- 标题
- 摘要
- 关键词
- 实验方案
- 产品
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[IEEE 2018 China International SAR Symposium (CISS) - Shanghai (2018.10.10-2018.10.12)] 2018 China International SAR Symposium (CISS) - Reconstruction Full-Pol SAR Data from Single-Pol SAR Image Using Deep Neural Network
摘要： Compared with single channel polarimetric (single-pol) SAR image, full polarimetric (full-pol) data convey richer information, but with compromises on higher system complexity and lower resolution or swath. In order to balance these factors, a deep neural networks based method is proposed to recover full-pol data from single-pol data in this paper. It consists of two parts: a feature extractor network is applied first to extract hierarchical multi-scale spatial features, followed by a feature translator network to predict polarimetric features with which full-pol SAR data can be recovered. Both qualitative and quantitative results show that the recovered full-pol SAR data agrees well with the real full-pol data. No prior information is assumed for scatterer media, and the framework can be easily expanded to recovery full-pol data from non-full-pol data. Traditional PolSAR applications such as model-based decomposition and unsupervised classification can now be applied directly to recovered full-pol SAR image to interpret the physical scattering mechanism.
关键词： synthetic aperture radar (SAR),deep neural network (DNN),polarimetric reconstruction
更新于2025-09-23 15:22:29
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[IEEE IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - A Multi-Direction Subbands and Deep Neural Networks Bassed Pan-Sharpening Method
摘要： This paper proposes a pan-sharpening method based on multi-direction subbands and deep neural networks. First, by utilizing the multi-scale and multi-direction properties of the nonsubsampled contourlet transform (NSCT), panchromatic (PAN) image is decomposed into the low frequency subbands in different resolutions and the high frequency subbands in different directions. Pan-sharpening method aims to fuse the high frequency subband coefficients of PAN image and the low frequency subband coefficients of multispectral (MS) image. Second, in order to better extract the feature of the high frequency subbands in different directions of PAN image, the deep neural network (DNN) is trained using the image patches of high frequency subbands of PAN image. Third, in the fusion stage, we exploit NSCT on the principal component of resampled low resolution (LR) MS image. The high frequency subbands of output high resolution (HR) MS image is obtained by forward propagation of the trained DNN, which input is the high frequency subbands of LR MS image. Finally, a new subband set is obtained by fusing the reconstructed high frequency subband and the original low frequency subband of LR MS image. The HR MS image is produced by executing the inverse transform of NSCT and adaptive PCA (A-PCA) on the new subband set. The experimental results show the proposed method outperforms other well-known methods in terms of both objective measurements and visual evaluation.
关键词： adaptive Principal Component Analysis (A-PCA),deep neural network (DNN),pan-sharpening,nonsubsampled contourlet transform (NSCT)
更新于2025-09-23 15:22:29
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[IEEE 2019 Conference on Lasers and Electro-Optics Europe & European Quantum Electronics Conference (CLEO/Europe-EQEC) - Munich, Germany (2019.6.23-2019.6.27)] 2019 Conference on Lasers and Electro-Optics Europe & European Quantum Electronics Conference (CLEO/Europe-EQEC) - A Multi-Copy Approach to Quantum Entanglement Characterization
摘要： Automatic speech recognition (ASR) systems are used daily by millions of people worldwide to dictate messages, control devices, initiate searches or to facilitate data input in small devices. The user experience in these scenarios depends on the quality of the speech transcriptions and on the responsiveness of the system. For multilingual users, a further obstacle to natural interaction is the monolingual character of many ASR systems, in which users are constrained to a single preset language. In this work, we present an end-to-end multi-language ASR architecture, developed and deployed at Google, that allows users to select arbitrary combinations of spoken languages. We leverage recent advances in language identification and a novel method of real-time language selection to achieve similar recognition accuracy and nearly-identical latency characteristics as a monolingual system.
