Scilab From Theory To Practice I Fundamentals

Scilab: from Theory to Practice - I. Fundamentals

This book provides a comprehensive, hands-on introduction to the powerful,open source computing environment of Scilab. It will teach you all the basic Scilab concepts you need for computing, analyzing and visualizing data, for developing algorithms, and creating models. Based on the latest versions of Scilab, it focuses on the most recent recommended practices. It offers a lot of advice and words of caution to help you take full advantage of Scilab’s capabilities, and efficiently create your own projects. Best practices have been certified by Dr. Claude Gomez, co-founder and advisor of Scilab Enterprises. After performing a quick overview of the software, three parts will successively deal with computing, programming and creating plots. The first one shows you how to perform and optimize all the mathematical calculations that an engineer may come across. The second one examines how to go beyond the simple calculations and study complex systems with scripting and interface building. The last one gives you a thorough description of Scilab's numerous graphics capabilities. Level: Intermediate/Advanced Table Of Contents: Getting Started 1. Preview of Scilab 2. The Console 3. The Graphical Interface 4. Inputs/Outputs 5. Finding Information on Scilab 6. Downloading and Installing Scilab Computing 7. Numbers and First Calculations 8. Variables, Constants and Types 9. Matrices 10. Booleans 11. Character Strings and Text Files 12. Other Common Types 13. Calculation Examples Programming 14. Scripts 15. Control Flow Statements 16. Functions 17. Advanced Programming 18. Example: Programming a Sudoku Game Creating Plots 19. Graphics Entities and Windows 20. Two-dimensional Plot 21. Three-dimensional Plots 22. Other Two-dimensional Geometrical Elements 23. To Go Even Further 24. Two Case Studies: a Pendulum and Comet Orbit

Scilab from theory to practice 1, Fundamentals

Applications of Image Processing and Soft Computing Systems in Agriculture

The variety and abundance of qualitative characteristics of agricultural products have been the main reasons for the development of different types of non-destructive methods (NDTs). Quality control of these products is one of the most important tasks in manufacturing processes. The use of control and automation has become more widespread, and new approaches provide opportunities for production competition through new technologies. Applications of Image Processing and Soft Computing Systems in Agriculture examines applications of artificial intelligence in agriculture and the main uses of shape analysis on agricultural products such as relationships between form and genetics, adaptation, product characteristics, and product sorting. Additionally, it provides insights developed through computer vision techniques. Highlighting such topics as deep learning, agribusiness, and augmented reality, it is designed for academicians, researchers, agricultural practitioners, and industry professionals.