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4. Kullback-Leibler Distance Based Generalized Grey Target Decision Method With Index and Weight Both Containing Mixed Attribute Values

Author

Di Tian, Jinshan Ma, Ma Xiaolin, and Jinmeng Yue

Subjects
Kullback–Leibler divergence, Index (economics), General Computer Science, generalized grey target decision method, weight function, mixed attributes, General Engineering, Kullback-Leibler distance, Statistics, General Materials Science, binary connection number, lcsh:Electrical engineering. Electronics. Nuclear engineering, Electrical and Electronic Engineering, Decision model, lcsh:TK1-9971, Mathematics
Abstract

This paper proposes a generalized grey target decision method (GGTDM) with index and weight both containing mixed attribute values based on Kullback-Leibler (K-L) distance. The proposed approach builds the weight function converting the mixed attribute-based weights into the certain number-based weights and takes the comprehensive weighted K-L distance as the decision-making basis (DMB). The proposed approach conducts its task in the following steps. First, all indices of alternatives are converted into binary connection numbers. Second, the two-tuple (determinacy, uncertainty) numbers originated from index binary connection numbers are obtained. Third, the two-tuple (determinacy, uncertainty) numbers of target center are calculated. Following that the certain number-based weights are obtained by the weight function. Then the comprehensive weighted K-L distance of each alternative and its target center is calculated. And the final decision making is based on the value of comprehensive weighted K-L distance with which the smaller the better. A case study illustrates the proposed approach with its effectiveness of converting the uncertain weights into the certain weights and the accurate results comparing with other decision-making methods.

Published
2020

34. An End-to-End Network for Continuous Human Motion Recognition via Radar Radios.

Author

Zhao, Running, Ma, Xiaolin, Liu, Xinhua, and Liu, Jian

Abstract

Micro-Doppler-based continuous human motion recognition (HMR) has gained considerable attention recently. However, existing methods mainly rely on individual recurrent neural network or sliding-window-based approaches, which makes them hard to effectively exploit all the temporal information to predict motions. Additionally, they need to represent the raw radar data into other domains and then perform feature extraction and classification. Thus, the representation cannot be optimized, and its high computational complexity and independence from learning model make the network consume significant time. In this article, to address these issues, we propose a new end-to-end network that uses radar radios to recognize continuous motion. Specifically, the fusion layer fuses the raw I & Q radar data without the need of representations, and it is integrated with subsequent networks in an end-to-end manner for jointly optimization. Moreover, the attention-based encoder-decoder structure encodes the fused data and selects useful temporal information for recognition, which guarantees the effective use of all the temporal information. The experiments show that in continuous HMR, the proposed network outperforms existing methods in terms of accuracy and inference time. [ABSTRACT FROM AUTHOR]

Published
2021
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35. Continuous Human Motion Recognition Using Micro-Doppler Signatures in the Scenario With Micro Motion Interference.

Author

Zhao, Running, Ma, Xiaolin, Liu, Xinhua, and Li, Fangmin

Abstract

The application of micro-Doppler-based continuous human motion recognition (HMR) is greatly hindered by non-target micro motion interference, due to the deformation of micro-Doppler signatures of target human motion caused by such interference. In this paper, we propose a novel continuous HMR method using micro-Doppler signatures that can work in the scenario with non-target micro motion interference. Specifically, a signal preprocessing architecture is designed, where the empirical mode decomposition is employed to remove the interference in radar raw signal and the multiwindow time-frequency representation is used to generate the time-frequency distribution (TFD) with high concentration. Moreover, a tailored network, that integrates multiscale squeeze-and-excitation network for feature sequence extraction, stacked bidirectional long short-term memory for sequence labeling and connectionist temporal classification algorithm for label transcription, is employed to recognize continuous human motion from TFD. The experimental results show that the proposed method outperforms existing methods in terms of recognition accuracy and generalization. [ABSTRACT FROM AUTHOR]

Published
2021
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36. RCRM-UC interference alignment algorithm for two-cell downlink cellular networks

Author

Ma Xiaolin, Xia Caiyun, Li Fangmin, and Liu Xinhua

Subjects
Computer science, MIMO, 020302 automobile design & engineering, 020206 networking & telecommunications, 02 engineering and technology, Linear subspace, Base station, 0203 mechanical engineering, Telecommunications link, 0202 electrical engineering, electronic engineering, information engineering, Cellular network, Algorithm design, Radio resource management, Algorithm, Coding (social sciences)
Abstract

Interference Alignment (IA) as an effective interference management mechanism has become a hot spot in the field of wireless communication systems in recent years. In order to investigate the significance of IA technique to downlink cellular networks, we propose a new way based on the rank constrained rank minimization theory in this paper. Our proposed algorithm aims at finding out the pre coding and receive matrices to align inter-user and inter-cell interference signals into the lowest dimension subspaces as much as possible while keeping the useful signals span the maximum available dimensions. In that way, we not only ensure the desired signal power but also reduce the interference signal power. Also we elaborate the scheme's derivation process in detail and give out the simulation analysis results of the algorithm. Experimental results show that our design is able to effectively improve the system capacity with equivalent system overhead compared to the existing iterative IA algorithms, and thus has better engineering applicability.

Published
2016

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