Read_RadiationCalibrationFactor

说明：  UniSolder-5.2C 俄罗斯开源(UniSolder-5.2C A short list of features: - power: 12-24V, AC or DC (24V 6A toroidal transformer is the best possible power source) - automatic 1/2 and 1/4 power selection, so only simple single winding 24V can drive all the instruments, from the smallest JBC Nano, to the largest JBC C245. - 2 separate heater control channels - 2 independent sensor inputs - current source on any sensor input 3uA - 12mA, wuth 2 bands (x1, x16) and 256 steps per band - flexible differential amplifier input selection - amplifier gain from 0 to 750 in 256 steps - negative offset selection in 1024 steps - resistive instrument identification (upto 625 different instruments can be identified by 2 resistors on the connector) - polynomial floating point voltage/resistance to temperature calculation - wave shaping to filter out the inductive peaks from series sensor signal - PID control with power limit - isolated USB port for firmware updates and live data - 128x64 OLED display with rich user interface.)

波束形成就是从传感器阵列重构源信号。（1）、通过增加期望信源的贡献来实现；（2）、通过抑制掉干扰源来实现。经典的波束形成需要观测方向(期望信源的方向)的知识。盲波束形成试图在没有期望信源方向信息的情况下进行信源的恢复。 波束形成技术的基本思想是：通过将各阵元输出进行加权求和，在一时间内将天线阵列波束“导向”到一个方向上，对期望信号得到最大输出功率的导向位置即给出DOA估计。 虽然阵列天线的方向图是全方向的，但阵列的输出经过加权求和后，却可以被调整到阵列接收的方向增益聚集在一个方向上，相当于形成了一个”波束”。这就是波束形成的物理意义所在。

毕业论文，一个考试系统-Thesis, an examination system

QR分解计算矩阵特征值、特征向量的 C语言实现(QR decomposition to calculate matrix eigenvalues and eigenvectors by C language)

Optical communication system book for Matlab

操作简单的一款Delphi医学证明登记管理系统，具有数据录入、修改、打补及数据浏览功能。用于医疗机构对新生儿信息进行录入登记的程序，前几年的程序，对学习一些基础的Delphi/ACCESS操作有不小的帮助。

VC的托盘程序,可供参考 VC tray procedures for reference()

WSN中基于博弈论知识的功率控制建模。无线传感器网络路由技术是利用节点相互协作完成数据传输，在保证网络可靠性的同时，尽量减少节点的能量消耗，均衡整个网络的能量消耗，从而延长网络的生命周期。(Power control modeling based on game theoretic knowledge in WSN. Routing technology of wireless sensor network node for data transmission is the use of mutual cooperation, to ensure the reliability of the network at the same time, to reduce the energy consumption of the nodes, balance the energy consumption of the whole network, so as to extend the network life cycle.)

说明：  A commented bash script to generate our final 2nd place solution can be found in make_kaggle_solution.sh. Running all the commands sequentially will probably take 7 - 10 days on recent consumer grade hardware. If you have multiple GPUs you can speed things up by doing training and feature extraction for the two networks in parallel. However, due to the computationally heavy data augmentation it may be far less than twice as fast especially when working with 512x512 pixel input images. You can also obtain a quadratic weighted kappa score of 0.839 on the private leaderboard by just training the 4x4 kernel networks and by performing only 20 feature extraction iterations with the weights that gave you the best MSE validation scores during training. The entire ensemble only achieves a slightly higher score of 0.845.

Programming a STM32F103 board using its USB port (Blue Pill)