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PubMed1985

A new algorithm for voltage clamp by iteration: a learning control of a nonlinear neuronal system.

Voltage-clamp of excitable membrane allows the measurement of membrane currents associated with electrical potential changes across the membrane. However, it has been impossible in practice to apply the conventional analog feedback voltage-clamp circuits to single electrode voltage clamping in central neurons. The reason for this is that the feedback system becomes unstable because of the positive feedback required for compensation of capacitative loss through the wall of the microelectrode. Park et al. (1981) proposed a new iterative technique to solve this problem. It requires that the potential to be clamped repeats itself with little or no change. The amount of current needed to clamp the membrane potential is not determined at once, but in a step-wise, trial and error fashion in the course of a set of repetitions. Since the feedback loop is open in real time, the system has great stability, and this advantage can be exploited in single electrode preparations. The computation algorithm which calculates the current waveform based on the voltage deviation during the last trial is the central part of the iterative voltage-clamp system. In this paper, we propose a new algorithm, which has several theoretical and practical advantages over the original one proposed by Park et al. First, two parameters used in the new algorithm are predetermined by a current-clamp experiment. Second, the speed of convergence of the new algorithm is faster than that of the Park's original algorithm. This was shown by computer simulation of iterative voltage clamp of artificial membrane following Hodgkin-Huxley equations for squid axon membrane and Rall's compartment model for a neuron with dendrites. Finally, we offer proof that the new algorithm is certain to converge for the general cases of voltage-clamp experiments with active membrane properties, synaptic membranes, etc. Consequently, the new algorithm for iterative voltage clamp is very suitable for single electrode voltage clamp in the central neurons. The new algorithm has been successfully applied to voltage-clamp experiments on rubrospinal neurons of cats (Tsukahara, Murakami, Kawato, Oda, and Etoh, in preparation).

Animals↗

PubMed1985

Identification and estimation algorithm for stochastic neural system. II.

The algorithm for identifying the stochastic neural system and estimating the system process which reflects the dynamics of the neural network are presented in this paper. The analogous algorithm has been proposed in our preceding paper (Nakao et al., 1984), which was based on the randomly missed observations of a system process only. Since the previous algorithm mentioned above was subject to an unfavorable effect of consecutively missed observations, to reduce such an effect the algorithm proposed here is designed additionally to observe an intensity process in a neural spike train as the information for the estimation. The algorithm is constructed with the extended Kalman filters because it is naturally expected that a nonlinear and time variant structure is necessary for the filters to realize the observation of an intensity process by means of mapping from a system process to an intensity process. The performance of the algorithm is examined by applying it to some artificial neural systems and also to cat's visual nervous systems. The results in these applications are thought to prove the effectiveness of the algorithm proposed here and its superiority to the algorithm proposed previously.

Animals↗

PubMed2005

Experimental implementation of local adiabatic evolution algorithms by an NMR quantum information processor.

Quantum adiabatic algorithm is a method of solving computational problems by evolving the ground state of a slowly varying Hamiltonian. The technique uses evolution of the ground state of a slowly varying Hamiltonian to reach the required output state. In some cases, such as the adiabatic versions of Grover's search algorithm and Deutsch-Jozsa algorithm, applying the global adiabatic evolution yields a complexity similar to their classical algorithms. However, using the local adiabatic evolution, the algorithms given by J. Roland and N.J. Cerf for Grover's search [J. Roland, N.J. Cerf, Quantum search by local adiabatic evolution, Phys. Rev. A 65 (2002) 042308] and by Saurya Das, Randy Kobes, and Gabor Kunstatter for the Deutsch-Jozsa algorithm [S. Das, R. Kobes, G. Kunstatter, Adiabatic quantum computation and Deutsh's algorithm, Phys. Rev. A 65 (2002) 062301], yield a complexity of order N (where N=2(n) and n is the number of qubits). In this paper, we report the experimental implementation of these local adiabatic evolution algorithms on a 2-qubit quantum information processor, by Nuclear Magnetic Resonance.

Journal Article↗

PubMed2000

Innovation sequence application to aircraft sensor fault detection: comparison of checking covariance matrix algorithms

In this paper, the algorithms verifying the covariance matrix of the Kalman filter innovation sequence are compared with respect to detected minimum fault rate and detection time. Four algorithms are dealt with; the algorithm verifying the trace of the covariance matrix of the innovation sequence, the algorithm verifying the sum of all elements of the inverse covariance matrix of the innovation sequence, the optimal algorithm verifying the ratio of two quadratic forms of which matrices are theoretic and selected covariance matrices of Kalman filter innovation sequence, and the algorithm verifying the generalized variance of the covariance matrix of the innovation sequence. The algorithms are implemented for longitudinal dynamics of an aircraft to detect sensor faults, and some suggestions are given on the use of the algorithms in flight control systems.

