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Contains more than 230 figures that present experimental CCD and CMOS data products and modeling simulations connected to photon transfer. This title also provides hundreds of relations that support photon transfer theory, simulations, and data.
This thesis offers an excellent, comprehensive introduction to the physics of the quark–gluon plasma. It clearly explains the connection between theory and experiment, making the topic accessible to non-specialists in this field. The experimental work, which contributes significantly to our understanding of the quark–gluon plasma, is described in great detail. The results described in the final chapters of the thesis provide interesting new ideas about the connection between proton-proton and Pb-Pb collisions. Simone Schuchmann received the 'ALICE Thesis Award 2016' for this excellent work.
This book constitutes the refereed proceedings of the 12th International Conference on Logic for Programming, Artificial Intelligence, and Reasoning, LPAR 2005, held in Montego Bay, Jamaica in December 2005. The 46 revised full papers presented together with abstracts of 3 invited talks were carefully reviewed and selected from 108 full paper submissions. The papers address all current issues in logic programming, logic-based program manipulation, formal method, automated reasoning, and various kinds of AI logics.
This text introduces the fundamental techniques for controlling dead-time processes from simple monovariable to complex multivariable cases. Dead-time-process-control problems are studied using classical proportional-integral-differential (PID) control for the simpler examples and dead-time-compensator (DTC) and model predictive control (MPC) methods for progressively more complex ones. Downloadable MATLAB® code makes the examples and ideas more convenient and simpler.
Hofstadter's collection of quirky essays is unified by its primary concern: to examine the way people perceive and think.
Throughout successive generations of information technology, the importance of the performance evaluation of software, computer architectures, and computer networks endures. For example, the performance issues of transaction processing systems and redundant arrays of independent disks replace the virtual memory and input-output problems of the 70s.
This volume contains the proceedings of FroCoS 2002, the 4th International Workshop on Frontiers of Combining Systems, held April 8-10, 2002 in Santa Margherita Ligure (near Genova), Italy. Like its predecessors, organized in - nich (1996), Amsterdam (1998), and Nancy (2000), FroCoS 2002 o?ered a c- mon forum for the presentation and discussion of research activities on the c- bination and integration of systems in various areas of computer science, such as logic, computation, program development and proof, arti?cial intelligence, mechanical veri?cation, and symbolic computation. There were 35 submissions of high quality, authored by researchers from countries including Australia, Belgium, Brazil, Finland, France, Germany, Italy, Portugal, Spain, Singapore, United Kingdom, United States of America, and - goslavia. All the submissions were thoroughly evaluated on the basis of at least three referee reports, and an electronic program committee meeting was held through the Internet. The program committee selected 14 research contributions. The topics covered by the selected papers include: combination of logics, c- bination of constraint solving techniques, combination of decision procedures, combination problems in veri?cation, modular properties of theorem proving, integration of decision procedures and other solving processes into constraint programming and deduction systems.
Targeted at students and researchers in computational sciences who need to develop computer codes for solving PDEs, the exposition here is focused on numerics and software related to mathematical models in solid and fluid mechanics. The book teaches finite element methods, and basic finite difference methods from a computational point of view, with the main emphasis on developing flexible computer programs, using the numerical library Diffpack. Diffpack is explained in detail for problems including model equations in applied mathematics, heat transfer, elasticity, and viscous fluid flow. All the program examples, as well as Diffpack for use with this book, are available on the Internet. XXXXXXX NEUER TEXT This book is for researchers who need to develop computer code for solving PDEs. Numerical methods and the application of Diffpack are explained in detail. Diffpack is a modern C++ development environment that is widely used by industrial scientists and engineers working in areas such as oil exploration, groundwater modeling, and materials testing. All the program examples, as well as a test version of Diffpack, are available for free over the Internet.
This book is a comprehensive guide to machine learning with worked examples in MATLAB. It starts with an overview of the history of Artificial Intelligence and automatic control and how the field of machine learning grew from these. It provides descriptions of all major areas in machine learning. The book reviews commercially available packages for machine learning and shows how they fit into the field. The book then shows how MATLAB can be used to solve machine learning problems and how MATLAB graphics can enhance the programmer’s understanding of the results and help users of their software grasp the results. Machine Learning can be very mathematical. The mathematics for each area is introduced in a clear and concise form so that even casual readers can understand the math. Readers from all areas of engineering will see connections to what they know and will learn new technology. The book then provides complete solutions in MATLAB for several important problems in machine learning including face identification, autonomous driving, and data classification. Full source code is provided for all of the examples and applications in the book. What you'll learn: An overview of the field of machine learning Commercial and open source packages in MATLAB How to use MATLAB for programming and building machine learning applications MATLAB graphics for machine learning Practical real world examples in MATLAB for major applications of machine learning in big data Who is this book for: The primary audiences are engineers and engineering students wanting a comprehensive and practical introduction to machine learning.
In the history of mathematics there are many situations in which cal- lations were performed incorrectly for important practical applications. Let us look at some examples, the history of computing the number ? began in Egypt and Babylon about 2000 years BC, since then many mathematicians have calculated ? (e. g. , Archimedes, Ptolemy, Vi` ete, etc. ). The ?rst formula for computing decimal digits of ? was disc- ered by J. Machin (in 1706), who was the ?rst to correctly compute 100 digits of ?. Then many people used his method, e. g. , W. Shanks calculated ? with 707 digits (within 15 years), although due to mistakes only the ?rst 527 were correct. For the next examples, we can mention the history of computing the ?ne-structure constant ? (that was ?rst discovered by A. Sommerfeld), and the mathematical tables, exact - lutions, and formulas, published in many mathematical textbooks, were not veri?ed rigorously [25]. These errors could have a large e?ect on results obtained by engineers. But sometimes, the solution of such problems required such techn- ogy that was not available at that time. In modern mathematics there exist computers that can perform various mathematical operations for which humans are incapable. Therefore the computers can be used to verify the results obtained by humans, to discovery new results, to - provetheresultsthatahumancanobtainwithoutanytechnology. With respectto our example of computing?, we can mention that recently (in 2002) Y. Kanada, Y. Ushiro, H. Kuroda, and M.