Eigen-beamforming technique is introduced in two typical multi-antenna systems: space-time block coding and VBALST. When the channel covariance information is known by the transmitter, optimal power allocation algorithm for eigen-beamforming is proposed based on minimizing the transmission errors. Also, it is indicated in this paper that this algorithm can be simplified when the channel is independent - identically distributed (I.I.D.) channel. By simulation and analysis, eigen-beamforming can greatly improve the performance of multi-antenna system when this power allocation algorithm is adopted.

An optimal deformable contour approximation algorithm using a cardinal-form piecewise cubic spline (PCS) curve representation is presented. The approximation is optimal in the sense of least square errors in both location and orientation. The knots are set automatically at high curvature positions. The sample data are generated by a robust edge fragment detection algorithm which is optimal in the sense of a weighted absolute error. An initial contour placement algorithm uses a penalized maximum likelihood algorithm to group the edge fragments together for an initial contour. A controlled deformable contour algorithm refines the initial contour to cover meaningful edge features.>

A multidimensional edge model is established and a first-order estimation for multidimensional edge profiles is proposed. An optimal edge location detection algorithm is developed. The advantages of the algorithm are that (1) it has little dependence on assumptions of edge models, noise models, or smoothing filters, (2) it has better abilities for detecting very weak edges and making less edge orientation errors than other edge detectors, (3) it can handle corners and complicated multidimensional image structures, and (4) it detects different edge types at the same time.>

An algorithm for the optimal edge contour estimation in medical images without a priori shape models is described. The proposed method is an intermediate-level approach compared with the usual low-level edge-detection operators. An optimal and robust contour estimator is derived by the minimization of a risk function which measures the error from both an inappropriate choice of edge contour and an inappropriate choice of the noise model in the image. The result includes Huber's function. If a parametric statistical noise model and the Neyman-Pearson criterion are used, the result is an extension of maximum-likelihood function. A recursive formulation can be implemented by assuming an independent random field and a Markov path model. The assumption of independent statistics can be satisfied by the use of an autoregressive moving-average preprocessor. The problem of varying edge strength is lessened using an adaptive trimmed mean. The robust algorithm is implemented using a priority-tree (stack) structure. The system's performance is illustrated by estimation of lesion boundaries in medical images.>