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PubMed2005

Using the GRID to improve the computation speed of electrical impedance tomography (EIT) reconstruction algorithms.

In our group at University College London, we have been developing electrical impedance tomography (EIT) of brain function. We have attempted to improve image quality by the use of realistic anatomical meshes and, more recently, non-linear reconstruction methods. Reconstruction with linear methods, with pre-processing, may take up to a few minutes per image for even detailed meshes. However, iterative non-linear reconstruction methods require much more computational resources, and reconstruction with detailed meshes was taking far too long for clinical use. We present a solution to this timing bottleneck, using the resources of the GRID, the development of coordinated computing resources over the internet that are not subject to centralized control using standard, open, general-purpose protocols and are transparent to the user. Optimization was performed by splitting reconstruction of image series into individual jobs of one image each; no parallelization was attempted. Using the GRID middleware 'Condor' and a cluster of 920 nodes, reconstruction of EIT images of the human head with a non-linear algorithm was speeded up by 25-40 times compared to serial processing of each image. This distributed method is of direct practical value in applications such as EIT of epileptic seizures where hundreds of images are collected over the few minutes of a seizure and will be of value to clinical data collection with similar requirements. In the future, the same resources could be employed for the more ambitious task of parallelized code.

Algorithms↗
PubMed2003

FAIR: a hardware architecture for real-time 3-D image registration.

Mutual information-based image registration, shown to be effective in registering a range of medical images, is a computationally expensive process, with a typical execution time on the order of minutes on a modern single-processor computer. Accelerated execution of this process promises to enhance efficiency and therefore promote routine use of image registration clinically. This paper presents details of a hardware architecture for real-time three-dimensional (3-D) image registration. Real-time performance can be achieved by setting up a network of processing units, each with three independent memory buses: one each for the two image memories and one for the mutual histogram memory. Memory access parallelization and pipelining, by design, allow each processing unit to be 25 times faster than a processor with the same bus speed, when calculating mutual information using partial volume interpolation. Our architecture provides superior per-processor performance at a lower cost compared to a parallel supercomputer.

Algorithms↗
PubMed2003

A biomolecular computing method based on Rho family GTPases.

In this paper, we propose a new biomolecular computing method based on Rho family GTPases, and discuss the schemes of representation and operations of molecular computing by Rho family GTPases applied to solve large-scale 3-SAT problems. We also present the optimal condition for the regulation schemes dependent on the temperature, kinase activity, and types of cells. This work is important for potential implementation of biomolecular computers using Rho family GTPases in which an optimized controlling scheme can make the best use of the interactions of signaling pathways in a computing system made by the large-scale abundance of kinases and phosphatases in cells.

Algorithms↗
PubMed2005

Fast parallel molecular algorithms for DNA-based computation: factoring integers.

The RSA public-key cryptosystem is an algorithm that converts input data to an unrecognizable encryption and converts the unrecognizable data back into its original decryption form. The security of the RSA public-key cryptosystem is based on the difficulty of factoring the product of two large prime numbers. This paper demonstrates to factor the product of two large prime numbers, and is a breakthrough in basic biological operations using a molecular computer. In order to achieve this, we propose three DNA-based algorithms for parallel subtractor, parallel comparator, and parallel modular arithmetic that formally verify our designed molecular solutions for factoring the product of two large prime numbers. Furthermore, this work indicates that the cryptosystems using public-key are perhaps insecure and also presents clear evidence of the ability of molecular computing to perform complicated mathematical operations.

Algorithms↗
PubMed2004

Hidden space support vector machines.

Hidden space support vector machines (HSSVMs) are presented in this paper. The input patterns are mapped into a high-dimensional hidden space by a set of hidden nonlinear functions and then the structural risk is introduced into the hidden space to construct HSSVMs. Moreover, the conditions for the nonlinear kernel function in HSSVMs are more relaxed, and even differentiability is not required. Compared with support vector machines (SVMs), HSSVMs can adopt more kinds of kernel functions because the positive definite property of the kernel function is not a necessary condition. The performance of HSSVMs for pattern recognition and regression estimation is also analyzed. Experiments on artificial and real-world domains confirm the feasibility and the validity of our algorithms.