Journal Article↗

PubMed1998

A second-order learning algorithm for multilayer networks based on block Hessian matrix.

This article proposes a new second-order learning algorithm for training the multilayer perceptron (MLP) networks. The proposed algorithm is a revised Newton's method. A forward-backward propagation scheme is first proposed for network computation of the Hessian matrix, H, of the output error function of the MLP. A block Hessian matrix, H(b), is then defined to approximate and simplify H. Several lemmas and theorems are proved to uncover the important properties of H and H(b), and verify the good approximation of H(b) to H; H(b) preserves the major properties of H. The theoretic analysis leads to the development of an efficient way for computing the inverse of H(b) recursively. In the proposed second-order learning algorithm, the least squares estimation technique is adopted to further lessen the local minimum problems. The proposed algorithm overcomes not only the drawbacks of the standard backpropagation algorithm (i.e. slow asymptotic convergence rate, bad controllability of convergence accuracy, local minimum problems, and high sensitivity to learning constant), but also the shortcomings of normal Newton's method used on the MLP, such as the lack of network implementation of H, ill representability of the diagonal terms of H, the heavy computation load of the inverse of H, and the requirement of a good initial estimate of the solution (weights). Several example problems are used to demonstrate the efficiency of the proposed learning algorithm. Extensive performance (convergence rate and accuracy) comparisons of the proposed algorithm with other learning schemes (including the standard backpropagation algorithm) are also made.

Journal Article↗

PubMed2000

The influence of the regularization parameter and the first estimate on the performance of tikhonov regularized non-linear image restoration algorithms

This paper reports studies on the influence of the regularization parameter and the first estimate on the performance of iterative image restoration algorithms. We discuss regularization parameter estimation methods that have been developed for the linear Tikhonov-Miller filter to restore images distorted by additive Gaussian noise. We have performed experiments on synthetic data to show that these methods can be used to determine the regularization parameter of non-linear iterative image restoration algorithms, which we use to restore images contaminated by Poisson noise. We conclude that the generalized cross-validation method is an efficient method to determine a value of the regularization parameter close to the optimal value. We have also derived a method to estimate the regularization parameter of a Tikhonov regularized version of the Richardson-Lucy algorithm. These iterative image restoration algorithms need a first estimate to start their iteration. An obvious and frequently used choice for the first estimate is the acquired image. However, the restoration algorithm could be sensitive to the noise present in this image, which may hamper the convergence of the algorithm. We have therefore compared various choices of first estimates and tested the convergence of various iterative restoration algorithms. We found that most algorithms converged for most choices, but that smoothed first estimates resulted in a faster convergence.

Journal Article↗

PubMed2002

Parallel Monte Carlo simulations using a residence weight algorithm.

A parallel Monte Carlo (MC) algorithm, the residence weight algorithm, is proposed. This algorithm is an extension to continuous ensembles of the residence time algorithm. The proposed algorithm consists of generating several trial MC moves in parallel, selecting one of them using appropriate relative probabilities and correcting the non-Boltzmannian sampling procedure by means of appropriate configuration weights. The corresponding parallel Boltzmannian scheme is based on a configurational-bias Monte Carlo scheme and will also be considered. The efficiency of both parallel algorithms has been compared in a case study: the slow relaxation dynamics in a model silicate. For a small number of trial moves generated in parallel, we observe that the residence weight algorithm performs less efficiently than its corresponding configurational-bias scheme by a factor of about 2. However, when the number of parallel trial moves increases and becomes larger than ten, we observe that the residence weight algorithm and the corresponding configurational-bias scheme perform with equivalent efficiencies.

Journal Article↗

PubMed2004

Modeling and convergence analysis of distributed coevolutionary algorithms.

A theoretical foundation is presented for modeling and convergence analysis of a class of distributed coevolutionary algorithms applied to optimization problems in which the variables are partitioned among p nodes. An evolutionary algorithm at each of the p nodes performs a local evolutionary search based on its own set of primary variables, and the secondary variable set at each node is clamped during this phase. An infrequent intercommunication between the nodes updates the secondary variables at each node. The local search and intercommunication phases alternate, resulting in a cooperative search by the p nodes. First, we specify a theoretical basis for a class of centralized evolutionary algorithms in terms of construction and evolution of sampling distributions over the feasible space. Next, this foundation is extended to develop a model for a class of distributed coevolutionary algorithms. Convergence and convergence rate analyzes are pursued for basic classes of objective functions. Our theoretical investigation reveals that for certain unimodal and multimodal objectives, we can expect these algorithms to converge at a geometrical rate. The distributed coevolutionary algorithms are of most interest from the perspective of their performance advantage compared to centralized algorithms, when they execute in a network environment with significant local access and internode communication delays. The relative performance of these algorithms is therefore evaluated in a distributed environment with realistic parameters of network behavior.

