I've posted similar questions before, so i apologize in advance, but i'm just not able to find where i'm going wrong here.
I am implementing Shamir secret sharing using OpenSSL's BIGNUM library in C.
After i do a round of Lagrange interpolation, i multiply key * numerator and then i need to divide by the denominator.
Because there is no BN_mod_div function, i instead use BN_mod_inverse() on the denominator, then multiply, like so:
(key * numerator) * (inverse of denominator)
What i've noticed is that if i use BN_mod_inverse(denom, denom, q, ctx); then the value that should be inverted remains the same:
Round Key: 2E
Numerator: 14
Denominator: 6 **<---- ORIGINAL DENOMINATOR**
Multiply key with numerator: 398 (POSITIVE)
Invert Denominator: 6 (POSITIVE) **<---------- INVERSE IS THE SAME???**
(Key*Numerator)*inv.Denom: 3FC (POSITIVE)
Round Key: 562
Numerator: A
Denominator: -2
Multiply key with numerator: 118 (POSITIVE)
Invert Denominator: -2 (NEGATIVE)
(Key*Numerator)*inv.Denom: 3AC (POSITIVE)
Round Key: 5D1
Numerator: 8
Denominator: 3
Multiply key with numerator: 584 (POSITIVE)
Invert Denominator: 3 (POSITIVE)
(Key*Numerator)*inv.Denom: 4D4 (POSITIVE)
Recovered Key: C4 (POSITIVE)
Key should = 4D2
If I change that to BN_mod_inverse(newBN, denom, q, ctx); it just turns into a zero:
Round Key: 2E
Numerator: 14
Denominator: 6 **<---- ORIGINAL DENOMINATOR**
Multiply key with numerator: 398 (POSITIVE)
Invert Denominator: 0 (NEGATIVE) **<------------ DENOMINATOR IS NOW ZERO??**
(Key*Numerator)*inv.Denom: 0 (NEGATIVE)
Round Key: 562
Numerator: A
Denominator: -2
Multiply key with numerator: 118 (POSITIVE)
Invert Denominator: 0 (NEGATIVE)
(Key*Numerator)*inv.Denom: 0 (NEGATIVE)
Round Key: 5D1
Numerator: 8
Denominator: 3
Multiply key with numerator: 584 (POSITIVE)
Invert Denominator: 0 (NEGATIVE)
(Key*Numerator)*inv.Denom: 0 (NEGATIVE)
Recovered Key: 0 (NEGATIVE)
Key should = 4D2
In either case, the combined key is wrong. What's going on here? Is there a workaround for this?
Here is my code:
BIGNUM *int2BN(int i)
{
BIGNUM *tmp = BN_new();
BN_zero(tmp);
int g;
if(i < 0) { //If 'i' is negative
for (g = 0; g > i; g--) {
BN_sub(tmp, tmp, one);
}
} else { //If 'i' is positive
for (g = 0; g < i; g++) {
BN_add(tmp, tmp, one);
}
}
return(tmp);
}
static void
blah() {
int denomTmp, numTmp, numAccum, denomAccum;
int s, j;
BIGNUM *accum[3], *bnNum, *bnDenom;
bnNum = BN_new();
bnDenom = BN_new();
/* Lagrange Interpolation */
for (s = 0; s < 3; s++) {
numAccum = 1;
denomAccum = 1;
for (j = 0; j < 3; j++) {
if(s == j) continue;
else {
/* 0 - i[k] = numTmp */
numTmp = 0 - key[j].keynum;
/* share - i[k] = denomTmp */
denomTmp = key[s].keynum - key[j].keynum;
/* Numerator accumulation: */
numAccum *= numTmp;
/* Denominator accumulation: */
denomAccum *= denomTmp;
}
}
accum[s] = BN_new();
bnNum = int2BN(numAccum);
bnDenom = int2BN(denomAccum);
/* Multiply result by share */
BN_mod_mul(accum[s], key[s].key, bnNum, q, ctx);
/* Invert denominator */
BN_mod_inverse(bnDenom, bnDenom, q, ctx);
/* Multiply by inverted denominator */
BN_mod_mul(accum[s], accum[s], bnDenom, q, ctx);
}
int a;
BIGNUM *total = BN_new();
BN_zero(total);
for(a = 0; a < 3; a++) {
BN_mod_add(total, total, accum[a], q, ctx);
}
}
Use BN_div. The remainder is the modulo. That is, rem = a % d.
