Principles and practice of information theory
Principles and practice of information theory
Distance measures for signal processing and pattern recognition
Signal Processing
Elements of information theory
Elements of information theory
Distributed Detection and Data Fusion
Distributed Detection and Data Fusion
Array Signal Processing: Concepts and Techniques
Array Signal Processing: Concepts and Techniques
Measuring information transfer in the spike generator of crayfish sustaining fibers
Biological Cybernetics
Face Recognition from Face Motion Manifolds using Robust Kernel Resistor-Average Distance
CVPRW '04 Proceedings of the 2004 Conference on Computer Vision and Pattern Recognition Workshop (CVPRW'04) Volume 5 - Volume 05
A Mathematical Theory of Communication
A Mathematical Theory of Communication
On the asymptotics of M-hypothesis Bayesian detection
IEEE Transactions on Information Theory
To code, or not to code: lossy source-channel communication revisited
IEEE Transactions on Information Theory
Cultural Specific Effects on the Recognition of Basic Emotions: A Study on Italian Subjects
USAB '09 Proceedings of the 5th Symposium of the Workgroup Human-Computer Interaction and Usability Engineering of the Austrian Computer Society on HCI and Usability for e-Inclusion
Progress in nonlinear speech processing
Information theory and neural information processing
IEEE Transactions on Information Theory - Special issue on information theory in molecular biology and neuroscience
Weave analysis of paintings on canvas from radiographs
Signal Processing
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Information processing theory endeavors to quantify how well signals encode information and how well systems, by acting on signals, process information. We use information-theoretic distance measures, the Kullback-Leibler distance in particular, to quantify how well signals represent information. The ratio of distances calculated between two informationally different signals at a system's output and input quantifies the system's information processing properties. Using this approach, we derive the fundamental processing capabilities of simple system architectures that apply universally: the systems and the kinds of signals they process and produce do not affect our general results. Applications in array signal processing and in neural signal analysis illustrate how to apply the theory.