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Old 08-22-2011, 06:52 AM   #1
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Old 08-22-2011, 07:03 AM   #2
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Old 08-22-2011, 07:04 AM   #3
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Old 08-22-2011, 09:08 AM   #4
t1ei2dw8ve
 
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Chinese name: Final Fantasy Tactics (FFT) game console: PS Production companies: Square Enix Genre: strategy role-playing class (S ● RPG) Speaking of groups: Han Chinese group day Magic Level: Full finished version Chinese name: Final Fantasy Tactics game Category: PS strategy role-playing class (S ● RPG) Gaming Platform: PC Developer: Square Enix Released: 1997/6/20
directory
PS version of the Chinese name Game companies make the game formerly known type of game platform team finished the game finished level of the number of the Game release date Final Fantasy Tactics PSP version of the game formerly known as developer of game genre game platform vendors release date games available number of groups to adapt to the crowd finished the game DFT algorithm About Us DFT FFT algorithm algorithm in C under the source in C + + environment, the source code in Matlab environment, the source PS version
PS version
Chinese name of the Final Fantasy Tactics (referred to as: FFT) the game formerly known as フ ァ イ ナ Hikaru フ ァ ン タ ジ タ ク Te ィ ク ス Final Fantasy Tactics game platform production company Square Enix PS type of strategy role-playing game (S ● RPG) Han Chinese group fantasy team finished day finished version of the full extent of release date 1997/6/20 (PS Japanese version) 1998/1/29 (PS U.S. Edition) Game number 1 Game chapter in history is opened, historians bring you into Yifalisi continent in the past. The era of strife in the war, two young changed history. One is the intelligence and the Lawrence DiRita, one justice of the Ramsar. Nobility provoked them in an unjust war in the search for truth, but found that elderly people have confidence, but holding the hands of fine-sounding name of the treasures of sacred stone, one by one into the face of ferocious beasts, perhaps this is their noble token and the gorgeous robe nature. You can not ask a teenager in such an environment spotless, Lawrence DiRita of the kind in the Walled City was bombed to ashes Jike Dan, a former civilian children vowed to rewrite history to trample on the exhaustion of his wisdom to his dislike of the nobility. The Ramsar while undergoing a series of horrible things, but still the faith,air force one low, for their loved ones fight to the last. This is the fate, when sitting on the grass blowing leaves two teenagers, and finally embarked on a completely different path in life, you will find actually familiar. This is a game Ye Hao, this is way of history, this is Final Fantasy Tactics: Final Fantasy Tactics. PSP version
PSP version
Final Fantasy Tactics Lion War (referred to as: FFT) the game formerly known as フ ァ イ ナ Hikaru フ ァ ン タ ジ タ ク Te ィ ク ス Lions Final Fantasy Tactics: Shishi Sensou Game Platform PSP Genre strategy role-playing class (S ● RPG ) development company offering vendor Square Enix Square Enix Release Date 2007-05-10 adapt the game 1-2, the number of people over the age of 12 team finished a Chinese group Magic Game Day two young heroes ... buried in the shadow of history in the real This time the chaos of secession,nike air force 1, is the history known as the In the swirling smoke, there were two young men on the scene. One is the end of the war hero lion king Dirui Da, while the other is the hero of the story - the history without knowing about Minglamuzha waves. The existence of two, so buried in the shadow of history in the Wet streets, dark gods, praying girl, clouds of the sky, the story of the Queen of gas will be kicked off on May 10, disaster, war, smoke, killing all waiting for you to end! About FFT algorithms FFT, is the fast Fourier transform, discrete Fourier transform is a fast algorithm, which is the discrete Fourier transform of the odd, even, true, real and other characteristics of the discrete Fourier transform algorithm was improved to obtain. Fourier transform theory it is not new
FFT algorithm Figure
found, but in a computer system or digital systems using discrete Fourier transform, can be said to carry a big step. Let x (n) for the complex sequence of N items, the DFT transform, either X (m) requires the calculation of N complex multiplications and complex additions N-1 times, and a complex multiplication is equal to four real multiplications and two real additions and one complex addition is equal to two real additions, even if a complex multiplication and one complex addition is defined as an ), that is, about N point DFT transform operations need to N2 times. When N = 1024 points or more, the need for N2 = 1048576 second operation, in the FFT,air force one high, using the periodicity and symmetry of WN, to an N key sequence (set N = 2k, k is a positive integer), divided two N / 2 sequence entries, each N / 2 点 DFT transform requires (N / 2) 2 times operator, then N of operations of the two N / 2 point DFT transform into a single N point DFT transformation. After this transformation, the total number of operations becomes N 2 (N / 2) 2 = N N2 / 2. Continuing the example, N = 1024, the total becomes 525,312 operations per second, saving about 50% of the computation. If we call this 