DENCLUE2.0 algorithm for python

说明：  用matlab自带的GUI界面设计了一个成绩管理界面，可进行成绩的载入、查询、绘图、求平均分和存储！可直接用！ (Matlab GUI interface that comes with the design of a performance management interface, the results of the load, query, plot, find the average and store! Can be used directly!)

least square system identification

MUNK剖面传播损失仿真，数据验证在TXT文件中，希望有所帮助。(MUNK sectional propagation loss simulation, data validation TXT file, hope that helps.)

MATLAB中textscantool,非常好，大家支持啊(MATLAB, textscantool, very good, we support ah)

对三轴加速度出传感器的原始数据进行处理，平滑噪声，傅里叶fft(The raw data of the three-axis acceleration sensor is processed, smoothing the noise)

一种混合贪婪随机自适应搜索（GRACH）算法，用于解决单车辆段车辆路径问题(a hybrid Greedy Randomized Adaptive Search (GRASP) algorithm that solves a single depot vehicle routing problem)

雷达信号压缩，一维信号的生成以及如何让拖尾压缩。(The radar signal compression, the generation of one-dimensional signals as well as how tailing compression.)

实现脉冲消隐以在通信系统去除较大干扰,保证误码率性能(Realization of pulse blanking to remove larger interference in communication system, guaranting the bit error rate performance)

独立成份分析（ＩＣＡ）以及ｗｉｎｎｅｒ滤波 Source separation of complex signals with JADE. Jade performs `Source Separation in the following sense: X is an n x T data matrix assumed modelled as X = A S + N where o A is an unknown n x m matrix with full rank. o S is a m x T data matrix (source signals) with the properties a) for each t, the components of S(:,t) are statistically independent b) for each p, the S(p,:) is the realization of a zero-mean `source signal . c) At most one of these processes has a vanishing 4th-order cumulant. o N is a n x T matrix. It is a realization of a spatially white Gaussian noise, i.e. Cov(X) = sigma*eye(n) with unknown variance sigma. This is probably better than no modeling at all...( Source separation of complex signals with JADE. Jade performs `Source Separation in the following sense: X is an n x T data matrix assumed modelled as X = A S+ N where o A is an unknown n x m matrix with full rank. o S is a m x T data matrix (source signals) with the properties a) for each t, the components of S(:,t) are statistically independent b) for each p, the S(p,:) is the realization of a zero-mean `source signal . c) At most one of these processes has a vanishing 4th-order cumulant. o N is a n x T matrix. It is a realization of a spatially white Gaussian noise, i.e. Cov(X) = sigma*eye(n) with unknown variance sigma. This is probably better than no modeling at all...)

用HFSS仿真的微带天线,初学HFSS的朋友可以使用一下(using HFSS simulation microstrip antenna, beginner HFSS friends can use this)