A probabilistic constrained nonlinear optimization framework to optimize RED parameters

The random early detection (RED) technique has seen a lot of research over the years. However, the functional relationship between RED performance and its parameters viz,, queue weight (omega(q)), marking probability (max(p)), minimum threshold (min(th)) and maximum threshold (max(th)) is not analytically availa ble. In this paper, we formulate a probabilistic constrained optimization problem by assuming a nonlinear relationship between the RED average queue length and its parameters. This problem involves all the RED parameters as the variables of the optimization problem. We use the barrier and the penalty function approaches for its Solution. However (as above), the exact functional relationship between the barrier and penalty objective functions and the optimization variable is not known, but noisy samples of these are available for different parameter values. Thus, for obtaining the gradient and Hessian of the objective, we use certain recently developed simultaneous perturbation stochastic approximation (SPSA) based estimates of these. We propose two four-timescale stochastic approximation algorithms based oil certain modified second-order SPSA updates for finding the optimum RED parameters. We present the results of detailed simulation experiments conducted over different network topologies and network/traffic conditions/settings, comparing the performance of Our algorithms with variants of RED and a few other well known adaptive queue management (AQM) techniques discussed in the literature.

High Resoulution Electron Microscopy of Chloritoid Minerals From Different Geological Melieu

Structural defects of three chloritoid minerals from distinet geologic melieu have been investigated by high resolution electron microscopy. X-ray powder and electron diffraction patterns indicate that the chloritoid from one geological source (A) is2M 1+2M2 monoclinic variant while those from another geological source (B) are 2M 2 monoclinic variants. In a typical one-dimensional lattice image of a crystal from sourceA, the 2M 2 matrix is broken by insertion of triclinic inter-growths. Another crystal with the 2M 2 matrix showed single, triple, quadruple and quintuple layers displaying an unusually high degree of disorder. Lattice images of 2M 2 monoclinic variants from sourceB yielded more homogeneous micrographs. The important finding from the present studies is that the chloritoid from sourceA is a severely disordered low-temperature intermediate phase in the conversion of the triclinic chloritoid to the high-temperature ordered monoclinic variants of sourceB. Severely disordered chloritoids, marking the beginning of low grade metamorphism, are generated as intermediates between the state of complete disordered arrangement towards the end of low grade metamorphism within the narrow stability range of 400°–500°C.

Hydrologic drought prediction under climate change: Uncertainty modeling with Dempster-Shafer and Bayesian approaches

Representation and quantification of uncertainty in climate change impact studies are a difficult task. Several sources of uncertainty arise in studies of hydrologic impacts of climate change, such as those due to choice of general circulation models (GCMs), scenarios and downscaling methods. Recently, much work has focused on uncertainty quantification and modeling in regional climate change impacts. In this paper, an uncertainty modeling framework is evaluated, which uses a generalized uncertainty measure to combine GCM, scenario and downscaling uncertainties. The Dempster-Shafer (D-S) evidence theory is used for representing and combining uncertainty from various sources. A significant advantage of the D-S framework over the traditional probabilistic approach is that it allows for the allocation of a probability mass to sets or intervals, and can hence handle both aleatory or stochastic uncertainty, and epistemic or subjective uncertainty. This paper shows how the D-S theory can be used to represent beliefs in some hypotheses such as hydrologic drought or wet conditions, describe uncertainty and ignorance in the system, and give a quantitative measurement of belief and plausibility in results. The D-S approach has been used in this work for information synthesis using various evidence combination rules having different conflict modeling approaches. A case study is presented for hydrologic drought prediction using downscaled streamflow in the Mahanadi River at Hirakud in Orissa, India. Projections of n most likely monsoon streamflow sequences are obtained from a conditional random field (CRF) downscaling model, using an ensemble of three GCMs for three scenarios, which are converted to monsoon standardized streamflow index (SSFI-4) series. This range is used to specify the basic probability assignment (bpa) for a Dempster-Shafer structure, which represents uncertainty associated with each of the SSFI-4 classifications. These uncertainties are then combined across GCMs and scenarios using various evidence combination rules given by the D-S theory. A Bayesian approach is also presented for this case study, which models the uncertainty in projected frequencies of SSFI-4 classifications by deriving a posterior distribution for the frequency of each classification, using an ensemble of GCMs and scenarios. Results from the D-S and Bayesian approaches are compared, and relative merits of each approach are discussed. Both approaches show an increasing probability of extreme, severe and moderate droughts and decreasing probability of normal and wet conditions in Orissa as a result of climate change. (C) 2010 Elsevier Ltd. All rights reserved.

