LBM-raoliu

matlab下的神经网络感知机实验代码，提供给有需要的人(matlab neural network under the experimental code Perceptron)

modification programme piece sur CN SIEMENS

是一个基于遗传算法改进的模糊C均值算法，很不错的代码。(It is a improved fuzzy C-means algorithm based on genetic algorithm , a very good code. )

Suggestion Cursor Util extends Assert Source Code for Andriod.

802.16eCTC的logmap算法仿真，MATLAB环境(802.16eCTC the logmap algorithm simulation, MATLAB environment)

The constant reverse voltage of the zener diode makes it a valuable component for the regulation of the output voltage against both variations in the input voltage from an unregulated power supply or variations in the load resistance. The current through the zener will change to keep the voltage at within the limits of the threshold of zener action and the maximum power it can dissipate.

干扰对齐中，两种经典的分布式算法（Max_SINR与Min_MLI）对比matlab代码(Interference alignment, the two classical distributed algorithm (Max_SINR and Min_MLI) contrast matlab code)

The receiver performance is quantified by depicting the receiver operating char- acteristics (ROC) curves. This curves serve as a tool to and study the performance of a sensing scheme. ROC graphs are employed to show trade-of vs between detection probability and false alarm rates, (i.e. PD versus PFA), thus allowing the determination of an optimal threshold. To illustrate the performance of energy detectors, we generate complementary ROC curves (the average probability of miss detection $ P_{md} 1 - P_d$ versus the false alarm probability $P_f$ for different receiver structures and in different fading environments.Fig. 1 illustrates the complementary ROC curves for a single channel reception (no diversity) over AWGN channels. This figure depicts the complementary ROC curves for energy detectors.

层次分析法，相关教材以及MATLAB源代码(AHP, related materials and MATLAB source code)

粒子滤波跟踪算法学习的好文档。为个人读书心得，主要从三个角度去理解粒子滤波，由浅入深，有学习价值！(Particle filter tracking algorithm to learn a good document. For personal reading experience, mainly from the three perspectives to understand the particle filter, Deep learning value!)