A comparison of three methods for downscaling daily precipitation in the Punjab region

Many downscaling techniques have been developed in the past few years for projection of station-scale hydrological variables from large-scale atmospheric variables simulated by general circulation models (GCMs) to assess the hydrological impacts of climate change. This article compares the performances of three downscaling methods, viz. conditional random field (CRF), K-nearest neighbour (KNN) and support vector machine (SVM) methods in downscaling precipitation in the Punjab region of India, belonging to the monsoon regime. The CRF model is a recently developed method for downscaling hydrological variables in a probabilistic framework, while the SVM model is a popular machine learning tool useful in terms of its ability to generalize and capture nonlinear relationships between predictors and predictand. The KNN model is an analogue-type method that queries days similar to a given feature vector from the training data and classifies future days by random sampling from a weighted set of K closest training examples. The models are applied for downscaling monsoon (June to September) daily precipitation at six locations in Punjab. Model performances with respect to reproduction of various statistics such as dry and wet spell length distributions, daily rainfall distribution, and intersite correlations are examined. It is found that the CRF and KNN models perform slightly better than the SVM model in reproducing most daily rainfall statistics. These models are then used to project future precipitation at the six locations. Output from the Canadian global climate model (CGCM3) GCM for three scenarios, viz. A1B, A2, and B1 is used for projection of future precipitation. The projections show a change in probability density functions of daily rainfall amount and changes in the wet and dry spell distributions of daily precipitation. Copyright (C) 2011 John Wiley & Sons, Ltd.

Methods for Automatic Evaluation of Sentence Extract Summaries

This paper describes three novel techniques to automatically evaluate sentence extract summaries. Two of these techniques called FuSE and DeFuSE evaluate the quality of the generated extract summary based on the degree of similarity to the model summary. They use a fuzzy set theoretic basis to generate a match score. DeFuSE is an enhancement to FuSE and uses WordNet based hypernymy structures to detect similarity between sentences at abstracted levels. The third technique focuses on quantifying the quality of an extract summary based on the difficulty in generating such a summary. Advantages of these techniques are described with examples.

Multigrid algorithms for symmetric discontinuous Galerkin methods on graded meshes

We study a class of symmetric discontinuous Galerkin methods on graded meshes. Optimal order error estimates are derived in both the energy norm and the L 2 norm, and we establish the uniform convergence of V-cycle, F-cycle and W-cycle multigrid algorithms for the resulting discrete problems. Numerical results that confirm the theoretical results are also presented.

Coloured Petri net models for automated manufacturing systems

In this paper, we propose an approach, using Coloured Petri Nets (CPN) for modelling flexible manufacturing systems. We illustrate our methodology for a Flexible Manufacturing Cell (FMC) with three machines and three robots. We also consider the analysis of the FMC for deadlocks using the invariant analysis of CPNs.

“Post silicon debug of SOC designs”

Continuous advances in VLSI technology have made implementation of very complicated systems possible. Modern System-on -Chips (SoCs) have many processors, IP cores and other functional units. As a result, complete verification of whole systems before implementation is becoming infeasible; hence it is likely that these systems may have some errors after manufacturing. This increases the need to find design errors in chips after fabrication. The main challenge for post-silicon debug is the observability of the internal signals. Post-silicon debug is the problem of determining what's wrong when the fabricated chip of a new design behaves incorrectly. This problem now consumes over half of the overall verification effort on large designs, and the problem is growing worse.Traditional post-silicon debug methods concentrate on functional parts of systems and provide mechanisms to increase the observability of internal state of systems. Those methods may not be sufficient as modern SoCs have lots of blocks (processors, IP cores, etc.) which are communicating with one another and communication is another source of design errors. This tutorial will be provide an insight into various observability enhancement techniques, on chip instrumentation techniques and use of high level models to support the debug process targeting both inside blocks and communication among them. It will also cover the use of formal methods to help debug process.

