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# Serial-Turbo-Trellis-Coded Modulation With Rate-1 Inner Code

Monday, 16 April 2007

Coders and decoders for bandwidth- and power-limited systems could be less complex.
Serially concatenated turbo codes have been proposed to satisfy requirements for low bit- and word-error rates and for low (in comparison with related previous codes) complexity of coding and decoding algorithms and thus low complexity of coding and decoding circuitry. These codes are applicable to such high-level modulations as octonary phase-shift keying (8PSK) and 16-state quadrature amplitude modulation (16QAM); the signal product obtained by applying one of these codes to one of these modulations is denoted, generally, as "serially concatenated trellis-coded modulation" ("SCTCM"). These codes could be particularly beneficial for communication systems that must be designed and operated subject to limitations on bandwidth and power.

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# More About the Phase-Synchroized Enhancement Method

Monday, 16 April 2007

A report presents further details regarding the subject matter of "Phase-Synchronized Enhancement Method for Engine Diagnostics" (MFS-26435), NASA Tech Briefs, Vol. 22, No. 1 (January 1998), page 54. To recapitulate: The phase-synchronized enhancement method (PSEM) involves the digital resampling of a quasi-periodic signal in synchronism with the instantaneous phase of one of its spectral components. This resampling transforms the quasi-periodic signal into a periodic one more amenable to analysis. It is particularly useful for diagnosis of a rotating machine through analysis of vibration spectra that include components at the fundamental and harmonics of a slightly fluctuating rotation frequency. The report discusses the machinery-signal-analysis problem, outlines the PSEM algorithms, presents the mathematical basis of the PSEM, and presents examples of application of the PSEM in some computational simulations.

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# Utilizing Expert Knowledge in Estimating Future STS Costs

Monday, 16 April 2007

A method of estimating the costs of future space transportation systems (STSs) involves classical activity-based cost (ABC) modeling combined with systematic utilization of the knowledge and opinions of experts to extend the process-flow knowledge of existing systems to systems that involve new materials and/or new architectures. The expert knowledge is particularly helpful in filling gaps that arise in computational models of processes because of inconsistencies in historical cost data. Heretofore, the costs of planned STSs have been estimated following a "top-down" approach that tends to force the architectures of new systems to incorporate process flows like those of the space shuttles. In this ABC-based method, one makes assumptions about the processes, but otherwise follows a "bottoms up" approach that does not force the new system architecture to incorporate a space-shuttle-like process flow. Prototype software has been developed to implement this method. Through further development of software, it should be possible to extend the method beyond the space program to almost any setting in which there is a need to estimate the costs of a new system and to extend the applicable knowledge base in order to make the estimate.

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# Improved Discrete Approximation of Laplacian of Gaussian

Monday, 16 April 2007

This method reduces the amount of circuitry needed for filtering of video data.
An improved method of computing a discrete approximation of the Laplacian of a Gaussian convolution of an image has been devised. The primary advantage of the method is that without substantially degrading the accuracy of the end result, it reduces the amount of information that must be processed and thus reduces the amount of circuitry needed to perform the Laplacian-of-Gaussian (LOG) operation.

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# Autonomous Environment-Monitoring Networks

Monday, 16 April 2007

These neural networks recognize novel features in streams of input data.
Autonomous environment-monitoring networks (AEMNs) are artificial neural networks that are specialized for recognizing familiarity and, conversely, novelty. Like a biological neural network, an AEMN receives a constant stream of inputs. For purposes of computational implementation, the inputs are vector representations of the information of interest. As long as the most recent input vector is similar to the previous input vectors, no action is taken. Action is taken only when a novel vector is encountered. Whether a given input vector is regarded as novel depends on the previous vectors; hence, the same input vector could be regarded as familiar or novel, depending on the context of previous input vectors. AEMNs have been proposed as means to enable exploratory robots on remote planets to recognize novel features that could merit closer scientific attention. AEMNs could also be useful for processing data from medical instrumentation for automated monitoring or diagnosis.

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# Hexagonal Pixels and Indexing Scheme for Binary Images

Friday, 13 April 2007

For some purposes, this scheme is superior to rectangular pixels.
A scheme for resampling binaryimage data from a rectangular grid to a regular hexagonal grid and an associated tree - structured pixel - indexing scheme keyed to the level of resolution have been devised. This scheme could be utilized in conjunction with appropriate image - data - processing algorithms to enable automated retrieval and/or recognition of images. For some purposes, this scheme is superior to a prior scheme that relies on rectangular pixels: One example of such a purpose is recognition of fingerprints, which can be approximated more closely by use of line segments along hexagonal axes than by line segments along rectangular axes. This scheme could also be combined with algorithms for query - image - based retrieval of images via the Internet.

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# Finding Minimum-Power Broadcast Trees for Wireless Networks

Friday, 13 April 2007

Algorithms for identifying viable trees have been derived.
Some algorithms have been devised for use in a method of constructing tree graphs that represent connections among the nodes of a wireless ommunication network. These algorithms provide for determining the viability of any given candidate connection tree and for generating an initial set of viable trees that can be used in any of a variety of search algorithms (e.g., a genetic algorithm) to find a tree that enables the network to broadcast from a source node to all other nodes while consuming the minimum amount of total power. The method yields solutions better than those of a prior algorithm known as the broadcast incremental power algorithm, albeit at a slightly greater computational cost.

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