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Description: /*
* EULER S ALGORITHM 5.1
*
* TO APPROXIMATE THE SOLUTION OF THE INITIAL VALUE PROBLEM:
* Y = F(T,Y), A<=T<=B, Y(A) = ALPHA,
* AT N+1 EQUALLY SPACED POINTS IN THE INTERVAL [A,B].
*
* INPUT: ENDPOINTS A,B INITIAL CONDITION ALPHA INTEGER N.
*
* OUTPUT: APPROXIMATION W TO Y AT THE (N+1) VALUES OF T.
*/-/ EULER * S * 5.1 * * ALGORITHM TO APPROXIMATE THE SOLUTION OF THE INITIAL VALUE PROBLEM : * Y = F (T, Y), A
Platform: |
Size: 105117 |
Author: JackHou |
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Description: PlotSphereIntensity(azimuth, elevation)
PlotSphereIntensity(azimuth, elevation, intensity)
h = PlotSphereIntensity(...)
Plots the intensity (as color) of a number of points on a unit sphere.
Input:
azimuth (phi), in degrees
elevation (theta), in degrees
intensity (optional, if not provided, a green sphere is produced)
All inputs must be vectors or matrices of the same size. Data does not have to be evenly spaced. When there aren t enough points to draw a smooth sphere, additional points (with color) are interpolated.
Output:
h - a handle to the patch object
The axes are also plotted:
positive x axis is red
positive y axis is green
positive z axis is blue
Platform: |
Size: 2668 |
Author: dake |
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Description: /*
* EULER S ALGORITHM 5.1
*
* TO APPROXIMATE THE SOLUTION OF THE INITIAL VALUE PROBLEM:
* Y = F(T,Y), A<=T<=B, Y(A) = ALPHA,
* AT N+1 EQUALLY SPACED POINTS IN THE INTERVAL [A,B].
*
* INPUT: ENDPOINTS A,B INITIAL CONDITION ALPHA INTEGER N.
*
* OUTPUT: APPROXIMATION W TO Y AT THE (N+1) VALUES OF T.
*/-/ EULER* S* 5.1** ALGORITHM TO APPROXIMATE THE SOLUTION OF THE INITIAL VALUE PROBLEM :* Y = F (T, Y), A
Platform: |
Size: 422912 |
Author: JackHou |
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Description: PlotSphereIntensity(azimuth, elevation)
PlotSphereIntensity(azimuth, elevation, intensity)
h = PlotSphereIntensity(...)
Plots the intensity (as color) of a number of points on a unit sphere.
Input:
azimuth (phi), in degrees
elevation (theta), in degrees
intensity (optional, if not provided, a green sphere is produced)
All inputs must be vectors or matrices of the same size. Data does not have to be evenly spaced. When there aren t enough points to draw a smooth sphere, additional points (with color) are interpolated.
Output:
h - a handle to the patch object
The axes are also plotted:
positive x axis is red
positive y axis is green
positive z axis is blue
-PlotSphereIntensity(azimuth, elevation)
PlotSphereIntensity(azimuth, elevation, intensity)
h = PlotSphereIntensity(...)
Plots the intensity (as color) of a number of points on a unit sphere.
Input:
azimuth (phi), in degrees
elevation (theta), in degrees
intensity (optional, if not provided, a green sphere is produced)
All inputs must be vectors or matrices of the same size. Data does not have to be evenly spaced. When there aren t enough points to draw a smooth sphere, additional points (with color) are interpolated.
Output:
h- a handle to the patch object
The axes are also plotted:
positive x axis is red
positive y axis is green
positive z axis is blue
Platform: |
Size: 2048 |
Author: dake |
Hits:
Description: 自适应均衡器设计，采用分数间隔、判决反馈结构，MMSE算法。-Adapt a FSE+DFE combination for square M-QAM over T/2-spaced microwave channels with AWGN initialized by Unbiased MMSE-FSE receiver
Platform: |
Size: 8192 |
Author: xiaopai |
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Description: These M-files generate received T/-spaced, noisy signals from empirically-measured T/2-spaced AppSigTec channels and simulates a blind adaptive equalizer updated with Constant Modulus Algorithm.
