Based on fundamental principles from mathematics, linear systems, and signal analysis, digital signal processing (DSP) algorithms are useful for extracting information from signals collected all around us. Combined with today’s powerful computing capabilities, they can be used in a wide range of application areas, including...

Full of biological applications, exercises, and interactive graphical examples, Foundational and Applied Statistics for Biologists Using R presents comprehensive coverage of both modern analytical methods and statistical foundations. The author harnesses the inherent properties of the R environment to enable students...

• Grasp the skills needed to write efficient Hive queries to analyze the Big Data
• Discover how Hive can coexist and work with other tools within the Hadoop...

• Implement machine learning algorithms to build ensemble-efficient models
• Explore powerful R packages to create predictive models using ensemble methods
• Learn to...

Applied statistics covers both the theory and the application of modern statistical and mathematical modelling techniques to applied problems in industry, public services, commerce, and research. It proceeds...

Surrogate or metamodels are now...

The first part describes how to build an integrated system, capable of properly handling the kinematics and dynamics of the robot along the...

This book discusses a variety of topics related to industrial and applied mathematics, focusing on wavelet theory, sampling theorems, inverse problems and their applications, partial differential equations as a model of real-world problems, computational linguistics, mathematical models and methods for meteorology, earth systems,...

This comprehensive account of the deadliest Ebola outbreak in history examines its devastating effects on West Africa’s most vulnerable populations: pregnant women and children. Noted experts across disciplines assess health care systems’ responses to the epidemic in Liberia, Guinea, and Sierra Leone, emphasizing key...