Arthur A. Giordano – författare

A comprehensive and detailed treatment of the program SIMULINK® that focuses on SIMULINK® for simulations in Digital and Wireless CommunicationsModeling of Digital Communication Systems Using SIMULINK® introduces the reader to SIMULINK®, an extension of the widely-used MATLAB modeling tool, and the use of SIMULINK® in modeling and simulating digital communication systems, including wireless communication systems. Readers will learn to model a wide selection of digital communications techniques and evaluate their performance for many important channel conditions. Modeling of Digital Communication Systems Using SIMULINK® is organized in two parts. The first addresses Simulink® models of digital communications systems using various modulation, coding, channel conditions and receiver processing techniques. The second part provides a collection of examples, including speech coding, interference cancellation, spread spectrum, adaptive signal processing, Kalman filtering and modulation and coding techniques currently implemented in mobile wireless systems. Covers case examples, progressing from basic to complexProvides applications for mobile communications, satellite communications, and fixed wireless systems that reveal the power of SIMULINK modelingIncludes access to useable SIMULINK® simulations onlineAll models in the text have been updated to R2018a; only problem sets require updating to the latest release by the user Covering both the use of SIMULINK® in digital communications and the complex aspects of wireless communication systems, Modeling of Digital Communication Systems UsingSIMULINK® is a great resource for both practicing engineers and students with MATLAB experience.

A comprehensive and detailed treatment of the program SIMULINK® that focuses on SIMULINK® for simulations in Digital and Wireless CommunicationsModeling of Digital Communication Systems Using SIMULINK® introduces the reader to SIMULINK®, an extension of the widely-used MATLAB modeling tool, and the use of SIMULINK® in modeling and simulating digital communication systems, including wireless communication systems. Readers will learn to model a wide selection of digital communications techniques and evaluate their performance for many important channel conditions. Modeling of Digital Communication Systems Using SIMULINK® is organized in two parts. The first addresses Simulink® models of digital communications systems using various modulation, coding, channel conditions and receiver processing techniques. The second part provides a collection of examples, including speech coding, interference cancellation, spread spectrum, adaptive signal processing, Kalman filtering and modulation and coding techniques currently implemented in mobile wireless systems.

Covers case examples, progressing from basic to complex Provides applications for mobile communications, satellite communications, and fixed wireless systems that reveal the power of SIMULINK modeling Includes access to useable SIMULINK® simulations online

All models in the text have been updated to R2018a; only problem sets require updating to the latest release by the user

Covering both the use of SIMULINK® in digital communications and the complex aspects of wireless communication systems, Modeling of Digital Communication Systems UsingSIMULINK® is a great resource for both practicing engineers and students with MATLAB experience.

A comprehensive and detailed treatment of the program SIMULINK® that focuses on SIMULINK® for simulations in Digital and Wireless CommunicationsModeling of Digital Communication Systems Using SIMULINK® introduces the reader to SIMULINK®, an extension of the widely-used MATLAB modeling tool, and the use of SIMULINK® in modeling and simulating digital communication systems, including wireless communication systems. Readers will learn to model a wide selection of digital communications techniques and evaluate their performance for many important channel conditions. Modeling of Digital Communication Systems Using SIMULINK® is organized in two parts. The first addresses Simulink® models of digital communications systems using various modulation, coding, channel conditions and receiver processing techniques. The second part provides a collection of examples, including speech coding, interference cancellation, spread spectrum, adaptive signal processing, Kalman filtering and modulation and coding techniques currently implemented in mobile wireless systems.

Covers case examples, progressing from basic to complex Provides applications for mobile communications, satellite communications, and fixed wireless systems that reveal the power of SIMULINK modeling Includes access to useable SIMULINK® simulations online

All models in the text have been updated to R2018a; only problem sets require updating to the latest release by the user

Covering both the use of SIMULINK® in digital communications and the complex aspects of wireless communication systems, Modeling of Digital Communication Systems UsingSIMULINK® is a great resource for both practicing engineers and students with MATLAB experience.

