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1. |
Record Nr. |
UNINA9910808290003321 |
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Autore |
Rhea Randall W. |
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Titolo |
Filter Synthesis Using Genesys S / / Filter |
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Pubbl/distr/stampa |
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Norwood : , : Artech House, , 2014 |
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[Piscataqay, New Jersey] : , : IEEE Xplore, , [2014] |
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ISBN |
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Descrizione fisica |
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1 online resource (342 p.) |
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Collana |
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Artech House microwave library |
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Disciplina |
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Soggetti |
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Electric filters |
Electric filters - Design and construction |
Electric filters - Mathematical models |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Note generali |
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Nota di bibliografia |
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Includes bibliographical references and index. |
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Nota di contenuto |
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Title; Contents; Preface; 1 Transmission Zeros; 1.1 Determining TZ by Inspection; 1.2 Filter Degree; 1.3 Canonical Realization; 1.4 Influence of TZs on the Response; 2 All-Pole Lowpass and Highpass; 2.1 Initial All-Pole Lowpass Parameters; 2.2 Dual Topologies; 2.3 Chebyshev Approximation with Even Order; 2.4 All-Pole Highpass Example; 3 Lowpass with Finite Zeros; 3.1 Introduction; 3.2 Alternative Topologies; 4 Conventional Bandpass; 4.1 Bandpass Transform; 4.2 Classification Symmetry or Antimetry; 4.3 A 75- to 125-MHz Bandpass; 4.4 A 96- to 104-MHz Bandpass Filter. |
4.5 Comparative Analysis of the Wide and Narrow Filters5 Extraction Sequences; 5.1 The Extraction Tab; 6 Customized Bandpass Filters; 6.1 Custom Filter Specification; 6.2 Partial Extractions of FTZs; 6.3 Inexact Extractions; 6.4 Inexact Example; 7 Norton Transforms; 7.1 Norton Series Transform; 7.2 Removing a Transformer with the Series Norton; 7.3 Norton S. |
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Sommario/riassunto |
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S/Filter includes tools beyond direct synthesis, including a wide variety of both exact and approximate equivalent network transforms, methods for selecting the most desirable out of potentially thousands of synthesized alternatives, and a transform history record that simplifies design attempts requiring iteration. Very few software programs are based on direct synthesis, and the additional features of |
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S/Filter make it a uniquely effective tool for filter design. This resource presents a practical guide to using Genesys software for microwave and RF filter design and synthesis. The focus of the book is common filter design problems and how to use direct synthesis to solve those problems. This book covers the application of S/Filter features to solving important and common filter problems. Both lumped element and distributed filters are discussed, with extensions to dielectric and quartz crystal resonators. |
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2. |
Record Nr. |
UNINA9910552714203321 |
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Autore |
Chatterjee Chanchal |
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Titolo |
Adaptive Machine Learning Algorithms with Python : Solve Data Analytics and Machine Learning Problems on Edge Devices / / by Chanchal Chatterjee |
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Pubbl/distr/stampa |
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Berkeley, CA : , : Apress : , : Imprint : Apress, , 2022 |
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ISBN |
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Edizione |
[1st ed. 2022.] |
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Descrizione fisica |
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1 online resource (290 pages) |
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Disciplina |
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Soggetti |
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Machine learning |
Artificial intelligence |
Python (Computer program language) |
Machine Learning |
Artificial Intelligence |
Python |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Nota di bibliografia |
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Includes bibliographical references and index. |
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Nota di contenuto |
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Chapter 1. Introducing Data Representation Features -- Chapter 2. General Theories and Notations -- Chapter 3. Square Root and Inverse Square Root -- Chapter 4. First Principal Eigenvector -- Chapter 5. Principal and Minor Eigenvectors -- Chapter 6. Accelerated Computation eigenvectors -- Chapter 7. Generalized Eigenvectors -- Chapter 8. Real – World Applications Linear Algorithms. |
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Sommario/riassunto |
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Learn to use adaptive algorithms to solve real-world streaming data problems. This book covers a multitude of data processing challenges, ranging from the simple to the complex. At each step, you will gain insight into real-world use cases, find solutions, explore code used to solve these problems, and create new algorithms for your own use. Authors Chanchal Chatterjee and Vwani P. Roychowdhury begin by introducing a common framework for creating adaptive algorithms, and demonstrating how to use it to address various streaming data issues. Examples range from using matrix functions to solve machine learning and data analysis problems to more critical edge computation problems. They handle time-varying, non-stationary data with minimal compute, memory, latency, and bandwidth. Upon finishing this book, you will have a solid understanding of how to solve adaptive machine learning and data analytics problems and be able to derive new algorithms for your own use cases. You will also come away with solutions to high volume time-varying data with high dimensionality in a low compute, low latency environment. You will: Apply adaptive algorithms to practical applications and examples Understand the relevant data representation features and computational models for time-varying multi-dimensional data Derive adaptive algorithms for mean, median, covariance, eigenvectors (PCA) and generalized eigenvectors with experiments on real data Speed up your algorithms and put them to use on real-world stationary and non-stationary data Master the applications of adaptive algorithms on critical edge device computation applications. |
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