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 …

I and i

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Using Multivariate Statistics

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

The Design and Analysis of Experiments

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.).

Machine learning

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.

/pdf/linear-matrix-inequalities-in-system-and-control-theory-2q5cf0pqm2.pdf

Linear Matrix Inequalities in System and Control Theory

Peak-power limits for 775nm SHG in PPKTP and MgO:sPPLT is experimentally assessed. No nonlinear absorption detected in PPKTP up to 16MW/cm2. SHG efficiency in MgO:sPPLT showed higher susceptibility to the intensity of 775nm SH radiation.

Operation Limits of Flux-Grown PPKTP and Stoichiometric PPLT for High Power SHG around 775nm

89. Why are yield sensors seldom used by farmers – a French case study

In this memoire I will present my journey and the knowledge that I have gained within the Precision Agriculture domain over the past two decades. The first section broadly introduces variation in cropping systems and serves as a very general introduction to the research domain. 
Section 2 provides a more detailed overview of PA and develops some of the main issues and challenges that have been historically faced by PA practitioners. This includes clearly defining the domain and a recognition of the diversity and multi-disciplinary nature of PA. The role and importance of variability in production systems is then introduced along with the challenges in correctly and properly defining production variance. The final three parts of Section 2 turn the focus to how information, particularly multiple layers and multiple types of information, start to be translated into decisions. This shifts PA from a technical question on measuring and managing variation to a socio-technical innovation question on how technology is perceived and adopted and the potential disconnect between what scientists consider important and what commercial users want.
Section 3 further develops two key areas – the disconnect between academic research and commercial application (innovation) and the disconnect in the data that we have and what we want (in terms of data type and the quality). There is a deliberate focus on how PA technologies and methodologies are effectively translated into successful commercial services and what I have learnt about this from my various placements and from my attempts to define industry-facing protocols at various stages of my research career. 
Section 4 provides an overview of how my research activities and publications that have (I hope) helped to address the issues raised in Sections 2 and 3. A focus is given to a large body of work that provides a descriptive reference base to observed spatial variation in yield and crop quality parameters in various annual and perennial systems. This is followed by the role and the evolution in zonage approaches over the past 2 decades and how these zones or management units have been used as a basis for more advantaged analysis to improve crop production knowledge and ultimately crop management decisions. The latter parts of Section 4 shift from natural, agronomic applications to more recent research that has started to interrogate and to build models to understand socio-technical aspects of PA. It develops ideas on how PA translation and adoption can be enhanced by better considering barriers and drivers of adoption.
The last section, Section 5, outlines my own vision for the evolution of Precision Agriculture over the next decade. It outlines how Precision Agriculture needs to develop so that producers have a more ‘personalised’ agricultural decision system. It highlights key areas of research that I would like to pursue in my future career, and how advances in digital technology will help to achieve this research and translation. There are some concluding remarks.
Throughout the memoire, I have deliberately avoided the incorporation of mathematical notation. I have preferred to keep the discussion more general and not too specific for a broader audience. The cited references contain more specific details for those wishing to understand the derivation of the geostatistical approaches and models discussed. By convention, I have indicated in bold the references that are self-citations within the document. These are all listed in the Bibliography. A more detailed publication list is appended with my full publication list. The publication list contains work that is not cited in the document, particularly research that I have performed in soil science, rather than in PA, and teaching and industry-oriented publications.

https://hal.archives-ouvertes.fr/tel-02548498/document

Towards Incorporating Within-Field Variation into Spatial Agronomic Decision Processes

1-THz optical pulse train generation at 1.5 µm

We demonstrate a novel, diode-pumped ytterbium-doped fiber laser, which combines the unique characteristics of a high brightness supercontinuum together with simplicity of operation and compactness for application in dispersion measurement using both time delay and interferometric techniques and has considerable potential for implementation into a simple yet highly practical apparatus for dispersion measurement. The high brightness and intensity of the source is particularly applicable to nondestructive dispersion-distribution measurements as well as real time control of dispersion during fiber drawing. The potential of these have been examined.

James Taylor

Papers

Operation Limits of Flux-Grown PPKTP and Stoichiometric PPLT for High Power SHG around 775nm

89. Why are yield sensors seldom used by farmers – a French case study

Towards Incorporating Within-Field Variation into Spatial Agronomic Decision Processes

1-THz optical pulse train generation at 1.5 µm

Chromatic dispersion measurement in the 1-2 /spl mu/m spectral region using a high brightness supercontinuum fiber laser source