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At least 1,135 records · Page 63
All sourcesNASA NTRSDOE OSTI
NASA NTRS2021

Design and Modeling of a Coded-Interleaving System in the Presence of Fading

Future deep space links are migrating towards higher frequency bands such as Ka band and optical. These links are susceptible to non linear effects (e.g. cloud coverage, atmospheric turbulence, scintillation, antenna mis-pointing, jitter, relative motion, etc.) that cause various degrees of fading loss over various time scales. We consider a channel experiencing block fading where the channel coherence time is a lot longer than the length of a codeword. As long as the fading duration is not excessive, an interleaver can be used in a coded-communication system that randomizes the fades among symbols of multiple codewords. This achieves an “averaging” effect that spreads the signal energy more evenly among the codewords, and improves the likelihood that each of them can be decoded correctly. For simplicity, we consider a block interleaver where adjacent symbols of a codeword have a fixed delay (or separation) with each other, and investigate the error-rate performance of a coded-interleaving system (with fixed and finite interleaving depth) in the presence of block fading. Unlike prior research which assumes theoretical channel coherence models, we aim to build upon our analysis using empirical spacecraft signal measurements obtained with the Deep Space Network, and to use the fundamental theories of probability and statistics (e.g. variations of Law of Large Numbers) to quantify the error-rate performance of the coded-interleaving system. We propose to utilize these results in the design of a two-dimensional coded-interleaving system for fading channels.

Sanchez-Net, Marc↗
NASA NTRS2021

A High Dynamic-Range Photon-Counting Receiver for Deep Space Optical Communication

The Deep Space Optical Communication (DSOC) project will demonstrate free-space optical communication at almost 3 AU, or 3 orders of magnitude further than any previous attempt. DSOC will utilize the 5m Palomar Hale Telescope to receive the downlink signal, which will couple the downlink light onto an optical table and into a superconducting nanowire single photon detector (SNSPD). The output of the SNSPD is digitized by the Ground Laser Receiver Signal Processing Assembly (GSPA) using a high throughput streaming time to digital converter (TDC). The GSPA is a scalable FPGA-based receiver which demodulates and decodes the DSOC downlink signal through novel signal processing algorithms implemented on Xilinx UltraScale+ FPGAs, as well as Python-based software monitor and control routines. Exploiting the unique TDC-based architecture, the GSPA supports over four orders of magnitude of downlink data rates across multiple orders of magnitude of signal and background powers. In this paper we present an overview of the hardware, firmware and software architectures to implement this system, as well as performance analysis for links ranging from near-Earth to 2.8 AU.

Srinivasan, Meera↗
NASA NTRSDate not supplied

A Real-Time Optical Ground Receiver for Photon Starved Environments

The National Aeronautics and Space Administration (NASA) Glenn Research Center (GRC) has developed a photon-counting optical ground receiver for pulse-position modulated signals. The real-time receiver system includes a fiber interconnect, superconducting nanowire single-photon detectors (SNSPDs), and a real-time field programmable gate array (FPGA) based receiver. The fiber interconnect and SNSPDs are implemented with two different configurations. In the first, a 7-channel few-mode fiber photonic lantern couples the light from the telescope to 7 single-pixel few-mode fiber coupled SNSPDs. In the second configuration, a few-mode fiber couples light to a 16-pixel monolithic SNSPD array. The real-time FPGA-based receiver performs combining of up to 16 SNSPD channels, symbol timing recovery, demodulation, and decoding. The system is scalable with data rates ranging from 20 Mbps to 267 Mbps. It is compliant with the Consultative Committee for Space Data Systems (CCSDS) Optical Communications Coding and Synchronization Standard. This standard will be used in NASA deep space and other low photon flux missions, such as in the Orion Artemis-2 Optical Communications System (O2O) demonstration, planned for the first crewed flight of Orion. This paper describes the scalable real-time optical receiver system and presents characterization test results.

optical communications↗
NASA NTRSDate not supplied

A Real-Time Optical Ground Receiver for Photon Starved Environments

The National Aeronautics and Space Administration (NASA) Glenn Research Center (GRC) has developed a photon-counting optical ground receiver for pulse-position modulated signals. The real-time receiver system includes a fiber interconnect, superconducting nanowire single-photon detectors (SNSPDs), and a real-time field programmable gate array (FPGA) based receiver. The fiber interconnect and SNSPDs are implemented with two different configurations. In the first, a 7-channel few-mode fiber photonic lantern couples the light from the telescope to 7 single-pixel few-mode fiber coupled SNSPDs. In the second configuration, a few-mode fiber couples light to a 16-pixel monolithic SNSPD array. The real-time FPGA-based receiver performs combining of up to 16 SNSPD channels, symbol timing recovery, demodulation, and decoding. The system is scalable with data rates ranging from 20 Mbps to 267 Mbps. It is compliant with the Consultative Committee for Space Data Systems (CCSDS) Optical Communications Coding and Synchronization Standard. This standard will be used in NASA deep space and other low photon flux missions, such as in the Orion Artemis-2 Optical Communications System (O2O) demonstration, planned for the first crewed flight of Orion. This paper describes the scalable real-time optical receiver system and presents characterization test results.

