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We propose a method of predicting intrauterine pressure (IUP) from external electrohysterograms (EHG) using a causal FIR Wiener filter. IUP and 8-channel EHG data were collected simultaneously from 14 laboring patients at term, and prediction models were trained and tested using 10-min windows for each patient and channel. RMS prediction error varied between 5-14 mmHg across all patients. We performed a 4-way analysis of variance on the RMS error, which varied across patients, channels, time (test window) and model (train window). The patient-channel interaction was the most significant factor while channel alone was not significant, indicating that different channels produced significantly different RMS errors depending on the patient. The channel-time factor was significant due to single-channel bursty noise, while time was a significant factor due to multichannel bursty noise. The time-model interaction was not significant, supporting the assumption that the random process generating the IUP and EHG signals was stationary. The results demonstrate the capabilities of optimal linear filter in predicting IUP from external EHG and offer insight into the factors that affect prediction error of IUP from multichannel EHG recordings. Index Terms--Electrohysterography, intrauterine pressure catheter, Wiener filter prediction.

In the design of brain-machine interface (BMI) algorithms, the activity of hundreds of chronically recorded neurons is used to reconstruct a variety of kinematic variables. A significant problem introduced with the use of neural ensemble inputs for model building is the explosion in the number of free parameters. Large models not only affect model generalization but also put a computational burden on computing an optimal solution especially when the goal is to implement the BMI in low-power, portable hardware. In this paper, three methods are presented to quantitatively rate the importance of neurons in neural to motor mapping, using single neuron correlation analysis, sensitivity analysis through a vector linear model, and a model-independent cellular directional tuning analysis for comparisons purpose. Although, the rankings are not identical, up to sixty percent of the top 10 ranking cells were in common. This set can then be used to determine a reduced-order model whose performance is similar to that of the ensemble. It is further shown that by pruning the initial ensemble neural input with the ranked importance of cells, a reduced sets of cells (between 40 and 80, depending upon the methods) can be found that exceed the BMI performance levels of the full ensemble. Index Terms--Brain-machine interface, cosine tuning, information in neural ensembles, sensitivity-based model pruning.

The use of electromechanical reciprocity relations to model ultrasonic measurement processes of interest in nondestructive materials evaluation is reviewed.

A study on non-uniformity correction approaches for the calibrations for most infrared sensing applications proposes a constant-statistics algorithm for non-uniformity correction of infrared focal point and other imaging arrays. The convergence rate of estimates was derived and verified with simulations. The calibrated results of the algorithm were also applied on both synthetic and real infrared image sequences. A novel technique was proposed to eliminate most of the ghost artifacts presented in all scene-based non-uniformity correction algorithms.

A linear approximation model that depends on a probability distribution function and two functions representing the mechanical response of tissue and an equilibrium bubble helps represent the steady-state response evoked by a harmonically driven ultrasonic wave propagating through a bubbly liquid in tissues. Numerical data reveal the spatial velocity and pressure distributions in the fluid and tissue and the occurrence of spatial attenuation in the fluid.

The acoustic signature, probed by an acoustic microscope, of a surface-breaking crack in an elastic material is calculated by the boundary element method. The analysis considered the crack-caused wave scattering phenomena in terms of hypersingular integral equations. The feasibility of using the acoustic signature to measure the extent of a surface-breaking crack is also shown.

Speech quality assessment methods are necessary for evaluating and documenting treatment outcomes of patients suffering from degraded speech due to Parkinson's disease, stroke, or other disease processes. Subjective methods of speech quality assessment are more accurate and more robust than objective methods but are time-consuming and costly. We propose a novel objective measure of speech quality assessment that builds on traditional speech processing techniques such as dynamic time warping (DTW) and the Itakura-Saito (IS) distortion measure. Initial results show that our objective measure correlates well with the more expensive subjective methods.