Results for “lab data” — Page 10

NASA NTRSDate not supplied

Biological Research and Space Health Enabled by Machine Learning to Support Deep Space Missions

A key science goal of the NASA “Moon to Mars” campaign is to understand how biology responds to the Lunar, Martian, and deep space environments in order to advance fundamental knowledge, reduce risk, and support safe, productive human space missions. Through the powerful emerging approaches of artificial intelligence (AI) and machine learning (ML), a paradigm shift has begun in biomedical science and engineered astronaut health systems, to enable Earth independence and autonomy of mission operations. Here we present an overview of AI/ML architecture to support deep space mission goals, developed with leaders in the field. First, we focus on the fundamental biological research that supports our understanding of physiological responses to spaceflight, and we describe current efforts to support AI/ML research including data standardization and data engineering through maximally open and FAIR (findable, accessible, interoperable, reusable) databases and the generation of AI-ready datasets for reuse and analysis. We also discuss remote data management frameworks for research data as well as environmental and health data that are generated during deep space missions. We highlight several research projects that leverage data standardization and management for fundamental biological discovery to uncover the complex effects of space travel on living systems. Next, we provide an overview of cutting-edge AI/ML approaches that can be integrated to support remote monitoring and analysis during deep space missions, including generative models and large language models to learn the underlying biomedical patterns and predict outcomes or answer questions during off world medical scenarios. We also describe current AI/ML methods to support this research and monitoring through automated cloud-based labs which enable limited human intervention and closed-loop experimentation in remote settings. These labs could support mission autonomy by analyzing environmental data streams, and would be facilitated through in situ analytics capabilities to avoid sending large raw data files through low bandwidth communications. Finally, in the context of deep space missions with limited communications or access to medical advice from Earth, we describe a solution for integrated, real-time mission biomonitoring across hierarchical levels from continuous environmental monitoring, to wearables and point-of-care devices, to molecular and physiological monitoring. We introduce a precision space health system that will ensure that the future of space health is predictive, preventative, participatory and personalized.

artificial intelligence↗

NASA NTRS1995

Facility for the evaluation of space communications and related systems

NASA Lewis Research Center's Communications Projects Branch has developed a facility for the evaluation of space communications systems and related types of systems, called the Advanced Space Communications (ASC) Laboratory. The ASC Lab includes instrumentation, testbed hardware, and experiment control and monitor software for the evaluation of components, subsystems, systems, and networks. The ASC lab has capabilities to perform radiofrequency (RF), microwave, and millimeter-wave characterizations as well as measurements using low, medium, or high data rate digital signals. In addition to laboratory measurements, the ASC Lab also includes integrated satellite ground terminals allowing experimentation and measurements accessing operational satellites through real space links.

Kerczewski, Robert J.↗

NASA NTRS1993

Distributed Active Archive Center

The Goddard Space Flight Center Version 0 Distributed Active Archive Center (GSFC V0 DAAC) is being developed to enhance and improve scientific research and productivity by consolidating access to remote sensor earth science data in the pre-EOS time frame. In cooperation with scientists from the science labs at GSFC, other NASA facilities, universities, and other government agencies, the DAAC will support data acquisition, validation, archive and distribution. The DAAC is being developed in response to EOSDIS Project Functional Requirements as well as from requirements originating from individual science projects such as SeaWiFS, Meteor3/TOMS2, AVHRR Pathfinder, TOVS Pathfinder, and UARS. The GSFC V0 DAAC has begun operational support for the AVHRR Pathfinder (as of April, 1993), TOVS Pathfinder (as of July, 1993) and the UARS (September, 1993) Projects, and is preparing to provide operational support for SeaWiFS (August, 1994) data. The GSFC V0 DAAC has also incorporated the existing data, services, and functionality of the DAAC/Climate, DAAC/Land, and the Coastal Zone Color Scanner (CZCS) Systems.