optical communications↗
NASA NTRSDate not supplied

Image-to-Image Wildfire Detection via Quantum-Compatible Variational Segmentation from Remotely-sensed Data

Over the last decade, the incidence of wildfires has surged, causing widespread destruction globally. To better comprehend and manage these incidents, remote sensing and aerial missions have been implemented in recent efforts. However, this has resulted in an exponential rise in the amount of remote sensing data utilization, leading to a need for intelligent automation of data extraction in wildfire studies. Machine learning provides an accurate automated approach for detecting these natural anomalies and facilitates decision-makers to take prompt actions. To make insightful decisions in wildfire management, it is imperative to move beyond simple detection and explore the potential of probabilistic generative machine learning for creating "what-if" scenarios for various wildfire conditions. Such models offer improved representation of the stochastic nature of wildfire events. However, the optimization of these models can be computationally expensive, especially when using classical computers. Quantum computers have recently emerged as a promising solution to reduce the computational cost of training such models and improve their performance. In this study, we aim to utilize quantum-compatible machine learning techniques to implement our probabilistic generative approach. To that end, we propose a supervised probabilistic variational model consisting of a U-NET-based image-to-image component along with encoder and decoder networks which work as a variational autoencoder (VAE) component. Additionally, we explore the type of latent distribution type in the VAE component and implement different means for modeling the prior distribution. We further investigate the quantum-compatible versions of the model compared to the classical counterpart and benchmark potential benefits of quantum compatibility over the classical model.

quantum machine learning↗
NASA NTRSDate not supplied

Impact Ice Microstructure Segmentation Using Transfer Learned Model

A process of using machine learning to segment impact ice microstructure is presented and analyzed. The segmentation was conducted with the goal of obtaining average grain size estimations. The model was trained on a set of micrographs of impact ice grown at NASA Glenn’s Icing Research Tunnel. The model leveraged a model pre-trained on a large set of micrographs of various materials as a starting point. Post-processing of the segmented images was done to connect broken boundaries. An automatic method of determining grain size following an ASTM standard was implemented. Segmentation results using different training sets as well as different encoder and decoder pairs are presented. Calculated sizes are compared to manual grain size measurement methods. Results show promise in accuracy as well as a possible improvement in repeatability and consistency. Next steps for improving the model are suggested.

Machine learning↗
NASA NTRS2022

Comparing Performance of Coded Communications over Fading Channels between the Lunar South Pole & Earth

In this paper we design and analyze an end-to-endcommunication system between a lander/rover on the surface ofthe lunar South Pole and an Earth station. Various communicationsystems on the lander or rover could be used for the Earthto-Moon communication in both the uplink and downlink direction.To communicate to and from the lander/rover on the lunarSouth Pole, low and/or medium directional antennas onboardthe lander/rover will have to be pointed at low elevation anglesbetween 2 to 10 degrees, thus causing multipath fading effectsdue to reflection of a portion of the transmitted electromagneticwaves from the surface of the Moon. These are not commonlyencountered in traditional deep space communications betweena spacecraft and a ground station.We investigate various design methods and analyze such communicationsystems, in the presence of multipath fading. Wemodel the fading channel based on existing and simulated data.For coherent reception, the acquisition and tracking loop shouldacquire and track incoming carrier phase in presence of Ricianmultipath fading. For this communication system in additionto estimating the received carrier phase, the amplitude of thefading signal should also be estimated, in particular to be used inthe decoder. We consider simple modulation and coding schemesin particular those specified in the CCSDS standards for spaceapplications.After designing various components of the communication system,we use Simulink models to obtain the end-to-end performanceof the communication link under investigation. Basedon the available data, the fading channel can be accuratelymodeled as a Rician fading channel with various Rician parametersdepending on the Earth elevation angle, which alsoaffects the Doppler spread. Therefore, the challenge is todesign a communication system robust in the presence of themultipath fading where the channel conditions change in timeand thus produce fading. In summary, this paper comparespossible communication system designs, performance analysis,and simulations for coded system with/without interleaving withhard/soft decision and with/without channel state information(CSI), over a communication link between a lander/rover at theLunar south pole and a Deep Space Network station in presenceof Rician fading.

