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3,262 results on '"Wang Bowen"'

120. Double-Phase-Shifter based Hybrid Beamforming for mmWave DFRC in the Presence of Extended Target and Clutters

Author

Cheng, Ziyang, Wu, Linlong, Wang, Bowen, R., Bhavani Shankar M., and Ottersten, Björn

Subjects
Electrical Engineering and Systems Science - Signal Processing
Abstract

In millimeter-wave (mmWave) dual-function radar-communication (DFRC) systems, hybrid beamforming (HBF) is recognized as a promising technique utilizing a limited number of radio frequency chains. In this work, in the presence of extended target and clutters, a HBF design based on the subarray connection architecture is proposed for a multiple-input multiple-output (MIMO) DFRC system. In this HBF, the double-phase-shifter (DPS) structure is embedded to further increase the design flexibility. We derive the communication spectral efficiency (SE) and radar signal-to-interference-plus-noise-ratio (SINR) with respect to the transmit HBF and radar receiver, and formulate the HBF design problem as the SE maximization subjecting to the radar SINR and power constraints. To solve the formulated nonconvex problem, the joinT Hybrid bRamforming and Radar rEceiver OptimizatioN (THEREON) is proposed, in which the radar receiver is optimized via the generalized eigenvalue decomposition, and the transmit HBF is updated with low complexity in a parallel manner using the consensus alternating direction method of multipliers (consensus-ADMM). Furthermore, we extend the proposed method to the multi-user multiple-input single-output (MU-MISO) scenario. Numerical simulations demonstrate the efficacy of the proposed algorithm and show that the solution provides a good trade-off between number of phase shifters and performance gain of the DPS HBF.

Published
2021

121. Panoptic-aware Image-to-Image Translation

Author

Zhang, Liyun, Ratsamee, Photchara, Wang, Bowen, Luo, Zhaojie, Uranishi, Yuki, Higashida, Manabu, and Takemura, Haruo

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Despite remarkable progress in image translation, the complex scene with multiple discrepant objects remains a challenging problem. The translated images have low fidelity and tiny objects in fewer details causing unsatisfactory performance in object recognition. Without thorough object perception (i.e., bounding boxes, categories, and masks) of images as prior knowledge, the style transformation of each object will be difficult to track in translation. We propose panoptic-aware generative adversarial networks (PanopticGAN) for image-to-image translation together with a compact panoptic segmentation dataset. The panoptic perception (i.e., foreground instances and background semantics of the image scene) is extracted to achieve alignment between object content codes of the input domain and panoptic-level style codes sampled from the target style space, then refined by a proposed feature masking module for sharping object boundaries. The image-level combination between content and sampled style codes is also merged for higher fidelity image generation. Our proposed method was systematically compared with different competing methods and obtained significant improvement in both image quality and object recognition performance., Comment: In 2023 IEEE winter conference on applications of computer vision (WACV)

Published
2021

124. Targeted NAD+ repletion via biomimetic nanoparticle enables simultaneous management of intimal hyperplasia and accelerated re-endothelialization: A proof-of-concept study toward next-generation of endothelium-protective, anti-restenotic therapy

Author

Yin, Li, Tong, Yao, Xie, Ruosen, Zhang, Zhanpeng, Islam, Zain Husain, Zhang, Kaijie, Burger, Jacobus, Hoyt, Nicholas, Kent, Eric William, Marcum, William Aaron, Johnston, Campbell, Kanchetty, Rohan, Tetz, Zoe, Stanisic, Sophia, Huang, Yitao, Guo, Lian-Wang, Gong, Shaoqin, and Wang, Bowen

Published
2024
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129. Manifold Optimization Methods for Hybrid beamforming in mmWave Dual-Function Radar-Communication System

Author

Wang, Bowen, Cheng, Ziyang, and He, Zishu

Subjects
Electrical Engineering and Systems Science - Signal Processing
Abstract

As a cost-effective alternative, hybrid analog and digital beamforming architecture is a promising scheme for millimeter wave (mmWave) system. This paper considers two hybrid beamforming architectures, i.e. the partially-connected and fully-connected structures, for mmWave dual-function radar communication (DFRC) system, where the transmitter communicates with the downlink users and detects radar targets simultaneously. The optimization problems are formulated by minimizing a weighted summation of radar and communication performance, subject to constant modulus and power constraints. To tackle the non-convexities caused by the two resultant problems, effective Riemannian optimization algorithms are proposed. Specifically, for the fully-connected structure, a manifold algorithm based on the alternating direction method of multipliers (ADMM) is developed. While for the partially-connected structure, a low-complexity Riemannian product manifold trust region (RPM-TR) algorithm is proposed to approach the near-optional solution. Numerical simulations are provided to demonstrate the effectiveness of the proposed methods.

Published
2021

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