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Advanced Microwave Sensors and Their Applications in Measurement

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Electronic Sensors".

Deadline for manuscript submissions: 25 December 2024 | Viewed by 585

Special Issue Editor


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Guest Editor
School of Electronics and Information Engineering, Sichuan University, Chengdu 610064, China
Interests: microwave sensors; microwave power combining; microwave wireless power transmission (WPT); microwave energy industrial applications

Special Issue Information

Dear Colleagues,

Sensors have undoubtedly become an essential part of the modern world. Sensors, as key tools for capturing changes in physical, chemical, or biological parameters, greatly expand their range of applications and performance when combined with microwave technology. Microwave sensors are widely used in fields such as radar and aerospace, automotive safety, industrial automation, communication systems, health and safety monitoring, environmental monitoring, and agriculture. One application of microwave energy is wireless power transmission, which involves transmitting energy wirelessly through microwaves. There is a close relationship between microwave measurement and wireless power transfer. In wireless power transfer, the generation, transmission, reception, and conversion of microwaves involve precise microwave measurement to ensure the efficiency and safety of energy transmission.

The non-contact measurement capabilities, high precision and sensitivity, powerful penetration, and adaptability to harsh environmental conditions of microwave sensors demonstrate their tremendous advantages in various application scenarios. This Special Issue aims to delve into the development and applications of microwave sensors, and we invite authors to submit high-quality manuscripts to advance the progress of microwave sensors in the measurement field. The themes of microwave energy applications in production and life include, but are not limited to, the following:

Wireless power supply for sensors;

Real-time monitoring of environmental conditions, including air, water, and soil pollution, by microwave sensors;

Sustainable green agriculture monitoring and power supply;

Process monitoring in drying and heating fields;

Real-time microwave sensors in biomedicine;

Microwave power transmission and monitoring on the Internet of Things (IoT);

New applications of real-time microwave sensing systems;

Electromagnetic materials and microwave measurement principles

Real-time monitoring in industrial production.

Prof. Dr. Changjun Liu
Guest Editor

Manuscript Submission Information

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Keywords

  • microwave energy applications in the industry
  • microwave sensors and advanced sensing systems
  • real-time industrial monitoring
  • sensor performance benchmarking: simulation vs. validation
  • permittivity measurement sensor
  • emerging wireless sensor

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Published Papers (1 paper)

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Research

15 pages, 5540 KiB  
Article
Mobile Network Coverage Prediction Using Multi-Modal Model Based on Deep Neural Networks and Semantic Segmentation
by Sheng Zeng, Yuhang Ji, Weiwei Chen, Liping Yan and Xiang Zhao
Sensors 2024, 24(16), 5178; https://doi.org/10.3390/s24165178 - 10 Aug 2024
Viewed by 425
Abstract
A coverage prediction model helps network operators find coverage gaps, plan base station locations, evaluate quality of service, and build radio maps for spectrum sharing, interference management, localization, etc. Existing coverage prediction models rely on the height and transmission power of the base [...] Read more.
A coverage prediction model helps network operators find coverage gaps, plan base station locations, evaluate quality of service, and build radio maps for spectrum sharing, interference management, localization, etc. Existing coverage prediction models rely on the height and transmission power of the base station, or the assistance of a path loss model. All of these increase the complexity of large-scale coverage predictions. In this paper, we propose a multi-modal model, DNN-SS, which combines a DNN (deep neural network) and SS (semantic segmentation) to perform coverage prediction for mobile networks. Firstly, DNN-SS filters the samples with a geospatial-temporal moving average filter algorithm, and then uses a DNN to extract numerical features. Secondly, a pre-trained model is used to perform semantic segmentation of satellite images of the measurement area. Thirdly, a DNN is used to extract features from the results after semantic segmentation to form environmental features. Finally, the prediction model is trained on the dataset consisting of numerical features and environmental features. The experimental results on campus show that for random location prediction, the model achieves a RMSE (Root Mean Square Error) of 1.97 dB and a MAE (Mean Absolute Error) of 1.41 dB, which is an improvement of 10.86% and 10.2%, respectively, compared with existing models. For the prediction of a test area, the RMSE and MAE of the model are 4.32 dB and 3.45 dB, respectively, and the RMSE is only 0.22 dB lower than that of existing models. However, the DNN-SS model does not need the height, transmission power, and antenna gain of the base station, or a path loss model, which makes it more suitable for large-scale coverage prediction. Full article
(This article belongs to the Special Issue Advanced Microwave Sensors and Their Applications in Measurement)
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