资源列表
LMS-Simulation
- 通过LMS算法建立自适应滤波器，实现语音信号降噪处理(An adaptive filter is established by LMS algorithm to reduce the noise of speech signal)
道路仿真
- Through simulation, we can take the real city traffic road as the basic, combine the three-dimensional virtual simulation effect of the traffic facilities such as streets, greening, vehicles and so on, so that users can immerse in the virtual city tr
VoiceProcess
- 通过激励信号与合成滤波器，获得重建信号，生成语音变速滤波器与语音变调滤波器，得到变速变调音频。(Through the excitation signal and the synthetic filter, the reconstruction signal is obtained, the voice transmission filter and the voice modulation filter are generated, and the variable tone audio is
test2
- 利用堆栈的形式对Huffman编码算法进行编程，编码过程还是比较高效的(Huffman encoding algorithm using the form of the stack programming, the encoding process is more efficient)
QueuingTheory
- 通过修改单位时间到达量\lambda，服务窗口数r，服务效率\mu等排队论经典参数，观察在不同参数下的等待时间与窗口使用效率(By modifying the classical parameters of queuing theory, such as modifying the arrival time of unit time \lambda, the number of service windows r, and the service efficiency \mu, we obser
FaceRecognition
- 基于主成分分析的人脸识别，应用K-L变换作特征处理(Face recognition based PCA)
myrbm2batch1epoch
- 通过解读RBM算法，利用代码理解RBM算法。(Through the interpretation of RBM algorithm, using code to understand RBM algorithm.)
demb
- 差分进化算法，用于优化计算，根据自己的情况进行编辑(Differential evolution algorithm)
write and read segy files matlab
- 地震勘探干专用软件，小的程序，地震勘探行业，matlab(The QT language reads and writes segy, a special program for the seismic exploration industry, and can read and write segy standard documents)
short circuit
- 电力系统短路分析 三相短路 潮流计算 还有相关的功率 电路节点 电流 电压 相内短路(short circuit calculation)
卷积神经网络详述
- 从卷积神经网络的发展历史开始，详细阐述了卷积神经网络的网络结构、神经元模型和训练算法。在此基础上以卷积神经网络在人脸检测和形状识别方面的应用为例，简单介绍了卷积神经网络在工程上的应用，并给出了设计思路和网络结构。(Starting from the history of the convolution neural network, the network structure, neuron model and training algorithm of the convolution neur
FeatureExtractionUsingAlexNetExample
- 本示例展示了怎样从一个预处理的卷积神经网络中提取特征，并用这些特征去训练一个图像分类器。(This example shows how to extract learned features from a pretrained convolutional neural network, and use those features to train an image classifier. Feature extraction is the easiest and fastest way use