In this paper, a highly isolated multiple-input multiple-output (MIMO) antenna array is proposed for fifth-generation (5G) metal frame smartphones. The eight identical small-sized inverted F-shaped folded slots are etched on the metal frame as a MIMO antenna. The bandwidth of the antenna can be adjusted by changing one of the short branches of the antenna. The bandwidth of the antenna can reach the N79 band (4.4~5.0 GHz). By carefully arranging the positions of the eight antenna elements, ideal spatial diversity can be successfully achieved to mitigate the coupling between the antenna elements effectively. What is more, a small combination slot of C-shape (0.0078 × 0.047λ2) and vertical I-shape (0.12 × 0.004λ2) between each antenna element is introduced to improve the element isolation of the MIMO antenna system. The proposed MIMO array has been simulated, fabricated, and measured. The results show good impedance matching (return loss > 6 dB) and high isolation (>22 dB). Due to the decent element isolation, the envelope correlation coefficient (ECC) between each antenna element is below 0.049. It can provide a reliable anti-interference performance for the MIMO antenna system. In addition, the measured radiation efficiencies of the MIMO antenna system are higher than 50%. The interaction of the hand model with the MIMO antenna system is also investigated, including the specific absorption rate (SAR).
With the development of the times, the society is slowly progressing, the era of information technology has come. With the research of computer technology and the development of artificial intelligence technology, various algorithms have been put forward and popularized. Among them are widely used expert algorithms, genetic algorithms, and a special co product neural network algorithm. In this paper, the mechanical monitoring terminal monitoring system is designed by using the reel neural network. After consulting a large number of literatures at home and abroad and watching the relevant models and starting to model them simply, this paper uses the existing resources of the laboratory to apply distributed network control through the use of multiple computers to assemble the central control system, so as to carry out experimental simulation, and get the analysis results. The final experimental results show that the co product neural network algorithm has better application in the monitoring system than other algorithms, and the data capture speed is faster and the analysis is more accurate.
Mechanical vibrations have been noticed since they were discovered a long time ago. Because almost all machinery in the process of motion will produce vibration, some is due to the external force caused by vibration, and some external forces act on a substance to produce vibration caused by the resonance phenomenon, these will have an impact on the efficiency of the original machinery or internal performance. To solve these problems, scientists have been trying for many years to determine exactly why vibrations occur to achieve their goals by using vibrations or reducing them. Therefore, artificial intelligence technology is used to design and study the monitoring terminal of mechanical vibration wireless sensor. After querying the relevant data at home and abroad and carrying out a simple simulation experiment, this paper designs it by using a variety of algorithms, and finally selects a better algorithm to carry out the final fitting design and experiment. Finally, we chose the electronic tag image matching algorithm for system design. The experimental results show that the electronic label image matching algorithm is more accurate and the imaging speed is faster than other algorithms.
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