Yin Long

Southwest University of Science and Technology

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Zhi Chen

University of Electronic Science and Technology of China

Jun Fang

Huzhou University

Simon Murphy

Cardiff University

Xujian Zhao

University of Science and Technology of China

Jie Xu

Chinese University of Hong Kong, Shenzhen

Xujian Zhao

Southwest University of Science and Technology

Xinmin Li

Chinese University of Hong Kong, Shenzhen

Shuguang Cui

Chinese University of Hong Kong, Shenzhen

Chunming Yang

Southwest University of Science and Technology

Siyao Zhang

Jiangsu University

Cooperative Institutions

Southwest University of Science and Technology

University of Electronic Science and Technology of China

Guangdong University of Petrochemical Technology

University of Science and Technology of China

Northwestern Polytechnical University

National University of Singapore

Chinese Academy of Sciences

Chinese University of Hong Kong, Shenzhen

Sichuan University

University of Chinese Academy of Sciences

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Multiple-level Omics Data based Cancer Subtypes Prediction by Multi-view Subspace Learning

2021 14th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI) (2023)

Zhihao Liu Nan Qiu Xinxin Li Weidong Zhang Yin Long

Accurate forecast of cancer subtypes is rather important in the malignancy diagnosis. Disease etiology is sophisticated, which manifests in diverse omics levels. Therefore, the elaborate fusion of multiple-level omics is very helpful to the analysis of cancer subtypes. In this paper, multiple-level omics data based cancer subtypes prediction is treated as a multi-view subspace clustering problem, where integrating the complemental information among multiple omics data to build up a unified representation is a pivotal challenge. Due to the fact that the great inconsistency lies among diverse omics levels, which leads to an unsatisfactory integrative effect via traditional multi-view clustering. To address this issue, different data on omics levels is fused under the spectral embedding domain, since even the high-inconsistency data across all omics level still have a rather analogous spectral block structure. Owning to the fact that the spectral block structure across all omics levels satisfies the great consistency, the data across all omics levels would be melted into a common representation with slight information damage. Apart from that, to enhance the global structure of subspace across all omics levels, an innovative low-rank relaxation is established by resorting to the the logarithmic determinant function. Finally, experimental outcome exhibits that the raised approach achieves superior behavior than existing prediction schemes.

Omics

Rank (graph theory)

10.1109/cisp-bmei60920.2023.10373357

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Novel Closed-Form Expressions for Ergodic Capacity of MIMO Ad-Hoc Networks

Proceedings of the 2021 5th International Conference on Electronic Information Technology and Computer Engineering (2021)

Md Foysal Ahmed Jiankang Yin Yin Long

In this letter, we study the ergodic capacity of MIMO Ad-Hoc networks under imperfect channel state information (CSI). Evaluating MIMO capacity under imperfect CSI is still an open problem, and only approximate results for it can be obtained in asymptotic regions so far. In this letter, different from the previous method, we apply the exact probability density function (PDF) of the ratio of two Wishart matrices to evaluate the ergodic capacity of MIMO Ad-Hoc networks. For utilizing the most cutting-edge PDF to solve our problem, we first derive an equivalent Wishart matrix for the sum of interference covariance matrices. And then, based on the equivalent matrix, we obtain an expression for the ergodic capacity by directly applying the PDF of the ratio of two Wishart matrices, which requires M-fold (M is the number of receive antennas) multiple integrals. To avoid the time-consuming M-fold multiple integral, based on Laplace transform, we provide an alternative method which only involves several single integrals.

Ergodicity

Matrix (chemical analysis)

10.1145/3501409.3501690

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Optimized Joint Beamforming for Wireless Powered Over-the-Air Computation

2022 IEEE Wireless Communications and Networking Conference (WCNC) (2024)

Siyao Zhang Xinmin Li Yin Long Jie Xu Shuguang Cui

10.1109/wcnc57260.2024.10570664

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Spectral Embedding and Novel Low-rank Approximation Based Multi-view Clustering

2022 26th International Conference on Pattern Recognition (ICPR) (2022)

Xiaobo Liu Yin Long Yiannis Nomikos

In the multi-view subspace clustering, it is a key challenge to incorporate the complementary information between different views to establish a unified representation (UR). Currently, the problem of establishing a UR tends to be solved in the original data space. However, if there exists the great inconsistency in each view, we cannot obtain a decent UR by above method. To address this issue, by resorting to the latest discovery that the data across all views have a quite similar spectral block structure, we attempt to solve this problem in the spectral embedding domain. Since the spectral block structure among all views has great consistency, the complementary information across all views can be incorporated into a UR with little information loss. Besides, to obtain the high-quality global structure of data on each view, a novel low-rank approximation is proposed by resorting to the tight lower bound on the rank function. In the end, the experimental results demonstrate that, the proposed method is both effective and robust, compared with the cutting-edge methods.

