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- 2018
- Conditional Random Fields (CRF)
- Convolutional Neural Network (CNN)
- Fine Classification
- Airborne hyperspectral
- green tide
- Elegant End-to-End Fully Convolutional Network (E3FCN)
- deep learning
- remote sensing
- Moderate Resolution Imaging Spectroradiometer (MODIS)
- Optoelectronic Information Science and Engineering
- Ocean University of China
- Wuhan University
- Central South University
- Hubei University
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Phasor Quaternion Neural Networks for Singular Point Compensation in Polarimetric-Interferometric Synthetic Aperture Radar
摘要： Interferograms obtained by synthetic aperture radar often include many singular points (SPs), which makes it difficult to generate an accurate digital elevation model. This paper proposes a filtering method to compensate SPs adaptively by using polarization and phase information around the SPs. Phase value is essentially related to polarization changes in scattering as well as propagation. In order to handle the polarization and phase information simultaneously in a consistent manner, we define a new number, phasor quaternion (PQ), by combining quaternion and complex amplitude, with which we construct the theory of PQ neural networks (PQNNs). Experiments demonstrate that the proposed PQNN filter compensates SPs very effectively. Even in the situations where the conventional methods deteriorate in their performance, it realizes accurate compensation, thanks to its good generalization characteristics in integrated Poincare-sphere polarization space and the complex-amplitude space. We find that PQNN is an excellent framework to deal with the polarization and phase of electromagnetic wave adaptively and consistently.
关键词： Complex-valued neural network (CVNN),phase singular point,polarimetric interferometric synthetic aperture radar (PolInSAR),quaternion neural network (QNN),digital elevation model (DEM)
更新于2025-09-23 15:22:29
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[IEEE 2018 41st International Conference on Telecommunications and Signal Processing (TSP) - Athens, Greece (2018.7.4-2018.7.6)] 2018 41st International Conference on Telecommunications and Signal Processing (TSP) - Field Trial of Alien Wavelengths on GARR Optical Network
摘要： At the Italian national level, GARR optical network is composed of two separate optical network domains. With the aim to integrate them we decided to use the alien wavelength technique. This is a hybrid solution based on the transmission and reception of optical signals, called alien wavelengths, that are transported on an infrastructure that is different from the one that generated them. We tested the technique first on a field trial, on two portions of our network of about 350 and 1200km long in order to measure the achievable performance. Based on the successful results we then implemented such technique on the production environment on about 3000km to deliver high-performance services. In this way we improved the overall efficiency of the Italian research and education network in a cost-effective way. This paper describes the overall activity, results and our experience in integrating the alien wavelengths in a production environment, with a special emphasis on deployment and operational issues.
关键词： Network Disaggregation,Open Network,Optical Networking,Alien Wavelengths,Field trial,DWDM
更新于2025-09-23 15:22:29
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[IEEE 2018 3rd International Conference on Mechanical, Control and Computer Engineering (ICMCCE) - Huhhot (2018.9.14-2018.9.16)] 2018 3rd International Conference on Mechanical, Control and Computer Engineering (ICMCCE) - A Remote Sensing Image Key Target Recognition System Design Based on Faster R-CNN
摘要： Aiming at the problem of traditional low-level recognition of key targets in remote sensing images, a method for target detection and recognition based on Faster R-CNN is proposed. Firstly, the open source remote sensing image data set NWPU VHR-10 dataset is converted into VOC 2007 format as the training sets and test sets. Secondly, according to the training set category information, the hyper-parameters of the neural network are refined, and then the training set is trained using the Faster R-CNN neural network to generate a model. Finally, this model is used to detect unknown remote sensing images and identify important targets. The simulation results show that the method has high recognition accuracy and speed, and can provide reference for recognition of the key targets of remote sensing images.
关键词： Faster R-CNN,convolution neural network,deep learning,key target recognition,remote sensing image detection
更新于2025-09-23 15:22:29
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[IEEE 2018 10th International Conference on Modelling, Identification and Control (ICMIC) - Guiyang (2018.7.2-2018.7.4)] 2018 10th International Conference on Modelling, Identification and Control (ICMIC) - Automatic Segmentation and 3D Reconstruction of Spine Based on FCN and Marching Cubes in CT Volumes
摘要： The spine is of great significance in the course of radiotherapy. The accurate location of the spine can provide reference for the determination of the tumor target area and the endanger organ in the radiotherapy plan. However, for some low-resolution areas of CT images, traditional methods cannot achieve a good segmentation effect. Due to the lack of data marked by doctors, there are few studies on the use of deep learning methods for segmentation of the spine. We use threshold segmentation and manual labeling methods to make our own data sets. This article combines the Fully Convolutional Neural Network (FCN) and the Marching Cubes (MC) algorithms to automatically segment and reconstruct the spine in the CT images. And we improved the network structure of FCN because FCN finally lost many details in one step down sampling. In the study, we used data from 40 patients, of which 30 were for training and 10 for testing. The final segmentation accuracy of the improved network is over 93%. The experimental results show that this method has a good segmentation effect and can better restore the shape of the spine and ribs. This preliminary result showed that our spine segmentation method had a great potential to reduce human efforts in labeling CT images in radiation therapy.
关键词： Fully Convolutional Neural Network,Spine,Medical Image,Marching Cubes
更新于2025-09-23 15:22:29
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[IEEE 2018 2nd International Conference on Data Science and Business Analytics (ICDSBA) - Changsha, China (2018.9.21-2018.9.23)] 2018 2nd International Conference on Data Science and Business Analytics (ICDSBA) - Detection of Diabetic Retinopathy Images Using a Fully Convolutional Neural Network
摘要： The paper discusses the development and application of a convolutional neural network (CNN) model for digital image processing in the context of data science and business analytics. It focuses on improving the accuracy and efficiency of image classification tasks.
