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Multimodal-Boost: Multimodal Medical Image Super-Resolution using Multi-Attention Network with Wavelet Transform

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arXiv 2021年

作者： Dharejo, Fayaz Ali Zawish, Muhammad Deeba, Farah Zhou, Yuanchun Dev, Kapal Khowaja, Sunder Ali Qureshi, Nawab Muhammad Faseeh The Computer Net-Work Information Center Chinese Academy of Sciences University of Chinese Academy of Sciences Beijing100190 China Walton Institute for Information and Communication Systems Science Waterford Institute of Technology Ireland Department of institute of intelligent systems University of Johannesburg South Africa Faculty of Engineering and Technology University of Sindh Jamshoro Pakistan The Department of Computer Education Sungkyunkwan University Seoul Korea Republic of

Multimodal medical images are widely used by clinicians and physicians to analyze and retrieve complementary information from high-resolution images in a non-invasive manner. Loss of corresponding image resolution adversely affects the overall performance of medical image interpretation. Deep learning based single image super resolution (SISR) algorithms has revolutionized the overall diagnosis framework by continually improving the architectural components and training strategies associated with convolutional neural networks (CNN) on low-resolution images. However, existing work lacks in two ways: i) the SR output produced exhibits poor texture details, and often produce blurred edges, ii) most of the models have been developed for a single modality, hence, require modification to adapt to a new one. This work addresses (i) by proposing generative adversarial network (GAN) with deep multi-attention modules to learn high-frequency information from low-frequency data. Existing approaches based on the GAN have yielded good SR results;however, the texture details of their SR output have been experimentally confirmed to be deficient for medical images particularly. The integration of wavelet transform (WT) and GANs in our proposed SR model addresses the aforementioned limitation concerning textons. While the WT divides the LR image into multiple frequency bands, the transferred GAN uses multi-attention and upsample blocks to predict high-frequency components. Additionally, we present a learning method for training domain-specific classifiers as perceptual loss functions. Using a combination of multi-attention GAN loss and a perceptual loss function results in an efficient and reliable performance. Applying the same model for medical images from diverse modalities is challenging, our work addresses (ii) by training and performing on several modalities via transfer learning. Using two medical datasets, we validate our proposed SR network against existing state-of-the-art

关键词： Wavelet transforms

Low-Complexity Hybrid Beamforming for Multi-Cell mmWave Massive MIMO: A Primitive Kronecker Decomposition Approach

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arXiv 2025年

作者： Sun, Teng Zhu, Guangxu Li, Xiaofan Fan, Jiancun Xia, Minghua School of Electronic and Information Engineering Xi’an Jiaotong University Xi’an710049 China 54th Research Institute of China Electronics Technology Group Corporation Shijiazhuang050081 China Shenzhen Research Institute of Big Data Shenzhen710049 China School of Intelligent Systems Science and Engineering Jinan University Zhuhai519070 China School of Electronics and Information Technology Sun Yat-sen University Guangzhou510006 China

To circumvent the high path loss of mmWave propagation and reduce the hardware cost of massive multiple-input multiple-output antenna systems, full-dimensional hybrid beamforming is critical in 5G and beyond wireless communications. Concerning an uplink multi-cell system with a large-scale uniform planar antenna array, this paper designs an efficient hybrid beamformer using primitive Kronecker decomposition and dynamic factor allocation, where the analog beamformer applies to null the inter-cell interference and simultaneously enhances the desired signals. In contrast, the digital beamformer mitigates the intra-cell interference using the minimum mean square error (MMSE) criterion. Then, due to the low accuracy of phase shifters inherent in the analog beamformer, a low-complexity hybrid beamformer is developed to slow its adjustment speed. Next, an optimality analysis from a subspace perspective is performed, and a sufficient condition for optimal antenna configuration is established. Finally, simulation results demonstrate that the achievable sum rate of the proposed beamformer approaches that of the optimal pure digital MMSE scheme, yet with much lower computational complexity and hardware cost. © 2025, CC BY-NC-ND.