Algorithms↗
PubMed2005

Incremental training of support vector machines.

We propose a new algorithm for the incremental training of support vector machines (SVMs) that is suitable for problems of sequentially arriving data and fast constraint parameter variation. Our method involves using a "warm-start" algorithm for the training of SVMs, which allows us to take advantage of the natural incremental properties of the standard active set approach to linearly constrained optimization problems. Incremental training involves quickly retraining a support vector machine after adding a small number of additional training vectors to the training set of an existing (trained) support vector machine. Similarly, the problem of fast constraint parameter variation involves quickly retraining an existing support vector machine using the same training set but different constraint parameters. In both cases, we demonstrate the computational superiority of incremental training over the usual batch retraining method.

Algorithms↗
PubMed2005

Data classification with radial basis function networks based on a novel kernel density estimation algorithm.

This paper presents a novel learning algorithm for efficient construction of the radial basis function (RBF) networks that can deliver the same level of accuracy as the support vector machines (SVMs) in data classification applications. The proposed learning algorithm works by constructing one RBF subnetwork to approximate the probability density function of each class of objects in the training data set. With respect to algorithm design, the main distinction of the proposed learning algorithm is the novel kernel density estimation algorithm that features an average time complexity of O(n log n), where n is the number of samples in the training data set. One important advantage of the proposed learning algorithm, in comparison with the SVM, is that the proposed learning algorithm generally takes far less time to construct a data classifier with an optimized parameter setting. This feature is of significance for many contemporary applications, in particular, for those applications in which new objects are continuously added into an already large database. Another desirable feature of the proposed learning algorithm is that the RBF networks constructed are capable of carrying out data classification with more than two classes of objects in one single run. In other words, unlike with the SVM, there is no need to resort to mechanisms such as one-against-one or one-against-all for handling datasets with more than two classes of objects. The comparison with SVM is of particular interest, because it has been shown in a number of recent studies that SVM generally are able to deliver higher classification accuracy than the other existing data classification algorithms. As the proposed learning algorithm is instance-based, the data reduction issue is also addressed in this paper. One interesting observation in this regard is that, for all three data sets used in data reduction experiments, the number of training samples remaining after a naive data reduction mechanism is applied is quite close to the number of support vectors identified by the SVM software. This paper also compares the performance of the RBF networks constructed with the proposed learning algorithm and those constructed with a conventional cluster-based learning algorithm. The most interesting observation learned is that, with respect to data classification, the distributions of training samples near the boundaries between different classes of objects carry more crucial information than the distributions of samples in the inner parts of the clusters.

Algorithms↗
PubMed2005

Sequential information processing using time-delay connections in ontogenic CALM networks.

In this paper, a variant of the categorization-and-learning-module (CALM) network is presented that is not only capable of categorizing sequential information with feedback, but can adapt its resources to the current training set. In other words, the modules of the network may grow or shrink depending on the complexity of the presented sequence-set. In the original CALM algorithm, modules did not have access to activations from earlier stimulus presentations. To bypass this limitation, we introduced time-delay connections in CALM. These connections allow for a delayed propagation of activation, such that information at a given time will be available to a module at a later timestep. In addition, modules can autonomously add and remove resources depending on the structure and complexity of the task domain. The performance of this ontogenic CALM network with time-delay connections is demonstrated and analyzed using a sample set of overlapping sequences from an existing problem domain.

Algorithms↗
PubMed2005

Estimating optimal feature subsets using efficient estimation of high-dimensional mutual information.

