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All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Soft syndrome iterative decoding of quantum LDPC codes and hardware architectures

In practical quantum error correction implementations, the measurement of syndrome information is an unreliable step—typically modeled as a binary measurement outcome flipped with some probability. However, the measured syndrome is in fact a discretized value of the continuous voltage or current values obtained in the physical implementation of the syndrome extraction. In this paper, we use this “soft” or analog information to benefit iterative decoders for decoding quantum low-density parity-check (QLDPC) codes. Syndrome-based iterative belief propagation decoders are modified to utilize the soft syndrome to correct both data and syndrome errors simultaneously. We demonstrate the advantages of the proposed scheme not only in terms of comparison of thresholds and logical error rates for quasi-cyclic lifted-product QLDPC code families but also with faster convergence of iterative decoders. Additionally, we derive hardware (FPGA) architectures of these soft syndrome decoders and obtain similar performance in terms of error correction to the ideal models even with reduced precision in the soft information. The total latency of the hardware architectures is about 600 ns (for the QLDPC codes considered) in a 20 nm CMOS process FPGA device, and the area overhead is almost constant—less than 50% compared to min-sum decoders with noisy syndromes.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
NASA NTRS1980

Voltage controller/current limiter for ac

Circuit protects ac power systems for overload failures, limits power surge and short-circuit currents to 150 percent of steady state level, regulates ac output voltage, and soft starts loads. Limiter generates dc error signal in response to line fluctuations and dumps power when overload is reached. Device is inserted between ac source and load.

Wu, T. T.↗
NASA NTRS1998

Trellises and Trellis-Based Decoding Algorithms for Linear Block Codes: The Map and Related Decoding Algirithms - Part 3

In a coded communication system with equiprobable signaling, MLD minimizes the word error probability and delivers the most likely codeword associated with the corresponding received sequence. This decoding has two drawbacks. First, minimization of the word error probability is not equivalent to minimization of the bit error probability. Therefore, MLD becomes suboptimum with respect to the bit error probability. Second, MLD delivers a hard-decision estimate of the received sequence, so that information is lost between the input and output of the ML decoder. This information is important in coded schemes where the decoded sequence is further processed, such as concatenated coding schemes, multi-stage and iterative decoding schemes. In this chapter, we first present a decoding algorithm which both minimizes bit error probability, and provides the corresponding soft information at the output of the decoder. This algorithm is referred to as the MAP (maximum aposteriori probability) decoding algorithm.

Lin, Shu↗
NASA NTRS1998

Trellises and Trellis-Based Decoding Algorithms for Linear Block Codes

A code trellis is a graphical representation of a code, block or convolutional, in which every path represents a codeword (or a code sequence for a convolutional code). This representation makes it possible to implement Maximum Likelihood Decoding (MLD) of a code with reduced decoding complexity. The most well known trellis-based MLD algorithm is the Viterbi algorithm. The trellis representation was first introduced and used for convolutional codes [23]. This representation, together with the Viterbi decoding algorithm, has resulted in a wide range of applications of convolutional codes for error control in digital communications over the last two decades. There are two major reasons for this inactive period of research in this area. First, most coding theorists at that time believed that block codes did not have simple trellis structure like convolutional codes and maximum likelihood decoding of linear block codes using the Viterbi algorithm was practically impossible, except for very short block codes. Second, since almost all of the linear block codes are constructed algebraically or based on finite geometries, it was the belief of many coding theorists that algebraic decoding was the only way to decode these codes. These two reasons seriously hindered the development of efficient soft-decision decoding methods for linear block codes and their applications to error control in digital communications. This led to a general belief that block codes are inferior to convolutional codes and hence, that they were not useful. Chapter 2 gives a brief review of linear block codes. The goal is to provide the essential background material for the development of trellis structure and trellis-based decoding algorithms for linear block codes in the later chapters. Chapters 3 through 6 present the fundamental concepts, finite-state machine model, state space formulation, basic structural properties, state labeling, construction procedures, complexity, minimality, and sectionalization of trellises. Chapter 7 discusses trellis decomposition and subtrellises for low-weight codewords. Chapter 8 first presents well known methods for constructing long powerful codes from short component codes or component codes of smaller dimensions, and then provides methods for constructing their trellises which include Shannon and Cartesian product techniques. Chapter 9 deals with convolutional codes, puncturing, zero-tail termination and tail-biting.Chapters 10 through 13 present various trellis-based decoding algorithms, old and new. Chapter 10 first discusses the application of the well known Viterbi decoding algorithm to linear block codes, optimum sectionalization of a code trellis to minimize computation complexity, and design issues for IC (integrated circuit) implementation of a Viterbi decoder. Then it presents a new decoding algorithm for convolutional codes, named Differential Trellis Decoding (DTD) algorithm. Chapter 12 presents a suboptimum reliability-based iterative decoding algorithm with a low-weight trellis search for the most likely codeword. This decoding algorithm provides a good trade-off between error performance and decoding complexity. All the decoding algorithms presented in Chapters 10 through 12 are devised to minimize word error probability. Chapter 13 presents decoding algorithms that minimize bit error probability and provide the corresponding soft (reliability) information at the output of the decoder. Decoding algorithms presented are the MAP (maximum a posteriori probability) decoding algorithm and the Soft-Output Viterbi Algorithm (SOVA) algorithm. Finally, the minimization of bit error probability in trellis-based MLD is discussed.

