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NASA NTRS1992

Experiments with recursive estimation in astronomical image processing

Recursive estimation concepts were applied to image enhancement problems since the 70's. However, very few applications in the particular area of astronomical image processing are known. These concepts were derived, for 2-dimensional images, from the well-known theory of Kalman filtering in one dimension. The historic reasons for application of these techniques to digital images are related to the images' scanned nature, in which the temporal output of a scanner device can be processed on-line by techniques borrowed directly from 1-dimensional recursive signal analysis. However, recursive estimation has particular properties that make it attractive even in modern days, when big computer memories make the full scanned image available to the processor at any given time. One particularly important aspect is the ability of recursive techniques to deal with non-stationary phenomena, that is, phenomena which have their statistical properties variable in time (or position in a 2-D image). Many image processing methods make underlying stationary assumptions either for the stochastic field being imaged, for the imaging system properties, or both. They will underperform, or even fail, when applied to images that deviate significantly from stationarity. Recursive methods, on the contrary, make it feasible to perform adaptive processing, that is, to process the image by a processor with properties tuned to the image's local statistical properties. Recursive estimation can be used to build estimates of images degraded by such phenomena as noise and blur. We show examples of recursive adaptive processing of astronomical images, using several local statistical properties to drive the adaptive processor, as average signal intensity, signal-to-noise and autocorrelation function. Software was developed under IRAF, and as such will be made available to interested users.

Busko, I.↗

NASA NTRS2011

Model Adaptation for Prognostics in a Particle Filtering Framework

One of the key motivating factors for using particle filters for prognostics is the ability to include model parameters as part of the state vector to be estimated. This performs model adaptation in conjunction with state tracking, and thus, produces a tuned model that can used for long term predictions. This feature of particle filters works in most part due to the fact that they are not subject to the "curse of dimensionality", i.e. the exponential growth of computational complexity with state dimension. However, in practice, this property holds for "well-designed" particle filters only as dimensionality increases. This paper explores the notion of wellness of design in the context of predicting remaining useful life for individual discharge cycles of Li-ion batteries. Prognostic metrics are used to analyze the tradeoff between different model designs and prediction performance. Results demonstrate how sensitivity analysis may be used to arrive at a well-designed prognostic model that can take advantage of the model adaptation properties of a particle filter.

Saha, Bhaskar↗

NASA NTRSDate not supplied

Enhancing Air Traffic Control Planning with Automatic Speech Recognition

The decisions made during the Federal Aviation Administration Air Traffic Control System Command Center's planning teleconferences hold significant sway over the National Airspace System. Held every two hours, these teleconferences convene air traffic managers and stakeholders from across the nation to discuss airspace conditions, weather, and constraints, leading to the formulation and adjustment of traffic management initiatives. Given the critical nature of these decisions, the need for accurate and efficient record-keeping is paramount. In recent years, the application of automatic speech recognition has gained popularity across diverse industries, including aviation. While traditional applications focus on transcribing air traffic control communication, this paper explores a unique application of automatic speech recognition by converting the audio from planning teleconferences into text transcriptions. This innovative approach addresses key challenges in the field, presenting potential benefits for quality assurance, real-time participation, and downstream natural language processing tasks. A notable breakthrough in the machine learning community, namely the transformer neural network architecture, forms the backbone of the proposed solution in this paper. The transformer architecture's role in this research represents a paradigm shift in the efficiency of automatic speech recognition models. By reducing the amount of in-domain training data required, this architecture allows for the fine-tuning of such models like Whisper, originally pretrained on vast English speech datasets. The adaptability of the transformer architecture proves invaluable in capturing the nuances of aviation terminology and specific language used in planning teleconferences. Leveraging the Whisper model as a baseline, our research details the fine-tuning and validation using a dataset comprising 20 hours of meticulously transcribed planning teleconferences. Notably, the baseline pretrained Whisper model exhibited a word error rate of 18.77%. Through the fine-tuning process, the model achieved a substantial improvement, demonstrating an impressive performance with a reduced word error rate of 6.82%. This substantial decrease in WER not only highlights the effectiveness of the transformer architecture but also emphasizes the practical advancements achieved through the application of automatic speech recognition in this specific domain. The utilization of automatic speech recognition in planning teleconferences in this work introduces several novelties. Firstly, the creation of text transcriptions offers a valuable tool for quality assurance and facilitates the efficient review of teleconferences. This is an important aspect of the proposed solution, given the time-sensitive and high-stakes nature of decisions made during these meetings. Furthermore, text-searchable transcriptions provide a streamlined approach for locating and validating critical information, potentially saving hours of manual effort in searching through audio recordings. Moreover, our research identifies a key use case for external facilities and stakeholders. In situations where attendance at the planning teleconference is not feasible, having access to text transcriptions in real-time or shortly after the teleconference ends, proves to be a time-saving and informative resource. This feature enhances collaboration and ensures that stakeholders can stay abreast of important discussions and decisions even in their absence. Despite the efficiency gains facilitated by the transformer architecture in automatic speech recognition technology, it is essential to acknowledge the human factors in data creation. Subject matter experts play a crucial role in accurately transcribing planning teleconferences due to the specificity and complexity of the information discussed. The research dataset, consisting of 20 hours of transcribed planning teleconferences, forms the foundation for fine-tuning and validating the Whisper model. The achieved word error rate of 6.82% demonstrates promising advancements, particularly in recognizing essential aviation terminology within the teleconferences. In conclusion, this paper presents a comprehensive exploration of the application of automatic speech recognition in Air Traffic Control System Command Center planning teleconferences, leveraging the transformer architecture for enhanced efficiency. The novel contributions lie in the improved accessibility of decision-making records, real-time participation opportunities for external stakeholders, and the potential for downstream natural language processing advancements. As the aviation industry continues to evolve, the integration of automatic speech recognition technologies holds the promise of revolutionizing decision-making processes and contributing to the overall safety and efficiency of air traffic management.

