Análisis de eficiencia en sistemas paralelos

El objetivo de esta línea de investigación es estudiar problemas de consumo y eficiencia energética en arquitecturas de procesamiento paralelo, analizando la relación que guarda con los paradigmas de programación de algoritmos paralelos. Por otro lado se pretende investigar las consecuencias de las fallas que puedan presentarse en arquitecturas paralelas y distribuidas y proponer estrategias de detección y recuperación. Finalmente, resulta necesario estudiar casos concretos sobre arquitecturas con procesadores de múltiples núcleos. Específicamente se pretende analizar la factibilidad del procesamiento paralelo en algoritmos de procesamiento de imágenes, evaluando la eficiencia sobre sistemas multicore basados en plataformas FPGAs.Eje: Arquitectura , Redes y Sistemas OperativosRed de Universidades con Carreras en Informátic

Análisis de eficiencia en sistemas paralelos

El objetivo de esta línea de investigación es estudiar problemas de consumo y eficiencia energética en arquitecturas de procesamiento paralelo, analizando la relación que guarda con los paradigmas de programación de algoritmos paralelos. Por otro lado se pretende investigar las consecuencias de las fallas que puedan presentarse en arquitecturas paralelas y distribuidas y proponer estrategias de detección y recuperación. Finalmente, resulta necesario estudiar casos concretos sobre arquitecturas con procesadores de múltiples núcleos. Específicamente se pretende analizar la factibilidad del procesamiento paralelo en algoritmos de procesamiento de imágenes, evaluando la eficiencia sobre sistemas multicore basados en plataformas FPGAs.Eje: Arquitectura , Redes y Sistemas OperativosRed de Universidades con Carreras en Informátic

Análisis de eficiencia en sistemas paralelos

El objetivo de esta línea de investigación es estudiar problemas de consumo y eficiencia energética en arquitecturas de procesamiento paralelo, analizando la relación que guarda con los paradigmas de programación de algoritmos paralelos. Por otro lado se pretende investigar las consecuencias de las fallas que puedan presentarse en arquitecturas paralelas y distribuidas y proponer estrategias de detección y recuperación. Finalmente, resulta necesario estudiar casos concretos sobre arquitecturas con procesadores de múltiples núcleos. Específicamente se pretende analizar la factibilidad del procesamiento paralelo en algoritmos de procesamiento de imágenes, evaluando la eficiencia sobre sistemas multicore basados en plataformas FPGAs.Eje: Arquitectura , Redes y Sistemas OperativosRed de Universidades con Carreras en Informátic

