PADS Power Aware Distributed Systems Algorithms

1. PADSPower Aware Distributed SystemsAlgorithms USC Information Sciences InstituteBrian Schott, Bob Parker, Ron Riley UCLAMani Srivastava Rockwell Science CenterCharles Chien

2. Algorithm DevelopmentOUTLINE • Multi-resolution acoustic beam-forming for tracking and cueing. • Wireless video conferencing over sensor network. • Surveillance video processing. • Laplacian-Pyramid Los-less Image Compaction for distributed ATR.

3. Algorithm DevelopmentAcoustic Beam Forming • Accomplishments • Obtained baseline tracking algorithms and acoustic data, for one and two targets, from the Army Research Laboratory. • Key algorithms modified by Alice Wang at MIT to run on 16-bit StrongARM. • Distributed data and algorithms on CD-ROMs to PACC developers. • ARL does not want these algorithms and data distributed to the public. • Progress • Developing lower power version of algorithms that do not require FFT. • Plans • Measure the power and error in line of bearing resulting from varying: • Number of microphones • Length of signal • Resolution of signal • Algorithm • Investigate using single microphone on a number of nodes for collaborative processing. • Apply algorithms to other data sets.

4. Baseline algorithms and acoustic/seismic signal database with 1 and 2 targets provided by the Army Research Laboratory. Remote surveillance for monitoring / targeting enemy vehicles. Microphone array with ~ 8’ diameter (geometry not restricted). Local processing at the sensor arrays for real-time tracking. Inexpensive, passive and non-line of sight capabilities. ARL Remote NettedAcoustic Detection System Acoustic Sensor Array Courtesy of N. Srour, Army Research Lab

5. LOB2 LOB3 LOB1 ARL Tracking Each cluster sends its LOB estimate to the end-user where tracking is done

6. Beamforming Algorithm • 2-sec of each channel is Fourier Transformed with Hamming window. • Frequency domain beamforming algorithm estimates target bearing along 12 beams. • Delay-sum BF delays each signal by a time specified by mic array geometry for coincidence on a given beam. • Code developed in C++ at ARL. • Alice Wang (MIT) converted key subroutines to integer math for implementation on StrongARM processor. • ISI-East is developing an algorithm to shift the signals in the time domain requiring interpolation rather than FFT. This is expected to have little impact on results but require significantly less power.

7. Algorithm DevelopmentWireless Video • Accomplishments • Adapted MBONE open-source multicast video tool, VIC, to support USB cameras on Laptop PCs over wireless network. • Demonstrated at 29 Palms data collection, with enthusiastic response. • Improved exposure/white balance compensation between video frames for enhanced compression. • Progress • Nearly completed adaptation of VIC and Linux drivers to support 16-bit PCMCIA cameras enabling video collection on iPAQ handheld PC. • Plans • Adapt VIC and Linux drivers to support Mercury sleeve with built-in camera for iPAQ handheld PCs.

8. Wireless Surveillance and Video Conferencing • Live video display demonstrated at 29 Palms using multicast MBONE video tools over wireless network. • We’re very interested in integrating some video capability into fielded sensor node for future experiments.

9. Compaq iPAQ H3600 iPAQ handheld PC provides user interface to sensor network. • 206 MHz StrongARM processor • 16 MB persistent flash • 32/64 MB RAM • Serial/USB ports • Expansion BUS • 1, 2-slot PCMCIA sleeves • VGA out, microdrive, networking, etc. • CF sleeve • Custom sleeves • 320x240 display with 4096 colors • Rechargeable batteries (Li-ion) X

10. Compaq supports Linux through http://www.handhelds.org All iPAQ hardware now supported under Linux ISI-East contributed support for suspend/resume iPKG (Itsy Package Management System) Embedded Linux packaging and distribution system Developed at ISI-East Now an integrated part of standard handhelds.org Linux Distributions. Linux on iPAQ

11. BackPAQ Research Sleeve • Digital Camera • 32 MB Flash RAM • 2 PCMCIA Sleeves • Accelerometer • Programmable Logic, (FPGA) • Digital IO Port http://crl.research.compaq.com/projects/mercury/

12. Algorithm Development Video Surveillance • Accomplishments • Improved exposure/white balance compensation between video frames for enhanced change-detection. • Progress • Improving continuous update to background image for long term operation. • Plans • Create a baseline of surveillance video relevant to the application for testing algorithms for power-aware change-detection, compression, …. • Compare power efficiency of various available lossy compression codecs.

