Dapeng Wu

City University of Hong Kong

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Xianbin Cao

Beihang University

Pan Zhou

Wuhan University of Science and Technology

Qiuyuan Huang

Fujian Medical University

Peng Yang

Beihang University

Tony Q. S. Quek

Singapore University of Technology and Design

Tie Qiu

Tianjin University

Yuguang Fang

City University of Hong Kong

Zhengchuan Chen

Chongqing University

Xing Xi

Princeton University

Zhenyu Xiao

Beihang University

Cooperative Institutions

Chinese Academy of Sciences

144

Tsinghua University

116

University of Florida

116

Huazhong University of Science and Technology

110

University of Electronic Science and Technology of China

Peking University

Beihang University

National Technical University of Athens

Beijing Institute of Technology

Shanghai Jiao Tong University

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Depth-based image registration via three-dimensional geometric segmentation

IET Computer Vision (2012)

Bing Han Christopher Paulson Dapeng Wu

Image registration is a fundamental task in computer vision and it significantly contributes to high-level computer vision and benefits numerous practical applications. Although many image registration techniques have been proposed in the past, there is still a need for further research because many issues such as the parallax problem remain to be solved. The traditional image registration algorithms suffer from the parallax problem due to their underlying assumption that the scene can be regarded approximately planar which is not satisfied when large depth variations exist in the images with high-rise objects. To address the parallax problem, we present a new strategy for two-dimensional (2D) image registration by leveraging the depth information from a 3D image reconstruction. The novel idea is to recover the depth in the image region with high-rise objects to build an accurate transform function for image registration. We use a geometric segmentation algorithm to partition 3D point cloud to multiple geometric structures and at the same time, estimate the parameters of each geometric structure. Experimental results show that the proposed method is able to mitigate the parallax problem and achieve better performance than the existing image registration scheme.

Parallax

Image registration

10.1049/iet-cvi.2011.0017

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Citations (2)

Optimal video sensing strategy and performance optimization for mobile wireless video sensors

Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE (2006)

Zhihai He Milind Desphande Dapeng Wu

Wireless video sensor networks have been envisioned for a wide variety of important applications. In this work, we investigate the following research problem: given a mobile video sensor with an initial resource configuration, what is the maximum video quality it can provide, and what is the best video encoding and streaming strategy to achieve this performance? To address this problem, we study the energy consumption of each sensor node in video compression and wireless data transmission. We statistically model the node mobility and study its impact on wireless video transmission. We develop a task-oriented performance metric to measure the video sensing performance of the sensor node. We develop an optimal resource allocation and scheduling scheme for the video sensor to maximize the performance metric. The results provide an important step stone for our future performance study of wireless video sensor networks.

Sensor node

10.1117/12.641855

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Citations (0)

An enhanced V-BLAST with non-stationary interference avoidance capability

Youngho Jo Dapeng Wu

Reliable communication under interferences is desired by both military and commercial parties. This paper proposes a vertical-Bell Laboratories Layered Space-Time (V-BLAST) architecture with non-stationary interference avoidance capability. A transform domain processing (TDP) and minimum mean square error (MMSE) detector are combined with the V-BLAST to enhance performance in a narrow band interference (NBI) environment. A key idea of TDP is to generate an adaptive modulated information-bearing signal avoiding spectrally crowded regions in the transform domain at both transmitters and receivers of the V-BLAST system. The adaptive interference-free signal is synthesized by a non-parametric spectral estimator, called Caponpsilas method (CM). The proposed V-BLAST system also incorporates the MMSE detector. These approaches offer better bit error performance than other interference mitigation methodologies that process signals at the receiver only, and better than other similar systems that use a parametric spectral estimator with zero-forcing detector. Our simulation results indicate that the proposed system mitigates not only stationary interferences but also non-stationary interferences. Therefore, the proposed system is a viable option for the highly reliable multiple-input multiple-output (MIMO) system.

SIGNAL (programming language)

10.1109/milcom.2008.4753255

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Citations (1)

Airborne Communication Networks: A Survey

IEEE Journal on Selected Areas in Communications (2018)

Xianbin Cao Peng Yang Mohamed Alzenad Xing Xi Dapeng Wu

Owing to the explosive growth of requirements of rapid emergency communication response and accurate observation services, airborne communication networks (ACNs) have received much attention from both industry and academia. ACNs are subject to heterogeneous networks that are engineered to utilize satellites, high-altitude platforms (HAPs), and low-altitude platforms (LAPs) to build communication access platforms. Compared to terrestrial wireless networks, ACNs are characterized by frequently changed network topologies and more vulnerable communication connections. Furthermore, ACNs have the demand for the seamless integration of heterogeneous networks such that the network quality-of-service (QoS) can be improved. Thus, designing mechanisms and protocols for ACNs poses many challenges. To solve these challenges, extensive research has been conducted. The objective of this special issue is to disseminate the contributions in the field of ACNs. To present this special issue with the necessary background and offer an overall view of this field, three key areas of ACNs are covered. Specifically, this paper covers LAP-based communication networks, HAP-based communication networks, and integrated ACNs. For each area, this paper addresses the particular issues and reviews major mechanisms. This paper also points out future research directions and challenges.

