Exploring Real‐Time Geoprocessing in Cloud Computing: Navigation Services Case Study

Today, many real‐time geospatial applications (e.g. navigation and location‐based services) involve data‐ and/or compute‐intensive geoprocessing tasks where performance is of great importance. Cloud computing, a promising platform with a large pool of storage and computing resources, could be a practical solution for hosting vast amounts of data and for real‐time processing. In this article, we explored the feasibility of using Google App Engine (GAE), the cloud computing technology by Google, for a module in navigation services, called Integrated GNSS (iGNSS) QoS prediction. The objective of this module is to predict quality of iGNSS positioning solutions for prospective routes in advance. iGNSS QoS prediction involves the real‐time computation of large Triangulated Irregular Networks (TINs) generated from LiDAR data. We experimented with the Google App Engine (GAE) and stored a large TIN for two geoprocessing operations (proximity and bounding box) required for iGNSS QoS prediction. The experimental results revealed that while cloud computing can potentially be used for development and deployment of data‐ and/or compute‐intensive geospatial applications, current cloud platforms require improvements and special tools for handling real‐time geoprocessing, such as iGNSS QoS prediction, efficiently. The article also provides a set of general guidelines for future development of real‐time geoprocessing in clouds.     

Oceanographic variability in shallow-water acoustics and the dualrole of the sea bottom

Acoustic propagation in shallow water is examined. Multipath propagation and extensive boundary interactions, which along with a host of other phenomena produce a highly variable and often unpredictable acoustic field, are discussed. The responsible mechanisms, and hence the acoustic effects, cover a wide range of temporal and spatial scales and are classified as either deterministic or random, although the two types often act in concert. Because of extensive interactions with the sound field, the bottom can severely degrade waterborne propagation, although the sea bottom (and subbottom) can provide a seismic path that not only is relatively stable, but exists even under environmental conditions that preclude an effective waterborne path. Propagation in the bottom is particularly significant at very low frequencies. These various aspects of shallow-water acoustics are illustrated using the results of experiments conducted in diverse geographic areas     

<Emphasis Type="Italic">3D</Emphasis> Modelling of Urban Area Using Synthetic Aperture Radar (SAR)

Synthetic aperture radar (SAR) is a newly-developed remote sensing technology that works in all weather and independent of daylight. Recent satellite designs such as TerraSAR-x, which have resolutions of a couple of meters and sub-meters, have provided appropriate data for modelling and monitoring of urban areas. Image classification and height information extraction is possible considering the nature of SAR data. In this paper, a proper classification method for high-resolution SAR images has been used in urban areas. This classifier is based on statistical models. First, statistical models that are well adapted to urban SAR images are selected. Initial labelling is performed using the maximum likelihood method. A method based on Markov random fields is applied to improve the results by considering neighbourhood information. Meanwhile, topographic information is extracted using the phase difference obtained from SAR interferometry. After classification and height extraction, the homogeneous regions consisting of locations with similar objects are determined. The homogeneous region adjacency graph are generated using vectors containing classification information, extracted objects, height of pixels forming each region, and information on the neighbouring areas. Height and classification information are then merged by assigning height conditions based on the nature of objects and optimizing an energy function. The results obtained, including buildings, streets, and corner reflectors, are easily recognizable. The overall accuracy is improved from 57% in the initial classification to 95% in the employed procedure. Moreover, the accuracy of height estimation is about 2.74 m, which is acceptable for height estimations of buildings with more than one floor.     

Finite orbital angular momentum states and Laguerre–Gaussian potential in two-temperature electron plasmas

Electron-acoustic waves are studied with orbital angular momentum (OAM) in an unmagnetized collisionless uniform plasma, whose constituents are the Boltzmann hot electrons, inertial cold electrons and stationary ions. For this purpose, we employ the fluid equations to obtain a paraxial equation in terms of cold electron density perturbations, which admits both the Gaussian and Laguerre–Gaussian (LG) beam solutions. Furthermore, an approximate solution for the electrostatic potential problem is found, which also allows us to express the components of the electric field in terms of LG potential perturbations. Calculating the energy flux of the electron-acoustic waves, an OAM density for these waves is obtained. Numerically, it is found that the parameters, such as, azimuthal angle, radial and angular mode numbers, and the beam waist strongly modify the LG potential profiles associated with electron-acoustic waves. The present results should be helpful to study the trapping and transportation of plasma particles and energy as well as to understand the electron-acoustic mode excitations produced by the Raman backscattering of laser beams in a uniform plasma.     

Artificial neural network simulation for prediction of suspended sediment concentration in the River Ramganga,Ganges Basin,India

The relation between the water discharge (Q) and suspended sediment concentration (SSC) of the River Ramganga at Bareilly, Uttar Pradesh, in the Himalayas, has been modeled using Artificial Neural Networks (ANNs). The current study validates the practical capability and usefulness of this tool for simulating complex nonlinear, real world, river system processes in the Himalayan scenario. The modeling approach is based on the time series data collected from January to December (2008-2010) for Q and SSC. Three ANNs (T1-T3) with different network configurations have been developed and trained using the Levenberg Marquardt Back Propagation Algorithm in the Matlab routines. Networks were optimized using the enumeration technique, and, finally, the best network is used to predict the SSC values for the year 2011. The values thus obtained through the ANN model are compared with the observed values of SSC. The coefficient of determination (R2), for the optimal network was found to be 0.99. The study not only provides insight into ANN modeling in the Himalayan river scenario, but it also focuses on the importance of understanding a river basin and the factors that affect the SSC, before attempting to model it. Despite the temporal variations in the study area, it is possible to model and successfully predict the SSC values with very simplistic ANN models.     

Is the age problem resolved?

The cosmological, astrophysical, and nucleocosmochronological methods for estimating the age of the universe and the corresponding uncertainties are comparatively studied in the present paper. We are led to the conclusion that the new measurements of cosmological parameters, and the recent estimates of the age of globular clusters have led to the gradual disappearance of the age problem from the arena of modern cosmology.