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In order to help students learn and master SCM serial interface technology, one self-teaching circuit board on serial interface technology has been designed. This paper mainly discusses the design method of the circuit board. First of all, the on-system programming module, I 2 C module, SPI module, 1-Wire bus module, serial keyboard and digital tube and LCD display interface module are introduced in detail; then the design method of on-system programming software is discussed. With this circuit board, students can easily master all kinds of serial interface technology usually used in the development of single chip microcomputer. It has been used by many colleges and universities and SCM technology training, the good teaching effect has been achieved.

WiFi technology has been used pervasively in finegrained indoor localization, gesture recognition, and adaptive communication. Achieving better performance in these tasks generally boils down to differentiating Line-Of-Sight (LOS) from Non-Line-Of-Sight (NLOS) signal propagation reliably which generally requires expensive/specialized hardware due to the complex nature of indoor environments. Hence, the development of low-cost accurate positioning systems that exploit available infrastructure is not entirely solved. In this paper, we develop a framework for indoor localization and tracking of ubiquitous mobile devices such as smartphones using on-board sensors. We present a novel deep LOS/NLOS classifier which uses the Received Signal Strength Indicator (RSSI), and can classify the input signal with an accuracy of 85%. The proposed algorithm can globally localize and track a smartphone (or robot) with a priori unknown location, and with a semi-accurate prior map (error within 0.8 m) of the WiFi Access Points (AP). Through simultaneously solving for the trajectory and the map of access points, we recover a trajectory of the device and corrected locations for the access points. Experimental evaluations of the framework show that localization accuracy is increased by using the trained deep network; furthermore, the system becomes robust to any error in the map of APs.