Journal Article↗

PubMed2001

A study of time-domain FXLMS algorithms with control output constraint.

A single input, single output active noise control system using the time-domain Filtered-X LMS algorithm with output constraint is investigated. The constraint on the output of the control filter is applied by three different methods: the leakage algorithm based on the transformation method using a penalty function; the re-scaling algorithm based on the active set method; and the simple practical (clipping) algorithm which just clips the output if a constraint is encountered. A comparison of the three algorithms shows that the re-scaling algorithm can usually work successfully under the constraint, while the leakage algorithm usually needs a large leakage coefficient to satisfy the constraint with a resulting performance loss. The clipping algorithm has potential problems both with the stability and convergence speed.

Journal Article↗

PubMed2002

Experimental evaluation of leaky least-mean-square algorithms for active noise reduction in communication headsets.

An adaptive leaky normalized least-mean-square (NLMS) algorithm has been developed to optimize stability and performance of active noise cancellation systems. The research addresses LMS filter performance issues related to insufficient excitation, nonstationary noise fields, and time-varying signal-to-noise ratio. The adaptive leaky NLMS algorithm is based on a Lyapunov tuning approach in which three candidate algorithms, each of which is a function of the instantaneous measured reference input, measurement noise variance, and filter length, are shown to provide varying degrees of tradeoff between stability and noise reduction performance. Each algorithm is evaluated experimentally for reduction of low frequency noise in communication headsets, and stability and noise reduction performance are compared with that of traditional NLMS and fixed-leakage NLMS algorithms. Acoustic measurements are made in a specially designed acoustic test cell which is based on the original work of Ryan et al. ["Enclosure for low frequency assessment of active noise reducing circumaural headsets and hearing protection," Can. Acoust. 21, 19-20 (1993)] and which provides a highly controlled and uniform acoustic environment. The stability and performance of the active noise reduction system, including a prototype communication headset, are investigated for a variety of noise sources ranging from stationary tonal noise to highly nonstationary measured F-16 aircraft noise over a 20 dB dynamic range. Results demonstrate significant improvements in stability of Lyapunov-tuned LMS algorithms over traditional leaky or nonleaky normalized algorithms, while providing noise reduction performance equivalent to that of the NLMS algorithm for idealized noise fields.

Journal Article↗

PubMed2004

Alternatives for potentially inappropriate medications in the elderly population: treatment algorithms for use in the Fleetwood Phase III Study.

OBJECTIVE: To provide estimates of the prevalence of potentially inappropriate medications used in eligible nursing facilities, to describe the development of evidence-based treatment algorithms for recommending safer alternative treatments to potentially inappropriate medications, and to provide the actual treatment algorithms developed for the Fleetwood Phase III study. DESIGN: Literature review, cross-sectional design. SETTING: Thirty North Carolina nursing facilities eligible for Fleetwood Phase III. PATIENTS, PARTICIPANTS: Algorithms developed for use by all pharmacists in the long-term care pharmacy serving the intervention facilities site for the Fleetwood Phase III study. INTERVENTIONS: Pharmacists are prospectively intervening directly with the prescriber to recommend a safer alternative to inappropriate medications using the standardized treatment algorithms developed for the study. MAIN OUTCOME MEASURE(S): Prevalence of potentially inappropriate medications used among residents and the development of 14 treatment algorithms suggesting appropriate alternatives to inappropriate medications. RESULTS: The percentage of potentially inappropriate medications used ranged from 0% to 13.2% at baseline in March 2002. We also found that evidence-based treatment algorithms were well received by consultant pharmacists at the intervention sites of the Fleetwood Phase III study. CONCLUSION: We have provided prevalence rates of potentially inappropriate medication use in nursing homes and developed treatment algorithms for pharmacists to use when making clinical recommendations regarding safer alternatives to potentially inappropriate medications in the elderly population. We are in the process of evaluating the effect of pharmacists' prospective interventions by using these standardized evidence-based treatment algorithms to reduce the prevalence of inappropriate medication use in intervention facilities.

