project2_students

基于小波分析的BPSK的OFDM的分析，将对其的频谱进行分类及预测，(Based on the wavelet analysis of the analysis of BPSK OFDM, will the classification and prediction of the spectrum。 )

使用统一资源定位符类URL实现文件下载，同时ImageIO也可以实现(class URL download file)

提取二值图像中的所有非零点坐标，进行矩阵标准化，最后进行二次曲线拟合(Extracting the binary image of all non-zero coordinates, matrix standardized last quadratic curve fitting)

能够成功实现傅里叶变换，包括32点 64点 128点 256点 512点 1024点。(Fourier transform can be successfully achieved, including 32 points 64 points 128 points 256 points 512 points 1024 points.)

说明：  输入灰度图像，采用pcnn算法进行边缘检测(Input gray image, use PCNN algorithm for edge detection)

说明：  实现旋转运动模糊图像的仿真，采用matlab工具来实现。(emluate rotation blurred images)

在matlab中实现透视变换。算法正确高效，这种仿射变换经过修改后可用于图像矫正或者摄像机标定等用途。(Matlab in perspective transformation. Correct and efficient algorithm modified this affine transformation can be used to image correction or camera calibration purposes.)

此MATLASB源代码是LSB的图像数字水印的嵌入与检测，希望对需要者有用!(This source code is MATLASB image watermarking LSB embedding and detection, in the hope that those who need it useful!)

此代码为传统的snake模型算法代码，运行其中的egsnake.m即可。该代码实现对图像的边缘检测，仅供大家一块学习交流(This code is the code algorithm of traditional snake model, which can run egsnake.m. The code to achieve the image edge detection, we only a learning exchange.)

说明：  该课题为基于MATLAB的汽车出入库识别系统，带有丰富的人机交互GUI界面。目前毕业设计选题中，传统的中规中矩的车牌识别不易得到高分，必须要在此基础上有所创新方得可以避开其他雷同课题，不会轻易被导师被否决而导致毕设不过。因此建议在车牌识别基础上加入多种方法的对比，看看哪种方法的精度高。虽然目前有很多方法的车牌识别，但是都是在各自的测试库里面测试的，比如测试的车牌图像不同，字符库不同，导致无法做对比。整个设计在一个GUI界面上完成。(This project is based on MATLAB vehicle in and out of the warehouse identification system, with rich human-computer interaction GUI interface. At present, in the topic selection of graduation project, the traditional regular license plate recognition is not easy to get high scores, so it is necessary to innovate on this basis to avoid other similar topics, and it will not be rejected easily by the tutor, leading to the failure of graduation project. Therefore, it is suggested to add a variety of methods on the basis of license plate recognition to see which method has high accuracy. Although there are many methods of license plate recognition, they are tested in their own test library, for example, the test license plate image is different, the character library is different, so it can not be compared. The whole design is completed on a GUI interface.)