Conduction_2d

简单模拟了bpsk的编解码，以及在高斯信道传输的后的译码和误码率的分析。另附功率谱。(Simple simulated bpsk codec, and the transmission of Gaussian channel bit error rate after decoding and analysis. Attached power spectrum.)

说明：  codes related to optimization

vba, to get point in an autocad

对时变信道下差分放大-前向中继的选择性合并性能的研究，这是IEEE Transactions on Wireless Communications, 2014 一篇文章的对应仿真，是作者本人写的程序(Description simulation of the following paper: M. R. Avendi and Ha H. Nguyen, Performance of Selection Combining for Differential Amplify-and- Forward Relaying Over Time-Varying Channels, IEEE Transactions on Wireless Communications, 2014 Required Products MATLAB MATLAB release MATLAB 7.13 (R2011b) )

说明：  基于Simulink搭建的燃料电池系统级仿真模型，适合相关matlab专业学生交流学习。(The fuel cell system level simulation model based on Simulink is suitable for the students majoring in MATLAB.)

MATLAB c++ 可以实在嵌入式的环境下联合编程。联合仿真(MATLAB c++ can be embedded in the environment is the joint programming. Co-simulation)

数值线性代数Gauss迭代法解决课后习题(Numerical linear algebra, Gauss iterative method to solve after-school exercise)

在matlab下，利用monte carlo方法模拟光在混浊介质中的传输(In matlab, monte carlo simulation of the transmission of light in turbid media)

oqpsk调制与解调，包括星座图，误码率，awgn加入信道后的系统性能仿真(OQPSK modulation and demodulation, including constellation diagram, bit error rate, AWGN after the addition of the system performance simulation)

在机动目标跟踪中，机动目标模型是机动目标跟踪的基本要素之一，一般希望机动目标模型能准确表征目标机动时的各种运动状态。比较常用的模型有匀速运动(CV)模型、匀加速运动(CA) 模型、时间相关模型（Singer）和机动目标“当前”统计模型。上述模型均采用机动频率表征目标的机动情况。在应用当中，通常采用固定的机动频率，这就表示机动目标的机动时间是一定的，而实际上机动目标的机动时间是不断变化的，也就是说机动频率是不断变化的，采用固定机动频率必然会带来误差。采样周期在0.5—2S时，机动频率越小跟踪精度越高[1]，但机动频率仍然是固定值。本文提出的基于神经网络的机动频率自适应调整方法可以使机动频率随机动而变化，从而提高状态估计的准确性，提高跟踪精度。本文将小波神经网络用于机动目标跟踪中机动频率的自适应调整，该算法对机动目标“当前”统计模型中的机动频率进行实时修改, 从而自适应的改变机动频率,使跟踪算法与目标的真实状态更接近。该算法采用小波神经网络的离线训练，实时性好。(The maneuver of the maneuvering target is uncertain. The maneuvering frequency is constantly changeable, but traditionally it is beforehand determined as a constant based on the target state estimation in the state model of the maneuvering target. The maneuver of the maneuvering target makes the kinematics equation of the target model mismatch with the practical motion model and the tracking error will be increased. Based on the advantages of the self-learning, the rapid convergence rate and the nonlinear approximation ability of the wavelet neural network, it was put forward to be used in the field of target tracking in the paper. The new residual is used as the input of the wavelet neural network, the output of the network is used to adjust adaptively the maneuvering frequency of the CS model. The algorithm is more close to the real state of the target. The simulation results showed that tracking error can be reduced and the tracking accuracy can be improved.)