Bodden, Lee↗

NASA NTRSDate not supplied

rHealth One Demonstration Aboard ISS: A Microfluidic Bioanalyzer Based on Sheath/Hydrodynamic Focusing Flow Cytometry

The Exploration Medical Capability (ExMC) element aims to provide astronauts with the means for their own health monitoring, diagnosis, and treatment during exploration missions. As space flight ventures further from earth, the need for autonomous medical care increases under greater constraints on size, mass, and resources. One pillar for diagnosis that crew would be separated from is laboratory analysis. Even now on ISS, blood samples must be collected and returned to earth for testing. In response, ExMC is assessing how assays for hematology, bone health, radiation exposure etc. could be addressed through commercial-off-the-shelf (COTS) and Small Business Innovation Research (SBIR) funded bioanalyzers that are miniaturizing lab technology. Since missions will reach distances where there are no timely replacements, validation on the International Space Station (ISS) is a necessary part of that assessment. In partnership with NASA Johnson Space Center (JSC) Immunology Lab and the Research Operations and Integration (ROI) element, ExMC conducted a technology demonstration on ISS of the rHEALTH ONE, a flow cytometry based bioanalyzer, to assess future devices based on this design. In flow cytometry, there are predominantly three ways to focus the cells (or particles) into a single file stream: hydrodynamic focusing, microcapillary, and acoustic focusing. The rHEALTH ONE utilizes the commercial standard, sheath-based hydrodynamic focusing. rHEALTH itself represents both a company and a suite of medical tools NASA has funded through SBIR grants towards the development of a promising diagnosis instrument for exploration missions. rHEALTH ONE is the interim version of the technology, functional as a benchtop analyzer and test bed for the next generation of rHEALTH in development. Several modifications were made to the rHEALTH ONE analyzer for operation in microgravity. For function, fluid management was key. A sheath-based analyzer uses sheath fluid to flow the sample, cleaning fluid to prevent biological contamination, and a reservoir to collect the liquid waste. The rHEALTH ONE analyzer uses bottles dependent on gravity to separate the air and liquid pathways and keep the liquids contained. It uses only 1 psig of air pressure to directly push the liquid through the device, requiring little-to-no resistance at inlet and outlet. A microtubing assembly with self-sealing luer connectors was designed – featuring 0.014 mm thick durable medical balloons to hold water inside the supply bottles – to create safe containment and easy access for the crew while maintaining the analyzer’s function. For safety, copper tape was added to the interior of the plastic housing to reduce electromagnetic interference, fluid and electrical connections were secured against vibration and leaks, and gaps were further sealed to ensure containment of the optical block and lasers. Water as the sheath and cleaning fluid and TOX 0 samples were used to reduce the biohazard risk to the crew. In May 2022, European Space Agency astronaut Samantha Cristoforetti demonstrated the rHEALTH ONE aboard ISS for its sample loading, flow cytometry, and data collection capability in microgravity. The JSC Immunology Lab provided flow cytometry expertise, designed the sample test protocol, and manufactured and benchmarked the flight samples on a ‘gold-standard’ flow cytometer. The analyzer was primed with water, purged of air, and four calibration solutions of polystyrene microparticles were tested to characterize its performance. Tightly controlled procedures and excellent execution prevented air bubble interference during air/water separation, fluid transfer, sample mixing and loading. Data showed a slight increase in signal noise on 3 of 5 channels and an anomaly of fluorophores migrating in one sample (confirmed by JSC post-flight). Flight results correctly detected the change in sample, matched the analyzer’s ground performance, and were consistent with the gold-standard’s ground results. Although areas were noted for improvement, these outcomes signified complete mission success.

