ObjectDtction

现代通信系统中广泛应用的GMSK调制与解调方式仿真模拟 多图 有很强的说明性 呕心沥血啊(In modern communications systems widely used in GMSK modulation and demodulation method simulation of multi-map has a strong descriptive heart out ah)

计算机械振动烈度，由采集的振振动位移数据来求得可直接使用。 (Calculation of the mechanical vibration intensity, obtained by the vibration vibration displacement data collected can be used directly.)

说明：  产生水平地震波的小程序，希望对作地震分析的同仁有用处！(level of seismic waves generated by small program, and I hope to make seismic analysis of the usefulness of us!)

在调制信道下以sin函数为载波的BPSK的调制与解调程序，及误码率曲线(Channel in the modulation of the carrier to the sin function for BPSK modulation and demodulation process, and BER curves)

说明：  一种ofdm频偏估计误差的方差MATLAB程序，可以运行，能得到结果图 (A frequency offset estimation error variance ofdm MATLAB program, you can run, can get results map)

关于 CoSaMP 算法介绍 压缩传感压缩感知理论应用的算法(CoSaMP algorithm introduced)

matlab数据拟合的ppt，内有详细介绍(data fitting matlab ppt, with detailed description)

选择“BreastCancer”数据集，使用支持向量机(SVM)对其进行分类。作为对比，第一次对特征集直接进行支持向量机分类，第二次对特征集进行主成分分析法的特征提取后，再对特征提取后的特征集进行支持向量机分类。并且对比和分析了两次分类的结果。(The BreastCancer data set is selected and classified by Support Vector Machine (SVM). For comparison, the first time the feature set is classified directly by support vector machine, the second time the feature set is extracted by principal component analysis, and then the feature set is classified by support vector machine. The results of the two classifications are compared and analyzed.)

fis for detect fire using fuzzy

以实际所测随机中子脉冲数据的自相关函数为研究对象，借助仿真实验，开展Elman神经网络对不同浓度核材料进行识别的研究。在实测数据的基础上，通过叠加随机噪声，模拟产生了不同浓度核材料的相关函数样本用于神经网络的训练与测试，实验结果表明，(Calculating the stochastic neutron pulse autocorrelation function with the actually measured data as the research object, we carry out different simulate experiments in concentration identification of varied nuclear materials using Elman neural network. Superimposing random noise on the measured data, we get correlation function samples simulating the different concentrations of nuclear material for training and testing the neural network.)