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bme240 > final exam studying > Flashcards

final exam studying Flashcards

1
Q

linspace

A

generates row vector of n-points (default is 100) between and including a & b
linspace(a, b, n)

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2
Q

transpose (special case)

A

.’ conjugates complex numbers

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3
Q

to conduct operations on vectors/matrices, the dimensions must be _______

A

the same

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4
Q

random function

A

generates random numbers based on input specifications

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5
Q

rand(3)

A

3 by 3 matrix with random numbers 0-1

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6
Q

rand(10, 1, 5)

A

10 by 10 of random numbers between 1-5

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7
Q

find function

A

locates non-zero elements and outputs indices

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8
Q

min/max functions

A

returns smallest/largest in each vector (for matrices, it’s for each column)

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9
Q

[M, I] = min(x)
what are M and I

A

M is the values, I is the indices

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10
Q

sortrows function

A

sorts matrix by specified column from smallest to largest (add ‘descend’ to flip)

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11
Q

symmetric error-bar function

A

errorbar(x, y, e)
e is the error

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12
Q

asymmetric error-bar function

A

errorbar(x, y, d, u)
histograms –> d is low error and u is high error
line graph–> d is negative error and u is positive

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13
Q

meshgrid function

A

creates 2d matrix for input vectors, (repeats the vector over and over)
[ X, Y] = meshgrid(x,y)

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14
Q

how to generate Z with results of meshgrid function?

remember [X, Y] = meshgrid(x,y)

A

Z = sin(X) .* cos(Y)

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15
Q

linear interpolation

A

estimating values at certain points based on existing ones
interp1(x,y, new_x)

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16
Q

cubic spline interpolation

A

smooths curve and minimizes errors (fitting interpolation)

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17
Q

least squares fitting

A

line of best fit for data based on the slope and intersection (goal is to minimize R)

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18
Q

linear regression

A

finds the linear function that best fits the data

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19
Q

polyfit function

A

returns coefficients of degree n that’s the best fit with least squares
polyfit(x, y, n)

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20
Q

polyval function

A

evaluates polynomial at each point of x
polyval(p, x)

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21
Q

diff function

A

calculates difference between consecutive elements

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22
Q

resistance and current (I = )

A

I = V/R

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23
Q

systems (in bioengineering)

A

groups of things that affect signal of interest

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24
Q

model (in bioengineering)

A

symbolic representations of process, system, or data collection

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25
Q

signals (at their core)

A

variations in energy that carry info

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26
Q

types of signals

A

chemical, mechanical, electrical, and thermal energy

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27
Q

signals for communication in body?

A

chemical and bioelectrical energy

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28
Q

biotransducer

A

converts non-electrical energy to electrical signal

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29
Q

sensitivity

A

change in output for given change in input (dy/dx)

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30
Q

resolution

A

smallest change in input that changes the measurement

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31
Q

linearity

A

consistency of input/output relationship

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32
Q

repeatability

A

expected change in output for repeated changes in input

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33
Q

noise

A

change in output not related to input, unrelated to signal

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34
Q

measured signal

A

biosignal of interest + noise

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35
Q

encoding

A

step of processing signals, transducing body signals so they can be read, can be analog or digital

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36
Q

continous/analog signal

A

in terms of amplitude, has a defined amplitude at every point

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37
Q

continuous/analog signal

A

x(t)

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38
Q

continuous/analog domain

A

uncountable, may or may not be finite

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39
Q

discrete/digital signal

A

defined over discrete time domain

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40
Q

discrete/digital signal

A

x[t]

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41
Q

Ts

A

sample interval

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42
Q

t =

A

n*Ts = n/fs

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43
Q

fs =

A

1/Ts

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44
Q

signal measurement system

A

signal (analog)–> sensor/transducer –> signal processing –> sampling (digital)

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45
Q

sampling

A

slicing the signal into sequences at time interval (Ts)

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46
Q

systematic error

A

each measurement deviates from the value by a fixed amount (DC shift)

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47
Q

random error

A

noise (random variations)

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48
Q

biomedical sources of noise

A

physiological variability noise, environmental noise/interference, artifact, electronic noise (thermal, shot, white noise)

49
Q

sinusoidal wave

A

Asin(wt) = Asin(2pift) = Asin(2pit/T)
w = frq in radians

50
Q

root mean squared (RMS)

A

square the signal, calculate the mean, take sqrt

51
Q

SNR (signal noise ratio)

A

higher SNR is better system

52
Q

positive SNR means…

A

signal > noise

53
Q

negative SNR means…

A

noise > signal

54
Q

aliasing

A

when signal isn’t sampled enough to capture changes, different signals become indistinguishable and high frq components and mistaken for low frq components

55
Q

nyquist sampling theorum

A

to avoid aliasing, the sample rate should e twice the highest frequency component in signal

ex: if highest is 12, signal should be sampled 24 times per cycle

56
Q

smallest frequency component

A

defines the period of a signal (T), too low = jagged graph, too high= loopy graph

