%% 有限差分显式方法（迭代），D = 扩散率：Fick 的第二扩散定律% by Prof. Roche C. de Guzman 清除; clc; 关闭所有'）; %% 给定xi = 0; xf = 0.6; dx = 0.04; % x 范围和步长 = dx [m] xL = 0; xU = 0.1; % 初始值 x 下限和上限 [m] ti = 0; tf = 0.05; dt = 4e-4; % t 范围和步长 = dt [s] ci = 2; % 初始浓度值 [ng/L] cLU = 8; x 上下限范围内的 % 初始浓度值 [ng/L] D = 1.5； % 扩散率或扩散系数 [m^2/s] %% 计算％自变量：x和t X = xi:dx:xf; nx = 数字（X）； T = ti:dt:tf; nt = numel(T); % x 和 t 向量及其元素数[x,t] = me

专注于应用于音频信号的频谱建模。 读者应该已经熟悉傅里叶变换和基本数字信号处理。

The Elements of Computing Systems Building a Modern Computer from First Principles 英文epub 本资源转载自网络，如有侵权，请联系上传者或csdn删除 查看此书详细信息请在美国亚马逊官网搜索此书

OFDMA权威技术书籍，原版pdf文档。LTE, WiFi工程开发必备手册，同时也是通信高年级研究生提升专业素养的极佳参考书。作者是Qualcomm Flarion核心成员。

The fourth edition of "Probability, Random Variables and Stochastic Processes" has been updated significantly from the previous edition, and it now includes co-author S. Unnikrishna Pillai of Polytechnic University. The book is intended for a senior/graduate level course in probability and is aimed at students in electrical engineering, math, and physics departments. The authors' approach is to develop the subject of probability theory and stochastic processes as a deductive discipline and to illustrate the theory with basic applications of engineering interest.

该书的作者Rogers是大牛，虽然内容比较老，但是里面都是经典的基础的算法，如果能把里面的算法都能实现出来，绝对会脱颖而出。

Mac OS操作系统 Mac OS X Internal: A System Approach

Stochastic Models Estimation and Control 1,2,3卷，高清PDF，文字可选，是时候收藏一波了

This is a practical guide to building Kalman filters that shows how the filtering equations can be applied to real-life problems. Numerous examples are presented in detail, showing the many ways in which Kalman filters can be designed. Computer code written in FORTRAN, MATLAB®, and True BASIC accompanies all of the examples so that the interested reader can verify concepts and explore issues beyond the scope of the text. In certain instances, the authors intentionally introduce mistakes to the initial filter designs to show the reader what happens when the filter is not working properly. The text carefully sets up a problem before the Kalman filter is actually formulated, to give the reader an intuitive feel for the problem being addressed. Because real problems are seldom presented as differential equations, and usually do not have unique solutions, the authors illustrate several different filtering approaches. Readers will gain experience in software and performance tradeoffs for determining the best filtering approach. The material that has been added to this edition is in response to questions and feedback from readers. The third edition has three new chapters on unusual topics related to Kalman filtering and other filtering techniques based on the method of least squares.Chapter 17 presents a type of filter known as the fixed or finite memory filter, which only remembers a finite number of measurements from the past. Chapter 18 shows how the chain rule from calculus can be used for filter initialization or to avoid filtering altogether. A realistic three-dimensional GPS example is used to illustrate the chain-rule method for filter initialization. Finally, Chapter 19 shows how a bank of linear sine-wave Kalman filters, each one tuned to a different sine-wave frequency, can be used to estimate the actual frequency of noisy sinusoidal measurements and obtain estimates of the states of the sine wave when the measurement noise is low.

S. R. Pliska, A stochastic calculus model of continuous trading: Optimal portfolios optimiza- tion, Math. Oper. Res., 11(1986), pp. 371-384.