الصفحة 1
الصفحة 1
Multirate Statistical Signal Processing
This book introduces a statistical theory for extracting information from signals that have di?erent sampling rates. This new theory generalizes the conventional (deterministic) theory of multirate systems beyond many of its constraints.Furthermore,itallowsfortheformulationofseveralnewproblems such as spectrum estimation, time-delay estimation and sensor fusion in the realm of multirate signal processing. I have arrived at the theory presented here by integrating concepts from diverse areas such as information theory, inverse problems and theory of - equalities. The process of merging a variety of concepts of di?erent origin results in both merits and shortcomings. The former include the fresh and - di?erentiated view of an amateur, providing scope of application. The latter include a lack of in-depth experience in each of the original ?elds. Granted, this may lead to gaps in continuity, however it goes without saying that a complete theory can seldom be achieved by one person and in a short time.
Modern Sliding Mode Control Theory : New Perspectives and Applications
This book is a collection of invited chapters covering several areas of modern sliding mode control theory. The authors identify key contributions defining the theoretical and applicative state of the art of the sliding mode control theory and the most promising trends of the ongoing research activities. The contributions is divided in four main parts: Part I: Basic Theory. Part II: Design Methods. Part III: Observers and Fault Detection. Part IV: Applications.
Instrumaster
Experiments with different neural network structures and algorithms in order to achieve musical note recognition as well as musical instrument recognition, all bundled in a mobile application. It also aims to create the most effective music-learning application that works completely offline, which is hard to find in modern music applications. The paper also explores why the instrument identifying AI is solely based on Multi-Layer Perceptron (MLP) and why the note-identifying AI system was chosen to be a ML system over CNN or other deep-learning trained AI. The paper presents feature extraction methods for audio signals and files and dives deep into the process, such as FFT, MFCCs, Wavelengths, sampling rates, etc. It also touches on Logistic Regression Algorithms, their limitations, and their performance with the different use cases in the application. All these techniques are then compared side by side for maximally added value, making this research paper a good reference for any future developers looking to find optimal neural networks techniques when it comes to audio processing and analysis.
Digital Signal Processing with Field Programmable Gate Arrays
Field-Programmable Gate Arrays (FPGAs) are revolutionizing digital signal processing as novel FPGA families are replacing ASICs and PDSPs for front-end digital signal processing algorithms. So the efficient implementation of these algorithms is critical and is the main goal of this book. It starts with an overview of today's FPGA technology, devices, and tools for designing state-of-the-art DSP systems. A case study in the first chapter is the basis for more than 40 design examples throughout. The following chapters deal with computer arithmetic concepts, theory and the implementation of FIR and IIR filters, multirate digital signal processing systems, DFT and FFT algorithms, advanced algorithms with high future potential, and adaptive filters. Each chapter contains exercises. The VERILOG source code and a glossary are given in the appendices. This edition has a new chapter on microprocessors, new sections on special functions using MAC calls, intellectual property core design and arbitrary sampling rate converters, and over 100 new exercises.
Digital Signal Processing for Measurement Systems : Theory and Applications
Digital Signal Processing for Measurement Systems: Theory and Applications covers the theoretical as well as the practical issues which form the basis of the modern DSP-based instruments and measurement methods. It covers the basics of DSP theory before discussing the critical aspects of DSP unique to measurement science. Includes important topics, for example, problems that arise when sampling periodic signals and the relationship between the sampling rate and the SNR