int BN_div(BIGNUM *dv, BIGNUM *rem, const BIGNUM *a, const BIGNUM *d, BN_CTX *ctx);
BN_div() divides a by d and places the result in dv and the remainder in rem
(dv=a/d, rem=a%d). Either of dv and rem may be NULL, in which case the respective
value is not returned. The result is rounded towards zero; thus if a is negative,
the remainder will be zero or negative. For division by powers of 2, use
BN_rshift(3).
Related
Find the nth int with 10 set bits
n is an int in the range 0<= n <= 30 045 014
The 0th int = 1023, the 1st = 1535 and so on
snob() same number of bits,
returns the lowest integer bigger than n with the same number of set bits as n
int snob(int n) {
int a=n&-n, b=a+n;
return b|(n^b)/a>>2;
}
calling snob n times will work
int nth(int n){
int o =1023;
for(int i=0;i<n;i++)o=snob(o);
return o;
}
example
https://ideone.com/ikGNo7
Is there some way to find it faster?
I found one pattern but not sure if it's useful.
using factorial you can find the "indexes" where all 10 set bits are consecutive
1023 << x = the (x+10)! / (x! * 10!) - 1 th integer
1023<<1 is the 10th
1023<<2 is the 65th
1023<<3 the 285th
...
Btw I'm not a student and this is not homework.
EDIT:
Found an alternative to snob()
https://graphics.stanford.edu/~seander/bithacks.html#NextBitPermutation
int lnbp(int v){
int t = (v | (v - 1)) + 1;
return t | ((((t & -t) / (v & -v)) >> 1) - 1);
}
I have built an implementation that should satisfy your needs.
/** A lookup table to see how many combinations preceeded this one */
private static int[][] LOOKUP_TABLE_COMBINATION_POS;
/** The number of possible combinations with i bits */
private static int[] NBR_COMBINATIONS;
static {
LOOKUP_TABLE_COMBINATION_POS = new int[Integer.SIZE][Integer.SIZE];
for (int bit = 0; bit < Integer.SIZE; bit++) {
// Ignore less significant bits, compute how many combinations have to be
// visited to set this bit, i.e.
// (bit = 4, pos = 5), before came 0b1XXX and 0b1XXXX, that's C(3, 3) + C(4, 3)
int nbrBefore = 0;
// The nth-bit can be only encountered after pos n
for (int pos = bit; pos < Integer.SIZE; pos++) {
LOOKUP_TABLE_COMBINATION_POS[bit][pos] = nbrBefore;
nbrBefore += nChooseK(pos, bit);
}
}
NBR_COMBINATIONS = new int[Integer.SIZE + 1];
for (int bits = 0; bits < NBR_COMBINATIONS.length; bits++) {
NBR_COMBINATIONS[bits] = nChooseK(Integer.SIZE, bits);
assert NBR_COMBINATIONS[bits] > 0; // Important for modulo check. Otherwise we must use unsigned arithmetic
}
}
private static int nChooseK(int n, int k) {
assert k >= 0 && k <= n;
if (k > n / 2) {
k = n - k;
}
long nCk = 1; // (N choose 0)
for (int i = 0; i < k; i++) {
// (N choose K+1) = (N choose K) * (n-k) / (k+1);
nCk *= (n - i);
nCk /= (i + 1);
}
return (int) nCk;
}
public static int nextCombination(int w, int n) {
// TODO: maybe for small n just advance naively
// Get the position of the current pattern w
int nbrBits = 0;
int position = 0;
while (w != 0) {
final int currentBit = Integer.lowestOneBit(w); // w & -w;
final int bitPos = Integer.numberOfTrailingZeros(currentBit);
position += LOOKUP_TABLE_COMBINATION_POS[nbrBits][bitPos];
// toggle off bit
w ^= currentBit;
nbrBits++;
}
position += n;
// Wrapping, optional
position %= NBR_COMBINATIONS[nbrBits];
// And reverse lookup
int v = 0;
int m = Integer.SIZE - 1;
while (nbrBits-- > 0) {
final int[] bitPositions = LOOKUP_TABLE_COMBINATION_POS[nbrBits];
// Search for largest bitPos such that position >= bitPositions[bitPos]
while (Integer.compareUnsigned(position, bitPositions[m]) < 0)
m--;
position -= bitPositions[m];
v ^= (0b1 << m--);
}
return v;
}
Now for some explanation. LOOKUP_TABLE_COMBINATION_POS[bit][pos] is the core of the algorithm that makes it as fast as it is. The table is designed so that a bit pattern with k bits at positions p_0 < p_1 < ... < p_{k - 1} has a position of `\sum_{i = 0}^{k - 1}{ LOOKUP_TABLE_COMBINATION_POS[i][p_i] }.