关键词： Butler matrix

Gru-Inc: An Inception-Attention Based Approach Using Gru for Human Activity Recognition

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SSRN

SSRN 2022年

作者： Mim, Taima Rahman Amatullah, Maliha Afreen, Sadia Yousuf, Mohammad Abu Uddin, Shahadat Alyami, Salem A. Hasan, Khondokar Fida Moni, Mohammad Ali Information and Communication Technology Bangladesh University Of Professionals Mirpur Cantonment Dhaka Bangladesh Institute of Information Technology Jahangirnagar University Savar Dhaka Bangladesh Complex Systems Research Group Faculty of Engineering The University of Sydney DarlingtonNSW2008 Australia Riyadh13318 Saudi Arabia Centre for Cyber Security Research & Innovation RMIT University 124 La Trobe Street MelbourneVIC3000 Australia Artificial Intelligence & Data Science School of Health and Rehabilitation Sciences Faculty of Health and Behavioural Sciences The University of Queensland St LuciaQLD4072 Australia

Human Activity Recognition is one of the most popular research fields nowadays, and many machine learning algorithms have been successfully implemented to achieve high-performance results. Although handcrafted feature extraction techniques were used in the past, ANN is now more popular. In this work, the GRU-INC model has been proposed, which is an Inception-Attention based approach using GRU that effectively makes use of the temporal and spatial information of the time-series data. The proposed model achieved an F1-score of 96.27%, 90.05%, 90.30%, 99.12% and 95.99% on the publicly available datasets such as UCI-HAR, OPPORTUNITY, PAMAP2, WISDM and Daphnet respectively. GRU along with Attention Mechanism was utilized for the temporal part and Inception module along with CBAM was exploited for the spatial part of the model. The proposed architecture was evaluated against the state-of-the-art as well as few existing works and it is proved that the GRU-INC model has a higher recognition rate as well as lower computational cost. © 2022, The Authors. All rights reserved.

关键词： Machine learning

EAO-SLAM: Monocular Semi-Dense Object SLAM Based on Ensemble data Association

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EAO-SLAM: Monocular Semi-Dense Object SLAM Based on Ensemble...

2020 IEEE/RSJ International Conference on intelligent Robots and systems (IROS)

作者： Yanmin Wu Yunzhou Zhang Delong Zhu Yonghui Feng Sonya Coleman Dermot Kerr Faculty of Robot Science and Engineering Northeastern University Shenyang China College of Information Science and Engineering Northeastern University Shenyang China The Chinese University of Hong Kong China School of Computing and Intelligent Systems Ulster University N. Ireland UK

ISBN: (数字)9781728162126

ISBN: (纸本)9781728162133

Object-level data association and pose estimation play a fundamental role in semantic SLAM, which remain unsolved due to the lack of robust and accurate algorithms. In this work, we propose an ensemble data associate strategy for integrating the parametric and nonparametric statistic tests. By exploiting the nature of different statistics, our method can effectively aggregate the information of different measurements, and thus significantly improve the robustness and accuracy of data association. We then present an accurate object pose estimation framework, in which an outliers-robust centroid and scale estimation algorithm and an object pose initialization algorithm are developed to help improve the optimality of pose estimation results. Furthermore, we build a SLAM system that can generate semi-dense or lightweight object-oriented maps with a monocular camera. Extensive experiments are conducted on three publicly available datasets and a real scenario. The results show that our approach significantly outperforms state-of-the-art techniques in accuracy and robustness. The source code is available on https://***/yanmin-wu/EAO-SLAM.

关键词： Simultaneous localization and mapping Navigation Pose estimation Semantics Robustness Nonparametric statistics intelligent robots

Integrating ARIMA and Bidirectional LSTM to Predict ETA in Multi-Airport systems

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Integrating ARIMA and Bidirectional LSTM to Predict ETA in M...

