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Description: 模拟退火和对称
*欧几里德旅行商问题。
*
*为基础的解决办法的本地搜索启发式
*非过境道路和近邻
-/*
* Simulated annealing and the Symetric
* Euclidian Traveling Salesman Problem.
*
* Solution based on local search heuristics for
* non-crossing paths and nearest neighbors
*
* Storage Requirements: n^2+4n ints
*
* Problem: given the coordinates of n cities in the plane, find a
* permutation pi_1, pi_2, ..., pi_n of 1, 2, ..., n that minimizes
* sum for 1<=i<n D(pi_i,pi_i+1), where D(i,j) is the euclidian
* distance between cities i and j
*
* Note: with n cities, there is (n-1)!/2 possible tours.
* factorial(10)=3628800 factorial(50)=3E+64 factorial(150)=5.7E+262
* If we could check one tour per clock cycle on a 100 MHZ computer, we
* would still need to wait approximately 10^236 times the age of the
* universe to explore all tours for 150 cities.
*
* gcc-O4-o tsp tsp.c-lm tsp | ghostview-
*
* Usage: tsp [-v] [n=dd] [s=dd] [filename]
* -v : verbose
* n= : nb of cities (cities generated randomly on E^2
Platform: |
Size: 86016 |
Author: 孙博 |
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Description: soft Demapping QPSK : LLR computation using Euclidian distance approach, Parallel-to-Serial converter : needs I and Q components of QPSK symbols at the input
Platform: |
Size: 1024 |
Author: IMM |
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Description: good version of soft Demapping QPSK : LLR computation using Euclidian distance approache, Parallel-to-Serial converter, needs I and Q componets of QPSK symbols at the input
Platform: |
Size: 1024 |
Author: IMM |
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Description: soft Demapping 8PSK : LLR computation using Euclidian distance approach, Parallel-to-Serial converter, needs I and Q componets of 8PSK symbols at the input
Platform: |
Size: 1024 |
Author: IMM |
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Description: corrected version of Demapping QPSK : LLR computation using Euclidian distance approach, Parallel-to-Serial converter, needs I and Q componets of QPSK symbols at the input
Platform: |
Size: 1024 |
Author: IMM |
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Description: Demapping 8PSK : LLR computation using Euclidian distance approach, Parallel-to-Serial converter, Hard decision, needs I and Q componets of 8PSK symbols at the input
Platform: |
Size: 1024 |
Author: IMM |
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Description: Euclidean Distance Transform has been widely studied in computational geometry, image processing, computer graphics and pattern recognition. Euclidean distance has been computed through different algorithms like parallel, linear time algorithms etc. On the basis of efficiency, accuracy and numerical computations, existing and proposed techniques has been compared. This study proposed a new technique of finding Euclidian distance using sequential algorithm. An experimental evaluation has shown that proposed technique has reduced the drawbacks of existing techniques. And the use of sequential algorithm scans has reduced the computational cost.
Platform: |
Size: 101376 |
Author: yasora |
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Description: Focused on the disadvantage of classical Euclidian
distance in data clustering analysis, we propose an improved
distance calculation formula, which describes the local
compactness and global connectivity between data points.
Furthermore, we improve ant-colony clustering algorithm
by using the improved distance calculation formula.
Theoretical analysis and experiments show that this method
is more efficient and has the ability to identify complex nonconvex
clusters.
Platform: |
Size: 353280 |
Author: rishi |
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Description: it is a collection of matlab program.
1.to find the euclidian distance
2.to create mel frequency filter bank
3.
Platform: |
Size: 2048 |
Author: sandeep |
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Description: After identifying the best MNT− 1 nodes with the simplified branch metric and the accumulated branch metric at stage NT − 1, we then calculate the LLR of each coded bit utilizing the standard squared Euclidian distance metric.
The log-likelihood ratio (LLR) of a posteriori probability (APP) of each coded bit conditioned on the received signal y is normally calculated using the max-log approximation.-After identifying the best MNT− 1 nodes with the simplified branch metric and the accumulated branch metric at stage NT − 1, we then calculate the LLR of each coded bit utilizing the standard squared Euclidian distance metric.
The log-likelihood ratio (LLR) of a posteriori probability (APP) of each coded bit conditioned on the received signal y is normally calculated using the max-log approximation.
Platform: |
Size: 329728 |
Author: yuva |
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Description: Theme: Object recognition using Geometric Properties(area, perimeter,circularity,etc) . In order to obtain the results the project was implemented using C++ and the tool C++Builder. For every geometric feature has been created a method that return a numeric value. Having these results I implemented a function which calculates the Euclidian distance that is often used for detecting and object recognition. The application it s used to recognize the symbols of playing cards. -Theme: Object recognition using Geometric Properties(area, perimeter,circularity,etc) . In order to obtain the results the project was implemented using C++ and the tool C++Builder. For every geometric feature has been created a method that return a numeric value. Having these results I implemented a function which calculates the Euclidian distance that is often used for detecting and object recognition. The application it s used to recognize the symbols of playing cards.
Platform: |
Size: 808960 |
Author: Lup Anca |
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Description: this code calculate the euclidian distance.
h = waitbar(0, Distance Computation )
switch nargin
case 1-this code calculate the euclidian distance.
h = waitbar(0, Distance Computation )
switch nargin
case 1
Platform: |
Size: 2048 |
Author: safa younes |
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Description: it s matlab code for Euclidian distane in data miningin pattern recognition
Platform: |
Size: 1024 |
Author: hana |
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Description: Returns the square of the Euclidian distance to (dx,dy).
Platform: |
Size: 6144 |
Author: pewuixs |
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