NMR methods for fast data acquisition

NMR spectroscopy has witnessed tremendous advancements in recent years with the development of new methodologies for structure determination and availability of high-field strength spectrometers equipped with cryogenic probes. Supported by these advancements, a new dimension in NMR research has emerged which aims to increase the speed with data is collected and analyzed. Several novel methodologies have been proposed in this direction. This review focuses on the principles on which these different approaches are based with an emphasis on G-matrix Fourier transform NMR spectroscopy.

Prototype learning methods for online handwriting recognition

In this paper, we study different methods for prototype selection for recognizing handwritten characters of Tamil script. In the first method, cumulative pairwise- distances of the training samples of a given class are used to select prototypes. In the second method, cumulative distance to allographs of different orientation is used as a criterion to decide if the sample is representative of the group. The latter method is presumed to offset the possible orientation effect. This method still uses fixed number of prototypes for each of the classes. Finally, a prototype set growing algorithm is proposed, with a view to better model the differences in complexity of different character classes. The proposed algorithms are tested and compared for both writer independent and writer adaptation scenarios.

Clonal propagation of Phyllanthus amarus: A hepatoprotector

Background: The micropropagation protocol for Phyllanthus amarus, an important medicinal herb used widely for the treatment of hepatitis in ethnomedicinal systems, was standardized with shoot tip and single node explants. Materials and Methods: The micropropagation was carried out for the hyperproducing ecotype (phyllanthin content 463.828 ppm; hypophyllanthin content: 75.469 ppm) collected from Aanaikatti, Coimbatore, and grown in mist chamber, CPMB, TNAU. For micropropagation studies, the leaves were trimmed off and the shoot tips (6 mm long) and nodal segments (single node) were used for initiation. Results: Shoot tips and single node explants gave a maximum of 6.00 and 7.00 multiple shoots per explant with Benzyl Amino Purine (BAP) (1.0mg/L mg/L). Upon subculturing, a shoot length of around 7 cm with an average of eight internodes per shoot was observed after 20 days in the elongation medium supplemented with BAP (0.2 mg/Lmg/L) and Indole Acetic Acid (IAA) (2.0 mg/L). Seven to ten adventitious roots developed when the elongated microshoots were cultured in half strength MS medium with Indole Butyric Acid (IBA) (2.0 mg/Lmg/L) and NAA (1.0 mg/L mg/L) in 15-20 days after transfer. The rooted shoots acclimatized successfully to field conditions. Conclusion: A method for successful micropropagation of the valuable medicinal plant was established which will provide a better source for continuous supply of plants for manufacturing drugs.

Five-stage Milstein methods for SDEs

In this paper, we consider the problem of computing numerical solutions for Ito stochastic differential equations (SDEs). The five-stage Milstein (FSM) methods are constructed for solving SDEs driven by an m-dimensional Wiener process. The FSM methods are fully explicit methods. It is proved that the FSM methods are convergent with strong order 1 for SDEs driven by an m-dimensional Wiener process. The analysis of stability (with multidimensional Wiener process) shows that the mean-square stable regions of the FSM methods are unbounded. The analysis of stability shows that the mean-square stable regions of the methods proposed in this paper are larger than the Milstein method and three-stage Milstein methods.

Scheduling identical parallel machines with machine eligibility restrictions to minimize total weighted flowtime in automobile gear manufacturing

In this paper, we address a scheduling problem for minimizing total weighted flowtime, observed in automobile gear manufacturing. Specifically, the bottleneck operation of the pre-heat treatment stage of gear manufacturing process has been dealt with in scheduling. Many real-life scenarios like unequal release times, sequence dependent setup times, and machine eligibility restrictions have been considered. A mathematical model taking into account dynamic starting conditions has been proposed. The problem is derived to be NP-hard. To approach the problem, a few heuristic algorithms have been proposed. Based on planned computational experiments, the performance of the proposed heuristic algorithms is evaluated: (a) in comparison with optimal solution for small-size problem instances and (b) in comparison with the estimated optimal solution for large-size problem instances. Extensive computational analyses reveal that the proposed heuristic algorithms are capable of consistently yielding near-statistically estimated optimal solutions in a reasonable computational time.

