Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

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 Analysis of Time Series: An Introduction, 4th edn. By C. Chatfield. ISBN 0 412 31820 2. Chapman and Hall, London, 1989. 242 pp. £13.50.

The Analysis of Time Series: An Introduction

Digital processing of speech signals

Vehicular networks are very likely to be deployed in the coming years and thus become the most relevant form of mobile ad hoc networks. In this paper, we address the security of these networks. We provide a detailed threat analysis and devise an appropriate security architecture. We also describe some major design decisions still to be made, which in some cases have more than mere technical implications. We provide a set of security protocols, we show that they protect privacy and we analyze their robustness and efficiency.

/pdf/securing-vehicular-ad-hoc-networks-4sgegi8hgk.pdf

Securing vehicular ad hoc networks

This paper explores the architecture, theoretical foundations, and paradigms of contemporary cybernetics from perspectives of cognitive informatics (CI) and computational intelligence. The modern domain and the hierarchical behavioral model of cybernetics are elaborated at the imperative, autonomic, and cognitive layers. The CI facet of cybernetics is presented, which explains how the brain may be mimicked in cybernetics via CI and neural informatics. The computational intelligence facet is described with a generic intelligence model of cybernetics. The compatibility between natural and cybernetic intelligence is analyzed. A coherent framework of contemporary cybernetics is presented toward the development of transdisciplinary theories and applications in cybernetics, CI, and computational intelligence.

Contemporary Cybernetics and Its Facets of Cognitive Informatics and Computational Intelligence

This paper presents a method of extracting and modelling radio transmitter transients for classification. It is motivated by the real possibility of identifying radio transmitters used in violation of federal and international regulations. A system has been developed which takes the beginning of a radio transmission and separates it from ambient channel noise using a multifractal segmentation technique. Then, significant features are extracted from the transient and a more compact multifractal model is obtained. Finally, this model is analysed by a neural network for classification. Preliminary results indicate that classification using multifractal models is feasible. More specifically, a probabilistic neural network has been trained using 160 out of 415 available transients. Testing the system with the remaining 255 transients produced results in which 92.5% of the transients were classified correctly.

Multifractal modelling of radio transmitter transients for classification

This paper presents a method of generating unique fingerprints of radio transmitter turn-on transients. The fingerprinting system consists of the application of multiresolution wavelet analysis used to characterize the features contained in the transient followed by the use of a genetic algorithm to extract the wavelet coefficients that represent critical features of the transient. To measure the ability of the system to generate efficient and unique fingerprints, a neural network is used to classify the transients by their fingerprints. To test the noise sensitivity of the system, noisy transients were applied to a trained neural network, the network was able to positively classify noisy transients with 20 dB signal to noise ratios (SNR) and up. Experiments with real radio transients show that the system is able generate uniqiue fingerprints for absolute classification by a neural network for radios of differing model type as well as radios of the same model type.

Transient analysis and genetic algorithms for classification

This paper presents a new method for the capture, analysis, and classification of radio transmitter transients. This method involves the use of a capturing subsystem consisting of an Icom IC-R7000 communications receiver and a Sound Blaster 16 sound card running on a PC. The radio transients are sampled at 44,100 samples per second and have 16 bits accuracy. Once the transmitter transient has been captured, a genetic algorithm selects the critical features from the wavelet coefficients for classification. The selected wavelet coefficients are considered to be fingerprints, and are presented to a back propagation neural network for transmitter classification. The capturing and analysis system, ODO-1, is able to classify both transients of the same model type as well as individual transmitters with 100% accuracy on a small data base of transmitter fingerprints.

Witold Kinsner

Papers

Contemporary Cybernetics and Its Facets of Cognitive Informatics and Computational Intelligence

Multifractal modelling of radio transmitter transients for classification

Transient analysis and genetic algorithms for classification

A radio transmitter fingerprinting system ODO-1

Editorial Recent Advances in Cognitive Informatics