James Taylor

Bio: James Taylor is an academic researcher from Newcastle University. The author has contributed to research in topics: Laser & Fiber laser. The author has an hindex of 95, co-authored 1161 publications receiving 39945 citations. Previous affiliations of James Taylor include Institut national de la recherche agronomique & European Spallation Source.

Optical Solitons: Theory and Experiment

Abstract: 1. Optical solitons in fibres: theoretical review A. Hasegawa 2. Solitons in optical fibres: an experimental account L. F. Mollenauer 3. All-optical long-distance soliton-based transmission systems K. Smith and L. F. Mollenauer 4. Nonlinear propagation effects in optical fibres: numerical studies K. J. Blow and N. J. Doran 5. Soliton-soliton interactions C. Desem and P. L. Chu 6. Soliton amplification in erbium-doped fibre amplifiers and its application to soliton communication M. Nakazawa 7. Nonlinear transformation of laser radiation and generation of Raman solitons in optical fibres E. M. Dianov, A. B. Grudinin, A. M. Prokhorov and V. N. Serkin 8. Generation and compression of femtosecond solitons in optical fibers P. V. Mamyshev 9. Optical fibre solitons in the presence of higher order dispersion and birefringence C. R. Menyuk and Ping-Kong A. Wai 10. Dark optical solitons A. M. Weiner 11. Soliton Raman effects J. R. Taylor Bibliography Index.

Supercontinuum Generation in Optical Fibers

Abstract: 1. Introduction and history J. R. Taylor 2. Supercontinuum generation in microstructure fiber - an historical note J. K. Ranka 3. Nonlinear fiber optics overview J. C. Travers, M. H. Frosz and J. M. Dudley 4. Fiber supercontinuum generation overview J. M. Dudley 5. Silica fibers for supercontinuum generation J. C. Knight and W. Wadsworth 6. Supercontinuum generation and nonlinearity in soft glass fibers J. H. V. Price and D. J. Richardson 7. Increasing the blue-shift of a picosecond pumped supercontinuum M. H. Frosz, P. M. Moselund, P. D. Rasmussen, C. L. Thomsen and O. Bang 8. Continuous wave supercontinuum generation J. C. Travers 9. Theory of supercontinuum and interactions of solitons with dispersive waves D. V. Skryabin and A. V. Gorbach 10. Interaction of four-wave mixing and stimulated Raman scattering in optical fibers S. Coen, S. G. Murdoch and F. Vanholsbeeck 11. Nonlinear optics in emerging waveguides: revised fundamentals and implications S. V. Afshar, M. Turner and T. M. Monro 12. Supercontinuum generation in dispersion varying fibers G. Genty 13. Supercontinuum generation in chalcogenide glass waveguides Dong-Il Yeom, M. R. E. Lamont, B. Luther Davies and B. J. Eggleton 14. Supercontinuum generation for carrier-envelope phase stabilization of mode-locked lasers S. T. Cundiff 15. Biophotonics applications of supercontinuum generation C. Dunsby and P. M. W. French 16. Fiber sources of tailored supercontinuum in nonlinear microspectroscopy and imaging A. M. Zheltikov Index.

Estimating Value at Risk and Expected Shortfall Using Expectiles

Abstract: Expectile models are derived using asymmetric least squares. A simple formula relates the expectile to the expectation of exceedances beyond the expectile. We use this as the basis for estimating expected shortfall. It has been proposed that the quantile be estimated by the expectile for which the proportion of observations below the expectile is ?. In this way, an expectile can be used to estimate value at risk. Using expectiles has the appeal of avoiding distributional assumptions. For univariate modelling, we introduce conditional autoregressive expectiles (CARE). Empirical results for the new approach are competitive with established benchmarks methods.

Tm-doped fiber laser mode-locked by graphene-polymer composite

Abstract: We demonstrate mode-locking of a thulium-doped fiber laser operating at 1.94 μm, using a graphene-polymer based saturable absorber. The laser outputs 3.6 ps pulses, with ~0.4 nJ energy and an amplitude fluctuation ~0.5%, at 6.46 MHz. This is a simple, low-cost, stable and convenient laser oscillator for applications where eye-safe and low-photon-energy light sources are required, such as sensing and biomedical diagnostics.

I and i

Abstract: There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality. Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

Using Multivariate Statistics

Abstract: Thank you for downloading using multivariate statistics. As you may know, people have look hundreds times for their favorite novels like this using multivariate statistics, but end up in infectious downloads. Rather than reading a good book with a cup of tea in the afternoon, instead they juggled with some harmful bugs inside their laptop. using multivariate statistics is available in our digital library an online access to it is set as public so you can download it instantly. Our books collection saves in multiple locations, allowing you to get the most less latency time to download any of our books like this one. Merely said, the using multivariate statistics is universally compatible with any devices to read.

The Design and Analysis of Experiments

Abstract: THE DESIGN AND ANALYSIS OF EXPERIMENTS. By Oscar Kempthorne. New York, John Wiley and Sons, Inc., 1952. 631 pp. $8.50. This book by a teacher of statistics (as well as a consultant for \"experimenters\") is a comprehensive study of the philosophical background for the statistical design of experiment. It is necessary to have some facility with algebraic notation and manipulation to be able to use the volume intelligently. The problems are presented from the theoretical point of view, without such practical examples as would be helpful for those not acquainted with mathematics. The mathematical justification for the techniques is given. As a somewhat advanced treatment of the design and analysis of experiments, this volume will be interesting and helpful for many who approach statistics theoretically as well as practically. With emphasis on the \"why,\" and with description given broadly, the author relates the subject matter to the general theory of statistics and to the general problem of experimental inference. MARGARET J. ROBERTSON

Machine learning

Abstract: Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Linear Matrix Inequalities in System and Control Theory

Abstract: Preface 1. Introduction Overview A Brief History of LMIs in Control Theory Notes on the Style of the Book Origin of the Book 2. Some Standard Problems Involving LMIs. Linear Matrix Inequalities Some Standard Problems Ellipsoid Algorithm Interior-Point Methods Strict and Nonstrict LMIs Miscellaneous Results on Matrix Inequalities Some LMI Problems with Analytic Solutions 3. Some Matrix Problems. Minimizing Condition Number by Scaling Minimizing Condition Number of a Positive-Definite Matrix Minimizing Norm by Scaling Rescaling a Matrix Positive-Definite Matrix Completion Problems Quadratic Approximation of a Polytopic Norm Ellipsoidal Approximation 4. Linear Differential Inclusions. Differential Inclusions Some Specific LDIs Nonlinear System Analysis via LDIs 5. Analysis of LDIs: State Properties. Quadratic Stability Invariant Ellipsoids 6. Analysis of LDIs: Input/Output Properties. Input-to-State Properties State-to-Output Properties Input-to-Output Properties 7. State-Feedback Synthesis for LDIs. Static State-Feedback Controllers State Properties Input-to-State Properties State-to-Output Properties Input-to-Output Properties Observer-Based Controllers for Nonlinear Systems 8. Lure and Multiplier Methods. Analysis of Lure Systems Integral Quadratic Constraints Multipliers for Systems with Unknown Parameters 9. Systems with Multiplicative Noise. Analysis of Systems with Multiplicative Noise State-Feedback Synthesis 10. Miscellaneous Problems. Optimization over an Affine Family of Linear Systems Analysis of Systems with LTI Perturbations Positive Orthant Stabilizability Linear Systems with Delays Interpolation Problems The Inverse Problem of Optimal Control System Realization Problems Multi-Criterion LQG Nonconvex Multi-Criterion Quadratic Problems Notation List of Acronyms Bibliography Index.