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Signal Processing With Computations on Sensed Data

Sparsity is characteristic of a signal that potentially allows us to represent information efficiently. We present an approach that enables efficient representations based on sparsity to be utilized throughout a signalprocessing system, with the aim of reducing the energy and/or resources required for computation, communication, and storage. The representation we focus on is compressive sensing. Its benefit is that compression is achieved with minimal computational cost through the use of random projections; however, a key drawback is that reconstruction is expensive. We focus on inference frameworks for signal analysis. We show that reconstruction can be avoided entirely by transforming signalprocessing operations (e.g., wavelet transforms, finite impulse response filters, etc.) such that they can be applied directly to the compressed representations. We present a methodology and a mathematical framework that achieve this goal and also enable significant computational-energy savings through operations over fewer input samples.

This enables explicit energy-versus-accuracy tradeoffs that are under the control of the designer. We demonstrate the approach through two case studies. First, we consider a system for neural prosthesis that extracts wavelet features directly from compressively sensed spikes. Through simulations, we show that spike sorting can be achieved with$54times$ fewer samples, providing an accuracy of 98.63% in spike count, 98.56% in firing-rate estimation, and 96.51% in determining the coefficient of variation; this compares with a baseline Nyquist-domain detector with corresponding performance of 98.97%, 99.69%, and 97.09%, respectively. Second, we consider a system for detecting epileptic seizures by extracting spectral-energy features directly from compressively sensed electroencephalogram. Through simulations of the end-to-end algorithm, we show that detection can be ach- eved with$21times$ fewer samples, providing a sensitivity of 94.43%, false alarm rate of 0.1543/h, and latency of 4.70 s; this compares with a baseline Nyquist-domain detector with corresponding performance of 96.03%, 0.1471/h, and 4.59 s, respectively.

Related Signal Processing Project Titles:

  1. Comparative Study of Advanced Signal Processing Techniques for Islanding Detection in a Hybrid Distributed Generation System.
  2. Localization Algorithm for the PD Source in Substation Based on L-Shaped Antenna Array Signal Processing.
  3. Digital High-Resolution Torque Sensor and Signal Processing.
  4. Signal Processing Challenges in Quantitative 3-D Cell Morphology: More than meets the eye.
  5. Razor Based Programmable Truncated Multiply and Accumulate, Energy-Reduction for Efficient Digital Signal Processing.
  6. Automated Histology Analysis: Opportunities for signal processing.
  7. Electric Machine Drive Design Improvements Through Control and Digital Signal Processing Techniques.
  8. On Sparse Methods for Array Signal Processing in the Presence of Interference.
  9. A Home Sleep Apnea Screening Device With Time-Domain Signal Processing and Autonomous Scoring Capability.
  10. Acoustic micro-Doppler signal processing with foveated electronic cochlea.]
  11. Signal Processing Oriented Approach for Big Data Privacy.
  12. Nonlinear Cognitive Signal Processing in Ultra-Low-Power Programmable Analog Hardware.
  13. Signal Processing in the Workplace [Social Sciences].
  14. Toward a Morphodynamic Model of the Cell: Signal processing for cell modeling.
  15. Soft-Core Dataflow Processor Architecture Optimized for Radar Signal Processing.
  16. Linewidth-Tolerant Joint Digital Signal Processing for 16QAM Nyquist WDM Superchannel.
  17. Block-Skew-Circulant Matrices in Complex-Valued Signal Processing.
  18. Signal Processing in Next-Generation Prosthetics [Special Reports].
  19. A UWB Radar Signal Processing Platform for Real-Time Human Respiratory Feature Extraction Based on Four-Segment Linear Waveform Model.

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