Obtaining high resolution images from low data with clipped noise is algorithmically challenging due to the ill-posed nature of problem. So far such problems have hardly been tackled, and few existing approaches use simplistic regularisers. We show usefulness two adaptive regularisers based on anisotropic diffusion ideas: Apart evaluating classical edge-enhancing regulariser, we introduce a nov...

We consider a MIMO fading broadcast channel and compare the achievable ergodic rates when the channel state information at the transmitter is provided by “analog” noisy feedback or by quantized (digital) feedback. The superiority of digital feedback is shown, with perfect or imperfect CSIR, whenever the number of feedback channel uses per channel coefficient is larger than 1. Also, we show that...

Traditionally, neural networks such as multi-layer perceptrons handle acoustic context by increasing the dimensionality of the observation vector, in order to include information of the neighbouring acoustic vectors, on either side of the current frame. As a result the monolithic network is trained on a high multi-dimensional space. The trend is to use the same fixed-size observation vector acr...

A technique for designing frames to use with vector selection algorithms, for example Matching Pursuits (MP), is presented. The design algorithm is iterative and requires a training set of signal vectors. An MP algorithm chooses frame vectors to approximate each training vector. Each vector in the frame is then adjusted by using the residuals for the training vectors which used that particular ...

Permutation source codes are a class of structured vector quantizers with a computationally-simple encoding procedure. In this thesis, we provide two extensions that preserve the computational simplicity but yield improved operational rate–distortion performance. In the first approach, the new class of vector quantizers has a codebook comprising several permutation codes as subcodes. Methods fo...