10.1109/jsac.2018.2864423

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Citations (299)

DyScore: A Boosting Scoring Method with Dynamic Properties for Identifying True Binders and Nonbinders in Structure-Based Drug Discovery

Journal of Chemical Information and Modeling (2022)

Yanjun Li Daohong Zhou Guangrong Zheng Xiaolin Li Dapeng Wu

The accurate prediction of protein–ligand binding affinity is critical for the success of computer-aided drug discovery. However, the accuracy of current scoring functions is usually unsatisfactory due to their rough approximation or sometimes even omittance of many factors involved in protein–ligand binding. For instance, the intrinsic dynamics of the protein–ligand binding state is usually disregarded in scoring function because these rapid binding affinity prediction approaches are only based on a representative complex structure of the protein and ligand in the binding state. That is, the dynamic protein–ligand binding complex ensembles are simplified as a static snapshot in calculation. In this study, two novel features were proposed for characterizing the dynamic properties of protein–ligand binding based on the static structure of the complex, which is expected to be a valuable complement to the current scoring functions. The two features demonstrate the geometry-shape matching between a protein and a ligand as well as the dynamic stability of protein–ligand binding. We further combined these two novel features with several classical scoring functions to develop a binary classification model called DyScore that uses the Extreme Gradient Boosting algorithm to classify compound poses as binders or non-binders. We have found that DyScore achieves state-of-the-art performance in distinguishing active and decoy ligands on both enhanced DUD data set and external test sets with both proposed novel features showing significant contributions to the improved performance. Especially, DyScore exhibits superior performance on early recognition, a crucial requirement for success in virtual screening and de novo drug design. The standalone version of DyScore and Dyscore-MF are freely available to all at: https://github.com/YanjunLi-CS/dyscore.

Boosting

Protein ligand

10.1021/acs.jcim.2c00926

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Citations (8)

Multiple Content Dissemination in Roadside-Unit-Aided Vehicular Opportunistic Networks

IEEE Transactions on Vehicular Technology (2014)

Yong Li Xiangming Zhu Depeng Jin Dapeng Wu

Roadside units (RSUs), which enable vehicle-to-infrastructure communications, are deployed along roadsides to handle the growing communication demands as the number of vehicles increases. The current opportunistic RSU-aided content dissemination schemes, however, do not address heterogeneous networks in terms of data items and users. We establish a mathematical framework to study the problem of multiple content dissemination under realistic RSU-aided opportunistic network assumptions, where: 1) mobile content items are heterogeneous in terms of size and lifetime; 2) vehicles' interests are different to different data; and 3) the RSU' storage for content dissemination is limited in size. We formulate the maximum data dissemination as a submodular function maximization (SFM) problem with multiple linear constraints (MLCs) of limited storage. Then, we propose an efficient heuristic algorithm to solve this NP-hard problem. Finally, we demonstrate the effectiveness of our algorithm through extensive simulations using realistic vehicular traces. The simulation results show that our proposed low-complexity heuristic algorithm performs much better than the existing feasible solutions, and it achieves similar performance to that of the most accurate algorithm currently available, whose computational complexity is unacceptable in practice.

Submodular set function

Maximization

10.1109/tvt.2014.2308149

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Citations (37)

Joint 3D Deployment and Beamforming for RSMA-Enabled UAV Base Station With Geographic Information

IEEE Transactions on Wireless Communications (2023)

Meng Xiao Huanxi Cui Zhongliang Zhao Xianbin Cao Dapeng Wu

This paper studies the joint three-dimensional (3D) deployment and beamforming problem for a rate-splitting multiple access (RSMA)-enabled unmanned aerial vehicle base station (UBS) assisted by geographic information. Specifically, we maximize the minimum achievable rate among users by optimizing the beamforming, rate allocation and UBS deployment considering the power and building blockages constraints. To solve the intractable problem, an alternating optimization scheme is proposed. In particular, we first split the formulated problem into three sub-problems of deployment region modeling, joint beamforming and rate allocation, and 3D UBS deployment. For the first sub-problem, we define the allowable deployment region with geographic information with the aim of ensuring line-of-sight connections between the UBS and users. The feasible region is expressed as tractable constraints via the Big-M method and penalty function method. For the other sub-problems, semi-definite programming and successive convex approximation are employed to design the joint beamforming and rate allocation, and UBS deployment, respectively. These two sub-problems are optimized iteratively until convergence. Finally, numerical results validate the superiority of our proposed solution in comparison with the benchmark schemes with regard to the minimum achievable rate.

