Rice University ELEC 301 project looking at the use of Weiner filters in the deconvolution of multiple noisy astronomical images into a single, clean image.
Final remarks on our deconvolution project.
Conclusion of determining room inpulse responses and deconvolving the response out of a recorded signal.
How to isolate two convolved signals.
Comparison of deconvolved recorded signal and input. We also compare the theoretical and actual impulse responses.
This subject describes and illustrates computational approaches to solving problems in systems biology. A series of case-studies will be explored that demonstrate how an effective match between the statement of a biological problem and the selection of an appropriate algorithm or computational technique can lead to fundamental advances. The subject will cover several discrete and numerical algorithms used in simulation, feature extraction, and optimization for molecular, network, and systems models in biology.
The module provides an introduction into the concepts of image restoration and filtering.
Background information regarding the recreation of sound using deconvolution.
Theory behind deconvolution of recorded sound to reproduce an input.
ELEC 301 project by William Howison, Chris Lamontagne, Bryce Luna, and David Newell. Given the output of a system and the system characteristics we can determine the input. We will determine the system characteristics of two rooms by playing an (approximate) impulse and recording the impulse response, and then we will play music into the same rooms and record the output. Using MATLAB we will deconvolve the output with the system response to determine a rough approximation of the input.
Conclusion of determining room inpulse responses and deconvolving the response out of a recorded signal.
These are the m-files our group found necessary to produce a deconvolved signal.