关键词： Image Classification,Digital Image Processing,Business Analytics,Data Science,Convolutional Neural Network
更新于2025-09-23 15:22:29
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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) - Supporting Low-Latency Applications through Hybrid Cost-Optimised Cloudlet Placement
摘要： Low-latency applications such as augmented reality, cognitive assistance, and context-aware computation are better supported by augmenting cloud networks with cloudlet enabled edge computing solution. This overcomes the high-transmission latency arising from the edge devices in the access segment to cloud servers located in the core. In this talk, we will review our recently proposed hybrid cost-optimization framework for optimal cloudlet placement over existing passive optical access networks, subject to capacity and latency constraints. In our work, we formulate a mixed-integer non-linear program to identify ideal locations (either at the central office (CO), remote node (RN), or in the field) for cloudlet placement over three deployment areas of differing population densities. Our results point to the fact that the installation of more RN and CO-located cloudlets will yield an improved cost optimal solution than the installation of field cloudlets alone, and that the percentage of the incremental energy budget arising from the installation of cloudlets are low.
关键词： non-linear mixed-integer programming,passive optical access network,cloud computing,low-latency,Tactile Internet,cloudlets,cost-optimization
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 - Desnet: Deep Residual Networks for Descalloping of Scansar Images
摘要： Scalloping is one of the critical problems in ScanSAR images. It not only affects image visualization, but also influences the quantitative applications such as surface wind and wave retrievals in the ocean area. The existing method of descalloping needs artificial parameter setting and lacks generality in the image domain. A novel deep neural network based on residual learning for descalloping of ScanSAR images is proposed in this paper. The proposed method can eliminate scalloping patterns and has strong adaptive ability, which can handle inhomogeneous scalloping patterns and different scenarios. Experiments on GF-3 ScanSAR images verify the good performance of this method. The code for our models is available online.
关键词： synthetic aperture radar (SAR),deep neural network,scalloping patterns,ScanSAR,Residual learning
更新于2025-09-23 15:22:29
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Compensation of the fluctuations of differential delay for frequency transfer in DWDM networks
摘要： This paper investigates the possibility of improving the stability of radio frequency transfer in telecommunication Dense Wavelength Division Multiplexing (DWDM) fiber optic networks. As it has been identified, the dispersion compensation in these networks, cause fibers (DCFs), frequently used temperature-induced substantial differential delay, whose fluctuations have the most significant impact on deterioration of the stability of frequency transfer. The authors present a method that allows achieving significant improvement of the long-term stability of frequency transfer. The developed method is based on modeling the impact of DCFs with the help of remotely accessible temperature sensors factory-installed by the manufactures in DCF modules. The effectiveness of the proposed solution has been tested on three different long haul routes (up to 1550 km), set up in the operational PIONER network.
关键词： frequency transfer,alien wavelength,DWDM network,optical fiber
更新于2025-09-23 15:22:29
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[IEEE 2018 IEEE International Conference on Imaging Systems and Techniques (IST) - Krakow (2018.10.16-2018.10.18)] 2018 IEEE International Conference on Imaging Systems and Techniques (IST) - Robust Estimation of Product Amount on Store Shelves from a Surveillance Camera for Improving On-Shelf Availability
摘要： This paper proposes a method to robustly estimate product amount on store shelves from a surveillance camera for improving on-shelf availability. We focus on changes of products on the shelves such as “product taken (decrease)” and “product replenished/returned (increase)”, and compute product amount by accurately accumulating them. The proposed method first detects change regions of products on the shelves in an image using background subtraction followed by moving object removal. The detected change regions are then classified into several classes representing the actual changes on the shelves such as “product taken” by using convolutional neural networks. Finally, the changes of products on the shelves are accumulated using classification results, and product amount on the shelves visible in the image is computed as on-shelf availability. Experimental results using two videos captured in a real store show that our method achieves success rate of 89.6% for on-shelf availability when an error margin is within one product. With high accuracy, store clerks can keep high on-shelf availability, enabling the improvement of business profit in retail stores.
关键词： product amount,image processing,on-shelf availability,convolutional neural network,surveillance camera,retail,background subtraction
更新于2025-09-23 15:22:29
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Innovation capability, network embeddedness and economic performance: profiling solar power innovators in China
摘要： This paper discusses the technological upgrading of China in photovoltaics technology. It explores the patterns of innovation and network embeddedness and their impact on economic performance at the firm level. Identifying the main innovators over 1995–2014 with patent and market share indicators, the landscape of their activities is inspected through two hierarchical cluster analyses in parallel: first, against the quantity, quality and diversity of patents, and second, against global-integration, component-size and position in technological knowledge networks. The resulting clusters are cross-related to understand their interrelations with age, size, turnover and productivity of actors. The multivariate analysis of variance shows a significant relationship between turnover and productivity. Global-integration in small-world networks is significantly related with economic performance. Quality of innovation shows higher importance than quantity and diversity. While specialisation in high-tech fields has positive impact on turnover, production-oriented firms with low-tech focus have higher productivity.
关键词： patent profiles,MANOVA,productivity,cluster analysis,concurrency matrix,technological upgrading,China,emerging economy,innovation system,solar photovoltaics,economic performance,network embeddedness patterns
更新于2025-09-23 15:22:29