On maximum entropy and minimum KL-divergence optimization by Grobner basis methods

In this paper we study constrained maximum entropy and minimum divergence optimization problems, in the cases where integer valued sufficient statistics exists, using tools from computational commutative algebra. We show that the estimation of parametric statistical models in this case can be transformed to solving a system of polynomial equations. We give an implicit description of maximum entropy models by embedding them in algebraic varieties for which we give a Grobner basis method to compute it. In the cases of minimum KL-divergence models we show that implicitization preserves specialization of prior distribution. This result leads us to a Grobner basis method to embed minimum KL-divergence models in algebraic varieties. (C) 2012 Elsevier Inc. All rights reserved.

Methods for Detecting Early Warnings of Critical Transitions in Time Series Illustrated Using Simulated Ecological Data

Many dynamical systems, including lakes, organisms, ocean circulation patterns, or financial markets, are now thought to have tipping points where critical transitions to a contrasting state can happen. Because critical transitions can occur unexpectedly and are difficult to manage, there is a need for methods that can be used to identify when a critical transition is approaching. Recent theory shows that we can identify the proximity of a system to a critical transition using a variety of so-called `early warning signals', and successful empirical examples suggest a potential for practical applicability. However, while the range of proposed methods for predicting critical transitions is rapidly expanding, opinions on their practical use differ widely, and there is no comparative study that tests the limitations of the different methods to identify approaching critical transitions using time-series data. Here, we summarize a range of currently available early warning methods and apply them to two simulated time series that are typical of systems undergoing a critical transition. In addition to a methodological guide, our work offers a practical toolbox that may be used in a wide range of fields to help detect early warning signals of critical transitions in time series data.

Simple and efficient methods for discrimination of chiral diacids and chiral alpha-methyl amines

The three-component chiral derivatization protocols have been developed for H-1, C-13 and F-19 NMR spectroscopic discrimination of chiral diacids by their coordination and self-assembly with optically active (R)-alpha-methylbenzylamine and 2-formylphenylboronic acid or 3-fluoro-2-formylmethylboronic acid. These protocols yield a mixture of diastereomeric imino-boronate esters which are identified by the well-resolved diastereotopic peaks with significant chemical shift differences ranging up to 0.6 and 2.1 ppm in their corresponding H-1 and F-19 NMR spectra, without any racemization or kinetic resolution, thereby enabling the determination of enantiopurity. A protocol has also been developed for discrimination of chiral alpha-methyl amines, using optically pure trans-1,2-cyclohexanedicarboxylic acid in combination with 2-formylphenylboronic acid or 3-fluoro-2-fluoromethylboronic acid. The proposed strategies have been demonstrated on large number of chiral diacids and chiral alpha-methyl amines.

AIWIN - A fast and jigless inspection technique for machined parts

Precision inspection of manufactured components having multiple complex surfaces and variable tolerance definition is an involved, complex and time-consuming function. In routine practice, a jig is used to present the part in a known reference frame to carry out the inspection process. Jigs involve both time and cost in their development, manufacture and use. This paper describes 'as is where is inspection' (AIWIN), a new automated inspection technique that accelerates the inspection process by carrying out a fast registration procedure and establishing a quick correspondence between the part to inspect and its CAD geometry. The main challenge in doing away with a jig is that the inspection reference frame could be far removed from the CAD frame. Traditional techniques based on iterative closest point (ICP) or Newton methods require either a large number of iterations for convergence or fail in such a situation. A two-step coarse registration process is proposed to provide a good initial guess for a modified ICP algorithm developed earlier (Ravishankar et al., Int J Adv Manuf Technol 46(1-4):227-236, 2010). The first step uses a calibrated sphere for local hard registration and fixing the translation error. This transformation locates the centre for the sphere in the CAD frame. In the second step, the inverse transformation (involving pure rotation about multiple axes) required to align the inspection points measured on the manufactured part with the CAD point dataset of the model is determined and enforced. This completes the coarse registration enabling fast convergence of the modified ICP algorithm. The new technique has been implemented on complex freeform machined components and the inspection results clearly show that the process is precise and reliable with rapid convergence. © 2011 Springer-Verlag London Limited.