Typically, a photomultiplier tube (PMT) can be leveraged as the photon detector in the underwater optical wireless communication (UOWC) system to further extend the transmission distance, whose output electric signal can be generally classified into three regimes named discrete-pulse regime, transition regime, and continuous waveform regime according to various received optical power (ROP). In this paper, we experimentally demonstrate a low-power UOWC system that adopted a PMT as the receiver, and a detection method by taking the cumulative sum of the sampled values in a symbol duration is proposed and experimentally evaluated under different ROP and symbol rates. Experimental results show that the proposed detection method outperforms various statistics-based comparison benchmarks. Furthermore, the effectiveness of the proposed method in alleviating strong ambient radiation is experimentally verified.

We propose a novel radio-frequency (RF) receiving architecture based on the microelectromechanical system (MEMS) and optical coherent detection module. The architecture converts the received electrical signal into mechanical vibration through the piezoelectric effect and adopts an optical detection module to detect the mechanical vibration. We analyze the response function of the piezoelectric film to an RF signal, the noise-limited sensitivity of the optical detection module, and the system transfer function in the frequency domain. A simple on–off keying (OOK) modulation with carrier frequency 1 GHz is used to numerically evaluate the detection sensitivity. The result shows that considering the main noise sources in system, the signal detection sensitivity can reach around −116.6 dBm at bandwidth 5 MHz. Such sensitivity significantly outperforms that of the currently deployed long-term evolution (LTE) system. Finally, we conduct preliminary experiments based on an off-the-shelf aluminium nitride piezoelectric film, which shows that the vibration amplitude based on the theoretical prediction can be achieved.

We adopt artificial$\Lambda $-type three-level system with superconducting devices for microwave signal detection, where the signal intensity reaches the level of discrete photons instead of continuous waveform. Based on the state transition principles of the three-level system, we propose a statistical model for microwave signal detection. In addition, achievable transmission rates and signal detection based on the proposed statistical models are investigated for low temperature conditions in deep space communication scenario. It is predicted that high sensitivity can be achieved by the proposed system. We further characterize the received signal considering the saturation phenomenon of three-level system, which reveals negligible performance degradation caused by saturation under weak received power regime.

After the reform and the opening up, the economy of my country has grown rapidly and people’s lives have become better and better. As a result, there is a lot of time to pay attention to their health, which has promoted the rapid development of my country’s sports industry. Since the 2008 Beijing Olympics, the successful hosting of the Beijing Olympics has been further strengthened. With the rise of the development of sports in our country, the use of machine learning in a large amount of information can process this data and analyze it well. Based on this, this article is aimed at making volleyball players and coaches better understand the technical structure of hiking and the technique of hiking. The paper understands the characteristics of muscle activity over time and uses machine learning methods to analyze a large number of volleyball sports data. In this experiment, 12 volleyball players from a college of physical education were selected. According to the actual situation of the students’ physical fitness and skills, it is more reasonable to divide them into two arms with preswing technology (A type) group and two‐arms without preswing technology (B type) group. Mainly study the volleyball spiking action, select the representative front‐row 4th position strong attack and the back‐row 6th position for comparison and analysis, and analyze the process from the take‐off stage to the aerial shot stage in the four stages of the smash through the kinematics, dynamics, and surface electromyography parameters. Experiments have shown that for type A, the left gluteus maximus integral EMG sum value is significantly different between the front and rear rows (P < 0.05). The discharge volume of the left gluteus maximus during the front‐row spiking process is greater than that of the back‐row spiking. This difference is mainly reflected in the kicking stage and the air attack stage. It shows that volleyball data analysis has a very broad prospect of exploration and application, which can create huge social and economic benefits. How to analyze kinematics is also a very demanding research project and is also part of the future analysis of sports data. Academic value and broad practical significance are important.