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121227s1978 xxu| s |||| 0|eng d |
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|a 9781461339700
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|a 10.1007/978-1-4613-3970-0
|2 doi
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|a (DE-He213)978-1-4613-3970-0
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|a E-Book
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|a Marmarelis, Vasilis.
|e author.
|4 aut
|4 http://id.loc.gov/vocabulary/relators/aut
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| 245 |
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|a Analysis of Physiological Systems
|h [electronic resource] :
|b The White-Noise Approach /
|c by Vasilis Marmarelis.
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| 250 |
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|a 1st ed. 1978.
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| 264 |
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1 |
|a New York, NY :
|b Springer US :
|b Imprint: Springer,
|c 1978.
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| 300 |
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|a XVI, 488 p. 75 illus.
|b online resource.
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|a text
|b txt
|2 rdacontent
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|2 rdamedia
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|a online resource
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| 347 |
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|a text file
|b PDF
|2 rda
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| 490 |
1 |
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|a Computers in Biology and Medicine
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| 490 |
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|a Springer eBook Collection
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| 505 |
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|a 1. The Problem of System Identification in Physiology -- 1.1. The Problem of Systems Analysis in Physiology -- 1.2. Functional and Structural Identification of Physiological Systems -- 1.3. “Black Box” vs. Parameter Identification in Physiological Systems -- 2. Analysis of Physiological Signals -- 2.1. Physiological Systems Data: Deterministic and Stochastic Descriptions -- 2.2. Some Statistical Tools and Concepts -- 2.3. Autocorrelation and Crosscorrelation Functions -- 2.4. Frequency Domain Description of Signals -- 2.5. Certain Properties of Gaussian Signals -- 2.6. Sampling Considerations -- 2.7. Statistical Estimation from Physiological Signals -- 2.8. Filtering of Physiological Signals -- 2.9. Considerations in Computing Power Spectra -- 3. Traditional Approaches to Physiological System Identification -- 3.1. Stimulus-Response Relations in Linear Systems -- 3.2. Transfer Functions and Bode Plots -- 3.3. Transfer Functions from Stimulus-Response Spectra -- 3.4. Coherence Function -- 3.5. Mufti-Input Linear Systems -- 3.6. Nonlinear Systems: Identification Using “Describing Functions”. -- 3.7. Effects of Feedback in Physiological Systems -- 3.8. Feedback Analysis in a Neurosensory System -- 4. The White-Noise Method in System Identification -- 4.1. Linear and Nonlinear Systems-The Volterra Series -- 4.2. The Wiener Theory -- 4.3. Schemes for the Estimation of the System Kernels -- 4.4. Multi-Input, Multi-Output Systems -- 4.5. Other Formulations of the White-Noise Approach -- 5. Applicability of the White-Noise Method and the Use of Quasiwhite Test Signals -- 5.1. The Band-Limited Gaussian White Noise -- 5.2. The Pseudorandom Signals Based on m Sequences -- 5.3. The Constant-Switching-Pace Symmetric Random Signals -- 5.4. Comparative Study of the Use of GWN, PRS, and CSRS in System Identification -- 5.5. Validation of Generated Quasiwhite Test Signals -- 6. Methods of Computation of System Kernels -- 6.1. Computational Considerations for Kernel Measurement -- 6.2. Time-Domain Approaches to Kernel Computation -- 6.3. Frequency-Domain Approach: Use of the Fast Fourier Transform Algorithm -- 6.4. Special Cases of Kernel Computation -- 6.5. Analog (Hybrid) Methods for the Computation of Kernels -- 6.6. Evaluation of the System Kernels -- 6.7. Evaluation of Results of Experiment -- 7. Errors in the Estimation of System Kernels -- 7.1. Estimation Errors Using GWN Stimulus -- 7.2. Estimation Errors Using PRS Stimuli -- 7.3. Estimation Errors Using CSRS Stimuli -- 7.4. Errors Due to the Presence of Contaminating Noise -- 8. Tests and Analyses Preliminary to Identification Experiment -- 8.1. Determination of the System Input and Output and Region of Operation -- 8.2. Examination of System Stationarity and Noise Conditions -- 8.3. Removal of Drifts in the Response Data -- 8.4. The Measurement of System Memory and Bandwidth -- 8.5. Measurement of Extent of System Nonlinearity -- 8.6. Recording and Digitalization of Stimulus-Response Data -- 8.7. Choice of GWN Bandwidth and Record Length -- 8.8. Optimal Choice of CSRS Step and Record Length -- 9. Peeking into the Black Box -- 9.1. Analysis of Cascades in Physiological Systems -- 9.2. Zero-Memory Systems -- 9.3. Combinations of Systems -- 10. Applications of the White-Noise Method to Neural Systems -- 10.1. Practical Considerations in Application of the White-Noise Method to Neural Systems -- 10.2. Identification of One-Input Neural Systems Using GWN Stimulus -- 10.3. Identification of Two-Input Neural Systems Using GWN Stimulus -- 10.4. Identification of One-Input Neural System Using Pseudorandom Binary Stimulus -- 10.5. Identification of One-Input Neural System Using CSRS Stimulus -- 10.6. Applications of Alternate Identification Techniques to Neural Systems with Discrete Input or Output -- 11. Physiological Systems Requiring Special Treatment -- 11.1. Physiological Systems with Point Process Inputs and Outputs -- 11.2. Systems with Spatiotemporal Inputs -- 11.3. Nonstationary Systems -- 11.4. Systems with Nonwhite Random Inputs -- 12. Dialogue for Epilogue -- References -- Related Literature.
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| 520 |
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|a In studying physiological systems bioscientists are continually faced with the problem of providing descriptions of cause-effect relationships. This task is usually carried out through the performance of stimulus-response experiments. In the past, the design of such experiments has been ad hoc, incomplete, and certainly inefficient. Worse yet, bioscientists have failed to take advantage of advances in fields directly related to their problems (specifically, advances in the area of systems analysis). The raison d'etre of this book is to rectify this deficiency by providing the physiologist with methodological tools that will be useful to him or her in everyday labora tory encounters with physiological systems. The book was written so that it would be practical, useful, and up-to date. With this in mind, parts of it give step-by-step descriptions of in the laboratory. It is hoped that this systematic procedures to be followed will increase the usefulness of the book to the average research physiologist and, perhaps, reduce the need for in-depth knowledge of some of the associated mathematics. Even though the material deals with state-of-the art techniques in systems and signal analysis, the mathematical level has been kept low so as to be comprehensible to the average physiologist with no extensive training in mathematics. To this end, mathematical rigor is often sacrificed readily to intuitive simple arguments.
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| 590 |
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|a Loaded electronically.
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| 590 |
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|a Electronic access restricted to members of the Holy Cross Community.
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| 650 |
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|a Neurosciences.
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| 690 |
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|a Electronic resources (E-books)
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| 710 |
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|a SpringerLink (Online service)
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| 773 |
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|t Springer eBooks
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| 830 |
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|a Computers in Biology and Medicine
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| 830 |
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|a Springer eBook Collection.
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| 856 |
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|u https://holycross.idm.oclc.org/login?auth=cas&url=https://doi.org/10.1007/978-1-4613-3970-0
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