ATM↗

NASA NTRS2001

How Cyanobacterial Distributions Reveal Flow and Irradiance Conditions of Photosynthetic Biofilm Formation

Microbial life on Earth is enormously abundant at sediment-water interfaces. The fossil record in fact contains abundant evidence of the preservation of life on such surfaces. It is therefore critical to our interpretation of early Earth history, and potentially to history of life on other planets, to be able to recognize life forms at these interfaces. On Earth this life often occurs as organized structures of microbes and their extracellular exudates known as biofilms. When such biofilms occur in areas receiving sunlight photosynthetic biofilms are the dominant form in natural ecosystems due to selective advantage inherent in their ability to utilize solar energy. Cyanobacteria are the dominant phototrophic microbes in most modern and ancient photosynthetic biofilms, microbial mats and stromatolites. Due to their long (3.5 billion year) evolutionary history, this group has extensively diversified resulting in an enormous array of morphologies and physiological abilities. This enormous diversity and specialization results in very specific selection for a particular cyanobacterium in each available photosynthetic niche. Furthermore these organisms can alter their spatial orientation, cell morphology, pigmentation and associations with heterotrophic organisms in order to fine tune their optimization to a given micro-niche. These adaptations can be detected, and if adequate knowledge of the interaction between environmental conditions and organism response is available, the detectable organism response can be used to infer the environmental conditions causing that response. This presentation will detail two specific examples which illustrate this point, Light and water are essential to photosynthesis in cyanobacteria and these organisms have specific detectable behavioural responses to these parameters. We will present cyanobacterial responses to quantified flow and irradiance to demonstrate the interpretative power of distribution and orientation information. This study presents new results, but many such examples are already found in the literature.

Prufert-Bebout, Lee↗

NASA NTRS2001

How Cyanobacterial Distributions Reveal Flow and Irradiance Conditions of Photosynthetic Biofilm Formation

