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The Global ETD Search service is a free service for researchers
to find electronic theses and dissertations. This service is
provided by the
Networked Digital Library of Theses and Dissertations.
Our metadata is collected from universities around the world. If you manage a university/consortium/country archive and want to be added, details can be found on the NDLTD website.
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Showing 411 to 420 of 1738 (0.071 seconds)Spelling suggestions: "subject:"filtering"" "subject:"iltering""
| 411 |
An Adaptive filtering algorithm and its application to adaptive beamforming in spread-spectrum systems for interference rejectionKwag, Young Kil January 1987 (has links)
No description available.
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| 412 |
Estimation of a wideband fading HF channel using modified adaptive filtering and center clipping techniquesMatherne, Marcus McLenn January 1994 (has links)
No description available.
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| 413 |
DGPS/ILS integration for an automatic landing system using Kalman FilteringHill, Steven James January 1996 (has links)
No description available.
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| 414 |
The design and implementation of tracking and filtering algorithms for an aircraft Beacon collision warning systemEwing, Jr, Paul Lee January 1989 (has links)
No description available.
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| 415 |
MLS Flight inspection techniques: Digital filtering and coordinate transformationMurphy, Timothy A. January 1985 (has links)
No description available.
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| 416 |
Fast Adaptive Block Based Motion Estimation for Video CompressionLuo, Yi 11 August 2009 (has links)
No description available.
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| 417 |
Short Text Classification in Twitter to Improve Information FilteringSriram, Bharath 03 September 2010 (has links)
No description available.
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| 418 |
Bio-Inspired Inertial Sensors for Human Body Motion MeasurementZeng, Hansong 19 June 2012 (has links)
No description available.
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| 419 |
Unknown input structural health monitoringImpraimakis, Marios January 2022 (has links)
The identification of a structural system deterministically or probabilistically is a topic of considerable interest and importance for its condition assessment and prediction. Many identification approaches, however, require the input which is not always available. Specifically, it may be impossible to know the input or, alternately, the measurement of the input is much more unreliable than the dynamic state measurement. Along these lines, engineers try to extract as much information as possible from the available output data to reduce the need for knowing the input. Three new methodologies are developed here to address this challenge.
Initially, the input-parameter-state estimation capabilities of a novel unscented Kalman filter, for real time monitoring applications, is examined on both linear and nonlinear systems. The unknown input is estimated in two stages within each time step. Firstly, the predicted dynamic states and the system's parameters provide an estimation of the input. Secondly, the corrected with measurements (updated) dynamic states and parameters provide a final input estimation for the current time step.
Subsequently, the estimation of the dynamic states, the parameters, and the input of systems subjected to wind loading is examined using a sequential Kalman filter. The procedure considers two Kalman filters in order to estimate initially the dynamic states using kinematic constraints, and subsequently the system parameters along with the input, in an online fashion.
Finally, the input-parameter-state estimation capabilities of a new residual-based Kalman filter are examined for both complete and limited output information conditions. The filter is based on the residual of the predicted and measured dynamic state output, as well as on the residual of the system model estimation. The considered sensitivity analysis is developed using a real time sensitivity matrix formulated by the filtered dynamic states.
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| 420 |
A technique for dual adaptive control.Alster, Jacob January 1972 (has links)
No description available.
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