Lin, Shu↗
DOE OSTI2022

In-Situ Sensing and Dynamics Predictions for Electrothermally-Actuated Soft Robot Limbs

Untethered soft robots that locomote using electrothermally-responsive materials like shape memory alloy (SMA) face challenging design constraints for sensing actuator states. At the same time, modeling of actuator behaviors faces steep challenges, even with available sensor data, due to complex electrical-thermal-mechanical interactions and hysteresis. This article proposes a framework for in-situ sensing and dynamics modeling of actuator states, particularly temperature of SMA wires, which is used to predict robot motions. A planar soft limb is developed, actuated by a pair of SMA coils, that includes compact and robust sensors for temperature and angular deflection. Data from these sensors are used to train a neural network-based on the long short-term memory (LSTM) architecture to model both unidirectional (single SMA) and bidirectional (both SMAs) motion. Predictions from the model demonstrate that data from the temperature sensor, combined with control inputs, allow for dynamics predictions over extraordinarily long open-loop timescales (10 min) with little drift. Prediction errors are on the order of the soft deflection sensor’s accuracy. This architecture allows for compact designs of electrothermally-actuated soft robots that include sensing sufficient for motion predictions, helping to bring these robots into practical application.

Sabelhaus, Andrew P.↗
NASA NTRS1995

Hologic QDR 2000 whole-body scans: a comparison of three combinations of scan modes and analysis software

This study reports on the short-term in vivo precision and absolute measurements of three combinations of whole-body scan modes and analysis software using a Hologic QDR 2000 dual-energy X-ray densitometer. A group of 21 normal, healthy volunteers (11 male and 10 female) were scanned six times, receiving one pencil-beam and one array whole-body scan on three occasions approximately 1 week apart. The following combinations of scan modes and analysis software were used: pencil-beam scans analyzed with Hologic's standard whole-body software (PB scans); the same pencil-beam analyzed with Hologic's newer "enhanced" software (EPB scans); and array scans analyzed with the enhanced software (EA scans). Precision values (% coefficient of variation, %CV) were calculated for whole-body and regional bone mineral content (BMC), bone mineral density (BMD), fat mass, lean mass, %fat and total mass. In general, there was no significant difference among the three scan types with respect to short-term precision of BMD and only slight differences in the precision of BMC. Precision of BMC and BMD for all three scan types was excellent: < 1% CV for whole-body values, with most regional values in the 1%-2% range. Pencil-beam scans demonstrated significantly better soft tissue precision than did array scans. Precision errors for whole-body lean mass were: 0.9% (PB), 1.1% (EPB) and 1.9% (EA). Precision errors for whole-body fat mass were: 1.7% (PB), 2.4% (EPB) and 5.6% (EA). EPB precision errors were slightly higher than PB precision errors for lean, fat and %fat measurements of all regions except the head, although these differences were significant only for the fat and % fat of the arms and legs. In addition EPB precision values exhibited greater individual variability than PB precision values. Finally, absolute values of bone and soft tissue were compared among the three combinations of scan and analysis modes. BMC, BMD, fat mass, %fat and lean mass were significantly different between PB scans and either of the EPB or EA scans. Differences were as large as 20%-25% for certain regional fat and BMD measurements. Additional work may be needed to examine the relative accuracy of the scan mode/software combinations and to identify reasons for the differences in soft tissue precision with the array whole-body scan mode.

