This paper deals with the problem of speech enhancement when a
corrupted speech signal with an additive colored noise is the only
information available for processing. Kalman filtering is known as
an effective speech enhancement technique, in which speech signal
is usually modeled as autoregressive (AR) process and represented
in the state-space domain.
This paper deals with the problem of speech enhancement when
only a corrupted speech signal is available for processing. Kalman
filtering is known as an effective speech enhancement technique,
in which speech signal is usually modeled as autoregressive (AR)
model and represented in the state-space domain.
If we have two individually sorted vectors "a" and "b" but they are not sorted with respect to each other and we want to merge them into vector "c" such that "c" is also a sorted vector. Then c=mergesorted(a,b) can be used.
This lab exercise will cover the use of AccelDSP’s design exploration capabilities that include mapping variables to memory and unrolling loop and vector operations. You will learn how to create different hardware architectures without modifying the MATLAB source to explore different area/performance tradeoffs.
來自澳大利亞Qeensland大學(xué)的計算機視覺Matlab工具箱。
This Toolbox provides a number of functions that are useful in computer vision,
machine vision and related areas. It is a somewhat eclectic collection reflecting
the author s personal interest in areas of photometry, photogrammetry, colorimetry. It
covers functions such as image file reading and writing, filtering, segmentation,
feature extraction, camera calibration, camera exterior orientation, display,
color space conversion and blackbody radiators. The Toolbox, combined
with MATLAB and a modern workstation computer, is a useful and convenient
environment for investigation of machine vision algorithms. It is possible to use
MEX files to interface with image acquisition hardware ranging from simple
framegrabbers to Datacube servers.
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!-- Fortran code for FDTD with Berenger PMLs, version 1.0, May 1999
!-- by Jos Bergervoet.
!-- Plot field and/or Poynting vector S around radiating linear dipole
A general technique for the recovery of signi cant
image features is presented. The technique is based on
the mean shift algorithm, a simple nonparametric pro-
cedure for estimating density gradients. Drawbacks of
the current methods (including robust clustering) are
avoided. Feature space of any nature can be processed,
and as an example, color image segmentation is dis-
cussed. The segmentation is completely autonomous,
only its class is chosen by the user. Thus, the same
program can produce a high quality edge image, or pro-
vide, by extracting all the signi cant colors, a prepro-
cessor for content-based query systems. A 512 512
color image is analyzed in less than 10 seconds on a
standard workstation. Gray level images are handled
as color images having only the lightness coordinate