Microbial life on Earth is enormously abundant at sediment-water interfaces. The fossil record in fact contains abundant evidence of the preservation of life on such surfaces. It is therefore critical to our interpretation of early Earth history, and potentially to history of life on other planets, to be able to recognize life forms at these interfaces. On Earth this life often occurs as organized structures of microbes and their extracellular exudates known as biofilms. When such biofilms occur in areas receiving sunlight photosynthetic biofilms are the dominant form in natural ecosystems due to selective advantage inherent in their ability to utilize solar energy. Cyanobacteria are the dominant phototrophic microbes in most modern and ancient photosynthetic biofilms, microbial mats and stromatolites. Due to their long (3.5 billion year) evolutionary history, this group has extensively diversified resulting in an enormous array of morphologies and physiological abilities. This enormous diversity and specialization results in very specific selection for a particular cyanobacterium in each available photosynthetic niche. Furthermore these organisms can alter their spatial orientation, cell morphology, pigmentation and associations with heterotrophic organisms in order to fine tune their optimization to a given micro-niche. These adaptations can be detected, and if adequate knowledge of the interaction between environmental conditions and organism response is available, the detectable organism response can be used to infer the environmental conditions causing that response. This presentation will detail two specific examples which illustrate this point. Light and water are essential to photosynthesis in cyanobacteria and these organisms have specific detectable behavioral responses to these parameters. We will present cyanobacterial responses to quantified flow and irradiance to demonstrate the interpretative power of distribution and orientation information. This study presents new results, but many such examples are already found in the literature. However this information exists in such a wide variety of journals, spanning decades of research that the utility of the vast storehouse of information is limited, not by the ability of cyanobacteria to respond in recognizable ways to environmental stimuli, but by our ability to compile and use this information. Recent advances in information technology will soon allow us to overcome these difficulties and utilize the detailed responses of cyanobacteria to environmental microniches as powerful records of the interaction between the biosphere and lithosphere.

Prufert-Bebout, Lee↗

NASA NTRS2002

Observer-Based Magnetic Bearing Controller Developed for Aerospace Flywheels

A prototype of a versatile, observer-based magnetic bearing controller for aerospace flywheels was successfully developed and demonstrated on a magnetic bearing test rig (see the photograph) and an actual flywheel module. The objective of this development included a fast, yet low risk, control development process, and a robust, high-performance controller for a large variety of flywheels. This required a good system model, an efficient development procedure, and a model-based controller that addressed the key problems associated with flywheel and bearing imbalance, sensor error, and vibration. The model used in this control development and tuning procedure included the flexible rotor dynamics and motor-induced vibrations. Such a model was essential for low-risk scheduling of speed-dependent control parameters and for reliable evaluation of novel control strategies. The successfully tested control prototype utilized an extended Kalman filter to estimate the true rotor principal-axis motion from the raw sensor position feedback. For control refinement, the extended Kalman filter also estimated and eliminated the combined effects of mass-imbalance and sensor runouts from the input data. A key advantage of the design based on the extended Kalman filter is its ability to accurately estimate both the rotor's principal-axis position and gyroscopic rates with the least amount of phase lag. This is important for control parameter scheduling to dampen the gyroscopic motions. Because of large uncertainties in the magnetic bearing and imbalance characteristics, this state-estimation scheme alone is insufficient for containing the rotor motion within the desired 1-mil excursion radius. A nonlinear gain adjustment based on an estimation of the principal-axis orbit size was needed to provide a coarse (nonoptimal), but robust, control of the orbit growth. Control current minimization was achieved with a (steepest gradient) search of synchronous errors in the principal-axis position input data. Actual flywheel tests of this observer-based controller (developed entirely in-house) at the NASA Glenn Research Center showed that the model correctly predicted the rotor orbit growth as a function of rotational speed, and it demonstrated the capability of gain adjustments to arrest this growth. Data from these tests on an actual flywheel module spun to 26,000 rpm proved that the controller was able to contain the shaft motion to within much less than 0.5 mils of radial excursion with axis currents less than 300 mA in root-mean-square estimate. The test speed range was limited because of thermal expansion concerns for this particular flywheel unit, not because of any deficiency in the controller. Simulations for this unit indicated that the controller should be robust up to its top operating speed of 60,000 rpm. Aside from these important achievements, and most significantly, it took less than 1 week to adapt this controller from the simple test rig to the actual flywheel and to demonstrate full five-axis levitation and control. This demonstration showed that both the controller and the model-based development and tuning framework are easily adaptable to a wide range of rotors and bearing configurations and, hence, are capable of reducing design risks and costs for many future flywheel technology developments.

