Based on fundamental principles from mathematics, linear systems, and signal analysis, digital signal processing (DSP) algorithms are useful for extracting information from signals collected all around us. Combined with today's powerful computing capabilities, they can be used in a wide range of application areas, including engineering, communicati

Presents the effects of the predictive filtering of waveform data in multiple passes as the first stage of a two-stage lossless compression algorithm. Predictive compression has a proven track record when applied to high dynamic range waveform data, wherein the waveform data are input to a linear predictor or perhaps an adaptive predictor for decorrelation, and the resultant residue is then subjected to an entropy coder to (ideally) represent the signal with a minimum number of bits. This compression is commonly applied with no loss of information. In this work, an adaptive filter is used for prediction, but instead of a single run through the predictor, the residue is continually passed back through the predictor in an attempt to further decorrelate the residue. Multiple passes of a gradient adaptive lattice filter has given the best decorrelation, yielding improved compression ratios. We run the compression technique on a seismic database, then provide some comparative lossless compression results using several coding schemes and show that using multiple-pass predictive filtering can improve the compression rates attainable.

A technique for lossless compression of seismic signals is proposed. The algorithm employed is based on the equation-error structure, which approximates the signal by minimizing the error in the least-square sense and estimates the transfer characteristic as a rational function or equivalently, as an autoregressive moving average process. The algorithm is implemented in the frequency domain. The performance of the proposed technique is compared with the lossless linear predictor and the differentiator approaches for compressing seismic signals, The residual sequence of these schemes is coded using arithmetic coding. The suggested approach yields compression measures (in terms of bits per sample) lower than the lossless linear predictor and the differentiator for compressing different classes of seismic signals.

The two-dimensional adaptive correlation enhancer algorithm (2DACE) is an open-loop adaptive filtering technique that can be applied to synthetic aperture radar (SAR) images for the purpose of reducing speckle. This paper includes the development of the 2DACE algorithm and the optimum filter parameters for this specific task. The unique implementation of 2DACE with a data amplitude pre-compression operation was proven to effectively reduce speckle, enhance fine features, and maintain image resolution.

We describe efficient lossless compression methods applicable to mixed types of data. The methods are applicable to situations in which there is a data base containing files with signal and text data, and the files are accessed via a narrow-band channel such as a modem. Theoretical principles are presented, including a discussion of measures of compressibility. Experimental results are also presented.

A technique for lossless compression of seismic signals is proposed. The algorithm employed is based on the equation-error structure which approximates the signal by minimizing the error in the least square sense, as a rational function, or equivalently as an auto-regressive moving-average (ARMA) process. The algorithm is implemented in the frequency domain. The performance of the proposed technique is compared with the lossless linear predictor for compressing seismic signals. The residual sequence of these schemes is coded using arithmetic coding. The suggested approach yields compression measures, in terms of bits per sample, lower than the lossless linear predictor for compressing different classes of seismic signals.

An algorithm for the lossless compression of two-dimensional signals is proposed. This approach is based on modeling the original signal by a rational function which consists of poles and zeros, or equivalently an auto-regressive moving average process. The equation-error structure, which approximates the signal by minimizing the error in the least square sense, is used to obtain the optimal coefficients of the transfer function. This technique is implemented in the frequency domain. The performance of the proposed approach for the lossless compression of different classes of images is evaluated and compared with the lossless linear predictor. The residual sequence of these schemes is coded using arithmetic coding. The suggested approach yields compression measures, in terms of bits per pixel, lower than the lossless linear predictor for compressing 8-bit gray-scale images.

This report is a part of the Test Information Program (TIP) at Sandia National Laboratories. It is an interim report, written primarily as an instruction document to aid in current work on the project. It addresses some found in storing and retrieving data from nuclear field tests conducted over the past five decades, primarily instrumentation data recorded from tests at the Nevada Test Site. First, the TIP data unit for storing and transporting TIP data is described. The data in the TIP data unit is typically recorded in a universal medium such as the portable optical or magnetic disk, or the tape cassette. Each TIP data unit is portable, and is also self-contained in the sense that it includes a set of related test data files, along with complete instructions and software for retrieval of the data by an unknown user, possibly on an unknown platform. Secondly, we describe the use of current software for compressing and waveform data, for authenticating and checking for errors in data processing files to be used on foreign platforms.