13. Video SurveillanceBackground Segmentation • Collect reference frame as part of sensor set up. • Compress and transmit static frame to potential viewers. • Collect image based on schedule or trigger. • Determine and invert changes in background from reference due to lighting and sensor artifacts. • Segment foreground based on threshold of color distance from reference. • Erode & dilate to remove speckle noise and voids. • Transmit foreground, if large enough, for overlay on static reference image.

14. Video SurveillanceBackground Correction • Cameras employ dynamic white-balance and gain to optimize use of dynamic range of 8-bit / color images. • Changes in sun angle, clouds, and adding foreground objects can lead to changes in brightness and color of all pixels in image. • Find relative gain of background between reference and current image for each color channel. • Compute histogram of ratio of current to reference pixels, and chose the most common (mode.) • Relative exposure of background should be constant, relative exposure of foreground to background is more random and often contains fewer pixels. • Only blocks of foreground pixels (often none) are encoded and transmitted for overlay on a reference image. • Lossy compression for viewing, lossless compaction for distributed processing ATR.

15. Algorithm Development Laplacian Pyramid • Accomplishments • Developed Laplacian-Pyramid Lossless Image Compaction for distributed multi-resolution image processing. • Progress • Making current version available to group as baseline. • Plans • Create Huffman code tables from Laplacian distribution estimate based on standard deviation of image differences. • Send distribution parameters rather than transmit code table. • Can use different code tables for each layer.

16. Laplacian PyramidHierarchical Image Coding Original Image Compaction (lossless encoding), not lossy compression. Scenario: • Distributed processing of objects found in video. • Compact image to save power on data transmission. • Avoid degradation of target recognition by lossy compression. • Previous video frame poor estimate of foreground objects. • Immediate results based on low resolution image. • Quality improves with each increment in resolution. • Stop transmitting when sufficient quality attained. Concept: • Image is successively downsampled by 2 and subtracted from previous layer to form a pyramid. • Averaging of blocks of pixels during downsampling is a gaussian blur, the difference is a Laplacian. Image Pyramid Laplacian Pyramid

17. Differential Pulse Code ModulationIntra-frame Compaction • Use pixels on previous lines and columns to predict current x = A + B + C where 1 =  +  + . • Best predictions are typically for the coefficients x = B + C - A • Encode difference between pixel and its predicted value with variable length encoding such as Huffman. • Predictive differencing maps histogram to Laplacian dist. P(d) = exp(-2 |d| / ) / [2] • Better prediction leads to a more peaked histogram, smaller standard deviation, producing more efficient compaction.

18. Variable Length Encoding • Based on image histogram, create coding table that translates most common values to fewer bits. • To minimize entropy, maximize coding efficiency, codeword should be chosen with lengths based on prob L(d) = -log2[P(d)], P(d) = Hist[d] / N • This produces an estimate of the minimum average length <L> = - { P(d) log2[P(d)] } • Huffman code iteratively combines the two least probable symbols to a single symbol, appending 0 to the code word for one and 1 to the other until there is only one symbol. • Modified Huffman: combine all outliers to single symbol and append 9-bit integer difference or 8-bit original. Sym -1 0 1 0.3 0.60.1 P - - - Code 0.4 0.6 0.4 1 - 0 1.0 1.0 1.0 01 1 00

19. Laplacian PyramidMethod • Use previously transmitted lower resolution image. • Simply encoding is inefficient due to 33% more pixels. • Only 3 in 2x2 downsample block required, fourth can be reconstructed from other 3 and downsampled image. • Downsample horizontally. • Save row difference on left. (9-bit) • Save row average on right. (8-bit) • Downsample right column vertically • Save column difference on top. (9-bit) • Save column average in downsampled image. (8-bit) • Similar to Ken Knowlton’s encoding.

20. Laplacian PyramidResults • Enables progressive reconstruction and achieves compaction nearly as good as intra-frame DPCM. • Use DPCM for initial frame, LP for increments in resolution. Average bits per pixel