Electric Circuits Class (Handout 1)

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- Maximum Likelihood Estimation: Part II - MLE for transformed parameters - Invariance property of MLE and its proof - Extension to vector parameter - Examples: ML estimation of power of Gaussian noise in dB - ML estimation of tangent of sinusoidal phase - Joint ML estimation of mean and variance of Gaussian random variables - Equivalency between MLE and Efficient estimator in linear Gaussian model - Joint estimation of amplitude and phase of sinusoidal wave in Gaussian noise - Joint estimation of amplitude, phase and frequency of sinusoidal wave in Gaussian noise

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Using circuit theories, analysis tools, and simplification methods presented in this course, the student is required to write a computer program that can do the following: 1. Accept the elements and connections 2. Compute the required circuit response (Current, Voltage, Power, …) at any arbitrary location specified by the program user. The computer program is required to be completed without any call or use of any circuit simulation tool (e.g. PSpice).

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