ESE 531: Digital Signal Processing

Transcription

1 ESE 531: Digital Signal Processing Lec 23: April 20, 2017 Compressive Sensing Penn ESE 531 Spring 2017 Khanna

2 Previously! Today " DTFT, DFT, FFT practice " Compressive Sampling/Sensing Penn ESE 531 Spring 2017 Khanna 2

3 Example 2: Penn ESE 531 Spring Khanna 3

4 Example 2: Penn ESE 531 Spring Khanna 4

5 Example 2: Penn ESE 531 Spring Khanna 5

6 Compressive Sampling

7 Compressive Sampling 0! What is the rate you need to sample at? " At least Nyquist Anything t T Penn ESE 531 Spring 2017 Khanna Adapted from M. Lustig, EECS Berkeley 7

8 Compressive Sampling 0 Something! What is the rate you need to sample at? " Maybe less than Nyquist t T Penn ESE 531 Spring 2017 Khanna Adapted from M. Lustig, EECS Berkeley 8

9 First: Compression! Standard approach " First collect, then compress " Throw away unnecessary data Penn ESE 531 Spring 2017 Khanna Adapted from M. Lustig, EECS Berkeley 9

10 First: Compression! Examples " Audio 10x " Raw audio: 44.1kHz, 16bit, stereo = 1378 Kbit/sec " MP3: 44.1kHz, 16 bit, stereo = 128 Kbit/sec " Images 22x " Raw image (RGB): 24bit/pixel " JPEG: 1280x960, normal = 1.09bit/pixel " Videos 75x " Raw Video: (480x360)p/frame x 24b/p x 24frames/s kHz x 16b x 2 = 98,578 Kbit/s " MPEG4: 1300 Kbit/s Penn ESE 531 Spring 2017 Khanna Adapted from M. Lustig, EECS Berkeley 10

11 First: Compression! Almost all compression algorithm use transform coding " mp3: DCT " JPEG: DCT " JPEG2000: Wavelet " MPEG: DCT & time-difference Penn ESE 531 Spring 2017 Khanna Adapted from M. Lustig, EECS Berkeley 11

12 First: Compression! Almost all compression algorithm use transform coding " mp3: DCT " JPEG: DCT " JPEG2000: Wavelet " MPEG: DCT & time-difference Penn ESE 531 Spring 2017 Khanna Adapted from M. Lustig, EECS Berkeley 12

13 First: Compression! Almost all compression algorithm use transform coding " mp3: DCT " JPEG: DCT " JPEG2000: Wavelet " MPEG: DCT & time-difference Penn ESE 531 Spring 2017 Khanna Adapted from M. Lustig, EECS Berkeley 13

14 Sparse Transform Penn ESE 531 Spring 2017 Khanna Adapted from M. Lustig, EECS Berkeley 14

15 Sparse Transform Penn ESE 531 Spring 2017 Khanna Adapted from M. Lustig, EECS Berkeley 15

16 Compressive Sensing/Sampling! Standard approach " First collect, then compress " Throw away unnecessary data Penn ESE 531 Spring 2017 Khanna Adapted from M. Lustig, EECS Berkeley 16

17 Compressive Sampling! Sample at lower than the Nyquist rate and still accurately recover the signal, and in some cases exactly recover 17

18 Compressive Sampling! Sample at lower than the Nyquist rate and still accurately recover the signal, and in some cases exactly recover Sparse signal in time Frequency spectrum 18

19 Compressive Sampling! Sample at lower than the Nyquist rate and still accurately recover the signal, and in some cases exactly recover Undersampled in time 19

20 Compressive Sampling! Sample at lower than the Nyquist rate and still accurately recover the signal, and in some cases exactly recover Undersampled in time 20

21 Compressive Sampling! Sample at lower than the Nyquist rate and still accurately recover the signal, and in some cases exactly recover Undersampled in time Undersampled in frequency (reconstructed in time with IFFT) 21

22 Compressive Sampling! Sample at lower than the Nyquist rate and still accurately recover the signal, and in some cases exactly recover Undersampled in time Undersampled in frequency (reconstructed in time with IFFT) Requires sparsity and incoherent sampling 22

23 Compressive Sampling! Sense signal randomly M times " M > C μ 2 (Φ,Ψ) S log N! Recover with linear program 23

24 Compressive Sampling! Sense signal randomly M times " M > C μ 2 (Φ,Ψ) S log N! Recover with linear program 24

25 Compressive Sampling: Simple Example 25

26 Example: Sum of Sinusoids! Two relevant knobs " percentage of Nyquist samples as altered by adjusting optimization factor, C " input signal duration, T " Data block size 26

27 Example: Increasing C C=1 7% C=2 14% C= % C=3 20.9% C=5 34.7% C= % 27

28 Example: Increasing C # f err,max within 10 mhz # p err,max decreasing 28

29 Example: Increasing T T=5 T=10 T=15 T=30 T=60 T=120 29

30 Example: Increasing T # f err,max decreasing # p err,max decreasing 30

31 Biometric Example: Parkinson s Tremors! 6 Subjects of real tremor data " collected using low intensity velocity-transducing laser recording aimed at reflective tape attached to the subjects finger recording the finger velocity " All show Parkinson s tremor in the 4-6 Hz range. " Subject 8 shows activity at two higher frequencies " Subject 4 appears to have two tremors very close to each other in frequency 31

32 Compressive Sampling: Real Data 32

33 Biometric Example: Parkinson s Tremors # C=10.5, T=30 # 20% Nyquist required samples 33

34 Biometric Example: Parkinson s Tremors # Tremors detected within 100 mhz 34

35 Implementing Compressive Sampling! Devised a way to randomly sample 20% of the Nyquist required samples and still detect the tremor frequencies within 100mHz 35

36 Implementing Compressive Sampling! Devised a way to randomly sample 20% of the Nyquist required samples and still detect the tremor frequencies within 100mHz " Requires post processing to randomly sample! 36

37 Implementing Compressive Sampling! Devised a way to randomly sample 20% of the Nyquist required samples and still detect the tremor frequencies within 100mHz " Requires post processing to randomly sample!! Implement hardware on chip to choose samples in real time " Only write to memory the chosen samples " Design random-like sequence generator " Only convert the chosen samples " Design low energy ADC 37

38 Big Ideas! Compressive Sampling " Integrated sensing/sampling, compression and processing " Based on sparsity and incoherency 38

39 Admin! Tania extra office hours " M 4-6pm! Tuesday lecture " Review " Final exam details " Old exam posted! Project " Due 4/25 39