NASA Discipline Musculoskeletal↗
DOE OSTI2021

Encoder–decoder neural network for solving the nonlinear Fokker–Planck–Landau collision operator in XGC

An encoder–decoder neural network has been used to examine the possibility for acceleration of a partial integro-differential equation, the Fokker–Planck–Landau collision operator. This is part of the governing equation in the massively parallel particle-in-cell code XGC, which is used to study turbulence in fusion energy devices. The neural network emphasizes physics-inspired learning, where it is taught to respect physical conservation constraints of the collision operator by including them in the training loss, along with the ℓ 2 loss. In particular, network architectures used for the computer vision task of semantic segmentation have been used for training. A penalization method is used to enforce the ‘soft’ constraints of the system and integrate error in the conservation properties into the loss function. During training, quantities representing the particle density, momentum and energy for all species of the system are calculated at each configuration vertex, mirroring the procedure in XGC. This simple training has produced a median relative loss, across configuration space, of the order of 10 –4 , which is low enough if the error is of random nature, but not if it is of drift nature in time steps. The run time for the current Picard iterative solver of the operator is O(n 2 ), where n is the number of plasma species. As the XGC1 code begins to attack problems including a larger number of species, the collision operator will become expensive computationally, making the neural network solver even more important, especially since its training only scales as O(n). Here, a wide enough range of collisionality has been considered in the training data to ensure the full domain of collision physics is captured. An advanced technique to decrease the losses further will be subject of a subsequent report. Eventual work will include expansion of the network to include multiple plasma species.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
NASA NTRS1993

Task planning with uncertainty for robotic systems

In a practical robotic system, it is important to represent and plan sequences of operations and to be able to choose an efficient sequence from them for a specific task. During the generation and execution of task plans, different kinds of uncertainty may occur and erroneous states need to be handled to ensure the efficiency and reliability of the system. An approach to task representation, planning, and error recovery for robotic systems is demonstrated. Our approach to task planning is based on an AND/OR net representation, which is then mapped to a Petri net representation of all feasible geometric states and associated feasibility criteria for net transitions. Task decomposition of robotic assembly plans based on this representation is performed on the Petri net for robotic assembly tasks, and the inheritance of properties of liveness, safeness, and reversibility at all levels of decomposition are explored. This approach provides a framework for robust execution of tasks through the properties of traceability and viability. Uncertainty in robotic systems are modeled by local fuzzy variables, fuzzy marking variables, and global fuzzy variables which are incorporated in fuzzy Petri nets. Analysis of properties and reasoning about uncertainty are investigated using fuzzy reasoning structures built into the net. Two applications of fuzzy Petri nets, robot task sequence planning and sensor-based error recovery, are explored. In the first application, the search space for feasible and complete task sequences with correct precedence relationships is reduced via the use of global fuzzy variables in reasoning about subgoals. In the second application, sensory verification operations are modeled by mutually exclusive transitions to reason about local and global fuzzy variables on-line and automatically select a retry or an alternative error recovery sequence when errors occur. Task sequencing and task execution with error recovery capability for one and multiple soft components in robotic systems are investigated.

Cao, Tiehua↗
NASA NTRS1986

Evidence for explosive chromospheric evaporation in a solar flare observed with SMM

SMM soft X-ray data and Sacramento Peak Observatory H-alpha observations are combined in a study of the impulsive phase of a solar flare. A blue asymmetry, indicative of upflow motions, was observed in the coronal Ca XIX line during the soft X-ray rise phase. H-alpha redshifts, indicative of downward motions, were observed simultaneously in bright flare kernels during the period of hard X-ray emission. It is shown that, to within observational errors, the impulsive phase momentum transported by the upflowing soft X-ray plasma is equivalent to that of the downward moving chromospheric material.

Zarro, D. M.↗
NASA NTRS1977

Minimax decoding of cyclic block codes

A minimax decoding algorithm utilizing soft bit detection of an (n,k) cyclic block code is described which will permit the correction of up to n-k bit errors interspersed at random locations throughout the block. The decoding solution consists of: (1) identifying the ordered soft bit set and, (2) finding the minimum order solution to the resulting syndrome equations where the nonzero error vector components are constrained to be a subset of the soft bit set. An efficient implementation of the decoding operation is described. In essence, this algorithm focuses the correction capability of the code on those bit positions which have the lowest a posteriori probabilities of correct detection.

