Vijay Kumar

The GRASP Multiple Micro-UAV Testbed

In the last five years, advances in materials, electronics, sensors, and batteries have fueled a growth in the development of microunmanned aerial vehicles (MAVs) that are between 0.1 and 0.5 m in length and 0.1-0.5 kg in mass [1]. A few groups have built and analyzed MAVs in the 10-cm range [2], [3]. One of the smallest MAV is the Picoftyer with a 60-mmpropellor diameter and a mass of 3.3 g [4]. Platforms in the 50-cm range are more prevalent with several groups having built and flown systems of this size [5]-[7]. In fact, there are severalcommercially available radiocontrolled (PvC) helicopters and research-grade helicopters in this size range [8].

An integrated framework for performance-based optimization of scientific workflows

Data analysis processes in scientific applications can be expressed as coarse-grain workflows of complex data processing operations with data flow dependencies between them. Performance optimization of these workflows can be viewed as a search for a set of optimal values in a multi-dimensional parameter space. While some performance parameters such as grouping of workflow components and their mapping to machines do not affect the accuracy of the output, others may dictate trading the output quality of individual components (and of the whole workflow) for performance. This paper describes an integrated framework which is capable of supporting performance optimizations along multiple dimensions of the parameter space. Using two real-world applications in the spatial data analysis domain, we present an experimental evaluation of the proposed framework.

Analysis and Semantic Querying in Large Biomedical Image Datasets

Biomedical image analysis plays an important role in diagnosing, prognosing, and treating complex diseases. The authors describe a set of techniques for analyzing, processing, and querying large image datasets using semantic and spatial information.

Large-Scale Biomedical Image Analysis in Grid Environments

This paper presents the application of a component-based Grid middleware system for processing extremely large images obtained from digital microscopy devices. We have developed parallel, out-of-core techniques for different classes of data processing operations employed on images from confocal microscopy scanners. These techniques are combined into a data preprocessing and analysis pipeline using the component-based middleware system. The experimental results show that: 1) our implementation achieves good performance and can handle very large datasets on high-performance Grid nodes, consisting of computation and/or storage clusters and 2) it can take advantage of Grid nodes connected over high-bandwidth wide-area networks by combining task and data parallelism.

Parameterized specification, configuration and execution of data-intensive scientific workflows

Data analysis processes in scientific applications can be expressed as coarse-grain workflows of complex data processing operations with data flow dependencies between them. Performance optimization of these workflows can be viewed as a search for a set of optimal values in a multidimensional parameter space consisting of input performance parameters to the applications that are known to affect their execution times. While some performance parameters such as grouping of workflow components and their mapping to machines do not affect the accuracy of the analysis, others may dictate trading the output quality of individual components (and of the whole workflow) for performance. This paper describes an integrated framework which is capable of supporting performance optimizations along multiple such parameters. Using two real-world applications in the spatial, multidimensional data analysis domain, we present an experimental evaluation of the proposed framework.

Large image correction and warping in a cluster environment

This paper is concerned with efficient execution of a pipeline of data processing operations on very large images obtained from confocal microscopy instruments. We describe parallel, out-of-core algorithms for each operation in this pipeline. One of the challenging steps in the pipeline is the warping operation using inverse mapping based methods. We propose and investigate a set of algorithms to handle the warping computations on storage clusters. Our experimental results show that the proposed approaches are scalable both in terms of number of processors and the size of images

HPC and Grid Computing for Integrative Biomedical Research

Integrative biomedical research projects query, analyze, and integrate many different data types and make use of datasets obtained from measurements or simulations of structure and function at multiple biological scales. With the increasing availability of high-throughput and high-resolution instruments, the integrative biomedical research imposes many challenging requirements on software middleware systems. In this paper, we look at some of these requirements using example research pattern templates. We then discuss how middleware systems, which incorporate Grid and high-performance computing, could be employed to address the requirements.

Intrarenal pure yolk sac tumor: an extremely rare entity.

Yolk sac tumor (endodermal sinus tumor) is a malignant germ cell tumor that usually arises in the gonads. Extragonadal germ cell tumors are rare and have been described in case reports. We report a pure intrarenal yolk sac tumor in a 1-year-old boy who presented with a huge abdominal mass and was operated for suspected Wilms tumor. The tumor exhibited histopathologic and immunohistochemical features identical to those of an endodermal sinus tumor of gonadal origin. The purpose of this report is to add a rare tumor to the differential diagnosis of pediatric renal neoplasms.

High-performance systems for in silico microscopy imaging studies

High-resolution medical images from advanced instruments provide rich information about morphological and functional characteristics of biological systems. However, most of the information available in biomedical images remains underutilized in research projects. In this paper, we discuss the requirements and design of system support for composing, executing, and exploring in silico experiments involving microscopy images. This framework aims to provide building blocks for large scale, high-performance analytical image exploration systems, through rich metadata models, comprehensive query and data access capabilities, and efficient database and HPC support.

Architectural implications for spatial object association algorithms

Spatial object association, also referred to as crossmatch of spatial datasets, is the problem of identifying and comparing objects in two or more datasets based on their positions in a common spatial coordinate system. In this work, we evaluate two crossmatch algorithms that are used for astronomical sky surveys, on the following database system architecture configurations: (1) Netezza Performance Server®, a parallel database system with active disk style processing capabilities, (2) MySQL Cluster, a high-throughput network database system, and (3) a hybrid configuration consisting of a collection of independent database system instances with data replication support. Our evaluation provides insights about how architectural characteristics of these systems affect the performance of the spatial crossmatch algorithms. We conducted our study using real use-case scenarios borrowed from a large-scale astronomy application known as the Large Synoptic Survey Telescope (LSST).