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Advanced Coupled-cavity Etalon (ACE) significantly improves both in-band transmission and out-of-band rejection. In some cases, 12% more light is transmitted inside the passband and >3x more light is rejected outside the passband. Incorporating ACE into the recirculating etalon receiver (RER) improves performance significantly. ACE increases the wavelength resolution and enables closer channel spacing resulting in a very efficient, high resolution spectrometer. RACER has both high resolution and a high photon efficiency which allows flexibility for trading different combinations of reduced cross-talk and closer channel spacing.

NASAs Goddard Space Flight Centers method for the extraction of Lunar data and/or planetary features is a method developed to extract Lunar features based on the combination of several image processing techniques. The technology was developed to register images from multiple sensors and extract features from images in low-contrast and uneven illumination conditions. The image processing and registration techniques can include, but is not limited to, a watershed segmentation, marked point processes, graph cut algorithms, wavelet transforms, multiple birth and death algorithms and/or the generalized Hough Transform.

The in situ inspection technology for additive manufacturing combines different types of cameras strategically placed around the part to monitor its properties during construction. The IR cameras collect accurate temperature data to validate thermal math models, while the visual cameras obtain highly detailed data at the exact location of the laser to build accurate, as-built geometric models. Furthermore, certain adopted techniques (e.g., single to grouped pixels comparison to avoid bad/biased pixels) reduce false positive readings. NASA has developed and tested prototypes in both laser-sintered plastic and metal processes. The technology detected errors due to stray powder sparking and material layer lifts. Furthermore, the technology has the potential to detect anomalies in the property profile that are caused by errors due to stress, power density issues, incomplete melting, voids, incomplete fill, and layer lift-up. Three-dimensional models of the printed parts were reconstructed using only the collected data, which demonstrates the success and potential of the technology to provide a deeper understanding of the laser-metal interactions. By monitoring the print, layer by layer, in real-time, users can pause the process and make corrections to the build as needed, reducing material, energy, and time wasted in nonconforming parts.

Currently, HSEG software is being used by Bartron Medical Imaging as a diagnostic tool to enhance medical imagery. Bartron Medical Imaging licensed the HSEG Technology from NASA Goddard adding color enhancement and developing MED-SEG, an FDA approved tool to help specialists interpret medical images. HSEG is available for licensing outside of the medical field (specifically for soft-tissue analysis).

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.

A time-varying electrical field E, having a root-mean-square intensity of 2rms, with a non-zero gradient in a direction transverse to the liquid or fluid flow direction, is produced by a nanostructure electrode array with a very high magnitude gradient near exposed electrode tips. A dielectrophoretic force causes the selected particles to accumulate near the electrode tips, if the medium and selected particles have substantially different dielectric constants. An insulating material surrounds most of the nanostructure electrodes, and a region of the insulating material surface is functionalized to promote attachment of the selected particle species to the surface. An electrical property value Z(meas) is measured at the functionalized surface, and is compared with a reference value Z(ref) to determine if the selected species particles are attached to the functionalized surface. An advantage of this innovation is that an array of nanostructure electrodes can provide an electric field intensity gradient that is one or more orders of magnitude greater than the corresponding gradient provided by a conventional microelectrode arrangement. As a result of the high magnitude field intensity gradients, a nanostructure concentrator can trap particles from high-speed microfluidic flows. This is critical for applications where the entire analysis must be performed in a few minutes.

Passive Smart Container system comprises four major components: RFID circuits embedded in or around the container, an antenna and RF distribution system, and an interrogator/reader. The system uses passive RFID circuits placed on a bulk item container to track consumption and quantify items as the items are removed, added or replaced in the container. The antenna is strategically integrated with the lid or elsewhere in or around the container and is constantly coupling RFID signals to/from the RFID circuits. The circuits reply with information regarding the fill level in the container. A processor connected to the reader/interrogator can infer the fill level according to which RFID circuits respond and the magnitude and phase of the returned signals. The technology is compatible with the EPCglobal Class-1 Generation-2 RFID standard. This setup can be modified to track all kinds of items, large and small, making this technology suitable and applicable to an array of commercial fields. RFID is a disruptive technology that has made a large impact on several industries, especially in supply chain and asset management. Passive Smart Container is well positioned to tap into this growing market. Its ability to account for discrete items as well as liquids and bulk goods that were deemed impossible or impractical to tag makes this technology relevant for an array of applications and industries.

Bacterial contamination of water systems used in microgravity is a major issue for NASA because biofilms can clog or interfere with water system functions and bacterial ingestion can be harmful to astronaut health. To address this problem, NASA innovators developed a microwave based technology to purify contaminated water by eradicating and eliminating bacteria that grows in systems that generate potable water, in equipment utilizing cooling loops and heat exchangers, and removing bacterial contamination that is present on a variety of surfaces. This decontamination system is chemical free and requires minimal to no consumables. Initial testing identified a specific microwave frequency band and exposure times for killing bacteria (Burkholderia cepacia) and biofilm. Test results show that exposing static water to microwave energy for 90 seconds can effectively kill waterborne bacteria and biofilm within a water filtration system. Additional testing, using a circulating water test bed, demonstrated that microwave energy at the selected frequency can effectively eradicate waterborne bacteria within 30 seconds. This technology could be further developed into a portable, lightweight system for use in remote locations as well as commercial space applications. The microwave decontamination system could also be added to existing water systems to extend the life of the system.

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.