tfidf

本文主要介绍了数字信号的基带传输原理及其在systemview下的实现方法(In this paper, the digital baseband signal transmission principle and its implementation under way in systemview)

说明：  利用C#创建窗体，通过设置按钮，同时利用按钮实现各种功能。此代码实现的功能是1、调用函数；2、显示照片，同时还能消除照片；3、通过调用MATLAB，实现图像显示的函数。(Use C# to create a form, by setting the button using the button at the same time to achieve various functions. This code implements the function is 1, call the function 2, display photos, while eliminating photographs 3, by calling MATLAB, the function of image display.)

一种使用GMM（Gaussian Mixture Model）聚类方法,亲测，好使(A clustering method using GMM (Gaussian Mixture Model).)

无模型自适应控制程序，使用MATLAB编写(Model free adaptive control program, coding with MATLAB.)

采用加权网络中节点强度和权重都是幂率分布的模型，分析了该信号的时域、频域、倒谱，循环谱等,粒子图像分割及匹配均为自行编制的子例程。( Using weighted model nodes in the network strength and weight are power law distribution, Analysis of the signal time domain, frequency domain, cepstrum, cyclic spectrum, etc. Particle image segmentation and matching subroutines themselves are prepared.)

在机动目标跟踪中，机动目标模型是机动目标跟踪的基本要素之一，一般希望机动目标模型能准确表征目标机动时的各种运动状态。比较常用的模型有匀速运动(CV)模型、匀加速运动(CA) 模型、时间相关模型（Singer）和机动目标“当前”统计模型。上述模型均采用机动频率表征目标的机动情况。在应用当中，通常采用固定的机动频率，这就表示机动目标的机动时间是一定的，而实际上机动目标的机动时间是不断变化的，也就是说机动频率是不断变化的，采用固定机动频率必然会带来误差。采样周期在0.5—2S时，机动频率越小跟踪精度越高[1]，但机动频率仍然是固定值。本文提出的基于神经网络的机动频率自适应调整方法可以使机动频率随机动而变化，从而提高状态估计的准确性，提高跟踪精度。本文将小波神经网络用于机动目标跟踪中机动频率的自适应调整，该算法对机动目标“当前”统计模型中的机动频率进行实时修改, 从而自适应的改变机动频率,使跟踪算法与目标的真实状态更接近。该算法采用小波神经网络的离线训练，实时性好。(The maneuver of the maneuvering target is uncertain. The maneuvering frequency is constantly changeable, but traditionally it is beforehand determined as a constant based on the target state estimation in the state model of the maneuvering target. The maneuver of the maneuvering target makes the kinematics equation of the target model mismatch with the practical motion model and the tracking error will be increased. Based on the advantages of the self-learning, the rapid convergence rate and the nonlinear approximation ability of the wavelet neural network, it was put forward to be used in the field of target tracking in the paper. The new residual is used as the input of the wavelet neural network, the output of the network is used to adjust adaptively the maneuvering frequency of the CS model. The algorithm is more close to the real state of the target. The simulation results showed that tracking error can be reduced and the tracking accuracy can be improved.)

Speed Control of a DC Motor Using BJT H-Bridge This demonstration illustrates simulation of a H bridge used to generated a chopped voltage and control speed of a DC motor, in open loop, in both directions

实现机器人学ppt课件，带有matlab仿真程序(failed to translate)

分析了该信号的时域、频域、倒谱，循环谱等,结合PCA的尺度不变特征变换(SIFT)算法，DC-DC部分采用定功率单环控制。( Analysis of the signal time domain, frequency domain, cepstrum, cyclic spectrum, etc. Combined with PCA scale invariant feature transform (SIFT) algorithm, DC-DC power single-part set-loop control.)

这是MATLAB7.0编程基础的源程序，全部的源程序，自己使用后感觉不错的 大家可以分享一下(MATLAB7.0编程基础-源程序)