Integrated Communications, Navigation and Surveillance Conference, ICNS

作者： Lechen Wang Xuechun Li Jianfeng Mao School of Science and Engineering The Chinese University of Hong Kong Shenzhen Guangdong China School of Science and Engineering Shenzhen Key Laboratory of IoT Intelligent Systems and Wireless Network Technology The Chinese University of Hong Kong Shenzhen Shenzhen Research Institute of Big Data Shenzhen Guangdong China

ISBN: (数字)9781728172705

ISBN: (纸本)9781728172712

Traffic states prediction in air transportation systems is a challenging problem and has not been fully explored because it is subject to many more highly correlated factors and a more complicated traffic management scheme compared to urban transportation systems. It becomes a more formidable task when facing a multi-airport system (MAS), in which several major airports are closely located and tightly coupled with each other through limited terminal airspace. In this work, we propose a novel method using a time series model and recurrent neural network to make the estimated time of arrival (ETA) for a flight to an MAS, which can be potentially utilized for flight delay prediction and congestion analysis. The experiment utilizes two months of 4D trajectories data from Beijing Capital International Airport (PEK) to Shenzhen Bao'an International airport (ZGSZ). The entire prediction work is decomposed into two sub-problems, en-route travel time prediction which is from flight origin to the entering gate of MAS, defined as the location is 300km from the airport in MAS, and terminal maneuvering area (TMA) travel time prediction which is from the entrance to flight's destination. The auto-regressive integrated moving average (ARIMA), a time series prediction model, is used to predict travel time in en-route under given the flight departure time. Bidirectional long short term memory (LSTM), a recurrent neural network, is developed to forecast travel time in the arrival approach by utilizing spatio-temporal features. To design the input features, we use density-based spatial clustering (DBSCAN) with the help of the Voronoi diagram to extract spatial information from every historical flight trajectory of aircraft operated in an MAS, then select the observation time window to capture the temporal information for each flight. The Multivariate Stacked Fully connected-Bidirectional LSTM (MSFCB-LSTM) model is constructed to make shortterm forecasting using spatio-temporal feature

关键词：

Measurement of the Gamma-Ray Energy Spectrum beyond 100 TeV from the HESS J1843-033 Region