The intuition is that we try to move back the bits one by one until we reach the pattern where are all bits are at the lowest possible positions. Moving the i-th bit from position to k + 1 to k moves back by C(k-1, i-1) positions, provided that all lower bits are at the right-most position (no moving bits into or through each other) since we skip over all possible combinations with the i-1 bits in k-1 slots.
We can thus "decode" a bit pattern to a position, keeping track of the bits encountered. We then advance by n positions (rolling over in case we enumerated all possible positions for k bits) and encode this position again.
To encode a pattern, we reverse the process. For this, we move bits from their starting position forward, as long as the position is smaller than what we're aiming for. We could, instead of a linear search through LOOKUP_TABLE_COMBINATION_POS, employ a binary search for our target index m but it's hardly needed, the size of an int is not big. Nevertheless, we reuse our variant that a smaller bit must also come at a less significant position so that our algorithm is effectively O(n) where n = Integer.SIZE.
I remain with the following assertions to show the resulting algorithm:
nextCombination(0b1111111111, 1) == 0b10111111111;
nextCombination(0b1111111111, 10) == 0b11111111110;
nextCombination(0x00FF , 4) == 0x01EF;
nextCombination(0x7FFFFFFF , 4) == 0xF7FFFFFF;
nextCombination(0x03FF , 10) == 0x07FE;
// Correct wrapping
nextCombination(0b1 , 32) == 0b1;
nextCombination(0x7FFFFFFF , 32) == 0x7FFFFFFF;
nextCombination(0xFFFFFFEF , 5) == 0x7FFFFFFF;
Let us consider the numbers with k=10 bits set.
The trick is to determine the rank of the most significant one, for a given n.
There is a single number of length k: C(k, k)=1. There are k+1 = C(k+1, k) numbers of length k + 1. ... There are C(m, k) numbers of length m.
For k=10, the limit n are 1 + 10 + 55 + 220 + 715 + 2002 + 5005 + 11440 + ...
For a given n, you easily find the corresponding m. Then the problem is reduced to finding the n - C(m, k)-th number with k - 1 bits set. And so on recursively.
With precomputed tables, this can be very fast. 30045015 takes 30 lookups, so that I guess that the worst case is 29 x 30 / 2 = 435 lookups.
(This is based on linear lookups, to favor small values. By means of dichotomic search, you reduce this to less than 29 x lg(30) = 145 lookups at worse.)
Update:
My previous estimates were pessimistic. Indeed, as we are looking for k bits, there are only 10 determinations of m. In the linear case, at worse 245 lookups, in the dichotomic case, less than 50.
(I don't exclude off-by-one errors in the estimates, but clearly this method is very efficient and requires no snob.)
I'm trying to compute batch 1D FFTs using cufftPlanMany. The data set comes from a 3D field, stored in a 1D array, where I want to compute 1D FFTs in the x and y direction. The data is stored as shown in the figure below; continuous in x then y then z.
