With the emergence of COVID-19, social distancing detection is a crucial technique for epidemic prevention and control. However, the current mainstream detection technology cannot obtain accurate social distance in real-time. To address this problem, this paper presents a first study on smartphone-based social distance detection technology based on near-ultrasonic signals. Firstly, according to auditory characteristics of the human ear and smartphone frequency response characteristics, a group of 18 kHz–23 kHz inaudible Chirp signals accompanied with single frequency signals are designed to complete ranging and ID identification in a short time. Secondly, an improved mutual ranging algorithm is proposed by combining the cubic spline interpolation and a two-stage search to obtain robust mutual ranging performance against multipath and NLoS affect. Thirdly, a hybrid channel access protocol is proposed consisting of Chirp BOK, FDMA, and CSMA/CA to increase the number of concurrencies and reduce the probability of collision. The results show that in our ranging algorithm, 95% of the mutual ranging error within 5 m is less than 10 cm and gets the best performance compared to the other traditional methods in both LoS and NLoS. The protocol can efficiently utilize the limited near-ultrasonic channel resources and achieve a high refresh rate ranging under the premise of reducing the collision probability. Our study can realize high-precision, high-refresh-rate social distance detection on smartphones and has significant application value during an epidemic.

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Music is an important part of human civilization, which is accompanied by the emergence and development of human civilization. While music is developing, different genres of music or musicians’ styles are all influencing each other. Therefore, it is significant to understand and measure the influence of music influencers and their followers in different genres of music with social development. Address to identify the influence network of American music, firstly, a PageRank model was proposed to calculate the music influence of every artist among the network. According to the results, we built the subnetworks to illustrate the influence of different sets. Then, we did research on distinguishing between genres, which utilized the random forest classifier. Furthermore, Gini impurity is used to demonstrate the feature's importance. Our result shows that Pop is the best influential genre, and acousticness is the most distinguishing feature.