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[matlab] Invariant_Line_Segment_Matching DL : 0: function Invariant_Line_Feature_Matching ___DESCRIPTION___ Compare segmented line pairs as 4 dimentional line pair features ( Q1 , Q2 , Drelative , D? ) Example : Invariant_Line_Feature_Matching ( ) ___REFERENCE___ Paper 1 : Line Feature Matching Technique Based on an Eigenvector Approach Park, Lee, Lee - Ideal - CV and Im. Und. - 77, 263-283 - (2000)- function Invariant_Line_Feature_Matching ___DESCRIPTION___ Compare segmented line pairs as 4 dimentional line pair features ( Q1 , Q2 , Drelative , D? ) Example : Invariant_Line_Feature_Matching ( ) ___REFERENCE___ Paper 1 : Line Feature Matching Technique Based on an Eigenvector Approach Park, Lee, Lee- Ideal- CV and Im. Und.- 77, 263-283- (2000)
Date : 2025-12-26 Size : 27kb User : Mehmet
[matlab] walsh-decoding-with-hamming-distance DL : 0: Aim: do walsh coding with hamming distance.To compare WALSH decoding for synchronous CDMA with decoding using hamming distance. a) System should be supplied with correct/ corrupted version (one/ two bit) of transmitted walsh code(s) b) System should also have table for correct codes(walsh codes) c) System should ab able to deocde the same using hamming distance property. d) Copmare resutls for corrupted code(s) to check the efficacy of WALSH decoding technique (as in above experiment) compared to hamming distance decoding.-Aim: do walsh coding with hamming distance.To compare WALSH decoding for synchronous CDMA with decoding using hamming distance. a) System should be supplied with correct/ corrupted version (one/ two bit) of transmitted walsh code(s) b) System should also have table for correct codes(walsh codes) c) System should ab able to deocde the same using hamming distance property. d) Copmare resutls for corrupted code(s) to check the efficacy of WALSH decoding technique (as in above experiment) compared to hamming distance decoding.
Date : 2025-12-26 Size : 40kb User : dhara
[matlab] hw2d DL : 0: Obtain the solution using graphical method c. Obtain the solution using analytical method, i.e., conditions of optimality d. Obtain the solution using Steepest Descent Method e. Obtain the solution using Netwon’s method f. Obtain the solution using Marquart’s method g. Compare convergence speeds/efficiencies of the algorithms in (d), (e) and (f) h. Use different parameter value set (other than the ones suggested below) with the numerical methods to see how the performance of the methods would change under different parameter values and makes some comments on parameter sensitivity of the methods. Notes- Obtain the solution using graphical method c. Obtain the solution using analytical method, i.e., conditions of optimality d. Obtain the solution using Steepest Descent Method e. Obtain the solution using Netwon’s method f. Obtain the solution using Marquart’s method g. Compare convergence speeds/efficiencies of the algorithms in (d), (e) and (f) h. Use different parameter value set (other than the ones suggested below) with the numerical methods to see how the performance of the methods would change under different parameter values and makes some comments on parameter sensitivity of the methods. Notes
Date : 2025-12-26 Size : 2kb User : Volkan
[matlab] hw2e DL : 0: Obtain the solution using analytical method, i.e., conditions of optimality d. Obtain the solution using Steepest Descent Method e. Obtain the solution using Netwon’s method f. Obtain the solution using Marquart’s method g. Compare convergence speeds/efficiencies of the algorithms in (d), (e) and (f) h. Use different parameter value set (other than the ones suggested below) with the numerical methods to see how the performance of the methods would change under different parameter values and makes some comments on parameter sensitivity of the methods-Obtain the solution using analytical method, i.e., conditions of optimality d. Obtain the solution using Steepest Descent Method e. Obtain the solution using Netwon’s method f. Obtain the solution using Marquart’s method g. Compare convergence speeds/efficiencies of the algorithms in (d), (e) and (f) h. Use different parameter value set (other than the ones suggested below) with the numerical methods to see how the performance of the methods would change under different parameter values and makes some comments on parameter sensitivity of the methods
Date : 2025-12-26 Size : 2kb User : Volkan
[matlab] Dynamic-Time-Warping DL : 0: If you pass in 2 vectors it returns the unnormalized distance between the vectors, the accumulated distance between them, the length of the warping path (the normalizing factor), and the warping path points. To compare 2 vectors A and B call: [Dist,D,k,w]=dtw(A,B) Dist is the unnormalized distance D is the accumulated distance k is the length of the warping path (the normalizing factor) w is a matrix containing the points along the warping path
Date : 2025-12-26 Size : 118kb User : Khun PJP