Noise Enhanced Sensory Signal Processing

Abstract

Understanding and emulating sensory information systems is a challenging task. The goal of this project was to develop the theory of noise enhanced signal processing (NESP) where the performance of some nonlinear systems may be enhanced by adding a suitable amount of noise to the input signal. The main objective of this project was to explore the applicability of NESP based approaches to enhance the performance of ``source blind'' signal processing algorithms. During this effort, we have explored the NESP mechanism for signal detection and estimation problems in a non-stationary and dynamic environment and developed some iterative learning algorithms to apply NESP based procedure with incomplete knowledge. We investigated image enhancement algorithms based on stochastic resonance (SR) noise which improve the performance of suboptimal image enhancers. We further explored the recently developed Compressive Sensing based measurement scheme in performing detection, classification and estimation with sparse signals and derived achievable performance limits. Results obtained have been documented in a number of technical publications.

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Document Details

Document Type
Technical Report
Publication Date
Jan 31, 2012
Accession Number
ADA567093

Entities

People

  • Pramod Varshney

Organizations

  • Syracuse University

Tags

Communities of Interest

  • Human Systems
  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Algorithms
  • Classification
  • Compressed Sensing
  • Detection
  • Detectors
  • False Alarms
  • Image Processing
  • Image Segmentation
  • Information Processing
  • Information Systems
  • Information Theory
  • Mathematical Models
  • Measurement
  • Multiobjective Optimization
  • Nonlinear Systems
  • Signal Detection
  • Signal Processing

Fields of Study

  • Engineering

Readers

  • Image Processing and Computer Vision.
  • Positioning, Navigation, and Timing (PNT) Technology.
  • Theoretical Analysis.