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All sourcesNASA NTRSDOE OSTI
NASA NTRS2018

Considerations for Implementing Voice-Controlled Spacecraft Systems Through a Human-Centered Design Approach

As computational power and speech recognition algorithms improve, the consumer market will see better-performing speech recognition applications. The cell phone and Internet-related service industry have further enhanced speech recognition applications using artificial intelligence and statistical data-mining techniques. These improvements to speech recognition technology (SRT) may one day help astronauts on future deep space human missions that require control of complex spacecraft systems or spacesuit applications by voice. Though SRT and more advanced speech recognition techniques show promise, use of this technology for a space application such as vehicle/habitat/spacesuit requires careful considerations. There are still recognition challenges to overcome such as background noise, human speech variability, and task loading. However, implemented correctly, a voice-controlled spacecraft system (VCSS) can provide a useful, natural and efficient form of human-machine communications during complex tasks such as when the hands and eyes are busy or as an aid for vehicle situational awareness inquiries or collaborative human-robot tasks. This document provides considerations and guidance for the use of SRT in VCSS applications for space missions, specifically in command-and-control (C2) applications where the commanding is user-initiated. First, current SRT limitations as known at the time of this report are given. Then, highlights of SRT used in the space program provide the reader with a history of some of the human spaceflight applications and research. Next, an overview of the speech production process and the intrinsic variations of speech are provided. Finally, general guidance and considerations are given for development of a VCSS using a human-centered design approach for space applications that includes vocabulary selection and performance testing, as well as VCSS considerations for C2 dialogue management design, feedback, error handling, and evaluation/usability testing. Available from

Salazar, George A.↗
NASA NTRS2019

Development Considerations for Implementing a Voice-Controlled Spacecraft System

As computational power and speech recognition algorithms improve, the consumer market will see better-performing speech recognition applications. The cell phone and Internet-related service industry have further enhanced speech recognition applications using artificial intelligence and statistical data-mining techniques. These improvements to speech recognition technology (SRT) may one day help astronauts on future deep space human missions that require control of complex spacecraft systems or spacesuit applications by voice. Though SRT and more advanced speech recognition techniques show promise, use of this technology for a space application such as vehicle/habitat/spacesuit requires careful considerations. This paper provides considerations and guidance for the use of SRT in voice-controlled spacecraft systems (VCSS) applications for space missions, specifically in command-and-control (C2) applications where the commanding is user-initiated. First, current SRT limitations as known at the time of this report are given. Then, highlights of SRT used in the space program provide the reader with a history of some of the human spaceflight applications and research. Next, an overview of the speech production process and the intrinsic variations of speech are provided. Finally, general guidance and considerations are given for the development of a VCSS using a human-centered design approach for space applications that includes vocabulary selection and performance testing, as well as VCSS considerations for C2 dialogue management design, feedback, error handling, and evaluation/usability testing.

Salazar, George↗
DOE OSTI2025

Efficient Training of Deep Neural Operator Networks via Randomized Sampling

Neural operators (NOs) employ deep neural networks to learn the mappings between infinitedimensional function spaces. Deep operator network (DeepONet), a popular NO architecture, has demonstrated success in the real-time prediction of complex dynamics across various scientific and engineering applications. In this work, we introduce a random sampling technique to be adopted during the training of DeepONet, aimed at improving the generalization ability of the model, while significantly reducing the computational time. The proposed approach targets the trunk network of the DeepONet model that outputs the basis functions corresponding to the spatiotemporal locations of the bounded domain on which the physical system is defined. While constructing the loss function, DeepONet training traditionally considers a uniform grid of spatiotemporal points at which all the output functions are evaluated for each iteration. This approach leads to a larger batch size, resulting in poor generalization and increased memory demands, due to the limitations of the stochastic gradient descent (SGD) optimizer. The proposed random sampling over the inputs of the trunk net mitigates these challenges, improving generalization and reducing the memory requirements during training, resulting in significant computational gains. We validate our hypothesis through three benchmark examples, demonstrating substantial reductions in training time while achieving comparable or lower overall test errors relative to the traditional training approach. Our results indicate that incorporating randomization in the trunk network inputs during training enhances the efficiency and robustness of DeepONet, offering a promising avenue for improving the framework’s performance in modeling complex physical systems.

