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...

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Bibliographic Details
Main Author: Tranquillo, Joseph Vincent, 1975- (Author)
Corporate Author: Bucknell University.
Format: eBook
Language:English
Published: [San Rafael, Calif.] : Morgan & Claypool Publishers, [2014]
Series:Synthesis lectures on biomedical engineering ; #52.
Subjects:
Online Access:View fulltext via EzAccess
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100 1 # |a Tranquillo, Joseph Vincent,  |d 1975-  |e author. 
245 1 0 |a Biomedical signals and systems /  |c Joseph V. Tranquillo. 
264 # 1 |a [San Rafael, Calif.] :  |b Morgan & Claypool Publishers,  |c [2014] 
264 # 4 |c Ã2014 
300 # # |a 1 online resource (xxi, 211 pages) :  |b illustrations (some color). 
336 # # |a text  |b txt  |2 rdacontent 
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490 1 # |a Synthesis lectures on biomedical engineering,  |v #52  |x 1930-0336 ; 
500 # # |a Author-affiliated organization: Bucknell University. 
504 # # |a Includes bibliographical references (page xviii). 
505 0 # |a 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 -- 
505 8 # |a 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 -- 
505 8 # |a 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 -- 
505 8 # |a 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 -- 
505 8 # |a 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 -- 
505 8 # |a 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 -- 
505 8 # |a 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 -- 
505 8 # |a 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 -- 
505 8 # |a 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 -- 
505 8 # |a 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 -- 
505 8 # |a 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 -- 
505 8 # |a 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. 
520 3 # |a 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 allows for systems thinking to be applied to electrical, mechanical, fluid, chemical, thermal and even optical systems. Each chapter focuses on a topic from classic signals and systems theory: System block diagrams, mathematical models, transforms, stability, feedback, system response, control, time and frequency analysis and filters. Embedded within each chapter are examples from the biological world, ranging from medical devices to cell and molecular biology. While the focus of the book is on the theory of analog signals and systems, many chapters also introduce the corresponding topics in the digital realm. Although some derivations appear, the focus is on the concepts and how to apply them. Throughout the text, systems vocabulary is introduced which will allow the reader to read more advanced literature and communicate with scientist and engineers. Homework and Matlab simulation exercises are presented at the end of each chapter and challenge readers to not only perform calculations and simulations but also to recognize the real-world signals and systems around them. 
588 # # |a Description based on online resource; title from PDF title page (Morgan & Claypool, viewed December 17, 2013). 
650 # 0 |a Biological systems  |x Mathematical models. 
650 # 0 |a System analysis. 
650 # 0 |a Signal processing  |x Mathematics. 
650 # 0 |a Biomedical engineering. 
650 1 2 |a Systems Analysis. 
650 2 2 |a Algorithms. 
650 2 2 |a Biomedical Engineering. 
650 2 2 |a Models, Theoretical. 
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650 # 7 |a SCIENCE / Life Sciences / Biology  |2 bisacsh 
650 # 7 |a SCIENCE / Life Sciences / General  |2 bisacsh 
653 # # |a Biological systems  |a Biological signals  |a System response  |a Stability  |a Feedback  |a Medical devices  |a PID control  |a Laplace transform  |a Correlations  |a Convolution  |a Signal processing  |a Filters 
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655 # 0 |a Electronic books. 
710 2 # |a Bucknell University. 
776 0 8 |i Print version:  |z 9781627053310 
830 # 0 |a Synthesis lectures on biomedical engineering ;  |v #52.  |x 1930-0328 
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