A major new professional reference work on fingerprint security systems and technology from leading international researchers in the field Handbook provides authoritative and comprehensive coverage of all major topics, concepts, and methods for fingerprint security systems This unique reference work is an absolutely essential resource for all biometric security professionals, researchers, and systems administrators

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Handbook of Fingerprint Recognition

Forecasting with artificial neural networks: the state of the art

A review and tutorial of the fundamental ideas and methods of joint time-frequency distributions is presented. The objective of the field is to describe how the spectral content of a signal changes in time and to develop the physical and mathematical ideas needed to understand what a time-varying spectrum is. The basic gal is to devise a distribution that represents the energy or intensity of a signal simultaneously in time and frequency. Although the basic notions have been developing steadily over the last 40 years, there have recently been significant advances. This review is intended to be understandable to the nonspecialist with emphasis on the diversity of concepts and motivations that have gone into the formation of the field. >

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Time-frequency distributions-a review

1980 Preface * 1999 Preface * 1999 Acknowledgements * Introduction * 1 Circular Logic * 2 Phase Singularities (Screwy Results of Circular Logic) * 3 The Rules of the Ring * 4 Ring Populations * 5 Getting Off the Ring * 6 Attracting Cycles and Isochrons * 7 Measuring the Trajectories of a Circadian Clock * 8 Populations of Attractor Cycle Oscillators * 9 Excitable Kinetics and Excitable Media * 10 The Varieties of Phaseless Experience: In Which the Geometrical Orderliness of Rhythmic Organization Breaks Down in Diverse Ways * 11 The Firefly Machine 12 Energy Metabolism in Cells * 13 The Malonic Acid Reagent ('Sodium Geometrate') * 14 Electrical Rhythmicity and Excitability in Cell Membranes * 15 The Aggregation of Slime Mold Amoebae * 16 Numerical Organizing Centers * 17 Electrical Singular Filaments in the Heart Wall * 18 Pattern Formation in the Fungi * 19 Circadian Rhythms in General * 20 The Circadian Clocks of Insect Eclosion * 21 The Flower of Kalanchoe * 22 The Cell Mitotic Cycle * 23 The Female Cycle * References * Index of Names * Index of Subjects

https://www.cambridge.org/core/services/aop-cambridge-core/content/view/596B270197FE27DE5FABB598B6210622/S0025557200150892a.pdf/div-class-title-span-class-italic-the-geometry-of-biological-time-span-by-a-t-winfree-pp-544-dm68-corrected-second-printing-1990-isbn-3-540-52528-9-springer-div.pdf

The geometry of biological time , by A. T. Winfree. Pp 544. DM68. Corrected Second Printing 1990. ISBN 3-540-52528-9 (Springer)

The organization of behavior: A neuropsychological theory

This paper presents the application of collective swarm intelligence in solving problems of non-multiplexing communication in powerline sensor network. Based on collective intelligence behavior in nature, we design a system that allows local sensors to collect local information acquired from the powerline channel. As the information becomes available, the sensor can adaptively adjust its parameters in order to achieve a common goal of non-multiplexing sensor communication throughout entire network. The non-multiplexing sensor communications is implemented using underdetermined blind source separation (uBSS) based on the finite alphabet property of the data. The details of hardware implementation are provided as well as the result from the test-bed network.

Collective behavior implementation in powerline surveillance sensor network

Constraint optimization neural network for adaptive early vision

Progress of Unsupervised Learning of Early Vision.

This paper introduces the data-driven signal decomposition method based on the empirical mode decomposition (EMD) technique. The decomposition process uses the data themselves to derive the base function in order to decompose the one-dimensional signal into a finite set of intrinsic mode signals. The novelty of EMD is that the decomposition does not use any artificial data windowing which implies fewer artifacts in the decomposed signals. The results show that the method can be effectively used in analyzing non-stationary signals. Furthermore, we applied this method to analyze closing equity prices of a financial stock. The result demonstrates the usefulness of the method in analyzing financial time series data, and some practical considerations in envelope estimation.

http://ieeexplore.ieee.org/iel5/10894/34292/01635133.pdf

Data-driven signal decomposition method

We present a fully Bayesian approach to Non-Negative Matrix Factorisation (NMF) by developing a Reversible Jump Markov Chain Monte Carlo (RJMCMC) method which provides full posteriors over the matrix components. In addition the NMF model selection issue is addressed, for the first time, as our RJMCMC procedure provides the posterior distribution over the matrix dimensions and therefore the number of components in the NMF model. A comparative analysis is provided with the Bayesian Information Criterion (BIC) and model selection employing estimates of the marginal likelihood. An illustrative synthetic example is provided using blind mixtures of images. This is then followed by a large scale study of the recovery of component spectra from multiplexed Raman readouts. The power and flexibility of the Bayesian methodology and the proposed RJMCMC procedure to objectively assess differing model structures and infer the corresponding plausible component spectra for this complex data is demonstrated convincingly.

Harold H. Szu

Papers

Collective behavior implementation in powerline surveillance sensor network

Constraint optimization neural network for adaptive early vision

Progress of Unsupervised Learning of Early Vision.

Data-driven signal decomposition method

Independent Component Analyses, Wavelets, Neural Networks, Biosystems, and Nanoengineering VII