Doing batch FFTs in the x-direction is (I believe) straighforward; with input stride=1, distance=nx and batch=ny * nz, it computes the FFTs over elements {0,1,2,3}, {4,5,6,7}, ..., {28,29,30,31}. However, I can't think of a way to achieve the same for the FFTs in the y-direction. A batch for each xy plane is again straightforward (input stride=nx, dist=1, batch=nx results in FFTs over {0,4,8,12}, {1,5,9,13}, etc.). But with batch=nx * nz, going from {3,7,11,15} to {16,20,24,28}, the distance is larger than 1. Can this somehow be done with cufftPlanMany?
I think that the short answer to your question (possibility of using a single cufftPlanMany to perform 1D FFTs of the columns of a 3D matrix) is NO.
Indeed, transformations performed according to cufftPlanMany, that you call like
cufftPlanMany(&handle, rank, n,
inembed, istride, idist,
onembed, ostride, odist, CUFFT_C2C, batch);
must obey the Advanced Data Layout. In particular, 1D FFTs are worked out according to the following layout
input[b * idist + x * istride]
where b addresses the b-th signal and istride is the distance between two consecutive items in the same signal. If the 3D matrix has dimensions M * N * Q and if you want to perform 1D transforms along the columns, then the distance between two consecutive elements will be M, while the distance between two consecutive signals will be 1. Furthermore, the number of batched executions must be set equal to M. With those parameters, you are able to cover only one slice of the 3D matrix. Indeed, if you try increasing M, then the cuFFT will start trying to compute new column-wise FFTs starting from the second row. The only solution to this problem is an iterative call to cufftExecC2C to cover all the Q slices.
For the record, the following code provides a fully worked example on how performing 1D FFTs of the columns of a 3D matrix.
#include <thrust/device_vector.h>
#include <cufft.h>
/********************/
/* CUDA ERROR CHECK */
/********************/
#define gpuErrchk(ans) { gpuAssert((ans), __FILE__, __LINE__); }
inline void gpuAssert(cudaError_t code, const char *file, int line, bool abort=true)
{
if (code != cudaSuccess)
{
fprintf(stderr,"GPUassert: %s %s %d\n", cudaGetErrorString(code), file, line);
if (abort) exit(code);
}
}
int main() {
const int M = 3;
const int N = 4;
const int Q = 2;
thrust::host_vector<float2> h_matrix(M * N * Q);
for (int k=0; k<Q; k++)
for (int j=0; j<N; j++)
for (int i=0; i<M; i++) {
float2 temp;
temp.x = (float)(j + k * M);
//temp.x = 1.f;
temp.y = 0.f;
h_matrix[k*M*N+j*M+i] = temp;
printf("%i %i %i %f %f\n", i, j, k, temp.x, temp.y);
}
printf("\n");
thrust::device_vector<float2> d_matrix(h_matrix);
thrust::device_vector<float2> d_matrix_out(M * N * Q);
// --- Advanced data layout
// input[b * idist + x * istride]
// output[b * odist + x * ostride]
// b = signal number
// x = element of the b-th signal
cufftHandle handle;
int rank = 1; // --- 1D FFTs
int n[] = { N }; // --- Size of the Fourier transform
int istride = M, ostride = M; // --- Distance between two successive input/output elements
int idist = 1, odist = 1; // --- Distance between batches
int inembed[] = { 0 }; // --- Input size with pitch (ignored for 1D transforms)
int onembed[] = { 0 }; // --- Output size with pitch (ignored for 1D transforms)
int batch = M; // --- Number of batched executions
cufftPlanMany(&handle, rank, n,
inembed, istride, idist,
onembed, ostride, odist, CUFFT_C2C, batch);
for (int k=0; k<Q; k++)
cufftExecC2C(handle, (cufftComplex*)(thrust::raw_pointer_cast(d_matrix.data()) + k * M * N), (cufftComplex*)(thrust::raw_pointer_cast(d_matrix_out.data()) + k * M * N), CUFFT_FORWARD);
cufftDestroy(handle);
for (int k=0; k<Q; k++)
for (int j=0; j<N; j++)
for (int i=0; i<M; i++) {
float2 temp = d_matrix_out[k*M*N+j*M+i];
printf("%i %i %i %f %f\n", i, j, k, temp.x, temp.y);
}
}
The situation is different for the case when you want to perform 1D transforms of the rows. In that case, the distance between two consecutive elements is 1, while the distance between two consecutive signals is M. This allows you to set a number of N * Q transformations and then invoking cufftExecC2C only one time. For the record, the code below provides a full example of 1D transformations of the rows of a 3D matrix.
