slide_window_filter

逻辑蒂斯回归模型，用于概率预测，或者分类。内附matlab代码，以及运行数据，可直接运行。(this is an matlab code of logistic regression which could use for data classification and properbility predict. u can direcctly use this code and th data inside.)

zigbee模块，基于开发板进行的一些基础性入门实验，没有添加协议栈的裸机实验。(zigbee module development board based on some basic entry test, did not add the protocol stack bare metal experiments.)

利用kafka实现生产者消费者模型，生产者读取文件内容发送到kafka上，消费者消费kafka上的消息存入数据库(Making use of Kafka to realize producer consumers)

MOCA方案介绍，moca为当前使用比较多的EOC解决方案，供大家参考！-MOCA program introduction, moca for the current use of a relatively large number of EOC solution, for your reference!

说明：  Ka频段大气影响的信道模型仿真用simulink实现。(The simulation of the Ka-band atmospheric influence channel model is implemented with simulink.)

回溯算法程序，-Backtracking algorithm program

说明：  根据此算法可以将补偿函数与刘维尔分数阶导数相结合，则可以得出高精度的caputo分数阶导数。(According to this algorithm, the compensation function can be combined with the Liouville fractional derivative, and the Caputo fractional derivative with high accuracy can be obtained.)

5G 的网络架构和业务特征相对于 3G/4G 时代出现了较大变化，对 5G 承载 网提出了挑战性的需求。针对大带宽、超低时延、高可靠、高精度同步、灵活性、网 络切片、智能协同等七大特征，探讨了 5G 承载技术方案，涵盖前传、中传和回传承 载方案，并分别对应有源天线单元（AAU）和分布单元（DU）之间，DU 和集中单元 （CU）之间，以及 CU 和核心网之间的通信承载。(Comparing with 3G/4G, 5G network architecture and service characteristics have changed great|y, which brings cha||enging requirements for 5G bearing network. |n this paper, seven features of 5G are proposed, such as wide bandwidth, u|tra |ow |atency, high re|iabi|ity, high-precision synchronization, f|exibi|ity, network section, and inte||igent cooperation. And 5G bearing technica| proposa|s are a|so discussed, inc|uding fronthau|, midd|ehau| and backhau| which correspond to the bearing network between active antenna unit (AAU) and distributed unit (DU), the bearing network between DU and centra|ized unit (CU), and the bearing network between CU and core network)

串口重定向，调试要用到，非常重要。printf函数重定向。(Serial port redirection is very important for debugging. Pritf function redirection.)

说明：  协同移动边缘计算中联合卸载决策和资源分配的双层优化方法(A bi level optimization method for joint unloading decision and resource allocation in collaborative mobile edge computing)