Abstract

e propose set of guidelines for virtual labs to improve end user experience based on analysis of experimental results. Virtual labs were designed and developed by people with different technical backgrounds based on their familiarity with the technologies and the complexity of virtual lab. As a result each lab developer decided to use his or her own set of tools and technologies leading to performance issues during execution of the virtual labs. Performance of a web application depends upon the content of the web page. Therefore, we did analysis on web pages of virtual lab and listed guidelines to enhance user experience.

Abstract

Cooperative spectrum sensing (CSS) is one of the efficient scheme that helps in improving the spectrum sensing performance of a cognitive radio network. In this paper we propose a Dempster-Shafer theory (DST) based CSS for cognitive radio network. The fusion rule is the D-S combination rule which is well known for its ability to handle uncertainty and has been used in quite a different number of fields. Noise uncertainty, which is unavoidable in practical field, can gravely limit the detection performance of CSS. In this paper we specially investigate sensor nodes undergoing uncertainty in noise power when the detection scheme is based on energy of the received signal and use DS theory to minimize such effects. Simulation results reveal significant improvement in CSS gain as compared to previous traditional methods.

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Abstract

In this paper, we discuss our efforts to annotate nominals in the Hindi Treebank with the semantic property of animacy. Although the treebank already encodes lexical information at a number of levels such as morph and part of speech, the addition of animacy information seems promising given its relevance to varied linguistic phenomena. The suggestion is based on the theoretical and computational analysis of the property of animacy in the context of anaphora resolution, syntactic parsing, verb classification and argument differentiation.

Abstract

In this paper, we discuss our efforts to annotate nominals in the Hindi Treebank with the semantic property of animacy. Although the treebank already encodes lexical information at a number of levels such as morph and part of speech, the addition of animacy information seems promising given its relevance to varied linguistic phenomena. The suggestion is based on the theoretical and computational analysis of the property of animacy in the context of anaphora resolution, syntactic parsing, verb classification and argument differentiation.

Abstract

Recognizing human faces in the wild is emerging as a critically important, and technically challenging computer vision problem. With a few notable exceptions, most previous works in the last several decades have focused on recognizing faces captured in a laboratory setting. However, with the intro- duction of databases such as LFW and Pubfigs, face recogni- tion community is gradually shifting its focus on much more challenging unconstrained settings. Since its introduction, LFW verification benchmark is getting a lot of attention with various researchers contributing towards state-of-the-results. To further boost the unconstrained face recognition research, we introduce a more challenging Indian Movie Face Database (IMFDB) that has much more variability compared to LFW and Pubfigs. The database consists of 34512 faces of 100 known actors collected from approximately 103 Indian movies. Unlike LFW and Pubfigs which used face detectors to automatically detect the faces from the web collection, faces in IMFDB are detected manually from all the movies. Manual selection of faces from movies resulted in high degree of variability (in scale, pose, expression, illumination, age, occlusion, makeup) which one could ever see in natural world. IMFDB is the first face database that provides a detailed annotation in terms of age, pose, gender, expression, amount of occlusion, for each face which may help other face related

Abstract

We present an approach to simulate both Newto-nian and generalized Newtonian fluids using Lattice BoltzmannMethod. The focus has been on accurately modelling non-Newtonian fluids at the micro channel level from biological fluidsin the past. Our method can model macroscopic behaviour of suchfluids by simulating the variation of properties such as viscositythrough the bulk of the fluid. The method works regardless ofthe magnitude of flow, be it through a thin tube or a largequantity of liquid splashing in a container. We simulate thechange in viscosity of a generalized Newtonian fluid and its freesurface interactions with obstacles and boundaries. We harnessthe inherent parallelism of Lattice Boltzmann Method to give afast GPU implementation for the same

Abstract

—Traditional video editing interfaces model and represent videos as a collection of frames against a timeline, which makes object-centric manipulation of videos a laborious task. We enable simple and meaningful interaction for object-centric navigation and manipulation of long shot videos, by introducing operators on three high-level video semantics: background mosaics, object motions, and camera motions. We estimate the scene background and represent the object motion using 3D space-time trajectories. We use the 3D object trajectories as basic interaction elements and define several object and camera operations as simple and intuitive curve manipulations. These allow users to perform various video object temporal manipulations by interactively manipulating the object trajectories. The camera operations model the camera as a movable and scalable aperture and allow the users to simulate pan, tilt, and zoom effects by creating new camera trajectories. With several example compositions we demonstrate that our representation and operations allow users to simply and interactively perform numerous seemingly complex, high-level video manipulation tasks.

Abstract

Divide and Conquer Ray Tracing (DACRT) is a recent technique which constructs no explicit acceleration structure. It creates and traverses an implicit hierarchy in a depth-first fashion recursively and is suited for dynamic scenes that change constantly. In this paper, we present a parallel version of DACRT that runs entirely on the GPU, which exploits efficient primitives like sort and reduce. Our approach suits the GPU well, with a low memory footprint. Our implementation outperforms the serial CPU algorithm for both primary and secondary ray passes. We show good performance on primary pass and on advanced effects.

Abstract

String sorting or variable-length key sorting has lagged in performance on the GPU even as the fixed-length key sorting has improved dramatically. Radix sorting is the fastest on the GPUs. In this paper, we present a fast and efficient string sort on the GPU that is built on the available radix sort. Our method sorts strings from left to right in steps, moving only indexes and small prefixes for efficiency. We reduce the number of sort steps by adaptively consuming maximum string bytes based on the number of segments in each step. Performance is improved by using Thrust primitives for most steps and by removing singleton segments from consideration. Over 70% of the string sort time is spent on Thrust primitives. This provides high performance along with high adaptability to future GPUs. We achieve speed of up to 10 over current GPU methods, especially on large datasets. We also scale to much larger input sizes. We present results on easy and difficult strings defined using their after-sort tie lengths.