Topics in Computer Architecture
Lab Chap #3
1. Implement the following Boolean function with a 4 x 1 multiplexer and external gates. Connect A and
B to select lines. The input requirements for the four data lines will be a function of the variables C
and

Topics in Computer Architecture
Final Exam Part1 - Datapath
Using the VHDL Tutorial here:
http:/www.seas.upenn.edu/~ese171/vhdl/vhdl_primer.html#_Toc526061347
the components provided:
final_P1.zip
draw the schematic for the datapath shown below.
if (input

Topics in Computer Architecture
Midterm Exam
Name _
Date _
1. Design a circuit with a 4 bit BCD input A, B, C, D that produces an output W, X, Y, Z that is equal
to the input +6 in binary. For example, 9 (1001) + 6 (0110) =15 (1111). The outputs for inval

3 Autocorrelation
Autocorrelation refers to the correlation of a time series with its own past and future values.
Autocorrelation is also sometimes called lagged correlation or serial correlation, which
refers to the correlation between members of a serie

4
Spectrum
The spectrum of a time series is the distribution of variance of the series as a function of
frequency. The object of spectral analysis is to estimate and study the spectrum. The spectrum
contains no new information beyond that in the autocovar

6 Spectral Analysis - Smoothed Periodogram Method
6.1 Historical background
There are many available methods for estimating the spectrum of a time series. In lesson 4 we
looked at the Blackman-Tukey method, which is based on Fourier transformation of the

5 Autoregressive-Moving-Average Modeling
5.1 Purpose.
Autoregressive-moving-average (ARMA) models are mathematical models of the persistence,
or autocorrelation, in a time series. ARMA models are widely used in hydrology,
dendrochronology, econometrics, a

2 Probability distribution
The probability distribution of a time series describes the probability that an observation
falls into a specified range of values. An empirical probability distribution for a time series can
be arrived at by sorting and ranking

12 Validating the Regression Model
Regression R-squared, even if adjusted for loss of degrees of freedom due to the number of
predictors in the model, can give a misleading, overly optimistic view of accuracy of prediction
when the model is applied outsid

1 Organizing time series in Matlab structures
The first step is to store your time series and its metadata in structures with defined fields.
This step assures uniformity in storage of the time series data of different students, and is
necessary to ensure

9 Correlation
The Pearson product-moment correlation coefficient is probably the single most widely
used statistic for summarizing the relationship between two variables. Statistical significance and
caveats of interpretation of the correlation coefficien

8 Filtering
The estimated spectrum of a time series gives the distribution of variance as a function of
frequency. Depending on the purpose of analysis, some frequencies may be of greater interest
than others, and it may be helpful to reduce the amplitude

10.
Lagged Correlation
Lagged relationships are characteristic of many natural physical systems. Lagged
correlation refers to the correlation between two time series shifted in time relative to one
another. Lagged correlation is important in studying the

7 Detrending
Trend in a time series is a slow, gradual change in some property of the series over the whole
interval under investigation. Trend is sometimes loosely defined as a long term change in the
mean (Figure 7.1), but can also refer to change in ot

11 Multiple Linear Regression
Multiple linear regression (MLR) is a method used to model the linear relationship between a
dependent variable and one or more independent variables. The dependent variable is sometimes
also called the predictand, and the in