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EE449 Computer Architecture

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    1. EE449 Computer Architecture

    3. How Do We Detect a Pulse? Two Methods for Measuring the Signal Analog Solution Transmitted signal is amplified and compared to a reference (threshold) voltage. Triggers board interrupts and declares a found signal Digital Processing Solution Transmitted signal is amplified and sampled (via on board ADC) Sampled signal is processed and correlated with a similar signal in memory Analog Processing: Discrete components = easy ($) Digital Processing: Algorithms = difficult ($$$)

    4. Why Not Always Use Analog?! Analog Signal Processing - Problems Error In Measurement Noise Delay in measurement due to threshold voltage level (small in this example)

    5. Why Not Always Use Analog?! (2) Delay is small who cares? Time of Flight Measurement Accurate prediction of distance from 1 node to the next depends upon reliable estimate of pulse travel time To minimize error threshold must be set low enough to accurately predict initial signal, but not low enough to trigger from noise Analog Detection adds delay to TOF measurement! Small delays = inaccurate results

    6. Analog Processing Techniques How is this done? Initialize board at transmitter to trigger transducer At receiver end, signal processing is done with discrete components Operation Amplifiers (Op-Amps) amplify signal Resistor networks generate reference voltage Capacitors filter out AC components of signal to allow for amplifiers work

    7. Circuit Schematic

    8. Greater Accuracy DSP Techniques What is our alternative? Use the on board processor! Digital Signal Processing Detect a Signal Quickly Sample the waveform Convert sample signal to the frequency domain Perform the correlation of the received signal with one in memory The resulting waveform gives an estimate of how well the received signal represents the signal in memory

    9. Greater Accuracy DSP Techniques (2) The Frequency Domain Assume some signal processing background any signals and systems book for information A Fourier transform allows us to look at the frequency components of any signal Implementation: DFT, FFT: DFT simpler easier to code for initial tests

    10. Greater Accuracy DSP Techniques (3) The DFT makes our lives simpler Convolution in time domain: Convolution in frequency domain: Correlation in frequency domain:

    11. Correlation: What Actually Happens? Different, but similar data sets Correlation: comparing the superposition of the signals, with the reference (memory) sliding to the left or right Example: If the reference is a close copy of the received signal and lags for some t, the correlation will be a positive value; if it leads the correlation will produce large negative values Correlation, like convolution is cyclic: wrap around problem Pad sampled function

    12. Correlation: What Actually Happens? (2)

    13. Results: Analog Analog Solution Circuit used closed loop gains for ~90 and ~600 Approximately 1:6 gain ratio Test case: supply voltage of 3.3V, and driving the transmitter with 3.3Vpp 40KHz square wave Good response: 8m (worst case) 100mV Vpp output still usable

    14. Results: Algorithm DSP Solution Coded DFT, IDFT (simplicity) in C Test Case: echo waveform generated in MATLAB was processed and compared to the same algorithm using MATLABS FFT, IFFT functions Little error between test cases, due to rounding in C program, MATLAB FFT computation Algorithm is successful!

    15. Results: Algorithm (2)

    16. Results: Algorithm (3)

    17. Future Work (Dimitrios) Analog further work Currently, gain ratio between two stages may be high Solution: add two more stages, reduce step up ratio Added power consumption Next step: create board Algorithm further work Correlation routine needs to be compared with a threshold to declare if waveforms are similar Requires ADC to sample real data Possible need to zero pad received data? What's left? ADC initialization Algorithm testing on actual platform

    18. Lessons Learned Good overall project Well suited for my background: circuit design, some programming Utilized wide range of knowledge, analog design, signal processing methods, C programming, MATLAB Good understanding of Ultrasound detection, processing Obstacles Algorithm development Simulink yields wrong results (!!!) Transition to using board (ran out of time) Thanks: Professor Savvides, Professor Kindlmann