Hierarchical Domain Adaptation Projective Dictionary Pair Learning Model for EEG Classification in IoMT Systems

Cai, Weiwei; Gao, Ming; Jiang, Yizhang; Gu, Xiaoqing; Ning, Xin; Qian, Pengjiang; Ni, Tongguang

doi:10.1109/tcss.2022.3176656

Cited by 54 publications

(49 citation statements)

References 28 publications

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“…From this point of view, there is no need to build an indoor distribution system. e most meaningful classi cation method [15] for wireless networks is classi cation by tra c bearer. at is, services are rst divided into circuit domain services and packet domain services and then classi ed according to the bearer rate.…”

Section: Wireless Network Indoor Coveragementioning

confidence: 99%

Development of Wireless Network Indoor Coverage System Based on Optical Fiber Distribution System

Song

Xiong

et al. 2022

Computational Intelligence and Neuroscience

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With the continuous development of network communication technology, the domestic network has covered the homes of ordinary people, but the indoor coverage system of the network cable network needs to be further improved. The optical fiber distribution system is a signal coverage system integrating GSM, TD-SCDMA, and other standards. It is mainly composed of system access unit, system expansion unit, and system remote unit. Wireless network is an important communication technology, which is easy to use and can be connected wirelessly over long distances. The purpose of this paper is to study an optical fiber distribution system for indoor coverage design of wireless networks, so as to achieve faster and more efficient coverage. In this paper, a wireless network signal processing algorithm is proposed, and a systematic method for designing the wireless network indoor coverage of the optical fiber distribution system is proposed, and the algorithms are compared and tested. The test results in this paper show that the signal strength obtained by the algorithm in this paper is −54.83 dB, while the traditional artificial design algorithm is only −45.97 dB. In addition, the wireless network base point of the algorithm in this paper is also 3 lower than the traditional one. Moreover, the coverage rate achieved by the algorithm in this paper is 3.17% higher than that of the network cable network base point layout given by the genetic algorithm, and the average received signal strength is 3.34 dB higher. In addition, the algorithm in this paper takes 9.1 s, 11.2 s, and 5.3 s for the three scenarios of underground parking lot, large shopping mall, and residential residential area, while the traditional manual design algorithm takes 87.9 s, 108.8 s, and 51.7 s, respectively. The algorithm in this paper is only about 1/10 of the traditional artificial design algorithm. Therefore, it can be concluded that the coverage effect of the wireless network indoor coverage system of the optical fiber distribution system designed in this paper is significantly improved.

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Section: Wireless Network Indoor Coveragementioning

confidence: 99%

Development of Wireless Network Indoor Coverage System Based on Optical Fiber Distribution System

Song

Xiong

et al. 2022

Computational Intelligence and Neuroscience

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“…As a new digital technology that began to rise in the twentieth century, it is the result of the long-term development of computer display technology, computer graphics technology, sensor technology, artificial intelligence [23,24], and other fields [25]. Positive and unrestricted interaction can provide users with a similar immersive experience in the virtual world as they can in the real world.…”

Section: An Overview Of Virtual Reality Technologymentioning

confidence: 99%

Digital Protection and Utilization of Folk Art Resources Using Virtual Reality Technology

Wang

2022

Mobile Information Systems

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Folk art is a “living fossil” of hisory, whose existence and evolution reflect the way people live and think. It is a one-of-a-kind way of communicating the process of human development to the rest of the world. However, as China’s economic development and cultural globalization accelerate, many folk arts are being severely harmed and lost. The emergence of virtual reality technology, which benefits from the advantages of virtual reality technology, has greatly strengthened the utilization and protection of folk art resources in recent years, with the rapid development of Internet technology. Virtual reality technology is a human-computer interaction experience technology that uses complex calculations to provide corresponding operational feedback to the senses of touch, hearing, vision, and balance. It is more reasonable and perfect to reconstruct and integrate the resource content and realize the digital utilization of folk art resources in this paper, which is based on the ALS collaborative filtering algorithm. This paper will make some necessary improvements to the original algorithm, which has met the requirements of virtual reality technology, through the comparison of algorithms. Finally, experiments show that the proposed algorithm’s accuracy is 85 percent higher than the original algorithms, and the data retrieval processing interval is more reasonable.

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“…Alternatively, the user can be directly recommended items that are most similar to their preferences. Today's content-based recommendation can extract the feature vectors of the processed objects more accurately, thanks to the continuous development of machine learning [20][21][22] and statistical analysis technology. Bayesian classification neural networks and principal component analysis are two of the most commonly used technologies.…”

Section: Personalized Recommendation and Cf Algorithmmentioning

confidence: 99%

College English Precise Teaching Model Using Combination Optimization Collaborative Filtering Algorithm

Yang

2022

Mobile Information Systems

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With the rapid development of the Internet and various learning platforms, the explosive growth of resources in the field of education has made the recommendation of learning resources increasingly important, and it has gradually become the research focus of academic circles. This article develops an English precision teaching platform based on the CF algorithm of combinatorial optimization to accurately recommend English learning materials that meet the learners’ personal preferences and achieve precision teaching. This optimization algorithm takes full advantage of the background information of users, calculates the similarity between users, and forms the best neighbor set, and then recommends the learning resources evaluated by neighbors with similar interests to the target users, addressing the problem of data sparseness and cold start, which leads to a decline in recommendation accuracy. On different data sets, experimental results show that this method’s recommendation accuracy is 94.6%, which is higher than the CF algorithm of multifeature fusion by 4.5% and the traditional CF algorithm by 9.7%. The findings show that when applied to English instructional resources, this method is accurate and practical and that it can effectively recommend resources that are appropriate for users to achieve accurate CET.

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Hierarchical Domain Adaptation Projective Dictionary Pair Learning Model for EEG Classification in IoMT Systems

Cited by 54 publications

References 28 publications

Development of Wireless Network Indoor Coverage System Based on Optical Fiber Distribution System

Development of Wireless Network Indoor Coverage System Based on Optical Fiber Distribution System

Digital Protection and Utilization of Folk Art Resources Using Virtual Reality Technology

College English Precise Teaching Model Using Combination Optimization Collaborative Filtering Algorithm

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