Minimal Randomness and Information Theory - Information Theory and Statistics, 1994. Proceedings., 1994 IEEE-IMS Workshop on

Unsupervised Medical Image Analysis by Multiscale FNM Modeling and MRF Relaxation Labeling - Information Theory and Statistics, 1994. Proceedings., 1994 IEEE-IMS Workshop on

We derive two types of block-wise FNM model for pixel images by incorporating local context. The self-learning is then formulated as an information match problem and solved by first estimating model parameters to initialize ML solution and then conducting flner segmentation through MRF relaxation.

Blind identification and equalization based on second-order statistics: a time domain approach

FUZZY INFORMATION AND STOCHASTICS

In applications there occur different forms of uncertainty. The twomost important types are randomness (stochastic variability) and imprecision(fuzziness). In modelling, the dominating concept to describe uncertainty isusing stochastic models which are based on probability. However, fuzzinessis not stochastic in nature and therefore it is not considered in probabilisticmodels.Since many years t...

Sequence Comparisons via Algorithmic Mutual Information

One of the main problems in DNA and protein sequence comparisons is to decide whether observed similarity of two sequences should be explained by their relatedness or by mere presence of some shared internal structure, e.g., shared internal tandem repeats. The standard methods that are based on statistics or classical information theory can be used to discover either internal structure or mutua...

Comments on 'Generating and counting binary Bent sequences'

We prove that the conjecture on bent sequences stated in Generating and counting bent sequences IEEE Transactions on Information Theory IT No by C M Adams and S E Tavares is false Let Vn be the vector space of n tuples of elements from GF Any map from Vn to GF f can be uniquely written as a polynomial in coordinates x xn f x xn M