BarraCUDA – a Fast Sequence Mapping Software using Graphics Processing Units

High-throughput DNA sequencing (HTS) instruments today are capable of generating millions of sequencing reads in a short period of time, and this represents a serious challenge to current bioinformatics pipeline in processing such an enormous amount of data in a fast and economical fashion. Modern graphics cards are powerful processing units that consist of hundreds of scalar processors in parallel in order to handle the rendering of high-definition graphics in real-time. It is this computational capability that we propose to harness in order to accelerate some of the time-consuming steps in analyzing data generated by the HTS instruments. We have developed BarraCUDA, a novel sequence mapping software that utilizes the parallelism of NVIDIA CUDA graphics cards to map sequencing reads to a particular location on a reference genome. While delivering a similar mapping fidelity as other mainstream programs , BarraCUDA is a magnitude faster in mapping throughput compared to its CPU counterparts. The software is also capable of supporting multiple CUDA devices in parallel to further accelerate the mapping throughput. BarraCUDA is designed to take advantage of the parallelism of GPU to accelerate the mapping of millions of sequencing reads generated by HTS instruments. By doing this, we could, at least in part streamline the current bioinformatics pipeline such that the wider scientific community could benefit from the sequencing technology. BarraCUDA is currently available at http://seqbarracuda.sf.net

The assembly of computational models for the collaborative development of virtual prototypes

In the context of collaborative product development, new requirements need to be accommodated for Virtual Prototyping Simulation (VPS), such as distributed processing and the integration of models created using different tools or languages. Existing solutions focus mainly on the implementation of distributed processing, but this paper explores the issues of combining different models (some of which may be proprietary) developed in different software environments. In this paper, we discuss several approaches for developing VPS, and suggest how it can best be integrated into the design process. An approach is developed to improve collaborative work in a VPS development by combining disparate computational models. Specifically, a system framework is proposed to separate the system-level modeling from the computational infrastructure. The implementation of a simple prototype demonstrates that such a paradigm is viable and thus provides a new means for distributed VPS development. © 2009 by ASME.

Customizing the technology roadmapping technique for software companies

Software importance keeps growing fast and consistently for many organizations. The growth of software functionality in manufactured products and the emergence of digital media, convergent spaces including digital content, software, and multi-channels to the market, are recent examples of organizational changes where software assumed a central position for the corporate strategy. This paper analyzes the alignment between strategic objectives and software development processes at software companies and proposes a methodology to ensure that development processes are aligned with the corporate capabilities required to exploit future market opportunities. The methodology includes the categorization of different software companies according to their core capabilities and the customization of the technology roadmapping technique for software companies. The research process included the realization of case studies and a survey. (c) 2006 PICMET.

Everywhere and nowhere: nearshore software development in the context of globalisation

Offshore software development has been identified as one of the most striking manifestations of contemporary globalisation and as evidence of placelessness, the idea that information and communication technologies have rendered location irrelevant. Research in the International Business and Information Systems fields, in contrast, has suggested that all locations are not equal and has identified a number of characteristics that may influence the attractiveness of a location for multinational investment and offshoring, respectively. These literatures, however, focus almost exclusively on quantitative, economic characteristics that are seen as fixed and applying uniformly throughout a whole country. They therefore offer little guidance on the suitability of particular locations as offshoring destinations, especially in countries without a track record in offshore software development. Drawing on two cases of nearshore software development centres set up by offshore service providers in the Caribbean, this paper illustrates that, while the initial decision to establish the ventures reflected a logic of placelessness, characteristics of these particular locations affected their subsequent success. Through the findings, we therefore develop a typology of espoused, unanticipated and remediable locational characteristics, which illustrates that locational attractiveness may vary significantly within countries and that offshore service providers and government agencies can modify locational characteristics to their advantage.

Progressive 3D reconstruction of infrastructure with videogrammetry

A number of methods are commonly used today to collect infrastructure's spatial data (time-of-flight, visual triangulation, etc.). However, current practice lacks a solution that is accurate, automatic, and cost-efficient at the same time. This paper presents a videogrammetric framework for acquiring spatial data of infrastructure which holds the promise to address this limitation. It uses a calibrated set of low-cost high resolution video cameras that is progressively traversed around the scene and aims to produce a dense 3D point cloud which is updated in each frame. It allows for progressive reconstruction as opposed to point-and-shoot followed by point cloud stitching. The feasibility of the framework is studied in this paper. Required steps through this process are presented and the unique challenges of each step are identified. Results specific to each step are also presented.

