PID电机调速模型_matlab_simulink仿真

论文《As-Projective-As-Possible Image Stitching with Moving DLT》中的拼接算法，对于视差图像拼接具有一定的鲁棒性，但是对特征点数量及其分布均匀性有较高的要求。 The success of commercial image stitching tools often leads to the impression that image stitching is a “solved problem”.The reality, however, is that many tools give unconvincing results when the input photos violate fairly restrictive imaging assumptions;the main two being that the photos correspond to views that differ purely by rotation, or that the imaged scene is effectively planar.Such assumptions underpin the usage of 2D projective transforms or homographies to align photos. In the hands of the casual user,such conditions are often violated, yielding misalignment artifacts or “ghosting” in the results. Accordingly, many existing imagestitching tools depend critically on post-processing routines to conceal ghosting. In this paper, we propose a novel estimationtechnique called Moving Direct Linear Transformation (Moving DLT) that is able to tweak or fine-tune the projective warp toaccommodate the deviations of the input data from the idealized conditions. This produces as-projective-as-possible image alignmentthat significantly reduces ghosting without compromising the geometric realism of perspective image stitching. Our technique thuslessens the dependency on potentially expensive postprocessing algorithms. In addition, we describe how multipleas-projective-as-possible warps can be simultaneously refined via bundle adjustment to accurately align multiple images for largepanorama creation.

实时显示质点弹道

目录一、贝叶斯分类器.....................................................................3二、分类流程图.........................................................................3三、最大似然估计.....................................................................4四、分类的三个过程.................................................................4五、具体代码实现.....................................................................5六、结果展示.............................................................................11

IIR  uwb脉冲，然后经PPM调制，经Cassioli信道获得其冲激响应h，再经卷积求得接收方获得信号，增加高斯噪声，再然后用uwb信号与接收信号作互相关函数求取延迟时间。根据各个延迟时间，由TOA算法定位。 【文件目录】 TOA_uwb ├── TOA_LS.m├── TOA_main.m├── constrained_weighted_NLOS│   ├── TOA_LS.m│   ├── add_noise.m│   ├── cir_cir_intersection.asv│   ├── cir_cir_intersection.m│   ├── constrained_weighted_NLOS.asv│   ├── constrained_weighted_NLOS.m│   ├── constrained_weighted_NLOS_ireative.m│   ├── cost_est.asv│   ├── cost_est.m│   ├── hs_err_pid404.log│   ├── limda_est.asv│   ├── limda_est.m│   ├── weights_cal.asv│   └── weights_cal.m├── dist_t.m├── kalmanfltr.m├── main.m├── monocycle.m├── tdoa_method.m├── tempt.m├── tempt1.m├── testing.asv├── testing.m├── toa.m├── uwb_channel.asv└── uwb_channel.m1 directory, 28 files

计算不同大小球形粒子集合的散射,粒子大小分布

【实例简介】中性点经线圈接地单相对地短路仿真

很多初入深度学习的学生都会遇到各种环境配置问题，环境搭建不好模型就跑不了，所以这是限制新手的一大难点，MATLAB具有成熟的运行环境，无需配置，这点对于想跑通一个深度学习模型的新手是非常有利的。所以，本教程手把手教你使用MATLAB中的深度学习框架，完成遥感影像分类的具体操作步骤。本教程给出完整的代码、操作手擦、原始训练及测试样本，旨在最大限度的简化操作步骤，让深度学习零基础的学生也可以轻松跑通深度学习代码，增加自信心和学习兴趣，为遥感影像分类提供一个可借鉴的新型的方法。

【实例简介】

基于hough变换的车牌倾斜校正

%chirp信号脉冲压缩实验,f0=5.321GHz,B=40MHz,Tp=6μs