- 标题
- 摘要
- 关键词
- 实验方案
- 产品
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LONG-REACH OPTICAL ACCESS NETWORKS (LR-OANS): A PROMISING CANDIDATE FOR FUTURE OPTICAL ACCESS
摘要： The ever-increasing demand for broader bandwidth per user, which results from the continuous development of new bandwidth-hungry services and applications, creates the motivation to upgrade the currently deployed Time-Division Multiplexing Passive Optical Networks (TDM-PONs) to Next-Generation Optical Access Networks (NG-OANs). Beside the need for more bandwidth per user, a further extension in the range and an increase in the split ratio are highly desirable in PONs. These additional requirements can be achieved by adopting so-called Long-Reach Optical Access Networks (LR-OANs). LR-OANs offer a promising solution that ensures a significant number of users can be supported over a longer range. Moreover, they introduce a cost-effective approach in which both the access and metro segments of the telecommunication network are combined into one backhaul segment, which results in the consolidation of many central offices into one trunk-exchange. This cost-effective approach gave us the motivation to provide a comprehensive survey on the LR-OANs. In this study, we first provide a brief review of different potential technologies, proposed for next-generation optical access. We then provide a review of different stat-of-the-art LR-OAN architectures including opportunities and challenges in each one. A comparison among them based on key network specification is also provided.
关键词： Long-Reach Optical Access Network (LR-OANs),Next-Generation Optical Access,Fiber-to-The Home FTTH,DWDM,Passive Optical Networks (PONs),Optical Hybrid Schemes
更新于2025-09-04 15:30:14
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Elementos histórico-culturais para o ensino dos instrumentos ópticos
摘要： In this paper we presented a proposal for the teaching of optical instruments with a historical-cultural approach. For this, we used a network of social relations that has Constantijn Huygens (1596-1687) as its center (hub). Through primary sources and other documents dealing with Dutch culture in the seventeenth century, we showed how this network of scientists, artists, and philosophers combined to factors such as religion and trade, can offer an interdisciplinary and complex view of the advent of optical instruments. The contextual view of this historical period may provide subsidies for the construction of an interdisciplinary curriculum from optical instruments.
关键词： History of Science,Nature of Science,Science and Art,Historical Networks,Interdisciplinarity
更新于2025-09-04 15:30:14
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[IEEE ICASSP 2018 - 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) - Calgary, AB (2018.4.15-2018.4.20)] 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) - Image Reconstruction for Quanta Image Sensors Using Deep Neural Networks
摘要： Quanta Image Sensor (QIS) is a single-photon image sensor that oversamples the light field to generate binary measurements. Its single-photon sensitivity makes it an ideal candidate for the next generation image sensor after CMOS. However, image reconstruction of the sensor remains a challenging issue. Existing image reconstruction algorithms are largely based on optimization. In this paper, we present the first deep neural network approach for QIS image reconstruction. Our deep neural network takes the binary bitstream of QIS as input, learns the nonlinear transformation and denoising simultaneously. Experimental results show that the proposed network produces significantly better reconstruction results compared to existing methods.
关键词： single-photon imaging,Quanta Image Sensor,deep neural networks,image reconstruction
更新于2025-09-04 15:30:14
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[IEEE 2018 25th IEEE International Conference on Image Processing (ICIP) - Athens, Greece (2018.10.7-2018.10.10)] 2018 25th IEEE International Conference on Image Processing (ICIP) - Near InfraRed Imagery Colorization
摘要： This paper proposes a stacked conditional Generative Adversarial Network-based method for Near InfraRed (NIR) imagery colorization. We propose a variant architecture of Generative Adversarial Network (GAN) that uses multiple loss functions over a conditional probabilistic generative model. We show that this new architecture/loss-function yields better generalization and representation of the generated colored IR images. The proposed approach is evaluated on a large test dataset and compared to recent state of art methods using standard metrics.
关键词： Convolutional Neural Networks (CNN),Infrared Imagery colorization,Generative Adversarial Network (GAN)
更新于2025-09-04 15:30:14
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[IEEE 2018 24th International Conference on Pattern Recognition (ICPR) - Beijing, China (2018.8.20-2018.8.24)] 2018 24th International Conference on Pattern Recognition (ICPR) - A New Method for Face Alignment under Extreme Poses and Occlusion
摘要： In real-world conditions, robust face alignment is challenging due to the large variability of occlusion and pose. Many methods aim to solve the problem, but can handle either images with occlusion only or with arbitrary poses only. In this paper, we propose a unified framework by ignoring the points which cannot be seen under occlusion and extreme poses, in which we get facial parts first by classification and then train regression models to get key points. It leads to higher accuracy when locating the truly existing points without considering the occluded and non-existent points. Besides, we observed that the drift and shape of face detection results affect face alignment .As far as we know, we are the first to explicitly raise the issue and solve it to some extent. Finally, our method outperforms the state-of-the-art methods on AFLW and COFW datasets. It is also comparable to other methods on LFPW dataset.
