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Utilizing Expert Knowledge in Estimating Future STS Costs

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.

Posted in: Briefs

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Improved Discrete Approximation of Laplacian of Gaussian

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.

Posted in: Briefs, TSP

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Autonomous Environment-Monitoring Networks

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.

Posted in: Briefs, TSP

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Current Attractions

The ScanWorks(R) hand-held 3D laser scanner from Perceptron (Plymouth, MI) was named Photonics Tech Briefs Product of the Month for April. The instrument features a scanning rate of up to 458,000 points per second and can maintain a dense point resolution of approximately 14 microns. The device projects the sensor's field of view onto the target scanning area to visualize the best scanning strategy, and may be used on dark or highly reflective surfaces. The scanning software includes a MS Windows XP-style interface, intelligent sensor calibration, real-time point shading, and automatic exposure control.

Posted in: Blog

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Technology Business Brief

New CNT Array Adhesion Tape and the Opportunity to Collaborate Company is seeking industry support for further testing and development of its carbon nanotube (CNT) adhesive. Developed in conjunction with the National Science Foundation, NASA, and the University of California-Berkeley, this company is looking for industry investment to support tests on the adhesion strength and mechanical stability of its nano adhesive.

Posted in: Blog

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PTB Product of the Year Awards



Posted in: Blog

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Hexagonal Pixels and Indexing Scheme for Binary Images

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.

Posted in: Briefs

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