Le, Dzu K.↗

NASA NTRS2003

Software Searches for Better Spacecraft-Navigation Models

ADAPT is a computer program that searches for better mathematical models for spacecraft navigation. The task of tuning trajectory-determination models for interplanetary navigation is complex, requiring an intensive search of multiple dynamical and nondynamical models that yield trajectory solutions with minimal errors. By automating the search, ADAPT eases the task of human analysts and enables them to consider wider ranges of potential solutions. ADAPT uses genetic algorithms to search a range of relevant parameters in a user-selected design space to arrive at values for those parameters that best fit the measured spacecraft-tracking data. The user s guide for ADAPT reviews the theoretical basis of the program and presents two example applications. One example is that of selecting a solar-radiation model for the Mars Pathfinder (MPF) mission using MPF tracking data and an extended Kalman filter from prior spacecraft-navigation software. The second example is of the use of tracking data from the Stardust spacecraft mission combined with a pseudo-epoch-state batch filter and an empirical small-forces model to find improved impulse models for use during Stardust attitude adjustments.

Ely, Todd↗

NASA NTRS1991

Hybrid state-space self-tuning control using dual-rate sampling

This paper presents a hybrid state-space self-tuning control scheme using dual-rate sampling for suboptimal digital adaptive control of linear time-invariant continuous-time multivariable stochastic systems with unknown parameters. An equivalent fast-rate discrete-time state-space innovation model (with estimated states) of the continuous-time system is constructed by using the estimated system parameters and Kalman gain. To utilize the existing optimal regional-pole assignment method developed in the continuous-time domain, the constructed fast-rate discrete-time model is converted into an equivalent continuous-time model for the development of a state-feedback optimal control law with pole placement in a specific region. The developed analog optimal control law is then converted into an equivalent pseudo-slow-rate digital control law via the proposed digital redesign technique, which can be realized via slow-rate digital electronics. The proposed method enables the development of a digitally implementable advanced control algorithm for digital adaptive control of continuous-time multivariable stochastic systems which may be unstable and/or have nonminimum phase.

Shieh, Leang S.↗

NASA NTRS1992

Self-tuning control of attitude and momentum management for the Space Station

This paper presents a hybrid state-space self-tuning design methodology using dual-rate sampling for suboptimal digital adaptive control of attitude and momentum management for the Space Station. This new hybrid adaptive control scheme combines an on-line recursive estimation algorithm for indirectly identifying the parameters of a continuous-time system from the available fast-rate sampled data of the inputs and states and a controller synthesis algorithm for indirectly finding the slow-rate suboptimal digital controller from the designed optimal analog controller. The proposed method enables the development of digitally implementable control algorithms for the robust control of Space Station Freedom with unknown environmental disturbances and slowly time-varying dynamics.

Shieh, L. S.↗

NASA NTRS2006

NASA Tech Briefs, May 2006

T-Shaped Emitter Metal Structures for HBTs Rigorous Estimation of SNR of a PSK Communication Link Advanced Ka-Band Transceiver With Monopulse Tracking EMI Filters for Low-Temperature Applications Lightweight Electronic Camera for Research on Clouds Pilot Weather Advisor System Waveguide Power-Amplifier Module for 80 to 150 GHz Better Back Contacts for Solar Cells on Flexible Substrates Topics covered include:Tunable, Highly Stable Lasers for Coherent Lidar; Improved Photon-Emission-Microscope System; Program Synthesizes UML Sequence Diagrams; Aspect-Oriented Subprogram Synthesizes UML Sequence Diagrams; Updated Computational Model of Cosmic Rays Near Earth; Software for Alignment of Segments of a Telescope Mirror; Simulation of Dropping of Cargo With Parachutes; DAVE-ML Utility Program; Robust Control for the Mercury Laser Altimeter; Thermally Stable Piezoelectric and Pyroelectric Polymers; Combustion Synthesis of Ca3(PO4)2 Net-Shape Surgical Implants; Stochastic Representation of Chaos Using Terminal Attractors; Two High-Temperature Foil Journal Bearings; Using Plates To Represent Fillets in Finite-Element Modeling; Repairing Chipped Silicide Coatings on Refractory Metal Substrates; Simplified Fabrication of Helical Copper Antennas; Graded-Index "Whispering-Gallery" Optical Microresonators; Optical Profilometers Using Adaptive Signal Processing; Manufacture of Sparse-Spectrum Optical Microresonators; Exact Tuning of High-Q Optical Microresonators by Use of UV; Automation for "Direct-to" Clearances in Air-Traffic Control; Improved Traps for Removing Gases From Coolant Liquids; and Lunar Constellation of Frozen Elliptical Inclined Orbits.