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arXiv 2023年

作者： Amenomori, M. Asano, S. Bao, Y.W. Bi, X.J. Chen, D. Chen, T.L. Chen, W.Y. Chen, Xu Chen, Y. Cirennima Cui, S.W. Danzengluobu Ding, L.K. Fang, J.H. Fang, K. Feng, C.F. Feng, Zhaoyang Feng, Z.Y. Gao, Qi Gomi, A. Gou, Q.B. Guo, Y.Q. Guo, Y.Y. He, H.H. He, Z.T. Hibino, K. Hotta, N. Hu, Haibing Hu, H.B. Hu, K.Y. Huang, J. Jia, H.Y. Jiang, L. Jiang, P. Jin, H.B. Kasahara, K. Katayose, Y. Kato, C. Kato, S. Kawashima, T. Kawata, K. Kozai, M. Kurashige, D. Labaciren Le, G.M. Li, A.F. Li, H.J. Li, W.J. Li, Y. Lin, Y.H. Liu, B. Liu, C. Liu, J.S. Liu, L.Y. Liu, M.Y. Liu, W. Liu, X.L. Lou, Y.-Q. Lu, H. Meng, X.R. Meng, Y. Munakata, K. Nagaya, K. Nakamura, Y. Nakazawa, Y. Nanjo, H. Ning, C.C. Nishizawa, M. Ohnishi, M. Okukawa, S. Ozawa, S. Qian, L. Qian, X. Qian, X.L. Qu, X.B. Saito, T. Sakakibara, Y. Sakata, M. Sako, T. Sako, T.K. Shao, J. Shibata, M. Shiomi, A. Sugimoto, H. Takano, W. Takita, M. Tan, Y.H. Tateyama, N. Torii, S. Tsuchiya, H. Udo, S. Wang, H. Wang, Y.P. Wangdui Wu, H.R. Wu, Q. Xu, J.L. Xue, L. Yang, Z. Yao, Y.Q. Yin, J. Yokoe, Y. Yu, N.P. Yuan, A.F. Zhai, L.M. Zhang, C.P. Zhang, H.M. Zhang, J.L. Zhang, X. Zhang, X.Y. Zhang, Y. Zhang, Yi Zhang, Ying Zhao, S.P. Zhaxisangzhu Zhou, X.X. Department of Physics Hirosaki University Hirosaki036-8561 Japan Department of Physics Shinshu University Matsumoto390-8621 Japan School of Astronomy and Space Science Nanjing University Nanjing210093 China Key Laboratory of Particle Astrophysics Institute of High Energy Physics Chinese Academy of Sciences Beijing100049 China National Astronomical Observatories Chinese Academy of Sciences Beijing100012 China Department of Mathematics and Physics Tibet University Lhasa850000 China Department of Physics Hebei Normal University Shijiazhuang050016 China University of Chinese Academy of Sciences Beijing100049 China Shandong University Qingdao266237 China Institute of Modern Physics SouthWest Jiaotong University Chengdu610031 China Faculty of Engineering Yokohama National University Yokohama240-8501 Japan Faculty of Engineering Kanagawa University Yokohama221-8686 Japan Faculty of Education Utsunomiya University Utsunomiya321-8505 Japan Faculty of Systems Engineering Shibaura Institute of Technology Omiya330-8570 Japan Institute for Cosmic Ray Research University of Tokyo Kashiwa277-8582 Japan Polar Environment Data Science Center Joint Support-Center for Data Science Research Research Organization of Information and Systems Tachikawa190-0014 Japan National Center for Space Weather China Meteorological Administration Beijing100081 China School of Information Science and Engineering Shandong Agriculture University Taian271018 China Department of Astronomy School of Physical Sciences University of Science and Technology of China Hefei230026 China Tsinghua University Beijing100084 China Joint Research Center for Astrophysics Tsinghua University Beijing100084 China Department of Astronomy Tsinghua University Beijing100084 China College of Industrial Technology Nihon University Narashino275-8575 Japan National Institute of Informatics Tokyo101-8430 Japan National Institute of Information and Communications Technology Tokyo

HESS J1843-033 is a very-high-energy gamma-ray source whose origin remains unidentified. This work presents, for the first time, the energy spectrum of gamma rays beyond 100 TeV from the HESS J1843-033 region using the data recorded by the Tibet air shower array and its underground muon detector array. A gamma-ray source with an extension of 0◦. 34 ± 0◦. 12 is successfully detected above 25 TeV at (α, δ) = (281◦. 09 ± 0◦. 10, −3◦. 76 ± 0◦. 09) near HESS J1843-033 with a statistical significance of 6.2 σ, and the source is named TASG J1844-038. The position of TASG J1844-038 is consistent with those of HESS J1843-033, eHWC J1842-035, and LHAASO J1843-0338. The measured gamma-ray energy spectrum in 25 TeV −16(E/40 TeV)−3.26±0.30 TeV−1cm−2s−1, and the spectral fit to the combined spectra of HESS J1843-033, LHAASO J1843-0338, and TASG J1844-038 implies the existence of a cutoff at 49.5 ± 9.0 TeV. Associations of TASG J1844-038 with SNR G28.6-0.1 and PSR J1844-0346 are also discussed in detail for the first time. © 2023, CC BY.