Greene, E. P.↗
NASA NTRS1984

Analysis of a Coded, M-ary Orthogonal Input Optical Channel with Random-gain Photomultiplier Detection

Performance of two coding systems is analyzed for a noisy optical channel with M(=2(L)-ary orthogonal signaling and random gain photomultiplier detection. The considered coding systems are the Reed Solomon (RS) coding with error only correction decoding and the interleaved binary convolutional system with soft decision Viterbi decoding. The required average number of received signal photons per information bit, N sub b, for a desired bit error of 0.000001 is found for a set of commonly used parameters and with a high background noise level. We find that the interleaved binary convolutional coding system is preferable to the RS coding system in performance complexity tradeoffs.

Lee, P. J.↗
NASA NTRS1996

Bit Error Probability for Maximum Likelihood Decoding of Linear Block Codes

In this paper, the bit error probability P(sub b) for maximum likelihood decoding of binary linear codes is investigated. The contribution of each information bit to P(sub b) is considered. For randomly generated codes, it is shown that the conventional approximation at high SNR P(sub b) is approximately equal to (d(sub H)/N)P(sub s), where P(sub s) represents the block error probability, holds for systematic encoding only. Also systematic encoding provides the minimum P(sub b) when the inverse mapping corresponding to the generator matrix of the code is used to retrieve the information sequence. The bit error performances corresponding to other generator matrix forms are also evaluated. Although derived for codes with a generator matrix randomly generated, these results are shown to provide good approximations for codes used in practice. Finally, for decoding methods which require a generator matrix with a particular structure such as trellis decoding or algebraic-based soft decision decoding, equivalent schemes that reduce the bit error probability are discussed.

Lin, Shu↗
NASA NTRS1991

Multiple single event upsets in CMOS static rams

The occurrence of multiple upset errors during ground tests can contaminate the data and lead to error cross sections which are too high. In space, multiple errors may produce higher upsets than predicted and if they occur in single words they can defeat error detection and correction hardware. This investigation involves data which were taken during an experimental study of dose imprint effects in static memories. The results show that multiple errors occurred mainly for heavy ions with high linear energy transfers and with the majority of these in the soft upset sections of the dose-imprinted memory samples. The percentages of the total number of errors which were singles, doubles, and triplets, were determined as a function of LET, dose, and soft or hard section of the devices. The experimental observations are compared to the predictions of simple binomial statistics.

Stassinopoulos, E. G.↗
NASA NTRS1992

Least Reliable Bits Coding (LRBC) for high data rate satellite communications

An analysis and discussion of a bandwidth efficient multi-level/multi-stage block coded modulation technique called Least Reliable Bits Coding (LRBC) is presented. LRBC uses simple multi-level component codes that provide increased error protection on increasingly unreliable modulated bits in order to maintain an overall high code rate that increases spectral efficiency. Further, soft-decision multi-stage decoding is used to make decisions on unprotected bits through corrections made on more protected bits. Using analytical expressions and tight performance bounds it is shown that LRBC can achieve increased spectral efficiency and maintain equivalent or better power efficiency compared to that of Binary Phase Shift Keying (BPSK). Bit error rates (BER) vs. channel bit energy with Additive White Gaussian Noise (AWGN) are given for a set of LRB Reed-Solomon (RS) encoded 8PSK modulation formats with an ensemble rate of 8/9. All formats exhibit a spectral efficiency of 2.67 = (log2(8))(8/9) information bps/Hz. Bit by bit coded and uncoded error probabilities with soft-decision information are determined. These are traded with with code rate to determine parameters that achieve good performance. The relative simplicity of Galois field algebra vs. the Viterbi algorithm and the availability of high speed commercial Very Large Scale Integration (VLSI) for block codes indicates that LRBC using block codes is a desirable method for high data rate implementations.

Vanderaar, Mark↗
DOE OSTI2021

Relationship between the contact force strength and numerical inaccuracies in piecewise-smooth systems

This work studies the different types of behavior and inaccuracies that can occur when contact is not adequately accounted for in a dynamical system with freeplay, as the strength of the contact stiffness increases. The MATLAB® ode45 time integration solver, with the built-in Event Location capability, is first validated using past experimental data from a forced Duffing oscillator with freeplay. Next, numerical results utilizing event location are compared to results neglecting event location in order to highlight possible numerical errors and effects on multistable dynamical responses. Inaccuracies tend to occur in two different ways. First, neglecting event location can affect the boundaries between basins of attraction. Second, neglecting event location has little effect on the behaviors of the attractor solutions themselves besides merely resembling poorly converged solutions. Errors are less pronounced at the limits of soft or hard contact stiffness. Finally, this study shows the importance of accurately solving piecewise-smooth systems and the existing correlation between the strength of the contact force and possible numerical inaccuracies.