A novel feature selection method using the concept of mutual information (MI) is proposed in this paper. In all MI based feature selection methods, effective and efficient estimation of high-dimensional MI is crucial. In this paper, a pruned Parzen window estimator and the quadratic mutual information (QMI) are combined to address this problem. The results show that the proposed approach can estimate the MI in an effective and efficient way. With this contribution, a novel feature selection method is developed to identify the salient features one by one. Also, the appropriate feature subsets for classification can be reliably estimated. The proposed methodology is thoroughly tested in four different classification applications in which the number of features ranged from less than 10 to over 15,000. The presented results are very promising and corroborate the contribution of the proposed feature selection methodology.

Algorithms↗
PubMed2005

A new class of wavelet networks for nonlinear system identification.

A new class of wavelet networks (WNs) is proposed for nonlinear system identification. In the new networks, the model structure for a high-dimensional system is chosen to be a superimposition of a number of functions with fewer variables. By expanding each function using truncated wavelet decompositions, the multivariate nonlinear networks can be converted into linear-in-the-parameter regressions, which can be solved using least-squares type methods. An efficient model term selection approach based upon a forward orthogonal least squares (OLS) algorithm and the error reduction ratio (ERR) is applied to solve the linear-in-the-parameters problem in the present study. The main advantage of the new WN is that it exploits the attractive features of multiscale wavelet decompositions and the capability of traditional neural networks. By adopting the analysis of variance (ANOVA) expansion, WNs can now handle nonlinear identification problems in high dimensions.

Algorithms↗
PubMed2006

Associative memory design using support vector machines.

The relation existing between support vector machines (SVMs) and recurrent associative memories is investigated. The design of associative memories based on the generalized brain-state-in-a-box (GBSB) neural model is formulated as a set of independent classification tasks which can be efficiently solved by standard software packages for SVM learning. Some properties of the networks designed in this way are evidenced, like the fact that surprisingly they follow a generalized Hebb's law. The performance of the SVM approach is compared to existing methods with nonsymmetric connections, by some design examples.

Algorithms↗
PubMedDate not supplied

Extensions of the Zwart-Powell box spline for volumetric data reconstruction on the cartesian lattice.

In this article we propose a box spline and its variants for reconstructing volumetric data sampled on the Cartesian lattice. In particular we present a tri-variate box spline reconstruction kernel that is superior to tensor product reconstruction schemes in terms of recovering the proper Cartesian spectrum of the underlying function. This box spline produces a C2 reconstruction that can be considered as a three dimensional extension of the well known Zwart-Powell element in 2D. While its smoothness and approximation power are equivalent to those of the tri-cubic B-spline, we illustrate the superiority of this reconstruction on functions sampled on the Cartesian lattice and contrast it to tensor product B-splines. Our construction is validated through a Fourier domain analysis of the reconstruction behavior of this box spline. Moreover, we present a stable method for evaluation of this box spline by means of a decomposition. Through a convolution, this decomposition reduces the problem to evaluation of a four directional box spline that we previously published in its explicit closed form.

Computer Graphics↗
PubMedDate not supplied

Progressive volume rendering of large unstructured grids.

We describe a new progressive technique that allows real-time rendering of extremely large tetrahedral meshes. Our approach uses a client-server architecture to incrementally stream portions of the mesh from a server to a client which refines the quality of the approximate rendering until it converges to a full quality rendering. The results of previous steps are re-used in each subsequent refinement, thus leading to an efficient rendering. Our novel approach keeps very little geometry on the client and works by refining a set of rendered images at each step. Our interactive representation of the dataset is efficient, light-weight, and high quality. We present a framework for the exploration of large datasets stored on a remote server with a thin client that is capable of rendering and managing full quality volume visualizations.

Computer Graphics↗
PubMed2006

Spectral embedding finds meaningful (relevant) structure in image and microarray data.

