Signal ProcessingES & BM MUET1 Lecture 2. Signal ProcessingES & BM MUET2 This lecture Concept of...
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Transcript of Signal ProcessingES & BM MUET1 Lecture 2. Signal ProcessingES & BM MUET2 This lecture Concept of...
Signal Processing ES & BM MUET 1
Lecture 2
Signal Processing ES & BM MUET 2
This lecture
• Concept of Signal Processing• Introduction to Signals• Classification of Signals• Basic elements of SP System• Analog to Digital Conversion
– Sampling – Quantization
• Nyquist Theorem• Applications of Signal Processing
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Signal Processing
• Representation, transformation, manipulation of signals and the information they contain.
• Classification:
Depends upon the type of signal to be processed.
• Analog Signal Processing
• Digital Signal Processing
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Signal Processing
• Analog SP
Continuous time signals are processed.
• Digital SP
Discrete - time discrete - valued signals processed by digital computers or other data processing machines.
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Signal??
• Any indication / information
• A change in which some information is residing
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Classification of Signals
• Continuous-time / Discrete-time Signals
• Continuous-valued / Discrete-valued Signals
• Deterministic / Random Signals
• One-dimensional / Multi-dimensional Signals
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Fundamental SP system
• Most signals – Analog in nature.
• Analog to Digital Converter is used as an interface between analog signal and Digital Signal Processor.
A/D Converter D/A ConverterDigital Signal
Processor
Analog
Input Signal
Analog
Output Signal
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A-D Conversion
1. Sampling• First step in going from analog to digital.• In signal processing, sampling is the
reduction of a continuous signal to a discrete signal. A common example is the conversion of a sound wave (a continuous-time signal) to a sequence of samples (a discrete-time signal).
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Sampling
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Nyquist Theorem
• In order the samples represent correctly the analog signal, the sampling frequency must be greater than twice the maximum frequency of the analog signal:
• fs≥2FM
• The limiting frequency 2FM is called Nyquist rate.
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Aliasing (Time Domain)
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Aliasing (Frequency Domain)
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Methods of avoiding Aliasing
• To avoid aliasing, there are two approaches: One is to raise the sampling frequency to satisfy the sampling theorem.The other is to filter off the unnecessary high-frequency components from the continuous-time signal. We limit the signal frequency by an effective low-pass filter, called anti-aliasing prefilter, so that the highest frequency left in the signal is less than half of the intended sampling rate.
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General DSP System
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Quantization
• Slide 143 CCN module 2• MIT OCW
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Applications of SP
• RADAR• SONAR• Medical• Image Processing
– Pattern recognition– Edge detection
• Audio Signal Processing– Speech generation– Speech recognition– Speaker identification
• Telecommunications– Multiplexing– Compression– Echo control