Cheung, Kar-Ming↗
NASA NTRSDate not supplied

Anomaly Detection in Flight Operational Data Using Deep Learning

In this session, we demonstrate two recently developed deep learning models for anomaly detection in flight operational data by the Data Sciences Group at NASA Ames Research Center. The first model is Convolutional Variational Auto-Encoder (CVAE) [1], which is an unsupervised deep encoder-decoder model, designed specifically for finding anomalies in heterogeneous multivariate time series data. We will demonstrate its application to finding anomalies in streaming data from NASA’s Digital Information Platform’s Fuser source. CVAE identifies data instances that are not representative of expected nominal behavior as anomalous. Since it is an unsupervised approach, the flagged anomalies will need to be reviewed by the subject matter experts (SMEs) for validation and labeling and is designed to assist with vulnerability discovery within Safety Monitoring System programs. The second model is Robust and Explainable Semi-supervised Anomaly Detection (RESAD) model [2], which builds on CVAE to allow learning from both minimally labeled data (previously reviewed by the SMEs) as well as majority unlabeled data. RESAD takes advantage of graph theoretic techniques to propagate the labels from the labeled data to the unlabeled data based on a pre-defined similarity metric and structures the learned feature space from flight time-series so that data of the same class would cluster tightly together. This model characteristic is enabled by training with an augmented loss function and allows learning of a more informative feature space for down-stream tasks such as search and active learning. We demonstrate RESAD using data from the NASA DASHlink project [3].

anomaly detection↗
NASA NTRSDate not supplied

Harnessing Large Language Models for Scientific Endeavors

The rapid proliferation of Large Language Models (LLMs) such as GPT, Bard, and Llama has revolutionized various sectors, including the scientific community. These models, with their potential to automate and augment tasks, are increasingly being recognized as both a valuable asset and a potential challenge in the realm of scientific research and data management. However, the current LLMs, primarily trained on general corpora, exhibit a limited understanding of scientific concepts and terminologies due to the lack of scientific corpus in their training data. Recognizing this gap, several groups are now advocating for the development of LLMs specifically tailored for scientific applications. A notable initiative in this direction is the Large Language Model effort initiated by NASA's CSDO. This endeavor aims to align LLM efforts across NASA’s Science Mission Directorate, develop a science-specific corpus and validation test set for model training, and create an encoder-only model for various downstream tasks. Moreover, the initiative also plans to develop a decoder-only model to explore the potential benefits and risks associated with a generative LLM for science. Lastly, the project aims to create a science evaluation suite, encompassing various categories of downstream scientific tasks, to serve as a benchmark for assessing the value of any LLM for future use. This presentation will provide an overview and current status of this ongoing initiative, highlighting its potential to reshape the use of LLMs in the scientific domain.

Rahul Ramachandran↗
NASA NTRS2022

Deep Interacting Multiple Model Filtering

In this paper, a deep learning-based multiple model estimation framework is presented for the state estimation of hybrid dynamical systems from high dimensional observations such as camera images. A low dimensional vector which represents the measurement of the latent dynamical system and its corresponding variance are learned using a deep encoder neural network. An Interacting Multiple Model (IMM) filter is used to generate the latent state estimates and covariances using multiple dynamical models, which can be learned using backpropagation through time. The state estimates of the dynamical system and the corresponding covariance matrix are generated from the latent state estimates and covariance using a deep decoder neural network. The whole network is trained in an end-to-end manner using a loss function which minimizes the negative log-likelihood of the neural network parameters. Simulation results are presented using a 2D bouncing ball example and estimation error statistics are computed which demonstrates the accuracy and consistency of the estimation.

Ghananeel Rotithor↗
NASA NTRSDate not supplied

A Photon Counting Ground Receiver for Free-Space Optical Communications

The National Aeronautics and Space Administration is developing a photon counting ground receiver for free-space optical communications. The receiver consists of a fiber interconnect, superconducting nanowire single photon counting detectors (SNSPDs), and a field programmable gate array (FPGA) based receive modem. This receiver is compliant with the Consultative Committee for Space Data Systems (CCSDS) Optical Communications High Photon Efficiency standard, which is used in the low photon flux communications regime, including on the upcoming Optical Artemis-2 Orion communications demonstration. The receiver architecture is modular, can be scaled for data rate, and is designed with primarily commercial-off-the-shelf technology. Two fiber interconnect and detector architectures are being prototyped. The first uses a few-mode-fiber coupled to a 16-channel SNSPD array and the second architecture uses a photonic lantern coupled to 7 single-pixel SNSPDs. Photon counting, symbol timing recovery, demodulation and decoding is done using several FPGA cards. This presentation describes the unique features of the ground receiver, performance test results, and challenges with using commercial SNSPDs in free-space optical communications.

Jennifer N Downey↗
NASA NTRSDate not supplied

Improvements on Low-Density Parity-Check (LDPC) Codes and High-Performance Neuromorphic Engineering for Communication Systems

Belief propagation (BP) on LDPC codes is an iterative decoding algorithm that performs information transfer on the Tanner graph, which represents the code. In each iteration, the algorithm exchanges information (LLR) between variable nodes and check nodes through the edges of the graph. LLR values represent the probability that a given bit in a transmitted codeword equals 0 or 1, given a received word. In the hardware part, recent advancements in intelligent technologies, such as artificial intelligence, big data analytics, autonomous vehicles, and speech/image recognition, have heightened the demand for faster calculations and reduced energy consumption.