关键词： Cosmology

Observation of gamma rays up to 320 TeV from the middle-aged TeV pulsar wind nebula HESS J1849-000

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arXiv 2023年

作者： Amenomori, M. Asano, S. Bao, Y.W. Bi, X.J. Chen, D. Chen, T.L. Chen, W.Y. Chen, Xu Chen, Y. Cirennima Cui, S.W. Danzengluobu Ding, L.K. Fang, J.H. Fang, K. Feng, C.F. Feng, Zhaoyang Feng, Z.Y. Gao, Qi Gomi, A. Gou, Q.B. Guo, Y.Q. Guo, Y.Y. Hayashi, Y. He, H.H. He, Z.T. Hibino, K. Hotta, N. Hu, Haibing Hu, H.B. Hu, K.Y. Huang, J. Jia, H.Y. Jiang, L. Jiang, P. Jin, H.B. Kasahara, K. Katayose, Y. Kato, C. Kato, S. Kawahara, I. Kawashima, T. Kawata, K. Kozai, M. Kurashige, D. Labaciren Le, G.M. Li, A.F. Li, H.J. Li, W.J. Li, Y. Lin, Y.H. Liu, B. Liu, C. Liu, J.S. Liu, L.Y. Liu, M.Y. Liu, W. Lu, H. Meng, X.R. Meng, Y. Munakata, K. Nagaya, K. Nakamura, Y. Nakazawa, Y. Nanjo, H. Ning, C.C. Nishizawa, M. Noguchi, R. Ohnishi, M. Okukawa, S. Ozawa, S. Qian, X. Qian, X.L. Qu, X.B. Saito, T. Sakakibara, Y. Sakata, M. Sako, T. Sako, T.K. Sasaki, T. Shao, J. Shibata, M. Shiomi, A. Sugimoto, H. Takano, W. Takita, M. Tan, Y.H. Tateyama, N. Torii, S. Tsuchiya, H. Udo, S. Wang, H. Wang, S.F. Wang, Y.P. Wangdui Wu, H.R. Wu, Q. Xu, J.L. Xue, L. Yang, Z. Yao, Y.Q. Yin, J. Yokoe, Y. Yu, Y.L. Yuan, A.F. Zhai, L.M. Zhang, H.M. Zhang, J.L. Zhang, X. Zhang, X.Y. Zhang, Y. Zhang, Yi Zhang, Ying Zhao, S.P. Zhaxisangzhu Zhou, X.X. Zou, Y.H. Department of Physics Hirosaki University Hirosaki036-8561 Japan Department of Physics Shinshu University Matsumoto390-8621 Japan School of Astronomy and Space Science Nanjing University Nanjing210093 China Key Laboratory of Particle Astrophysics Institute of High Energy Physics Chinese Academy of Sciences Beijing100049 China National Astronomical Observatories Chinese Academy of Sciences Beijing100012 China Department of Mathematics and Physics Tibet University Lhasa850000 China Department of Physics Hebei Normal University Shijiazhuang050016 China University of Chinese Academy of Sciences Beijing100049 China Shandong University Qingdao266237 China Institute of Modern Physics SouthWest Jiaotong University Chengdu610031 China Faculty of Engineering Yokohama National University Yokohama240-8501 Japan Faculty of Engineering Kanagawa University Yokohama221-8686 Japan Faculty of Education Utsunomiya University Utsunomiya321-8505 Japan Faculty of Systems Engineering Shibaura Institute of Technology Omiya330-8570 Japan Institute for Cosmic Ray Research University of Tokyo Kashiwa277-8582 Japan Polar Environment Data Science Center Joint Support-Center for Data Science Research Research Organization of Information and Systems Tachikawa190-0014 Japan National Center for Space Weather China Meteorological Administration Beijing100081 China School of Information Science and Engineering Shandong Agriculture University Taian271018 China Department of Astronomy School of Physical Sciences University of Science and Technology of China Hefei230026 China College of Industrial Technology Nihon University Narashino275-8575 Japan National Institute of Informatics Tokyo101-8430 Japan National Institute of Information and Communications Technology Tokyo184-8795 Japan Department of Mechanical and Electrical Engineering Shangdong Management University Jinan250357 China College of Science China University of Petroleum Qingdao266555 China Tok

