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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 55 records · Page 3

Burst error correction extensions for large Reed Solomon codes

Reed Solomon codes are powerful error correcting codes that include some of the best random and burst correcting codes currently known. It is well known that an (n,k) Reed Solomon code can correct up to (n - k)/2 errors. Many applications utilizing Reed Solomon codes require corrections of errors consisting primarily of bursts. In this paper, it is shown that the burst correcting ability of Reed Solomon codes can be increased beyond (n - k)/2 with an acceptable probability of miscorrect.

Owsley, P.↗

Single-event upset (SEU) in a DRAM with on-chip error correction

Results are given of SEU measurements on 256K dynamic RAMs with on-chip error correction. They are claimed to be the first ever reported. A (12/8) Hamming error-correcting code was incorporated in the layout. Physical separation of the bits in each code word was used to guard against multiple bits being disrupted in any given word. Significant reduction in observed errors is reported.

Zoutendyk, J. A.↗

Using ridge regression in systematic pointing error corrections

A pointing error model is used in the antenna calibration process. Data from spacecraft or radio star observations are used to determine the parameters in the model. However, the regression variables are not truly independent, displaying a condition known as multicollinearity. Ridge regression, a biased estimation technique, is used to combat the multicollinearity problem. Two data sets pertaining to Voyager 1 spacecraft tracking (days 105 and 106 of 1987) were analyzed using both linear least squares and ridge regression methods. The advantages and limitations of employing the technique are presented. The problem is not yet fully resolved.

Guiar, C. N.↗

Polar Coding For Forward Error Correction In Space Communications With LDPC Comparisons

With the surging development of optical telecommunicationsfor space applications, the importance of errorcorrection has become more apparent than ever. Specifically,the exploration of forward error correction code (FEC) methodologieswill be instrumental in developing the standards foroptical communications in space. Despite the widespread useof low-density parity-check (LDPC) codes, alternate FEC codessuch as polar codes have shown immense promise in assistingspace communications error correction with their ability tobypass the error floors that plague LDPC codes. Extremelypromising techniques including cyclic redundancy checks (CRC),successive cancellation (SC), and successive cancellation lists(SCL) that assist polar coding in achieving the Shannon limitin a timely manner are evaluated. MATLAB simulations areconducted with AWGN and burst noise to test each technique'sability to handle noise typically encountered in space and eachtechnique's ability to correct unexpected errors. Results ofsimulations for different rates and message lengths are alsoreported to determine each technique's ability to handle largedata volumes and fix errors. Similar simulations are conductedfor LDPC codes with additional tests for convolutional and nointerleavers. Finally, a discussion regarding the future ability ofpolar codes to satisfy current missions in the place of, or inconjunction with, LDPC codes along with the merits of eachFEC technique's ability to process data efficiently and handledata while maintaining adequate performance will be provided.Preliminary recommendations will be made for each technique'seffectiveness for GEO related missions along with discussionsregarding each technique's ability to fit within the CCSDS standards for optical communications.

Polar Coding↗

Introduction to Forward-Error-Correcting Coding

This reference publication introduces forward error correcting (FEC) and stresses definitions and basic calculations for use by engineers. The seven chapters include 41 example problems, worked in detail to illustrate points. A glossary of terms is included, as well as an appendix on the Q function. Block and convolutional codes are covered.

Freeman, Jon C.↗

Codes with multi-level error-correcting capabilities

In conventional channel coding, all the information symbols of a message are regarded equally significant, and hence codes are devised to provide equal protection for each information symbol against channel errors. However, in some circumstances, some information symbols in a message are more significant than the other symbols. As a result, it is desirable to devise codes with multilevel error-correcting capabilities. In this paper, block codes with multilevel error correcting capabilities, which are also known as unequal error protection (UEP) codes, are investigated. Several classes of UEP codes are constructed. One class of codes satisfies the Hamming bound on the number of parity-check symbols for systematic linear UEP codes and hence is optimal.

Lin, Mao-Chao↗

An investigation of error correcting techniques for OMV and AXAF

The original objectives of this project were to build a test system for the NASA 255/223 Reed/Solomon encoding/decoding chip set and circuit board. This test system was then to be interfaced with a convolutional system at MSFC to examine the performance of the concantinated codes. After considerable work, it was discovered that the convolutional system could not function as needed. This report documents the design, construction, and testing of the test apparatus for the R/S chip set. The approach taken was to verify the error correcting behavior of the chip set by injecting known error patterns onto data and observing the results. Error sequences were generated using pseudo-random number generator programs, with Poisson time distribution between errors and Gaussian burst lengths. Sample means, variances, and number of un-correctable errors were calculated for each data set before testing.

Ingels, Frank↗

Links between N-modular redundancy and the theory of error-correcting codes

N-Modular Redundancy (NMR) is one of the best known fault tolerance techniques. Replication of a module to achieve fault tolerance is in some ways analogous to the use of a repetition code where an information symbol is replicated as parity symbols in a codeword. Linear Error-Correcting Codes (ECC) use linear combinations of information symbols as parity symbols which are used to generate syndromes for error patterns. These observations indicate links between the theory of ECC and the use of hardware redundancy for fault tolerance. In this paper, we explore some of these links and show examples of NMR systems where identification of good and failed elements is accomplished in a manner similar to error correction using linear ECC's.

Bobin, V.↗

Dose Error Correction Using Simulation Extrapolation for Community Noise Dose-Response Modeling

The objective of this work is to provide a framework to account and correct for dose error in dose-response modeling due to measurement uncertainty. Error in noise measurements, especially in the case of limited monitoring locations in a community, can lead to an attenuation or misestimation of parameters in dose-response models. This error can result in overpredicted annoyance at lower doses and underpredicted annoyance at higher doses. Simulated data in the present work are based on previous NASA community studies and incorporate a notional design for future studies with the X-59 aircraft. Several populations of different annoyance response sensitivities are included. Simulation extrapolation (SIMEX, Cook and Stefanski 1994) is used to correct for the dose error in a simple, fully pooled logistic regression. Results indicate the negative impact of attenuation is greatly diminished for all amounts of dose error considered, regardless of a population’s annoyance sensitivity. Therefore, SIMEX can help produce a more accurate dose-response relationship.

SIMEX↗

A NASA Perspective on Quantum AI, Error Correction, and Beyond

Quantum computing is one of the most enticing computational paradigms with the potential to revolutionize diverse areas of future-generation computational systems. While quantum computing hardware has advanced rapidly, from tiny laboratory experiments to quantum chips that can outperform even the largest supercomputers on specialized computational tasks, these noisy-intermediate scale quantum (NISQ) processors are still too small and non-robust to be directly useful for any real-world applications. We discuss the prospects for quantum computing and AI, highlighting advances in algorithms, both near- and longer-term. Quantum error correction is critical to the realization of any such vision. The talk with touch on some recent exciting advanced in quantum error correction, particularly in dynamical codes. The talk will conclude with an example of how the combination of quantum computing and artificial intelligence can help probe fundamental aspect of quantum physics.

quantum optimization algorithms and sampling↗