Biosignal and Biomedical Image Processing MATLAB-Based Applications Muya phần 6 pptx

... image I(22:54,22:52) = .25; % Four squares of different shades I( 76; 1 06, 22:52) = .5; % against a black background I(22:52, 76: 1 06) = .75; I( 76: 1 06, 76: 1 06) = 1; % % Construct projections: Fan and ... routines provide local maxima and min- ima, and allows for manipulating the image s maxima and minima, which im- plement various fill-in effects. TLFeBOOK 360 Chapter 12 Th...

Ngày tải lên: 23/07/2014, 19:20

... 0.5) and the unsharpe filter (alpha = 0.2) are shown below: h unsharp = ͫ −0. 166 7 −0 .66 67 −0. 166 7 −0 .66 67 4.3333 −0 .66 67 −0. 166 7 −0 .66 67 −0. 166 7 ͬ ; h gaussian = ͫ 0.0113 0.0838 0.0113 0.0838 0 .61 93 ... frame 16 of the MRI image ( mri.tif ). Make a multiframe image of the basic image by rotating that image through 360 degrees. Use 36 frames (10 degrees per rotati...

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... logical, and pro- duce an output of type uint8 if the associated map length is less than or equal to 64 , or uint 16 if greater that 64 . N specifies the colormap depth and must be less than 65 ,5 36. For gray2ind the ... of the operations in Example 10 .6 below. Example 10 .6 Load the image of blood cells in blood.tiff in MATLAB’s image files. Convert the image to class inten...

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... proportions D(1,:) = .5*y ؉ .5*x ؉ .1*rand(1,N); D(2,:) = .2*y ؉ .7*x ؉ .1*rand(1,N); D(3,:) = .7*y ؉ .2*x ؉ .1*rand(1,N); D(4,:) = 6* y ؉ 24*x ؉ .2*rand(1,N); D(5,:) = .6* rand(1,N); % Noise only % % Center ... section. Adaptive filtering has a number of applications in biosignal processing. It can be used to suppress a narrowband noise source such as 60 Hz that is corrupt- ing a broa...

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... analysis applications only the subband signals are of interest and reconstruction is not needed, but in many wavelet applications, some operation is performed on the subband signals, y lp (n) and y hp (n), ... with a small bandwidth and a p eak a t 10 H z. TLFeBOOK 8 Advanced Signal Processing Techniques: Optimal and Adaptive Filters OPTIMAL SIGNAL PROCESSING: WIENER FILTERS The...

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... time and TLFeBOOK 150 Chapter 6 r xx (τ) = ∫ ∞ −∞ x(t) x(t +τ) dt (4) and r xx (n) = ∑ M k=1 x(k) x(k + n)(5) where τ and n are the shift of the waveform with respect to itself. In the standard ... standard graphics and are illustrated in the example below. Example 6. 1 Construct a time series consisting of two sequential sinu- soids of 10 and 40 Hz, each active for 0.5 sec (see...

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... Example 4.5 and Figure 4.13 % Bandstop filter with a passband gain of 1 between 0 and 100, % a stopband gain of -40 db between 300 and 400 Hz, % and an upper passband gain of 1 between 500 and fs/2 ... Design a bandstop filter having the following characteris- tics: a passband gain of 1 (0 db) between 0 and 100, a stopband gain of −40 db between 300 and 400 Hz, and an upper pa...

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... from Problem 6 in Chapter 2. Applying the Hamming widow to the data before calculating the FFT. 5. Repeat problem 4 above using the Welch method with 2 56 and 65 segment lengths and the window ... [-10 -10], 2 56) ;) Sig_noise assumes a sampling rate of 1 kHz. Use the Welch method. Find the spectrum of the waveform for segment lengths of 2 56 (no overlap) and 64 points with 0%, 50...

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... Generate bandlimited noise and compute and plot the auto- correlation function for two different bandwidths. % Example 2.2 and Figure 2.8 % Generate colored noise having two different bandwidths % and ... construct and plot the ensemble standard deviation. 2. Use the MATLAB autocorrelation and random number routine to plot the autocorrelation sequence of white noise. Use arrays of...

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... Implementation 265 Problems 270 10 Fundamentals of Image Processing: MATLAB Image Processing Toolbox 271 Image Processing Basics: MATLAB Image Formats 271 General Image Formats: Image Array Indexing ... signal processing and data acqui- sition while Chapter 2 includes topics that are basic to all aspects of signal and image processing. Chapters 3 and 4 cover class...

Ngày tải lên: 23/07/2014, 19:20