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[mathematica] qyahhmo DL : 0: 数字通信系统中信噪比与误码率关系的Matlab模拟-The relationship between citic to noise ratio and bit error rate of digital communication systems Matlab simulation
Date : 2025-12-29 Size : 131kb User : Emmmbr
[mathematica] xdesp DL : 0: 这是消除信号中噪声的程序,是国外编制的,比较适合初学,-This procedure is to eliminate the noise in the signal, is compiled by abroad, suitable for beginners,
Date : 2025-12-29 Size : 4kb User : ctpyjg
[mathematica] cxmpiledbeginnersprocedure DL : 0: 这是消除信号中噪声的程序,是国外编制的,比较适合初学,-This procedure is to eliminate the noise in the signal, is compiled by abroad, suitable for beginners,
Date : 2025-12-29 Size : 4kb User : ctpyjg
[mathematica] orincirle-environment DL : 0: 通信原理课程原理课题：数字幅度调制的抗噪声性能,程序运行于MATLAB环境下-Communication principle course principle: digital amplitude modulation noise resistance, the program runs in the MATLAB environment
Date : 2025-12-29 Size : 334kb User : Donzjkm
[mathematica] asxzee DL : 0: 设计最小二乘逆滤波器求解反卷积问题,假设有观测噪声,为降低观测噪声的影响-Design least-square deconvolution problem solving inverse filter, assume that the observation noise, in order to reduce the effects of observation noise
Date : 2025-12-29 Size : 26kb User : insuantiafjzn
[mathematica] KalmanFiltering DL : 0: 扩展卡尔曼滤波算法C语言实现：实现对二维状态变量的预测及跟踪。包括卡尔曼滤波初始化函数，负责初始化状态变量的值，过程噪声，测量噪声，以及状态转移矩阵。卡尔曼滤波函数负责对状态变量进行迭代滤波，整个过程包括状态预测，协方差预测，测量预测，计算卡尔曼增益，状态量更新，协方差更新。(To realize the prediction and tracking of two dimensional state variables. It includes the initialization function of Kalman filter, which is responsible for initializing the value of state variables, process noise, measurement noise, and state transfer matrix. Kalman filter function is responsible for iterative filtering of state variables. The whole process includes state prediction, covariance prediction, measurement and prediction, computing Kalman gain, updating state variables and updating covariance.)
Date : 2025-12-29 Size : 1kb User : hnuduanyang
[mathematica] fp DL : 0: Latent fingerprint matching has played a critical role in identifying suspects and criminals. However, compared to rolled and plain fingerprint matching, latent identification accuracy is significantly lower due to complex background noise, poor ridge quality and overlapping structured noise in latent images. Accordingly, manual markup of various features (e.g., region of interest, singular points and minutiae) is typically necessary to extract reliable features from latents. To reduce this markup cost and to improve the consistency in feature markup,
Date : 2018-10-17 Size : 87.09kb User : uud123