Karumuri, Sharmila [Department of Civil & Systems ↗
NASA NTRS2014

The Auto-Gopher Deep Drill

Subsurface penetration by coring, drilling or abrading is of great importance for a large number of space and earth applications. An Ultrasonic/Sonic Drill/Corer (USDC) has been in development at JPL's Nondestructive Evaluation and Advanced Actuators (NDEAA) lab as an adaptable tool for many of these applications. The USDC uses a novel drive mechanism to transform the high frequency ultrasonic or sonic vibrations of the tip of a horn into a lower frequency sonic hammering of a drill bit through an intermediate free-flying mass. The USDC device idea has been implemented at various scales from handheld drills to large diameter coring devices. A series of computer programs that model the function and performance of the USDC device were developed and were later integrated into an automated modeling package. The USDC has also evolved from a purely hammering drill to a rotary hammer drill as the design requirements increased form small diameter shallow drilling to large diameter deep coring. A synthesis of the Auto-Gopher development is presented in this paper.

Badescu, Mircea↗
NASA NTRS1987

Advanced development of double-injection, deep-impurity semiconductor switches

Deep-impurity, double-injection devices, commonly refered to as (DI) squared devices, represent a class of semiconductor switches possessing a very high degree of tolerance to electron and neutron irradiation and to elevated temperature operation. These properties have caused them to be considered as attractive candidates for space power applications. The design, fabrication, and testing of several varieties of (DI) squared devices intended for power switching are described. All of these designs were based upon gold-doped silicon material. Test results, along with results of computer simulations of device operation, other calculations based upon the assumed mode of operation of (DI) squared devices, and empirical information regarding power semiconductor device operation and limitations, have led to the conculsion that these devices are not well suited to high-power applications. When operated in power circuitry configurations, they exhibit high-power losses in both the off-state and on-state modes. These losses are caused by phenomena inherent to the physics and material of the devices and cannot be much reduced by device design optimizations. The (DI) squared technology may, however, find application in low-power functions such as sensing, logic, and memory, when tolerance to radiation and temperature are desirable (especially is device performance is improved by incorporation of deep-level impurities other than gold.

Hanes, M. H.↗
NASA NTRS2010

Performance Bounds on Two Concatenated, Interleaved Codes

A method has been developed of computing bounds on the performance of a code comprised of two linear binary codes generated by two encoders serially concatenated through an interleaver. Originally intended for use in evaluating the performances of some codes proposed for deep-space communication links, the method can also be used in evaluating the performances of short-block-length codes in other applications. The method applies, more specifically, to a communication system in which following processes take place: At the transmitter, the original binary information that one seeks to transmit is first processed by an encoder into an outer code (Co) characterized by, among other things, a pair of numbers (n,k), where n (n > k)is the total number of code bits associated with k information bits and n k bits are used for correcting or at least detecting errors. Next, the outer code is processed through either a block or a convolutional interleaver. In the block interleaver, the words of the outer code are processed in blocks of I words. In the convolutional interleaver, the interleaving operation is performed bit-wise in N rows with delays that are multiples of B bits. The output of the interleaver is processed through a second encoder to obtain an inner code (Ci) characterized by (ni,ki). The output of the inner code is transmitted over an additive-white-Gaussian- noise channel characterized by a symbol signal-to-noise ratio (SNR) Es/No and a bit SNR Eb/No. At the receiver, an inner decoder generates estimates of bits. Depending on whether a block or a convolutional interleaver is used at the transmitter, the sequence of estimated bits is processed through a block or a convolutional de-interleaver, respectively, to obtain estimates of code words. Then the estimates of the code words are processed through an outer decoder, which generates estimates of the original information along with flags indicating which estimates are presumed to be correct and which are found to be erroneous. From the perspective of the present method, the topic of major interest is the performance of the communication system as quantified in the word-error rate and the undetected-error rate as functions of the SNRs and the total latency of the interleaver and inner code. The method is embodied in equations that describe bounds on these functions. Throughout the derivation of the equations that embody the method, it is assumed that the decoder for the outer code corrects any error pattern of t or fewer errors, detects any error pattern of s or fewer errors, may detect some error patterns of more than s errors, and does not correct any patterns of more than t errors. Because a mathematically complete description of the equations that embody the method and of the derivation of the equations would greatly exceed the space available for this article, it must suffice to summarize by reporting that the derivation includes consideration of several complex issues, including relationships between latency and memory requirements for block and convolutional codes, burst error statistics, enumeration of error-event intersections, and effects of different interleaving depths. In a demonstration, the method was used to calculate bounds on the performances of several communication systems, each based on serial concatenation of a (63,56) expurgated Hamming code with a convolutional inner code through a convolutional interleaver. The bounds calculated by use of the method were compared with results of numerical simulations of performances of the systems to show the regions where the bounds are tight (see figure).

