EE263 HOMEWORK SOLUTIONS

We consider an integrated circuit IC that contains N cells or modules that are connected by K wires. The path gain from transmitter j to receiver i is Gij which are all nonnegative, and Gii are positive. Explain how you know that there is no equalizer that rejects more disturbance patterns than yours does. We assume that measurement spots do not coincide with the source locations, i. If you need to assume that one or more matrices that arise in your solution are invertible, full rank, etc. The prediction error depends on the time-series data, and also A, the parameter in our model. Since you are generating the data randomly, it is remotely possible that the second method will work better than the first, at least for one run.

Note that there is no requirement on the vehicle velocity at the way-points. The roughness measure R is the sum of the squares of the differences of each element in the array and its neighbors. These matrices are close to each other, but not exactly the same. Pick the statement that is true, and justify it completely. You can probably imagine many practical applications where the ability to make a prediction of the next value of a time series or signal is very valuable. If your method works provided some condition holds such as some matrix being full rank , say so. The data is a set of frequency response measurements, with some measurement errors.

ee263 homework solutions

The iteration is stopped when or more accurately, if successive re263 are very close, i. The command conv convolves two vectors; the command hist plots a histogram of the amplitude distribution.

Ee263 homework 5 solutions

It is also possible to store a message at a node during any time period, presumably for transmission during a later period. In this data set the locations are arranged on a grid, with edges between adjacent locations except when there is an obstruction. Briefly justify your answer. Give a histogram plot of the amplitude distribution of both y and z.

  VANN WOODWARD THESIS

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Your job is to fill all details of the argument. Contact Us name Please enter your name. The sum of the squares of any three consecutive symbols cannot exceed 2: The model is called auto-regressive since it expresses the next signal value as a linear combination of the hhomework last values, plus a noise.

By examining the offsets for the different vehicles, suggest a vehicle you might want to have a maintenance crew check out. Also, plot the optimal forces for each of the vehicles as a function of time.

ee263 homework solutions

Express the population dynamics as an autonomous linear system with state x ti. Another approach is to represent complex matrices and vectors using real matrices and vectors of twice the dimensions, and then you apply what you already know about real linear algebra. Explain how to obtain the two extreme cases: Solktions must explain the math; you may not refer to any matlab notation or operators and especially, backslash in your explanation.

We are not looking for a complicated method e.

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Before we get to the questions, we define a matrix that may be useful. We say that a signal ed263 is piecewise-affine PWAwith kink points i1. Such a function is called a rational function of degree m.

To find the predictive ability of an AR model with coefficients a0b1.

Small R corresponds to smooth, or smoothly varying, U. For example, suppose that reaction R2 corresponds to the flow of metabolite M1 into the cell, with v2 giving the flow rate.

  FRODE GRYTTEN ESSAY

In the general case the output would also be a vector; but for simplicity here we consider the scalar output case. Give an explicit expression for k. Be sure to tell us which spot measurement you believe to be faulty, and what your guess of the emission rates is.

We consider the usual measurement setup: You can quote any result given in the lecture notes. In the first time period, y1 becomes available, in the next time period y2 becomes available, and so on, so that all m measurements are available in the mth time period. Show that the set of points in Rn that are closer to a than b is a halfspace, i. Running this M-file will define ydes and the matrices A i given as a 3 dimensional array; for example, A: As an example, consider node 3 in the network of part 2.

Give the value of the trend parameter a that you find. It is generated by a nonlinear recursion, which has infinite memory.

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We are given noisy measurements of H at some points on the imaginary axis, i. For each symbol we give a set of symbols which are allowed to follow the symbol. And it takes a few homeowrk to evaluate it, so calling the p-file function is something like a carrying out a real physical experiment.