Signals: Spectra & Processing Flashcards
1
Q
The most important piece that needs to be shared and safeguarded from noise.
A
Signal
2
Q
An action, a gesture, or sign used as a means of communcation
A
Means of Communication
3
Q
A piece of information communicated by an action, gesture, or sign
A
Communicated Infromation
4
Q
Something that incites somebody to action
A
Incitement
5
Q
Information transmitted by means of a modulated current or an electromagnetic wave and received by telephone, telegraph, etc.
A
Electronics Transmitted Infromation
6
Q
It is defined as ta single-valued function of time that conveys information
A
Signal
7
Q
Describes the frequency content of the signal
A
Spectra
8
Q
Condition that is not limited to a specific set of values but can vary infinitely within a continuum
A
Spectrum
9
Q
Separation of visible light through a prism forming a rainbow
A
Prismatic Diffraction
10
Q
Any variable signal continuous in both time and amplitude
A
Analog Signal
11
Q
Electrical properties used to a signal
A
Voltage
Frequency
Current
Charge
12
Q
The digital representation of discrete-time signal which is often derived from analog signal
A
Digital Signal
13
Q
A sampled version of an analog signal
A
Discrete-time Signal
14
Q
The result of individual time values of the discrete-time signal being approximated to a certain precision
A
Digital Signal
15
Q
The process of approximating the precise value within a fixed number of digits or bits
A
Quantization
16
Q
It is quantized discrete-time signal
A
Digital Signal
17
Q
Any signal processing conducted on analog signals by analog means
A
Analog Signal Processing
18
Q
The study of signals in a digital representation and the processing methods of these signals
A
Digital Signal Processing
19
Q
The four major subfields of DSP
A
Audio Signal Processing
Control Engineering
Digital Image Processing
Speech Processing
20
Q
The domains of digital signals
A
Time Domain Spatial Domain Frequency Domain Autocorrelation Domain Wavelet Domain
21
Q
It can be produced by a sequence of samples from a measuring device
A
Time and Spatial domain Representation
22
Q
Can be obtained by a discrete Fourier transform
A
Frequency Domain Information
23
Q
The cross-correlation of the signal with itself over varying intervals of time or space
A
Autocorrelation
24
Q
The most common enhancement processing approach of the input signal in the time or space domain
A
Filtering
25
It is a linear transformation of input samples
Linear Filter
26
A filter that uses only previous samples of the input or output signals
Causal Filters
27
A filter that uses only future input samples
Non-causal
28
Means to change a non-causal filter to a causal filter
Adding a delay
29
A filter that has constant properties over time
Time-invariant Filter
30
A filter that changes in time
Adaptive Filter
31
A filter that produces an output that converges to a constant value with time or remains bounded within a finite interval
Stable Filter
32
A filter that produces output that diverges
Unstable
33
A filter that is always stable and only uses the input signals
Finite Impulse Response (FIR) Filter
34
A filter that uses both the input signal and previous samples of the output signal and can be unstable
Infinite Impulse Response (IIR) Filter
35
Converts the signal information to a magnitude and phase component of each frequency
Fourier Transform
36
A function that satisfies the condition:
x(t)=x(t+To)
where,
t = time
To = a constant
Periodic Signal
37
Any signal for which there is no value of To to satisfy the equation
x(t)=x(t+To)
Nonperiodic or Aperiodic Signal
38
A signal where there is no uncertainty with respect to ts value at any time
Deterministic Signal
39
A signal about which there is uncertainty before its actual occurrence
Random Signal
40
The condition of an energy signal
It is an energy signal IF AND ONLY IF the total energy of the signal satisfies the condition
0
41
The condition of a power signal
It is a power signal IF AND ONLY IF the average power of the signal satisfies the condition
0
42
It is function where there is a single 1 output and 0 elsewhere