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NASA NTRS2024

An On-Board Off-Board Framework for Online Replanning: Applied to UAVs in Urban Environments

Autonomous systems are being used in a multitude of areas at an increasing rate and require a high level of adaptivity and intelligence to operate safely, especially under faulty conditions. This paper introduces a novel genetic algorithm tailored for UAV trajectory replanning, with an improved execution time via search space reduction based on the operating conditions of the UAV and its remaining mission. A unique characteristic of the replanning agent is its fast-start and adaptive properties, pre-seeding candidates with partial solutions and dynamically tuning elitism, crossover, and mutation rates in correspondence to the average fitness and diversity of the population. A population restart mechanism and early stopping mechanism are evaluated as well to assess their effect on solution quality and runtime. Previous work on genetic algorithms for UAV replanning were conducted with short trajectories in a small state space. Our UAV operates in a 56,000 square meter simulated urban environment, with static obstacles and a total of 53 possible waypoints. The agent increases the safety and reliability of UAV autonomy when operating under faulty conditions and when replanning is required.

Machine Learning↗

NASA NTRS1996

Optimizing Input/Output Using Adaptive File System Policies

Parallel input/output characterization studies and experiments with flexible resource management algorithms indicate that adaptivity is crucial to file system performance. In this paper we propose an automatic technique for selecting and refining file system policies based on application access patterns and execution environment. An automatic classification framework allows the file system to select appropriate caching and pre-fetching policies, while performance sensors provide feedback used to tune policy parameters for specific system environments. To illustrate the potential performance improvements possible using adaptive file system policies, we present results from experiments involving classification-based and performance-based steering.

Madhyastha, Tara M.↗

NASA NTRS1985

Experiments in structural dynamics and control using a grid

Future spacecraft are being conceived that are highly flexible and of extreme size. The two features of flexibility and size pose new problems in control system design. Since large scale structures are not testable in ground based facilities, the decision on component placement must be made prior to full-scale tests on the spacecraft. Control law research is directed at solving problems of inadequate modelling knowledge prior to operation required to achieve peak performance. Another crucial problem addressed is accommodating failures in systems with smart components that are physically distributed on highly flexible structures. Parameter adaptive control is a method of promise that provides on-orbit tuning of the control system to improve performance by upgrading the mathematical model of the spacecraft during operation. Two specific questions are answered in this work. They are: What limits does on-line parameter identification with realistic sensors and actuators place on the ultimate achievable performance of a system in the highly flexible environment? Also, how well must the mathematical model used in on-board analytic redundancy be known and what are the reasonable expectations for advanced redundancy management schemes in the highly flexible and distributed component environment?

Montgomery, R. C.↗

NASA NTRS2002

Test Platform for Advanced Digital Control of Brushless DC Motors (MSFC Center Director's Discretionary Fund)

A FY 2001 Center Director's Discretionary Fund task to develop a test platform for the development, implementation. and evaluation of adaptive and other advanced control techniques for brushless DC (BLDC) motor-driven mechanisms is described. Important applications for BLDC motor-driven mechanisms are the translation of specimens in microgravity experiments and electromechanical actuation of nozzle and fuel valves in propulsion systems. Motor-driven aerocontrol surfaces are also being utilized in developmental X vehicles. The experimental test platform employs a linear translation stage that is mounted vertically and driven by a BLDC motor. Control approaches are implemented on a digital signal processor-based controller for real-time, closed-loop control of the stage carriage position. The goal of the effort is to explore the application of advanced control approaches that can enhance the performance of a motor-driven actuator over the performance obtained using linear control approaches with fixed gains. Adaptive controllers utilizing an exact model knowledge controller and a self-tuning controller are implemented and the control system performance is illustrated through the presentation of experimental results.

