Publications

The role of cyber forensics in criminal investigations has greatly increased in recent years due to the wealth of data that is collected and available to investigators. Physical forensics has also experienced a data volume and fidelity revolution due to advances in methods for DNA and trace evidence analysis. Key to extracting insight is the ability to correlate across multi-modal data, which depends critically on identifying a touch-point connecting the separate data streams. Separate data sources may be connected because they refer to the same individual, entity or event. In this paper we present a data source classification system tailored to facilitate the investigation of potential terrorist activity. This taxonomy is structured to illuminate the defining characteristics of a particular terrorist effort and designed to guide reporting to decision makers that is complete, concise, and evidence-based. The classification system has been validated and empirically utilized in the forensic analysis of a simulated terrorist activity. Next-generation analysts can use this schema to label and correlate across existing data streams, assess which critical information may be missing from the data, and identify options for collecting additional data streams to fill information gaps.

Within DOT, the FAA has initiated an effort known as the NextGen Surveillance and Weather Radar Capability (NSWRC) to analyze the need for the next generation radar replacement and assess viable implementation alternatives. One concept under analysis is multifunction radar using phased-array technology -- Multifunction Phased Array Radar or MPAR.

What is believed to be a new concept for the measurement of micrometer-sized particle trajectories in an inlet air stream is introduced. The technique uses a light source and a mask to generate a spatial pattern of light within a volume in space. Particles traverse the illumination volume and elastically scatter light to a photodetector where the signal is recorded in time. The detected scattering waveform is decoded to find the particle trajectory. A design is presented for the structured laser beam, and the accuracy of the technique in determining particle position is demonstrated. It is also demonstrated that the structured laser beam can be used to measure and then correct for the spatially dependent instrument-response function of an optical-scattering-based particle-sizing system for aerosols.

One of the largest sources of error in the current automated convective weather forecast systems is due to its inability to accurately account for new convective storm development. In many situations the initiation of new convection is preceded by low altitude convergence in the horizontal winds. These regions of low altitude convergence, often referred to as boundaries, are typically associated with synoptic scale fronts, drylines, and thunderstorm outflows. Gridded wind analyses that utilize Doppler weather radar, surface, and aircraft measurements are one of the best sources of low altitude winds that can be used to identify wind boundaries over large domains. This study summarizes the preliminary results of a study which examined the feasibility of using gridded wind analyses from operational wind analysis systems to make automated detections of wind boundaries. The analysis focused on two operational wind analysis systems both capable of producing high update, and high spatial resolution wind analyses over a domain that covers the eastern half of the Continental United Sates (CONUS), the Space Time Mesoscale Analysis System (STMAS) and the Corridor Boundary layer wind analysis system (CBOUND). Wind analyses from both systems were first processed with a Lagrangian temporal filter and then passed through an automated boundary detection algorithm based on the Terminal Doppler Weather Radar (TDWR) Machine Intelligent Gust Front Algorithm (MIGFA). The results indicate that the temporal filter improves the boundary signal to noise ratio such that it is technically feasible to make fully automated boundary detections with image processing techniques.