Midterm Examination Flashcards by Gerand Parawan

DSP stands for?

Digital Signal Processing

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is the process of analyzing and modifying a
signal to optimize or improve its efficiency or
performance.

Digital Signal Processing

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is concerned with the representation of signals
by sequence of numbers or symbols and the
processing of these sequences

Digital Signal Processing

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is an electrical or electromagnetic
current that is used for carrying data from one
device or network to another

Signal

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can be define as a function that
conveys information, generally about the state
or behavior of a physical system

Signal

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are those signals for which both time and amplitude are continuous.

Analog signal

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are those signals that are defined only at discrete units of time

Discrete signal

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Involves analyzing, modifying, and synthesizing signals to
pull meaning out of it.

Signal Processing

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Two classifications of signal processing:

Analog Signal Processing
Digital Signal Processing

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deals with transformation of
analog signals

Analog signal processing

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deals with the processing of discrete signals

Digital signal processing

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Block diagram of a Digital Processing system

Pre-filter
ADC
DSP
DAC
Post-filter

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used to filter out unwanted high-frequency components from
raw analog input signal.

Pre-filter

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converts analog signals to digital signals

ADC

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the digital signal is analyzed and processed and the synthesized output is fed to DAC

DSP

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converts digital signals back to analog signals.

DAC

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Used to filter out unwanted high-frequency components in
the generated analog signal

Post-filter

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Applications of DSP System: (5)

Speech and Audio Processing;
Image and Video Processing;
Military and Telecommunications;
Healthcare and Biomedical sector; and
Consumer electronics

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This involves speech recognition and analysis
noise filtering, echo cancellation, etc.

Speech and Audio Processing

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This involves compression, enhancement,
reconstruction and restoration of images and
videos.

Image and Video Processing

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Example radar tracking, modulation and
demodulation

Military and Telecommunications

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Example analysis of ECG and X-ray signal

Healthcare and Biomedical sector

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Most digital equipment like smartphones,
televisions, digital cameras, etc. it has DSP
embedded on it to accelerate its
performance

Consumer electronics

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In electrical engineering, the fundamental
quantity of representing some information is
called a?

Signal

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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)