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An introduction to the world of electrical and optical sensors with applications in bioengineering, environmental remote sensing fields and many more. The topics cover description of sensor performance, temperature sensors, light sensors, force sensors, displacement sensors, motion sensors, environmental sensors, sound sensors, chemical sensors, sensor interfacing, instrumentation techniques and switching mechanisms. The lab component of this course introduced sensors control using NI LabView software and NI ELVIS hardware

An introduction to the world of electrical and optical sensors with applications in bioengineering, environmental remote sensing fields and many more. The topics cover description of sensor performance, temperature sensors, light sensors, force sensors, displacement sensors, motion sensors, environmental sensors, sound sensors, chemical sensors, sensor interfacing, instrumentation techniques and switching mechanisms. The lab component of this course introduced sensors control using NI LabView software and NI ELVIS hardware

Topics for the course include sample spaces and probabilities, discrete distributions (Binomial, Negative Binomial, Geometric, Hypergeometric, Poisson, and Gamma), continuous distributions (Uniform, Normal, Chi-squared), expectation and variance, hypothesis testing, interval estimation and confidence intervals. There will be extensive use of MS Excel and R, a statistical software program. At the end of the course, students should be able make meaningful connections between statistics and other areas of study, including and social sciences.

Topics for the course include sample spaces and probabilities, discrete distributions (Binomial, Negative Binomial, Geometric, Hypergeometric, Poisson, and Gamma), continuous distributions (Uniform, Normal, Chi-squared), expectation and variance, hypothesis testing, interval estimation and confidence intervals. There will be extensive use of MS Excel and R, a statistical software program. At the end of the course, students should be able make meaningful connections between statistics and other areas of study, including and social sciences.