关键词： Automatic speech recognition (ASR),multilingual,deep neural network (DNN),language identification (LID)
更新于2025-09-23 15:21:01
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[IEEE 2019 IEEE/CIC International Conference on Communications in China (ICCC) - Changchun, China (2019.8.11-2019.8.13)] 2019 IEEE/CIC International Conference on Communications in China (ICCC) - Enhanced Performance of a Phosphorescent White LED CAP 64QAM VLC system utilizing Deep Neural Network (DNN) Post Equalization
摘要： In this paper, a phosphorescent white LED CAP 64QAM VLC system utilizing deep neural network (DNN) and LMS linear equalization (LE) has been experimentally demonstrated. We successfully achieve a data rate of 2.4Gb/s at a 1.1-m indoor free space transmission with the bit error rate (BER) below the 7% forward error correction (FEC) limit of 3.8 (cid:104)(cid:104) 10-3. Compared to the LE and volterra NLE+LE, DNN+LE brings a better performance to mitigate nonlinear distortion. The results show that DNN+LE will be a promising solution for indoor high-speed VLC system
关键词： Deep Neural Network (DNN),Visible Light Communication (VLC),CAP modulation
更新于2025-09-19 17:13:59
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[IEEE 2019 18th International Conference on Optical Communications and Networks (ICOCN) - Huangshan, China (2019.8.5-2019.8.8)] 2019 18th International Conference on Optical Communications and Networks (ICOCN) - Post Equalization Scheme Based on Deep Neural Network for a Probabilistic Shaping 128 QAM DFT-S OFDM Signal in Underwater Visible Light Communication System
摘要： We have presented a post equalization scheme based on Deep Neural Network (DNN) for DFT-S OFDM modulation using (PS) technique in underwater visible light communication (VLC) system. By this method, we successfully demonstrated a data rate of 1.74Gbit/s PS128QAM DFT-S OFDM modulation over 1.2meter underwater optical transmission with bit error rate (BER) below 7% FEC threshold of 3.8×10-3. Compared to the typical PS128QAM DFT-S OFDM modulation without DNN, the proposed method would lead to an improvement of system capacity of 5.4% by increasing the data rate by 90 Mbps. The experimental results validate that the proposed DNN-based post equalization scheme for odd order QAM PS technique can be a promising solution for future high speed underwater VLC system.
关键词： Probabilistic Shaping (PS),odd order QAM,Deep Neural Network (DNN),Underwater VLC,Discrete Fourier Transform-Spread (DFT-S) OFDM
更新于2025-09-16 10:30:52
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[IEEE 2019 IEEE 8th International Conference on Advanced Optoelectronics and Lasers (CAOL) - Sozopol, Bulgaria (2019.9.6-2019.9.8)] 2019 IEEE 8th International Conference on Advanced Optoelectronics and Lasers (CAOL) - Laser-controlled Interaction of Cytocrome c with Lipids May Not Disrupt Apoptotic Pathway
摘要： Automatic speech recognition (ASR) systems are used daily by millions of people worldwide to dictate messages, control devices, initiate searches or to facilitate data input in small devices. The user experience in these scenarios depends on the quality of the speech transcriptions and on the responsiveness of the system. For multilingual users, a further obstacle to natural interaction is the monolingual character of many ASR systems, in which users are constrained to a single preset language. In this work, we present an end-to-end multi-language ASR architecture, developed and deployed at Google, that allows users to select arbitrary combinations of spoken languages. We leverage recent advances in language identification and a novel method of real-time language selection to achieve similar recognition accuracy and nearly-identical latency characteristics as a monolingual system.
关键词： Automatic speech recognition (ASR),multilingual,deep neural network (DNN),language identification (LID)
更新于2025-09-16 10:30:52
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[IEEE 2018 37th Chinese Control Conference (CCC) - Wuhan (2018.7.25-2018.7.27)] 2018 37th Chinese Control Conference (CCC) - Deep Forest-Based Classification of Hyperspectral Images
摘要： The classi?cation of hyperspectral images (HSIs) is a hot topic in the ?eld of remote sensing technology. In recent years, convolutional neural network (CNN) has achieved great success for HSI classi?cation. However, CNN has to do a great effort in parameters tuning which is time-consuming. Furthermore, a large number of samples are required to train CNN, nevertheless, it is expensive to obtain enough training samples from HSIs. In this paper, we propose a novel classi?cation approach based on deep forest. To reduce the dimension of hyperspectral data, principal component analysis (PCA) is performed during the pre-processing. In contrast to the CNN, our method has fewer hyper-parameters and faster training speed. To the best of our knowledge, this is among the ?rst deep forest-based hyperspectral spectral information classi?cation. Extensive experiments are conducted on two real-world HSI datasets to show the proposed method is signi?cantly superior to the state-of-the-art methods.
关键词： Deep Neural Network(DNN),Hyperspectral Image (HSI),Principal Component Analysis (PCA),Deep Forest
更新于2025-09-10 09:29:36