For a directional (a.k.a. directive) transceiver misaligned towards an omni-directional transceiver, the multipaths' time-of-arrival (TOA) distribution is herein derived in closed form explicitly in terms of the "geometric modeling" parameters. This derivation allows the directional beamwidth to mis-point to any arbitrary azimuth-direction, even if entirely away from the intended omni-directional transceiver.

The fundamental point of this paper is that the fusion of several simple, somewhat unreliable, and somewhat inefficient frontal face detectors results in an efficient and reliable frontal face detector which, without any training, performs similarly to a state-ofthe- art neural network based face detector trained on 60,000 images. The simple detectors used include a skin detector, symmetry detectors, as well as structural face detectors. On a test set of 30 color images containing frontal faces, the fused face detector had an accuracy of 93% with a RMSE of 4.96 pixels, as compared to an accuracy of 87% and a RMSE of 8.00 pixels for the neural network based face detector. On the Caltech Face Database, the fused face detector had a 90% detection rate which is on par with state-of-the-art face detection methods that utilize extensive prior training, including the neural network approach which achieves a detection rate of 94%.

Speckle noise has been found to rotate an image's homomorphic elliptically distributed complex-wavelet coefficients. This work proposes a counter-rotation of these complex-wavelet coefficients as a means of mitigating speckle noise using principle component analysis. Simulations suggest that the proposed denoising technique offers superior visual quality, though its signal-to-mean-square-error ratio (S/MSE) is numerically comparable to the improvement obtained in customary median filtering and adaptive Wiener filtering.