To assure the parallel implementation of an algorithm performs to its maximum potential, a knowledge of the specific parallel machine being used is required. Mapping gray-scale morphological operators and a filter in ...
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To assure the parallel implementation of an algorithm performs to its maximum potential, a knowledge of the specific parallel machine being used is required. Mapping gray-scale morphological operators and a filter in a SIMD, a MIMD, and a mixed-mode environment is analyzed. The matching of several algorithmic techniques and machine features are examined analytically and experimentally. Issues considered include concurrent execution of subtasks, data layout, choice of data transfer protocols, and the mode of parallelism used. Experiments are performed using the MIMD Intel Paragon, SIMD MasPar MP-I, and the mixed-mode PASM prototype. The analytical results and experimental procedures can be applied to other systems as well.
In this paper, we present the design and evaluation of a compiler system, called APE, for automatic parallelization of scientific and engineering applications on distributed memory computers. APE is built on top of SU...
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In this paper, we present the design and evaluation of a compiler system, called APE, for automatic parallelization of scientific and engineering applications on distributed memory computers. APE is built on top of SUIF compiler. It extends SUIF with capabilities in parallelizing loops with non-uniform cross-iteration dependencies, and in handling loops that have indirect access patterns. We have evaluated the effectiveness of SUIF with several CFD test codes, and found that SUIF handles uniform loops over dense and regular data structures very well. For non-uniform loops, an innovative and efficient parallelization approach based on convex theory have been proposed and is being implemented. We have also presented a class of scalable algorithms for parallel distribution and redistribution of unstructured data structures during parallelizing irregular loops.
A novel 3D motion compensation algorithm for the coding of 3D medical image sequences is presented. The temporal correlation existing between successive in time 3D data sets is exploited using a 3D cube matching algor...
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A novel 3D motion compensation algorithm for the coding of 3D medical image sequences is presented. The temporal correlation existing between successive in time 3D data sets is exploited using a 3D cube matching algorithm (CMA). In order to cope with more complex motions and to adapt compensation to the high-activity regions, a 3D warping based motion estimation technique is introduced. The methods are evaluated experimentally for the coding of an 3D image sequence of a beating heart.
A new, robust and computationally attractive approach to the problem of time series classification is discussed in this paper. Both the Bayesian as well as a new adaptive classification scheme for source selection are...
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A new, robust and computationally attractive approach to the problem of time series classification is discussed in this paper. Both the Bayesian as well as a new adaptive classification scheme for source selection are discussed. Simulation results are included to demonstrate the effectiveness of the new methodology.
Methods are proposed for coding of the depth map and disparity fields for stereo or multiview image communication applications. Block-based and wireframe modeling techniques are examined for the coding of isolated dep...
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Methods are proposed for coding of the depth map and disparity fields for stereo or multiview image communication applications. Block-based and wireframe modeling techniques are examined for the coding of isolated depth map information and 2-D and 3-D motion compensation techniques for the coding of depth map sequences are evaluated.
Hierarchical prioritized predictive image coding methods are presented for progressive image transmission. The three main coder stages are hierarchical transform, prioritized coefficient coding and adaptive multiple d...
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Hierarchical prioritized predictive image coding methods are presented for progressive image transmission. The three main coder stages are hierarchical transform, prioritized coefficient coding and adaptive multiple distribution entropy coding (AMDEC). Following a hierarchical subband decomposition of the original image, the quantized coefficients are scalar quantized and then coded using novel hierarchical partition priority coding (HPPC) and predictive HPPC (PHPPC) algorithms. Given a suitable partitioning of their absolute range, the quantized detail coefficients are ordered based on both their decomposition level and partition and, then, are DPCM coded along with the corresponding address map. The use of space filling scanning further reduces the coding cost. Finally, AMDEC is applied to the HPPC/PHPPC output. Experimental results demonstrate the performance of the proposed compression methods.
A novel approach is proposed for modeling speech parameter variations between neutral and stressed conditions and employed in a technique for stressed speech synthesis. The proposed method consists of modeling the var...
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A novel approach is proposed for modeling speech parameter variations between neutral and stressed conditions and employed in a technique for stressed speech synthesis. The proposed method consists of modeling the variations in pitch contour, voiced speech duration and average spectral structure using hidden Markov models (HMMs). While HMMs have traditionally been used for recognition applications, here they are used to statistically model the characteristics needed for generating pitch contour and spectral slope patterns to modify the speaking style of isolated neutral words. An algorithm is developed based on an analysis-synthesis speech model, and HMM pitch and spectral stress characteristics for stress perturbation. Informal listener evaluations of the stress-modified speech confirm the HMM's ability to capture the parameter variations under stressed conditions. The proposed HMM models are both speaker- and word-independent, but unique to each speaking style. While the modeling scheme is applicable to a variety of stress and emotional speaking styles, the evaluations presented in this study focus on angry, Lombard-effect and loud-spoken speech.
In this paper, we present a new system to segment and label CT brain slices using a self-organizing Kohonen network. Our aim is to extract reliable and robust measures from CT images of Traumatic Brain Injury (TBI) pa...
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In this paper, we present a new system to segment and label CT brain slices using a self-organizing Kohonen network. Our aim is to extract reliable and robust measures from CT images of Traumatic Brain Injury (TBI) patients that can accurately describe the morphological changes in the brain as recovery progresses. Segmentation is performed by assigning a feature pattern to each voxel, consisting of a scaled family of differential geometrical invariant features. The invariant feature pattern is input to Kohonen network for an unsupervised classification of the voxels into regions.
The detection of spatio-temporal scalp EEG patterns associated with voluntary motion preparation towards the development of a brain-computer interface (BCI) is explored. The rationale for the use of a spatio-temporal ...
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The detection of spatio-temporal scalp EEG patterns associated with voluntary motion preparation towards the development of a brain-computer interface (BCI) is explored. The rationale for the use of a spatio-temporal approach to this detection problem is explained. The need for a temporal or dynamic classifier is confirmed by demonstration of the lack of robustness in static neural network classifiers with respect to time alignment of the patterns under analysis. The results from dynamic classifiers, such as the Time Delay Neural Network (TDNN) and the Gamma Neural Network are presented in terms of their Receiver Operating Characteristic (ROC) Curves.
The existence of characteristic changes in the EEG of a subject preparing for the execution of a voluntary movement is reviewed. Some of those changes, i.e., the "readiness potentials" (RPs), are suggested a...
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The existence of characteristic changes in the EEG of a subject preparing for the execution of a voluntary movement is reviewed. Some of those changes, i.e., the "readiness potentials" (RPs), are suggested as a possible link between the internal processes associated with different forms of motion preparation and a digital signal processing system designed to classify the different intended movements. Such a system can potentially be used to provide input to a specialized graphic user interface, constituting a brain-computer interface (BCI). Our current work on neural-network classification of RPs is summarized and the results are presented.
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