R. S. Miller↗

NASA NTRS1998

Space Discovery: Teaching with Space. Evaluation: Summer, Fall 1998 Programs

This is the final report of the 1998 NASA-sponsored evaluation of the effectiveness of the United States Space Foundation's five-day Space Discovery Standard Graduate Course (Living and Working in Space), the five-day Space Discovery Advanced Graduate Course (Advanced Technology and Biomedical Research), the five-day introductory course Aviation and Space Basics all conducted during the summer of 1998, and the Teaching with Space two-day Inservice program. The purpose of the program is to motivate and equip K- 12 teachers to use proven student-attracting space and technology concepts to support standard curriculum. These programs support the America 2000 National Educational Goals, encouraging more students to stay in school, increase in competence, and have a better opportunity to be attracted to math and science. The 1998 research program continues the comprehensive evaluation begun in 1992, this year studying five summer five-day sessions and five Inservice programs offered during the Fall of 1998 in California, Colorado, New York, and Virginia. A comprehensive research design by Dr. Robert Ewell of Creative Solutions and Dr. Darwyn Linder of Arizona State University evaluated the effectiveness of various areas of the program and its applicability on diverse groups. Preliminary research methodology was a set of survey instruments administered after the courses, and another to be sent in April-4-5 months following the last inservice involved in this study. This year, we have departed from this evaluation design in two ways. First, the five-day programs used NASA's new EDCATS on-line system and associated survey rather than the Linder/Ewell instruments. The Inservice programs were evaluated using the previously developed survey adapted for Inservice programs. Second, we did not do a follow-on survey of the teachers after they had been in the field as we have done in the past. Therefore, this evaluation captures only the reactions of the teachers to the programs immediately after the instruction. Although EDCATS is designed for teachers to enter their data logged onto the appropriate internet web site, most surveys were completed on a printed copy and entered into the EDCATS system by USSF personnel. The Aviation and Space Basics class were taken to a computer lab where they responded to the survey on-line. Data from the Inservice surveys were manually entered into a computer spreadsheet program by US Space Foundation personnel and processed using the statistical program SPSS for Windows. The raw data and copy of the EDCATS survey for the five-day programs are in Appendix 1. The raw data and copy of the Inservice survey are in Appendix 2. Comments from both programs are in Appendices 3 and 4, respectively.

Ewell, Bob↗

NASA NTRS1991

Geomagnetic model investigations for 1980 - 1989: A model for strategic defense initiative particle beam experiments and a study in the effects of data types and observatory bias solutions

Two suites of geomagnetic field models were generated at the request of Los Alamos National Lab. concerning Strategic Defense Initiative (SDI) research. The first is a progression of five models incorporating MAGSAT data and data from a sequence of batches as a priori information. The batch sequence is: post 1979.5 observatory data, post 1980 land survey and selected aeromagnetic and marine survey data, a special White Sands (NM) area survey by Project Magnet with some additional post 1980 marine survey data, and finally DE-2 satellite data. These models are of 13th deg and order in their main field terms, and deg and order 10 in their first derivative temporal terms. The second suite consists of four models based solely upon post 1983.5 observatory and survey data. They are of deg and order 10 in main field and 8 in a first deg Taylor series. A comprehensive error analysis was applied to both series, which accounted for error sources such as the truncated core and crustal fields, and the neglected Sq and low deg crustal fields. Comparison of the power spectrum of the MGST (10/81) model with those of this series show good agreement.

Langel, Robert A.↗

NASA NTRSDate not supplied

Earth Independent Medical Operations (EIMO) Datascope: Challenges and Potential Solutions

