (db4) wavelet based feature extraction was used. We observed that the classification rate is almost high under the RBF neural network for all the dataset considered. © 2015 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 25, 33–40, 2015     

Quantification of human error in maintenance using graph theory and matrix approach

Assessment of human error in maintenance requires identification of the contributing factors that lead to human error(s). These factors are called human error inducing factors (HEIFs), which take into consideration both the active and latent error contributing aspects related to man, machine and environment. A systems approach of the Graph Theory is applied in this paper for quantifying human error in maintenance activities that models the identified factors and their interactions/interrelationships in terms of human error digraph. The nodes in the digraph represent the HEIFs and the edges represent their interrelationships. The digraph is converted into an equivalent matrix and an expression based on this is developed, which is characteristic of the human error in maintenance. This expression is used to evaluate a human error index by substituting the numerical value of the factors and their interrelations. The index is a measure of the human error potential involved in the maintenance of systems. A higher value of index indicates that the error likelihood is more for the associated tasks, and more efforts are required to make the system less prone to human error. The proposed methodology is illustrated using a case study. The approach is anticipated to play a significant role in identifying sources of human errors and predicting their impact; and will help to integrate human factors during design stage with the objective of reducing human error in maintenance. Copyright © 2011 John Wiley & Sons, Ltd.     

A multi-sensor climatological view of double ITCZs over the Indian Ocean

We characterize the climatological features of the double inter-tropical convergence zones (DITCZs) over the western Indian Ocean during November–December by a synergistic analysis of the Hamburg Ocean Atmosphere Parameters and Fluxes from Satellite (HOAPS III) data (1988–2005) and the National Aeronautics and Space Administration's (NASA's) A-Train data (2002–2009). We investigate rainfall, freshwater flux and cloud liquid water, cloud fraction and relative humidity over the DITCZs. In addition, the daily rainfall data from the Global Precipitation Climatology Project (GPCP) are used to document the DITCZs during the El Niño southern oscillation (ENSO) events. An analysis of the GPCP data shows that the DITCZs are clearly discernible during strong ENSO events (1997, 2002 and 2006), in sharp contrast to the DITCZs in the eastern Pacific Ocean, where they are absent during ENSOs. Further, these convergence zones on either side of the equator are of short duration, approximately 3–6 pentads during November and December. All satellite sensor data sets consistently capture the major features of DITCZs. As an accurate simulation of DITCZs in coupled global climate models remains a challenge, the results from the present study would provide a platform for evaluating these models.     

Synthesis and Characterization of in situ Polyethylene and Polypropylene Nanocomposites: Gas Phase Polymerization by Nanosilica Supported <Emphasis Type="Italic">bis</Emphasis> (Cyclopentadienyl) Zirconium (IV) Dichloride Catalyst System

In the present work, in situ polyethylene and polypropylene nanocomposites have been successfully synthesized by gas phase polymerization of ethylene and propylene in presence of surface treated nanofillers with nanosilica-supported-bis (cyclopentadienyl) zirconium (IV) dichloride as a coordination catalyst. The nanofillers used are Cloisite-20A and nanosilica, respectively. These were pre-treated with MAO (Methylaluminoxane) to inhibit their deactivating action on the catalyst because of the presence of either acidic or basic groups on their surfaces; in this way MAO acts as a co-catalyst for the polymerization reactions being discussed. Two in situ Polyethylene (PE) i.e., CFPE and SFPE (Cloisite-20A filled polyethylene and nanosilica filled polyethylene) and two in situ polypropylene (PP) i.e., CFPP and SFPP (Cloisite-20A filled polypropylene and nanosilica filled polypropylene) nanocomposites have been prepared by gas-phase polymerization. The in situ polyethylene and polypropylene nanocomposites were characterized using Fourier transform infrared Spectroscope (FTIR), Thermogravimetric analyzer (TGA), Differential Scanning Calorimeter (DSC) and Scanning electron microscope (SEM). By our approach, it has been observed that the nanofillers are completely encapsulated by a thin layer of polymer chains.     

Rec10- and Rec12-independent recombination in meiosis of Schizosaccharomyces pombe

The Rec10 protein, a component of the linear elements forming along sister chromatids in meiotic prophase of Schizosaccharomyces pombe, plays an important role in the activation of Rec12 for double-strand break formation, and thus the initiation of recombination between homologous chromosomes. Recombination between homologous chromosomes was moderately reduced in homozygous crosses of the C-terminal truncation mutant rec10-155 and strongly in the full deletion allele rec10-175. Both alleles were also tested in two assays for intrachromosomal recombination (PS1 and VL1) and showed only slight reductions, while deletion of rec12 led to a 13-fold reduction. The even stronger reductions in rec10 rec12 double deletion crosses indicate partially redundant functions of Rec10 and Rec12 in the initiation of intrachromosomal recombination. A low level of double-strand breaks has been detected in rec10-175 meiosis at the mbs1 hotspot of recombination, and spore viability in the double mutant was also lower than in the single-deletion mutants. Low levels of apparent crossover and conversion between homologous chromosomes in the absence of Rec12 have been quantified using a newly developed assay. The results also indicate that the functions of Rec10 differ in several respects from those of its distant homologue Red1 in Saccharomyces cerevisiae, including interactions with Hop1 and Mek1 for promotion of recombination between homologues at the expense of sister chromatid recombination.     

Application of ANN, Fuzzy Logic and Decision Tree Algorithms for the Development of Reservoir Operating Rules

Optimal use of scarce water resources is the prime objective for water resources development projects in the developing country like India. Optimal releases have been generally expressed as a function of reservoir state variables and hydrologic inputs by a relationship which ultimately allows the policy/water managers to determine the water to be released as a function of available information. Optimal releases were obtained by using optimal control theory with inflow series and revised reservoir characteristics such as elevation area capacity table, zero elevation level as input in this study. Operating rules for reservoir were developed as a function of demand, water level and inflow. Artificial Neural Network (ANN) with back propagation algorithm, Fuzzy Logic and decision tree algorithms such as M5 and REPTree were used for deriving the operating rules using the optimal releases for an irrigation and power supply reservoir, located in northern India. It was found that fuzzy logic model performed well compared to other soft computing techniques such as ANN, M5P and REPTree investigated in this study.