Danilo Barrionuevo↗
NASA NTRSDate not supplied

3D Scanning System to Assess Gravity-Dependent Body Shape Changes

The human body shows unique morphological changes when exposed to different gravity conditions, including muscle atrophy, fluid shift, and spinal elongation. Such changes need to be incorporated for human-system integration in the vehicle habitat, garment, and spacesuit designs, as inaccurate body measurements can result in suboptimal crew protection that can potentially decrease injury tolerance. However, measurement tools have not been available for accurate assessments of body shape changes. This work aimed to develop a prototype 3-D body scanning system with the configuration and performance optimized for in-flight crewmember body scanning. A scan hardware system was developed using Intel RealSense commercial off-the-shelf 3D sensors. The sensor parameters were iteratively optimized and tested to obtain the performance level needed for body scanning. A scan booth structure was fabricated, with the overall size 4 x 4 x 8 feet. The specific number of sensors and mounting positions were determined by iterative simulations, which indicated that 16 cameras can capture 94% and 96% of body surface area from the 1st percentile female and 99th percentile male crew population subject. The mounted sensors were linked through a mix of USB-C and USB-3 cables and operated for data acquisition from a Linux laptop computer. A software prototype was developed using Python and Tkinter graphical user interface toolkit. A calibration procedure was also built using a panel of QR codes. A computer vision tool detected and decoded the unique ID and pattern locations of the QR codes. The calibration information determined the position and orientation of the 3D sensors with respect to each other. The scanner performance was assessed using a set of 3D printed custom manikins. The manikin size and shape were derived from the previous ISS study, which measured the crewmembers’ anthropometry changes across the different flight phases. The average anthropometric measurements at the pre-flight and flight day 15 were sampled and projected onto the 1st percentile female and 99th percentile male body shapes. Another pair of manikins were also 3D printed to simulate the neutral body posture, estimated from ISS microgravity environments. A preliminary analysis assessed the performance of the newly developed scanner against the reference scanner, which has been used at the NASA JSC for crew and test subject anthropometry. Although the new scanner data showed several artifacts and missing geometries in the occluded body areas such as armpits and crotch, overall shape matched with the reference scan. When the manikin surface coordinates were compared, a root mean square error of 1.3 cm was observed from the manikin torso segment. Linear measurements including the stature, knee height and circumference measurements at the chest and calf showed differences from the reference scan measurements, ranging between 0.3 and 0.9 cm. Overall, this work demonstrated a development framework for an in-flight scanner with design and operation optimized for crewmember body scanning. Further improvement can potentially provide previously unavailable anthropometric data from different gravitational environments, including 0-g, 1/6-g, and 1-g. Such data can improve suit fit, habitat design, exercise efficacy quantification and sizing of orthostatic intolerance garments.

K H Kim↗
NASA NTRS2024

Transfer-AE: A novel autoencoder-based impact detection model for structural digital twin

Accurately detecting the location and intensity of impacts is crucial for ensuring structural safety. Currently, AI-based structural impact detection methods are widely used for their excellent detection accuracy. However, their generalization capability is limited by the scenarios present in the training data. Many complex and dangerous impact scenarios are difficult to conduct real-world experiments on to collect sufficient samples. To capture all impact scenarios and fully leverage the advantages of AI-based detection technologies, advanced methods involve combining real-world structural monitoring data with corresponding numerical models to construct digital twins. These methods continuously refine the created numerical models with limited real-world data and provide diverse impact scenarios through numerical model simulations. However, there are inevitable differences between digital models and physical models that are challenging to correct through mechanical means. This discrepancy in data distribution between the two models significantly hinders the application of digital twin technology in impact/event identification tasks. To address this challenge, this study proposes a novel model based on autoencoders, named Transfer-AE. Transfer-AE encodes the common features of digital twins in the latent space to bridge the uncertainty gap at a macro scale between numerical models and physical models and synchronously fits the magnitude and location of the impact load in the decoder. This enables consistent detection results for the same impact event, whether the sample comes from the numerical model or the physical model. Transfer-AE includes two operating modes: Mode 1 has a fixed computational complexity with stable inference speed, but the training cost and difficulty increase with data distribution. Mode 2's computational complexity increases with data distribution, but it has a fixed training cost and speed. In both cases involving the geodesic dome structure simulating a deep space habitat and the IASC-ASCE benchmark structure, Transfer-AE demonstrated the best performance in impact localization and quantification tasks compared to mainstream domain-adaptive transfer models.

Chengjia Han↗