Moision, Bruce↗
NASA NTRS1996

Advanced Flight Computing Technologies for Validation by NASA's New Millennium Program

The New Millennium Program (NMP) consists of a series of deep-space and earth-orbiting missions that are technology-driven, in contrast to the more traditional science-driven space exploration missions of the past. These flights are designed to validate technologies that will enable a new era of low-cost highly miniaturized and highly capable spaceborne applications in the new millennium.

New↗
DOE OSTI2024

Analyzing inference workloads for spatiotemporal modeling

Ensuring power grid resiliency, forecasting climate conditions, and optimization of transportation infrastructure are some of the many application areas where data is collected in both space and time. Spatiotemporal modeling is about modeling those patterns for forecasting future trends and carrying out critical decision-making by leveraging machine learning/deep learning. Once trained offline, field deployment of trained models for near real-time inference could be challenging because performance can vary significantly depending on the environment, available compute resources and tolerance to ambiguity in results. Users deploying spatiotemporal models for solving complex problems can benefit from analytical studies considering a plethora of system adaptations to understand the associated performance-quality trade-offs. To facilitate the co-design of next-generation hardware architectures for field deployment of trained models, it is critical to characterize the workloads of these deep learning (DL) applications during inference and assess their computational patterns at different levels of the execution stack. In this paper, we develop several variants of deep learning applications that use spatiotemporal data from dynamical systems. We study the associated computational patterns for inference workloads at different levels, considering relevant models (Long short-term Memory, Convolutional Neural Network and Spatio-Temporal Graph Convolution Network), DL frameworks (Tensorflow and PyTorch), precision (FP16, FP32, AMP, INT16 and INT8), inference runtime (ONNX and AI Template), post-training quantization (TensorRT) and platforms (Nvidia DGX A100 and Sambanova SN10 RDU). Overall, our findings indicate that although there is potential in mixed-precision models and post-training quantization for spatiotemporal modeling, extracting efficiency from contemporary GPU systems might be challenging. Instead, co-designing custom accelerators by leveraging optimized High Level Synthesis frameworks (such as SODA High-Level Synthesizer for customized FPGA/ASIC targets) can make workload-specific adjustments to enhance the efficiency.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1972

Hybrid and concatenated coding applications.

Results of a study to evaluate the performance and implementation complexity of a concatenated and a hybrid coding system for moderate-speed deep-space applications. It is shown that with a total complexity of less than three times that of the basic Viterbi decoder, concatenated coding improves a constraint length 8 rate 1/3 Viterbi decoding system by 1.1 and 2.6 dB at bit error probabilities of 0.0001 and one hundred millionth, respectively. With a somewhat greater total complexity, the hybrid coding system is shown to obtain a 0.9-dB computational performance improvement over the basic rate 1/3 sequential decoding system. Although substantial, these complexities are much less than those required to achieve the same performances with more complex Viterbi or sequential decoder systems.

Hofman, L. B.↗
NASA NTRS1992

The X-windows interactive navigation data editor

A new computer program called the X-Windows Interactive Data Editor (XIDE) was developed and demonstrated as a prototype application for editing radio metric data in the orbit-determination process. The program runs on a variety of workstations and employs pull-down menus and graphical displays, which allow users to easily inspect and edit radio metric data in the orbit data files received from the Deep Space Network (DSN). The XIDE program is based on the Open Software Foundation OSF/Motif Graphical User Interface (GUI) and has proven to be an efficient tool for editing radio metric data in the navigation operations environment. It was adopted by the Magellan Navigation Team as their primary data-editing tool. Because the software was designed from the beginning to be portable, the prototype was successfully moved to new workstation environments. It was also itegrated into the design of the next-generation software tool for DSN multimission navigation interactive launch support.

