Biomedical signals and systems /
Biomedical Signals and Systems is meant to accompany a one-semester undergraduate signals and systems course. It may also serve as a quick-start for graduate students or faculty interested in how signals and systems techniques can be applied to living systems. The biological nature of the examples a...
| Main Author: | |
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| Corporate Author: | |
| Format: | eBook |
| Language: | English |
| Published: |
[San Rafael, Calif.] :
Morgan & Claypool Publishers,
[2014]
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| Series: | Synthesis lectures on biomedical engineering ;
#52. |
| Subjects: | |
| Online Access: | View fulltext via EzAccess |
Table of Contents:
- 1. Introduction
- 1.1 What is a system?
- 1.1.1 Cause and effect
- 1.1.2 The systems of engineering
- 1.2 What is a signal?
- 1.2.1 Signals in engineering
- 1.2.2 Sensors
- 1.3 System boundaries
- 1.4 Design using signals and systems
- 2. System types
- 2.1 Introduction
- 2.2 conservative and non-conservative systems
- 2.3 Open and closed systems
- 2.4 Static and dynamic systems
- 2.5 Continuous and discrete signals and systems
- 2.6 Stable and unstable systems
- 2.7 Time varying and time invariant systems
- 2.8 Deterministic and non-deterministic systems
- 2.9 Finite and infinite systems
- 2.10 Linear and non-linear systems
- 2.11 Stationary and non-stationary
- 2.12 Memory and memoriless systems
- 2.13 Time constants
- 2.14 Conclusion
- 2.15 Exercises
- 3. System models
- 3.1 What is a model
- 3.2 Models using conservation
- 3.2.1 Conservation of momentum
- 3.2.2 Conservation of charge
- 3.2.3 Conservation of mass
- 3.2.4 Fluid mass and volume
- 3.2.5 Conservation of energy
- 3.2.6 Other models
- 3.3 State and compartment models
- 3.3.1 Volume balance
- 3.3.2 Models of ion channels
- 3.4 Reduction of a higher order equation
- 3.5 Exercises
- 4. Laplace transform
- 4.1 Introduction
- 4.2 Formal definitions
- 4.2.1 Laplace transform
- 4.2.2 Inverse Laplace transform
- 4.3 Transform tables
- 4.4 Four useful Laplace transforms
- 4.4.1 The impulse
- 4.4.The unit step
- 4.4.3 The sinusoid
- 4.4.4 The derivative
- 4.5 From differential to algebraic equations
- 4.6 From algebraic equations to a solution
- 4.7 Other interesting applications
- 4.7.1 The Fourier transform
- 4.7.2 Non-time mapping
- 4.8 The z-transform
- 4.9 Exercises
- 6. Stability
- 6.1 Introduction
- 6.2 Stability and transfer function poles
- 6.2.1 Finding poles and zeros
- 6.2.2 Visualizing poles and zeros
- 6.2.3 Relationship to stability in time
- 6.3 The role of zeros
- 6.4 Designing systems
- 6.5 Matlab and stability
- 6.6 Exercises
- 7. Feedback
- 7.1 Open and closed loop systems
- 7.2 Feedback transfer functions
- 7.3 Block diagram reductions
- 7.4 Stability and feedback
- 7.5 Feedforward
- 7.6 Opening the loop
- 7.7 Matlab and feedback
- 7.8 Exercises
- 8. System response
- 8.1 Zero input and zero state response
- 8.2 The impulse response
- 8.2.1 A first order example
- 8.2.2 A different first order example
- 8.2.3 A second order example
- 8.3 The step response
- 8.3.1 The importance of the step response
- 8.3.2 Comparing the step and impulse responses
- 8.4 Quantifying a response
- 8.4.1 Estimating a transfer function
- 8.4.2 A generic second order system
- 8.5 The sine response
- 8.5.1 decibels
- 8.5.2 The Bode plot
- 8.5.3 The 3dB point
- 8.6 Response to an arbitrary input
- 8.6.1 Convolution
- 8.6.2 Deconvolution
- 8.7 Other applications
- 8.7.1 Other useful test signals
- 8.8 Matlab and system responses
- 8.9 Exercises
- 9. Control
- 9.1 The generic control model
- 9.2 Evaluating a controlled response
- 9.2.1 Time domain evaluation
- 9.2.2 Frequency domain evaluation
- 9.3 On-off controllers
- 9.4 PID controllers
- 9.4.1 Proportional (P) control
- 9.4.2 Proportional derivative (PD) controller
- 9.4.3 Proportional integral (PI) controller
- 9.4.4 Proportional integral derivative (PID) controller
- 9.4.5 Choosing constants
- 9.4.6 Alternative formulation
- 9.5 Example of a PID controlled system
- 9.6 The problem of system delays
- 9.7 Other controllers
- 9.7.1 Lag-lead controllers
- 9.8 Reverse engineering biological systems
- 9.9 Matlab
- 9.10 Exercises
- 10. Time domain analysis
- 10.1 Basic signal processing
- 10.1.1 Average
- 10.1.2 Signal power
- 10.1.3 Variance and standard deviation
- 10.1.4 Signal to noise ratio
- 10.2 Correlations
- 10.2.1 Cross-correlation
- 10.2.2 Cross covariance
- 10.2.3 Auto correlation
- 10.3 Matlab
- 10.4 Exercises
- 11. Frequency domain analysis
- 11.1 Comparing a signal to sinusoids
- 11.1.1 Properties of sinusoids
- 11.1.2 A problem with the cross-correlation
- 11.2 The Fourier series
- 11.3 The Fourier transform
- 11.3.1 Power at a frequency
- 11.3.2 Fourier transform properties
- 11.3.3 The rectangle function
- 11.3.4 Inverse Fourier transform
- 11.4 The discrete Fourier transform
- 11.4.1 Aliasing and the Nyquist rate
- 11.4.2 The Nyquist rate and aliasing
- 11.5 Matlab
- 11.6 Exercises
- 12. Filters
- 12.1 Ideal filters
- 12.1.1 Ideal filter phase shift
- 12.1.2 The chirp signal
- 12.2 Filters in reality
- 12.2.1 Roll-off
- 12.2.2 Ripples
- 12.2.3 Phase shifts
- 12.3 First and second order filters
- 12.3.1 A first order filter
- 12.3.2 A second order filter
- 12.4 Higher order filters
- 12.4.1 Butterworth
- 12.4.2 Chebyshev
- 12.4.3 Elliptical
- 12.4.4 Bessel
- 12.4.5 Filter evaluation
- 12.4.6 High, bandpass and notch filter
- 12.4.7 Electrical implementation
- 12.5 Windowing in the time domain
- 12.6 Matlab
- 12.7 Exercises
- A. Complex numbers
- A.1 Introduction
- A.2 The complex plane
- A.3 Euler's identity
- A.4 Mathematical operations
- A.4.1 Addition and subtraction
- A.4.2 Multiplication
- A.4.3 Conjugation
- B. Partial fraction expansion
- C. Laplace transform table
- D. Fourier transform table
- Author's biography.