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Raspberry Pi 的代码
t5-l4-pi-boost-deboost-setsched.tst
#
# rt-mutex test
#
# Op: C(ommand)/T(est)/W(ait)
# | opcode
# | | threadid: 0-7
# | | | opcode argument
# | | | |
# C: lock: 0: 0
#
# Commands
#
# opcode opcode argument
# schedothe
simd_instructions.h
MMX_INSTRUCTION(paddb,_mm_add_pi8)
MMX_INSTRUCTION(paddsb,_mm_adds_pi8)
MMX_INSTRUCTION(paddusb,_mm_adds_pu8)
MMX_INSTRUCTION(paddw,_mm_add_pi16)
MMX_INSTRUCTION(paddsw,_mm_adds_pi16)
MMX_INSTRUCTION(
predict_true.m
function xv= predict_true(xv, V,G, WB,dt)
%
% INPUTS:
xv= [xv(1) + V*dt*cos(G+xv(3,:));
xv(2) + V*dt*sin(G+xv(3,:));
pi_to_pi(xv(3) + V*dt*sin(G)/WB)];
thread.h
//
// Copyright (C) 2007 Arne Steinarson
//
// This software is provided 'as-is', without any express or implied
// warranty. In no event will the authors be h
s1func.m
function [s1]=S1(y,sita,m1,k0,u,q)
k1=y^2;
g=9.8;
kc=(g/u^2)/(2*k0);
if m1==1
S=(0.0081/k1^4)*exp(-0.74*kc^2/k1^2);
G=(4/(3*pi))*(cos(sita/2-q/2))^4;
s1=S*G;
elsei
mc_stat_distrib.m
function pi = mc_stat_distrib(P)
% MC_STAT_DISTRIB Compute stationary distribution of a Markov chain
% function pi = mc_stat_distrib(P)
%
% Each row of P should sum to one; pi is a column vector
mc_stat_distrib.m
function pi = mc_stat_distrib(P)
% MC_STAT_DISTRIB Compute stationary distribution of a Markov chain
% function pi = mc_stat_distrib(P)
%
% Each row of P should sum to one; pi is a column vector
mc_stat_distrib.m
function pi = mc_stat_distrib(P)
% MC_STAT_DISTRIB Compute stationary distribution of a Markov chain
% function pi = mc_stat_distrib(P)
%
% Each row of P should sum to one; pi is a column vector
% K