This Innovate Practice Full Paper presents the past process of the full-time Master of Engineering (M. Eng.) education in China, which is designed to meet the requirements of the China's innovation of science and technologies. Under the current trend of the economic globalization and the internationalization of higher education, China's higher engineering education (HEE) must carry out certain reforms to adapt to the new changes and development of the country. In China's HEE, M. Eng. program can be divided into two categories: in-service M. Eng. program and full-time M. Eng. program. The graduate students of the former program come from the employees of enterprises and study in their spare time, while the graduate students of the latter program mainly come from the fresh graduates of universities and they spend their full time for study. Normally they both spend 2 to 3 years to finish their master programs. This paper mainly discusses the current situation of full-time M. Eng. education, and analyzes the corresponding difficulties from the following four aspects: insufficient clarity in the training goal, incompleted Dual Tutor System, unsatisfactory enterprise practice, and insufficient interaction between university and enterprise. Therefore, full-time M. Eng. education still faces some immature situation and has higher demand for the innovation. This paper takes the big data full-time Master of Engineering (BDFM) training as a case, and introduces a new training and practice mode in a Chinese university, including project management, establishment of Practice Education Base (PEB), course construction, design of mechanism of PEB, as well as the establishment of Dual Tutor Matching Mechanism (DTMM). Considering the internationalization of HEE under China's national conditions, we show how to speed up the educational reform of M. Eng., how to implement educational innovation, how to promote the engineering education to better adapt to the needs of economic and social development, and how to play the exemplary role of this new training mode. We hope our study could contribute to the sustainable development of HEE over the world.

Wireless sensor networks as the base support for the Internet of things have been a large number of popularity and application. Such as intelligent agriculture, we have to use the sensor network to obtain the growing environment data of crops and others. However, the difficulty of power supply of wireless nodes has seriously hindered the application and development of Internet of things. In order to solve this problem, people use low-power sleep scheduling and other energy-saving methods on the nodes. Although these methods can prolong the working time of nodes, they will eventually become invalid because of the exhaustion of energy. The use of solar energy, wind energy, and wireless signals in the environment to obtain energy is another way to solve the energy problem of nodes. However, these methods are affected by weather, environment, and other factors, and they are unstable. Thus, the discontinuity work of the node is caused. In recent years, the development of wireless power transfer (WPT) has brought another solution to this problem. In this paper, a three-layer framework is proposed for mobile station data collection in rechargeable wireless sensor networks to keep the node running forever, named TLFW which includes the sensor layer, cluster head layer, and mobile station layer. And the framework can minimize the total energy consumption of the system. The simulation results show that the scheme can reduce the energy consumption of the entire system, compared with a Mobile Station in a Rechargeable Sensor Network (MSiRSN).

Target material identification is playing an important role in our everyday life. This paper introduces a device-free target material identification system, implemented on ubiquitous and cheap commercial off-the-shelf (COTS) Wi-Fi devices. The intuition is that different materials produce different amounts of phase and amplitude changes when a target appears on the line-of-sight (LoS) of a radio frequency (RF) link. However, due to multipath and hardware imperfection, the measured phase and amplitude of the channel state information (CSI) are very noisy. We thus present novel CSI pre-processing schemes to address the multipath and hardware noise issues before they can be used for accurate material sensing. Comprehensive real-life experiments demonstrate that we can identify 10 commonly seen liquids at an overall accuracy higher than 95% with strong multipath indoors.

A novel approach of dynamic graphic software watermark is proposed. In this scheme, many fake watermarks created through encoding multi-constant, which's structure is similar to the only true one's, are introduced to enhance the stealth and anti-attack ability of dynamic graph watermark and keep software from sabotage. Meanwhile, a detailed analysis in theory is made in terms of the principle, feasibility and merits of this approach. A series of tests are made on the basis of the prototype system, analyzing the two sub-systems separately about their validity, robustness and performance overload caused by watermark embedding. Moreover, bit-rate of the three graph watermark structures in the system is analyzed in theory.

This paper proposes a new method aiming at integrating the safety analysis in software design and development. Model Driven Architecture is used for the basic framework. By making use of UML extension, UMLsec models the Platform Independent Model of software safety, which achieves more safety requirements in the initial stage of the system design cycle. This approach reduces the risk and the cost of software development and improves the reusability of software.