Automated sparse 3D point cloud generation of infrastructure using its distinctive visual features

The commercial far-range (>10 m) spatial data collection methods for acquiring infrastructure’s geometric data are not completely automated because of the necessary manual pre- and/or post-processing work. The required amount of human intervention and, in some cases, the high equipment costs associated with these methods impede their adoption by the majority of infrastructure mapping activities. This paper presents an automated stereo vision-based method, as an alternative and inexpensive solution, to producing a sparse Euclidean 3D point cloud of an infrastructure scene utilizing two video streams captured by a set of two calibrated cameras. In this process SURF features are automatically detected and matched between each pair of stereo video frames. 3D coordinates of the matched feature points are then calculated via triangulation. The detected SURF features in two successive video frames are automatically matched and the RANSAC algorithm is used to discard mismatches. The quaternion motion estimation method is then used along with bundle adjustment optimization to register successive point clouds. The method was tested on a database of infrastructure stereo video streams. The validity and statistical significance of the results were evaluated by comparing the spatial distance of randomly selected feature points with their corresponding tape measurements.

Visual pattern recognition models for remote sensing of civil infrastructure

As-built models have been proven useful in many project-related applications, such as progress monitoring and quality control. However, they are not widely produced in most projects because a lot of effort is still necessary to manually convert remote sensing data from photogrammetry or laser scanning to an as-built model. In order to automate the generation of as-built models, the first and fundamental step is to automatically recognize infrastructure-related elements from the remote sensing data. This paper outlines a framework for creating visual pattern recognition models that can automate the recognition of infrastructure-related elements based on their visual features. The framework starts with identifying the visual characteristics of infrastructure element types and numerically representing them using image analysis tools. The derived representations, along with their relative topology, are then used to form element visual pattern recognition (VPR) models. So far, the VPR models of four infrastructure-related elements have been created using the framework. The high recognition performance of these models validates the effectiveness of the framework in recognizing infrastructure-related elements.

Comparison of Civil Infrastructure Optical-based Spatial Data Acquisition Techniques

Infrastructure spatial data, such as the orientation and the location of in place structures and these structures' boundaries and areas, play a very important role for many civil infrastructure development and rehabilitation applications, such as defect detection, site planning, on-site safety assistance and others. In order to acquire these data, a number of modern optical-based spatial data acquisition techniques can be used. These techniques are based on stereo vision, optics, time of flight, etc., and have distinct characteristics, benefits and limitations. The main purpose of this paper is to compare these infrastructure optical-based spatial data acquisition techniques based on civil infrastructure application requirements. In order to achieve this goal, the benefits and limitations of these techniques were identified. Subsequently, these techniques were compared according to applications' requirements, such as spatial accuracy, the automation of acquisition, the portability of devices and others. With the help of this comparison, unique characteristics of these techniques were identified so that practitioners will be able to select an appropriate technique for their own applications.

Comparison of Civil Infrastructure Optical-based Spatial Data Acquisition Techniques

Infrastructure spatial data, such as the orientation and the location of in place structures and these structures' boundaries and areas, play a very important role for many civil infrastructure development and rehabilitation applications, such as defect detection, site planning, on-site safety assistance and others. In order to acquire these data, a number of modern optical-based spatial data acquisition techniques can be used. These techniques are based on stereo vision, optics, time of flight, etc., and have distinct characteristics, benefits and limitations. The main purpose of this paper is to compare these infrastructure optical-based spatial data acquisition techniques based on civil infrastructure application requirements. In order to achieve this goal, the benefits and limitations of these techniques were identified. Subsequently, these techniques were compared according to applications' requirements, such as spatial accuracy, the automation of acquisition, the portability of devices and others. With the help of this comparison, unique characteristics of these techniques were identified so that practitioners will be able to select an appropriate technique for their own applications.