Fetal heart rate patterns in postasphyxiated fetal lambs with brain damage

OBJECTIVE: We previously showed that in asphyxiated fetal lambs the duration of hypotension correlated well with the severity of histologic damage to the brain, whereas the duration of bradycardia did not. This study compares fetal heart rate patterns with the degree of histologic damage to the brain. STUDY DESIGN: Twelve chronically instrumented near-term fetal lambs were subjected to asphyxia by umbilical cord occlusion until fetal arterial pH was <6. 9 and base excess was <-20 mEq/L. An additional 4 fetuses served as sham-asphyxia controls. Fetal heart rate (from electrocardiogram), arterial blood pressure, fetal breathing movements, and electrocorticogram were continuously monitored before, during, and for 72 hours after asphyxia. Fetal brain histologic features were categorized as mild (group 1, n = 5), moderate (group 2, n = 4), and severe (group 3, n = 3). Long-term fetal heart rate variability expressed as amplitude range was assessed visually every 5 minutes from 30 minutes before asphyxia until 2 hours of recovery and at 6, 12, 24, 48, and 72 hours of recovery. RESULTS: Long-term fetal heart rate variability amplitude decreased from 32 +/- 17 beats/min (mean +/- SEM) preocclusion to 4 +/- 13 beats/min at the end of occlusion (P <.001) without significant differences among the 3 groups. During 10 to 45 minutes of recovery, the long-term variability of group 1 was significantly greater than that of groups 2 and 3. At 24 to 72 hours of recovery, the long-term variability of groups 1 and 2 was significantly higher than that of group 3, which was almost 0. The "checkmark" and sinusoidal fetal heart rate patterns were observed during the recovery period in groups 2 and 3. CONCLUSIONS: Decreased long-term fetal heart rate variability and the "checkmark" and sinusoidal fetal heart rate patterns were indicators of the severity of asphyxial histologic damage in the fetal brain.     

Automated 360° profilometry of a three-dimensional diffuse object and its reconstruction by use of the shading model

The 360° profilometry of a three-dimensional (3-D) diffuse object by use of the light intersection and its image reconstruction by surface shading are presented. The lack of data in one direction, which was due to occlusion, was compensated by the projection of two lines of light from different directions. Some experiments to profile objects and their reconstruction by computer are shown. The entire surface model was constructed, and a real shading image was obtained by means of computer graphics.     

Development of a pulse control-type MEMS microrobot with a hardware neural network

This article presents the micro-electro-mechanical systems (MEMS) microrobot which demonstrates locomotion controlled by hardware neural networks (HNN). The size of the microrobot fabricated by the MEMS technology is 4 × 4 × 3.5 mm. The frame of the robot is made of silicon wafer, and it is equipped with a rotary-type actuator, a link mechanism, and six legs. The rotary-type actuator generates rotational movement by applying an electrical current to artificial muscle wires. The locomotion of the microrobot is obtained by the rotation of the rotary-type actuator. As in a living organism, the HNN realized robot control without using any software programs, A/D converters, or additional driving circuits. A central pattern generator (CPG) model was implemented as an HNN system to emulate the locomotion pattern. The MEMS microrobot emulated the locomotion method and the neural networks of an insect with the rotary-type actuator, the link mechanism, and the HNN. The microrobot performed forward and backward locomotion, and also changed direction by inputting an external trigger pulse. The locomotion speed was 0.325 mm/s and the step width was 1.3 mm.     

Human motion database with a binary tree and node transition graphs

Database of human motion has been widely used for recognizing human motion and synthesizing humanoid motions. In this paper, we propose a data structure for storing and extracting human motion data and demonstrate that the database can be applied to the recognition and motion synthesis problems in robotics. We develop an efficient method for building a human motion database from a collection of continuous, multi-dimensional motion clips. The database consists of a binary tree representing the hierarchical clustering of the states observed in the motion clips, as well as node transition graphs representing the possible transitions among the nodes in the binary tree. Using databases constructed from real human motion data, we demonstrate that the proposed data structure can be used for human motion recognition, state estimation and prediction, and robot motion planning.     