Benchmark (surveying)

Transmitter power output

10.1109/twc.2023.3299650

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Citations (2)

BLS-Location: A Wireless Fingerprint Localization Algorithm Based on Broad Learning

IEEE Transactions on Mobile Computing (2021)

Xiaoqiang Zhu Tie Qiu Wenyu Qu Xiaobo Zhou Mohammed Atiquzzaman

With the rapid growth in the demand for location-based services in indoor environments, wireless fingerprint localization has attracted increasing attention because of its high precision and easy implementation. However, an effective method does not exist owing to the problems of data loss, noise interference in the fingerprint database, and being time-consuming during the offline training phase. Therefore, this paper presents a novel indoor wireless fingerprint localization algorithm, termed BLS-Location, based on a broad learning system (BLS) that utilizes channel state information (CSI) to overcome the aforementioned problems. It includes an offline training phase and an online localization phase. In the offline training phase, the Kalman filter and the expectation-maximization (EM) algorithm are utilized for completing and denoising the data. Moreover, principal component analysis (PCA) is used to reconstruct the CSI data to reduce complexity and train the weights by BLS. In the online localization phase, we employ a novel probabilistic method based on the regression results of BLS to obtain the estimated location. The experimental results show that BLS-Location can significantly reduce the training time with a high accuracy, compared to several machine learning algorithms and four existing methods in two representative indoor environments.

10.1109/tmc.2021.3073005

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Citations (48)

Joint Power Control and Beamforming for Uplink Non-Orthogonal Multiple Access in 5G Millimeter-Wave Communications

IEEE Transactions on Wireless Communications (2018)

Lipeng Zhu Jun Zhang Zhenyu Xiao Xianbin Cao Dapeng Wu

In this paper, we investigate the combination of two key enabling technologies for the fifth generation wireless mobile communication, namely millimeter-wave (mm-wave) communications and non-orthogonal multiple access (NOMA). In particular, we consider a typical two-user uplink mm-wave-NOMA system, where the base station equips an analog beamforming structure with a single radio-frequency chain and serves two NOMA users. An optimization problem is formulated to maximize the achievable sum rate of the two users while ensuring a minimal rate constraint for each user. The problem turns to be a joint power control and beamforming problem, i.e., we need to find the beamforming vectors to steer to the two users simultaneously subject to an analog beamforming structure, and meanwhile control appropriate power on them. As direct search for the optimal solution of the non-convex problem is too complicated, we propose decomposing the original problem into two sub-problems that are relatively easy to solve: one is a power control and beam gain allocation problem, and the other is an analog beamforming problem under a constant-modulus constraint. The rationale of the proposed solution is verified by extensive simulations, and the performance evaluation results show that the proposed sub-optimal solution achieves a close-to-bound uplink sum-rate performance.

Maximization

10.1109/twc.2018.2855151

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Citations (101)

Learning Topology and Dynamics of Large Recurrent Neural Networks

IEEE Transactions on Signal Processing (2014)

Yiyuan She Yuejia He Dapeng Wu

Large-scale recurrent networks have drawn increasing attention recently because of their capabilities in modeling a large variety of real-world phenomena and physical mechanisms. This paper studies how to identify all authentic connections and estimate system parameters of a recurrent network, given a sequence of node observations. This task becomes extremely challenging in modern network applications, because the available observations are usually very noisy and limited, and the associated dynamical system is strongly nonlinear. By formulating the problem as multivariate sparse sigmoidal regression, we develop simple-to-implement network learning algorithms, with rigorous convergence guarantee in theory, for a variety of sparsity-promoting penalty forms. A quantile variant of progressive recurrent network screening is proposed for efficient computation and allows for direct cardinality control of network topology in estimation. Moreover, we investigate recurrent network stability conditions in Lyapunov's sense, and integrate such stability constraints into sparse network learning. Experiments show excellent performance of the proposed algorithms in network topology identification and forecasting.

10.1109/tsp.2014.2358956

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Citations (9)