42 ENGINEERING↗
NASA NTRS1974

Soft X-ray bremsstrahlung and fluorescent line production in the atmosphere by low energy electrons

The effect of low energy quasi-trapped or precipitating electrons which impact on the counter windows of soft X-ray detectors are discussed. The errors caused by X-rays produced in the residual atmosphere above a rocket-borne detector because of the resemblance to X-rays of cosmic origin are examined. The design and development of counter windows which make it possible to identify the atmospherically produced X-rays are described. Curves are presented to show the following: (1) preliminary low energy electron data from Atmospheric Explorer C, (2) X-ray flux in electron-excited nitrogen and oxygen, (3) typical proportional counter response to low energy cosmic rays, and (4) proportional counter response to X-radiation produced by electrons incident upon a gas of oxygen to nitrogen number of 0.4.

Kraushaar, W. L.↗
NASA NTRS2008

Exploring Cosmic X-ray Source Polarization

Cosmic X-ray sources are expected to be polarized, either because of their asymmetry and the role of scattering in their emission or the role of magnetic fields. Polarization at other wavelengths has been useful. X-ray polarization will provide a new handle on black hole parameters, in particular the spin, on accretion flows and outflows, on neutron star spin orientations and emission mechanisms, on the quantum mechanical effects of super-strong magnetic fields of magnetars, and on the structure of supernovae shocks. The proposed Gravity and Extreme Magnetism SMEX (GEMS) will use high efficiency polarimeters behind thin foil mirrors. The statistical sensitivity and control of systematics will allow measurement of polarization fractions as small as 1% from many galactic and extragalactic sources. Targets which should be polarized at the level that GEMS can easily measure include stellar black holes, Seyfert galaxies and quasars, blazars, rotation-powered and accretion-powered pulsars, magnetars, shell supernova remnants and pulsar wind nebulae. The polarimeters are Time Projection Chambers that allow reconstruction of images of photoelectron tracks for 2-10 keV Xrays. They can be deep without sacrificing modulation. These polarimeters do not image the sky, but the telescope point spread function and detector collimation allow structure to be resolved at the 10 arcmin level. Rotation of the spacecraft is not needed for the signal measurement in the Time Projection Chambers, but provides for measurement and correction of systematic errors. It also allows a small Bragg reflection soft X-ray experiment to be included that can be used for isolated neutron stars and blazars.

Swank, Jean Hebb↗
NASA NTRS2003

Neural Network and Regression Methods Demonstrated in the Design Optimization of a Subsonic Aircraft

The neural network and regression methods of NASA Glenn Research Center s COMETBOARDS design optimization testbed were used to generate approximate analysis and design models for a subsonic aircraft operating at Mach 0.85 cruise speed. The analytical model is defined by nine design variables: wing aspect ratio, engine thrust, wing area, sweep angle, chord-thickness ratio, turbine temperature, pressure ratio, bypass ratio, fan pressure; and eight response parameters: weight, landing velocity, takeoff and landing field lengths, approach thrust, overall efficiency, and compressor pressure and temperature. The variables were adjusted to optimally balance the engines to the airframe. The solution strategy included a sensitivity model and the soft analysis model. Researchers generated the sensitivity model by training the approximators to predict an optimum design. The trained neural network predicted all response variables, within 5-percent error. This was reduced to 1 percent by the regression method. The soft analysis model was developed to replace aircraft analysis as the reanalyzer in design optimization. Soft models have been generated for a neural network method, a regression method, and a hybrid method obtained by combining the approximators. The performance of the models is graphed for aircraft weight versus thrust as well as for wing area and turbine temperature. The regression method followed the analytical solution with little error. The neural network exhibited 5-percent maximum error over all parameters. Performance of the hybrid method was intermediate in comparison to the individual approximators. Error in the response variable is smaller than that shown in the figure because of a distortion scale factor. The overall performance of the approximators was considered to be satisfactory because aircraft analysis with NASA Langley Research Center s FLOPS (Flight Optimization System) code is a synthesis of diverse disciplines: weight estimation, aerodynamic analysis, engine cycle analysis, propulsion data interpolation, mission performance, airfield length for landing and takeoff, noise footprint, and others.

Hopkins, Dale A.↗