Electromagnetic Signal Feature Fusion and Recognition based on Multi-Modal Deep Learning

Hou, Changbo; Zhang, Xiao; Chen, Xiang

doi:10.23940/ijpe.20.06.p12.941949

Cited by 21 publications

(6 citation statements)

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“…Utilization of Electromagnetic Data Is Difficult. On the one hand, there are many types of electromagnetic data [15], showing multisource heterogeneous characteristics [16]. As the types of electromagnetic equipment and stations continue to increase, spectrum usage methods continue to evolve [17], resulting in an increasing number of the electromagnetic data [18].…”

Section: The Hidden Information Mining and Comprehensivementioning

confidence: 99%

Electromagnetic Environment Portrait Based on Big Data Mining

Guo

Lin

2021

Wireless Communications and Mobile Computing

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With the development of IoT in smart cities, the electromagnetic environment (EME) in cities is becoming more and more complex. A full understanding of the characteristics of past spectrum resource utilization is the key to improving the efficiency of spectrum management. In order to explore the characteristics of spectrum utilization more comprehensively, this paper designs an EME portrait model. By checking the statistical information of the spectrum data, including changes in the noise floor and channel utilization in each individual wireless service, the correlation between the spectrum and time or space of different channels and the information is merged into a high-dimensional model through consistency transformation to form the EME portrait. The portrait model is not only convenient for storage and retrieval but also beneficial for transfer and expansion, which will become an important foundation for intelligent electromagnetic spectrum management.

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Section: The Hidden Information Mining and Comprehensivementioning

confidence: 99%

Electromagnetic Environment Portrait Based on Big Data Mining

Guo

Lin

2021

Wireless Communications and Mobile Computing

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“…This system has sent many outstanding piano talents to the country. It has proved to the world that although the piano is an "exotic product" [6], it has already taken root in China [7]. It also provides teaching methods, concepts, and levels with Chinese characteristics [8].…”

Section: Introductionmentioning

confidence: 99%

“…In Eq (7),. shows the mean of the original data, and refers to the standard deviation of the original data.…”

mentioning

confidence: 99%

Creation Techniques and Styles of Chinese Piano Works Supported by Edge Cloud Computing Technology

Liu

2022

Preprint

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The purpose is to explore the application of the deep learning (DL) algorithm and edge cloud computing technology in the creation and innovation of Chinese piano music, understand the development history of Chinese piano music, the evolution of creative techniques, style characteristics, and innovation. Firstly, on the basis of the combination of traditional and modern techniques, using scientific research methods such as surveys and documents, a preliminary discussion and arrangement of a large number of piano music works are carried out from the three aspects of creation characteristics, techniques, and styles. Secondly, according to the characteristics of edge cloud computing technology, the application and embodiment of this technology in Chinese piano music creation and innovation are explained. Moreover, the DL algorithm is further introduced and improved to implement the piano music creation technique and style recognition model based on the Convolution, Long-short Term Memory, and Deep Neural Network (CLDNN) algorithm. Finally, the performance of the constructed model is analyzed through comparative experiments. The results reveal that by sorting relevant literature, the five major periods of Chinese piano music experience and a large number of classic piano repertoires can be clearly sorted out. It can be understood that piano music creation techniques and style changes will be an endless process, requiring more arduous learning by composers and theorists. Further analysis of the constructed model denotes that the recognition accuracy of the proposed model algorithm reaches 91.33%, which is obviously better than other algorithms. Therefore, the integration of the DL algorithm and other methods has certain research significance for exploring the creative techniques and style innovation of Chinese piano works.

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“…With the increasingly complex electromagnetic environment of signals, DL has gradually become the mainstream algorithm in spectrum semantic sensing (SMS) and modulation recognition algorithms relying on powerful feature extraction capabilities and robustness. Although communication signal modulation recognition technology has gradually matured and the results have become more abundant [14], with the rapid development of wireless communication technology, signal transmission scenarios have become increasingly diversified, and application requirements have become increasingly updated [15], all of which promote the improvement of modulation methods. Therefore, modulation recognition technology always needs to be constantly updated according to changes in application scenarios and application requirements.…”

Section: Introductionmentioning

confidence: 99%

Research on Distributed Multisensor Spectrum Semantic Sensing and Recognition in Internet of Things Environment

Liu

Zhang

Liu³

et al. 2022

Wireless Communications and Mobile Computing

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The development of 5G technology has brought about a new era of Internet of Thing (IoT), and at the same time, electromagnetic spectrum monitoring and sensing have also ushered in huge challenges. Digital modulation recognition technology is an important content of electromagnetic spectrum sensing. In the increasingly complex wireless communication transmission environment, especially in noncooperative communication, it becomes more and more difficult to receive target signals and accurately extract effective semantic information from diverse modulation signals of the electromagnetic spectrum. At this stage, with the rapid development of network information and wireless communication technology, within a prescribed distance, the IoT built by many sensors has attracted wide attention from people in related fields. This paper proposes a distributed collaborative sensing spectrum semantic recognition architecture for communication signals based on feature fusion. Perform wireless communication and transmission between multiple sensors to form a self-organizing network to cooperatively sense signal semantic information, and extract the signal features of each sensor in the distributed network structure. Finally, the extracted sensor features are semantically analyzed and modeled, and the effective features are fused to complete the entire perception and recognition process. Even if the channel environment of a small number of receiving nodes deteriorates in a complex transmission environment, the signal quality features can still be accurately extracted, the classification and recognition effect like or higher than the best channel state performance can be achieved, and the fault tolerance of the system can be effectively improved. It can also enhance the performance of spectral semantic information sensing and recognition in the IoT environment.

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Electromagnetic Signal Feature Fusion and Recognition based on Multi-Modal Deep Learning

Cited by 21 publications

References 0 publications

Electromagnetic Environment Portrait Based on Big Data Mining

Electromagnetic Environment Portrait Based on Big Data Mining

Creation Techniques and Styles of Chinese Piano Works Supported by Edge Cloud Computing Technology

Research on Distributed Multisensor Spectrum Semantic Sensing and Recognition in Internet of Things Environment

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