Data flows and storage/retrieval capacity are severely constrained during missions in space and challenges will become even greater during exploration class missions. There is a need for an artificial intelligence (AI)-based clinical decision support system (CDSS) to monitor and analyze data to provide real-time consultative support for crew medical officer (CMO) decision-making. EIMO is defined as the gradual transition of medical care and decision making from terrestrial to space-based assets, enabling support of astronaut health and performance and reducing overall mission risk. While a hallmark of this paradigm shift from low-earth orbit is that on-board care will increasingly become the responsibility of the astronauts for primary management and decision making, terrestrial assets will continue to be paramount in pre-mission screening and planning, as well as prevention, health maintenance and long-term care contingencies. New capabilities and systems that enable progressively more robust and resilient systems and crews will be necessary to reduce risk and increase probability of deep space exploration mission success. An aspiration for EIMO is to develop AI-enhanced solutions for analysis of crew health & performance data and to facilitate clinical decision support for autonomous medical operations. A “system of systems” approach is envisioned whereby EIMO will deploy AI-supported natural language processing and machine learning (ML) techniques to utilize embedded reference databases and real-time data streams [input vectors] from multiple data sources. Constituent input vectors may include environmental controls, countermeasures data, behavioral data, physiologic wearables, point-of-care laboratory tests, personalized medical records, inventory trade space risk assessments, COTS medical databases, and ground support inputs. An ideal AI capability would possess trained fusion algorithms to cross reference input vectors with medical ‘knowledge’ [cultivated database] to stratify relevant data streams for predictive and actionable capabilities. In addition, EIMO will feature mobility, in that it can be accessed and can push/pull data within and between multiple vehicles/habitats. Large amounts and variable sources of data can be leveraged to diagnose, inform treatment strategies, and potentially predict medical events and performance decrements. Inclusion of advanced training tools using extended reality will enable increasingly autonomous medical care to aid a CMO when ground support is unavailable or time-delayed beyond required action window, e.g., emergent medical situations. EIMO CDSS would require very large datasets to train pre-flight and significant amounts of data are needed to support ML via in-flight CDSS operations. An additional challenge will be to find sufficient data to train a model relevant to astronaut demographics. The rapid, accelerating evolution of this field creates a propitious solution space to leverage multi-modal AI through public-private partnership(s). The status of multi-modal AI systems today would preclude their use for long duration missions as they remain unreliable and are subject to “digital hallucinations” and other errors that could pose operational risk. A federated labs structure is being considered to test and optimize data flow from the multiple input vectors leading to field testing in suitable ground/flight analogs. Critical to the success of an EIMO CDSS will be integration and interoperability and success will be defined by a system that can serve as an in-flight medical consult for the CMO providing critical support during medical contingencies. Benefits to terrestrial medicine may be significant as an outflow of the EIMO medical system, particularly for remote areas and communities lacking significant infrastructure, personnel and resources.

J Lemery↗

NASA NTRS2024

Earth Independent Medical Operations (EIMO) Datascope: Challenges and Potential Solutions

Data flows and storage/retrieval capacity are severely constrained during missions in space and challenges will become even greater during exploration class missions. There is a need for an artificial intelligence (AI)-based clinical decision support system (CDSS) to monitor and analyze data to provide real-time consultative support for crew medical officer (CMO) decision-making. EIMO is defined as the gradual transition of medical care and decision making from terrestrial to space-based assets, enabling support of astronaut health and performance and reducing overall mission risk. While a hallmark of this paradigm shift from low-earth orbit is that on-board care will increasingly become the responsibility of the astronauts for primary management and decision making, terrestrial assets will continue to be paramount in pre-mission screening and planning, as well as prevention, health maintenance and long-term care contingencies. New capabilities and systems that enable progressively more robust and resilient systems and crews will be necessary to reduce risk and increase probability of deep space exploration mission success. An aspiration for EIMO is to develop AI-enhanced solutions for analysis of crew health & performance data and to facilitate clinical decision support for autonomous medical operations. A “system of systems” approach is envisioned whereby EIMO will deploy AI-supported natural language processing and machine learning (ML) techniques to utilize embedded reference databases and real-time data streams [input vectors] from multiple data sources. Constituent input vectors may include environmental controls, countermeasures data, behavioral data, physiologic wearables, point-of-care laboratory tests, personalized medical records, inventory trade space risk assessments, COTS medical databases, and ground support inputs. An ideal AI capability would possess trained fusion algorithms to cross reference input vectors with medical ‘knowledge’ [cultivated database] to stratify relevant data streams for predictive and actionable capabilities. In addition, EIMO will feature mobility, in that it can be accessed and can push/pull data within and between multiple vehicles/habitats. Large amounts and variable sources of data can be leveraged to diagnose, inform treatment strategies, and potentially predict medical events and performance decrements. Inclusion of advanced training tools using extended reality will enable increasingly autonomous medical care to aid a CMO when ground support is unavailable or time-delayed beyond required action window, e.g., emergent medical situations. EIMO CDSS would require very large datasets to train pre-flight and significant amounts of data are needed to support ML via in-flight CDSS operations. An additional challenge will be to find sufficient data to train a model relevant to astronaut demographics. The rapid, accelerating evolution of this field creates a propitious solution space to leverage multi-modal AI through public-private partnership(s). The status of multi-modal AI systems today would preclude their use for long duration missions as they remain unreliable and are subject to “digital hallucinations” and other errors that could pose operational risk. A federated labs structure is being considered to test and optimize data flow from the multiple input vectors leading to field testing in suitable ground/flight analogs. Critical to the success of an EIMO CDSS will be integration and interoperability and success will be defined by a system that can serve as an in-flight medical consult for the CMO providing critical support during medical contingencies. Benefits to terrestrial medicine may be significant as an outflow of the EIMO medical system, particularly for remote areas and communities lacking significant infrastructure, personnel and resources.