#include <thrust/device_vector.h>
#include <cufft.h>
/********************/
/* CUDA ERROR CHECK */
/********************/
#define gpuErrchk(ans) { gpuAssert((ans), __FILE__, __LINE__); }
inline void gpuAssert(cudaError_t code, const char *file, int line, bool abort=true)
{
if (code != cudaSuccess)
{
fprintf(stderr,"GPUassert: %s %s %d\n", cudaGetErrorString(code), file, line);
if (abort) exit(code);
}
}
int main() {
const int M = 3;
const int N = 4;
const int Q = 2;
thrust::host_vector<float2> h_matrix(M * N * Q);
for (int k=0; k<Q; k++)
for (int j=0; j<N; j++)
for (int i=0; i<M; i++) {
float2 temp;
temp.x = (float)(j + k * M);
//temp.x = 1.f;
temp.y = 0.f;
h_matrix[k*M*N+j*M+i] = temp;
printf("%i %i %i %f %f\n", i, j, k, temp.x, temp.y);
}
printf("\n");
thrust::device_vector<float2> d_matrix(h_matrix);
thrust::device_vector<float2> d_matrix_out(M * N * Q);
// --- Advanced data layout
// input[b * idist + x * istride]
// output[b * odist + x * ostride]
// b = signal number
// x = element of the b-th signal
cufftHandle handle;
int rank = 1; // --- 1D FFTs
int n[] = { M }; // --- Size of the Fourier transform
int istride = 1, ostride = 1; // --- Distance between two successive input/output elements
int idist = M, odist = M; // --- Distance between batches
int inembed[] = { 0 }; // --- Input size with pitch (ignored for 1D transforms)
int onembed[] = { 0 }; // --- Output size with pitch (ignored for 1D transforms)
int batch = N * Q; // --- Number of batched executions
cufftPlanMany(&handle, rank, n,
inembed, istride, idist,
onembed, ostride, odist, CUFFT_C2C, batch);
cufftExecC2C(handle, (cufftComplex*)(thrust::raw_pointer_cast(d_matrix.data())), (cufftComplex*)(thrust::raw_pointer_cast(d_matrix_out.data())), CUFFT_FORWARD);
cufftDestroy(handle);
for (int k=0; k<Q; k++)
for (int j=0; j<N; j++)
for (int i=0; i<M; i++) {
float2 temp = d_matrix_out[k*M*N+j*M+i];
printf("%i %i %i %f %f\n", i, j, k, temp.x, temp.y);
}
}
I guess, idist=nx*nz could also jump a whole plane and batch=nz would then cover one yx plane. The decision should be made according to whether nx or nz is larger.
I've been working on this program to count how many twin primes between two values and it's been specified that twin primes come in the (6n-1, 6n+1) format, with the exception of (3, 5). My code seems to work fine, but it keeps giving me the wrong result....1 less couple of twin primes than i should get. Between 1 and 40, we should have 5 twin primes, but I'm always getting 4. é
What am I doing wrong? Am I not taking into account (3, 5)?
Here's my code:
#include <stdio.h>
int prime (int num) {
int div;
if (num == 2) return 1;
if (num % 2 == 0) return 0;
div = 3;
while (div*div <= num && num%div != 0)
div = div + 2;
if (num%div == 0)
return 0;
else
return 1;
}
int main(void) {
int low, high, i, count, n, m;
printf("Please enter the values for the lower and upper limits of the interval\n");
scanf("%d%d", &low, &high);
printf("THIS IS THE LOW %d\n AND THIS IS THE HIGH %d\n", low, high);
i = low;
count = 0;
while (6*i-1>=low && 6*i+1<=high) {
n = 6*i-1;
m = 6*i+1;
if (prime(n) && prime(m)) ++count;
i = i + 1;
}
printf("Number of twin primes is %d\n", count);
return 0;
}
Your program misses (3 5) because 3 is not trapped as a prime number, and because 4 is not a multiple of 6. Rather than the main loop stepping by (effectively) 6, this answer steps by 1.
