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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 199 records · Page 11

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NASA NTRS1988

Method and apparatus for implementing a maximum-likelihood decoder in a hypercube network

A method and a structure to implement maximum-likelihood decoding of convolutional codes on a network of microprocessors interconnected as an n-dimensional cube (hypercube). By proper reordering of states in the decoder, only communication between adjacent processors is required. Faster and more efficient operation is enabled, and decoding of large constraint length codes is feasible using standard VLSI technology.

Pollara-Bozzola, Fabrizio↗

NASA NTRS1991

Large constraint length high speed viterbi decoder based on a modular hierarchial decomposition of the deBruijn graph

A method of formulating and packaging decision-making elements into a long constraint length Viterbi decoder which involves formulating the decision-making processors as individual Viterbi butterfly processors that are interconnected in a deBruijn graph configuration. A fully distributed architecture, which achieves high decoding speeds, is made feasible by novel wiring and partitioning of the state diagram. This partitioning defines universal modules, which can be used to build any size decoder, such that a large number of wires is contained inside each module, and a small number of wires is needed to connect modules. The total system is modular and hierarchical, and it implements a large proportion of the required wiring internally within modules and may include some external wiring to fully complete the deBruijn graph. pg,14.

Collins, Oliver↗

NASA NTRS2009

Joint Carrier-Phase Synchronization and LDPC Decoding

A method has been proposed to increase the degree of synchronization of a radio receiver with the phase of a suppressed carrier signal modulated with a binary- phase-shift-keying (BPSK) or quaternary- phase-shift-keying (QPSK) signal representing a low-density parity-check (LDPC) code. This method is an extended version of the method described in Using LDPC Code Constraints to Aid Recovery of Symbol Timing (NPO-43112), NASA Tech Briefs, Vol. 32, No. 10 (October 2008), page 54. Both methods and the receiver architectures in which they would be implemented belong to a class of timing- recovery methods and corresponding receiver architectures characterized as pilotless in that they do not require transmission and reception of pilot signals. The proposed method calls for the use of what is known in the art as soft decision feedback to remove the modulation from a replica of the incoming signal prior to feeding this replica to a phase-locked loop (PLL) or other carrier-tracking stage in the receiver. Soft decision feedback refers to suitably processed versions of intermediate results of iterative computations involved in the LDPC decoding process. Unlike a related prior method in which hard decision feedback (the final sequence of decoded symbols) is used to remove the modulation, the proposed method does not require estimation of the decoder error probability. In a basic digital implementation of the proposed method, the incoming signal (having carrier phase theta theta (sub c) plus noise would first be converted to inphase (I) and quadrature (Q) baseband signals by mixing it with I and Q signals at the carrier frequency [wc/(2 pi)] generated by a local oscillator. The resulting demodulated signals would be processed through one-symbol-period integrate and- dump filters, the outputs of which would be sampled and held, then multiplied by a soft-decision version of the baseband modulated signal. The resulting I and Q products consist of terms proportional to the cosine and sine of the carrier phase cc as well as correlated noise components. These products would be fed as inputs to a digital PLL that would include a number-controlled oscillator (NCO), which provides an estimate of the carrier phase, theta(sub c).

Simon, Marvin↗

NASA NTRS2007

VLSI Design of a Turbo Decoder

A very-large-scale-integrated-circuit (VLSI) turbo decoder has been designed to serve as a compact, high-throughput, low-power, lightweight decoder core of a receiver in a data-communication system. In a typical contemplated application, such a decoder core would be part of a single integrated circuit that would include the rest of the receiver circuitry and possibly some or all of the transmitter circuitry, all designed and fabricated together according to an advanced communication-system-on-a-chip design concept. Turbo codes are forward-error-correction (FEC) codes. Relative to older FEC codes, turbo codes enable communication at lower signal-to-noise ratios and offer greater coding gain. In addition, turbo codes can be implemented by relatively simple hardware. Therefore, turbo codes have been adopted as standard for some advanced broadband communication systems.