BACKGROUND: Accurate methods for extraction of meaningful patterns in high dimensional data have become increasingly important with the recent generation of data types containing measurements across thousands of variables. Principal components analysis (PCA) is a linear dimensionality reduction (DR) method that is unsupervised in that it relies only on the data; projections are calculated in Euclidean or a similar linear space and do not use tuning parameters for optimizing the fit to the data. However, relationships within sets of nonlinear data types, such as biological networks or images, are frequently mis-rendered into a low dimensional space by linear methods. Nonlinear methods, in contrast, attempt to model important aspects of the underlying data structure, often requiring parameter(s) fitting to the data type of interest. In many cases, the optimal parameter values vary when different classification algorithms are applied on the same rendered subspace, making the results of such methods highly dependent upon the type of classifier implemented. RESULTS: We present the results of applying the spectral method of Lafon, a nonlinear DR method based on the weighted graph Laplacian, that minimizes the requirements for such parameter optimization for two biological data types. We demonstrate that it is successful in determining implicit ordering of brain slice image data and in classifying separate species in microarray data, as compared to two conventional linear methods and three nonlinear methods (one of which is an alternative spectral method). This spectral implementation is shown to provide more meaningful information, by preserving important relationships, than the methods of DR presented for comparison. Tuning parameter fitting is simple and is a general, rather than data type or experiment specific approach, for the two datasets analyzed here. Tuning parameter optimization is minimized in the DR step to each subsequent classification method, enabling the possibility of valid cross-experiment comparisons. CONCLUSION: Results from the spectral method presented here exhibit the desirable properties of preserving meaningful nonlinear relationships in lower dimensional space and requiring minimal parameter fitting, providing a useful algorithm for purposes of visualization and classification across diverse datasets, a common challenge in systems biology.

Algorithms↗
PubMed2002

Virtual medical devices.

Virtual medical devices are set to have a major impact on the industry. This article explains the practicalities and benefits of a new approach to designing and constructing many types of medical devices.

Algorithms↗
PubMedDate not supplied

Scalable data servers for large multivariate volume visualization.

Volumetric datasets with multiple variables on each voxel over multiple time steps are often complex, especially when considering the exponentially large attribute space formed by the variables in combination with the spatial and temporal dimensions. It is intuitive, practical, and thus often desirable, to interactively select a subset of the data from within that high-dimensional value space for efficient visualization. This approach is straightforward to implement if the dataset is small enough to be stored entirely in-core. However, to handle datasets sized at hundreds of gigabytes and beyond, this simplistic approach becomes infeasible and thus, more sophisticated solutions are needed. In this work, we developed a system that supports efficient visualization of an arbitrary subset, selected by range-queries, of a large multivariate time-varying dataset. By employing specialized data structures and schemes of data distribution, our system can leverage a large number of networked computers as parallel data servers, and guarantees a near optimal load-balance. We demonstrate our system of scalable data servers using two large time-varying simulation datasets.

Computer Graphics↗
PubMed1985

A computer-assisted electrocardiographic analysis system: methodology and potential application to cardiovascular toxicology.

An automated analysis of electrocardiographic (ECG) waveforms, based on a precise one-dimensional analysis of features within a generalized computer-enhanced ECG waveform, has been developed in our laboratory. ECG signals are monitored, amplified, and recorded using standard techniques. The recorder output signal is distributed to a microcomputer system. Software developed for the microcomputer slows the signal playback rate and permits operator review of the slowed signal for arrhythmia analysis. The analysis program identifies and superimposes 10-40 individual ECG complexes, depending on the heart rate, and generates an "ensembled" waveform. Operator interaction permits delineation of specific points on the displayed waveform and calculation of heart rate and duration of components within the ECG complex. The primary advantages of this system include (1) extensive automation--computer support decreases analytical time, increases precision, and permits rapid screening of large numbers of animals; (2) enhanced sensitivity--the use of functional parameters should provide a more sensitive index of toxicity than morphological parameters; (3) broad utility--this system provides the capability to utilize a variety of animals, both anesthetized and unanesthetized, ranging in age from fetuses to geriatrics, and permits studies of block as well as longitudinal design; and (4) ease of replication--standardization of equipment and techniques facilitates replication by other laboratories.

Animals↗