Midterm Examination Flashcards
DSP stands for?
Digital Signal Processing
is the process of analyzing and modifying a
signal to optimize or improve its efficiency or
performance.
Digital Signal Processing
is concerned with the representation of signals
by sequence of numbers or symbols and the
processing of these sequences
Digital Signal Processing
is an electrical or electromagnetic
current that is used for carrying data from one
device or network to another
Signal
can be define as a function that
conveys information, generally about the state
or behavior of a physical system
Signal
are those signals for which both time and amplitude are continuous.
Analog signal
are those signals that are defined only at discrete units of time
Discrete signal
Involves analyzing, modifying, and synthesizing signals to
pull meaning out of it.
Signal Processing
Two classifications of signal processing:
Analog Signal Processing
Digital Signal Processing
deals with transformation of
analog signals
Analog signal processing
deals with the processing of discrete signals
Digital signal processing
Block diagram of a Digital Processing system
Pre-filter
ADC
DSP
DAC
Post-filter
used to filter out unwanted high-frequency components from
raw analog input signal.
Pre-filter
converts analog signals to digital signals
ADC
the digital signal is analyzed and processed and the synthesized output is fed to DAC
DSP
converts digital signals back to analog signals.
DAC
Used to filter out unwanted high-frequency components in
the generated analog signal
Post-filter
Applications of DSP System: (5)
Speech and Audio Processing;
Image and Video Processing;
Military and Telecommunications;
Healthcare and Biomedical sector; and
Consumer electronics
This involves speech recognition and analysis
noise filtering, echo cancellation, etc.
Speech and Audio Processing
This involves compression, enhancement,
reconstruction and restoration of images and
videos.
Image and Video Processing
Example radar tracking, modulation and
demodulation
Military and Telecommunications
Example analysis of ECG and X-ray signal
Healthcare and Biomedical sector
Most digital equipment like smartphones,
televisions, digital cameras, etc. it has DSP
embedded on it to accelerate its
performance
Consumer electronics
In electrical engineering, the fundamental
quantity of representing some information is
called a?
Signal
is a function that conveys some information
Signal
Analog signals are denoted by?
Sine waves
are less accurate than analog signals because they are discrete samples of an analog signal taken over some period of time.
Digital signal
Digital signals are denoted by?
Square waves
is defined by the type of input and output it deals with.
System
In systems, the input is known as _____and the output is known as _____.
Excitation;
Response
Conversion of Analog to Digital Signals: (2)
Sampling;
Quantization
can be defined as taking samples. It is done on an independent variable.
Sampling
can be defined as dividing into quanta (partitions). It is done on a dependent variable.
Quantization
The types of systems whose input and output both are continuous signals or analog signals is called?
Continuous Systems
The type of systems whose input and output both are discrete signals or digital signals is called?
Discrete Systems
The signals which are defined only at discrete instants of time are known as?
Discrete time signals
A discrete time signal may be represented in any one of the following four ways −
Graphical Representation
Functional Representation
Tabular Representation
Sequence Representation
Who invented Fourier Series?
Jean Baptiste Joseph Fourier (Auxerre, France)
is a way of representing a periodic function as a (possibly infinite) sum of sine and cosine functions
Fourier Series
represents functions as possibly infinite sums of a monomial term
Taylor Series
Applications of Fourier Series: (6)
Signal processing
Image processing
Heat distribution mapping
Wave simplification
Light Simplification(Interference, Diffraction)
Radiation measurements and so on…
The time and frequency domains are alternative ways of representing signals. _____ is the mathematical relationship between these two representations.
Fourier transform
Applications of Fourier Transform: (6)
Image Processing
Voice recognition
Astronomy
Geophysics
Forensics
Fingerprint / Iris recognition
What are the Fourier Transform properties? (7)
Duality
Linearity
Scaling
Time Shifting
Frequency Shifting (Modulation)
Parseval’s Theorem
Convolution Theorem
only applicable to periodic signals
Fourier Series
Fourier developed a mathematical model to transform signals between the time domain to the frequency domain & vice versa, which is called?
Fourier Transform
can be represented using discrete frequencies
Periodic signals
The combination of periodic and aperiodic signals generates four categories:
Aperiodic-Continuous;
Periodic-Continuous;
Aperiodic-Discrete;
Periodic-Discrete
These signals extend to both positive and negative infinity without repeating in a periodic pattern.
Aperiodic-Continuous (Fourier Transform)
any waveform that repeats itself in a regular pattern from negative to positive infinity
Periodic-Continuous (Fourier Series)
These signals are only defined at discrete points between positive and negative infinity and do not repeat themselves in a periodic fashion.
Aperiodic-Discrete (Discrete Time Fourier Transform)
These are discrete signals that repeat themselves in a periodic fashion from negative to positive infinity.
Periodic-Discrete (Discrete Fourier Transform)
It is a mapping between domains
Transform
Filters: (4)
A. Low Pass Filter
B. High Pass Filter
C. Band pass Filter
D. Band Stop Filter or (Notch Filter)
is a mathematical operation used to express the relation between the input and output of an LTI system.
Convolution
There are two types of convolutions:
Continuous and Discrete convolution
Continuous Convolution formula:
y(t) = x(t) * h(t)
DiscreteConvolution formula:
y(n) = x(n) * h(n)
Is reverse process of convolution is widely used in signal and image processing.
Deconvolution
Convolution of two causal sequences is
Causal
The convolution of two anti-causal sequences is
Anti causal
Convolution of two unequal-length rectangles results a
Trapezium
Convolution of two equal-length rectangles results in a
Triangle
It is a measure of similarity between signals and is found using a process similar to convolution
Correlation
It is used to compare two signals
Correlation
Correlation has two types:
Cross-Correlation and Autocorrelation
Cross-Correlation:
R(n) = x(n) * y(-n)
