Search results for "methodologies"
showing 10 items of 2106 documents
CUSHAW2-GPU: Empowering Faster Gapped Short-Read Alignment Using GPU Computing
2014
We present CUSHAW2-GPU to accelerate the CUSHAW2 algorithm using compute unified device architecture (CUDA)-enabled GPUs. Two critical GPU computing techniques, namely intertask hybrid CPU-GPU parallelism and tile-based Smith-Waterman map backtracking using CUDA, are investigated to facilitate fast alignments. By aligning both simulated and real reads to the human genome, our aligner yields comparable or better performance compared to BWA-SW, Bowtie2, and GEM. Furthermore, CUSHAW2-GPU with a Tesla K20c GPU achieves significant speedups over the multithreaded CUSHAW2, BWA-SW, Bowtie2, and GEM on the 12 cores of a high-end CPU for both single-end and paired-end alignment.
An adaptive-PCA algorithm for reflectance estimation from color images
2008
This paper deals with the problem of spectral reflectance estimation from color camera outputs. Because the reconstruction of such functions is an inverse problem, stabilizing the reconstruction process is highly desirable. One way to do this is to decompose reflectance function on a basis functions like PCA. The present work proposes an algorithm making PCA adaptive in reflectance estimation from a color camera output. We propose to adapt the PCA basis derivation by selecting, for each sample, the more relevant elements from the training set elements. The adaptivity criterion is achieved by a likelihood measurement. Finally, the spectral reflectance estimation results are evaluated with th…
Reflectance-based surface saliency
2017
In this paper, we propose an original methodology allowing the computation of the saliency maps for high dimensional RTI data (Reflectance Transformation Imaging). Unlike most of the classical methods, our approach aims at devising an intrinsic visual saliency of the surface, independent of the sensor (image) and the geometry of the scene (light-object-camera). From RTI data, we use the DMD (Discrete Modal Decomposition) technique for the angular reflectance reconstruction, which we extend by a new transformation on the modal basis enabling a rotation-invariant representation of reconstructed reflectances. This orientation-invariance of the resulting reflectance shapes fosters a robust esti…
Restoration of out-of-focus images based on circle of confusion estimate
2002
In this paper a new method for a fast out-of-focus blur estimation and restoration is proposed. It is suitable for CFA (Color Filter Array) images acquired by typical CCD/CMOS sensor. The method is based on the analysis of a single image and consists of two steps: 1) out-of-focus blur estimation via Bayer pattern analysis; 2) image restoration. Blur estimation is based on a block-wise edge detection technique. This edge detection is carried out on the green pixels of the CFA sensor image also called Bayer pattern. Once the blur level has been estimated the image is restored through the application of a new inverse filtering technique. This algorithm gives sharp images reducing ringing and c…
Image Processing Chain For Digital Still Cameras Based On The Simpil Architecture
2005
The new generation of wireless devices herald the development of products for integrated portable image and video communication requiring to image and video applications high computing performance. Portable MultiMedia Supercomputers (PMMS), a new class of architectures, allow to combine high computational performance, needed by multimedia applications, and a big energy efficiency, needed by portable devices. Among PMMS, the SIMPil (SIMD processor pixel) architecture satisfies the above requirements, especially with video and digital images processing tasks. In this paper we, exploit the SIMPil computation and throughput efficiency to implement the whole image processing chain of a digital s…
Big Data in metagenomics: Apache Spark vs MPI.
2020
The progress of next-generation sequencing has lead to the availability of massive data sets used by a wide range of applications in biology and medicine. This has sparked significant interest in using modern Big Data technologies to process this large amount of information in distributed memory clusters of commodity hardware. Several approaches based on solutions such as Apache Hadoop or Apache Spark, have been proposed. These solutions allow developers to focus on the problem while the need to deal with low level details, such as data distribution schemes or communication patterns among processing nodes, can be ignored. However, performance and scalability are also of high importance when…
Cluster-based active learning for compact image classification
2010
In this paper, we consider active sampling to label pixels grouped with hierarchical clustering. The objective of the method is to match the data relationships discovered by the clustering algorithm with the user's desired class semantics. The first is represented as a complete tree to be pruned and the second is iteratively provided by the user. The active learning algorithm proposed searches the pruning of the tree that best matches the labels of the sampled points. By choosing the part of the tree to sample from according to current pruning's uncertainty, sampling is focused on most uncertain clusters. This way, large clusters for which the class membership is already fixed are no longer…
An efficient upper bound of the rotation distance of binary trees
2000
A polynomial time algorithm is developed for computing an upper bound for the rotation distance of binary trees and equivalently for the diagonal-flip distance of convex polygons triangulations. Ordinal tools are used.
Fast comparison of DNA sequences by oligonucleotide profiling
2008
Provisional abstact and full-text PDF files correspond to the article as it appeared upon acceptance. Fully formatted PDF and final abstract will be made available soon.
Protein Interaction Networks and Disease: Highlights of the 3rd Challenges in Computational Biology Meeting
2017
Cellular functions are managed by a complex network of protein interactions, the malfunction of which may derive in disease phenotypes. In spite of the incompleteness and noise present in our current protein interaction maps, computational biologists are making strenuous efforts to extract knowledge from these intricate networks and, through their integration with other types of biological data, expedite the development of novel and more effective treatments against human disorders. The 3rd Challenges in Computational Biology meeting revolved around the Protein Interaction Networks and Disease subject, bringing expert network biologists to the city of Mainz, Germany to debate the current st…