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Caught up in a space station turf war between gangs and corrupt law, a lone asteroid miner decides to take them all down. When an asteroid miner comes to Station 35 looking to sell her cargo and get back to the solitude she craves, she gets swept up in a three-way standoff with gangs and crooked cops. Faced with either taking sides or cleaning out the Augean Stables, she breaks out the flamethrower.
Refining Sound is a practical roadmap to the complexities of creating sounds on modern synthesizers. Perhaps the most difficult aspect of learning to create sounds on a synthesizer is understanding what all the individual synthesizer components contribute to the complex finished sound. Author and veteran synthesizer instructor Brian K. Shepard draws on his years of experience in synthesizer pedagogy in order to peel back the often-mysterious layers of sound synthesis one-by-one. The result is a book that allows readers to familiarize themselves with each individual step in the synthesis process, in turn empowering them in their own creative or experimental work. Refining Sound follows the stages of synthesis in chronological progression from the "raw materials" of sound waves through the various stages of the refinement process, ultimately bringing readers to the final "polishing" of their sounds with audio effects. Each chapter focuses on a particular aspect of the synthesis process, and contains easily digestible guided projects (entitled "Your Turn" sections) that focus on the topics of the chapter. Throughout the text, the material is supported by copious examples and illustrations and more than forty interactive synthesis demonstrations on the related companion website that allow the reader to experiment with and understand these concepts without the distraction of other synthesizer controls and modifiers. The final chapter brings everything together as the reader creates several common types of synthesizer sounds with detailed step-by-step instructions and explanations of the concepts behind those steps. With all of the sounds in the final chapter, readers are given suggestions and tips on ways to modify the sounds, with final outcomes left to the readers' own creativity. Refining Sound is essential for all electronic musicians from amateur to professional levels of accomplishment, students, teachers, libraries, and anyone interested in creating sounds on a synthesizer.
Part of the excitement in boundary-layer meteorology is the challenge associated with turbulent flow - one of the unsolved problems in classical physics. An additional attraction of the filed is the rich diversity of topics and research methods that are collected under the umbrella-term of boundary-layer meteorology. The flavor of the challenges and the excitement associated with the study of the atmospheric boundary layer are captured in this textbook. Fundamental concepts and mathematics are presented prior to their use, physical interpretations of the terms in equations are given, sample data are shown, examples are solved, and exercises are included. The work should also be considered as a major reference and as a review of the literature, since it includes tables of parameterizatlons, procedures, filed experiments, useful constants, and graphs of various phenomena under a variety of conditions. It is assumed that the work will be used at the beginning graduate level for students with an undergraduate background in meteorology, but the author envisions, and has catered for, a heterogeneity in the background and experience of his readers.
If you understand basic mathematics and know how to program with Python, you’re ready to dive into signal processing. While most resources start with theory to teach this complex subject, this practical book introduces techniques by showing you how they’re applied in the real world. In the first chapter alone, you’ll be able to decompose a sound into its harmonics, modify the harmonics, and generate new sounds. Author Allen Downey explains techniques such as spectral decomposition, filtering, convolution, and the Fast Fourier Transform. This book also provides exercises and code examples to help you understand the material. You’ll explore: Periodic signals and their spectrums Harmonic structure of simple waveforms Chirps and other sounds whose spectrum changes over time Noise signals and natural sources of noise The autocorrelation function for estimating pitch The discrete cosine transform (DCT) for compression The Fast Fourier Transform for spectral analysis Relating operations in time to filters in the frequency domain Linear time-invariant (LTI) system theory Amplitude modulation (AM) used in radio Other books in this series include Think Stats and Think Bayes, also by Allen Downey.
