Philips

QQ机器人在线状态监测－通过QQ官方QQ在线状态图标 用PYTHON控制机器人的下线重启,保证通用机器人24小时.(QQ Robot-line condition monitoring- the official QQ by QQ online status icon in control of the robot off the assembly line with the PYTHON restart, ensuring universal robot for 24 hours.)

此代码是关于利用51单片机实现步进，滞留等程序的参考用。(This code is on the use of 51 single-chip stepper, detention and other procedures for reference.)

监控日志的程序,发现程序异常就自动重启,这样不至于严重影响客户使用.多出时间来彻底解决问题.(moniter the processs)

说明：  使用STM32单片机驱动dds模块，这里的dds模块为正点原子的模块(Using STM32 MCU to drive the DDS module, where the DDS module is the module of positive atom)

NXP LPC11u24 cortex-M0 PWM 示例程序(NXP LPC11u24 cortex-M0 PWM sample programs)

用STM32控制AD7606采样,并口通信(Use STM32 AD7606 sampling control, parallel communication)

说明：  msp产生spwm波程序，通过对PWM的产生方波，然后spwm技术生成正弦波(The program of generating SPWM wave by MSP)

输入通道捕捉程序，适用于飞思卡尔JM60单片机，用于捕捉信号。(The input channels capture program for the Freescale JM60 microcontroller used to capture the signal.)

3pi小车各部分的性能能以及各模块的源代码(3pi trolley part of the performance capacity and the source code for each module)

于市售 STM32 开发板上实现特定人语音识别处理项目。识别流程是：预滤波、ADC、分帧、端点检测、预加重、加窗、特征提取、特征匹配。端点检测(VAD)采用短时幅度和短时过零率相结合。检测出有效语音后，根据人耳听觉感知特性,计算每帧语音的 Mel 频率倒谱系数(MFCC)。然后采用动态时间弯折(DTW)算法与特征模板相匹配,最终输出识别结果。先用Matlab对上述算法进行仿真，经数次试验求得算法内所需各系数的最优值。而后将算法移植到 STM32 开发板上，移植过程中根据 STM32 上存储空间相对较小、计算能力也相对较弱的实际情况，对算法进行优化。最终完成于 STM32 微处理器上的特定人语音识别系统。(Implement speech recognition processing project in commercially available STM32 development board. Identification is the process: pre-filter, ADC, framing, endpoint detection, pre-emphasis, windowing, feature extraction, feature matching. Endpoint detection (VAD) short-time amplitude and short-term zero rate combined. After detecting an effective voice, according to the characteristics of human auditory perception, calculated for each frame of speech Mel Frequency Cepstral Coefficients (MFCC). Then dynamic time warping (DTW) algorithm and feature template matches the final output recognition result. First with Matlab simulation algorithm described above, after several trials to get the optimal value of each coefficient within the desired algorithm. The algorithm will migrate to STM32 development board, the porting process according to the STM32 relatively small storage space, computing power is relatively weak situation of the optimization algorithm. Finally completed on the STM32 micr)