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At least 343 records · Page 19
All sourcesNASA NTRSDOE OSTI
NASA NTRS2011

Prioritized LT Codes

The original Luby Transform (LT) coding scheme is extended to account for data transmissions where some information symbols in a message block are more important than others. Prioritized LT codes provide unequal error protection (UEP) of data on an erasure channel by modifying the original LT encoder. The prioritized algorithm improves high-priority data protection without penalizing low-priority data recovery. Moreover, low-latency decoding is also obtained for high-priority data due to fast encoding. Prioritized LT codes only require a slight change in the original encoding algorithm, and no changes at all at the decoder. Hence, with a small complexity increase in the LT encoder, an improved UEP and low-decoding latency performance for high-priority data can be achieved. LT encoding partitions a data stream into fixed-sized message blocks each with a constant number of information symbols. To generate a code symbol from the information symbols in a message, the Robust-Soliton probability distribution is first applied in order to determine the number of information symbols to be used to compute the code symbol. Then, the specific information symbols are chosen uniform randomly from the message block. Finally, the selected information symbols are XORed to form the code symbol. The Prioritized LT code construction includes an additional restriction that code symbols formed by a relatively small number of XORed information symbols select some of these information symbols from the pool of high-priority data. Once high-priority data are fully covered, encoding continues with the conventional LT approach where code symbols are generated by selecting information symbols from the entire message block including all different priorities. Therefore, if code symbols derived from high-priority data experience an unusual high number of erasures, Prioritized LT codes can still reliably recover both high- and low-priority data. This hybrid approach decides not only "how to encode" but also "what to encode" to achieve UEP. Another advantage of the priority encoding process is that the majority of high-priority data can be decoded sooner since only a small number of code symbols are required to reconstruct high-priority data. This approach increases the likelihood that high-priority data is decoded first over low-priority data. The Prioritized LT code scheme achieves an improvement in high-priority data decoding performance as well as overall information recovery without penalizing the decoding of low-priority data, assuming high-priority data is no more than half of a message block. The cost is in the additional complexity required in the encoder. If extra computation resource is available at the transmitter, image, voice, and video transmission quality in terrestrial and space communications can benefit from accurate use of redundancy in protecting data with varying priorities.

Woo, Simon S.↗
NASA NTRS2006

System and method for forward error correction

A system and method are provided for transferring a packet across a data link. The packet may include a stream of data symbols which is delimited by one or more framing symbols. Corruptions of the framing symbol which result in valid data symbols may be mapped to invalid symbols. If it is desired to transfer one of the valid data symbols that has been mapped to an invalid symbol, the data symbol may be replaced with an unused symbol. At the receiving end, these unused symbols are replaced with the corresponding valid data symbols. The data stream of the packet may be encoded with forward error correction information to detect and correct errors in the data stream.

Cole, Robert M.↗
NASA NTRS2007

System and method for transferring data on a data link

A system and method are provided for transferring a packet across a data link. The packet may include a stream of data symbols which is delimited by one or more framing symbols. Corruptions of the framing symbol which result in valid data symbols may be mapped to invalid symbols. If it is desired to transfer one of the valid data symbols that has been mapped to an invalid symbol, the data symbol may be replaced with an unused symbol. At the receiving end, these unused symbols are replaced with the corresponding valid data symbols. The data stream of the packet may be encoded with forward error correction information to detect and correct errors in the data stream.

Cole, Robert M.↗
NASA NTRS1986

On the selection and evaluation of visual display symbology Factors influencing search and identification times

Three single-target visual search tasks were used to evaluate a set of cathode-ray tube (CRT) symbols for a helicopter situation display. The search tasks were representative of the information extraction required in practice, and reaction time was used to measure the efficiency with which symbols could be located and identified. Familiar numeric symbols were responded to more quickly than graphic symbols. The addition of modifier symbols, such as a nearby flashing dot or surrounding square, had a greater disruptive effect on the graphic symbols than did the numeric characters. The results suggest that a symbol set is, in some respects, like a list that must be learned. Factors that affect the time to identify items in a memory task, such as familiarity and visual discriminability, also affect the time to identify symbols. This analogy has broad implications for the design of symbol sets. An attempt was made to model information access with this class of display.

Remington, Roger↗
NASA NTRS1994

Effective declutter of complex flight displays using stereoptic 3-D cueing

The application of stereo technology to new, integrated pictorial display formats has been effective in situational awareness enhancements, and stereo has been postulated to be effective for the declutter of complex informational displays. This paper reports a full-factorial workstation experiment performed to verify the potential benefits of stereo cueing for the declutter function in a simulated tracking task. The experimental symbology was designed similar to that of a conventional flight director, although the format was an intentionally confused presentation that resulted in a very cluttered dynamic display. The subject's task was to use a hand controller to keep a tracking symbol, an 'X', on top of a target symbol, another X, which was being randomly driven. In the basic tracking task, both the target symbol and the tracking symbol were presented as red X's. The presence of color coding was used to provide some declutter, thus making the task more reasonable to perform. For this condition, the target symbol was coded red, and the tracking symbol was coded blue. Noise conditions, or additional clutter, were provided by the inclusion of randomly moving, differently colored X symbols. Stereo depth, which was hypothesized to declutter the display, was utilized by placing any noise in a plane in front of the display monitor, the tracking symbol at screen depth, and the target symbol behind the screen. The results from analyzing the performances of eight subjects revealed that the stereo presentation effectively offsets the cluttering effects of both the noise and the absence of color coding. The potential of stereo cueing to declutter complex informational displays has therefore been verified; this ability to declutter is an additional benefit from the application of stereoptic cueing to pictorial flight displays.

