The geometric arithmetic parallel processor

Cloud, Eugene L.

doi:10.1109/fmpc.1988.47455

Cited by 22 publications

(9 citation statements)

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“…This kind of architecture brings advantages due to its modularity and scalability (based on regular structures). For example Geometric-arithmetic Parallel Processor (GAPP) is a parallel processor with over 10,000 processing elements [17]. It consisted of very simple processing elements (one bit sum only) to perform matrix operations.…”

Section: Related Workmentioning

confidence: 99%

Distributed Sensing of Fluid Dynamic Phenomena with the XDense Sensor Grid Network

Loureiro

Rangarajan

Tovar

2015

2015 IEEE 3rd International Conference on Cyber-Physical Systems, Networks, and Applications

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XDense is a proposed wired mesh grid sensor network system tailored for scenarios that benefit from thousands of sensors per square meter. XDense has scalable network topology and protocols, customizable to application specifics, that enables complex feature extraction in realtime from observed phenomena by exploiting communication and distributed processing capabilities of such network topologies.XDense has been designed with closed-loop CPS applications like active flow control of aircraft wing surfaces in mind. It uses a plug-n-play architecture that allows dimensioning of application specific networks. In this paper, we evaluate the performance of XDense in a fluid dynamic application scenario. With experiments on feature detection and realtime scenarios, we demonstrate the potential of the architecture and discuss practical implementation issues. Abstract-XDense is a proposed wired mesh grid sensor network system tailored for scenarios that benefit from thousands of sensors per square meter. XDense has scalable network topology and protocols, customizable to application specifics, that enables complex feature extraction in realtime from observed phenomena by exploiting communication and distributed processing capabilities of such network topologies. XDense has been designed with closed-loop CPS applications like active flow control of aircraft wing surfaces in mind. It uses a plug-nplay architecture that allows dimensioning of application specific networks. In this paper, we evaluate the performance of XDense in a fluid dynamic application scenario. With experiments on feature detection and realtime scenarios, we demonstrate the potential of the architecture and discuss practical implementation issues.

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Section: Related Workmentioning

confidence: 99%

Distributed Sensing of Fluid Dynamic Phenomena with the XDense Sensor Grid Network

Loureiro

Rangarajan

Tovar

2015

2015 IEEE 3rd International Conference on Cyber-Physical Systems, Networks, and Applications

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“…where DISTANCE2 = (X1 -X)2 + (Y -Y)2 (7) xj,yj = the midpoint coordinates of segment i Xj,j = the midpoint coOrdinates of segmentj.…”

Section: Maximum Distance Testmentioning

confidence: 99%

<title>Real-time road detection in infrared imagery</title>

Andre¹,

McCoy²

1990

SPIE Proceedings

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Automatic road detection is an important part in many scene recognition applications. The extraction of roads provides a means of navigation and position update for remotely piloted vehicles or autonomous vehicles. Roads supply strong contextual information which can be used to improve the performance of automatic target recognition (ATh) systems by directing the search for targets and adjusting target classification confidences. This paper will describe algorithmic techniques for labeling roads in high-resolution infrared imagery. In addition, realtime implementation of this structural approach using a processor array based on the Martin Marietta Geometric Arithmetic Parallel Processor (GAPPTh) chip will be addressed.The algorithm described is based on the hypothesis that a road consists of pairs of line segments separated by a distance "d" with opposite gradient directions (antiparallel). The general nature of the algorithm, in addition to its parallel implementation in a single instruction, multiple data (SIMD) machine, are improvements to existing work.The algorithm seeks to identify line segments meeting the road hypothesis in a manner that performs well, even when the side of the road is fragmented due to occlusion or intersections.The use of geometrical relationships between line segments is a powerful yet flexible method of road classification which is independent of orientation. In addition, this approach can be used to nominate other types of objects with minor parametric changes. INTRODUCTIONTo begin nomination of potential road areas, an edge operator is used to extract the edges1'2'3. The edges are then partitioned into line segments and attributes such as length, midpoint coordinates, direction4, and slope are calculated. That information is then arranged in an adjacency matrix5 type of format. The relationships between line segments such as the distance between midpoints, the difference in slope, etc., can then be evaluated. A set oftwo line segments are part of a potential road if they meet the following criteria:

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“…The GAPPTN chip is a SIMD computer developed at Martin Marietta [2]. Each chip contains 72 individual processing cells arranged in 12 rows by 6 columns.…”

Section: Geometric Arithmetic Parallel Processor (Gapp)mentioning

confidence: 99%

<title>Real-time identification of fixed targets</title>

Rowland¹,

Kreeger²,

Sanders³

1992

SPIE Proceedings

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Fixed targets such as bridges, airfields and buildings are of military significance and their value is constantly being appraised as the battle scenario evolves. For example, a building thought to be of no significance may be reappraised, through intelligence reports, as a military command center. The ability to quickly strike these targets with a minimal amount of a priori information is necessary.The requirements placed on such a system are: 1) Rapid turnaround time from the moment the decision is made to attack. Depending on the user organization, this time ranges from fifteen minutes to twelve hours. 2) Minimal a priori target information. There is likely to be no imagery data base of the target, and the system may be required to operate with as little information as an overhead photograph. 3) Real time recognition of the target. Terminal guidance of the weapons delivery system to a specified destructive aiinpoint will be impacted by the recognition system. 4) Flexibility to attack a variety of targets. A data base of known high value fixed targets (HVFT) may be stored, but the sudden inclusion of new targets must be accommodated. This paper will discuss a real time implementation of a model based approach to automatically recognize high value fixed targets in forward looking infrared (FLIR) imagery. This approach generates a predictive model of the expected target features to be found in the image, extracts those feature types from the image, and matches the predictive model with the image features. A generic approach to the description of the target features has been taken to allow for rapid preparation of the models from minimal a priori target information.The real time aspect has been achieved by implementing the system on a massively parallel single instruction, multiple data (SIMD) architecture.An overview of an entire system approach to attack high value fixed targets will be discussed. The automatic target recognizer (ATR), which is a part of this system, will be discussed in detail and results of the ATR operating against HVFT in FLIR imagery will be shown. 2 / SPIE Vol. 1623 20th AIPR Workshop O-8194-0765-8/92/$4.OO Downloaded From: http://proceedings.spiedigitallibrary.org/ on 06/25/2016 Terms of Use: http://spiedigitallibrary.org/ss/TermsOfUse.aspx

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The geometric arithmetic parallel processor

Cited by 22 publications

References 0 publications

Distributed Sensing of Fluid Dynamic Phenomena with the XDense Sensor Grid Network

Distributed Sensing of Fluid Dynamic Phenomena with the XDense Sensor Grid Network

<title>Real-time road detection in infrared imagery</title>

<title>Real-time identification of fixed targets</title>

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