Fang, Wai-Chi↗

DOE OSTI2022

An efficient decoder for a linear distance quantum LDPC code

Recent developments have shown the existence of quantum low-density parity check (qLDPC) codes with constant rate and linear distance. A natural question concerns the efficient decodability of these codes. In this paper, we present a linear time decoder for the recent quantum Tanner codes construction of asymptotically good qLDPC codes, which can correct all errors of weight up to a constant fraction of the blocklength. Our decoder is an iterative algorithm which searches for corrections within constant-sized regions. At each step, the corrections are found by reducing a locally defined and efficiently computable cost function which serves as a proxy for the weight of the remaining error.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2022

Dynamic energy system modeling using hybrid physics-based and machine learning encoder–decoder models

Three model configurations are presented for multi-step time series predictions of the heat absorbed by the water and steam in a thermal power plant. The models predict over horizons of 2, 4, and 6 steps into the future, where each step is a 5-minute increment. The evaluated models are a pure machine learning model, a novel hybrid machine learning and physics-based model, and the hybrid model with an incomplete dataset. The hybrid model deconstructs the machine learning into individual boiler heat absorption units: economizer, water wall, superheater, and reheater. Each configuration uses a gated recurrent unit (GRU) or a GRU-based encoder–decoder as the deep learning architecture. Mean squared error is used to evaluate the models compared to target values. The encoder–decoder architecture is over 11% more accurate than the GRU only models. The hybrid model with the incomplete dataset highlights the importance of the manipulated variables to the system. The hybrid model, compared to the pure machine learning model, is over 10% more accurate on average over 20 iterations of each model. Automatic differentiation is applied to the hybrid model to perform a local sensitivity analysis to identify the most impactful of the 72 manipulated variables on the heat absorbed in the boiler. The models and sensitivity analyses are used in a discussion about optimizing the thermal power plant.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

NASA NTRS1966

Generalized minimum distance decoding.

Generalized minimum distance decoding that permits likelihood information to be used, yielding same probability of error as maximum likelihood decoding

DISTANCE MEASURING EQUIPMENT↗

NASA NTRS2004

Maximum likelihood decoding analysis of accumulate-repeat-accumulate codes

In this paper, the performance of the repeat-accumulate codes with (ML) decoding are analyzed and compared to random codes by very tight bounds. Some simple codes are shown that perform very close to Shannon limit with maximum likelihood decoding.

LDPC codes turbo-like codes maximum likelihood dec↗

NASA NTRS2000

Deep Space Network turbo decoder implementation

turbo decoder turbo code↗

NASA NTRS2001

Deep Space Network turbo decoder implementation

A new decoder is being developed by the Jet Propulsion Laboratory for NASA's Deep Space Network. This unit will decode the new turbo codes, which have recently been approved by the Consultative Committee for Space Data Systems (CCSDS). Turbo codes provide up to 0.8 dB improvement in Eb/No over the current best codes used by deep space missions.

turbo codes↗

NASA NTRS2006

An Interleaver Implementation for the Serially Concatenated Pulse-Position Modulation Decoder

We describe novel interleaver and deinterleaver architectures that support bandwidth efficient memory access for decoders of turbo-like codes that are used in conjunction with high order modulations. The presentation focuses on a decoder for serially concatenated pulse-position modulation (SCPPM), which is a forward-error-correction code designed by NASA to support laser communications from Mars at more than 50 megabits-per-second (Mbps). For 64-ary PPM, the new architectures effectively triple the fan-in of the interleaver and fan-out of the deinterleaver, enabling parallelization that doubles the overall throughput. The techniques described here can be readily modified for other PPM orders.

turbo decoding↗

DOE OSTI2021

Hayden-Preskill decoding from noisy Hawking radiation

In the Hayden-Preskill thought experiment, the Hawking radiation emitted before a quantum state is thrown into the black hole is used along with the radiation collected later for the purpose of decoding the quantum state. A natural question is how the recoverability is affected if the stored early radiation is damaged or subject to decoherence, and/or the decoding protocol is imperfectly performed. We study the recoverability in the thought experiment in the presence of decoherence or noise in the storage of early radiation.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2024

Single-Shot Decoding of Good Quantum LDPC Codes

Abstract Quantum Tanner codes constitute a family of quantum low-density parity-check codes with good parameters, i.e., constant encoding rate and relative distance. In this article, we prove that quantum Tanner codes also facilitate single-shot quantum error correction (QEC) of adversarial noise, where one measurement round (consisting of constant-weight parity checks) suffices to perform reliable QEC even in the presence of measurement errors. We establish this result for both the sequential and parallel decoding algorithms introduced by Leverrier and Zémor. Furthermore, we show that in order to suppress errors over multiple repeated rounds of QEC, it suffices to run the parallel decoding algorithm for constant time in each round. Combined with good code parameters, the resulting constant-time overhead of QEC and robustness to (possibly time-correlated) adversarial noise make quantum Tanner codes alluring from the perspective of quantum fault-tolerant protocols.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2017

