18 Jul Digital signal processing is a technology that is widely used in many ?applications, such as automotive, consumer, graphics/imaging, ?industrial, instrumentation, medical, military, tel
Digital signal processing is a technology that is widely used in many applications, such as automotive, consumer, graphics/imaging, industrial, instrumentation, medical, military, telecommunications, and voice/speech applications. Digital signal processing incorporates mathematics, software programming, and processing hardware to manipulate analog signals.
answer the following:
- Explain how analog signals are converted to digital form.
- Explain the basic concepts of a digital signal processor (DSP).
EET 230– Digital Systems II
Digital Signal Processing
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Outline of the lecture
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Digital Signal Processing Basics
Converting Analog Signals to Digital
Analog – to – Digital Conversion Methods
The Digital Signal Processor ( DSP)
Digital – to – Analog Conversion Methods
Objective of the Lecture
After successful completion of the lecture students will be able to:
List the essential elements of digital signal processing system
Explain how analog signals are converted to digital
Describe the sampling Process
State the purpose of ADC
Explain how different types of ADCs operate
State the purpose of DAC
Explain how a DAC operates
Enumerate sample functions a DSP can perform
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What is DSP?
Changing or analyzing information that is measured as discrete sequences of numbers
The representation, transformation, and manipulation of signals and the information they contain
A collection of algorithms used to manipulate signals.
A digital signal processor (DSP) is optimized for speed and working in real time (as events happen). It is basically a specialized microprocessor with a reduced instruction set.
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Unique Features of DSP
Signals come from the real world
Need to react in real time
Need to measure signals and convert them to digital numbers
Signals are discrete
Information in between discrete samples is lost
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Processing Real Signals
Most of the signals in our environment are analog such as sound, temperature and light
To processes these signals with a computer, we must:
1. convert the analog signals into electrical signals, e.g., using a transducer such as a microphone to convert sound into electrical signal
2. digitize these signals, or convert them from analog to digital, using an ADC (Analog to Digital Converter)
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Typical DSP System Components
Input lowpass filter (anti-aliasing filter)
Analog to digital converter (ADC)
Digital computer or digital signal processor
Digital to analog converter (DAC)
Output lowpass filter (anti-imaging filter)
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DSP Components
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DSP System Components
Analog input signal is filtered to be a band-limited signal by an input low pass filter
Signal is then sampled and quantized by an ADC
Digital signal is processed by a digital circuit, often a computer or a digital signal processor
Processed digital signal is then converted back to an analog signal by a DAC
The resulting step waveform is converted to a smooth signal by a reconstruction filter called an anti-imaging filter
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Advantages of DSP
Versatility
Digital systems can be reprogrammed for other applications
Digital systems can be ported to different hardware
Repeatability and stability
Digital systems can be easily duplicated
Digital systems do not depend on strict component tolerances
Digital system responses do not drift with temperature
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Advantages of DSP (cont.)
Simplicity
Some things can be done more easily digitally than with analog systems (e.g., linear phase filters)
Security can be introduced by encryption/scrambling
Digital signals easily stored on magnetic media without deterioration
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Applications of DSP
Speech Processing
Noise filtering
Coding
Compression
Recognition
Synthesis
Sampling rate changes
64 kbps-narrowband, 64 kbps-wideband
32 kbps-narrowband, 32 kbps-wideband
16 kbps-narrowband, 16 kbps-wideband
64 kbps Mu-Law PCM
32 kbps CCITT G.721 ADPCM
16 kbps LD-CELP
8 kbps CELP
4.8 kbps CELP for STU-3
2.4 kbps LPC-10E for STU-3
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Applications of DSP
Image Processing: enhancement, coding, compression, pattern recognition
Multimedia: transmission of sound, still images, motion pictures, digital TV, video conferencing
Music: recording, playback and manipulation (mixing, special effects), synthesis
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Image Processing Example
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Applications of DSP
Communication: encoding and decoding of digital communication signals, detection, equalization, filtering, direction finding, echo cancellation
Radar and Sonar: target detection, position and velocity estimation, tracking
Biomedical Engineering: analysis of biomedical signals, diagnosis, patient monitoring, preventive health care, artificial organs
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Application Areas
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Motivation Towards Sampling
Most signals of practical interest in electrical engineering such as speech, biological signals, seismic signals, radar signals, sonar signals and various communication signals such as audio and video signals are analog.
To process analog signals by digital means it is first necessary to convert the analog signals to a sequence of numbers having finite values.
Thus the motivation in sampling of analog signals is to make use of the advantage of digital systems which are growing in a faster rate due to the development of large scale integrated circuit technology.
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Sampling
Consider the sampling shown in the figure below
The figure shows that sampling these signals every T seconds is not fast enough for these signals
Thus we lose information in this case
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Sampling
Now if we take samples of x(t) fast enough we can recover it from its samples.
The question here is how fast we have to sample so that no information is lost during sampling?
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Sampling – Antialiasing Filter
Before sampling, the analog input must be filtered with a low-pass anti-aliasing filter. The filter eliminates frequencies that exceed a certain limit that is determined by the sampling rate.
