1. Stochastic Resonance n Adding Noise to a signal can help in its detection. n Just the right amount of noise must be added (resonance) n SR in Nature –Periodic ice age prediction –Crayfish warnings of approaching bass - a periodic fin motion –Dogfish spit noise to better detection. –Neurons

2. Stochastic Resonance n What Causes SR? –Non-Additive Noise –Nonlinearities n Mathematical Definition –There is none

3. n Example: Miyamoto Resonance Add noise to image and threshold. Vary noise. 90120 180 240 0 15 30 60

4. Most Studied System 1/s dx / dt x ax-bx 3 ++  sin(  t) (t)(t)

5. Numerics (Mataim et al. 1998)

8. Comparison of NP Optimal Detector n Coherent NP Correlator vs. Stochastic Resonance n Assumptions –Compared NP on input and after SR nonlinearity n Detector COHERENT n Noise = White Gaussian Galdi, Pierro, Pinto (Phys Review E, June 1998).

9. Comparison of NP Optimal Detectors n Coherent NP Correlator vs. Stochastic Resonance (4 dB) Galdi, Pierro, Pinto (Phys Review E, June 1998).

10. Noncoherent n Noncoherent SR sign detector Galdi, Pierro, Pinto (Phys Review E, June 1998).

11. Noncoherent Results n Noncoherent correlator is about 3dB worse than coherent. n Noncoherent SR is better than noncoherent correlator for low SNR Galdi, Pierro, Pinto (Phys Review E, June 1998).