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Sukumar et al.: Robotic three-dimensional imaging system¼ Fig. 6 6 Difference shell approach for finding anomalous objects of an undercarriage by comparing with previous scans: ͑a͒ photograph of the ...doi:10.1117/1.2238565 fatcat:tuwgyvyntbgzhg75rxj6oudcqy
PageRank is computed iteratively using the formula, r = αP T r + (1 − α) 1 N e, where N is the number of vertices in the graph, P is the transition matrix for the graph, r i is the PageRank value for node ...doi:10.1109/icdew.2015.7129544 dblp:conf/icde/LeeSL15 fatcat:vfwg42cbjbd6le3j6ra32kk3ji
Unmanned Systems Technology VIII
Towards the spatial alignment of data, if we can denote the Euler angles of roll, pitch and yaw from the IMU by ( ) T g t g t g t t z y x P ] , , [ = T r t r t r t t z y x D ] , , [ = Range κ φ ω , , and ... 3D range measurements (x t r , y t r, z t r ) by at a particular time t (note that we have already interpolated all the sensor data to synchronize in time) and let the GPS measurements after considering ...doi:10.1117/12.666249 fatcat:3h6rgmxxenc45ircotkrlhtrjy
In recent years, there has been a huge growth in the amount of genomic data available as reads generated from various genome sequencers. The number of reads generated can be huge, ranging from hundreds to billions of nucleotide, each varying in size. Assembling such large amounts of data is one of the challenging computational problems for both biomedical and data scientists. Most of the genome assemblers that have developed use de Bruijn graph techniques. A de Bruijn graph represents adoi:10.1109/bigdata.2015.7364056 dblp:conf/bigdataconf/SinghGLSS15 fatcat:j2rbjnyvfnavjnghp6k2hwnicm
more »... on of read sequences by billions of vertices and edges, which require large amounts of memory and computational power to store and process. This is the major drawback to de Bruijn graph assembly. Massively parallel, multithreaded, shared memory systems can be leveraged to overcome some of these issues. The objective of our research is to investigate the feasibility and scalability issues of de Bruijn graph assembly on Cray's Urika-GD system; Urika-GD is a high performance graph appliance with a large shared memory and massively multithreaded custom processor designed for executing SPARQL queries over large-scale RDF data sets. However, to the best of our knowledge, there is no research on representing a de Bruijn graph as an RDF graph or finding Eulerian paths in RDF graphs using SPARQL for potential genome discovery. In this paper, we address the issues involved in representing de Bruin graphs as RDF graphs and propose an iterative querying approach for searching cycles to find Eulerian paths in large RDF graphs. We evaluate the performance of our implementation on real world ebola genome datasets and illustrate how genome assembly can be accomplished with Urika-GD using iterative SPARQL queries.
Sukumar and R.K. Ferrell / 'Big Data' collaboration: Exploring, recording and sharing enterprise knowledge S.R. Sukumar and R.K. ... Sukumar and R.K. Ferrell / 'Big Data' collaboration: Exploring, recording and sharing enterprise knowledge S.R. Sukumar and R.K. ...doi:10.3233/isu-130712 fatcat:afcyvhrahnhh5bzqrdj4y76od4
The analysis for these case studies was conducted using the Hadoop/Hive data platform and used open source software such as Mahout, R, and Python networkx 8 and hence are repeatable in other contexts. ...doi:10.1145/2487575.2488205 dblp:conf/kdd/ChandolaSS13 fatcat:emqpbqkpurawlll2oj4gyf2di4
2010 IEEE Energy Conversion Congress and Exposition
Green Jobs is ready to invest $X in a state to generate s KWh of solar, w KWh of wind energy and r KWh of other renewable energy, how much encouragement (in the form of incentives) from each state can ...doi:10.1109/ecce.2010.5618006 fatcat:clphzip4lrfbba3nnvuf55jdta
In this paper, we derive a data mining framework to analyze 3D features on human faces. The framework leverages kernel density estimators, genetic algorithm and an information complexity criterion to identify discriminant feature-clusters of lower dimensionality. We apply this framework on human face anthropometry data of 32 features collected from each of the 300 3D face mesh models. The feature-subsets that we infer as the output establishes domain knowledge for the challenging problem of 3Ddoi:10.1109/icip.2008.4712154 dblp:conf/icip/SukumarBPKA08 fatcat:jraejddabrcpvnt424lq2vfgui
more »... ace recognition with dense 3D gallery models and sparse or low resolution probes. Index Terms-3D face recognition, feature learning, dimensionality reduction, informative-discrimant face features.