Platform: |
Size: 2048 |
Author: Morteza Babaee |
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Description: 画单位球体的以颜色为渐近的色彩图
由方位角 高程 密度等来构建-Plots the intensity (as color) of a number of points on a unit sphere.
Input:
azimuth (phi), in degrees
elevation (theta), in degrees
intensity (optional, if not provided, a green sphere is produced)
All inputs must be vectors or matrices of the same size.
Data does not have to be evenly spaced. When there aren t enough points
to draw a smooth sphere, additional points (with color) are
Platform: |
Size: 4096 |
Author: 凌子 |
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Description: T/2 -spaced FSE MMSE equalizer sim
chnnel: carrier offset-T/2-spaced FSE MMSE equalizer sim
chnnel: carrier offset
Platform: |
Size: 1024 |
Author: Kai |
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Description: Author: Vinay Uday Prabhu
E-mail: vinay_u_prabhu@yahoo.co.uk
Function: Comparison of the performances of the LS and the MMSE channel estimators
for a 64 sub carrier OFDM system based on the parameter of Mean square error
Assumptions: The channel is assumed to be g(t)=delta(t-0.5 Ts)+delta(t-3.5 Ts)
{Fractionally spaced}
For more information on the theory and formulae used , please do refer to the paper On
"Channel Estimation In OFDM systems" By Jan-Jaap van de Beek, Ove Edfors, Magnus Sandell
Sarah Kate wilson and Petr Ola Borjesson In proceedings Of VTC 95 Vol 2 pg.815-819
- Author: Vinay Uday Prabhu
E-mail: vinay_u_prabhu@yahoo.co.uk
Function: Comparison of the performances of the LS and the MMSE channel estimators
for a 64 sub carrier OFDM system based on the parameter of Mean square error
Assumptions: The channel is assumed to be g(t)=delta(t-0.5 Ts)+delta(t-3.5 Ts)
{Fractionally spaced}
For more information on the theory and formulae used , please do refer to the paper On
"Channel Estimation In OFDM systems" By Jan-Jaap van de Beek, Ove Edfors, Magnus Sandell
Sarah Kate wilson and Petr Ola Borjesson In proceedings Of VTC 95 Vol 2 pg.815-819
Platform: |
Size: 2048 |
Author: venkat |
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Description: it is a constant modulus algorithm in fractional spaced error
Platform: |
Size: 1024 |
Author: chakri143 |
Hits:
Description: FASTLOMB caculates the Lomb normalized periodogram (aka Lomb-Scargle, Gauss-Vanicek or Least-Squares spectrum) of a vector x with coordinates in t.
The coordinates need not be equally spaced. In fact if they are, it is probably preferable to use PWELCH or SPECTRUM. For more details on the Lomb normalized periodogram, see the excellent section 13.8 in [1], pp. 569-577.
This code is a transcription of the Fortran subroutine fasper in [1] (pp.575-577), so it is a really fast (albeit not really exact) implementation of the Lomb periodogram. Also Matlab s characteristics have been taken into account in order to make it even faster for Matlab. For an exact calculation of the Lomb periodogram use LOMB, which is however about 100 times slower.-FASTLOMB caculates the Lomb normalized periodogram (aka Lomb-Scargle, Gauss-Vanicek or Least-Squares spectrum) of a vector x with coordinates in t.
The coordinates need not be equally spaced. In fact if they are, it is probably preferable to use PWELCH or SPECTRUM. For more details on the Lomb normalized periodogram, see the excellent section 13.8 in [1], pp. 569-577.
This code is a transcription of the Fortran subroutine fasper in [1] (pp.575-577), so it is a really fast (albeit not really exact) implementation of the Lomb periodogram. Also Matlab s characteristics have been taken into account in order to make it even faster for Matlab. For an exact calculation of the Lomb periodogram use LOMB, which is however about 100 times slower.
Platform: |
Size: 8192 |
Author: csar |
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Description: FASTLOMB caculates the Lomb normalized periodogram (aka Lomb-Scargle, Gauss-Vanicek or Least-Squares spectrum) of a vector x with coordinates in t.
