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Biomedical Measurement Systems and Data Science [Kõva köide]

(University of Illinois, Urbana-Champaign)
  • Formaat: Hardback, 400 pages, kõrgus x laius x paksus: 250x173x23 mm, kaal: 910 g, Worked examples or Exercises
  • Ilmumisaeg: 17-Jun-2021
  • Kirjastus: Cambridge University Press
  • ISBN-10: 1107179068
  • ISBN-13: 9781107179066
Teised raamatud teemal:
Biomedical Measurement Systems and Data ScienceSuurem pilt
  • Formaat: Hardback, 400 pages, kõrgus x laius x paksus: 250x173x23 mm, kaal: 910 g, Worked examples or Exercises
  • Ilmumisaeg: 17-Jun-2021
  • Kirjastus: Cambridge University Press
  • ISBN-10: 1107179068
  • ISBN-13: 9781107179066
Teised raamatud teemal:
Püsilink: https://www.kriso.ee/db/9781107179066.html
Märksõnad:
Discover the fundamental principles of biomedical measurement design and performance evaluation with this hands-on guide. Whether you develop measurement instruments or use them in novel ways, this practical text will prepare you to be an effective generator and consumer of biomedical data. Designed for both classroom instruction and self-study, it explains how information is encoded into recorded data and can be extracted and displayed in an accessible manner. Describes and integrates experimental design, performance assessment, classification, and system modelling. Combines mathematical concepts with computational models, providing the tools needed to answer advanced biomedical questions. Includes MATLAB® scripts throughout to help readers model all types of biomedical systems, and contains numerous homework problems, with a solutions manual available online. This is an essential text for advanced undergraduate and graduate students in bioengineering, electrical and computer engineering, computer science, medical physics, and anyone preparing for a career in biomedical sciences and engineering.

Discover the core principles of biomedical measurement design and performance evaluation with this hands-on guide. With MATLAB® code, problems, and a solutions manual available online, it is an essential text for advanced undergraduate and graduate students, and practicing professionals in Bioengineering and Electrical and Computer Engineering.

Arvustused

'a good introductory text on the technical aspects of analyzing measured data sets for graduate students or advanced undergraduates in a premedical or related curriculum In an increasingly crowded publication space, this book offers something new and valuable, making implementation of data analysis approaches accessible for biomedical scientists and the engineers who work with them Highly recommended.' M. R. King, Choice Connect