#include <stdio.h>
int prime (int num) {
int div;
if (num == 1) return 0; // excluded 1
if (num == 2 || num == 3) return 1; // included 3 too
if (num % 2 == 0) return 0;
div = 3;
while (div*div <= num) {
if (num % div == 0) // moved to within loop
return 0;
div += 2;
}
return 1;
}
int main(void) {
int low, high, i, count, n, m;
printf("Please enter the values for the lower and upper limits of the interval\n");
scanf("%d%d", &low, &high);
printf("THIS IS THE LOW %d\n AND THIS IS THE HIGH %d\n", low, high);
count = 0;
for (i=low; i<=high; i++) {
n = i-1;
m = i+1;
if (prime(n) && prime(m)) {
printf ("%2d %2d\n", n, m);
++count;
}
}
printf("Number of twin primes is %d\n", count);
return 0;
}
Program output
1
40
THIS IS THE LOW 1
AND THIS IS THE HIGH 40
3 5
5 7
11 13
17 19
29 31
Number of twin primes is 5
Next run:
3
10
THIS IS THE LOW 3
AND THIS IS THE HIGH 10
3 5
5 7
Number of twin primes is 2
https://primes.utm.edu/lists/small/100ktwins.txt
The five twin primes under forty are (3,5), (5,7), (11,13), (17,19), (29,31) so if you know that your code isn't counting (3,5) then it is working correctly, counting (5,7), (11,13), (17,19), and (29,31).
A possible fix would be to add an if-statement which adds 1 to "count" if the starting number is less than 4. I'm not really that used to reading C syntax so I had trouble getting my head around your formulas, sorry.
edit: since comments don't format code snippets:
i = low;
count = 0;
if (low <= 3 && high >= 3){
count ++; // accounts for (3,5) twin primes if the range includes 3
}
You have a problem in your prime function, this is the output of your prime function for the first ten prime evaluations
for(i=1;i<=10;i++) printf("%d\t%d",i,prime(i));
1 1
2 1
3 0
4 0
5 1
6 0
7 1
8 0
Note the prime() function from Weather Vane, you should include 3 as prime (and exclude 1).
From [1], twin primes are the ones that have a prime gap of two, differing by two from another prime.
Examples are (3,5) , (5,7), (11,13). The format (6n-1,6n+1) is true but for (3,5) as you stated. Your program runs almost ok since it shows the number of twin primes that are in the interval AND follows the rule mentioned above. This doesn't include (3,5). You can make a kind of exception (like if low<=3 add 1 to total count), or use another algorithm to count twin primes (like verify if i is prime, then count distance from i to next prime, if distance=2 then they are twin primes)
[1] http://en.wikipedia.org/wiki/Twin_prime
Important update: I already figured out the answers and put them in this simple open-source library: http://bartolsthoorn.github.com/NVDSP/ Check it out, it will probably save you quite some time if you're having trouble with audio filters in IOS!
^
I have created a (realtime) audio buffer (float *data) that holds a few sin(theta) waves with different frequencies.
