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Titlebook: Biomedical Signals and Systems; Joseph V. Tranquillo Book 2014 Springer Nature Switzerland AG 2014

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發(fā)表于 2025-3-21 17:29:12 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
期刊全稱(chēng)Biomedical Signals and Systems
影響因子2023Joseph V. Tranquillo
視頻videohttp://file.papertrans.cn/189/188100/188100.mp4
學(xué)科分類(lèi)Synthesis Lectures on Biomedical Engineering
圖書(shū)封面Titlebook: Biomedical Signals and Systems;  Joseph V. Tranquillo Book 2014 Springer Nature Switzerland AG 2014
影響因子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 exerc
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1930-0328 ry is introduced which will allow the reader to read more advanced literature and communicate with scientist and engineers. Homework and Matlab simulation exerc978-3-031-00531-2978-3-031-01659-2Series ISSN 1930-0328 Series E-ISSN 1930-0336
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Gender, Development and Social Changest always the intent is to extract some particular information. For example, we may be confronted with a time series that represents the electrocardiogram from a patient and we would like to know their heart rate. Doing so will involve finding the QRS complex and marking the peak. Then to get the he
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Meena Gopal,Orlanda Ruthven,Bina Fernandez0.3 we used a sinewave as one of the signals. It turns out that this is a very fruitful and important way to think about decomposing a signal–by comparing a complex signal to many different sinewaves (of different frequencies) we can identify the frequency content of the complex signal. This is part
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Biomedical Signals and Systems978-3-031-01659-2Series ISSN 1930-0328 Series E-ISSN 1930-0336
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Synthesis Lectures on Biomedical Engineeringhttp://image.papertrans.cn/b/image/188100.jpg
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https://doi.org/10.1007/978-3-663-02655-6In previous chapters we discussed mathematical models of systems. But often a system is composed of many subsystems with several signals that allow those subsystems to interact. Looking at the many differential equations (or even the algebraic equations in the s-domain) often tells you little about how the system is put together.
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