Medical Operations↗

NASA NTRS1977

A model for dispersion from area sources in convective turbulence

Four independent estimates of the vertical distribution of the eddy coefficient for dispersion of a passive contaminant from an extensive area source in a convective layer have been presented. The estimates were based on the following methods: (1) a second-order closure prediction, (2) field data of pollutant concentrations over Los Angeles, (3) lab measurements of particle dispersion, and (4) assumption of equality between momentum and mass transfer coefficients in the free convective limit. It is suggested that K-values estimated both from second-order closure theory and from Los Angeles measurements are systematically underestimated.

Crane, G.↗

NASA NTRS1990

User's view of Space Station

It is pointed out that the current Space Station Freedom (SSF) design is the minimum station capable of satisfying the top-level user requirements, effectively incorporating the international partners, and providing the 'hooks' and 'scars' required to cost-effectively evolve the capabilites of the SSF. Research requirements have resulted in very important design drivers on the SS: three labs, three crewmembers per shift, low microgravity in the labs, minimum disturbances, high power for users, high data rates, multiple external attach points, and clean induced environment. Also addressed are the design capabilities that allow the SSF to support the development of technologies and the operations to expand the human presence in the solar system.

Holt, Alan C.↗

NASA NTRS2012

3D Vision Processing for the MER and MSL Mars Rover Missions

No abstract available

Multimission Instrument (Image) Processing Lab at ↗

NASA NTRS2013

Common Practice Lightning Strike Protection Characterization Technique to Quantify Damage Mechanisms on Composite Substrates

