3002-(1)

用matlab实现的jpeg2000 压缩 代码。(With jpeg2000 compression matlab code of the implementation.)

多目标检测，可用于视频监控，视觉目标跟踪，运动检测，及其它图像处理中的相关应用。(This code contains multiple object detection adaptable to visual surveillance, visual object tracking, motion estimation and other image processing applications.)

this is a matlab file for modified yule nielsen model for predicting the printed area color

信息论与编码中的自适应算术编码的程序，希望有帮助！(Information theory and coding of adaptive arithmetic coding process, want to help!)

永磁同步电机矢量控制，速度环电流环采用PI控制方法(Permanent magnet synchronous motor vector control, speed loop current loop PI control method)

inverse filter demo.this deblurs the image using inverse filter

说明：  ex2_1 利用Z变换求系统输出 ex2_2 求DFT ex2_3 求序列的圆周移位 ex2_4 求序列的圆周卷积 ex2_5 简单的DFT实例 (ex2_1 the use of Z transform ex2_2 demand for system output DFTex2_3 seeking ex2_4 shift sequence for the circumference of the circular convolution sequence ex2_5 simple example DFT)

这是自己的作业程序，可以用于小图像的特征提取。(This is their operating procedures, can be used for small image feature extraction.)

Matlab code - Cubic spline method

一个身体部位比较模型的新方法网络（BAN）的，可以容纳多个环节和多个科目。所述的绝对测量允许跨频谱可能刻画的比较 从单参数为整个合奏，通过基于参数化到每个活动，每个学科和每个环节模型。使用错误，并明确之间权衡复杂性，在一个善良的适应措施相结合，显示有重要的影响时，适用于一系列典型的禁止通道数据。它是有不同的 在模式的选择的影响，以及它相关的复杂性，混合活动的“日常”的数据，设置活动相比，动态数据（例如步行）。平均路径损耗的不足，甚至位数的路径损失的措施，作为唯一的表征还强调“禁止通道。 (A new approach to compare models for body area networks (BAN) that accommodates multiple links and multiple subjects is presented. The absolute measure described allows comparison across a spectrum of possible characterizations ranging from single-parameter for an entire ensemble, through to per-activity, per-subject and per-link based parameterized models. The use of an explicit trade-off between error and complexity, combined in a goodness-of-fit measure, is shown to have important consequences when applied to a range of typical BAN channel data. It is shown that there are different implications in choice of model, and it’s associated complexity, for mixed-activity “everyday” data, when compared with set-activity dynamic data (e.g. walking). The deficiency of mean path loss, or even median path loss measures, as a sole characterization of the BAN channel is also highlighted.)