Lake bathymetry from Indian Remote Sensing (P6‐LISS III) satellite imagery using artificial neural network model

The remote sensing technique provides a rapid and relatively inexpensive means of identifying silted areas in large water bodies, in order that desilting activities can be effectively conducted. This study developed lake bathymetry for a selected lake system (Akkulam–Veli Lake, Kerala, India) from the Indian Remote Sensing (IRS P6‐LISS III) satellite imagery, using an artificial neural network (ANN) model. The water depth was measured for 17 months at different points in the lake on the same date of overpass of the IRS satellite. The satellite imageries obtained for 12 December 2007 and 16 February 2009 were identified as cloud‐free images. ANN models were developed with the four input series of radiance values from green, red, NIR and MIR bands observed for the satellite imagery obtained on 12 December 2007 at the sampling sites, with actual water depth measurements also being taken on the same date. A three‐layered feed forward neural network with back propagation training algorithm was developed for this study. To train the model, it was run several times by changing the number of neurons, learning rate and the momentum constants until the mean square error was minimum. When the number of neurons is increased to 35, and the logsig function is used as ANN transfer function, the error becomes minimum. To test the model, the developed ANN was run for a new set of input from the satellite imagery taken on 16 February 2009. Comparing the predicted and measured values for the same sites for the same day, it was found that the model is best suited for predicting water depth using ANN and the radiance values for four bands of IRS satellite imagery. The results of this study indicated that, for the shallow lake with lower depth, the difference between the actual and predicted value was considerable. In contrast, this was not the case where the lake water depth was greater, indicating an increased prediction accuracy with ANN with increasing depths for shallow lakes. A bathymetry map prepared with ANN indicated only the lake shoreline, as well as the shallow littoral zones. The approach used in this study requires further refinement, including further of the model based on using more field measurements to obtain a better bathymetry map.     

Modularity in musical processing: The automaticity of harmonic priming.

Three experiments investigated the modularity of harmonic expectations that are based on cultural schemata despite the availability of more predictive veridical information. Participants were presented with prime–target chord pairs and made an intonation judgment about each target. Schematic expectation was manipulated by the combination of prime and target, with some transitions being schematically more probable than others. Veridical information in the form of prime–target previews, local transition probabilities, or valid versus invalid previews was also provided. Processing was facilitated when a schematically probable target chord followed the prime. Furthermore, this effect was independent of all manipulations of veridical expectation. A solution to L. B. Meyer's (1967b) query "On Rehearing Music" is suggested, in which schematic knowledge contributes to harmonic expectation in a modular manner regardless of whether any veridical knowledge exists. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

INVERTEBRATE AND FISH ASSEMBLAGE RELATIONS TO DISSOLVED OXYGEN MINIMA IN LOWLAND STREAMS OF SOUTHWESTERN LOUISIANA

Dissolved oxygen (DO) concentrations in lowland streams are naturally lower than those in upland streams; however, in some regions where monitoring data are lacking, DO criteria originally established for upland streams have been applied to lowland streams. This study investigated the DO concentrations at which fish and invertebrate assemblages at 35 sites located on lowland streams in southwestern Louisiana began to demonstrate biological thresholds. Average threshold values for taxa richness, diversity and abundance metrics were 2.6 and 2.3 mg/L for the invertebrate and fish assemblages, respectively. These thresholds are approximately twice the DO concentration that some native fish species are capable of tolerating and are comparable with DO criteria that have been recently applied to some coastal streams in Louisiana and Texas. DO minima >2.5 mg/L were favoured for all but extremely tolerant taxa. Extremely tolerant taxa had respiratory adaptations that gave them a competitive advantage, and their success when DO minima were <2 mg/L could be related more to reductions in competition or predation than to DO concentration directly. DO generally had an inverse relation to the amount of agriculture in the buffer area; however, DO concentrations at sites with both low and high amounts of agriculture (including three least‐disturbed sites) declined to <2.5 mg/L. Thus, although DO fell below a concentration that was identified as an approximate biological threshold, sources of this condition were sometimes natural (allochthonous material) and had little relation to anthropogenic activity. Copyright © 2012 John Wiley & Sons, Ltd.     

Hybridization of Metaheuristics Based Energy Efficient Scheduling Algorithm for Multi-Core Systems

The developments of multi-core systems (MCS) have considerably improved the existing technologies in the field of computer architecture. The MCS comprises several processors that are heterogeneous for resource capacities, working environments, topologies, and so on. The existing multi-core technology unlocks additional research opportunities for energy minimization by the use of effective task scheduling. At the same time, the task scheduling process is yet to be explored in the multi-core systems. This paper presents a new hybrid genetic algorithm (GA) with a krill herd (KH) based energy-efficient scheduling technique for multi-core systems (GAKH-SMCS). The goal of the GAKH-SMCS technique is to derive scheduling tasks in such a way to achieve faster completion time and minimum energy dissipation. The GAKH-SMCS model involves a multi-objective fitness function using four parameters such as makespan, processor utilization, speedup, and energy consumption to schedule tasks proficiently. The performance of the GAKH-SMCS model has been validated against two datasets namely random dataset and benchmark dataset. The experimental outcome ensured the effectiveness of the GAKH-SMCS model interms of makespan, processor utilization, speedup, and energy consumption. The overall simulation results depicted that the presented GAKH-SMCS model achieves energy efficiency by optimal task scheduling process in MCS.     

Calculation by the Method of Finite Differences of the Temperature Distribution in Layered Tissues

A numerical approach to obtain the temperature distribution in layered tissues with thermal source and cooling inside the tissues is presented in this paper. This approach can be applied to problems with a distributed internal thermal source produced by external radiation such as microwave, ultrasound, and shortwave, or by internal increase of metabolic rate in live subjects. The exact mechanism of cooling is not discussed here, but it is assumed to be due to blood flow in vivo. The calculation of the temperature distribution in layered tissues is based on a linear model of the tissues that consists of several layers of different kinds of tissues separated by parallel boundaries. Some simple mathematical forms of the cooling functions are also assumed in this paper. The results obtained agree well with six available experiments.     

A simple hourly all-sky solar radiation model based on meteorological parameters

An hourly solar radiation model for cloudy skies, based on meteorological data, was developed andtested. As a means of comparison, the SOLMET regression and Watt models were also tested. The present model was examined for individual cloud types using measured solar radiation to judge the effectiveness of the model in the presence of particular clouds.     

Using multi-modal 3D contours and their relations for vision and robotics

In this work, we make use of 3D contours and relations between them (namely, coplanarity, cocolority, distance and angle) for four different applications in the area of computer vision and vision-based robotics. Our multi-modal contour representation covers both geometric and appearance information. We show the potential of reasoning with global entities in the context of visual scene analysis for driver assistance, depth prediction, robotic grasping and grasp learning. We argue that, such 3D global reasoning processes complement widely-used 2D local approaches such as bag-of-features since 3D relations are invariant under camera transformations and 3D information can be directly linked to actions. We therefore stress the necessity of including both global and local features with different spatial dimensions within a representation. We also discuss the importance of an efficient use of the uncertainty associated with the features, relations, and their applicability in a given context.