Introduction to Communications Principles
Introduction to Communications Principles ECE 303
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This 1 page Class Notes was uploaded by Sarina Wintheiser on Tuesday September 22, 2015. The Class Notes belongs to ECE 303 at Colorado State University taught by Staff in Fall. Since its upload, it has received 21 views. For similar materials see /class/210304/ece-303-colorado-state-university in ELECTRICAL AND COMPUTER ENGINEERING at Colorado State University.
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Date Created: 09/22/15
ECE 303 Introduction to Communication Principles IN Sequences and Series MATH 161 Understands convergence of sequences and series Knows series and sequence representations for expx Can evaluate finite and infinite geometric sums r Knows Binomial theorem Differentiation MATH 161 261 a Can compute partial and total derivatives Knows chain rule Knows and can apply Liebnitz mle Integration MATH 161 261 a Can integrate by parts Can compute 2D integrals Prereguisites a MATH261 Optional As of 12908 Co n cepts Games and counting formulas Polling and the hypergeometric distribution The probability space of outcomes and events Probability measure Conditional probability total probability and Bayes rule Random variables cdfs pmfs and pdfs Moments and their uses Mean and conditional mean as minimum mean squared error estimators Classical distributions and their origins Averaging and Chebyshev s inequality WLLN Histograms and the multinomial distribution Simulations Multiple random variables Transformation of random variables The linear system theory of probability 0 sums of IID random variables 0 convolution ofpdfs 0 product of characteristic functions Noise through linear systems Binary communication and error probability Applications Optics SolidState Engineering Reliability and OR Control and Robotics Communication Signal Image Processing and Computer Vision Radar and Remote Sensoring Biomedicine Finance Tools MATLAB Programs and Simulations 0 Experiments amp Probabilitv Spac s Can specify probability space and probability measure for an experiment Understands independence and conditional probability Can apply Bayes Rule Random Variables a Can compute pmf pdf cdf characteristic functions and moments Can compute pdffor functions of independent random variables Probabilitv Distributions a Understands Bernoulli binomial geometric and Pa al distributions from Bernoulli experiment Understands exponential and Erlang distributions from Poisson experiment an derive uniform Rayleigh chisquared Cauchy andbeta distributions from bivariate normal e erimen Can derive hypergeometric distribution for polls Can derive multinomial distribution for histograms Binarv Communication a Can use standard Gaussian tables Can compute error probability for binary communication Averaging g Can use Chebyshev s inequality to design averag to meet specifications Understands Weak Law of Large Numbers Simulations a Can simulate samples and construct histograms Can generate arbitrarily distributed random variables Correlation and Spectrum Anal is Can compute correlatim and spectrum for WSS ran om process including white noise through a linear time invarian s em
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