PROJECTS ON SIGNAL PROCESSING

The important processes in signal processing

• Reconstruction
• Sampling

Recovering the original signal from sampled signal in which analyzes in frequency domain is made easier and simpler is reconstruction. The independent variable denotes time to modify terminology and notation which assume that unless noted. Statistical parameter applies kurtosis to measure the Gaussian nature of the signal.

Components required in changing AD to DA:

• Analog to Digital converter
• Analog filter
• Digital Processing
• Digital to analog conversion.

Signals are described as random signals as statistical models it is unusual using optimum linear filters called as wiener filters.

Sampling is transformation of a signal from continuous time to discrete time but taking in the values of continuous time signal that are multiple of quantity called sampling intervals.

Signal processing uses the following algorithms:

• Simulated Annealing
• Genetic algorithm
• Fuzzy logic system
• Particle colony optimization
• Particle swarm optimization

Process of signal manipulation:

• Signal delay/shifting
• Signal integration/differentiation
• Signal attenuation/amplification
• Signal subtraction/addition
• Signal division/multiplication

The block matlab simulink audio and multimedia file block must be chosen to get the acquired audio saved. There is several ways to reduce noise in the audio. The Bionic Wavelet transforms uses can be demonstrated as an adaptive wavelet change which is got from a non-linear auditory cochlear model to boost up speech signal.

Noteworthy capabilities of DSP methods:

• Equipment reproducibility is excellent
• The system design is accurate
• Exploitation characteristics is of high stability
• Supervision facility is outstanding