6859814
Three-dimensional Models for Cardiac Bioelectricity Simulation: Cell to Organ
Computer models of the heart have become useful tools in improving understanding of cardiac electrical activation at the cellular level, and in analyzing mechanisms of arrhythmias and antiarrhythmic therapies. In this paper, we propose to use spherical harmonic (SPHARM) surface reconstruction and alignment methods to generate curvilinear meshes for finite element models of ventricular anatomy which incorporate detailed structural information. The endocardial and epicardial surfaces are defined by SPHARM, and the volume between these surfaces is divided into nested shells. Consequently, the resulting mesh comprises hexahedral elements. Using this novel SPHARM-based meshing method, the transmembrane potential propagation is simulated, based on FitzHugh—Nagumo reaction—diffusion equations. The dynamic electrical activation propagations are simulated on realistic cardiac data during a heart cycle. The obtained results clearly demonstrate an accurate resolution of the cardiac potential during the excitation. Our novel curvilinear mesh models have a great potential to be used in an improved simulation of cardiovascular pathologies for testing therapy strategies and planning interventions.
15158980
Large-Scale Modeling of Parametric Surfaces Using Spherical Harmonics
We present an approach for large-scale modeling of parametric surfaces using spherical harmonics (SHs). A standard least square fitting (LSF) method for SH expansion is not scalable and cannot accurately model large 3D surfaces. We propose an iterative residual fitting (IRF) algorithm, and demonstrate its effectiveness and scalability in creating accurate SH models for large 3D surfaces. These large-scale and accurate parametric models can be used in many applications in computer vision, graphics, and biomedical imaging. As a simple extension of LSF, IRF is very easy to implement and requires few machine resources.
2480556
Data Brokers: Building Collections through Automated Negotiation
Collecting digital materials is time-consuming and can gain from automation. Since each source – and even each acquisition – may involve a separate negotiation of terms, a collector may prefer to use a broker to represent his interests with owners. This paper describes the Data Broker Framework (DBF), which is designed to automate the process of digital object acquisition. For each acquisition, a negotiation agent is assigned to negotiate on the collector’s behalf, choosing from strategies in a strategy pool to automatically handle most bargaining cases and decide what to accept and what counteroffers to propose. We introduce NOODLE (Negotiation OntOlogy Description LanguagE) to formally specify terms in the negotiation domain.
6802677
A surface-based approach for classification of 3D neuroanatomic structures
We present a new framework for 3D surface object classification that combines a powerful shape description method with suitable pattern classification techniques. Spherical harmonic parameterization and normalization techniques are used to describe a surface shape and derive a dual high dimensional landmark representation. A point distribution model is applied to reduce the dimensionality. Fisher's linear discriminants and support vector machines are used for classification. Several feature selection schemes are proposed for learning better classifiers. After showing the effectiveness of this framework using simulated shape data, we apply it to real hippocampal data in schizophrenia and perform extensive experimental studies by examining different combinations of techniques. We achieve best leave-one-out cross-validation accuracies of 93% (whole set, N = 56) and 90% (right-handed males, N = 39), respectively, which are competitive with the best results in previous studies using different techniques on similar types of data. Furthermore, to help medical diagnosis in practice, we employ a threshold-free receiver operating characteristic (ROC) approach as an alternative evaluation of classification results as well as propose a new method for visualizing discriminative patterns.
14116656
The design and implementation of a program development system based on rewriting method
In this paper, we present a program development system based on rewriting method with the goal of providing an powerful tool for automatic software development and rapid prototyping. New mechanisms for defining constrained types and optimal rules are introduced to functional programming languages to achieve strong expressiveness. The enhanced functional programming language is combined with an algebraic specification language. Thus, the design from specification to program can be supported, and the efficiency and flexibility of programming can also be improved. In this system, both static and dynamic techniques are used to deal with constrained type check. All function definitions, computation constraints and optimal rules are regarded as rewriting rules. In order to provide a strong support for a large group of TRSs which may be neither terminating nor orthogonal, we propose a method using structure measure to decide TRSs' confluences. Based on this method, we present a partial completion algorithm to generate a rewriting model from a specification. Depending on term rewriting system, the mixed language system computes with parallel outermost and needed reduction. We explain the principles and implementation techniques in detail, some examples are provided.
33719729
New Algorithms for Efficient Mining of Association Rules
Abstract Discovery of association rules is an important data mining task. Several algorithms have been proposed to solve this problem. Most of them require repeated passes over the database, which incurs huge I/O overhead and high synchronization expense in parallel cases. There are a few algorithms trying to reduce these costs. But they contain weaknesses such as often requiring high pre-processing cost to get a vertical database layout, containing much redundant computation in parallel cases, and so on. We propose new association mining algorithms to overcome the above drawbacks, through minimizing the I/O cost and effectively controlling the computation cost. Experiments on well-known synthetic data show that our algorithms consistently outperform a priori, one of the best algorithms for association mining, by factors ranging from 2 to 4 in most cases. Also, our algorithms are very easy to be parallelized, and we present a parallelization for them based on a shared-nothing architecture. We observe that our parallelization develops the parallelism more sufficiently than two of the best existing parallel algorithms.
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.
13746374
A Generalized Output-Coding Scheme with SVM for Multiclass Microarray Classification
Multiclass cancer classification based on microarray data is described. A generalized output-coding scheme combined with binary classifiers is used. Different coding strategies, decoding functions and feature selection methods are combined and validated on two cancer datasets: GCM and ALL. The effects of these different methods and their combinations are then discussed. The highest testing accuracies achieved are 78% and 100% for the two datasets respectively. The results are considered to be very good when compared with the other researchers’ work.
74980
PLS and SVD based penalized logistic regression for cancer classification using microarray data
Accurate cancer prediction is important for treatment of cancers. The combination of two dimension reduction methods, partial least squares (PLS) and singular value decomposition (SVD), with the penalized logistic regression (PLR) has created powerful classifiers for cancer prediction using microarray data. Comparing with support vector machine (SVM) on seven publicly available cancer datasets, the new algorithms can achieve very good performance and run much faster. They also have the advantage that the probabilities of predictions can be directly given. PLS based PLR is also combined with recursive feature elimination (RFE) to select a 16-gene subset for acute leukemia cancer classification. The testing error on this subset of genes is empirically zero.
35408245
A quick method for synthesizing photorealistic color images under various illumination conditions
We propose a fast method to synthesize realistic images under novel lighting directions using three color images. In the method, we use a look-up table procedure to estimate the surface orientations, instead of complex computations. Moreover, we explain a way to separate the diffuse and specular reflection components from the three color images.
11412243
Customizing computation accelerators for extensible multi-issue processors with effective optimization techniques
Compared with single-issue general purpose processors (GPPs), extensible multi-issue/VLIW processors can exploit instruction - level parallelism, which are more suitable for computation intensive tasks. Moreover, they offer the ability of customizing computation accelerators for an application domain. In this paper, we present an automated methodology that customizes computation accelerators for the multi-issue/VLIW extensible processors, where several techniques are also proposed to optimize the design of an accelerator.