δ(n) = 1
Impulse Function or δ Function
43
It is a function where it is equal to 1 for n≥0 and 0 elsewhere
u(n) = 1
Unit Step Function
44
It is a function where it is equal to 1 for n≤ -1 and 0 elsewhere
u(-n-1) = 1
Reversed Step Function
45
Formula for a sinusoidal signal
```
x(t) = Acos(ωt + ϕ)
x(t) = Acos(2πft + ϕ)
```
ω is in rad
f is in Hertz
ϕ is the phase angle
A is the dc level
46
General expression for complex signal
```
x(t) = Ae^±(ωt + θ)
x(t) = cos(ωt + θ) ± jsin(ωt + θ)
```
47
The Fourier Series Expansion formula
x(t) = a₀ + 2∑[aᵢcos((2πit)/T₀) + bᵢsin((2πit)/T₀)]
i is from 1 to ∞
where,
aᵢ and bᵢ = coefficients
i/T₀ = ith harmonic of the fundamental frequency f₀
a₀ = mean value of the periodic signal v(t)
48
Fourier series, Average (a₀) or Mean value of a periodic signal
a₀ = 1/T₀ ∫ x(t) dt
| from -T₀/2 to T₀/2
49
Fourier series, cosine coefficient aᵢ
aᵢ = 1/T₀ ∫x(t) cos((2πit)/T₀) dt
| from -T₀/2 to T₀/2
50
Fourier series, sine coefficient bᵢ
bᵢ= 1/T₀ ∫x(t) sin((2πit)/T₀) dt
| from -T₀/2 to T₀/2
51
A certain linear operator that maps functions to other functions to other function
Fourier Transform
52
Decomposes a function into a continuous spectrum of its frequency components
Fourier Transform
53
Synthesizes a function from its spectrum of frequency components.
Inverse Fourier Transform
54
Fourier Transform Pair condition from time domain to frequency domain and vv
x(t) = ∫X(f) e^(j2πft) df vv X(f) = ∫ x(t) e^(-j2πft) dt
from -∞ to ∞
55
Fourier Transform Property of Linearity or Superposition from time domain to frequency domain and vv
ax₁(t) + bx₂(t) vv aX₁(f) + bX₂(f)
56
Fourier Transform Property for Duality from time domain to frequency domain and vv
X(t) vv x(-f)
57
Fourier Transform Property for Time Scaling from time domain to frequency domain and vv
x(at) vv 1/a x(f/a)
58
Fourier Transform Property for Time Shifting from time domain to frequency domain and vv
x(t-t₀) vv X(f)e^(-2jπft₀)
59
Fourier Transform Property for Frequency Shifting from time domain to frequency domain and vv
x(t)e^(2jπfct) vv X(f-fc)
60
Fourier Transform Property for Differentiation in the Time Domain from time domain to frequency domain and vv
dⁿ/dtⁿ x(t) vv (2πft)ⁿX(f)
61
Fourier Transform Property for Integration in the Time Domain from time domain to frequency domain and vv
∫x(t) dt vv 1/(2πf) X(f)
| from -∞ to t
62
Fourier Transform Property for Multiplication in the Time Domain from time domain to frequency domain and vv
x₁(t)x₂(t) vv ∫X₁(λ)X₂(f-λ) dλ
| from -∞ to t
63
The multiplication of two signals in the same time domain is transformed into the convolution of their individual Fourier transforms in the frequency domain
Multiplication Theorem
64
Fourier Transform Property for Convolution in the Time Domain from time domain to frequency domain and vv
∫x₁(τ)x₂(t-τ) dτ vv X₁(f)X₂(f)
| from -∞ to t
65
The convolution of two signals in the time domain is transformed into the multiplication of their individual Fourier Transforms in the Frequency domain
Convolution Theorem
66
The operation used to measure the similarity between two signals or functions and the time relation of the similarity
Correlation
67
Steps to correlate two signals
(1) First shift the second signal
(2) Then multiply both signals
(3) Integrate under the curve
68
When the signals x₁(t) and x₂(t) are the same during the correlation process
Autocorrelation
69
Provides information about the time-domain structure of a noisy signal
Autocorrelation
70
Often used to discover periodic components in noisy signals
Autocorrelation
71
When the signals x₁(t) and x₂(t) are different during the correlation process
Cross correlation
72
Used to identify signal by comparison with a library of known reference signals
Cross Correlation
73
It is similar to correlation except that the second signal x(t-τ) is flipped back to front
Convolution
74
Steps for the convolution operation
(1) Flip the second signal
(2) Shift the second signal
(3) Multiply both signals
(4) Integrate under the curve
75
A filter whose response to an impulse ultimately settles to zero.