Rinker, G. C.↗
NASA NTRS1995

An Improved Ionospheric Correction Method for Wide-Area Augmentation Systems

A powerful approach for generating ionospheric corrections in wide area differential GPS applications has been developed, that can be applied to the Federal Aviation Administration's Wide Area Augmentation System (WAAS). This approach has been used to support ionospheric calibration for NASA's Deep Space Network and will be supporting real-time operations for global ionospheric specification and, possibly, single-frequency satellite altimeter calibration. It is a real-time, grid-based technique relying on a computationally efficient Kalman-type filter to produce accurate, smoothly varying ionospheric correction maps over the coverage area. Formal error maps are also computed, providing vertical delay errors over the WAAS grid, which are useful in integrity monitoring. This solution to the ionospheric correction problem is self-calibrating, since GPS transmitter inter-frequency biases are obtained as a by-product of the mapping procedure. This contrasts with other techniques in which bias values must be provided from some additional source.

Wide-Area↗
NASA NTRSDate not supplied

Graphical User Interface (GUI) Implementation for Agent-Based Microbial Radiobiology Model

Sending human life past the Low Earth Orbit (LEO) to explore the Moon and Mars will be challenging. The Earth’s magnetic field naturally protects life from deep-space particle radiation such as Galactic Cosmic Rays (GCR) and Solar Particle Events (SPE); these will pose health risks to humans in deep space. Research has been done to investigate these effects, like BioSentinel, the first biological CubeSat to fly beyond the LEO, designed to culture yeast in a microfluidic device and record optical measurements of growth and metabolism. However, experiments can only report cell damage as bulk growth curves, while deep-space radiation causes damage that is heterogeneous among individual cells. AMMPER is an open-source, agent-based, computational model coded in Python to simulate the effects of deep-space radiation on individual yeast cells (Saccharomyces cerevisiae) to facilitate interpretation of biological radiation experiments. Version 1.0 of the code ran in a command line interface (CLI), limiting use to those familiar with modularization, object-oriented programming, and computational models. Here we present a graphical user interface (GUI) for AMMPER to increase its accessibility. GUI development included converting input points and UI files, designing an application and logo, and expanding program packages. Additionally, we added optical assistance that corresponded with simulation parameters, which included simulation type, cell type, ROS model, and radiation dosage, as well as customizable display and file exportation features. Following a pilot testing period, its structure was updated further to enhance abilities, adding increased runs, video visualization, data plotting, and an educational/tutorial component. Future work will include creating a bit installer and runtime environment for AMMPER. Ultimately, the creation of the GUI has two main goals: to facilitate the integration of computational models into the work of researchers in microbial radiobiology, and to act as an interactive and visual resource for space biology education.

yeast↗
NASA NTRS1987

Planning the Voyager spacecraft's mission to Uranus

The application of the systems engineering process to the planning of the Voyager spacecraft mission is described. The Mission Planning Office prepared guidelines that controlled the use of the project and multimission resources and spacecraft consumables in order to obtain valuable scientific data at an acceptable risk level. Examples of mission planning which are concerned with the design of the Deep Space Network antenna, the uplink window for transmitting computer command subsystem loads, and the contingency and risk assessment functions are presented.

Plagemann, Stephen H.↗
NASA NTRS1987

Expert systems for space power supply - Design, analysis, and evaluation

The feasibility of applying expert systems to the conceptual design, analysis, and evaluation of space power supplies in particular, and complex systems in general is evaluated. To do this, the space power supply design process and its associated knowledge base were analyzed and characterized in a form suitable for computer emulation of a human expert. The existing expert system tools and the results achieved with them were evaluated to assess their applicability to power system design. Some new concepts for combining program architectures (modular expert systems and algorithms) with information about the domain were applied to create a 'deep' system for handling the complex design problem. NOVICE, a code to solve a simplified version of a scoping study of a wide variety of power supply types for a broad range of missions, has been developed, programmed, and tested as a concrete feasibility demonstration.

Cooper, Ralph S.↗
NASA NTRS2010

A Rat Body Phantom for Radiation Analysis

To reduce the uncertainties associated with estimating the biological effects of ionizing radiation in tissue, researchers rely on laboratory experiments in which mono-energetic, single specie beams are applied to cell cultures, insects, and small animals. To estimate the radiation effects on astronauts in deep space or low Earth orbit, who are exposed to mixed field broad spectrum radiation, these experimental results are extrapolated and combined with other data to produce radiation quality factors, radiation weighting factors, and other risk related quantities for humans. One way to reduce the uncertainty associated with such extrapolations is to utilize analysis tools that are applicable to both laboratory and space environments. The use of physical and computational body phantoms to predict radiation exposure and its effects is well established and a wide range of human and non-human phantoms are in use today. In this paper, a computational rat phantom is presented, as well as a description of the process through which that phantom has been coupled to existing radiation analysis tools. Sample results are presented for two space radiation environments.

