Writi Following Models

The purpose of this book is to describe in sufficient detail the mathematical models of hysteresis nonlinearities with "nonlocal memories. " The distinct feature of these nonlinearities is that their future states depend on past histories of input variations. It turns out that memories of hysteresis nonlinearities are quite selective. Indeed, experiments show that only some past input extrema (not the entire input variations) leave their marks upon future states of hysteresis nonlinearities. Thus, special mathematical tools are needed to describe nonlocal selective memories of hysteresis nonlinearities. The origin of such tools can be traced back to the landmark paper of Preisach. The book is primarily concerned with Preisach-type models of hysteresis.

This contributed volume introduces the innovative landscapes and business models used in tourism entrepreneurship initiatives of Portugal and Spain. It provides benchmarks for entrepreneurial initiatives covering tourism services, place-branded tourism, social networks, spiritual tourism, cross-border tourism initiatives, and tourism in low-density regions. It also provides guidelines for future strategic actions to foster rural and sustainable development in alternative tourism destinations, following the Iberian experience.

Channel your inner M.C. Escher with these brain teaser puzzles!

This book intends to provide the reader with an overview of the latest developments in computational techniques used in various engineering disciplines.

Learn how to integrate robust and reliable Machine Learning Pipelines in Production

This book provides a comprehensive overview of the state of the art in research and applications of Foundation Models and is intended for readers familiar with basic Natural Language Processing (NLP) concepts.

This fully updated new edition of a uniquely accessible textbook/reference provides a general introduction to probabilistic graphical models (PGMs) from an engineering perspective. It features new material on partially observable Markov decision processes, graphical models, and deep learning, as well as an even greater number of exercises.