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18. Performance Analysis of Millimeter Wave Wireless Power Transfer With Imperfect Beam Alignment.

Author

Wang, Man, Zhang, Chao, Chen, Xiaoming, and Tang, Suhua

Subjects
WIRELESS power transmission, MILLIMETER waves, OCEAN wave power, POISSON processes, WAVE analysis, PROBABILITY density function, RADIO frequency
Abstract

In this paper, the impact of imperfect beam alignment (IBA) on millimeter wave (mmWave) wireless power transfer (WPT) is investigated. We consider a mmWave WPT network, where the location of the energy transmitters follows a Poisson point process. Instead of the mostly used flat-top antenna model, we adopt the Gaussian antenna model suggested by the 3 rd Generation Partnership Project (3GPP) for better accuracy. Two beam alignment error (BAE) models, i.e., truncated Gaussian and uniform models, are introduced to represent different BAE sources. We derive the probability density functions (PDFs) of the cascaded antenna gain with both BAE models and then provide the approximated PDFs for tractability. With the help of Fox's H function, the analytic expression for the energy coverage probability with nonlinear energy harvesting model is derived. Besides, we deduce a closed-form expression of the average harvested radio frequency (RF) energy. The simulation results verify our theoretical results and demonstrate the performance degradation incurred by BAE. It also shows that in the imperfect beam alignment scenario, the Gaussian antenna model can accurately represent the performance of mmWave WPT networks with actual beam pattern, while the flat-top antenna model cannot always provide accurate performance evaluation. [ABSTRACT FROM AUTHOR]

Published
2021
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19. Ensemble Super-Resolution With a Reference Dataset.

Author

Jiang, Junjun, Yu, Yi, Wang, Zheng, Tang, Suhua, Hu, Ruimin, and Ma, Jiayi

Abstract

By developing sophisticated image priors or designing deep(er) architectures, a variety of image super-resolution (SR) approaches have been proposed recently and achieved very promising performance. A natural question that arises is whether these methods can be reformulated into a unifying framework and whether this framework assists in SR reconstruction? In this paper, we present a simple but effective single image SR method based on ensemble learning, which can produce a better performance than that could be obtained from any of SR methods to be ensembled (or called component super-resolvers). Based on the assumption that better component super-resolver should have larger ensemble weight when performing SR reconstruction, we present a maximum a posteriori (MAP) estimation framework for the inference of optimal ensemble weights. Especially, we introduce a reference dataset, which is composed of high-resolution (HR) and low-resolution (LR) image pairs, to measure the SR abilities (prior knowledge) of different component super-resolvers. To obtain the optimal ensemble weights, we propose to incorporate the reconstruction constraint, which states that the degenerated HR estimation should be equal to the LR observation one, as well as the prior knowledge of ensemble weights into the MAP estimation framework. Moreover, the proposed optimization problem can be solved by an analytical solution. We study the performance of the proposed method by comparing with different competitive approaches, including four state-of-the-art nondeep learning-based methods, four latest deep learning-based methods, and one ensemble learning-based method, and prove its effectiveness and superiority on some general image datasets and face image datasets. [ABSTRACT FROM AUTHOR]

Published
2020
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20. Context-Patch Face Hallucination Based on Thresholding Locality-Constrained Representation and Reproducing Learning.

Author

Jiang, Junjun, Yu, Yi, Tang, Suhua, Ma, Jiayi, Aizawa, Akiko, and Aizawa, Kiyoharu

Abstract

Face hallucination is a technique that reconstructs high-resolution (HR) faces from low-resolution (LR) faces, by using the prior knowledge learned from HR/LR face pairs. Most state-of-the-arts leverage position-patch prior knowledge of the human face to estimate the optimal representation coefficients for each image patch. However, they focus only the position information and usually ignore the context information of the image patch. In addition, when they are confronted with misalignment or the small sample size (SSS) problem, the hallucination performance is very poor. To this end, this paper incorporates the contextual information of the image patch and proposes a powerful and efficient context-patch-based face hallucination approach, namely, thresholding locality-constrained representation and reproducing learning (TLcR-RL). Under the context-patch-based framework, we advance a thresholding-based representation method to enhance the reconstruction accuracy and reduce the computational complexity. To further improve the performance of the proposed algorithm, we propose a promotion strategy called reproducing learning. By adding the estimated HR face to the training set, which can simulate the case that the HR version of the input LR face is present in the training set, it thus iteratively enhances the final hallucination result. Experiments demonstrate that the proposed TLcR-RL method achieves a substantial increase in the hallucinated results, both subjectively and objectively. In addition, the proposed framework is more robust to face misalignment and the SSS problem, and its hallucinated HR face is still very good when the LR test face is from the real world. The MATLAB source code is available at https://github.com/junjun-jiang/TLcR-RL. [ABSTRACT FROM AUTHOR]

Published
2020
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30. Category-Based Deep CCA for Fine-Grained Venue Discovery From Multimodal Data.

Author

Yu, Yi, Tang, Suhua, Aizawa, Kiyoharu, and Aizawa, Akiko

Subjects
*ART & state, *INDUSTRIAL location, *TOURIST attractions, *DEEP learning, *IMAGE retrieval, *CANONICAL correlation (Statistics)
Abstract

In this work, travel destinations and business locations are taken as venues. Discovering a venue by a photograph is very important for visual context-aware applications. Unfortunately, few efforts paid attention to complicated real images such as venue photographs generated by users. Our goal is fine-grained venue discovery from heterogeneous social multimodal data. To this end, we propose a novel deep learning model, category-based deep canonical correlation analysis. Given a photograph as input, this model performs: 1) exact venue search (find the venue where the photograph was taken) and 2) group venue search (find relevant venues that have the same category as the photograph), by the cross-modal correlation between the input photograph and textual description of venues. In this model, data in different modalities are projected to a same space via deep networks. Pairwise correlation (between different modality data from the same venue) for exact venue search and category-based correlation (between different modality data from different venues with the same category) for group venue search are jointly optimized. Because a photograph cannot fully reflect rich text description of a venue, the number of photographs per venue in the training phase is increased to capture more aspects of a venue. We build a new venue-aware multimodal data set by integrating Wikipedia featured articles and Foursquare venue photographs. Experimental results on this data set confirm the feasibility of the proposed method. Moreover, the evaluation over another publicly available data set confirms that the proposed method outperforms state of the arts for cross-modal retrieval between image and text. [ABSTRACT FROM AUTHOR]

Published
2019
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44. Potential throughput based access point selection

Author

Tang, Suhua, primary, Taniguchi, Noriyuki, additional, Shagdar, Oyunchimeg, additional, Tamai, Morihiko, additional, Yomo, Hiroyuki, additional, Hasegawa, Akio, additional, Ueda, Tetsuro, additional, Miura, Ryu, additional, and Obana, Sadao, additional

Published
2010
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