Parrish, Russell V.↗
NASA NTRS1995

Method for coding low entrophy data

A method of lossless data compression for efficient coding of an electronic signal of information sources of very low information rate is disclosed. In this method, S represents a non-negative source symbol set, (s(sub 0), s(sub 1), s(sub 2), ..., s(sub N-1)) of N symbols with s(sub i) = i. The difference between binary digital data is mapped into symbol set S. Consecutive symbols in symbol set S are then paired into a new symbol set Gamma which defines a non-negative symbol set containing the symbols (gamma(sub m)) obtained as the extension of the original symbol set S. These pairs are then mapped into a comma code which is defined as a coding scheme in which every codeword is terminated with the same comma pattern, such as a 1. This allows a direct coding and decoding of the n-bit positive integer digital data differences without the use of codebooks.

Yeh, Pen-Shu↗
NASA NTRS2012

Sample-Clock Phase-Control Feedback

To demodulate a communication signal, a receiver must recover and synchronize to the symbol timing of a received waveform. In a system that utilizes digital sampling, the fidelity of synchronization is limited by the time between the symbol boundary and closest sample time location. To reduce this error, one typically uses a sample clock in excess of the symbol rate in order to provide multiple samples per symbol, thereby lowering the error limit to a fraction of a symbol time. For systems with a large modulation bandwidth, the required sample clock rate is prohibitive due to current technological barriers and processing complexity. With precise control of the phase of the sample clock, one can sample the received signal at times arbitrarily close to the symbol boundary, thus obviating the need, from a synchronization perspective, for multiple samples per symbol. Sample-clock phase-control feedback was developed for use in the demodulation of an optical communication signal, where multi-GHz modulation bandwidths would require prohibitively large sample clock frequencies for rates in excess of the symbol rate. A custom mixedsignal (RF/digital) offset phase-locked loop circuit was developed to control the phase of the 6.4-GHz clock that samples the photon-counting detector output. The offset phase-locked loop is driven by a feedback mechanism that continuously corrects for variation in the symbol time due to motion between the transmitter and receiver as well as oscillator instability. This innovation will allow significant improvements in receiver throughput; for example, the throughput of a pulse-position modulation (PPM) with 16 slots can increase from 188 Mb/s to 1.5 Gb/s.

Quirk, Kevin J.↗
DOE OSTI2022

Hypothesis testing via AI: Generating physically interpretable models of scientific data with machine learning (Full Technical Report)

Deep learning has demonstrated an exceptional ability to solve complex tasks (an engineering success); however, it has done so at the expense of the ability to generate new knowledge (a scientific failure). We propose an alternative framework—entitled Deep Symbolic Regression (DSR)—in which artificial neural networks (NNs) rapidly generate hypotheses about physical relationships among inputs. This framework bypasses the need to interpret an NN altogether, while still leveraging the representational power of deep learning. The resulting models are tractable mathematical expressions, which are inherently and readily human interpretable and can provide insights into underlying physical phenomena. Further, we fold this methodology into the scientific process by allowing the scientist to directly integrate a priori knowledge and beliefs to accelerate learning. We demonstrate this methodology on symbolic regression—the problem of rediscovering underlying expressions describing a dataset—and achieve state-of-the-art performance across a wide variety of symbolic regression problems. Further, we generalize our DSR framework to apply to the more general class of symbolic optimization problems, in which one seeks to optimize a sequence of symbols or “tokens” under a black-box reward function. Examples of other symbolic optimization problems include neural architecture search and computational antibody design. Our generalized tool, Deep Symbolic Optimization (DSO), has been demonstrated on the task of learning symbolic control policies for reinforcement learning environments, and has been adopted as an enabling capability for computational antibody design.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1984

The evaluation of display symbology - A chronometric study of visual search

Three single-target visual search tasks were used to evaluate a set of CRT symbols for a helicopter traffic display. The search tasks were representative of the kinds of information extraction required in practice, and reaction time was used to measure the efficiency with which symbols could be located and identified. The results show that familiar numeric symbols were responded to more quickly than graphic symbols. The addition of modifier symbols such as a nearby flashing dot or surrounding square had a greater disruptive effect on the graphic symbols than the alphanumeric characters. The results suggest that a symbol set is like a list that must be learned. Factors that affect the time to respond to items in a list, such as familiarity and visual discriminability, and the division of list items into categories, also affect the time to identify symbols.

Remington, R.↗