To support FAA certification airworthiness standards, composite substrates are subjected to lightning direct-effect electrical waveforms to determine performance characteristics of the lightning strike protection (LSP) conductive layers used to protect composite substrates. Test results collected from independent LSP studies are often incomparable due to variability in test procedures & applied practices at different organizations, which impairs performance correlations between different LSP data sets. Under a NASA supported contract, The Boeing Company developed technical procedures and documentation as guidance in order to facilitate a test method for conducting universal common practice lightning strike protection test procedures. The procedures obtain conformity in future lightning strike protection evaluations to allow meaningful performance correlations across data sets. This universal common practice guidance provides the manufacturing specifications to fabricate carbon fiber reinforced plastic (CFRP) test panels, including finish, grounding configuration, and acceptable methods for pretest nondestructive inspection (NDI) and posttest destructive inspection. The test operations guidance elaborates on the provisions contained in SAE ARP5416 to address inconsistencies in the generation of damage protection performance data, so as to provide for maximum achievable correlation across capable lab facilities. In addition, the guidance details a direct effects test bed design to aid in quantification of the multi-physical phenomena surrounding a lightning direct attachment supporting validation data requirements for the development of predictive computational modeling. The lightning test bed is designed to accommodate a repeatable installation procedure to secure the test panel and eliminate test installation uncertainty. It also facilitates a means to capture the electrical waveform parameters in 2 dimensions, along with the mechanical displacement and thermal heating parameters which occur during lightning attachment. Following guidance defined in the universal common practice LSP test documents, protected and unprotected CFRP panels were evaluated at 20, 40 and 100KAmps. This report presents analyzed data demonstrating the scientific usefulness of the common practice approach. Descriptions of the common practice CFRP test articles, LSP test bed fixture, and monitoring techniques to capture the electrical, mechanical and thermal parameters during lightning attachment are presented here. Two methods of measuring the electrical currents were evaluated, inductive current probes and a newly developed fiberoptic sensor. Two mechanical displacement methods were also examined, optical laser measurement sensors and a digital imaging correlation camera system. Recommendations are provided to help users implement the common practice test approach and obtain LSP test characterizations comparable across data sets.

Szatkowski, George N.↗

NASA NTRS2024

Lessons Learned from Particulate Characterization Laboratory Anomalies

The White Sands Test Facility chemistry laboratory provides quality control for cleanroom operations including cleanliness verification of aerospace hardware by particulate counts and non-volatile residue determinations, particulate counts for liquid hypergolic propellants, gaseous helium and nitrogen propellant pressurizing agents used for ground support equipment and flight test article valve actuation, gaseous oxygen primarily used for component testing, and deionized water for refurbished propellant hardware flushing. Cleanliness verification includes particulate counts and non-volatile residue determinations to industry standard, NASA, and program specifications and levels. Particulate counts are typically to customer-specified specifications and levels including JPR 5322.1H (2016) Levels 50 and 100, Orion (MPCV 70156. Revision H (2018)) Level 100, RPTSTD-8070-0001 Revision 3 (2022), and IEST-STD-CC1246E (2013) Levels 50 and 100. The laboratory issues high pressure filter holders containing membrane filters to test operations personnel, who collect samples by flowing the required volumes of fluid through the filter holder, and the filter holder is returned to the lab for counting. A passing particulate count is required before testing may proceed. Rapid data reduction and issuing of reports indicating a pass or fail of the particulate specification are required. Corrective action and resampling invariably occurs if a sample fails. Consequently, the laboratory must maintain the highest degree of reliability to facilitate quality data used to decide if testing may proceed. Experience and continual improvements have enabled reliability. However, anomalies attributed to lab processes and hardware including filter holders, membranes, and Petri dishes have been encountered. This paper presents a summary of problems, solutions, successes, and lessons learned from particle counting experience for over 35 years.

Lessons Learned↗

NASA NTRS1990

Demo of three ways to use a computer to assist in lab

The objective is to help the slow learner and students with a language problem, or to challenge the advanced student. Technology has advanced to the point where images generated on a computer can easily be recorded on a VCR and used as a video tutorial. This transfer can be as simple as pointing a video camera at the screen and recording the image. For more clarity and professional results, a board may be inserted into a computer which will convert the signals directly to the TV standard. Using a computer program that generates movies one can animate various principles which would normally be impossible to show or would require time-lapse photography. For example, you might show the change in shape of grains as a piece of metal is cold worked and then show the recrystallization and grain growth as heat is applied. More imaginative titles and graphics are also possible using this technique. Remedial help may also be offered via computer to those who find a specific concept difficult. A printout of specific data, details of the theory or equipment set-up can be offered. Programs are now available that will help as well as test the student in specific areas so that a Keller type approach can be used with each student to insure each knows the subject before going on to the next topic. A computer can serve as an information source and contain the microstructures, physical data and availability of each material tested in the lab. With this source present unknowns can be evaluated and various tests simulated to create a simple or complex case study lab assignment.