Finite Impulse Response (FIR) Filter
76
A filter which have internal feedback and may continue to respond indefinitely
Infinite Impulse Response (IIR) Filter
77
Properties of FIR filters
Inherently Stable
Requires no Feedback
Can have linear phase
Can have minimum phase
Generally linear design methods
Efficient realizations with Fast Fourier Transforms
Startup Transients are finite in duration
Less sensitive to coefficient word lengths
78
A FIR filter design method where ideal impulse response is truncated with a window. The simplest method and suitable for short filters.
Window Design Method
79
A FIR filter design method uses the transition region to control sidelobes which specifies response at evenly-spaced frequency samples.
Frequency Sampling Method
80
A FIR filter design method minimizes the maximum ripple using the Remez exchange algorithm.
Equiripple Design Method or Minimax Method
81
An impulse response function that is non-zero over an infinite length of time.
Infinite Impulse Response (IIR) Filter
82
Examples of IIR filters
Chebyshev
Butterworth Filter
Bessel Filter
Elliptic Filter
83
This IIR filter method transforms filters into discrete-time domain using bilinear transform
Transforms of Continuous Filter Designs
84
Uses Yule-Walker Method for this IIR Filter modeling technique
Modeling of Desired Frequency Response
85
Uses Prony's Method for this IIR modeling technique
Modeling of Desired Impulse Response
86
The bandwidth, sampling rate, no.of bits, and data rate of high fidelity CD music
5 Hz to 20 kHz
41. kHz
16 bits
706 kbps
87
The bandwidth, sampling rate, no.of bits, and data rate of tele-phone quality speech
200 Hz to 3.2 kHz
8 kHz
12 bits
96 kbps
88
The bandwidth, sampling rate, no.of bits, and data rate for telephone with companding
200 Hz to 3.2 kHz
8 kHz
8 bits
64 bits
89
The bandwidth, , sampling rate, no.of bits, and data rate of speech encoded by linear predictive coding
200 Hz to 3.2 kHz
8 kHz
12 bits
4 kbps
90
The data rate that represents the straightforward application of sampling and quantization theory to audio signals
64 kbps
91
It has a shiny main surface and information is burned on the surface with a laser
Compact Disc (CD) or High Fidelity Audio
92
An audio processing application based on the model of human speech production
Speech Synthesis and Recognition
93
A data compression application where each time a zero is encountered in the input data, two values are written to the output file.
Run-Length Encoding
94
A data compression method which assign frequently used character fewer bits, and seldom used characters more bits.
Huffman Encoding
95
A data compression method that refers to several techniques that store data as the difference between successive samples, rather than directly storing the samples themselves
Delta Encoding
96
A data compression method which is the foremost technique for general purpose data compression due to its simplicity and versatility. This is always used in GIF image files.
LZW Compression
97
JPEG
Joint Photographers Experts Group
98
Another name for JPEG
Transform Compression
99
Data compression where the low frequency components of a signal are more important than the high frequency components
JPEG
100
MPEG
Moving Picture Experts Group
101
It is a compression standard for digital video sequence and also provides for the compression of the sound track associated with the video
MPEG
102
Two MPEG types
Within-the-frames
| Between-frame
103
A MPEG type where individual frames making up the video sequence are encoded as they were originally still images. This is performed using JPEG standard.
Within-the-Frame