Gwaltney, D. A.↗

NASA NTRS1985

Adaptive Inverse Control for Rotorcraft Vibration Reduction

This thesis extends the Least Mean Square (LMS) algorithm to solve the mult!ple-input, multiple-output problem of alleviating N/Rev (revolutions per minute by number of blades) helicopter fuselage vibration by means of adaptive inverse control. A frequency domain locally linear model is used to represent the transfer matrix relating the higher harmonic pitch control inputs to the harmonic vibration outputs to be controlled. By using the inverse matrix as the controller gain matrix, an adaptive inverse regulator is formed to alleviate the N/Rev vibration. The stability and rate of convergence properties of the extended LMS algorithm are discussed. It is shown that the stability ranges for the elements of the stability gain matrix are directly related to the eigenvalues of the vibration signal information matrix for the learning phase, but not for the control phase. The overall conclusion is that the LMS adaptive inverse control method can form a robust vibration control system, but will require some tuning of the input sensor gains, the stability gain matrix, and the amount of control relaxation to be used. The learning curve of the controller during the learning phase is shown to be quantitatively close to that predicted by averaging the learning curves of the normal modes. For higher order transfer matrices, a rough estimate of the inverse is needed to start the algorithm efficiently. The simulation results indicate that the factor which most influences LMS adaptive inverse control is the product of the control relaxation and the the stability gain matrix. A small stability gain matrix makes the controller less sensitive to relaxation selection, and permits faster and more stable vibration reduction, than by choosing the stability gain matrix large and the control relaxation term small. It is shown that the best selections of the stability gain matrix elements and the amount of control relaxation is basically a compromise between slow, stable convergence and fast convergence with increased possibility of unstable identification. In the simulation studies, the LMS adaptive inverse control algorithm is shown to be capable of adapting the inverse (controller) matrix to track changes in the flight conditions. The algorithm converges quickly for moderate disturbances, while taking longer for larger disturbances. Perfect knowledge of the inverse matrix is not required for good control of the N/Rev vibration. However it is shown that measurement noise will prevent the LMS adaptive inverse control technique from controlling the vibration, unless the signal averaging method presented is incorporated into the algorithm.

Jacklin, Stephen A.↗

NASA NTRS2004

Robust local search for spacecraft operations using adaptive noise

Randomization is a standard technique for improving the performance of local search algorithms for constraint satisfaction. However, it is well-known that local search algorithms are constraints satisfaction. However, it is well-known that local search algorithms are to the noise values selected. We investigate the use of an adaptive noise mechanism in an iterative repair-based planner/scheduler for spacecraft operations. Preliminary results indicate that adaptive noise makes the use of randomized repair moves safe and robust; that is, using adaptive noise makes it possible to consistently achieve, performance comparable with the best tuned noise setting without the need for manually tuning the noise parameter.

planning↗

NASA NTRSDate not supplied

Development of Concurrent Artificially-Intelligent Spectrometry and Adaptive Lidar System (CASALS) for Swath Mapping From Space

We present the design and performance of a Concurrent Artificially-intelligent Spectrometry and Adaptive Lidar System (CASALS) for 3D imaging from Space. With a single fast wavelength tuning laser, CASALS accomplishes a 1,200 resolvable spots swath mapping by grating dispersion wavelength steering. Any subset of these 1,200 spots can be selected by wavelength switching. The validation operating principle was accomplished and reported in IGASS-2022. With configurable base design, we report the designs and progress of the CASALS airplane campaign with 256 contiguous ground spots. It is accomplished with a single fast tuning lase at 1040-nm, 1.152MHz tuning rate, and pulse modulated 2-ns on each wavelength. Return pulses are mapped to an eight-pixel detector array with single-photon sensitivity. The lidar returns are time-multiplexed to two outputs that are digitized with two 1-GSPS-digitizer. A grating spectrometer rejects solar background noise spatially and spectrally. We are developing a 1040-nm CASALS intended for multiple LEO orbit missions: Earth Venture Mission on ESPA Grande, STV Mission on ESPA Grande SmallSat, STV Mission on spacecraft equivalent to ICESat power (SWaP,) and splitting the laser beam requires higher laser pulse energy.

ranging lidar↗

NASA NTRS1991

Spinoff From a Moon Suit

Al Gross transferred expertise obtained as an ILC engineer for NASA's Apollo program to the manufacture of athletic shoes. Gross substituted DuPont's Hytrel plastic for foam materials in the shoe's midsole, eliminating cushioning loss caused by body weight. An external pressurized shell applied from space suit technology was incorporated into the shoe. Stiffness and cushioning properties of the midsole were "tuned" by varying material thickness and styling lines. A stress free "blow molding" process adapted from NASA space suit design was also utilized. The resulting compression chamber midsole performed well in tests. It allows AVIA to re-configure for specific sports and is a "first step" toward a durable, foamless, non-fatiguing midsole.

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