Audio frequency train-to-wayside communication used in mass transit systems is subject to interference from harmonics of power and propulsion systems. The interference limits data rate in present modulation schemes. A multi-carrier modulation scheme, which offers higher data rate while ensuring orthogonality with interference, is presented.>

A two-dimensional (2D) linear predictor which has an autoregressive moving average (ARMA) representation well as a bias term is adapted for adaptive differential pulse code modulation (ADPCM) encoding of nonnegative images. The predictor coefficients are updated by using a 2D recursive LMS (TRLMS) algorithm. A constraint on optimum values for the convergence factors and an updating algorithm based on the constraint are developed. The coefficient updating algorithm can be modified with a stability control factor. This realization can operate in real time and in the spatial domain. A comparison of three different types of predictors is made for real images. ARMA predictors show improved performance relative to an AR algorithm.

An adaptive differential pulse code modulation (DPCM) algorithm is presented that determines, for each window, the best causal nonsymmetric half-plane region of support (ROS) in addition to the best prediction coefficients. First, a recursive algorithm is developed that determines the best (in the sense of minimizing the mean-square error) first-, second-, and third-order ROS from the covariance measurements. A procedure for determining the order of the ROS that maximizes the compression ratio, which is based on the window size as well as the covariance measurement, is then developed.>

There are many industrial, biomedical, and military applications for which there is a need to compress images using techniques that are as closely information-preserving as possible. The fact that images cannot be accurately modeled as stationary processes and that the image statistics are usually not Gaussian reduces the efficiency of commonly used predictive and transform-based coding procedures. An efficient multiple-source coding procedure capable of preserving sharp features in the image with small computational load is presented. The procedure uses a composite model to deal with the nonstationarity in the space variation of the mean function. The image is then modeled as the addition of two components: an "approximate" image, which represents the underlying structure of the image, and a "difference" image, which corresponds to random variations superimposed on that structure. The approximate image is constructed using a binary image that describes the boundaries of its homogeneous regions, the local means of these regions, and an interpolation between the regions. The difference image is the result of subtracting the approximate image from the original. The coding of the binary and the difference images can be done very efficiently: the binary image can be coded by a facsimile technique, and the difference image can be coded using a predictive technique since it can be modeled accurately as a stationary random field. A lower bound for the compressibility of an image using the proposed procedure is given. To illustrate the procedure, as well as some issues involved, an example is shown.

A predictive compression technique is examined, using maximum likelihood prediction of the image pixel based on the Markov mesh model, that encodes the differences via Gordon block-bit-plane (GBBP) encoding. The procedure is very efficient in that it requires a bit rate near the entropy of the source. For images with many quantization levels, maximum likelihood prediction can be cumbersome to implement. Thus, a suboptimal procedure called differential bit-plane coding (DBPC) is investigated. This is easily implemented, even for a large number of quantization levels, and is reasonably efficient.

Polarity-coincidence correlation (pcc) is usually analyzed under the assumption of independent noise inputs and small input-signal power. Thus the difficulty of evaluating the variance of the PCC statistic for inputs with arbitrary cross correlations is avoided. An expression for the variance that can be conveniently evaluated on a computer is discussed. As one example, the PCC statistic is analyzed for a strong Markovian signal that is added to two independent, white noise inputs. The effect of hard limiting is determined as a function of the input signal-to-noise ratio. In another example, the PCC detector is analyzed for a small Markovian signal that is added to two dependent Markov noise inputs. In this case, the cost of clipping increases substantially with the noise correlation.

This paper considers a detection theory approach to the restoration of digitized images. The images are modeled as second-order Markov meshes. This model is not only well suited to a decision approach to smoothing, but it enables computer simulations of images thereby permitting a statistical analysis of restoration techniques. Smoothing procedures that are near optimal in the sense of approaching a nonrealizable bound are demonstrated and evaluated. The achievable reduction in mean-square error is considerable for coarsely quantized pictures. This reduction, for the four-level pictures considered, is somewhat greater than that achievable by linear techniques. The approach actually minimizes the probability of error which may be important for preserving picture features.

Polarity Coincidence Array detectors (PCA) are considered for testing the hypothesis that a random signal is common to an array of receivers which contain noise processes that are independent representations of a given class of stochastic processes. A standard procedure is to reduce the received data by sampling and then hard limiting. Hard limiting is shown to introduce an inherent loss in input signal power of1.96dB when the input data is a sequence of independent samples from a stationary Gaussian process. However, when the stationary and/or Gaussian assumptions are violated, the relative efficiencies of the PCA detectors can greatly improve. When the input samples are dependent, it is necessary to assume Ganssian inputs in order to analyze the PCA detectors. However, these devices are still unaffected by a nonstationary noise level that is slowly varying relative to the inverse bandwidth of the pre-filter. Furthermore, the loss due to clipping is considerably reduced as the sample dependence (i.e., sampling rate) increases. For rapid sampling rates, the spectral shapes of the inputs must be known accurately in order to fix the false-alarm rate at some pre-assigned value.

A procedure is obtained for modifying given sampled-data parametric detectors to make them asymptotically nonparametric. Unlike standard nonparametric devices, these detectors do not require the assumption of independent samples but only a knowledge of the input spectral shapes. As examples of this technique, two types of conventional array detectors are modified to produce nonparametric systems.