Analytical model of Doppler spectra of coherent light backscattered from rotating cones and cylinders

The interaction between a rotating object and laser beams has been studied in the field of laser Doppler velocimetry, where two incident laser beams are focused on one small spot of the rotating surface and interferometry is used. In the case of a single incident laser beam illuminating a relatively large area of the rotating surface, both the Doppler broadening and the reflected-power level are dictated by points distributed over a wide curved area at varying angles of incidence. An analytical model of spectra in backscatter from cones and cylinders rotating around their axes is presented. This analytical solution may contribute to laser Doppler velocimetry as well as ladar applications.     

Feature-based detection of the K-complex wave in the humanelectroencephalogram using neural networks

The main difficulties in reliable automated detection of the K-complex wave in EEG are its close similarity to other waves and the lack of specific characterization criteria. We present a feature-based detection approach using neural networks that provides good agreement with visual K-complex recognition: a sensitivity of 90% is obtained with about 8% false positives. The respective contribution of the features and that of the neural network is demonstrated by comparing the results to those obtained with i) raw EEG data presented to neural networks, and ii) features presented to Fisher's linear discriminant.     

Identification of dynamic mechanical parameters of the human chestduring manual cadiopulmonary resuscitation

Survival from cardiac arrest is dependent on timely cardiopulmonary resuscitation (CPR). Since CPR is often unsuccessful, the outcome may be improved by a better understanding of the relationship between force applied to the sternum and the resulting hemodynamic effects. The first step in this complex chain of interactions is the mechanical response of the chest wall to cyclical compression. We formulated a dynamic mechanical model of the chest response and developed a method of identification of the model parameters based on force, displacement, and acceleration data acquired during cyclical compressions. The elasticity, damping, and equivalent mass of the human chest were estimated with a constrained nonlinear least-mean-square identification technique. The method was validated on data acquired from a test apparatus built for this purpose. The model fit was measured with the normalized chi-square statistic on residuals obtained between recorded force and force predicted by the model. In the analysis of one human chest, the elasticity was found to be nonlinear and statistically different during compression and release. A considerable amount of damping was found, with no significant difference between compression and release. The equivalent mass was too small to be determined accurately. This method can be used to obtain the dynamic mechanical parameters of the human chest and may lead to a better understanding of CPR.     

Optimal detection, classification, and superposition resolution inneural waveform recordings

The effects of noise autocorrelation on neural waveform recognition (detection, classification, and superposition resolution) are investigated, using microelectrode recordings from the cortex of a monkey. Optimal waveform recognition is accomplished by passing the data through a whitening filter before matched filtering for detection or template matching for classification and superposition resolution. Template matching without whitening requires about 40% higher signal-to-noise ratio (SNR) than template matching with whitening for comparable classification and superposition resolution. The comparable difference for detection is 15%     

Lidar system model for use with path obscurants and experimental validation

When lidar pulses travel through a short path that includes a relatively high concentration of aerosols, scattering phenomena can alter the power and temporal properties of the pulses significantly, causing undesirable effects in the received pulse. In many applications the design of the lidar transmitter and receiver must consider adverse environmental aerosol conditions to ensure the desired performance. We present an analytical model of lidar system operation when the optical path includes aerosols for use in support of instrument design, simulations, and system evaluation. The model considers an optical path terminated with a solid object, although it can also be applied, with minor modifications, to cases where the expected backscatter occurs from nonsolid objects. The optical path aerosols are characterized by their attenuation and backscatter coefficients derived by the Mie theory from the concentration and particle size distribution of the aerosol. Other inputs include the lidar system parameters and instrument response function, and the model output is the time-resolved received pulse. The model is demonstrated and experimentally validated with military fog oil smoke for short ranges (several meters). The results are obtained with a lidar system operating at a wavelength of 0.905 microm within and outside the aerosol. The model goodness of fit is evaluated using the statistical coefficient of determination whose value ranged from 0.88 to 0.99 in this study.     

Segmentation algorithms for detecting microcalcifications inmammograms

The presence of microcalcification clusters in mammograms contributes evidence for the diagnosis of early stages of breast cancer. In many cases, microcalcifications are subtle and their detection can benefit from an automated system serving as a diagnostic aid. The potential contribution of such a system may become more significant as the number of mammograms screened increases to levels that challenge the capacity of radiology clinics. Many techniques for detecting microcalcifications start with a segmentation algorithm that indicates all candidate structures for the subsequent phases. Most algorithms used to segment microcalcifications have aspects that might raise operational difficulties, such as thresholds or windows that must be selected, or parametric models of the data. We present a new segmentation algorithm and compare it to two other algorithms: the multi-tolerance region-growing algorithm, which operates without the aspects mentioned above, and the active contour model, which has not been applied previously to segment microcalcifications. The new algorithm operates without threshold or window selection or parametric data models, and it is more than an order of magnitude faster than the other two