High On-Off Ratio Bilayer Graphene Complementary Field Effect Transistors

In this paper, we propose a novel S/D engineering for dual-gated Bilayer Graphene (BLG) Field Effect Transistor (FET) using doped semiconductors (with a bandgap) as source and drain to obtain unipolar complementary transistors. To simulate the device, a self-consistent Non-Equilibrium Green's Function (NEGF) solver has been developed and validated against published experimental data. Using the simulator, we predict an on-off ratio in excess of 10(4) and a subthreshold slope of similar to 110mV/decade with excellent scalability and current saturation, for a 20nm gate length unipolar BLG FET. However, the performance of the proposed device is found to be strongly dependent on the S/D series resistance effect. The obtained results show significant improvements over existing reports, marking an important step towards bilayer graphene logic devices.

Comprehensive Probabilistic Seismic Hazard Analysis of the Andaman-Nicobar Regions

An updated catalog of earthquakes has been prepared for the Andaman-Nicobar and adjoining regions. The catalog was homogenized to a unified magnitude scale, and declustering of the catalog was performed to remove aftershocks and foreshocks. Eleven regional source zones were identified in the study area to account for local variability in seismicity characteristics. The seismicity parameters were estimated for each of these source zones, and the seismic hazard evaluation of the Andaman-Nicobar region has been performed using different source models and attenuation relations. Probabilistic seismic hazard analysis has been performed with currently available data and their best possible scientific interpretation using an appropriate instrument such as the logic tree to explicitly account for epistemic uncertainty by considering alternative models (source models, maximum magnitude, and attenuation relationships). The hazard maps for different periods have been produced for horizontal ground motion on the bedrock level.

Deterministic seismic hazard macrozonation of India

Earthquakes are known to have occurred in Indian subcontinent from ancient times. This paper presents the results of seismic hazard analysis of India (6 degrees-38 degrees N and 68 degrees-98 degrees E) based on the deterministic approach using latest seismicity data (up to 2010). The hazard analysis was done using two different source models (linear sources and point sources) and 12 well recognized attenuation relations considering varied tectonic provinces in the region. The earthquake data obtained from different sources were homogenized and declustered and a total of 27,146 earthquakes of moment magnitude 4 and above were listed in the study area. The sesismotectonic map of the study area was prepared by considering the faults, lineaments and the shear zones which are associated with earthquakes of magnitude 4 and above. A new program was developed in MATLAB for smoothing of the point sources. For assessing the seismic hazard, the study area was divided into small grids of size 0.1 degrees x 0.1 degrees (approximately 10 x 10 km), and the hazard parameters were calculated at the center of each of these grid cells by considering all the seismic sources within a radius of 300 to 400 km. Rock level peak horizontal acceleration (PHA) and spectral accelerations for periods 0.1 and 1 s have been calculated for all the grid points with a deterministic approach using a code written in MATLAB. Epistemic uncertainty in hazard definition has been tackled within a logic-tree framework considering two types of sources and three attenuation models for each grid point. The hazard evaluation without logic tree approach also has been done for comparison of the results. The contour maps showing the spatial variation of hazard values are presented in the paper.

Efficient method of moving shadow detection and vehicle classification

Moving shadow detection and removal from the extracted foreground regions of video frames, aim to limit the risk of misconsideration of moving shadows as a part of moving objects. This operation thus enhances the rate of accuracy in detection and classification of moving objects. With a similar reasoning, the present paper proposes an efficient method for the discrimination of moving object and moving shadow regions in a video sequence, with no human intervention. Also, it requires less computational burden and works effectively under dynamic traffic road conditions on highways (with and without marking lines), street ways (with and without marking lines). Further, we have used scale-invariant feature transform-based features for the classification of moving vehicles (with and without shadow regions), which enhances the effectiveness of the proposed method. The potentiality of the method is tested with various data sets collected from different road traffic scenarios, and its superiority is compared with the existing methods. (C) 2013 Elsevier GmbH. All rights reserved.