Decoding Signal Processing at the Single-Cell Level

The ability of cells to detect and decode information about their extracellular environment is critical to generating an appropriate response. In multicellular organisms, cells must decode dozens of signals from their neighbors and extracellular matrix to maintain tissue homeostasis while still responding to environmental stressors. How cells detect and process information from their surroundings through a surprisingly limited number of signal transduction pathways is one of the most important question in biology. Despite many decades of research, many of the fundamental principles that underlie cell signal processing remain obscure. However, in this issue of Cell Systems, Gillies et al present compelling evidence that the early response gene circuit can act as a linear signal integrator, thus providing significant insight into how cells handle fluctuating signals and noise in their environment.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2018

Automatic signal decoding and sensor stability of a 3-electrode mixed-potential sensor for NO x /NH 3 quantification

Sensors to detect mixtures of NO x /NH 3 are needed to monitor emissions of diesel automobiles where a selective catalytic reduction system uses an NH 3 mediated reaction to reduce NO x . We report on the application of a three electrode La 0.8 Sr 0.2 CrO 3 , Au 0.5 Pd 0.5 , Pt mixed potential sensor using yttria-stabilized-zirconia (YSZ) as a solid electrolyte to NO x /NH 3 sensing. Artificial neural networks were used to automatically decode the concentrations of NO x /NH 3 and errors of less than 15% are achieved. The optimal architecture for ANN decoding and the maximum density of training data points are also determined. The stability of the sensor was monitored by electrochemical impedance spectroscopy. The impedance associated with YSZ oxygen ion conduction and the electrochemical reactions at the three-phase interface are tracked for a period of over 100 days.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2023

Neural-network decoders for measurement induced phase transitions

Open quantum systems have been shown to host a plethora of exotic dynamical phases. Measurement-induced entanglement phase transitions in monitored quantum systems are a striking example of this phenomena. However, naive realizations of such phase transitions requires an exponential number of repetitions of the experiment which is practically unfeasible on large systems. Recently, it has been proposed that these phase transitions can be probed locally via entangling reference qubits and studying their purification dynamics. In this work, we leverage modern machine learning tools to devise a neural network decoder to determine the state of the reference qubits conditioned on the measurement outcomes. We show that the entanglement phase transition manifests itself as a stark change in the learnability of the decoder function. We study the complexity and scalability of this approach in both Clifford and Haar random circuits and discuss how it can be utilized to detect entanglement phase transitions in generic experiments.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2021

Decoding defect statistics from diffractograms via machine learning

Abstract Diffraction techniques can powerfully and nondestructively probe materials while maintaining high resolution in both space and time. Unfortunately, these characterizations have been limited and sometimes even erroneous due to the difficulty of decoding the desired material information from features of the diffractograms. Currently, these features are identified non-comprehensively via human intuition, so the resulting models can only predict a subset of the available structural information. In the present work we show (i) how to compute machine-identified features that fully summarize a diffractogram and (ii) how to employ machine learning to reliably connect these features to an expanded set of structural statistics. To exemplify this framework, we assessed virtual electron diffractograms generated from atomistic simulations of irradiated copper. When based on machine-identified features rather than human-identified features, our machine-learning model not only predicted one-point statistics (i.e. density) but also a two-point statistic (i.e. spatial distribution) of the defect population. Hence, this work demonstrates that machine-learning models that input machine-identified features significantly advance the state of the art for accurately and robustly decoding diffractograms.

36 MATERIALS SCIENCE↗

DOE OSTI2015

Discrete decoding based ultrafast multidimensional nuclear magnetic resonance spectroscopy

The three-dimensional (3D) nuclear magnetic resonance (NMR) spectroscopy constitutes an important and powerful tool in analyzing chemical and biological systems. However, the abundant 3D information arrives at the expense of long acquisition times lasting hours or even days. Therefore, there has been a continuous interest in developing techniques to accelerate recordings of 3D NMR spectra, among which the ultrafast spatiotemporal encoding technique supplies impressive acquisition speed by compressing a multidimensional spectrum in a single scan. However, it tends to suffer from tradeoffs among spectral widths in different dimensions, which deteriorates in cases of NMR spectroscopy with more dimensions. In this study, the discrete decoding is proposed to liberate the ultrafast technique from tradeoffs among spectral widths in different dimensions by focusing decoding on signal-bearing sites. For verifying its feasibility and effectiveness, we utilized the method to generate two different types of 3D spectra. The proposed method is also applicable to cases with more than three dimensions, which, based on the experimental results, may widen applications of the ultrafast technique.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