Similarity join has been widely used in many data analysis and data mining applications, we mainly focus on the scalability and performance problem of similarity join query on massive highdimensional data set. p-stable distribution based projection scheme can implement dimension reduction effectively. Three novel approaches based on projection scheme are proposed to deal with massive highdimensional data similarity join problem: Single projection method, Multiple projection method and Projection space partitioning method. Comprehensive experimental tests were performed to evaluate the performance of the above approaches. The experimental results show that the proposed approaches in this paper have good performance and scalability.

Considering that most of online training is not effectively supervised, this article presents an online leaning state assessment approach which combines blink detection, yawn detection, and head pose estimation. Blink detection is realized by computing the eye aspect ratio and the ratio of closed eye frames to the total frames per unit time to evaluate the degree of eye fatigue. Yawn detection is implemented by computing the aspect ratio of the mouth by using the feature points of the inner lip and combining it with the time of opening mouth to distinguish the mouth state. Head pose estimation is first implemented by calculating the head rotation matrix by matching the feature points of 2D face with the 3D face model and then calculating the Euler angle of the head according to the rotation matrix to evaluate the change of the head pose. Especially in yawn detection, we employ the feature points of inner lips in the calculation of the mouth aspect ratio to avoid the impact of lip thickness of various participants. Furthermore, the blink detection, yawn detection, and head pose estimation are first calculated based on the two-dimensional grayscale image of human face, which could reduce the computational complexity and improve the real-time performance of detection. Finally, combining the values of blinking, yawning, and head pose, multiple groups of experiments are carried out to assess the state of different online learners; then, the learning state is evaluated by analyzing the numerical changes of the three characteristics. Experimental results show that our approach could effectively evaluate the state of online learning and provide support for the development of online education.

In this paper, a one-dimensional acoustical topology by energy hopping within power-law variable section waveguides (VSWG) is proposed, in which a topological phase transition occurs due to the energy in the basic unit hopping to the nearby unit with the same energy mode resulting that its energy band is closed first and then opened. This research can realize the enhanced sound energy at the topological interface state and further regulated sound energy on the basis of enhancing sound energy. The large open hole determine the wide frequency range where the designable topological interface state is constructed and the power-law of the curve of the structure can adjust the size of the common forbidden band of the two topological states, so as to further improve the bandwidth. The small open hole control the magnitude of the acoustic energy at the topological interface state. This research will provide guidance for designing acoustic devices with different frequencies, different acoustic energy concentrations and realizing engineering applications of other multifunctional acoustic devices.

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With the rapid development of communication technology, the intelligent mobile terminal brings about great convenience to people’s life with rich applications, while its power consumption has become a great concern to researchers and consumers. Power modeling is the basis to understand and analyze the power consumption characteristics of the terminal. In this paper, we analyze the Bluetooth and hidden power consumption of the android platform and fix the power model of open-source Android platform. Then, a power consumption monitoring tool is implemented based on the model; the tool is divided into three layers, which are original information monitor layer, power consumption calculation layer, and application layer. The original monitor layer gets the power consumption data and running time of the different components under different states, the calculation layer calculates the power consumption of each hardware and each application based on the power model of each component, and the application layer displays the real-time power consumption of the software and hardware. Finally, we test our tool in real environment by using Xiaomi 9 Pro and perform comparison with actual instrument measurement; the error between the monitored value and the measured value is less than 5%.