Neville, J. P.↗

NASA NTRS1990

Geodetic positioning of the Aerospace Electronics Research Lab (ERL) Osborne Time Transfer Receiver (TTR) using the GPS NAVSTAR Block I satellites

Aerospace has routinely processed the Osborne Time Transfer Receiver (TTR) data for the purpose of monitoring the performance of ground and GPS atomic clocks in near real-time with on-line residual displays and characterizing clock stability with Allan Variance calculations. Recently, Aerospace added the ability to estimate the TTR's location by differentially correcting the TTR's location in the WGS84 reference system. This new feature is exercised on a set of TTR clock phase data and Sub-meter accurate station location estimates of the TTR at the Aerospace Electronic Research Lab (ERL) are obtained.

Liu, Anthony S.↗

NASA NTRS1988

Focus on the future

An assessment is made of what was learned from Halley and recommendations are made for future directions for infrared studies of comets and supporting lab investigations. The following issues are addressed: (1) What steps can be taken to achieve consistent interpretation of Halley infrared data; (2) How successful has the Halley Watch been for infrared studies; (3) What supporting lab research is needed; (4) What are the key infrared observations needed for future comets; and (5) How do current and future NASA programs relate to comet studies.

Hanner, Martha S.↗

NASA NTRS1992

Computer integrated laboratory testing

The objective is the integration of computers into the Engineering Materials Science Laboratory course, where existing test equipment is not computerized. The first lab procedure is to demonstrate and produce a material phase change curve. The second procedure is a demonstration of the modulus of elasticity and related stress-strain curve, plastic performance, maximum and failure strength. The process of recording data by sensors that are connected to a data logger which adds a time base, and the data logger in turn connected to a computer, places the materials labs into a computer integrated mode with minimum expense and maximum flexibility. The sensor signals are input into a spread sheet for tabular records, curve generation, and graph printing.

Dahl, Charles C.↗

NASA NTRS2009

System for Automated Calibration of Vector Modulators

Vector modulators are used to impose baseband modulation on RF signals, but non-ideal behavior limits the overall performance. The non-ideal behavior of the vector modulator is compensated using data collected with the use of an automated test system driven by a LabVIEW program that systematically applies thousands of control-signal values to the device under test and collects RF measurement data. The technology innovation automates several steps in the process. First, an automated test system, using computer controlled digital-to-analog converters (DACs) and a computer-controlled vector network analyzer (VNA) systematically can apply different I and Q signals (which represent the complex number by which the RF signal is multiplied) to the vector modulator under test (VMUT), while measuring the RF performance specifically, gain and phase. The automated test system uses the LabVIEW software to control the test equipment, collect the data, and write it to a file. The input to the Lab - VIEW program is either user-input for systematic variation, or is provided in a file containing specific test values that should be fed to the VMUT. The output file contains both the control signals and the measured data. The second step is to post-process the file to determine the correction functions as needed. The result of the entire process is a tabular representation, which allows translation of a desired I/Q value to the required analog control signals to produce a particular RF behavior. In some applications, corrected performance is needed only for a limited range. If the vector modulator is being used as a phase shifter, there is only a need to correct I and Q values that represent points on a circle, not the entire plane. This innovation has been used to calibrate 2-GHz MMIC (monolithic microwave integrated circuit) vector modulators in the High EIRP Cluster Array project (EIRP is high effective isotropic radiated power). These calibrations were then used to create correction tables to allow the commanding of the phase shift in each of four channels used as a phased array for beam steering of a Ka-band (32-GHz) signal. The system also was the basis of a breadboard electronic beam steering system. In this breadboard, the goal was not to make systematic measurements of the properties of a vector modulator, but to drive the breadboard with a series of test patterns varying in phase and amplitude. This is essentially the same calibration process, but with the difference that the data collection process is oriented toward collecting breadboard performance, rather than the measurement of output from a network analyzer.

Lux, James↗