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    Mimicking biology to improve sensory system designs and signal processing algorithms has thrived in the past and will continue to do so for decades to come. Technological advances have generally followed Moore’s Law (capability doubling every 2 years or so) since the 1960’s while our understanding of biological sensory systems is also rapidly advancing. These trends fuel the fertile grounds of bio-inspired sensory systems, a topic that is inherently multidisciplinary. This book is intended to be used as the primary text for a technical elective course in an undergraduate or graduate electrical engineering curriculum, but it could certainly be used for related purposes as well. Available student materials for such a course have previously been limited to biology sensory system texts, robotic application systems, collections of papers from numerous authors, current technical publications, and related material that have been useful but awkward as student study material due to the complexity of biology and the vast array of technical applications. There has not been a book or summary of study materials available that systematically covers natural photo-, mechano-, and chemo-sensory systems across the animal kingdom and also summarizes various novel engineering ideas that glean ideas from these natural sensory systems.

    For a one-semester course it is expected that the instructor of such a course would search the literature for recent sensory system designs inspired by biology such as examples already covered in this text . It is recommended that special assignments be given to students in the course to review the literature for relevant papers (or be given a set of papers to choose from) and present their findings to the class. A suggested assignment is given in Appendix A. It is also recommended that the instructor include the following free texts for more in-depth coverage of specific linear systems theory and image processing concepts when applicable (for example, when discussing a particular current application):

    Ulaby, F. and Yagle, A, Signals and Systems: Theory and Applications, Michigan Publishing, ISBN 978-1-60785-487-6, 2018. Available at

    Yagle, A. and Ulaby, F., Image Processing for Engineers, Michigan Publishing, ISBN 978-1-60785-489-0, 2018. Available at

    The following text is referenced frequently and is recommended for a much more thorough study of the structure and function of natural sensory systems. Breaking this topic into photo-, mechano- and chemo-sensory systems is inspired by the organization of this book:

    Smith, C. U. M., Biology of Sensory Systems, 2/e, John Wiley and Sons, ISBN: 978-0-470-51862-5, 2008.

    Since solutions manuals to textbooks are so readily available, it makes more sense to work example problems in this text and provide similar exercise problems (with only the answers provided) to assess the skill required to solve the problems. There are also sets of questions for the student to check their comprehension of the material covered. Most of these questions are directly answered in the text so an answer key is not provided separately.

    Specific problems are introduced and worked that are intended to reinforce specific concepts covered. There are many various other problems that could be introduced, but the goal is to keep within the scope of a one-semester course. The following is a partial list of the problem types and why they were chosen for this text:

    2D convolution problems show how image filtering is a 2D extension of 1D convolution covered in a standard linear systems course.

    Space constant problem shows significant attenuation of ionic signal as it travels down the axon.

    Neuronal circuit model problems emphasize that many times neuronal electronic signals are due to the movement of ions and not electrons and holes. In biology ions must be replenished; thus, the circuit includes dependent sources that model microbiological structures called ion pumps.

    Motion detection problems show that delayed responses of adjacent neurons are needed for most basic motion detection queues.

    Center-surround opponent processing problems show how three cone types with broadly overlapping responses across the visible electromagnetic spectrum can be combined to uniquely identify very specific colors.

    Wavelet analysis and synthesis problem demonstrate that broadly overlapping filters can be used to encode detailed signals and conserve signal energy, which is very important to biology.

    Auditory neuron response problem is another example of how broadly overlapping responses of adjacent neurons can be used to extract specific tonal information from incoming sound source.

    The author is grateful for a one-semester professional development leave (PDL) assignment in 2020 that made possible the completion of this project. Randy Hanna, Dean of Florida State University Panama City (FSU PC), is appreciated for his willingness to inaugurate PDL opportunities for dedicated teaching faculty on our campus. Shaun Saxon, FSU PC Librarian, and Laura Miller, FSU Open Publishing Librarian, were invaluable for the help and advice for making this available in the most practical sense. Betul Adalier is very much appreciated for sharing her design talent in creating the front and back cover.

    This book is provided for free in accordance with the Creative Commons license stated earlier. It is requested that you let us know how you plan to use the book and to let us know how we can make it better. The author may be contacted directly for comments and feedback at The book may be downloaded from the FSU Libraries at

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