Overview A linear predictive filter (LPF) is a short vector of coefficients whose inner product with a fragment of a time-sampled signal predicts one or more points of the 10 signal’s future within some error. When the prediction horizon is short, the filter is interpolative and constitutes a recognizer or classifier of the signal [3, 5]. When the prediction horizon is longer, the filter is ext...

This paper presents a new technique in digital communications, called modelbased-predictive adaptive-delta-modulation (MBP-ADM). MBP-ADM uses system identification tools to identify a model of the signal which is used for prediction. The prediction helps the system to respond adaptively to a varying input signal, in order to achieve improved performance. The results show a substantial improveme...

In robotic radiosurgery, a robot-mounted LINAC follows the motion of the target region. Due to delays in signal acquisition and robot motion, there is the need for prediction of the measured motion. In this work, we present a slight modification of the Recursive Least Squares (RLS) algorithm for the prediction of human respiration. We have modified the RLS algorithm as to include an exponential...

Autoregressive (AR) modeling by linear prediction (LP) provides the basis of a wide variety of signal processing and communication systems including parametric spectral estimation and system identification. Perhaps the greatest success of linear prediction techniques is to be found in speech analysis and audio coding. In this paper, we first reviewed the general frameworks of predictive signal ...

Methods employed in the construction of prediction bands for continuous curves require a different approach to those used for a data point. In many cases, the underlying function is unknown and thus a distribution-free approach which preserves sufficient coverage for the entire signal is necessary in the signal analysis. This paper discusses three methods for the formation of (1-α)100% bootstra...

Reversible data hiding (RDH) is desirable in applications where both the hidden message and the cover medium need to be recovered without loss. Among many RDH approaches is prediction-error expansion (PEE), containing two steps: i) prediction of a target pixel value, and ii) embedding according to the value of prediction-error. In general, higher prediction performance leads to larger embedding...