From the Nobel Prize-winning author of Thinking, Fast and Slow and the coauthor of Nudge, a revolutionary exploration of why people make bad judgments and how to make better ones—"a tour de force” (New York Times). Imagine that two doctors in the same city give different diagnoses to identical patients—or that two judges in the same courthouse give markedly different sentences to people who have committed the same crime. Suppose that different interviewers at the same firm make different decisions about indistinguishable job applicants—or that when a company is handling customer complaints, the resolution depends on who happens to answer the phone. Now imagine that the same doctor, the same judge, the same interviewer, or the same customer service agent makes different decisions depending on whether it is morning or afternoon, or Monday rather than Wednesday. These are examples of noise: variability in judgments that should be identical. In Noise, Daniel Kahneman, Olivier Sibony, and Cass R. Sunstein show the detrimental effects of noise in many fields, including medicine, law, economic forecasting, forensic science, bail, child protection, strategy, performance reviews, and personnel selection. Wherever there is judgment, there is noise. Yet, most of the time, individuals and organizations alike are unaware of it. They neglect noise. With a few simple remedies, people can reduce both noise and bias, and so make far better decisions. Packed with original ideas, and offering the same kinds of research-based insights that made Thinking, Fast and Slow and Nudge groundbreaking New York Times bestsellers, Noise explains how and why humans are so susceptible to noise in judgment—and what we can do about it.
The HilbertOCoHuang Transform (HHT) represents a desperate attempt to break the suffocating hold on the field of data analysis by the twin assumptions of linearity and stationarity. Unlike spectrograms, wavelet analysis, or the WignerOCoVille Distribution, HHT is truly a time-frequency analysis, but it does not require an a priori functional basis and, therefore, the convolution computation of frequency. The method provides a magnifying glass to examine the data, and also offers a different view of data from nonlinear processes, with the results no longer shackled by spurious harmonics OCo the artifacts of imposing a linearity property on a nonlinear system or of limiting by the uncertainty principle, and a consequence of Fourier transform pairs in data analysis. This is the first HHT book containing papers covering a wide variety of interests. The chapters are divided into mathematical aspects and applications, with the applications further grouped into geophysics, structural safety and visualization.
Uncertainty for Everyone The one thing that is certain about the world is that the world is uncertain. I have here, the question that apart of the matter, living matter, has to resolve in each and every one of its moments of existance. The environment of a living being is apart of the living being where it turns out, the rest of the living beings live. This is the drama of life on earth. Every living individual debates with his environment, exchanging matter, energy and information in the hope of staying alive, the same as all living beings who share that same environment. The adven ture of a living being (of all living beings ) is to maintain reasonable independ ence in face ofthe fluctuations ofuncertainty within the environment. The range of restrictions and mutual relationships is colossal. How is the tran seendental pretension of staying alive regulated? There is an equation imposed by the laws ofthermodynamics and the mathematical theory ofinformation about the interaction ofa living being with his environment which we could state like this: The complexity 01 a living individual plus his capacity for anticipation in re spect to his environment is identical to the uncertainty of the environmentplus the capacity of that living being to change the environment.
This book gives an overview of singular spectrum analysis (SSA). SSA is a technique of time series analysis and forecasting combining elements of classical time series analysis, multivariate statistics, multivariate geometry, dynamical systems and signal processing. SSA is multi-purpose and naturally combines both model-free and parametric techniques, which makes it a very special and attractive methodology for solving a wide range of problems arising in diverse areas. Rapidly increasing number of novel applications of SSA is a consequence of the new fundamental research on SSA and the recent progress in computing and software engineering which made it possible to use SSA for very complicated tasks that were unthinkable twenty years ago. In this book, the methodology of SSA is concisely but at the same time comprehensively explained by two prominent statisticians with huge experience in SSA. The book offers a valuable resource for a very wide readership, including professional statisticians, specialists in signal and image processing, as well as specialists in numerous applied disciplines interested in using statistical methods for time series analysis, forecasting, signal and image processing. The second edition of the book contains many updates and some new material including a thorough discussion on the place of SSA among other methods and new sections on multivariate and multidimensional extensions of SSA.