The code below shows how I created my buffer, and I've tried to do a bandpass filter but it just turns the signals to noise/blips:
// Multiple signal generator
__block float *phases = nil;
[audioManager setOutputBlock:^(float *data, UInt32 numFrames, UInt32 numChannels)
{
float samplingRate = audioManager.samplingRate;
NSUInteger activeSignalCount = [tones count];
// Initialize phases
if (phases == nil) {
phases = new float[10];
for(int z = 0; z <= 10; z++) {
phases[z] = 0.0;
}
}
// Multiple signals
NSEnumerator * enumerator = [tones objectEnumerator];
id frequency;
UInt32 c = 0;
while(frequency = [enumerator nextObject])
{
for (int i=0; i < numFrames; ++i)
{
for (int iChannel = 0; iChannel < numChannels; ++iChannel)
{
float theta = phases[c] * M_PI * 2;
if (c == 0) {
data[i*numChannels + iChannel] = sin(theta);
} else {
data[i*numChannels + iChannel] = data[i*numChannels + iChannel] + sin(theta);
}
}
phases[c] += 1.0 / (samplingRate / [frequency floatValue]);
if (phases[c] > 1.0) phases[c] = -1;
}
c++;
}
// Normalize data with active signal count
float signalMulti = 1.0 / (float(activeSignalCount) * (sqrt(2.0)));
vDSP_vsmul(data, 1, &signalMulti, data, 1, numFrames*numChannels);
// Apply master volume
float volume = masterVolumeSlider.value;
vDSP_vsmul(data, 1, &volume, data, 1, numFrames*numChannels);
if (fxSwitch.isOn) {
// H(s) = (s/Q) / (s^2 + s/Q + 1)
// http://www.musicdsp.org/files/Audio-EQ-Cookbook.txt
// BW 2.0 Q 0.667
// http://www.rane.com/note170.html
//The order of the coefficients are, B1, B2, A1, A2, B0.
float Fs = samplingRate;
float omega = 2*M_PI*Fs; // w0 = 2*pi*f0/Fs
float Q = 0.50f;
float alpha = sin(omega)/(2*Q); // sin(w0)/(2*Q)
// Through H
for (int i=0; i < numFrames; ++i)
{
for (int iChannel = 0; iChannel < numChannels; ++iChannel)
{
data[i*numChannels + iChannel] = (data[i*numChannels + iChannel]/Q) / (pow(data[i*numChannels + iChannel],2) + data[i*numChannels + iChannel]/Q + 1);
}
}
float b0 = alpha;
float b1 = 0;
float b2 = -alpha;
float a0 = 1 + alpha;
float a1 = -2*cos(omega);
float a2 = 1 - alpha;
float *coefficients = (float *) calloc(5, sizeof(float));
coefficients[0] = b1;
coefficients[1] = b2;
coefficients[2] = a1;
coefficients[3] = a2;
coefficients[3] = b0;
vDSP_deq22(data, 2, coefficients, data, 2, numFrames);
free(coefficients);
}
// Measure dB
[self measureDB:data:numFrames:numChannels];
}];
My aim is to make a 10-band EQ for this buffer, using vDSP_deq22, the syntax of the method is:
vDSP_deq22(<float *vDSP_A>, <vDSP_Stride vDSP_I>, <float *vDSP_B>, <float *vDSP_C>, <vDSP_Stride vDSP_K>, <vDSP_Length __vDSP_N>)
See: http://developer.apple.com/library/mac/#documentation/Accelerate/Reference/vDSPRef/Reference/reference.html#//apple_ref/doc/c_ref/vDSP_deq22
Arguments:
float *vDSP_A is the input data
float *vDSP_B are 5 filter coefficients
float *vDSP_C is the output data
I have to make 10 filters (10 times vDSP_deq22). Then I set the gain for every band and combine them back together. But what coefficients do I feed every filter? I know vDSP_deq22 is a 2nd order (butterworth) IIR filter, but how do I turn this into a bandpass?
Now I have three questions:
a) Do I have to de-interleave and interleave the audio buffer? I know setting stride to 2 just filters on channel but how I filter the other, stride 1 will process both channels as one.
b) Do I have to transform/process the buffer before it enters the vDSP_deq22 method? If so, do I also have to transform it back to normal?
c) What values of the coefficients should I set to the 10 vDSP_deq22s?
I've been trying for days now but I haven't been able to figure this on out, please help me out!
Your omega value need to be normalised, i.e. expressed as a fraction of Fs - it looks like you left out the f0 when you calculated omega, which will make alpha wrong too:
float omega = 2*M_PI*Fs; // w0 = 2*pi*f0/Fs
should probably be:
float omega = 2*M_PI*f0/Fs; // w0 = 2*pi*f0/Fs
where f0 is the centre frequency in Hz.
