Maximum system mutual information is considered for a group of interfering users employing single user detection and antenna selection of multiple transmit and receive antennas for flat Rayleigh fading channels with independent fading coefficients for each path. The case considered is that where there is very limited channel state information at the transmitter, but channel state information is assumed at the receiver. The focus is on extreme cases with very weak interference or very strong interference. It is shown that the optimum signaling covariance matrix is sometimes different from the standard scaled identity matrix. In fact this is true even for cases without interference if SNR is sufficiently weak. Further the scaled identity matrix is actually that covariance matrix that yields worst performance if the interference is sufficiently strong.
In this paper, some image registration algorithms are investigated for the purpose of image fusion. A hybrid scheme which uses both feature-based and intensity-based methods is proposed. In this scheme, an edge-based image registration approach is developed to guide the intensity- based registration which uses optical flow estimation. The idea of coarse-to-fine multiscale iterative refinement is also studied. Experiments show that this approach is robust and efficient.
Combining signal detection decisions from multiple sensors is useful in some practical communications, radar, and sonar applications. The optimum schemes for generating and combining the detector decisions have been studied for cases with independent observations from sensor to sensor. Designing schemes for cases with dependent observations from sensor to sensor is a much more difficult problem and to date very little progress has been made. Design approaches which have been suggested for these cases are quite complicated. Here a simple adaptive design approach is outlined for the important and difficult task of detecting a weak random signal in additive, possibly non-Gaussian noise. The approach is based on considering sensor decision rules and fusion rules which contain some unknown parameters. These rules have previously been shown to be optimum for cases with a larger number of observations. These previous results also show that the best parameters minimize the mean square error fit to the best centralized signal detection scheme. Based on these ideas a gradient descent algorithm is proposed for learning the best parameters. Results of the training are compared to known results for multisensor detection schemes.