57
Q

post-processing

A

operations after digitization, like shifting, scaling, and noise reduction

58
Q

2 methods of noise reduction

A

signal averaging and signal filtering

59
Q

signal averaging

A

reduces the noise power, best when the frq spectra of noise and signal overlap

noise = average_odd - average_even

60
Q

moving average filters

A

replace each data point with value of average and itself and nearby points

61
Q

symmetrical moving average filter

A

3 point moving average:
(Xt-1 + Xt + Xt+1) /3

62
Q

asymmetrical moving average filter

A

3 point moving average:
(Xt-2 + Xt-1 + Xt) / 3

63
Q

weighted moving average

A

more weight to point being replaced and less to furthest away
((Xt-2 * 1) + (Xt-1 * 2) + (Xt * 3) + (Xt+1 * 2) (Xt+2 *1) / 9

64
Q

3 classes of signal

A

periodic, aperiodic, transient (step-like)

65
Q

periodic signals

A

every phase is the same, biosignals can be taken as periodic for analysis

66
Q

aperiodic signals

A

exists over finite time frame, treated as periodic

67
Q

transient signal

A

increases infinitely, hardest to treat

68
Q

interpolate before averages

A

[rxy, lags] = xcorr(x, y, ‘coeff’)

69
Q

correlation

A

comparison of two signals

70
Q

correlation coefficient

A

number that signifies the correlation (-1 to 1)

71
Q

crosscorrelation

A

correlating over many shifts/lags

72
Q

lag

A

continuous variable of time to shift x(t) with respect to y(t)

73
Q

peak correlation

A

tells us where signals are most similar

74
Q

multiple correlations

A

shift wave to find best match for signal

75
Q

autocorrelation

A

correlating signal with itself

76
Q

plotting crosscorrelation

A

[rxy, lags] = xcorr(x,y, ‘coeff’)
figure; plot(lags*Ts, rxy)

77
Q

max correlation

A

[max_corr, max_shift] = max(rxy)

78
Q

crosscovariance

A

similarity in deviation of two signals about the mean

79
Q

covariance

A

similar to correlation but has the mean subtracted from the signal

80
Q

frequency domain

A

shows how signal energy is distributed over a range of frequencies

81
Q

2 components of frequency domain

A

magnitude (abs(Xf)) and phase (angle(Xf))

82
Q

fourier series theorum

A

any periodic signal can be represented as a sum of sinusoids, more sinusoids = better summation

X(f) = fft(x)

83
Q

bandwidth

A

range of frequency domain

84
Q

filters in frequency domain

A

reduce noise by limiting the bandwidth of range of interest

85
Q

filter properties

A

filter type, bandwidth, attenuation slops

86
Q

multiplication value

A

dependent on instantaneous frequency, (1 = keep, 0 = pass)

87
Q

pass band

A

part of input signal you want to keep, passed to output without change

88
Q

stop band

A

part of input signal you want to discard, ideally reduced to 0

89
Q

cut-off frequency

A

between pass and stop bands

90
Q

inverse fourier analysis

A

going back to time domain from frequency domain
ifft(Xf, n) –> n-point trandformation

91
Q

low pass filter

A

passes frequency (below fc) with minimal attenuation and attenuates higher frequency

92
Q

high pass filter

A

passes frequency (above fc) with minimal attenuation and attenuates lower frequency

93
Q

band-pass filter

A

allows specific interval of frequency to pass and stops the rest

94
Q

band-stop filter

A

attenuates specific interval of frequency and passes the rest

95
Q

notch-filter

A

attenuates signal within very narrow range of frequencies

96
Q

filtfilt

A

digital filtering by processing x data forwards and backgrounds
filfilt(B, A, X) filters in X direction w filter described in A and B

97
Q

[B, A] = butter(N, Wn)

A

designs nth order butterworth filter with 0.0 < Wn < 1.0 (cut-off frq) returns numerator B and denominator A

98
Q

butter(N, Wn, ‘high’)

A

high pass filter, removes low frequencies

99
Q

butter(N, Wn, ‘low’)

A

low pass filter, removes high frequencies

100
Q

butter(N, Wn, ‘stop’)

A

band-stop filter of WN [W1, W2]

101
Q

event time

A

when signal passes the threshold

102
Q

relative timing

A

time passed in between events

103
Q

single events

A

time of physiological response to stimulus

104
Q

latency

A

time of peak response

105
Q

event detection

A

detect by setting and finding the threshold (min or max), jump forward by interval time

106
Q

false negative

A

missing an event

107
Q

identification features of events

A

amplitude, slopes, peaks

108
Q

how do you avoid false positives/negatives?

A

with a good system and measurement system

109
Q

false positive

A

detecting an event when none occurred

110
Q

spatial data

A

data in space-time domain

111
Q

digital images

A

represented as 2D matrix of pixels

112
Q

resolution (in imaging)

A

number of pixels per unit length

112
Q

voxel

A

3D pixel

113
Q

dynamic range

A

number of gray levels in an image (2^bits)

113
Q

histogram function

A

represents grayscale distribution (x = gray level, y = frequency)

114
Q
A
115
Q

quantization

A

digitizing the amplitude

116
Q
A

bme240 (12 decks)

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