Signal processing is an EE subfield that focuses on analyzing, modifying, and synthesizing signals like sound, images, and scientific measurements. Signal processing techniques are often wont to improve transmission, storage efficiency, and subjective quality and to also emphasize or detect components of interest during a measured signal. Statistical signal processing is an approach that treats signals as stochastic processes, utilizing their statistical properties to perform signal processing tasks. Statistical techniques are widely utilized in signal processing applications. For example, one can model the probability distribution of noise incurred when photographing a picture and construct techniques supported this model to scale back the noise within the resulting image. Nonlinear signal processing involves the analysis and processing of signals produced from nonlinear systems and maybe within the time, frequency, or Spatio-temporal domains. Nonlinear systems can produce highly complex behaviors including bifurcations, chaos, harmonics, and subharmonics which can't be produced or analyzed using linear methods.