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.

Bright peak enhanced x-ray clear phase mask

Abstract: A clear bright peak enhanced x-ray phase mask (BPEXPM) is described. It employs the constructive interference of spatially coherent radiation as opposed to the destructive interference that is used in all the other phase-shifting schemes. This type of mask shows ultrahigh resolution imaging, feature reduction from the mask critical dimension (CD), and enhanced transmission. It has been simulated and verified experimentally with a 320 nm mask CD. Specifically, the BPEXPM exhibits: (1) approximately three times higher effective sensitivity, 40 mJ/cm2 dose to mask for Shipley XP-9947E, a positive-tone chemically amplified resist that normally has a sensitivity of 120 mJ/cm2 in the x-ray region and (2) resolution of 54 nm features in XP-9947E at a 50 μm gap for a 320 nm mask CD. The simulation for the BPEXPM also predicts a wafer image CD of 31 nm at 10 μm gaps with a 150 nm mask CD.

Use of dispersion managed fibers in parallel WDM systems

Abstract: The problem of serial electronic bottleneck in optical systems can be solved using parallel transmission. In these systems, there is no need for serial-to-parallel, and parallel-to-serial conversion since all bits in a digital word are transmitted simultaneously. Several parallel systems have been proposed in the literature and some of them are already commercially available. In this paper, the feasibility of a novel parallel WDM system using Dispersion Managed Fibers (DMF) is proposed. 10 GWord/s over 40 km of DMF at an aggregate bit rate-distance product of 160 Gbit/s/spl times/40-km is studied.

Spatial analysis and mapping of banana crop properties: issues of the asynchronicity of the banana production and proposition of a statistical method to take it into account

Abstract: Precision agriculture for banana crops has been little investigated so far. The main difficulty to implement precision agriculture methods lies in the asynchronicity of this crop: after a few cycles, each plant has its own development stage in the field. It results in a diversity of the phenological stages within a field and also a continuous production of bananas over time. Therefore, maps of agronomic interest derived from plant responses are difficult to produce using existing methods. This study proposes a mapping approach that handles the diversity of phenological stages and the temporal continuity of production. It explores the feasibility of applying this general approach to a plant response parameter which is the time between flowering and maturity (time to harvest) of banana denoted tfm. The tfm gives an insight into the spatial distribution of vigour. The study was conducted using a large database (more than 395 000 observations) generated by two commercial farms in 2015 and 2016 in Cameroon. The temporal variability of tfm, which is induced by meteorological and operational constraints, and the spatial variability, which is assumed to be due to environmental factors, was assessed by decomposing the tfm variance. This method allowed mapping of the effect of the temporal variability as well as the effect of agri-environmental variables on tfm using a block kriging method. Spatial structures highlighted by this decomposition either at the farm level or at the field level, suggest that the map of the effect of environmental factors on tfm could be used to support agronomic decisions. This idea is reinforced by the identification of factors explaining the environmental variability of tfm and by the temporal stability of the spatial structures.

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

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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.