Journal Article↗

PubMed2003

Speed-up of DNA melting algorithm with complete nearest neighbor properties.

We describe an optimized algorithm, which is faster and more accurate compared to previously described algorithms, for computing the statistical mechanics of denaturation of nucleic acid sequences according to the classical Poland-Scheraga type of model. Nearest neighbor thermodynamics has been included in a complete and general way, by rigorously treating nearest neighbor interactions, helix end interactions, and isolated base-pairs. This avoids the simplifications of previous approaches and achieves full generality and controllability with respect to thermodynamic modeling. The algorithm computes subchain partition functions by recursion, from which various quantitative aspects of the melting process are easily derived, for example the base-pairing probability profiles. The algorithm represents an optimization with respect to algorithmic complexity of the partition function algorithm of Yeramian et al. (Biopolymers 1990, 30, 481-497): we reduce the computation time for a base-pairing probability profile from O(N2) to O(N), where N is the sequence length. This speed-up comes in addition to the speed-up due to a multiexponential approximation of the loop entropy factor as introduced by Fixman and Freire22 and applied by Yeramian et al. The speed-up, however, is independent of the multiexponential approximation and reduces time from O(N3) to O(N2) in the exact case. A method for representing very large numbers is described, which avoids numerical overflow in the partition functions for genomic length sequences. In addition to calculating the standard base-pairing probability profiles, we propose to use the algorithm to calculate various other probabilities (loops, helices, tails) for a more direct view of the melting regions and their positions and sizes. This can provide a better understanding of the physics of denaturation and the biology of genomes.

Algorithms↗

PubMed2006

An algorithm for three-dimensional Voronoi S-network.

The paper presents an algorithm for calculating the three-dimensional Voronoi-Delaunay tessellation for an ensemble of spheres of different radii (additively-weighted Voronoi diagram). Data structure and output of the algorithm is oriented toward the exploration of the voids between the spheres. The main geometric construct that we develop is the Voronoi S-network (the network of vertices and edges of the Voronoi regions determined in relation to the surfaces of the spheres). General scheme of the algorithm and the key points of its realization are discussed. The principle of the algorithm is that for each determined site of the network we find its neighbor sites. Thus, starting from a known site of the network, we sequentially find the whole network. The starting site of the network is easily determined based on certain considerations. Geometric properties of ensembles of spheres of different radii are discussed, the conditions of applicability and limitations of the algorithm are indicated. The algorithm is capable of working with a wide variety of physical models, which may be represented as sets of spheres, including computer models of complex molecular systems. Emphasis was placed on the issue of increasing the efficiency of algorithm to work with large models (tens of thousands of atoms). It was demonstrated that the experimental CPU time increases linearly with the number of atoms in the system, O(n).

Algorithms↗

PubMed2001

Modified block uniform resampling (BURS) algorithm using truncated singular value decomposition: fast accurate gridding with noise and artifact reduction.

The block uniform resampling (BURS) algorithm is a newly proposed regridding technique for nonuniformly-sampled k-space MRI. Even though it is a relatively computationally intensive algorithm, since it uses singular value decomposition (SVD), its procedure is simple because it requires neither a pre- nor a postcompensation step. Furthermore, the reconstructed image is generally of high quality since it provides accurate gridded values when the local k-space data SNR is high. However, the BURS algorithm is sensitive to noise. Specifically, inaccurate interpolated data values are often generated in the BURS algorithm if the original k-space data are corrupted by noise, which is virtually guaranteed to occur to some extent in MRI. As a result, the reconstructed image quality is degraded despite excellent performance under ideal conditions. In this article, a method is presented which avoids inaccurate interpolated k-space data values from noisy sampled data with the BURS algorithm. The newly proposed technique simply truncates a series of singular values after the SVD is performed. This reduces the computational demand when compared with the BURS algorithm, avoids amplification of noise resulting from small singular values, and leads to image SNR improvements over the original BURS algorithm.

Algorithms↗

PubMed1998

A new correlation-based fuzzy logic clustering algorithm for fMRI.

Fuzzy logic clustering algorithms are a new class of processing strategies for functional MRI (fMRI). In this study, the ability of such methods to detect brain activation on application of a stimulus task is demonstrated. An optimization of the selected algorithm with regard to different parameters is proposed. These parameters include (a) those defining the pre-processing procedure of the data set; (b) the definition of the distance between two time courses, considered as p-dimensional vectors, where p is the number of sequential images in the fMRI data set; and (c) the number of clusters to be considered. Based on the assumption that such a clustering algorithm should cluster the pixel time courses according to their similarity and not their proximity (in terms of distance), cross-correlation-based distances are defined. A clear mathematical description of the algorithm is proposed, and its convergence is proven when similarity measures are used instead of conventional Euclidean distance. The differences between the membership function given by the algorithm and the probability are clearly exposed. The algorithm was tested on artificial data sets, as well as on data sets from six volunteers undergoing stimulation of the primary visual cortex. The fMRI maps provided by the fuzzy logic algorithm are compared to those achieved by the well established cross-correlation technique.

