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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.

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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.

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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.

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PTB Product of the Year Awards



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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.

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Celestial Software Scratches More Than the Surface

While NASA is preparing to send humans back to the Moon by 2020 and then eventually to Mars, the average person can explore the landscapes of these celestial bodies much sooner, without the risk and training and without even leaving the comfort of home.

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Finding Minimum-Power Broadcast Trees for Wireless Networks

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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