linux sk_buff resources


 
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Operating Systems Linux linux sk_buff resources
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Old 01-17-2008
linux sk_buff resources

Hi,

I'm trying to build linear sk_buffs, non linear sk_buffs with page frames (nr_frags) and non linear skbuffs with linked list (frag_list) for testing of a kernel module I wrote. Does anyone have any better resources than the API for doing this? Like a guide type thing. The best I've found so far is:

How SKBs work

but that doesn't really go deep enough. I'm not worried about filling the skbuff with any data, just building the actual memory object to a certain size with a certain number of frags attached.

Any help is greatly appreciated,
Best Regards,
Brendan
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mlib_SignalLPCCovariance_F32(3MLIB)			    mediaLib Library Functions			       mlib_SignalLPCCovariance_F32(3MLIB)

NAME
mlib_SignalLPCCovariance_F32 - perform linear predictive coding with covariance method SYNOPSIS
cc [ flag... ] file... -lmlib [ library... ] #include <mlib.h> mlib_status mlib_SignalLPCCovariance_F32(mlib_f32 *coeff, const mlib_f32 *signal, void *state); DESCRIPTION
The mlib_SignalLPCCovariance_F32() function performs linear predictive coding with covariance method. In linear predictive coding (LPC) model, each speech sample is represented as a linear combination of the past M samples. M s(n) = SUM a(i) * s(n-i) + G * u(n) i=1 where s(*) is the speech signal, u(*) is the excitation signal, and G is the gain constants, M is the order of the linear prediction fil- ter. Given s(*), the goal is to find a set of coefficient a(*) that minimizes the prediction error e(*). M e(n) = s(n) - SUM a(i) * s(n-i) i=1 In covariance method, the coefficients can be obtained by solving following set of linear equations. M SUM a(i) * c(i,k) = c(0,k), k=1,...,M i=1 where N-k-1 c(i,k) = SUM s(j) * s(j+k-i) j=0 are the covariance coefficients of s(*), N is the length of the input speech vector. Note that the covariance matrix R is a symmetric matrix, and the equations can be solved efficiently with Cholesky decomposition method. See Fundamentals of Speech Recognition by Lawrence Rabiner and Biing-Hwang Juang, Prentice Hall, 1993. PARAMETERS
The function takes the following arguments: coeff The linear prediction coefficients. signal The input signal vector. state Pointer to the internal state structure. RETURN VALUES
The function returns MLIB_SUCCESS if successful. Otherwise it returns MLIB_FAILURE. ATTRIBUTES
See attributes(5) for descriptions of the following attributes: +-----------------------------+-----------------------------+ | ATTRIBUTE TYPE | ATTRIBUTE VALUE | +-----------------------------+-----------------------------+ |Interface Stability |Committed | +-----------------------------+-----------------------------+ |MT-Level |MT-Safe | +-----------------------------+-----------------------------+ SEE ALSO
mlib_SignalLPCCovarianceInit_F32(3MLIB), mlib_SignalLPCCovarianceFree_F32(3MLIB), attributes(5) SunOS 5.11 2 Mar 2007 mlib_SignalLPCCovariance_F32(3MLIB)