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140122 ||| eng |
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|a 9781461542995
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| 100 |
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|a Devasahayam, Suresh R.
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| 245 |
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|a Signals and Systems in Biomedical Engineering
|h Elektronische Ressource
|b Signal Processing and Physiological Systems Modeling
|c by Suresh R. Devasahayam
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| 250 |
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|a 1st ed. 2000
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| 260 |
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|a New York, NY
|b Springer US
|c 2000, 2000
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| 300 |
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|a XVI, 333 p
|b online resource
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| 505 |
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|a 10.4. A Model for the Strength-Duration Curve -- Exercises -- Programming Exercise -- 11. Modeling Skeletal Muscle Contraction -- 11.1. Skeletal Muscle Contraction -- 11.2. Properties of Skeletal Muscle -- 11.3. The Cross-Bridge Theory of Muscle Contraction -- 11.4. A Linear Model of Muscle Contraction -- 11.5. Applications of Skeletal Muscle Modeling -- Exercises -- Programming Exercise -- 12. Modeling Myoelectric Activity -- 12.1. Electromyography -- 12.2. A Model of The Electromyogram -- Exercises -- Programming Exercise -- 13. System Identification in Physiology -- 13.1. Black Box Modeling of Physiological Systems -- 13.2. Sensory Receptors -- 13.3. Pupil Control System -- 13.4. Applications of System Identification in Physiology -- Exercises -- 14. Modeling the Cardiovascular System -- 14.1. The Circulatory System -- 14.2. Other Applications of Cardiovascular Modeling -- Exercises -- 15. A Model of the Immune Response to Disease -- 15.1. Behavior of the Immune System --
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|a 1. Introduction to Systems Analysis and Numerical Methods -- 1.1. The Systems Approach to Physiological Analysis -- 1.2. Numerical Methods for Data Analysis and Simulation -- 1.3. Examples of Physiological Models -- Exercises -- 2. Continuous Time Signals and Systems -- 2.1. Physiological Measurement and Analysis -- 2.2. Time Signals -- 2.3. Input - Output systems -- Exercises -- 3. Fourier Analysis for Continuous Time Processes -- 3.1. Decomposition of Periodic Signals -- 3.2. Fourier Conversions -- 3.3. System Transfer Function -- 3.4. Systems Representation of Physiological Processes -- Exercises -- 4. Discrete Time Signals and Systems -- 4.1. Discretization of Continuous-Time Signals -- 4.2. Discrete-Time Signals -- 4.3. Discrete-Time Systems -- 4.4. Random Signals -- Exercises -- Programming Exercise -- 5. Fourier Analysis for Discrete-Time Processes -- 5.1. Discrete Fourier Conversions -- 5.2. Applying the Discrete Fourier Transform -- 5.3. The Z-Transform --
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|a 5.4. Discrete Fourier Transform of Random Signals -- Exercises -- Programming Exercises -- 6. Time-Frequency and Wavelet Analysis -- 6.1. Time-Varying Processes -- 6.2. The Short Time Fourier Transform -- 6.3. Wavelet Decomposition of Signals -- 6.4. The Wavelet Transform -- 6.5. Comparison of Fourier and Wavelet Transforms -- Exercises -- 7. Estimation of Signals in Noise -- 7.1. Noise Reduction by Filtering -- 7.2. Time Series Analysis -- Exercises -- 8. Feedback Systems -- 8.1. Physiological Systems With Feedback -- 8.2. Analysis of Feedback Systems -- 8.3. Digital Control in Feedback Systems -- Exercises -- 9. Model Based Analysis of Physiological Signals -- 9.1. Modeling Physiological Systems -- 9.2. Model Based Noise Reduction and Feature Extraction -- Exercises -- 10. Modeling the Nerve Action Potential -- 10.1. Electrical Behavior of Excitable Tissue -- 10.2. The Voltage Clamp Experiment -- 10.3. Interpreting the Voltage-Clamp Experimental Data --
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|a 15.2. Linearized Model of the Immune Response -- Exercises
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| 653 |
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|a Biomedical engineering
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| 653 |
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|a Electrical and Electronic Engineering
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| 653 |
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|a Electrical engineering
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| 653 |
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|a Biomedical Engineering and Bioengineering
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| 653 |
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|a Signal, Speech and Image Processing
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| 653 |
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|a Signal processing
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| 653 |
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|a Biophysics
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| 041 |
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7 |
|a eng
|2 ISO 639-2
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| 989 |
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|b SBA
|a Springer Book Archives -2004
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| 490 |
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|a Topics in Biomedical Engineering
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| 028 |
5 |
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|a 10.1007/978-1-4615-4299-5
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| 856 |
4 |
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|u https://doi.org/10.1007/978-1-4615-4299-5?nosfx=y
|x Verlag
|3 Volltext
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| 082 |
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|a 610.28
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| 520 |
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|a In the past few years Biomedical Engineering has received a great deal of attention as one of the emerging technologies in the last decade and for years to come, as witnessed by the many books, conferences, and their proceedings. Media attention, due to the applications-oriented advances in Biomedical Engineering, has also increased. Much of the excitement comes from the fact that technology is rapidly changing and new technological adventures become available and feasible every day. For many years the physical sciences contributed to medicine in the form of expertise in radiology and slow but steady contributions to other more diverse fields, such as computers in surgery and diagnosis, neurology, cardiology, vision and visual prosthesis, audition and hearing aids, artificial limbs, biomechanics, and biomaterials. The list goes on. It is therefore hard for a person unfamiliar with a subject to separate the substance from the hype. Many of the applications of Biomedical Engineering are rather complex and difficult to understand even by the not so novice in the field. Much of the hardware and software tools available are either too simplistic to be useful or too complicated to be understood and applied. In addition, the lack of a common language between engineers and computer scientists and their counterparts in the medical profession, sometimes becomes a barrier to progress
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