{"id":1402,"date":"2019-02-16T23:29:58","date_gmt":"2019-02-17T07:29:58","guid":{"rendered":"http:\/\/wonghoi.humgar.com\/blog\/?page_id=1402"},"modified":"2019-02-16T23:48:36","modified_gmt":"2019-02-17T07:48:36","slug":"oversimplified-signals-and-systems-5-5-computing-convolution","status":"publish","type":"page","link":"https:\/\/wonghoi.humgar.com\/blog\/oversimplified-signals-and-systems-5-5-computing-convolution\/","title":{"rendered":"Oversimplified: Signals and Systems (5.5) \u2013 Computing convolution"},"content":{"rendered":"

As far as computing convolution is concerned, it turns out if you are doing discrete time convolution of finite number of samples by hand, the most intuitive way: copy-scale-paste perspective is the easiest. It’s done exactly the same way as your grade school long multiplication<\/a> except you don’t do any carries.<\/p>\n

I recommend ignoring flip-and-drag in the traditional teaching approach in signals and systems. It is a variable renaming trick that has no context associated with it whatsoever. It is counter-intuitive because it involves switching the roles between time (\"t\") and delay (\"\tau\") in your head.<\/p>\n

Flip-and-drag might be the only approach ONLY IF<\/strong> the signal is described in the form of functions where you have uncountable (continuous time) or infinite number of time spots to work on AND<\/strong>:<\/p>\n