FPGA implementations for parallel multidimensional filtering algorithms

PhD ThesisOne and multi dimensional raw data collections introduce noise and artifacts, which need to be recovered from degradations by an automated filtering system before, further machine analysis. The need for automating wide-ranged filtering applications necessitates the design of generic filtering architectures, together with the development of multidimensional and extensive convolution operators. Consequently, the aim of this thesis is to investigate the problem of automated construction of a generic parallel filtering system. Serving this goal, performance-efficient FPGA implementation architectures are developed to realize parallel one/multi-dimensional filtering algorithms. The proposed generic architectures provide a mechanism for fast FPGA prototyping of high performance computations to obtain efficiently implemented performance indices of area, speed, dynamic power, throughput and computation rates, as a complete package. These parallel filtering algorithms and their automated generic architectures tackle the major bottlenecks and limitations of existing multiprocessor systems in wordlength, input data segmentation, boundary conditions as well as inter-processor communications, in order to support high data throughput real-time applications of low-power architectures using a Xilinx Virtex-6 FPGA board. For one-dimensional raw signal filtering case, mathematical model and architectural development of the generalized parallel 1-D filtering algorithms are presented using the 1-D block filtering method. Five generic architectures are implemented on a Virtex-6 ML605 board, evaluated and compared. A complete set of results on area, speed, power, throughput and computation rates are obtained and discussed as performance indices for the 1-D convolution architectures. A successful application of parallel 1-D cross-correlation is demonstrated. For two dimensional greyscale/colour image processing cases, new parallel 2-D/3-D filtering algorithms are presented and mathematically modelled using input decimation and output image reconstruction by interpolation. Ten generic architectures are implemented on the Virtex-6 ML605 board, evaluated and compared. Key results on area, speed, power, throughput and computation rate are obtained and discussed as performance indices for the 2-D convolution architectures. 2-D image reconfigurable processors are developed and implemented using single, dual and quad MAC FIR units. 3-D Colour image processors are devised to act as 3-D colour filtering engines. A 2-D cross-correlator parallel engine is successfully developed as a parallel 2-D matched filtering algorithm for locating any MRI slice within a MRI data stack library. Twelve 3-D MRI filtering operators are plugged in and adapted to be suitable for biomedical imaging, including 3-D edge operators and 3-D noise smoothing operators. Since three dimensional greyscale/colour volumetric image applications are computationally intensive, a new parallel 3-D/4-D filtering algorithm is presented and mathematically modelled using volumetric data image segmentation by decimation and output reconstruction by interpolation, after simultaneously and independently performing 3-D filtering. Eight generic architectures are developed and implemented on the Virtex-6 board, including 3-D spatial and FFT convolution architectures. Fourteen 3-D MRI filtering operators are plugged and adapted for this particular biomedical imaging application, including 3-D edge operators and 3-D noise smoothing operators. Three successful applications are presented in 4-D colour MRI (fMRI) filtering processors, k-space MRI volume data filter and 3-D cross-correlator.IRAQI Government

Dwuetapowa metoda eksploracji danych pozyskiwanych z obrazów cyfrowych

The main aim of this work is to develop a two-step method of extracting knowledge from digital images. The method integrates the analysis of digital images directed at the extraction of quantitative and qualitative characteristics and knowledge extraction methods. The proposed method let to conduct a new kind of exploratory research aimed at extracting knowledge from digital images. This method allows to create rule set knowledge bases for decision support systems. Analysis of a coherent series of images allows the extraction of interesting qualitative and quantitative features. Put their further exploratory analysis may allow for the discovery of regularities occurring, generalizations, connections and relationships. The paper presents the research literature on both the available algorithms analysis and processing images as well as methods of exploration of knowledge. Realized computer program implements the proposed method, while providing the environment of its experimental verification. Checks on the correctness of the system were carried out on real data: computer tomographic images of the surface of the tooth, dermatoscopic skin cancer images and microscopic images of Friction Stir Welding joints. The proposed method of data mining of digital images was divided into two steps. The first step uses the selected methods of image analysis, focused on the extraction of quantitative and qualitative characteristics of objects presented on images. The basic input data for the selected methods are a series of images depicting the analyzed objects. At the step of extraction of characteristics: • Determined their number, type, the names of the attributes and names or ranges of features. • Establish a set of graphical transformations to be carried images to their standardization and to obtain the required characteristics. The result of this stage are data table - information system. These data are subjected to pre-processing, covering the processing of missing, outliers, digitization data. Pre-processed data make decision table. In the table determined attribute decision-making and conditional attributes. The decision table is input for the second step of the proposed method. This step involves data mining, ended generating decision rules. This process is preceded by an analysis of the consistency of the two available methods: qualitative and quantitative. Data mining is based on an approach based on rough set theory. The result of exploratory research are the decision rules generated by various methods. Cross validation allows to carry out the quality of the method. At each stage it is possible to support the use of domain experts who are using a dedicated system could verify the results obtained with regard to input images. The last element of the method is the ability to use established knowledge base for the implementation of subject decision support system. This is done by inference forward module. The inference can be used for both practical and experimental verification of the received rule base, as well as to achieve ready to implement the user system. The proposed method can maximize the automation capabilities of acquiring knowledge of the images, while allowing for the use of knowledge and competence domain experts