Signals
     Flow of information
     Measured quantity that varies with time (or 
      position)
     Electrical signal received from a transducer 
      (microphone, thermometer, accelerometer, 
      antenna, etc.)
     Electrical signal that controls a process
                               
     SVD background 
      The Singular Value Decomposition (SVD) of a rectangular matrix A 
        is a decomposition of the form 
                 T
        A = U S V  
      U and V are orthogonal matrices 
      and S is a diagonal matrix 
      The singular vectors form orthonormal bases, and the important 
        relation 
      A v = s u  
            i   i i
      shows that each right singular vectors is mapped onto the 
        corresponding left singular vector, and the "magnification factor" is 
        the corresponding singular value. 
                                          
    What is Digital Signal 
    Processing?
     Digital signal processing (DSP) is the study 
      of signals in a digital representation and the 
      processing methods of these signals.
      DSP includes subfields like: audio signal 
      processing, control engineering, digital image 
      processing and speech processing.
                               
                               
     Noise reduction 
      The SVD has also applications in digital signal processing, e.g., as a 
        method for noise reduction. The central idea is to let a matrix A 
        represent the noisy signal, compute the SVD, and then discard 
        small singular values of A. It can be shown that the small singular 
        values mainly represent the noise, and thus the rank-k matrix A  
                                                                       k
        represents a filtered signal with less noise.