International Telemetering Conference Proceedings / October 20-23, 2003 / Riviera Hotel and Convention Center, Las Vegas, Nevada / Calculating the power spectral density (PSD) at the transducer or data acquisition system offers advantages in a network-based telemetry system. The PSD is provided in real time to the users. The conversion to PSD can either be lossless (allowing a complete reconstruction of the transducer signal) or lossy (providing data compression). Post-processing can convert the PSD back to time histories if desired. A complete reconstruction of the signal is possible, including knowledge of the signal level between the sample periods. Properly implemented, this method of data collection provides a sharp anti-aliasing filter with minimal added cost. Currently no standards exist for generating PSDs on the vehicle. New standards could help telemetry system designers understand the benefits and limitations calculating the power spectral density in a network-based telemetry system.

International Telemetering Conference Proceedings / October 25-28, 1999 / Riviera Hotel and Convention Center, Las Vegas, Nevada / A high performance lossy data compression technique is currently being developed for space science applications under the requirement of high-speed push-broom scanning. The technique is also error-resilient in that error propagation is contained within a few scan lines. The algorithm is based on block-transform combined with bit-plane encoding; this combination results in an embedded bit string with exactly the desirable compression rate. The lossy coder is described. The compression scheme performs well on a suite of test images typical of images from spacecraft instruments. Hardware implementations are in development; a functional chip set is expected by the end of 2000.

This thesis deals with lossless and losing methods of data compressions and their possible applications in the measurement engineering. In the first part of the thesis there is a theoretical elaboration which informs the reader about the basic terminology, the reasons of data compression, the usage of data compression in standard practice and the division of compression algorithms. The practical part of thesis deals with the realization of the compress algorithms in Matlab and LabWindows/CVI.