To understand the need for an anti-aliasing filter, you need to understand the sampling theorem which essentially states:
In order to recover a signal, the sampling rate must be greater than twice the highest frequency in the signal.
where fsample = sampling frequency
fa(max) = highest harmonic in the analog signal
Stated as an equation, fsample > 2fa(max)
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Sampling
If the signal is sampled less than this, the recovery process will produce frequencies that are entirely different than in the original signal. These “masquerading” signals are called aliases.
The anti-aliasing filter is a low-pass filter that limits high frequencies in the input signal to only those that meet the requirements of the sampling theorem.
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Examples
A signal has a spectrum from dc to 5 kHz. Determine minimum sampling rate and maximum time between samples:
To provide some guard band, assume signal of Example 1 is sampled 25% above theoretical minimum. Repeat the analysis.
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Examples
3. For conditions established in Example 2, assume that the signal lasts for 30 minutes. Determine total number of samples required.
4. A pure sinusoid with a frequency of 1kHz is sampled at intervals of 0.1msec, and converted into digital numbers to be processed on a computer. List all positive frequencies below 45kHz in the spectrum
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Analog to Digital Conversion
To process naturally occurring analog quantities with a digital system, the analog signal is converted to digital form after the anti-aliasing filter.
The first step in converting a signal to digital form is to use a sample-and-hold circuit. This circuit samples the input signal at a rate determined by a clock signal and holds the level on a capacitor until the next clock pulse.
A positive half-wave from 0-10 V is shown in blue. The sample-and-hold circuit produces the staircase representation shown in red.
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Analog to Digital Conversion
The second step is to quantize these staircase levels to binary coded form using an analog-to-digital converter (ADC). The digital values can then be processed by a digital signal processor or computer.
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Analog to Digital Conversion
Most signals have higher frequency harmonic and noise. For most ADCs, the sampling and filter cutoff frequencies are selected to be able to reconstruct the desired signal without including unnecessary harmonics and noise.
An example of a reasonable sampling rate is in a digital audio CD. For audio CDs, sampling is done at 44.1 kHz because audio frequencies above 20 kHz are not detectable by the ear.
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Sample and Hold and ADC
Following the anti-aliasing filter, is the sample-and-hold circuit and the analog-to-digital converter. At this point, the original analog signal has been converted to a digital signal.
Many ICs can perform both functions on a single chip and include two or more channels. For audio applications, the AD1871 is an example of a stereo audio ADC.
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Analog and Digital Conversion Methods
The flash ADC: The flash ADC uses a series high-speed comparators that compare the input with reference voltages. Flash ADCs are fast but require 2n – 1 comparators to convert an analog input to an n-bit binary number.
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Analog to Digital Conversion Methods
The dual-slope ADC:
The dual-slope ADC integrates the input voltage for a fixed time while the counter counts to n.
Control logic switches to the VREF input.
A fixed-slope ramp starts from –V as the counter counts. When it reaches 0 V, the counter output is latched.
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Analog to Digital Conversion Methods
Successive Approximation ADC
Starting with the MSB, each bit in the successive approximation register (SAR) is activated and tested by the digital-to-analog converter (DAC).
After each test, the DAC produces an output voltage that represents the bit.
The comparator compares this voltage with the input signal. If the input is larger, the bit is retained; otherwise it is reset (0).
The method is fast and has
a fixed conversion time
for all inputs.
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Analog to Digital Conversion Methods
The Sigma-Delta ADC
With sigma-delta conversion, the difference between two samples of the analog input signal integrated and quantized. The density of 1s at the output is proportional to the input signal.
One option for the sigma-delta method is to count the one-bit quantized output for a set interval. The output of the counter is latched with the parallel binary code.
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Analog to Digital Conversion Methods
Sigma-delta ADCs can have high resolution and have advantages for rejecting noise signals (such as 60 Hz power line interference).
They are available in ICs with internal programmable amplifiers. For these reasons, they are widely used in instrumentation applications.
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Digital to Analog Conversion Methods
Binary Weighted Input DAC
The binary-weighted-input DAC is a basic DAC in which the input current in each resistor is proportional to the column weight in the binary numbering system. It requires very accurate resistors and identical HIGH level voltages for accuracy.
The MSB is represented by the largest current, so it has the smallest resistor. To simplify analysis, assume all current goes through Rf and none into the op-amp.
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Digital to Analog Conversion Methods
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Digital to Analog Conversion Methods
The R2R Ladder
The R-2R ladder requires only two values of resistors. By calculating a Thevenin equivalent circuit for each input, you can show that the output is proportional to the binary weight of inputs that are HIGH.
Digital to Analog Conversion Methods
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Digital to Analog Conversion Methods
Reconstruction Filter
After converting a digital signal to analog, it is passed through a low-pass “reconstruction filter” to smooth the stair steps in the output. The cutoff frequency of the reconstruction filter is often set to the same limit as the anti-aliasing filter, to block higher harmonics due to the digitizing process.
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Summary
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Digital Signal Processing Basics
Converting Analog Signals to Digital
Analog – to – Digital Conversion Methods
The Digital Signal Processor ( DSP)
Digital – to – Analog Conversion Methods
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