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The Spatial Standard Observer (SSO) provides a tool that allows measurement of the visibility of an element, or visual discriminability of two elements. The device may be used whenever it is necessary to measure or specify visibility or visual intensity. The SSO is based on a model of human vision, and has been calibrated by an extensive set of human test data. The SSO operates on a digital image or a pair of digital images. It computes a numerical measure of the perceptual strength of the single image, or of the visible difference between the two images. The visibility measurements are provided in units of Just Noticeable Differences (JND), a standard measure of perceptual intensity. A target that is just visible has a measure of 1 JND. The SSO will be useful in a wide variety of applications, most notably in the inspection of displays during the manufacturing process. It is also useful in for evaluating vision from unpiloted aerial vehicles (UAV) predicting visibility of UAVs from other aircraft, from the control tower of aircraft on runways, measuring visibility of damage to aircraft and to the shuttle orbiter, evaluation of legibility of text, icons or symbols in a graphical user interface, specification of camera and display resolution, inspection of displays during the manufacturing process, estimation of the quality of compressed digital video, and predicting outcomes of corrective laser eye surgery.

The advantage of using cardiac biometrics over existing methods is that heart signatures are more difficult to forge compared to other biometric devices. Iris scanners can be fooled by contact lenses and sunglasses, and a segment of the population does not have readable fingerprints due to age or working conditions. Previous electrocardiographic signals employed a single template and compared that template with new test templates by means of cross-correlation or linear-discriminant analysis.The benefit of this technology over competing cardiac biometric methods is that it is more reliable with a significant reduction in error rates. The benefit of this technology is that it creates a probabilistic model of the electrocardiographic features of a person instead of a single signal template of the average heartbeat. The probabilistic model described as Gaussian mixture model allows various modes of the feature distribution, in contrast to a template model that only characterizes a mean waveform. Another advantage is that the model uses both physiological and anatomical characterization of the heart, unlike other methods that mainly use only physiological characterization of the heart. By combining features from different leads, the heart of the person is better characterized in terms of anatomical orientation because each lead represents a different projection of the electrical vector of the heart. Thus, employing multiple electrocardiographic leads provides a better performance in subject verification or identification.

The Inductive Monitoring System (IMS) software provides a method of building an efficient system health monitoring software module by examining data covering the range of nominal system behavior in advance and using parameters derived from that data for the monitoring task. This software module also has the capability to adapt to the specific system being monitored by augmenting its monitoring database with initially suspect system parameter sets encountered during monitoring operations, which are later verified as nominal. While the system is offline, IMS learns nominal system behavior from archived system data sets collected from the monitored system or from accurate simulations of the system. This training phase automatically builds a model of nominal operations, and stores it in a knowledge base. The basic data structure of the IMS software algorithm is a vector of parameter values. Each vector is an ordered list of parameters collected from the monitored system by a data acquisition process. IMS then processes select data sets by formatting the data into a predefined vector format and building a knowledge base containing clusters of related value ranges for the vector parameters. In real time, IMS then monitors and displays information on the degree of deviation from nominal performance. The values collected from the monitored system for a given vector are compared to the clusters in the knowledge base. If all the values fall into or near the parameter ranges defined by one of these clusters, it is assumed to be nominal data since it matches previously observed nominal behavior. The IMS knowledge base can also be used for offline analysis of archived data.

The plasma system consists of a glass tube with a diameter of 0.5 mm or larger, if desired. The electrodes are separated by 10 mm. Helium, argon or cold dry air can be used as a plasma gas source. An applied high voltage between the electrodes causes the gas to breakdown within the central core of the glass capillary generating atmospheric plasma. Nanostructures colloids/organic/inorganic precursors are placed in a glass container with an inlet and outlet for carrier gas and are seated on an ultrasonic nebuliser. The aerosol is then carried into the plasma stream by the carrier gas and is deposited. The atmospheric plasma deposition system can be modified for depositing multiple materials, either simultaneously or sequentially, and for high-throughput processing by having multiple jets. Each capillary can either be connected to the container containing a single precursor material or to different containers containing different precursor materials to facilitate multiple depositions. The multi-jet plasma system can be automated and controlled individually to precisely control surface characteristics. This technique is independent of the chosen substrate, and has proven to work for many substrates, including paper, plastic, semiconductors and metals.

A typical sensor device includes a set of interdigitated microelectrodes fabricated by photolithography on silicon wafer or an electrically insulating substrate. In preparation for fabricating the SWCNT portion of such a sensor, a batch of treated (coated or doped) SWCNTs is dispersed in a solvent. The resulting suspension of SWCNTs is drop-deposited or injected onto the area containing the interdigitated electrodes. As the solvent evaporates, the SWCNTs form a mesh that connects the electrodes. The density of the SWCNTs in the mesh can be changed by varying the concentration of SWCNTs in the suspension and/or the amount of suspension dropped on the electrode area. To enable acquisition of measurements for comparison and to gain orthogonality in the sensor array, undoped SWCNTs can be similarly formed on another, identical set of interdigitated electrodes. Coating materials tested so far include chlorosulfonated polyethylene. Dopants that have been tested include Pd, Pt, Au, Cu and Rh nanoparticle clusters. To date, the sensor has been tested for NO2, NH3, CH4, Cl2, HCl, toluene, benzene, acetone, formaldehyde and nitrotoulene.

The technology involves a sophisticated system designed to detect conditions through the analysis of exhaled breath, utilizing an array of nanomaterial sensors fabricated upon a standard printed circuit board with interdigitated electrodes. These sensors are configured to interact with a sample gas that contains various Volatile Organic Compounds (VOCs) associated with a variety of biological conditions. Each sensor consists of nanomaterials, such as carbon nanotubes, composite nanotubes, nanoparticle-doped nanotubes, or polymer-coated nanotubes, all disposed on an electrically conductive structure. These sensors are highly sensitive to specific VOCs at a broad spectrum of concentrations, and each sensor generates a unique measurable electrical signal on interaction with VOCs in the breath that reflects the presence and concentration of specific components in the sample gas. The previously nanosensor diagnosis technology has been further developed to identify 64 specific formulations of nanomaterials that exhibit unique and varying sensitivities to VOCs, which enables unique response signatures to be developed for a wide range of VOCs. A single device may be developed using these principles to detect a variety of health conditions and diseases.