The theory of large deviations provides a simple unified basis for statistical mechanics, information theory and queueing theory. The objective of this paper is to use large deviation theory and the Laplace method of integration to provide an simple intuitive overview of the theory of effective bandwidth for high-speed digital networks, especially ATM networks. This includes (1) identification of the appropriate energy function, entropy function and effective bandwidth function of a source, (2) the calculus of the effective bandwidth functions, (3) bandwidth allocation and buffer management, (4) traffic descriptors, and (5) envelope processes and conjugate processes for fast simulation and bounds.>

The role of inequalities in information theory is reviewed, and the relationship of these inequalities to inequalities in other branches of mathematics is developed. The simple inequalities for differential entropy are applied to the standard multivariate normal to furnish new and simpler proofs of the major determinant inequalities in classical mathematics. The authors discuss differential entropy inequalities for random subsets of samples. These inequalities when specialized to multivariate normal variables provide the determinant inequalities that are presented. The authors focus on the entropy power inequality (including the related Brunn-Minkowski, Young's, and Fisher information inequalities) and address various uncertainty principles and their interrelations.>

The converse for the discrete memoryless multiple access channel is generalized and is used to derive strong bounds on the total capacity (sum of the rates of all the senders) of anm-user Gaussian multiple access channel in terms of the input covariance matrix. These bounds are used to show that the total capacity of the channel with feedback is less than twice the total capacity without feedback. The converse for the general multiple access channel is also used to show that for anym-user multiple access channel, feedback cannot increase the total capacity by more than a factor ofm.

A new noniterative technique for all pole modelling of noisy speech is proposed. We exploit the additive and high resolution properties of a group delay function to identify the high signal to noise ratio (SNR) regions of the short time spectrum and to separate out these regions from the low SNR regions. A modified spectrum is derived from the group delay function in these regions. The new spectrum is used to derive the all pole model for the speech segment. The main advantage of this method is that it is not necessary to have prior knowledge of the noise or speech characteristics as in the other methods of processing noisy speech.