Presents detailed guidance on Monte Carlo integration methods for complex applications Monte Carlo integration has become an indispensable computational tool across science, engineering, mathematics, and economics, offering effective solutions where traditional numerical integration methods fall short. Monte Carlo Integration with MATLAB and Simulink provides both a structured introduction to advanced integration techniques and a practical guide to applying them in real-world contexts. Author Arthur A. Giordano emphasizes the natural progression from traditional methods such as the use of MATLAB integral to Monte Carlo simulation-based approaches, highlighting the growing importance of random variable–driven computations in modern research and engineering applications. Covering topics from accept-rejection sampling and importance sampling to advanced algorithms such as Metropolis-Hastings, Gibbs Sampling, Slice, Hamiltonian Monte Carlo, and Sequential Monte Carlo (Particle Filtering), the book equips readers with the knowledge to handle both tractable and intractable integration problems. Extensive MATLAB examples are paired with detailed explanations, while dedicated Simulink models extend the scope of applications to robotics, control systems, neural networks, cosmology, and more. By integrating step-by-step examples, code snippets, and exploratory exercises, the book fosters an interactive learning process that encourages readers to replicate, modify, and expand on the provided material. Combining theoretical background with extensive computational demonstrations, Monte Carlo Integration with MATLAB and Simulink: Covers both deterministic and simulation-based integration methods with increasing depth and complexityIntroduces advanced Monte Carlo sampling algorithms, including Gibbs Sampling and Sequential Monte Carlo (Particle Filtering)Features over a dozen fully developed MATLAB examples with accompanying program codeProvides detailed Simulink models for robotics, control systems, and scientific applicationsIncludes problem sets with solutions available on a companion websiteHighlights the transition from classical integration to simulation methods for random processesIncorporating classical integration techniques and cutting-edge simulation methods, Monte Carlo Integration with MATLAB and Simulink is a valuable resource for advanced undergraduate and graduate students in applied mathematics, engineering, and computational sciences, as well as scientists, engineers, and researchers applying Monte Carlo integration in fields ranging from signal processing to robotics.

Presents detailed guidance on Monte Carlo integration methods for complex applications Monte Carlo integration has become an indispensable computational tool across science, engineering, mathematics, and economics, offering effective solutions where traditional numerical integration methods fall short. Monte Carlo Integration with MATLAB and Simulink provides both a structured introduction to advanced integration techniques and a practical guide to applying them in real-world contexts. Author Arthur A. Giordano emphasizes the natural progression from traditional methods such as the use of MATLAB integral to Monte Carlo simulation-based approaches, highlighting the growing importance of random variable driven computations in modern research and engineering applications. Covering topics from accept-rejection sampling and importance sampling to advanced algorithms such as Metropolis-Hastings, Gibbs Sampling, Slice, Hamiltonian Monte Carlo, and Sequential Monte Carlo (Particle Filtering), the book equips readers with the knowledge to handle both tractable and intractable integration problems. Extensive MATLAB examples are paired with detailed explanations, while dedicated Simulink models extend the scope of applications to robotics, control systems, neural networks, cosmology, and more. By integrating step-by-step examples, code snippets, and exploratory exercises, the book fosters an interactive learning process that encourages readers to replicate, modify, and expand on the provided material. Combining theoretical background with extensive computational demonstrations, Monte Carlo Integration with MATLAB and Simulink: Covers both deterministic and simulation-based integration methods with increasing depth and complexity Introduces advanced Monte Carlo sampling algorithms, including Gibbs Sampling and Sequential Monte Carlo (Particle Filtering) Features over a dozen fully developed MATLAB examples with accompanying program code Provides detailed Simulink models for robotics, control systems, and scientific applications Includes problem sets with solutions available on a companion website Highlights the transition from classical integration to simulation methods for random processes Incorporating classical integration techniques and cutting-edge simulation methods, Monte Carlo Integration with MATLAB and Simulink is a valuable resource for advanced undergraduate and graduate students in applied mathematics, engineering, and computational sciences, as well as scientists, engineers, and researchers applying Monte Carlo integration in fields ranging from signal processing to robotics.