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.

Solid-state DNA sizing by atomic force microscopy.

Atomic force microscopy (AFM) allows rapid, accurate, and reproducible visualization of DNA adsorbed onto solid supports. The images reflect the lengths of the DNA molecules in the sample. Here we propose a solid-state DNA sizing (SSDS) method based on AFM as an analytical method for high-throughput applications such as finger-printing, restriction mapping, +/- screening, and genotyping. For this process, the sample is first deposited onto a solid support by adsorption from solution. It is then dried and imaged under ambient conditions by AFM. The resulting images are subjected to automated determination of the lengths of the DNA molecules on the surface. The result is a histogram of sizes that is similar to densitometric scans of DNA samples separated on gels. A direct comparison of SSDS with agarose gel electrophoresis for +/- screening shows that it produces equivalent results. Advantages of SSDS include reduced sample size (i.e., lower reagent costs), rapid analysis of single samples, and potential for full automation using available technology. The high sensitivity of the method also allows the number of polymerase chain reaction cycles to be reduced to 15 or less. Because the high signal-to-noise ratio of the AFM allows for direct visualization of DNA-binding proteins, different DNA conformations, restriction enzymes, and other DNA modifications, there is potential for dramatically improving the information content in this type of analysis.

Automated sizing of DNA fragments in atomic force microscope images.

Current techniques used to measure lengths of DNA fragments in atomic force microscope (AFM) images require a user to operate interactive software and execute tedious error-prone cursor selections. An algorithm is proposed which provides an automated method for determining DNA fragment lengths from AFM images without interaction from the computer operator (e.g. cursor selections or mouse clicks). The approach utilises image processing techniques tailored to characteristics of AFM images of DNA fragments. The automated measurements have a mean absolute deviation of less than 1 pixel when compared to manual image-based measurements. The DNA length determined from the histogram of calculated lengths is accurate to within 3% of the actual DNA length in solution. For fragments that are 250 base-pairs long, the precision is estimated to be within 17 nm, which is about 20% of the total length. This precision was confirmed when the algorithm easily resolved fragments in one image that differed by only 17 nm. Fragment sizes up to 2000 base-pairs have been tested and successfully sized. This algorithm is being developed as part of a new solid-state DNA sizing technique for applications such as genotyping and construction of physical genome maps.

Segmentation algorithms for detecting microcalcifications in mammograms.

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 that operates without the aspects mentioned above, and the active contour model that 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.

Optimal detection, classification, and superposition resolution in neural waveform recordings.

The effects of noise autocorrelation on neural waveform recognition (detection, classification, and superposition resolution) are investigated in this study 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 than template matching with whitening for comparable classification and superposition resolution. The comparable difference for detection is 15%.

Feature-based detection of the K-complex wave in the human electroencephalogram 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.

Noise reduction in biological step signals: application to saccadic EOG.

A weighted filter for noise reduction in nonrecurrent step signals where adaptive filtering cannot be applied is described. An optimal correction of a conventional finite impulse response (FIR) filter is achieved by using a priori knowledge of noise variance and a continuous estimation of the error signal's power. The weighted filter provides an optimal compromise between noise filtering and distortionless tracking. The prior knowledge required is that of the noise power and the lowest frequency in the noise spectrum. Application of the weighted filter to the saccadic electro-oculogram (EOG) results in better estimations of saccade duration and velocity.

Identification of dynamic mechanical parameters of the human chest during manual cardiopulmonary 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.