Gamma rays from HESS J1849−000, a middle-aged TeV pulsar wind nebula (PWN), are observed by the Tibet air shower array and the muon detector array. The detection significance of gamma rays reaches 4.0 σ and 4.4 σ levels above 25 TeV and 100 TeV, respectively, in units of Gaussian standard deviation σ. The energy spectrum measured between 40 TeV −16(E/40 TeV)−2.24±0.41 TeV−1 cm−2 s−1. The gamma-ray energy spectrum from the sub-TeV (E p, cut is estimated at (Equation presented), suggesting that protons are accelerated up to the PeV energy range. Our study thus proposes that HESS J1849−000 should be further investigated as a new candidate for a Galactic PeV cosmic-ray accelerator, PeVatron. © 2023, CC BY.

关键词： Gamma rays

A reinforcement-learning-based energy-efficient framework for multi-task video analytics pipeline

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arXiv 2021年

作者： Zhao, Yingying Dong, Mingzhi Wang, Yujiang Feng, Da Lv, Qin Dick, Robert P. Li, Dongsheng Lu, Tun Gu, Ning Shang, Li School of Computer Science Fudan University Shanghai China Shanghai Key Laboratory of Data Science Fudan University Shanghai China Shanghai Institute of Intelligent Electronics & Systems Shanghai China Department of Computing Imperial College London London United Kingdom Software Service Company Limited Beijing China University of Colorado Boulder BoulderCO United States Department of Electrical Engineering and Computer Science College of Engineering University of Michigan Ann ArborMI United States Microsoft Research Asia Shanghai China School of Computer Science Fudan University Shanghai China

Deep-learning-based video processing has yielded transformative results in recent years. However, the video analytics pipeline is energy-intensive due to high data rates and reliance on complex inference algorithms, which limits its adoption in energy-constrained applications. Motivated by the observation of high and variable spatial redundancy and temporal dynamics in video data streams, we design and evaluate an adaptive-resolution optimization framework to minimize the energy use of multitask video analytics pipelines. Instead of heuristically tuning the input data resolution of individual tasks, our framework utilizes deep reinforcement learning to dynamically govern the input resolution and computation of the entire video analytics pipeline. By monitoring the impact of varying resolution on the quality of high-dimensional video analytics features, hence the accuracy of video analytics results, the proposed end-to-end optimization framework learns the best non-myopic policy for dynamically controlling the resolution of input video streams to globally optimize energy efficiency. Governed by reinforcement learning, optical flow is incorporated into the framework to minimize unnecessary spatio-temporal redundancy that leads to re-computation, while preserving accuracy. The proposed framework is applied to video instance segmentation which is one of the most challenging computer vision tasks, and achieves better energy efficiency than all baseline methods of similar accuracy on the YouTube-VIS dataset. Copyright © 2021, The Authors. All rights reserved.

关键词： Energy efficiency

Correction to: development of a cloud-assisted classification technique for the preservation of secure data storage in smart cities

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Journal of Cloud Computing 2023年第1期12卷 1-1页

作者： Kumar, Ankit Khan, Surbhi Bhatia Pandey, Saroj Kumar Shankar, Achyut Maple, Carsten Mashat, Arwa Malibari, Areej A. Department of Computer Engineering & Applications GLA University Mathura India Department of Electrical and Computer Engineering Lebanese American University Byblos Lebanon Department of Data Science School of Science Engineering and Environment University of Salford Manchester Byblos UK WMG University of Warwick Coventry UK Secure Cyber Systems Research Group (SCSRG) WMG University of Warwick Coventry UK Faculty of Computing and Information Science King Abdulaziz University Rabigh Saudi Arabia Department of Industrial and Systems Engineering College of Engineering Princess Nourah Bint Abdulrahman University Riyadh Saudi Arabia