NSGA-IIMATLAB

这是关于智能天线的自适应算法的MATLAB代码(This is the smart antenna on the adaptive algorithm of MATLAB code)

BPSK调制解调直接序列扩频系统源代码(白噪声及瑞利衰落误码率比较)(BPSK receiver simulation using Spread Spectrum Techniques )

设计一个阻带下限0.45*pi 阻带上限0.8*pi 通带下限0.55*pi 阻带上限0.7*pi 通带衰减0.15dB，阻带衰减40dB，分别用汉宁窗汉明窗凯瑟窗布莱克曼窗设计滤波器，对比分析各方法优劣(Design a stop-band limit 0.45* pi stopband limit 0.8* pi Passband lower limit 0.55* pi stopband limit 0.7* pi Passband attenuation 0.15dB, stop-band attenuation of 40dB, respectively Hanning Hamming window Kaiser window Blackman window filter design, comparative analysis of the pros and cons of each method)

sound localization code using the matlab. this is a simulation file

计算时域均衡器抽头系数和处理经过码间干扰的信号并测试时域均衡器的性能。(Calculate the time-domain equalizer tap coefficients and dealing with after the inter-symbol interference signals and test the performance of the equalizer.)

Arduino JSON Library

脉冲编码调制(PCM)实现 编程实现PCM技术的三个过程：采样、量化与编码。 采样：低通连续信号采样，以 x=sin(200*t) m=x./(200*t) m=m.*m 为例说明低通采样定理，绘出信号时、频图形；带通连续信号采样，以x=sin(20*t) m=x./t 为例说明带通采样定理，绘出信号时、频图形。 量化：均匀量化，以幅度 的正弦信号为例实现为64级电平的均匀量化；非均匀量化，输入A律PCM编码器的正弦信号 ，采样序列为 ，将其进行PCM编码，给出编码器的输出码组序列 编码：以上述信号为例，实现A律的13折线近似法及国际标准PCM对数A律量化编码。(Pulse code modulation (PCM) to achieve PCM technology programming three processes: sampling, quantization and coding. Sample: low-pass continuous signal sampling to x = sin (200* t) m = x./(200* t) m = m.* m an example low-pass sampling theorem, draw the signal, frequency graphics bandpass sampling continuous signals to x = sin (20* t) m = x./t an example bandpass sampling theorem, draw the signal, frequency graph. Quantization: uniform quantization, in order to realize the magnitude of sinusoidal signal as an example for the 64 level uniform quantization non-uniform quantization, input A law PCM encoder sine signal, the sampling sequence, to be PCM encoded, the encoder is given The output code sequence Code: A Case Study of the signal to achieve the 13 A line approximation law and international standards on the number of A law PCM coding quantization.)

Abstract—Numerous methods have been developed for holistic face recognition with impressive performance. However, few studies have tackled how to recognize an arbitrary patch of a face image. Partial faces frequently appear in unconstrained scenarios, with images captured by surveillance cameras or handheld devices (e.g., mobile phones) in particular.

基于LMS算法的自适应噪声消除仿真分析,内含详细的文档说明(LMS-based adaptive noise cancellation algorithm simulation analysis, contains a detailed documentation)

Apuntes del lenguaje C++