Licensed under Creative Common A COMPARATIVE ANALYSIS OF IMPLEMENTATION OF TACTICAL DECISIONS IN PUBLIC UNIVERSITIES IN KENYA

Abstract Today many Public Universities have failed to implement well thought out strategies. A revie

5th International Symposium on Satellite Navigation Technology and Applications

The classical least-squares processing of GPS measurements generates residuals, which contains the signature of both unmodelled systematic biases and random measurement noise. It is desirable to extract (or minimise) the systematic biases contained within the GPS measurements. This would be relatively straightforward if there were some apriori knowledge of the phenomena related to these errors. Common ways of dealing with this problem include (i) changes to the stochastic modelling, and (ii) redefinition of the functional model. In this study, we apply a method based on wavelets to decompose GPS double-differenced residuals into a low-frequency bias term and a high-frequency noise term. The extracted bias component is then applied directly to the GPS measurements to correct for this term. The remaining terms, largely characterised by the GPS range measurements and high-frequency measurement noise, are expected to give the best linear unbiased solutions from a leastsquares process. A robust VCV estimation, using the MINQUE procedure, controls the formulation of the stochastic model. The results show that this method can improve both the ambiguity resolution and the accuracy of the estimated baseline component

Principal Component Analysis of Wavelet

In a continuous GPS deformation monitoring scheme, any action taken generally relies on a description of the state of the process or events as given by GPS measurements. Timely and correct interpretation of the GPS data is essential to improved quality control, safer system operations, and reduction in the number of false alarms. Unfortunately, GPS data are contaminated by both random and systematic errors of unknown sources. Instrument failure, poor or uncalibrated instrumentation, receiver noise and multipath, all can contribute to data problems. Without proper pretreatment, the necessary interpretation is difficult, if not impossible. Data contaminated by outliers must be eliminated and noise levels reduced. In many cases, critical information occurs over a short duration, and hence is difficult to detect. Wavelets can be used to pre-process data in order to better locate and identify significant events. Combining this type of data pre-processing with multivariate statistics can generate useful insights into the problems of deformation monitoring, data analysis and data interpretation

Vertical displacement measurements for bridges using optical fiber sensors and CCD cameras : a preliminary study

Bridge managers all over the world are always looking for simple ways to measure bridge vertical displacements for structural health monitoring. However, traditional methods to obtain such data are either tedious or expensive. There is a need to develop a simple, inexpensive, and yet practical method to measure bridge vertical displacements. This paper proposes two methods using either optical fiber (FBG) sensors or a charge-coupled-device (CCD) camera, respectively, for vertical displacement measurements of bridges. The FBG sensor method is based on the measured horizontal strains together with the identified curvature functions obtained by a self-developed FBG Tilt sensor. CCD cameras use a large number of pixels to form an image. The CCD camera method utilizes image processing techniques for pixel identification and subsequent edge detection. A preliminary study to validate the proposed methods in laboratory was presented. The tests include applying the methods to determine the vertical displacements separately for a concrete beam and a steel beam under various loadings. The comparisons include their installations, costs, degrees of accuracy, external factors affecting the measurement, etc. It was concluded that both methods could be used for vertical displacement measurement, and they could be complementary with one another. It was suggested to further improve the two methods developed and a successful outcome will not only help to solve an important problem for bridge management, but also prepare the way for better structural health monitoring techniques