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. Table of Contents: Preface / Acknowledgments / Introduction / System Types / System Models / Laplace Transform / Block Diagrams / Stability / Feedback / System Response / Control / Time Domain Analysis / Frequency Domain Analysis / Filters / Author's Biography
Quantitative Neurophysiology is supplementary text for a junior or senior level course in neuroengineering. It may also serve as an quick-start for graduate students in engineering, physics or neuroscience as well as for faculty interested in becoming familiar with the basics of quantitative neuroscience. The first chapter is a review of the structure of the neuron and anatomy of the brain. Chapters 2-6 derive the theory of active and passive membranes, electrical propagation in axons and dendrites and the dynamics of the synapse. Chapter 7 is an introduction to modeling networks of neurons and artificial neural networks. Chapter 8 and 9 address the recording and decoding of extracellular potentials. The final chapter has descriptions of a number of more advanced or new topics in neuroengineering. Throughout the text, vocabulary is introduced which will enable students to read more advanced literature and communicate with other scientists and engineers working in the neurosciences. Numerical methods are outlined so students with programming knowledge can implement the models presented in the text. Analogies are used to clarify topics and reinforce key concepts. Finally, homework and simulation problems are available at the end of each chapter. Table of Contents: Preface / Neural Anatomy / Passive Membranes / Active Membranes / Propagation / Neural Branches / Synapses / Networks of Neurons / Extracellular Recording and Stimulation / The Neural Code / Applications / Biography / Index
In recent years, the life sciences have embraced simulation as an important tool in biomedical research. Engineers are also using simulation as a powerful step in the design process. In both arenas, Matlab has become the gold standard. It is easy to learn, flexible, and has a large and growing userbase. MATLAB for Engineering and the Life Sciences is a self-guided tour of the basic functionality of MATLAB along with the functions that are most commonly used in biomedical engineering and other life sciences. Although the text is written for undergraduates, graduate students and academics, those in industry may also find value in learning MATLAB through biologically inspired examples. For instructors, the book is intended to take the emphasis off of learning syntax so that the course can focus more on algorithmic thinking. Although it is not assumed that the reader has taken differential equations or a linear algebra class, there are short introductions to many of these concepts. Following a short history of computing, the MATLAB environment is introduced. Next, vectors and matrices are discussed, followed by matrix-vector operations. The core programming elements of MATLAB are introduced in three successive chapters on scripts, loops, and conditional logic. The last three chapters outline how to manage the input and output of data, create professional quality graphics and find and use Matlab toolboxes. Throughout, biomedical examples are used to illustrate MATLAB's capabilities. Table of Contents: Introduction / Matlab Programming Environment / Vectors / Matrices / Matrix -- Vector Operations / Scripts and Functions / Loops / Conditional Logic / Data In, Data Out / Graphics / Toolboxes
Thank you for visiting our website. Would you like to provide feedback on how we could improve your experience?
This site does not use any third party cookies with one exception — it uses cookies from Google to deliver its services and to analyze traffic.Learn More.