Introduction to Optimum Design, Third Edition describes an organized approach to engineering design optimization in a rigorous yet simplified manner. It illustrates various concepts and procedures with simple examples and demonstrates their applicability to engineering design problems. Formulation of a design problem as an optimization problem is emphasized and illustrated throughout the text. Excel and MATLAB® are featured as learning and teaching aids. - Basic concepts of optimality conditions and numerical methods are described with simple and practical examples, making the material highly teachable and learnable - Includes applications of optimization methods for structural, mechanical, aerospace, and industrial engineering problems - Introduction to MATLAB Optimization Toolbox - Practical design examples introduce students to the use of optimization methods early in the book - New example problems throughout the text are enhanced with detailed illustrations - Optimum design with Excel Solver has been expanded into a full chapter - New chapter on several advanced optimum design topics serves the needs of instructors who teach more advanced courses

The Optimum-Path Forest (OPF) classifier was first published in 2008 in its supervised and unsupervised versions with applications in medicine and image classification. Since then, it has expanded to a variety of other applications such as remote sensing, electrical and petroleum engineering, and biology. In recent years, multi-label and semi-supervised versions were also developed to handle video classification problems. The book presents the principles, algorithms and applications of Optimum-Path Forest, giving the theory and state-of-the-art as well as insights into future directions. Presents the first book on Optimum-path Forest Shows how it can be used with Deep Learning Gives a wide range of applications Includes the methods, underlying theory and applications of Optimum-Path Forest (OPF)

This book deals with a basic problem arising within the Bayesian approach 1 to scientific methodology, namely the choice of prior probabilities. The problem will be considered with special reference to some inference methods used within Bayesian statistics (BS) and the so-called theory of inductive 2 probabilities (T/P). In this study an important role will be played by the assumption - defended by Sir Karl Popper and the supporters of the current verisimilitude theory (VT) - that the cognitive goal of science is the achievement of a high degree of truthlikeness or verisimilitude. A more detailed outline of the issues and objectives of the book is given in Section 1. In Section 2 the historical background of the Bayesian approach and the verisimilitude theory is briefly illustrated. In Section 3, the methods used in TIP and BS for making multinomial inference~ are considered and some conceptual relationships between TIP and BS are pointed out. In Section 4 the main lines of a new approach to the problem of the choice of prior probabilities are illustrated. Lastly, in Section 5 >the structure of the book is described and a first explanation of some technical terms is provided.

Well-known authority, Dr. Van Trees updates array signal processing for today's technology This is the most up-to-date and thorough treatment of the subject available Written in the same accessible style as Van Tree's earlier classics, this completely new work covers all modern applications of array signal processing, from biomedicine to wireless communications

In this book, we study theoretical and practical aspects of computing methods for mathematical modelling of nonlinear systems. A number of computing techniques are considered, such as methods of operator approximation with any given accuracy; operator interpolation techniques including a non-Lagrange interpolation; methods of system representation subject to constraints associated with concepts of causality, memory and stationarity; methods of system representation with an accuracy that is the best within a given class of models; methods of covariance matrix estimation;methods for low-rank matrix approximations; hybrid methods based on a combination of iterative procedures and best operator approximation; andmethods for information compression and filtering under condition that a filter model should satisfy restrictions associated with causality and different types of memory.As a result, the book represents a blend of new methods in general computational analysis,and specific, but also generic, techniques for study of systems theory ant its particularbranches, such as optimal filtering and information compression.- Best operator approximation,- Non-Lagrange interpolation,- Generic Karhunen-Loeve transform- Generalised low-rank matrix approximation- Optimal data compression- Optimal nonlinear filtering

The transmitter and receiver are jointly optimized with a minimum average-probability-of-error criterion. The transmitted waveforms are binary and the receiver is nonlinear with zero memory. Errors are due to additive, Gaussian noise, with independent samples, and adjacent symbol interference. The channel impulse response is assumed to be known and time-invariant. The optimum waveforms are given by the eigenfunction corresponding to the maximum eigenvalue of a symmetric integral operator. In addition, the optimum receiver is given for extensive inter-symbol interference, and its probability of error is formulated. The results are applied to data representing an experimental, bandpass, telephone channel with quadratic delay. This example shows approximately 5.5-dB performance improvement over a rectangularly pulsed carrier and standard correlation receiver. The system developed here tends to minimize the effect of inter-symbol interference while transferring maximum energy through the channel. This is contrasted with systems that eliminate the inter-symbol interference at the expense of a reduction in energy transferred.