Spectral Clustering

Rank (graph theory)

Representation

Low-rank approximation

10.1109/icpr56361.2022.9956486

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Full-duplex massive multiple input multiple output systems under hardware impairments

Computers & Electrical Engineering (2017)

Yin Long Zhi Chen

Duplex (building)

10.1016/j.compeleceng.2017.07.001

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Nonasymptotic Analysis of Capacity in Massive MIMO Systems

IEEE Wireless Communications Letters (2015)

Yin Long Zhi Chen Jun Fang

In this letter, we analyze the capacity of a point-to-point massive MIMO system in the regime of a large (but finite) number of antennas. Different from previous works resorting to asymptotic random matrix theory, concentration inequalities are used in our nonasymptotic analysis for massive MIMO systems with a finite number of antennas. We first derive deterministic bounds on the ergodic capacity for massive MIMO systems. Furthermore, we obtain statistical bounds within which the instantaneous capacity falls with a falling-in probability. As the number of antennas increases, the statistical bounds on the instantaneous capacity become tighter and the corresponding falling-in probability increases, which reveals an interesting phenomenon that the instantaneous capacity has fewer random fluctuations as the number of antennas becomes larger. Simulations show that these theoretical bounds match well with our experimental results. Therefore, the bounds can be useful for the design and analysis of practical massive MIMO systems with a large but finite number of antennas.

10.1109/lwc.2015.2454509

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Spectral clustering with scale fairness constraints

Knowledge and Information Systems (2024)

Zhijing Yang Hui Zhang Chunming Yang Bo Li Xujian Zhao

Spectral Clustering

10.1007/s10115-024-02183-7

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CNN-Based Multiantenna Spectrum Sensing for Hybrid Circular and Noncircular Source Signals in Cognitive Radio

IEEE Sensors Letters (2023)

An-Zhi Chen Zhiping Shi Yin Long Guoxi Sun Limin Liu

Recently, multiantenna spectrum sensing (MSS) using de-ep neural network has received much attention. However, most of them are not explicitly formulated to account for non-circular sources. In practice, the primary users' signals may come from either circular or noncircular sources, or both. We are, therefore, motivated to investigate the MSS problem for hybrid circular and noncircular source signals in cognitive radio. Specifically, we first introduce the augmented sample covariance matrix (ASCM) to exploit the additional statistical information given by the noncircular property. Then, based on the ASCM, a simple convolutional neural network (CNN) structure is designed to extract the features embedded in the ASCM. Simulation results reveal that the proposed method is capable of providing performance improvement over conventional MSS methods, regardless of the spatially white/correlated noise.

10.1109/lsens.2023.3330109

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DMIS: Dual Model Index Structure for Enhanced Performance on Complexly Distributed Datasets

Lecture notes in computer science (2023)

Lanzhong Liu Xujian Zhao Yin Long

10.1007/978-3-031-39847-6_7

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Minimum Number of Antennas Required to Satisfy Outage Probability in Massive MIMO Systems

IEEE Wireless Communications Letters (2016)

Yin Long Zhi Chen Jun Fang

In practical massive multiple-input multiple-output (MIMO) systems, determining the number of antennas to satisfy different service demands is an important issue. In this letter, in contrast to the traditional method using the Gaussian approximation, we present a novel approach to determine the number of antennas required to at least satisfy pre-specified outage probability requirements. By exploiting the statistical bounds on instantaneous MIMO capacity, we derive the equivalent problem of determining the number of antennas needed to satisfy outage probability constraints. Through solving the equivalent problem, we obtain the minimum number of antennas required under pre-specified outage probability requirements. Analysis and simulation results demonstrate that, in massive MIMO systems, the required minimum number of antennas obtained by the proposed method is more accurate than that obtained by the traditional method.

Outage Probability

10.1109/lwc.2016.2558510

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Citations (13)