Interest point detectors are the starting point in image analysis for depth estimation using epipolar geometry and camera ego-motion estimation. With several detectors defined in the literature, some of them outperforming others in a specific application context, we introduce Multi-Feature Sample Consensus (MuFeSaC) as an adaptive and automatic procedure to choose a reliable feature detector among competing ones. Our approach is derived based on model selection criteria that we demonstrate fordoi:10.1109/icip.2007.4379543 dblp:conf/icip/SukumarPBKA07 fatcat:g42d3wvs6jdexgkjovu24nimxa
more »... obile robot self-localization in outdoor environments consisting of both man-made structures and natural vegetation. Index Terms-feature learning, RANSAC, interest point detector evaluation
Towards understanding what makes 3D objects appear simple or complex Sreenivas R. Sukumar, David L. Page, Andreas F. Koschan and Mongi A. ... h opt 5 1 2 2 35 243 ⎥ ⎥ ⎦ ⎤ ⎢ ⎢ ⎣ ⎡ = (4) where ( ) ( ) ( ) ( ) ∫ ∫ = = dt t G t G , dt t G G R 2 2 2 μ and σ is the absolute deviation of the curvature data κ i . ...doi:10.1109/cvprw.2008.4562975 dblp:conf/cvpr/SukumarPKA08 fatcat:ztiavyj4w5gmvew742n567xrpq
detail preserving 3D models that possess accurate depth information for characterization and visualization of cracks as a significant improvement over contemporary commercial video-based vision systems. r ... Hence, each range measurement r is processed through the filter that we describe below. ... T gps and R imu are GPS and IMU measurement matrices, respectively. They are combined to generate the pose matrix T hg . ...doi:10.1016/j.optlaseng.2006.12.007 fatcat:osagdny2n5f67aldr3gplod4di
Unmanned Ground Vehicle Technology VII
This large data set allows high fidelity geometry that other 3D sensors do not offer, but at the price of data redundancy and a potential data overload. 2 / t * c r ∆ = α α tan s f s - tan f B ) s ( r ... = (4) where ( ) ( ) ( ) ( ) ∫ ∫ = = dt t G t G , dt t G G R 2 2 2 µ and σ is the absolute deviation of the curvature data κ i . ...doi:10.1117/12.602930 fatcat:564h7aqwkfehnhhjwykwrigixa
Assuming that the calibration matrix (K) of the camera acquiring images of the scene from different viewpoints is available; F is instrumental in the estimation of the relative rotation (R) and translation ... form of the translation vector t in Equation 1. -1 x ] [ ' ; 0 ' RK t K F m F m = = i i (1) In vision-based vehicle navigation/localization applications, we note that the uncertainty about the pose (R, ...doi:10.1109/cvpr.2008.4587567 dblp:conf/cvpr/SukumarBPKA08 fatcat:kceybgh7xrh75ilzusi7ugqqha
Belief propagation methods are the state-of-the-art with multi-sensor state localization problems. However, when localization applications have to deal with multi-modality sensors whose functionality depends on the environment of operation, we understand the need for an inference framework to identify confident and reliable sensors. Such a framework helps eliminate failed/non-functional sensors from the fusion process minimizing uncertainty while propagating belief. We derive a frameworkdoi:10.1109/icpr.2008.4761125 dblp:conf/icpr/SukumarBPKA08 fatcat:ywhlatsfpzax3owvsd7toasdta
more »... d from model selection theory and demonstrate results on real world multi-sensor robot state localization and multi-camera target tracking applications.
Sukumar et al. ... The laser line (profile of r) conveys surface cues about the object. The digitization of the underlying surface is achieved by accumulating such surface profiles. ...doi:10.1080/17452751003743973 fatcat:5gdwydifuva5rb2gifvugtsubq
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