14935665
Optimal Matrix Computing Using Vector Division with Sub-word Parallel
Available computing is expected to be effective and efficient in ubiquitous multimedia services. And, matrix multiplication is particular to ubiquitous computing such as image transformation so that it is urgent to exploit potential parallelism as well as data reuse in matrix multiplication. Therefore, based on synchronized data triggered architecture (SDTA), we propose a method of parallel vector division (PVD) as well as sub-word parallel (SWP) supported on SDTA to optimize the matrix computation on specified architecture. Experimental results show that PVD with SWP is suitable for matrix multiplication on SDTA instruction set architecture. Besides, we could extend the proposed model into other data-intensive computing on SDTA.
17498280
A Hardware Approach for Reducing Interpretation Overhead
Abstract—An interpreter reads instructions of the source executable and perform each corresponding operation in turn on a software-maintained version of the old ISA’s state. Interpretation has emerged as an important tool with real world applications. Interpretation is always adopted to support instruction set emulation and handle the non-hotspot code in a two-stage dynamic binary translation strategy. An important consideration of an interpreter is the runtime overhead. We investigate that repeated re-decode operations are the bottleneck of interpretation overhead. We propose interpreted code cache (Pcache), a hardware assist, to save the information of the decoded instruction for reuse. We analyze and model Pcache performance via a real interpretation system. Results from SPEC2000 integer benchmarks show that Pcache could significantly reduce re-decode operations and the overhead of interpretation. We demonstrate that the hardware Pcache is a highly effective approach, and the extra overhead is neglectable compared to the performance gains.
33440658
A New Technique for Program Code Compression in Embedded Microprocessor
Program code compression has become a critical technology nowadays, and it can promote the performance of microprocessor systems. But compression techniques nowadays can hardly achieve both satisfactory compression ratio and low hardware complexity. A new program code compression method is proposed in this paper. Experiment results show that the code sizes of ARM and OR1200 microprocessor can be efficiently reduced by 32.2% and 36.9% individually with this algorithm, resulting to a sharp decrease in memory traffic. A hardware decompressing prototype is also presented, revealing its advantage in ultra high speed decompression and low hardware complexity.
6228733
Shape Analysis and Spatio-Temporal Tracking of Mesoscale Eddies in Miami Isopycnic Coordinate Ocean Model
Detection and analysis of ocean surface phenomena have so far relied on manual analysis of long sequences of satellite images or images produced from the mathematical models. In this paper a technique for the three-dimensional shape analysis and spato-temporall tracking of mesoscale eddies from MICOM dataset is presented. Mesoscale eddies play a strong role in carrying heat poleward. The shape analysis of eddies provides a volumetric estimation which can be further used to determine the heat trapped in eddies. Eddies are detected and the region of swirling currents around the detected center is segmented. Segmented eddies are stacked to generate 3D visualization. A 3D skeleton is obtained with the centroids of all the layers in the segmented eddy and represented parametrically. Coefficients of the parametric representation are used for motion tracking of eddies.
10374803
Hemispherical Harmonic Surface Description and Applications to Medical Image Analysis
The use of surface harmonics for rigid and nonrigid shape description is well known. In this paper we define a set of complete hemispherical harmonic basis functions on a hemisphere domain and propose a novel parametric shape description method to efficiently and flexibly represent the surfaces of anatomical structures in medical images. As the first application of hemispherical harmonic theory in shape description, our technique differs from the previous surface harmonics shape descriptors, all of which don't work efficiently on the hemisphere-like objects that often exist in medical anatomical structures (e.g., ventricles, atriums, etc.). We demonstrate the effectiveness of our approach through theoretic and experimental exploration of a set of medical image applications. Furthermore, an evaluation criterion for surface modeling efficiency is described and the comparison results demonstrated that our method outperformed the previous approaches using spherical harmonic models.
8270756
MetaDL: A Digital Library of Metadata for Sensitive or Complex Research Data
Traditional digital library systems have certain limitations when dealing with complex or sensitive (e.g. proprietary) data. Collections of digital libraries have to be accessed individually and through non-uniform interfaces. By introducing a level of abstraction, a Meta-Digital Library or MetaDL, users gain a central access portal that allows for prioritized queries, evaluation and rating of the results, and secure negotiations to obtain primary data. This paper demonstrates the MetaDL architecture with an application in brain imaging research, BrassDL, the Brain Support Access System Digital Library. BrassDL is currently under development. This paper describes a theoretical framework behind it, addressing aspects from metadata extraction and system-supported negotiations to legal, ethical and sustainability issues.
17743882
Morphometric Analysis of Brain Structures for Improved Discrimination
We perform discriminative analysis of brain structures using morphometric information. Spherical harmonics technique and point distribution model are used for shape description. Classification is performed using linear discriminants and support vector machines with several feature selection approaches. We consider both inclusion and exclusion of volume information in the discrimination. We perform extensive experimental studies by applying different combinations of techniques to hippocampal data in schizophrenia and achieve best jackknife classification accuracies of 95% (whole set) and 90% (right-handed males), respectively. Our results find that the left hippocampus is a better predictor than the right in the complete dataset, but that the right hippocampus is a stronger predictor than the left in the right-handed male subset. We also propose a new method for visualization of discriminative patterns.
31075540
Reducing multiclass cancer classification to binary by output coding and SVM
Multiclass cancer classification based on microarray data is presented. The binary classifiers used combine support vector machines with a generalized output-coding scheme. Different coding strategies, decoding functions and feature selection methods are incorporated and validated on two cancer datasets: GCM and ALL. Using random coding strategy and recursive feature elimination, the testing accuracy achieved is as high as 83% on GCM data with 14 classes. Comparing with other classification methods, our method is superior in classificatory performance.