10240 times the amount only, is the direct method previously 1%, more points, the greater the computational savings, this is the superiority of FFT. DFT algorithm For length N input vector x, the DFT is a length N vector X, with elements N X (k) = sum x (n) * exp (-j * 2 * pi * (k-1) * (n- 1) / N), 1 0), k is essentially a sampling of data n can be decomposed into even and odd power of power of the number of / / pr []: l = 0, the stored N point sampled data the real part of / / l = 1, the Fourier transform of the N-storage real part of / / pi [ ,],[the real part of / / l = 1, returns the inverse Fourier transform of the real part of the / / fi []: l = 0, returns the imaginary part of Fourier transform / / l = 1, returns the inverse Fourier transform of the imaginary part of / / pr [] : il = 1, i = 0, the return to the Fourier transform mode / / il = 1, i = 1, returns the inverse Fourier transform mode / / pi []: il = 1, i = 0, the return to the Fourier transform The Argument / / il = 1, i = 1, returns the inverse Fourier transform Argument void fft (double pr [], double pi [], int n, int k, double fr [], double fi [], int l, int il) {int it, m, is, i, j,nike air force one high, nv, l0; double p,air force 1 shoes, q, s, vr, vi, poddr, poddi; for (it = 0; it = 0; l0 -) { m / = 2; nv 0? 90.0-90.0; else pi = atan (fi / fr) * 360.0/6.283185306;} return;} --- in the C environment source code under the bool FFT (complex * TD, complex * FD, int r) {/ / Fast Fourier transform one-dimensional. / / Complex * TD - point to an array of time domain pointer; complex * FD - pointer to point to an array of frequency domain; r - 2 the power of numbers, ie, the number of iterations LONG count; / / Fourier transform points int i, j , k; / / loop variable int bfsize, p; / / intermediate variable double angle; / / angle of complex * W, * X1, * X2, * X; count = 1 0), k is essentially a sampling of data n can be decomposed into even and odd power of power of the number of / / pr []: l = 0, the storage of N sampling points the real part of the data / / l = 1, the Fourier transform of the N-storage real part of / / pi []: l = 0, the stored N point sampled data the imaginary part of / / l = 1, the Fourier transform of the N storage an imaginary part / / / / export parameters: / / fr []: l = 0, return to the real part of Fourier transform / / l = 1, returns the inverse Fourier transform of the real part of the / / fi []: l = 0, return Fourier transform of the imaginary part of / / l = 1, returns the inverse Fourier transform of the imaginary part of / / pr []: il = 1, i = 0, the return to the Fourier transform mode / / il = 1, i = 1, the return inverse Fourier transform mode / / pi []: il = 1, i = 0, the convergence angle of the Fourier transform to return / / il = 1, i = 1, returns the inverse Fourier transform of the convergence angle void fft (double pr [ ,],[s, vr, vi, poddr, poddi; for (it = 0; it = 0; l0 -) {m / = 2; nv 0? 90.0-90.0; else pi = atan (fi / fr) * 360.0/6.283185306;} return;} in C + + Environment Source bool FFT (complex * TD, complex * FD, int r) {/ / Fast Fourier transform one-dimensional. / / complex * TD - a pointer to point to an array of time-domain; complex * FD - pointer to point to an array of frequency domain; r - 2 the power of numbers, ie, the number of iterations LONG count; / / Fourier transform points int i, j, k; / / loop variable int bfsize, p ; / / intermediate variable double angle; / / angle of complex * W, * X1, * X2, * X; count = 1 <<r; / / calculation of Fourier transform, a number of points left for the W = r new complex [count / 2]; X1 = new complex [count]; X2 = new complex [count]; / / allocate the necessary memory operations / / calculate the weighted coefficients (twiddle factors w, i power of the table) for (i = 0; i <count / 2; i + +) {angle =-i * PI * 2 / count; W [i] = complex (cos (angle), sin (angle ));} / / write the time-domain points X1 memcpy (X1,nike air force one, TD, sizeof (complex ) * count); / / use of butterfly Fast Fourier Transform algorithm for (k = 0; k <r ; k + +) {for (j = 0; j <1 <<k; j + +) {bfsize = 1 <<(rk); for (i = 0; i <bfsize / 2; i + +) { p = j * bfsize; X2 [i + p] = X1 [i + p] + X1 [i + p + bfsize / 2] * W [i * (1 <<k)]; X2 [i + p + bfsize / 2] = X1 [i + p] - X1 [i + p + bfsize / 2] * W [i * (1 <<k)];}} X = X1; X1 = X2; X2 = X; } / / re-order for (j = 0; j <count; j + +) {p = 0; for (i = 0; i <r; i + +) {if (j & (1 <<i)) { p + = 1 <<(ri-1);}} FD [j] = X1 [p];} / / free memory delete W; delete X1; delete X2; return true;} in the Matlab environment, the source function X = myfft (x)% myfft Recursive function with N = length (x); t = log2 (N); t1 = floor (t); t2 = ceil (t); if t1 ~ = t2;% if the length of x N non-integer power of 2, the complement of 0 to the nearest integer power of 2 x = [x zeros (1,2 ^ t2-N)]; N = 2 ^ t2; end w0 = exp (-j * 2 * pi / N); X = zeros (1, N); if N == 2 X (1) = x (1) + x (2); X (2) = x (1)-x (2 ); else n = 1: N / 2; xe (n) = x (2 * n-1); xo (n) = x (2 * n); XE = myfft (xe);% recursive call XO = myfft (xo); for n = 1: N / 2 X (n) = XE (n) + XO (n) * (w0 ^ (n-1)); X (n + N / 2) = XE (n) -XO (n) * (w0 ^ (n-1)); end end FFT-related entries algorithm single game game online games platform for real-time communication software, sports video game game development IT network engineering software development for more chart entries Atlas Extended Reading List: 1
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