Algorithms↗

PubMed1995

Performance comparison of several published tissue near-infrared spectroscopy algorithms.

We have collected multiwavelength near-infrared (NIR) attenuation spectra on human forearm muscle, the adult rat head, and newborn piglet head to compare the changes in chromophore concentration derived from these data using published algorithms from four groups. We find differences between the results from the algorithms on each data set, particularly in their estimation of cytochrome oxidase (cyt-aa3) redox changes. We also find some differences when applying the same algorithm to the three data sets, suggesting possible difficulties in transferring algorithms between different physiological systems (e.g., Kurth, C. D., Steven, J. M., Benaron, D., and Chance, B. (1993) J. Clin. Monit. 9, 163-170). We have also compared the algorithms using simulated data generated using measured hemoglobin absorption spectra and a diffusion model for light transport in tissue. We find that while the algorithms from three groups are in broad agreement, that published by Piantadosi (Piantadosi, C. A. (1993) Methods Toxicol. 2, 107-126) produces significantly different results for cyt-aa3 and HbO2. Either the hemoglobin spectra used to produce the simulated data are inaccurate or the modeling is incorrect, or this algorithm is erroneous.

Adult↗

PubMed1996

A computer algorithm to impute interrupted heart rate data for the spectral analysis of heart rate variability--the ARIC study.

The shorter term beat-to-beat heart rate data collected from the general population are often interrupted by artifacts, and an arbitrary exclusion of such individuals from analysis may significantly reduce the sample size and/or introduce selection bias. A computer algorithm was developed to label as artifacts any data points outside the upper and lower limits generated by a 5-beat moving average +/- 25% (or set manually by an operator using a mouse) and to impute beat-to-beat heart rate throughout an artifact period to preserve the timing relationships of the adjacent, uncorrupted heart rate data. The algorithm applies Fast Fourier Transformation to the smoothed data to estimate low-frequency (LF; 0.025-0.15 Hz) and high-frequency (HF; 0.16-0.35 Hz) spectral powers and the HF/LF ratio as conventional indices of sympathetic, vagal, and vagal-sympathetic balance components, respectively. We applied this algorithm to resting, supine, 2-min beat-to-beat heart rate data collected in the population-based Atherosclerosis Risk in Communities study to assess the performance (success rate) of the algorithm (N = 526) and the inter-and intra-data-operator repeatability of using this computer algorithm (N = 108). Eighty-eight percent (88%) of the records could be smoothed by the computer-generated limits, an additional 4.8% by manually set limits, and 7.4% of the data could not be processed due to a large number of artifacts in the beginning or the end of the records. For the repeatability study, 108 records were selected at random, and two trained data operators applied this algorithm to the same records twice within a 6-month interval of each process (blinded to each other's results and their own prior results). The inter-data-operator reliability coefficients were 0.86, 0.92, and 0.90 for the HF, LF, and HF/LF components, respectively. The average intra-data-operator reliability coefficients were 0.99, 0.99, and 0.98 for the HF, LF, and HF/LF components, respectively. These results indicate that this computer algorithm is efficient and highly repeatable in processing short-term beat-to-beat heart rate data collected from the general population, given that the data operators are trained according to standardized protocol.

Algorithms↗

PubMed1998

Comparative investigations of algorithms for the detection of breaths in newborns with disturbed respiratory signals.

The correct detection of the beginning of inspiration and expiration in the respiratory signals is an essential prerequisite for accurate lung function testing in newborns. Five algorithms for breath detection using pneumotachographically measured flow and volume signals were investigated with regard to the error rate. To compare and to evaluate the reliability of these algorithms 12 minimally and 12 severely disturbed flow and volume signals from spontaneously breathing newborns were used. With the exception of an algorithm based on Walsh-transformed signals, all algorithms work reliably (error rate <1.1%) if disturbances are minimal. In severely disturbed signals there is a great difference between the algorithms. The most robust algorithm tested (trigger of the flow signal with an additional plausibility check of the recognized breath) resulted in an error rate of <3.4%. Not all algorithms tested are suitable for real-time applications because they differ considerably in delay time for breath detection.

Algorithms↗