DBR-protocol-master

说明：  四维超混沌系统，平衡点 图像，李雅普诺夫指数，完全同步(Four-dimensional hyperchaotic system, balance the image, Lyapunov exponent, fully synchronized)

实现数值分析中单纯形法改进方法的matlab代码，即改进单纯形法算法，能够实现大型稀疏矩阵的迭代，这是3/3..(Numerical analysis to achieve improved method simplex method matlab code that modified simplex method algorithm, to achieve large sparse matrix iteration, which is 3/3 ..)

说明：  无线感知网络的经典图书，无须多言，下了就知道(Wireless sensor network, classic books, do not say, under the know)

说明：  从图书馆找的matlab源程序的学习案例，方便大家学习，欢迎下载！！(Matlab source from the library to find case study, to facilitate learning, welcome to download! !)

it is helpful to konw the characreristics of bpsk modulation technique

第7章语音信号的减噪176 7.1自适应滤波器减噪176 7.1.1LMS算法基本原理176 7.1.2基本LMS自适应算法178 7.1.3LMS的自适应陷波器181 7.2谱减法减噪184 7.2.1基本谱减法184 7.2.2改进的谱减法187 7.3维纳滤波法减噪195 7.3.1维纳滤波的基本原理195 7.3.2维纳滤波减噪的具体步骤和函数WienerScalart96197 7.3.3维纳滤波的MATLAB例子199(Noise Chapter 7 of the speech signal noise reduction algorithm for adaptive filters 176 7.1 Fundamentals 7.1.2 Basic LMS adaptive algorithm 176 178 7.1.3LMS of 176 7.1.1LMS adaptive notch filter 181 7.2 184 7.2 spectral subtraction noise reduction 184 7.2.2 .1 basic spectral subtraction improved spectral subtraction method of noise reduction Wiener filter 187 195 7.3 Fundamentals 7.3.1 195 7.3.2 Wiener filtering Wiener filter noise of concrete steps and functions WienerScalart96197 7.3.3-dimensional MATLAB examples Carolina filter 199)

FIR Wiener Filter Applications: The following demos demonstrate the corresponding applications fir_f - FIR Wiener Filter fir_p - FIR Wiener Linear Predictor fir_nc - FIR Wiener Noise Cancellation (FIR Wiener Filter Applications: The following demos demonstrate the corresponding applications fir_f- FIR Wiener Filter fir_p- FIR Wiener Linear Predictor fir_nc- FIR Wiener Noise Cancellation )

WiMX File very usful simulink

We formulate a scheduling problem that takes into account different hardware delays experienced by the secondary users (SUs) in a centralized cognitive radio network (CRN) while switching to different frequency bands. We propose a polynomial-time suboptimal algorithm to address our formulated scheduling problem. We evaluate the impact of varying switching delay, number of frequencies, and number of SUs. Our simulation results indicate that our proposed algorithm is robust to changes in the hardware spectrum switching delay and its performance is very close to its upper bound. We also compare our proposed method with the corresponding constant switching delay-based algorithm and demonstrate that our suggestion of taking into account the different hardware delays while switching to different frequency bands is essential for scheduling in CRNs.

Here different algorithms which are suitable for watermarking are implemented and MATLAB results are shown for reference.