Replica Exchange Light Transport

We solve the light transport problem by introducing a novel unbiased Monte Carlo algorithm called replica exchange light transport, inspired by the replica exchange Monte Carlo method in the fields of computational physics and statistical information processing. The replica exchange Monte Carlo method is a sampling technique whose operation resembles simulated annealing in optimization algorithms using a set of sampling distributions. We apply it to the solution of light transport integration by extending the probability density function of an integrand of the integration to a set of distributions. That set of distributions is composed of combinations of the path densities of different path generation types: uniform distributions in the integral domain, explicit and implicit paths in light (particle/photon) tracing, indirect paths in bidirectional path tracing, explicit and implicit paths in path tracing, and implicit caustics paths seen through specular surfaces including the delta function in path tracing. The replica‐exchange light transport algorithm generates a sequence of path samples from each distribution and samples the simultaneous distribution of those distributions as a stationary distribution by using the Markov chain Monte Carlo method. Then the algorithm combines the obtained path samples from each distribution using multiple importance sampling. We compare the images generated with our algorithm to those generated with bidirectional path tracing and Metropolis light transport based on the primary sample space. Our proposing algorithm has better convergence property than bidirectional path tracing and the Metropolis light transport, and it is easy to implement by extending the Metropolis light transport.     

Robust loss functions for boosting

Boosting is known as a gradient descent algorithm over loss functions. It is often pointed out that the typical boosting algorithm, Adaboost, is highly affected by outliers. In this letter, loss functions for robust boosting are studied. Based on the concept of robust statistics, we propose a transformation of loss functions that makes boosting algorithms robust against extreme outliers. Next, the truncation of loss functions is applied to contamination models that describe the occurrence of mislabels near decision boundaries. Numerical experiments illustrate that the proposed loss functions derived from the contamination models are useful for handling highly noisy data in comparison with other loss functions.     

A new learning method using prior information of neural networks

In this paper, we present a new learning method using prior information for three-layer neural networks. Usually when neural networks are used for identification of systems, all of their weights are trained independently, without considering interrelated weights values. Thus, the training results are usually not good. The reason for this in that each parameter has its influence on others during learning. To overcome this problem, we first give an exact mathematical equation that describes the relation between weight values given a set of data conveying prior information. The we present a new learning method that trains part of the weights and calculates the others using these exact mathematical equations. This method often a priori keeps the given mathematical structure exactly the same during learning; in other words, training is done so that the network follows a predetermined trajectory. Numerical computer simulation results are provided to support this approach. This work was presented, in part, at the Fourth International Symposium on Artificial Life and Robotics, Oita, Japan, January 19–22, 1999.     

Rapid reinforcement for fusion mass-spliced fibers using low-power induction heating

A rapid reinforcement method using low-power induction heating is developed. The reinforcement of a mass-spliced fiber ribbon unit comprising five graded-index multimode fibers was completed in 30 sec with good performance by supplying 30 W of electric power.     

Flow and deformation of fresh concrete

The results of studies made from the standpoint of rheology on methods of predicting flows and deformations of fresh concrete and mortar as basic research for rationalization of concrete construction are described. This paper contains as follows:

1. method of measuring viscosity of fresh concrete and mortar;
2. method of estimating deformation of fresh concrete due to its own weight;
3. proposal of the “Inclined Pipe Test Method” as a new method of testing consistency of grout mortar.

     

Multiple kernel learning with gaussianity measures

Kernel methods are known to be effective for nonlinear multivariate analysis. One of the main issues in the practical use of kernel methods is the selection of kernel. There have been a lot of studies on kernel selection and kernel learning. Multiple kernel learning (MKL) is one of the promising kernel optimization approaches. Kernel methods are applied to various classifiers including Fisher discriminant analysis (FDA). FDA gives the Bayes optimal classification axis if the data distribution of each class in the feature space is a gaussian with a shared covariance structure. Based on this fact, an MKL framework based on the notion of gaussianity is proposed. As a concrete implementation, an empirical characteristic function is adopted to measure gaussianity in the feature space associated with a convex combination of kernel functions, and two MKL algorithms are derived. From experimental results on some data sets, we show that the proposed kernel learning followed by FDA offers strong classification power.