关键词： extreme poses,convolutional neural networks,occlusion,face alignment,different bounding box
更新于2025-09-04 15:30:14
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[IEEE 2018 20th International Conference on Transparent Optical Networks (ICTON) - Bucharest (2018.7.1-2018.7.5)] 2018 20th International Conference on Transparent Optical Networks (ICTON) - Elastic Networks Thematic Network Results I: Planning and Control of Flex-Grid/SDM
摘要： This paper overviews the approach of the Elastic Networks research network to address different issues of planning and control of Flex-Grid/SDM optical networks. Firstly, we present the Net2Plan open-source planning tool capabilities to model Flex-Grid/SDM networks; secondly a PCE-based Transport-SDN controller for packet over flex-grid optical networks is described. Finally results on machine-learning-based QoT classification techniques useful in planning and control tasks are reported.
关键词： space division multiplexing,elastic optical networks,network planning,control,machine-learning
更新于2025-09-04 15:30:14
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An Open-Source Artificial Neural Network Model for Polarization-Insensitive Silicon-on-Insulator Subwavelength Grating Couplers
摘要： We present an open-source deep artificial neural network (ANN) model for the accelerated design of polarization-insensitive subwavelength grating (SWG) couplers on the silicon-on-insulator platform. Our model can optimize SWG-based grating couplers for a single fundamental-order polarization, or both, by splitting them counter-directionally at the grating level. Alternating SWG sections are adopted to reduce the reflections (loss) of standard, single-etch devices—further accelerating the design time by eliminating the need to process a second etch. The model of this device is trained by a dense uniform dataset of finite-difference time-domain (FDTD) optical simulations. Our approach requires the FDTD simulations to be made up front, where the resulting ANN model is made openly available for the rapid, software-free design of future standard photonic devices, which may require slightly different design parameters (e.g., fiber angle, center wavelength, polarization) for their specific application. By transforming the nonlinear input–output relationship of the device into a matrix of learned weights, a set of simple linear algebraic and nonlinear activation calculations can be made to predict the device outputs 1,830 times faster than numerical simulations, within 93.2% accuracy of the simulations.
关键词： subwavelength devices,machine learning,Silicon photonics,polarization insensitivity,grating couplers,artificial neural networks
更新于2025-09-04 15:30:14
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Supervised band selection in hyperspectral images using single-layer neural networks
摘要： Hyperspectral images provide fine details of the scene under analysis in terms of spectral information. This is due to the presence of contiguous bands that make possible to distinguish different objects even when they have similar colour and shape. However, neighbouring bands are highly correlated, and, besides, the high dimensionality of hyperspectral images brings a heavy burden on processing and also may cause the Hughes phenomenon. It is therefore advisable to make a band selection pre-processing prior to the classification task. Thus, this paper proposes a new supervised filter-based approach for band selection based on neural networks. For each class of the data set, a binary single-layer neural network classifier performs a classification between that class and the remainder of the data. After that, the bands related to the biggest and smallest weights are selected, so, the band selection process is class-oriented. This process iterates until the previously defined number of bands is achieved. A comparison with three state-of-the-art band selection approaches shows that the proposed method yields the best results in 43.33% of the cases even with greatly reduced training data size, whereas the competitors have achieved between 13.33% and 23.33% on the Botswana, KSC and Indian Pines datasets.
关键词： supervised learning,neural networks,Hyperspectral images,band selection,filter-based approach
更新于2025-09-04 15:30:14
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[IEEE 2018 17th International Symposium on Distributed Computing and Applications for Business Engineering and Science (DCABES) - Wuxi, China (2018.10.19-2018.10.23)] 2018 17th International Symposium on Distributed Computing and Applications for Business Engineering and Science (DCABES) - MaxFlow: a Convolutional Neural Network Based Optical Flow Algorithm for Large Displacement Estimation
摘要： Optical ?ow estimation is a basic problem in computer vision. FlowNet is the ?rst convolutional neural network based optical algorithm that estimates optical ?ow by learning the relationship between image pair and the corresponding optical ?ow. In this paper, MaxFlow is proposed to improve the accuracy of FlowNet. The architecture of MaxFlow is similar to that of FlowNetSimple. MaxFlow uses two kinds of new layers, which are designed specially for estimating large displacements of small scale objects. The new down sampling layer makes the network to predict the maximum displacement in a region. Thus the large movements will not be missed. The new up sampling layer up samples the estimated optical ?ow ?elds without using any parameter. It simpli?es the network without decreasing the accuracy of the network. Experiments on synthetic datasets and real datasets illustrate that the two new layers are effective and the accuracy of MaxFlow is higher than that of FlowNet.
关键词： optical ?ow,convolutional neural networks,variational methods
更新于2025-09-04 15:30:14
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[IEEE 2018 20th International Conference on Transparent Optical Networks (ICTON) - Bucharest (2018.7.1-2018.7.5)] 2018 20th International Conference on Transparent Optical Networks (ICTON) - Network Service Slicing Supporting Ubiquitous Access in Passive Optical Networks
摘要： The various booming network applications and services require optical access networks to support ubiquitous access, not simply providing huge bandwidth. In this paper, we investigate the timeslot-based network slicing of passive optical networks to support various network services. We find that low latency services need critical service slicing settings for the benefit of achieving the lowest latency.
关键词： network slicing,optical access networks,low latency
更新于2025-09-04 15:30:14