本文主要总结 LaTeX 的安装配置和一些使用方法。

OpenCV (Open Source Computer Vision Library) is an open source computer vision and machine learning software library. OpenCV was built to provide a common infrastructure for computer vision applications and to accelerate the use of machine perception in the commercial products.
Being a BSD-licensed product, OpenCV makes it easy for businesses to utilize and modify the code.

Face recognition based on YOLO, You Only Look Once: Unified, Real-Time Object Detection.

1. Abstract

自YOLO算法提出以来，至今已经发展到了v3，性能、集成性等都得到了极大的提升，用YOLO来实现人脸识别算法，其特点是模型训练参数较少，可移植并且实时性很高。目前为止，集成现有技术实现一个基于YOLO算法的人脸识别系统是一项很有挑战性的工作。近几年来，目标检测算法取得了很大的突破。比较流行的算法可以分为两类，一类是基于Region Proposal的R-CNN系算法（R-CNN，Fast R-CNN, Faster R-CNN），它们是two-stage的，需要先使用启发式方法（selective search）或者CNN网络（RPN）产生Region Proposal，然后再在Region Proposal上做分类与回归。另一类是Yolo，SSD这类one-stage算法，其仅仅使用一个CNN网络直接预测不同目标的类别与位置。第一类方法准确度高，但是速度慢，第二类算法速度快，但是准确性较低。本文将介绍Yolo算法，其全称是You Only Look Once: Unified, Real-Time Object Detection，You Only Look Once说的是只需要一次CNN运算，Unified指的是这是一个统一的框架，提供end-to-end的预测，而Real-Time体现是Yolo算法速度快。这里我们谈的是Yolo-v1版本算法，其性能差于后来的SSD算法的，但是Yolo后来也继续进行改进，产生了Yolo9000算法。本文主要讲述Yolo-v1算法的原理。

This paper, Through-wall Human Pose Estimation Using Radio Signals, is extracted from a paper in CVPR2018 published by Dina Katabi, a famous team in the wireless communication field, and demonstrates accurate human pose estimation through walls and occlusions.
In this paper, the system RF-pose designed by wireless signals can accurately predict human activities, and it also has very accurate prediction results when the environment is blocked by walls and other obstacles.

Abstrcat

​ 隐私保护在各个领域都是一个很重要的研究课题，一般而言，隐私的威胁来自于两个方面，一是攻击者利用系统的漏洞或者非法权限访问数据库或云端，窃取隐私数据；二是数据系统内部具有权限的数据管理员对于隐私信息的探查和泄露。在云计算环境中，构建密文对敏感数据加密后存入云端数据库能有效防止以上两种威胁。

​ 目前流行的加密方法称作保序加密(order preserving encryption, OPE)，相比于传统的加密方案，其优点在于：传统的加密方案将会破坏明文数据原有的顺序信息, 使得数据的查询变得十分困难， 保序加密是一种密文保持明文顺序的特殊加密方案。它既能保护用户数据机密性, 也能够实现密文数据高效查询，保序加密可以使得云服务器根据密文的顺序信息来得到明文顺序信息，进而保证涉及顺序信息的查询操作可以在密文空间高效进行。

The paper named Precise Power Delay Profiling with Commodity Wi-Fi is written by Yaxiong Xie, Mo Li`s student in Nayang Technological University. This note is a recap in a easy-to-understand manner.

Tensorflow中一些简单但是容易忘记的：

import tensorflow as tf
a = tf.matmul(x,w1) #用来表示矩阵的乘法操作

weight = tf.Variable(tf.random_normal([2,3],stddev = 2)) 

bias = tf.Variable(tf.zeros([3]))
#偏置项

Data Download and Extract

Taking cifar10 as an example,

DATA_URL = 'https://www.cs.toronto.edu/~kriz/cifar-10-binary.tar.gz'

filename = DATA_URL.split('/')[-1]
filepath = os.path.join(path, filename)
#output: path\filename

Abstract

The presence of reflectors in the environment surrounding a transmitter and receiver create multiple paths that a transmitted signal can traverse.

As a result, the receiver sees the superposition of multiple copies of the transmitted signal, each traversing a different path. Each signal copy will experience differences in attenuation, delay and phase shift while travelling from the source to the receiver. This can result in either constructive or destructive interference, amplifying or attenuating the signal power seen at the receiver. Strong destructive interference is frequently referred to as a deep fade and may result in temporary failure of communication due to a severe drop in the channel signal-to-noise ratio.

These instructions are currently expected to work on Linux operating systems that are based on an upstream Linux kernel version between 3.2 (e.g. Ubuntu 12.04) and 4.2 (e.g. Ubuntu 14.04.4).
The IWL5300 provides 802.11n channel state information in a format that reports the channel matrices for 30 subcarrier groups, which is about one group for every 2 subcarriers at 20 MHz or one in 4 at 40 MHz. Each channel matrix entry is a complex number, with signed 8-bit resolution each for the real and imaginary parts. It specifies the gain and phase of the signal path between a single transmit-receive antenna pair.