The jaw movements can be measured by estimating the position and orientation of two small permanent magnets attached on the upper and lower jaws. It is a difficult problem to estimate the positions and orientations of the magnets from magnetic field because it is a typical inverse problem. The back propagation neural networks (BPNN) are applicable to solve this problem in short processing time. But its precision is not enough to apply to practical measurement. In the other hand, precise estimation is possible by using the nonlinear least-square (NLS) method. However, it takes long processing time for iterative calculation, and the solutions may be trapped in the local minima. In this paper, we propose a precise and fast measurement system which makes use of the estimation algorithm combining BPNN with NLS method. In this method, the BPNN performs an approximate estimation of magnet parameters in short processing time, and its result is used as the initial value of iterative calculation of NLS method. The cost function is solved by Gauss-Newton iteration algorithm. Precision, processing time and noise immunity were examined by computer simulations. These results shows the proposed system has satisfactory ability to be applied to practical measurement.

A continuous-wave ultrasonic Doppler system using wide field ultrasound transducers was applied to telemeter blood velocity from the carotid artery of exercising subjects. Velocity spectrogram was obtained by Hanning windowed fast Fourier transformation of the telemetered data. Distortion caused by a high-pass filter and transducers in the telemetry system was discussed in the paper. As the maximum Reynolds number in our experiment was 1478 which is smaller than the critical level of 2000, the blood flow should be laminar. Spatial velocity profiles were then reconstructed from the velocity spectrogram. In this paper, we defined a converging index Q of the velocity spectrum to measure the bluntness of the spatial velocity distribution across the blood vessel. Greater Q, the blunter the velocity profile will be. Simulation results for spatial velocity distributions of theoretical parabolic flow and Gaussian-distribution spectra with varied Q value showed that the cut-off effect by a high-pass filter of cut-off frequency fc=200Hz in our system could be ignored when the axial velocity is larger than 0.30 m/s and Q is greater than 2.0. Our experimental results, in contrast to those obtained from phantom systems by us and by Hein and O'Brien, indicate that the distribution of blood velocity is much blunter than previously thought. The Q index exceeded 10 during systole, whereas it was 0.5 in parabolic flow. The peak of Q index lagged behind that of axial blood velocity by approximately 0.02s. The phase delay of the Q index curve might be due to the time needed for the red blood cells to form the non-homogeneous distribution.

Almost all land animals coordinate their behavior with circadian rhythms, matching their functions to the daily cycles of lightness and darkness that result from the rotation of the earth corresponding to 24 hours. Through external stimuli, such as dairy life activities or other sources from our environment may influence the internal rhythmicity of sleep and waking properties. However, the rhythms are regulated to keep their activity constant by homeostasis while fluctuating by incessant influences of external forces. A modeling study has been developed to identify homeostatic dynamics properties underlying a circadian rhythm activity of Sleep and Wake data measured from normal subjects, using an MA (Moving Average) model associated with Backpropagation (BP) algorithm. As results, we found that the neural network can capture the regularity and irregularity components included in the data. The order of MA neural network model depends on subjects behavior, the first two orders are usually dominant in the case of no strong external forces. The adaptive dynamic changes are evaluated by the change of weight vectors, a kind of internal representation of the trained network. The dynamic is kept in a steady state for more than 20 days at most. Identified properties reflect the subject's behavior, and hence may be useful for medical diagnoses of disorders related to circadian rhythms.

This paper presents a detailed simulation method to estimate Doppler power spectrum and mean blood velocity using real CW Doppler transducers with twin-crystal arrangement. The method is based on dividing the sample volume into small cells and using the statistics of the Doppler power spectrum with the same Doppler shift frequency, which predicts the mean blood velocity. The acoustic fields of semicircular transducers across blood vessels were calculated and the effects of acoustical and physiological factors on Doppler power spectrum and mean blood velocity were analyzed. Results show that nonuniformity of the acoustic field of the ultrasonic beam in the blood vessel and blood velocity profiles significantly affect Doppler power spectrum and mean blood velocity. However, Doppler angle, vessel depth, and sample volume length are not sensitive functions. Comparisons between simulation and experimental results illustrated a good agreement for parabolic flow profile. These results will contribute to a better understanding of Doppler power spectrum and mean blood velocity in medical ultrasound diagnostics.

A problem of estimating biopotential sources in the brain based on EEG signals observed on the scalp is known as an important inverse problem of electrophysiology. Usually there is no closed-form solution for this problem and it requires iterative techniques such as the Levenberg-Marquardt algorithm. Considering the nonlinear properties of inverse problem, and signal to noise ratio inherent in EEG signals, a back propagation neural network has been recently proposed as a solution. In this paper, we investigated the properties of neural networks and its localization accuracy for single dipole source localization. Based on the results of extensive studies, we concluded the neural networks are highly feasible in single-source localization with a small number of electrodes (18 electrodes), also examined the usefulness of this method for clinical application with a case of epilepsy.

The goal of source localization in the brain is to estimate a set of parameters for representing source characteristics; one of such parameters is the source number. We here propose a method combining the Powell algorithm with the information criterion method for determining the optimal dipole number. The potential errors can be calculated by the Powell algorithm with the concentric 4-sphere head model and 32 electrodes, then the number of dipoles is determined by the information criterion method with the potential errors mentioned above. This method has the advantages of a high identification accuracy of dipole number and a small number of EEG data because in this method: (1) only one EEG topography is used in the computation, (2) 32 electrodes are used to obtain the EEG data, (3) the optimal dipole number can be obtained by this method. In order to prove our method to be efficient, precise and robust to noise, 10% white noise is introduced to test this method theoretically. Some investigations are presented here to show our method is an advanced approach for determining the optimal dipole number.

The electroencephalogram (EEG) has become a widely used tool for investigating brain function. Brain signal source localization is a process of inverse calculation from sensor information (electric potentials for EEG) to the identification of multiple brain sources to obtain the locations and orientation parameters. In this paper, we describe a combination of the backpropagation neural network (BPNN) with the nonlinear least-square (NLS) method to localize two dipoles with reasonable accuracy and speed from EEG data computerized by two dipoles randomly positioned in the brain. The trained BPNN, obtains the initial values for the two dipoles through fast calculation and also avoids the influence of noise. Then the NLS method (Powell algorithm) is used to accurately estimate the two dipole parameters. In this study, we also obtain the minimum distance between the assumed dipole pair, 0.8 cm, in order to localize two sources from a smaller limited distance between the dipole pair. The present simulation results demonstrate that the combined method can allow us to localize two dipoles with high speed and accuracy, that is, in 20 seconds and with the position error of around 6.5%, and to reduce the influence of noise.

To estimate inner multi-layer tissue conductivity distribution in a cross section of the local tissue by using bioresistance data measured noninvasively on the surface of the tissue, a measurement method using divided electrodes is proposed, where a current electrode is divided into several parts. The method is evaluated by computer simulations using a three-dimension (3D) model and two two-dimension (2D) models. In this paper, conductivity distributions of the simplified (2D) model are analyzed based on a combination of a finite difference method (FDM) and a steepest descent method (SDM). Simulation results show that conductivity values for skin, fat and muscle layers can be estimated with an error less than 0.1%. Even though different strength random noise is added to measured resistance values, the conductivities are estimated with reasonable precise, e.g., the average error is about 4.25% for 10% noise. The configuration of the divided electrodes are examined in terms of dividing pattern and the size of surrounding guard electrodes to confine and control the input currents from the divided electrodes within a cross sectional area in the tissue.