For your 10 band equaliser you'll need to pick 10 values of f0, spaced logarithmically, e.g. 25 Hz, 50 Hz, 100 Hz, 200 Hz, 400 Hz, 800 Hz, 1.6 kHz, 3.2 kHz, 6.4 kHz, 12.8 kHz.
I have a float and I am trying to get a random number between 1.5 - 2. I have seen tutorials on the web but all of them are doing the randomization for 0 to a number instead of 1.5 in my case. I know it is possible but I have been scratching my head on how to actually accomplish this. Can anyone help me?
Edit1: I found the following method on the web but I do not want all these decimals places. I only want things like 5.2 or 7.4 etc...
How would I adjust this method to do that?
-(float)randomFloatBetween:(float)num1 andLargerFloat:(float)num2
{
int startVal = num1*10000;
int endVal = num2*10000;
int randomValue = startVal + (arc4random() % (endVal - startVal));
float a = randomValue;
return (a / 10000.0);
}
Edit2: Ok so now my method is like this:
-(float)randomFloatBetween:(float)num1 andLargerFloat:(float)num2
{
float range = num2 - num1;
float val = ((float)arc4random() / ARC4RANDOM_MAX) * range + num1;
return val;
}
Will this produce numbers like 1.624566 etc..? Because I only want say 1.5,1.6,1.7,1.8,1.9, and 2.0.
You can just produce a random float from 0 to 0.5 and add 1.5.
EDIT:
You're on the right track. I would use the maximum random value possible as your divisor in order to get the smallest intervals you can between possible values, rather than this arbitrary division by 10,000 thing you have going on. So, define the maximum value of arc4random() as a macro (I just found this online):
#define ARC4RANDOM_MAX 0x100000000
Then to get a value between 1.5 and 2.0:
float range = num2 - num1;
float val = ((float)arc4random() / ARC4RANDOM_MAX) * range + num1;
return val;
This will also give you double precision if you want it (just replace float with double.)
EDIT AGAIN:
Yes, of course this will give you values with more than one decimal place. If you want only one, just produce a random integer from 15 to 20 and divide by 10. Or you could just hack off the extra places afterward:
float range = num2 - num1;
float val = ((float)arc4random() / ARC4RANDOM_MAX) * range + num1;
int val1 = val * 10;
float val2= (float)val1 / 10.0f;
return val2;
arc4random is a 32-bit generator. It generates Uint32's. The maximum value of arc4random() is UINT_MAX. (Do not use ULONG_MAX!)
The simplest way to do this is:
// Generates a random float between 0 and 1
inline float randFloat()
{
return (float)arc4random() / UINT_MAX ;
}
// Generates a random float between imin and imax
inline float randFloat( float imin, float imax )
{
return imin + (imax-imin)*randFloat() ;
}
// between low and (high-1)
inline float randInt( int low, int high )
{
return low + arc4random() % (high-low) ; // Do not talk to me
// about "modulo bias" unless you're writing a casino generator
// or if the "range" between high and low is around 1 million.
}
This should work for you:
float mon_rand() {
const u_int32_t r = arc4random();
const double Min = 1.5;
if (0 != r) {
const double rUInt32Max = 1.0 / UINT32_MAX;
const double dr = (double)r;
/* 0...1 */
const double nr = dr * rUInt32Max;
/* 0...0.5 */
const double h = nr * 0.5;
const double result = Min + h;
return (float)result;
}
else {
return (float)Min;
}
}
That was the simplest I could think of, when I had the same "problem" and it worked for me:
// For values from 0.0 to 1.0
float n;
n = (float)((arc4random() % 11) * 0.1);
And in your case, from 1.5 to 2.0:
float n;
n = (float)((arc4random() % 6) * 0.1);
n += 15 * 0.1;
For anybody who wants more digits:
If you just want float, instead of arc4random(3) it would be easier if you use rand48(3):
// Seed (only once)
srand48(arc4random()); // or time(NULL) as seed
double x = drand48();
The drand48() and erand48() functions return non-negative, double-precision, floating-point values, uniformly distributed over the interval [0.0 , 1.0].
Taken from this answer.