Muu info

Discover the fundamental principles of biomedical measurement design and performance evaluation with this hands-on guide.
Preface xi
1 Introduction to Measurement Systems
1(6)
1.1 Information Flow
1(2)
1.2 Design of the Book
3(1)
1.3 Notation
4(3)
2 Probability
7(8)
2.1 Introduction
7(2)
2.2 Probability Axioms
9(1)
2.3 Consequences of the Axioms
10(1)
2.4 Conditional Probability
11(1)
2.5 Bayes' Formula
12(1)
2.6 Statistically Independent Events
13(1)
2.7 Problems
14(1)
3 Statistics of Random Processes
15(50)
3.1 Univariate Continuous Random Variables
16(4)
3.2 Discrete Random Variables
20(4)
3.3 Jointly Distributed Random Variables
24(2)
3.4 Data Statistics
26(8)
3.5 Second-Order Statistics: Matrix Forms
34(5)
3.6 Stationary Random Processes
39(1)
3.7 Continuous-Time Covariance and Correlation
40(1)
3.8 Ergodic Processes
41(2)
3.9 Multivariate Normal Density
43(2)
3.10 Mixture Densities
45(1)
3.11 Functions of Random Variables
46(4)
3.12 Regression Analysis
50(5)
3.13 Central Limit Theorem
55(1)
3.14 Information
56(5)
3.15 Problems
61(4)
4 Spatiotemporal Models of the Measurement Process
65(27)
4.1 Introduction
65(1)
4.2 Measurement Equations
65(7)
4.3 Signal Modeling Tools
72(9)
4.4 An Ultrasonic Measurement
81(3)
4.5 1-D Convolution by Matrix Multiplication
84(1)
4.6 2-D Direct Numerical Convolution
85(2)
4.7 2-D Convolution by Matrix Multiplication
87(2)
4.8 Problems
89(3)
5 Basis Decomposition
92(57)
5.1 Introduction to Principal Components Analysis
92(5)
5.2 Data Spaces
97(4)
5.3 Orthonormal Basis
101(2)
5.4 Fourier Series
103(6)
5.5 Continuous-Time Fourier Transform
109(3)
5.6 Fourier Convolution Theorem
112(1)
5.7 Discrete-Time Fourier Transform
112(6)
5.8 Discrete Fourier Transform
118(4)
5.9 Fourier Analysis of 2-D LTI/LSI Measurement Systems
122(3)
5.10 Even-Odd Real-Imaginary Symmetries
125(1)
5.11 Short-Time Fourier Transform
126(1)
5.12 Power Spectral Density
127(3)
5.13 Passing Random Processes through Linear Systems
130(5)
5.14 Examples from Research
135(9)
5.15 Problems
144(5)
6 Basis Decomposition II
149(40)
6.1 Eigenanalysis with a Fourier Basis
150(1)
6.2 What Do Eigenstates Describe?
151(2)
6.3 Eigenanalysis Connections to DFT
153(7)
6.4 Measurements as Vector-Space Transformations
160(3)
6.5 Singular-Value Decomposition
163(8)
6.6 SVD Indicates Dimensionality of Motion
171(2)
6.7 Linear Shift-Varying Measurement Systems
173(2)
6.8 Compressed Sensing/Compressive Sampling Methods
175(5)
6.9 Analyzing Systems of Chemical Reactions
180(6)
6.10 Problems
186(3)
7 Projection Radiography
189(36)
7.1 Acquisition-Stage Processing: Modeling Photon Detection
189(5)
7.2 Contrast Transfer
194(6)
7.3 Noise Power Spectrum
200(2)
7.4 Spatial Resolution
202(9)
7.5 Relating Quality Measures to Task Performance
211(4)
7.6 Display-Stage Processing
215(3)
7.7 Problems
218(7)
8 Statistical Decision-Making
225(23)
8.1 Experimental Design
225(4)
8.2 Hypothesis Testing
229(8)
8.3 Receiver Operating Characteristic Analysis
237(5)
8.4 Other Performance Metrics
242(2)
8.5 Problems
244(4)
9 Statistical Pattern Recognition with Flow Cytometry Examples
248(23)
9.1 Simulations
250(3)
9.2 Covariance Diagonalization and Whitening Transformations
253(7)
9.3 Discriminant Analysis
260(6)
9.4 Clustering
266(2)
9.5 Problems
268(3)
10 ODE Models I: Biological Systems
271(40)
10.1 Mathematical Representations of Systems
272(7)
10.2 Linear Systems of Equations
279(1)
10.3 Differential Operators for Linear Systems
280(3)
10.4 Modeling Cell Growth
283(6)
10.5 Linearizing Equations via Taylor Series
289(2)
10.6 Nonlinear Continuous Models: Logistic Equation
291(3)
10.7 The Predator-Prey Problem
294(8)
10.8 Linear Stability, Model Limitations, and Generalizations
302(2)
10.9 Modeling Infectious Disease in Populations
304(4)
10.10 Problems
308(3)
11 ODE Models II: Sensors
311(12)
11.1 Second-Order Systems
311(2)
11.2 Sensor Impulse Response
313(7)
11.3 Laplace Transforms
320(3)
1 1.4 MATLAB Solutions via Symbolic Math
323(6)
11.5 Laplace Transform Approach to Solving ODEs
326(3)
1 1.6 Summary
329(3)
11.7 Problems
330(2)
Appendix A Review of Linear Algebra 332(26)
Appendix B Properties of Dirac Deltas 358(2)
Appendix C Signal Modulation 360(7)
Appendix D Fourier Transform Theorems and Special Functions 367(6)
Appendix E Constrained Optimization 373(3)
Appendix F One-Sided Laplace Transforms 376(2)
Appendix G Independent, Orthogonal, Uncorrelated 378(3)
Bibliography 381(6)
Index 387
Michael Insana is a Donald Biggar Willett Professor in Engineering at the University of Illinois at Urbana-Champaign. He is a Fellow of the AIMBE, the IEEE, and the IoP.