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.

Photochron Iic Streak Tube For 300Mhz Circular-Scan Operation

Abstract: The design features of a circular-scan streak tube which is intended as a primary receiver component in a spaceborne laser-ranging system are reviewed. Preliminary experimental data are also presented which indicate that a circular-scan Photochron streak camera has an instrumental function of less than 6ps in both single-shot and repetitive modes of streak operation.

The Intersection of Evidence and Values in Clinical Guidelines: Who Decides What Constitutes Acceptable Risk in the Care of Children?

Abstract: Risk is implicit in all clinical decision-making. Whether a clinician refrains from ordering a head computed tomography scan for a patient with a headache or decides to obtain blood work on a child with a fever, each decision involves a balance between accepting and limiting risk. Ideally, clinicians strive to achieve this balance. Clinicians accept some risk (eg, the risk of missing a rare disease) to avoid placing undue burden on the patient and the health care system with invasive, expensive, and/or potentially unnecessary testing. Yet, clinicians also strive not to assume too much risk so that timely diagnoses are made and morbidity and mortality prevented.1 Achieving this balance is difficult for a number of reasons. First, clinicians often lack precise estimates of the risks and harms for a given clinical scenario. There is simply an absence of or paucity of data to help quantify many of the risks, benefits, and outcomes associated with different possible therapeutic options. This confounds the ability to know whether pursuing 1 particular option corresponds to accepting too much or too little risk. As a result, decision-making is often intrinsically imperfect. Second, even if precise estimates of the involved risks are known, determining the threshold constituting acceptable risk (ie, the level above which too much risk would be assumed) is largely subjective. The issue of acceptable risk is inherently a matter of values, which, in pediatrics, includes not only the clinician’s values but also those of the parent and sometimes the child. Although there is general agreement that it is unacceptable for a parent to assume high levels of preventable risk and harm on behalf of his or her child, what constitutes a high level of risk and harm? Is it a 1 in 100 or 1 in 100 000 risk of the child …

Temporal evolution of within-season vineyard canopy response from a proximal sensing system

Abstract: A weekly survey of canopy NDVI with a proximal-mounted canopy sensor was undertaken in a cool-climate juicegrape vineyard. Sensing was performed at different positions in the canopy. Sensing around the top-wire led to saturation problems, however sensing in the growing region of the canopy led to consistently non-saturated results throughout the season. With this directed sensing, a spatial pattern in NDVI 2–4 weeks after flowering could be generated that approximated the spatial pattern in NDVI at the end of the season. This is earlier than has been previously reported and may allow for proactive within-season canopy management.

An Interface for Computer-Aided Control Engineering based on an Engineering Data-Base Manager

Abstract: A companion paper [1] describes an engineering data-base manager (EDBM) for computer-aided control engineering (CACE). The need for an EDBM to support the complete control system design cycle is also discussed. The obvious benefit of integrating an EDBM into a CACE environment, as demonstrated in [1,2], is knowing how all system models and analysis and design result data are interrelated. This results in a data base that is documentable and reproducible. If the user interface of the system is designed correctly, there can be several major secondary pay-offs as well: information hiding (eliminating the need to recall commands and file names, for example) and direct integration of CACE activity and EDBM functionality. Conversely, if the CACE environment and EDBM are not integrated well, many users will not be motivated to use the EDBM to track models and results. The features of an EDBM-based user interface to a CACE software environment are described in detail below. This includes demonstrating the execution of all data base operations, including browse, display, edit, purge, delete, and replicate, as well as executing certain CACE activity directly from the EDBM displays. Some of these features have been implemented in a rapid prototype software environment [3,4]; more of them are currently being incorporated in version 1.0 of our CACE environment [5]; a few will remain to be realized in future stages of this work. The primary goal of this user interface design is to make EDBM an integral part of the environment, not merely an appendage.

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.