Qualls, Garry D.↗
NASA NTRS2022

Radiation Tolerance and Mitigation for Neuromorphic Processors

Neuromorphic processors are designed to execute Deep Neural Networks (DNNs) at very high speed using only a fraction of the electrical power needed to run a DNN on a traditional CPU or GPU. This unique capability makes Neuromorphic processors a prime candidate for space systems, where advanced computational tasks like image analysis, depth map reconstruction, or rover control need to be executed in a power-starved environment. In contrast to the growing number of applications of Neuromorphic processors in smart phones, the automotive and robotics domain, the space environment is unforgiving because of extreme temperatures and high levels of radiation. Any space system, operating beyond LEO requires computing hardware that is resilient against radiation effects. However, Neuromorphic processors have not yet been designed or tested for their radiation tolerance. In this report, we consider traditional methods of detection of radiation events and mitigation via redundancy and gauge their effectiveness on DNNs. In contrast to traditional flight software, however, neural networks represent a statistical algorithm, which might affect its resilience against radiation events. We will focus on the analysis of the tolerance of DNNs with respect to radiation events and discuss techniques to detect radiation hits using on-chip triple modular redundancy (TMR) on an Intel Loihi neuromorphic processor and to mitigate radiation damage. We describe an architecture for on-chip TMR for the Intel Loihi and present results of initial experiments.

Neural Networks↗
NASA NTRS1990

Applications of different design methodologies in navigation systems and development at JPL

The NASA/JPL deep space navigation system consists of a complex array of measurement systems, data processing systems, and support facilities, with components located both on the ground and on-board interplanetary spacecraft. From its beginings nearly 30 years ago, this system has steadily evolved and grown to meet the demands for ever-increasing navigation accuracy placed on it by a succession of unmanned planetary missions. Principal characteristics of this system are its capabilities and great complexity. Three examples in the design and development of interplanetary space navigation systems are examined in order to make a brief assessment of the usefulness of three basic design theories, known as normative, rational, and heuristic. Evaluation of the examples indicates that a heuristic approach, coupled with rational-based mathematical and computational analysis methods, is used most often in problems such as orbit determination strategy development and mission navigation system design, while normative methods have seen only limited use is such applications as the development of large software systems and in the design of certain operational navigation subsystems.

Thurman, S. W.↗
DOE OSTI2024

Separable physics-informed DeepONet: Breaking the curse of dimensionality in physics-informed machine learning

The deep operator network (DeepONet) has shown remarkable potential in solving partial differential equations (PDEs) by mapping between infinite-dimensional function spaces using labeled datasets. However, in scenarios lacking labeled data, the physics-informed DeepONet (PI-DeepONet) approach, which utilizes the residual loss of the governing PDE to optimize the network parameters, faces significant computational challenges, particularly due to the curse of dimensionality. This limitation has hindered its application to high-dimensional problems, making even standard 3D spatial with 1D temporal problems computationally prohibitive. Additionally, the computational requirement increases exponentially with the discretization density of the domain. Here, to address these challenges and enhance scalability for high-dimensional PDEs, we introduce the Separable physics-informed DeepONet (Sep-PI-DeepONet). This framework employs a factorization technique, utilizing sub-networks for individual one-dimensional coordinates, thereby reducing the number of forward passes and the size of the Jacobian matrix required for gradient computations. By incorporating forward-mode automatic differentiation (AD), we further optimize computational efficiency, achieving linear scaling of computational cost with discretization density and dimensionality, making our approach highly suitable for high-dimensional PDEs. We demonstrate the effectiveness of Sep-PI-DeepONet through three benchmark PDE models: the viscous Burgers’ equation, Biot’s consolidation theory, and a parameterized heat equation. Our framework maintains accuracy comparable to the conventional PI-DeepONet while reducing training time by two orders of magnitude. Notably, for the heat equation solved as a 4D problem, the conventional PI-DeepONet was computationally infeasible (estimated 289.35 h), while the Sep-PI-DeepONet completed training in just 2.5 h. These results underscore the potential of Sep-PI-DeepONet in efficiently solving complex, high-dimensional PDEs, marking a significant advancement in physics-informed machine learning.

Neural operator↗