ECE 353 Introduction to Microprocessor Systems

1. ECE 353Introduction to Microprocessor Systems Michael G. Morrow, P.E. Week 14

2. Topics • Digital versus analog • Data acquisition systems • Quantization and aliasing • ADCs • DACs • Waveform Generation • ADuC7026 Analog Peripherals • Digital Filters and Audio Demos

3. Characteristics of Signals • Analog Signals • Infinite number of possible signal levels (values) • Can change at any instant to any other value • Bandwidth is potentially infinite • Analog signals are continuous in both time and value • There are no noise margins in analog! • Digital Signals • Signal level (value) only representable in fixed steps within a finite range • Only know the signals value at distinct instants in time • Bandwidth is limited to a finite value • Digital signals are discrete in time and value (they are a vector of values) • Signal can be exactly identified in the presence of some amount of noise

4. Why Use Digital Signals? • Pros • Digital signals can be faithfully stored and copied • Allows for numeric processing by digital computers (digital signal processing - DSP) • Lossy and lossless data compression possible • Can mathematically represent physically unrealizable systems • Cons • Cannot exactly represent or reconstruct the original analog signal • Requires greater bandwidth (uncompressed)

5. Data Acquisition Systems • Block Diagram • Isolation/Buffering • Amplification • Bandwidth-limiting • Sample and Hold • Analog-to-Digital Converter (ADC) • Shannon’s Sampling Theorem • FS > 2FMAX • Aliasing • Must be prevented - it can not be detected in the data • Anti-aliasing Filters

6. Data Acquisition Systems (cont) • Quantization • An ADC converts a continuous signal to a discrete digital value at each sample point. • The ADC uses some scheme to map the analog value to a digital code. • We will only discuss uniform (linear) quantization. • Quantization Noise • There is always uncertainty as to what the actual analog signal value was. • This is manifested as quantization noise.

7. Types of ADCs • Parallel (Flash) Converters • Successive Approximation Converters • Pipelined Converters • Also other types • Integrating (Dual-Slope) Converters • Slow, but noise immunity very good, can’t alias • Sigma-delta Converters • Commonly used for high resolution (16-24 bits) audio signal conversion at 44.1KHz or higher • Dramatically reduce anti-aliasing filter requirements by oversampling

8. Digital to Analog Converters (DACs) • Device Characteristics • Coding scheme • Output type and range • Resolution • Accuracy • Ideal DAC transfer characteristic • Errors • Offset • Gain • Nonlinearity • Latency and settling time • Output glitching

9. Digital to Analog Converters (DACs) • PWM DAC • R-2R Ladder DAC • Each input bit controls an analog switch • Op amp converts current sum to voltage • Reconstruction filters • What was the value of the signal between the samples?

10. Waveform Generation • DACs allow the generation of analog waveforms under digital control • Example – generate sinusoid • VOUT = VMAXsin(2πft) • Calculate directly as a function of t • Calculate as a function of the desired signal phase • Use lookup table to obtain sin/cos values, use index as a phase accumulator • Use complex vector rotation

11. ADuC7026 Analog Peripherals • 12-channel, 12-bit successive approximation ADC operating at up to 1MS/s • Bootloader code uses factory-programmed values to compensate for ADC gain and offset errors • Four 12-bit voltage output DACs • On-chip precision 2.5V voltage reference • External capacitor required • On-chip temperature sensor (+/-3°C)

12. Digital Filters • We can implement filters digitally that operate on digital signals • Advantages • No temperature/aging/drift characteristics • Repeatability • Can create identical filters • Implementation • Finite Impulse Response • No feedback • Stability guaranteed • Infinite Impulse Response • Uses feedback • Can be unstable

13. Hardware Quantization Aliasing FIR filter Audio Equalizer Audio Effects Echo Flanger Tremelo Frequency Translation Subharmonic Synthesis Karplus-Strong Guitar Synthesizer Vocoder DSP Demos

14. Wrapping Up • Homework #7 will be due on Friday, December 14th. • Final Exam on Tuesday, December 18th, at 5:05pm in room 2535, Engineering Hall. • Coverage is over all course material.

15. Pipelined ADC • Conversion is performed in stages by lower resolution (faster!) ADCs.

16. DSP Hardware • TMS320C6713 DSP, 225MHz • 1350 MFLOPs, 1800 MIPs • TLC320AIC23 16-bit stereo CODEC • 48KHz sample rate

17. Aliasing

18. Anti-aliasing Filters

19. Anti-aliasing Filters - Ideal

20. Anti-aliasing Filters - Ideal

21. Anti-aliasing Filters - Practical

22. Anti-aliasing Filters - Practical

23. Anti-aliasing Filters - Practical

24. Uniform Quantization Error function

25. Ideal DAC Transfer Characteristic

26. DAC Errors – Offset Error

27. DAC Errors – Gain Error

28. DAC Errors – Nonlinearity Errors

29. DigitalFilters

30. Reconstruction Filters • Back to our sampled signal – a sinusoid at ¼FS • How do we make the DAC output look like the original input?

31. PWM DAC • Use PWM digital output driver • LPF removes most of AC components

33. FIR Filter • The output y is the sum of the products of the last m samples x and the filter coefficients h.

34. Audio Equalizer

35. Audio Effects - Echo

36. Audio Effects - Flanger • The delay B is varied sinusoidally.

37. Audio Effects - Tremelo • Error in diagram – audio signal comes in where the sine generator is shown, modulating sinusoid comes in on upper port.

38. Audio Effects – Frequency Translation

39. Audio Effects – Subharmonic Synthesis

40. Karplus-Strong • Queue is filled with noise to start. • Output is the sum of the two elements at the head of the queue multiplied by a decay factor. • Output is fed back into the queue.

41. Vocoder • Uses the frequency spectrum of one signal to control the frequency response of the other signal. • Can also use white noise as the modulated signal.