C语言链表学习

资源描述本程序利用了opencv自带的haar人脸分类器，然后在vs编程环境下，实现了对静态图像中正脸、侧脸的检测，同时还实现了对动态视频中人脸的实时检测。

可计算生成巴特沃斯、切比雪夫原型滤波器，转换为数字滤波器，并可直接进行滤波-Computable generating Butterworth, Chebyshev prototype filter, is converted to digital filters, can be directly filtered

运动目标位置预测-moving target location forecast

纯C语言编写的输入法源代码，主要用于单片机，有需要的可以下载！ 使用说明： 拼音输入法杳询函数: unsigned char code * py_ime(unsigned char input_py_val[]); input_py_val为已输入的拼音码字符串头指针， 反回值为中文的起始地址，当为0时，杳询失败 应用举例： {     unsigned char input_string[]={"bang"};     unsigned char chines_string[100];     sprintf(chines_string,"%s",py_ime(input_string)); }

链表的快速排序法-List`s quick sort

关于主题的数据结构和 alogorithn aa 树的算法。项目在 c + + 编码，控制台 GUI，一步一步安排

矩阵的类，可以做矩阵运算-matrix of categories that can be done matrix calculation

资源描述This paper presents a lowcost indoor location identification and material tracking methodology for construction projects using Ultra High Frequency (UHF) passive Radio Frequency Identification (RFID) technology. Onsite location aware information is an emerging area that focuses on automating delivery of spatial information pertinent to location of materials, workforce, and equipment. This spatial information can be used to derive knowledge about construction project status. A two-step algorithm is presented to automate the process of location estimation and material tracking in near-real-time. In this methodology, a number of passive RFID tags are distributed onsite where work is progressing, and a mobile RFID reader is carried by a worker onsite. Each passive RFID tag is deployed as a reference point with a known location (landmark) within a predefined zone. Reference tags of known locations are used to d

边缘提取是图像处理的基础工作，如何精确有效地提取边缘是图像处理领域相关学者讨论的热点问题，由此产生的各种边缘检测方法层出不穷并且得到了很好的应用，但这些方法都无法达到人眼识别物体边缘的精确程度

应用背景Massive multiuser MIMO (MISO): ◮ M ≫ K ≫ 1 (think 100 × 10 or 500 × 50) ◮ coherent, but simple, processing ◮ Potential to dramatically improve rate & reliability ◮ Potential to drastically scale down TX power ◮ Not only theory, at least one known testbed (64× ∼10) 关键技术 Massive MIMO Operation Not enough resources for pilots & CSI feedback, so operate in TDD. ◮ On the uplink, ◮ acquire CSI from uplink pilots and/or blindly from data ◮ detect symbols ◮ M ≫ K ⇒ linear processing (MRC, ZF, MMSE) nearly optimal ◮ On the downlink, ◮ use CSI obtained on the uplink ◮ make necessary adjustments based on reciprocity calibration ◮ apply multiuser MIMO precoding ◮ simple precoders desirable (and very good!): MRT, ZF, MMSE, ... ◮ MRC/MRT operation ◮ intracell interference will appear as