RA0-estimation

to understand the Algorithm go to matlab help in page dct2 and idct2 to get the mathematical expression for M = N = 8, we can calculate the most of hard values and save it as LUTs to speed up the execution now compare our special 8X8 functions with the internal general functions use this code: A = int32(100*rand(8,8)) tic for i = 1 : 1000 IDCT_8X8(DCT_8X8(A)) end toc tic for i = 1 : 1000 idct2(dct2(A)) end toc i had built the general functions too, but with low speed (to understand the Algorithm go to matlab help in page dct2 and idct2 to get the mathematical expressionfor M = N = 8, we can calculate the most of hard values and save it as LUTs to speed up the executionnow compare our special 8X8 functions with the internal general functionsuse this code: A = int32 (100* rand (8,8)) tic for i = 1: 1000 IDCT_8X8 (DCT_8X8 (A)) end toc tic for i = 1: 1000 idct2 (dct2 (A)) end toc i had built the general functions too, but with low speed)

LSB图像水印的MATLAB实现，欢迎大家下载 (LSB watermark)

Matlab的常用命令下载 Matlab的常用命令下载 Matlab的常用命令下载 Matlab的常用命令下载(The common function of Matlab Welcome to use. The common function of Matlab Welcome to use.)

说明：  数字图像处理，利用matlab进行压缩，融合，分解，重构等等(Digital image processing using matlab for compression, fusion, decomposition, reconstruction, etc.)

Matlab curvelab toolbox

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阻塞矩阵多频信号主瓣干扰抑制-画自适应波束形成图f=6、7、8、9MHz,目标角度10，干扰11，20，-20强度分别为20db，40db，20db，20db-(Blocking matrix multi-frequency signal main lobe interference suppression- painting adaptive beamforming Figure f = 6, 7, 8, 9 MHz, target angle, interference 11,20,-20 strength to 20db, 40db, 20db, 20db-)

“Fast Tracking via Dense Spatio-Temporal Context Learning,” In ECCV 2014的源代码，效果非常好。(In this paper, we present a simple yet fast and robust algorithm which exploits the spatio-temporal context for visual tracking. Our approach formulates the spatio-temporal relationships between the object of interest and its local context based on a Bayesian framework, which models the statistical correlation between the low-level features (i.e., image intensity and position) from the target and its surrounding regions. The tracking problem is posed by computing a confidence map, and obtaining the best target location by maximizing an object location likelihood function. The Fast Fourier Transform is adopted for fast learning and detection in this work. Implemented in MATLAB without code optimization, the proposed tracker runs at 350 frames per second on an i7 machine. Extensive experimental results show that the proposed algorithm performs favorably against state-of-the-art methods in terms of efficiency, accuracy and robustness.)

说明：  一种相位调制信号的多普勒补偿方法，希望对大家有帮助。(Doppler signal of a phase modulation compensation, we want to help.)

利用Matlab生成GPS C/A 码的基带信号。 （1）产生卫星PRN 号码为“5”的一组C/A 码； （2）循环生成25 页的导航数据，GPS 时间可填入一固定的已知数值； （3）生成C/A 码扩频信号，每个C/A 码码片给出4 个采样数据； （4）基带信号采用方波信号，电平为0 和1，占空比为50 ％，不加入噪声，进一步生成某个特定的中频信号。(Matlab generated using GPS C/A code base band signal. (1) generation of satellite PRN number is " 5" a set of C/A code (2) loop generates 25 of the navigation data, GPS time can fill a fixed known value (3) to generate C/A code spread spectrum signal, each C/A code chip gives the four sampling data (4) baseband signals using square wave signal, level 0 and 1, 50 duty cycle, not to join the noise generated further a specific frequency signal.)