Biomimetic<\/strong><\/a> implies the mimicry of biology; note the word mime embedded in the term. In recent years biomimicry seems to be used more and more for sensory systems applications (our interest), while biomimetics implies molecular-level mimicry. This text is focused on biologically-inspired paradigms used for sensory systems and the signal processing that goes with such systems. The subject is sensory systems and not the research associated with mimicking organic chemistry, muscle tissue, etc. In this text cursory descriptions of biological phenomena will be followed by electronic sensor designs and the signal processing algorithms that emulating such phenomena for useful technological application. Bioprincipic is a similar term used recently implying the mimicry of biological principles.<\/p>\n Biometric<\/strong> <\/a>implies measuring biological features unique to an individual to determine the identity of the person. For example, authentication can be granted based on a pattern matching of a fingerprint, scanned iris image, or recorded voice pattern. This could be used for building and computer security purposes. Biomedical means the branch of medicine associated with survival in stressing environments. Bionic means enhancing normal biological capability with electronic or mechanical devices [Webster].<\/p>\n Bioinformatics<\/strong><\/a> is used to describe computer applications of extracting information about biological phenomena, primarily in the field of molecular biology. Bioinformatics is more formally defined as \u201c The collection, classification, storage, and analysis of biochemical and biological information using computers especially as applied to molecular genetics and genomics.\u201d [Webster].<\/p>\n Anatomy<\/strong><\/a> and Physiology<\/strong><\/a> imply structure and function, respectively. Scientists from many disciplines often organize their thoughts in similar ways. However, until there is a reason to communicate across disciplines, the terminology in each tends to develop into quite different terms. Table 1 is an observation of the separation of phenomena into physical and abstract categories. Genetic Algorithms refers to computational methods inspired by genetics. A genetic algorithm may consist of randomly choosing a solution to a problem or improving an existing solution based on an evaluation of fitness representing the problem solution. Improved solutions may be derived from fitness evaluation and genetic operators representing mutation and crossover.<\/p>\n Evolutionary Computation<\/strong><\/a> refers to the computational methods mimicking natural evolutionary forces.\u00a0Neural Networks<\/strong><\/a> is used to refer to networks of computational elements that process information in an analogous way to biological neuronal networks. Both natural and artificial neural networks perform a nonlinear transform on an aggregation of many weighted input signals. There are many artificial \u201cneural network\u201d paradigms (ANN\u2019s) that include many ideas ont found in biological neuronal networks, although the general concept has its original inspiration from biology.<\/p>\n","protected":false},"excerpt":{"rendered":"