Abstract

The present study outlines an approach to classify forest density and to estimate canopy closure of the forest of the Andaman and Nicobar archipelago. The vector layers generated for the study area using satellite data was validated with the field knowledge of the surveyed ground control points. The methodology adopted in this present analysis is three-tiered. First, the density stratification into five zones using visual

Abstract

A Special Economic Zone (SEZ) is a geographical region that has economic laws more liberal than a country's typical economic laws. In the recent years, India has witnessed a confrontation between farmers and the state, the reason being use of highly productive land for setting up SEZs with some of the cases occupying a centre stage in the World fora (Nandigram violence). In this paper, we propose an integrated solution for identifying nonagricultural lands for setting up SEZs using MODIS NDVI product and remote sensing techniques. The study also identifies multi- cropping practices and reinforces the potential of time-series MODIS data for mapping and land cover classification at regional scales.

Abstract

Computational approaches are being successfully utilized for the design of small molecules that could be potential substrates for various biological targets both in academia and pharmaceutical industry. Over the past two decades or so, various computational techniques have been developed and improvements on the existing methodologies were proposed to accomplish high accuracy/ predictability of molecular properties. Consideration of the flexible nature of proteins in structure based drug design studies has been a bottleneck mainly due to the huge demand of computational and actual time. Hence most of the docking studies consider the biological macromolecules to be rigid and allow only the conformational changes of the small molecules. Similarly, the ligand based drug design approaches tend to focus more on the global minima of the small molecules. This could lead to incorrect model and hence false predictions since this approximation may not be true in cases where the global minima essentially is not the bioactive conformer – the conformer that binds to the target. Recently, advanced methods have been proposed to consider the flexible nature of the molecules. Molecular dynamics (MD) simulations have been shown to be a straightforward means to include the flexibility of both ligands and biomolecular targets. MD simulations are invaluable computational methods that are extensively used for deriving information of the structure, dynamics and energetic properties of various biomolecules including proteins, nucleic acids and lipids. The need for consideration of dynamic nature of the biomolecular targets will be demonstrated and the advantages of MD simulations to overcome this limitation will be discussed. Basic principles of MD simulations will be discussed and an introduction to biomolecular force fields will be given. Illustrated examples of the applications of MD simulations to ligand based and structure based drug design approaches will be presented.

Abstract

We consider the problem of probabilistic reliable communication (PRC) over synchronous networks modeled as directed graphs in the presence of a Byzantine adversary when players’ knowledge of the network topology is not complete. We show that possibility of PRC is extremely sensitive to the changes in players’ knowledge of the topology. This is in complete contrast with earlier known results on the possibility of perfectly reliable communication over undirected graphs where the case of each player knowing only its neighbours gives the same result as the case where players have complete knowledge of the network. Specifically, in either case,(2t+ 1)-vertex connectivity is necessary and sufficient, where t is the number of nodes that can be corrupted by the adversary [DDWY93, SKR+05]. We introduce a novel model for quantifying players’ knowledge of network topology, denoted by T K. Given a directed graph G, influenced by a Byzantine adversary that can corrupt up to any t players, we give a necessary and sufficient condition for possibility of PRC over G for any arbitrary topology knowledge T K. It follows from our main characterization theorem that knowledge of up to d=⌊ n− 2t

Abstract

We address the problem of message authentication in communication networks which are resource constrained or are performance bound. Recent research has focused on development of symmetric key protocols for authentication in such networks. In these protocols, the sender generates a pool of keys -used to sign the messages, and distributes a different subset of keys -used to verify the signatures, to each user. However, in these protocols, users can collude to combine their keys and impersonate the sender by generating the sender signatures. In this work, we describe a family of collusion resistant symmetric key distribution protocols for authentication which address the problem of collusion. We show that the collusion resistance achieved using our protocols is practical (and hence, sufficient) for networks whose communication diameter is known or is within fixed bounds. Furthermore, we show that some existing protocols in literature are members of our family of protocols.

Abstract

This paper focuses on anti-magic labellings of planar graphs of type (a, b, c) introduced by Lih (Util. Math. 24 (1983), 165–197). We show that a certain class of planar graphs defined using the complete graphs and a class of planar graphs defined using the complete bipartite graphs are (1, 1, 0) and (1, 1, 1)-anti-magic under certain mild conditions.

Abstract

The study was conducted in order to compute the deviations in the Peak Ground Acceleration values as specified in the IS 1893 2002 for different regions from the values obtained from modern empirical attenuation formulae. For this purpose earthquake events catalogue was prepared for various earthquake that occurred from year 1984 to 2003 in peninsular India.

Abstract

Document image segmentation algorithms primarily aim at separating text and graphics in presence of complex layouts. However, for many non-Latin scripts, segmentation becomes a challenge due to the characteristics of the script. In this paper, we empirically demonstrate that successful algorithms for Latin scripts may not be very effective for Indic and complex scripts. We explain this based on the differences in the spatial distribution of symbols in the scripts. We argue that the visual information used for segmentation needs to be enhanced with other information like script models for accurate results.

Abstract

Super-resolution reconstruction algorithms assume the availability of exact registration and blur parameters. Inaccurate estimation of these parameters adversely affects the quality of the reconstructed image. However, traditional approaches for image registration are either sensitive to image degradations such as variations in blur, illumination and noise, or are limited in the class of image transformations that can be estimated. We propose an accurate registration algorithm that uses the local phase information, which is robust to the above degradations. We derive the theoretical error rate of the estimates in presence of non-ideal band-pass behavior of the filter and show that the error converges to zero over iterations. We also show the invariance of local phase to a class of blur kernels. Experimental results on images taken under varying conditions clearly demonstrates the robustness of our approach.

Abstract

We propose a novel approach to 3D hand geometry based person authentication using projected light patterns. Instead of explicitly computing a depth map of the palm for recognition, we capture the depth information in the deformations of a projected texture pattern, and use it directly for recognition. The deformed pattern is characterized using local texture measures, which can encode the certain depth characteristics of the palm. An authentication system built using the proposed technique achieves an equal error rate of 0.84% on a dataset of 1341 samples collected from 149 users, as opposed to 4.03% using traditional 2D features on an identical dataset. The approach is robust as well as computationally efficient and could be applied to other 3D object recognition problems as well.