Development of a field-portable imaging system for scene classification using multispectral data fusion algorithms

Preston, E.; Bergman, Tom; Gorenflo, R.; Hermann, David; Kopala, E.; Kuzma, Thomas J.; Lazofson, Laurence E.; Orkis, R.

doi:10.1109/naecon.1994.332921

Cited by 3 publications

(3 citation statements)

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“…Although, as we show in App. A, network topologies exist for which ρ max−con = ρ UB , thereby leading to rules that do not satisfy (14), in general the choice (18) yields AC rules. 1 However, the choice ρ max−con typically yields eigenvalues in (13) with larger magnitudes than can be achieved with the optimal choice ρ minmax from (17).…”

Section: Asymptotically Converging First-order Lti Rulesmentioning

confidence: 99%

“…To copy otherwise, to republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. use in various civilian and military applications, including target tracking and surveillance for robot navigation [11,16] source localization [13] and radar applications [19], data gathering for weather forecasting and environmental applications [5,7,12], and medical monitoring and imaging [2,3,14]. In general, the networks envisioned for many of these applications involve large numbers of possibly randomly distributed inexpensive sensor nodes, with limited sensing, processing, and communication power on board.…”

Section: Introductionmentioning

confidence: 99%

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Locally constructed algorithms for distributed computations in ad-hoc networks

Scherber

Papadopoulos

2004

Proceedings of the 3rd International Symposium on Information Processing in Sensor Networks

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In this paper we develop algorithms for distributed computation of a broad range of estimation and detection tasks over networks with arbitrary but fixed connectivity. The distributed algorithms we develop are linear dynamical systems that generate sequences of approximations to the desired computation. The algorithms are locally constructed at each node by exploiting only locally available and macroscopic information about the network topology. We present methods for designing these distributed algorithms so as to optimize the convergence rates to the desired computation and demonstrate their performance characteristics in the context of a problem of signal estimation from multi-node signal observations in Gaussian noise.

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Section: Asymptotically Converging First-order Lti Rulesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Locally constructed algorithms for distributed computations in ad-hoc networks

Scherber

Papadopoulos

2004

Proceedings of the 3rd International Symposium on Information Processing in Sensor Networks

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show abstract

“…A remote sensing example is given in [40], where a classifier is produced by using a series of CCD cameras to obtain images in the UV through near infrared regions and uses Maximum Likelihood algorithms for clustering.…”

Section: Region Based Techniquesmentioning

confidence: 99%