Abstract: Systems, apparatus, and methods for collecting, interpreting, and utilizing noise exposure data may include sensors to obtain an analog signal representative of impulse noise sound pressure and an analog signal representative of continuous noise sound pressure. At least one ADC may generate digital signals by sampling the analog signals at rates equal to or greater than twice the reciprocal of a minimum impulse noise rise time. Accelerometers may obtain data in close proximity to and remote from the sensors. At least one processor may include a first combining node to combine the digital signals to represent both the continuous noise and the impulse noise, a shock-artifact detection filter to identify a time frame including a shock artifact based on the accelerometry data, a frequency filter to generate a background-removed audio signal, an adaptive filter to estimate the shock artifact, and a second combining node to produce a shock-artifact-free audio signal.

Abstract: Systems, apparatus, and methods for collecting, interpreting, and utilizing noise exposure data may include sensors to obtain an analog signal representative of impulse noise sound pressure and an analog signal representative of continuous noise sound pressure. At least one ADC may generate digital signals by sampling the analog signals at rates equal to or greater than twice the reciprocal of a minimum impulse noise rise time. Accelerometers may obtain data in close proximity to and remote from the sensors. At least one processor may include a first combining node to combine the digital signals to represent both the continuous noise and the impulse noise, a shock-artifact detection filter to identify a time frame including a shock artifact based on the accelerometry data, a frequency filter to generate a background-removed audio signal, an adaptive filter to estimate the shock artifact, and a second combining node to produce a shock-artifact-free audio signal.

Abstract: Systems and methods for predicting exposure to an agent. One or more features are extracted from physiological data. For each respective classifier, (i) the respective classifier is identified, wherein the respective classifier is trained using training data for a respective physiological state, (ii) the respective classifier is applied to the one or more features to obtain a classifier output that represents a likelihood of exposure, (iii) a respective first threshold is applied to the classifier output to determine a patient state classification, and (iv) the patient state classifications are aggregated across a number of time intervals to obtain an aggregate patient state classification for each classifier. The aggregate patient state classifications are combined across the plurality of classifiers to obtain a combined classification, and an indication that the patient has been exposed to the agent is provided when the combined classification exceeds a second threshold.

Abstract: Microwave examination of individuals is carried out by transmitting microwave signals from multiple antenna locations into an individual and receiving the backscattered microwave signals at multiple antenna locations to provide received signals from the antennas. The received signals are processed to remove the skin interface reflection component of the signal and the corrected signal data are provided to a hypothesis testing process. In hypothesis testing for detecting tumors, image data are formed from the test statistic used to perform a binary hypothesis test at each voxel. The null hypothesis asserts that no tumor is present at a candidate voxel location. The voxel threshold is determined by specifying a false discovery rate to control the expected proportion of false positives in the image. When the test statistic value associated with a voxel is greater than the threshold, the null hypothesis is rejected and the test statistic is assigned to the voxel.