The coordinates need not be equally spaced. In fact if they are, it is probably preferable to use PWELCH or SPECTRUM. For more details on the Lomb normalized periodogram, see the excellent section 13.8 in [1], pp. 569-577.
This code is a transcription of the Fortran subroutine fasper in [1] (pp.575-577), so it is a really fast (albeit not really exact) implementation of the Lomb periodogram. Also Matlab s characteristics have been taken into account in order to make it even faster for Matlab. For an exact calculation of the Lomb periodogram use LOMB, which is however about 100 times slower.-FASTLOMB caculates the Lomb normalized periodogram (aka Lomb-Scargle, Gauss-Vanicek or Least-Squares spectrum) of a vector x with coordinates in t.
The coordinates need not be equally spaced. In fact if they are, it is probably preferable to use PWELCH or SPECTRUM. For more details on the Lomb normalized periodogram, see the excellent section 13.8 in [1], pp. 569-577.
This code is a transcription of the Fortran subroutine fasper in [1] (pp.575-577), so it is a really fast (albeit not really exact) implementation of the Lomb periodogram. Also Matlab s characteristics have been taken into account in order to make it even faster for Matlab. For an exact calculation of the Lomb periodogram use LOMB, which is however about 100 times slower.
Platform: |
Size: 8192 |
Author: csar |
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Description: 一个很好的Steerable图片过滤器，很强大，值得参考-This program can be used to evaluate the first directional derivative of an image. The orientation of the filter can be specified by the user. In general, these filters could be useful for edge detection and image analysis.
The filters created by this program are derived from the "steerable filters" presented in:
W. T. Freeman and E. H. Adelson, "The Design and Use of Steerable Filters", IEEE PAMI, 1991.
A demonstration program (runDemo.m) is included which will create an animation showing the directional derivatives evenly-spaced from 0 degrees to 360 degrees (in 15 degree increments
Platform: |
Size: 337920 |
Author: LiMingTian |
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Description: laplace_orbit_fit() implements the Laplace method for orbit determination from 3 distinct azimuth/elevation observations of a body. The underlying theory is described in Bate, White and Mueller. Note that the observations must be closely spaced in order to yield good results.
The other functions are used by laplace_orbit_fit(), but they can be used on their own because they provide useful algorithms for coordinate conversions and Julian Date calculations.
laplace_orbit_fit() Inputs:
<lat>: Observer latitude (radians). North latitudes are +ve.
<lon>: Observer longitude (radians). East longitudes are +ve.
<alt>: Observer altitude (meters)
<T> : Row vector with three distinct Julian Dates for the observations
<AZI_ELE>: 2x3 matrix. Row 1 must contain azimuth (radians) and row 2 the elevation (radians) for the 3 observations
Platform: |
Size: 4096 |
Author: manu |
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Description: The problem of channel equalization via channel
identification (CEQCID) that has previously been considered by a
handful of researchers is explored further. An efficient algorithm
for mapping the channel parameters to the equalizers coeficients
is proposed. The proposed scheme is compared with a lattice
least squares (LS) based receivers. For the particular application
of the high frequency (HF) radio channels, we find that the
CEQCID has lower computational complexity. In terms of the
tracking performance, also, the CEQCID has been found to be
superior to the LS based receivers [4]. We emphasize on the
implementation of a fractionally tap-spaced decision feedback
equalizer (DFE) and compare that with the T-spaced DFE of
[4]. We show that the former is a better choice for the multipath
HF channel-The problem of channel equalization via channel
identification (CEQCID) that has previously been considered by a
handful of researchers is explored further. An efficient algorithm
for mapping the channel parameters to the equalizers coeficients
is proposed. The proposed scheme is compared with a lattice
least squares (LS) based receivers. For the particular application
of the high frequency (HF) radio channels, we find that the
CEQCID has lower computational complexity. In terms of the
tracking performance, also, the CEQCID has been found to be
superior to the LS based receivers [4]. We emphasize on the
implementation of a fractionally tap-spaced decision feedback
equalizer (DFE) and compare that with the T-spaced DFE of
[4]. We show that the former is a better choice for the multipath
HF channel
Platform: |
Size: 420864 |
Author: hamed |
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