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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
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- Hubei University
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An Energy Efficient Clustering using Firefly and HML for Optical Wireless Sensor Network
摘要： An Energy Efficient Clustering using Firefly and HML for Optical Wireless Sensor Network. In this paper an energy efficient and dynamic cluster formation technique is proposed. Cluster formation is the first and the main priority for any Wireless Sensor Network (WSN). In our work, the proposed technique is combination of Firefly algorithm and Hierarchical Maximum Likelihood (HML) for an Optical Wireless Sensor Network (OWSN). It utilizes the unique property of Firefly algorithm and overcomes the problem of it by introducing HML in it. As a result the parameters of the network changes with respect to the requirement of suitable position of nodes. The power distribution in the nodes is also accurately done as HML works on the property of maximum likelihood property. It means the nodes become active when they are selected based on the closest value of source node to form a cluster. The cost function also minimized.
关键词： Energy Efficient,Optical Wireless Sensor Network (OWSN),Firefly Algorithm,Cluster,HML
更新于2025-09-23 15:22:29
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Task-Oriented GAN for PolSAR Image Classification and Clustering
摘要： Based on a generative adversarial network (GAN), a novel version named Task-Oriented GAN is proposed to tackle difficulties in PolSAR image interpretation, including PolSAR data analysis and small sample problem. Besides two typical parts in GAN, i.e., generator (G-Net) and discriminator (D-Net), there is a third part named TaskNet (T-Net) in the Task-Oriented GAN, where T-Net is employed to accomplish a certain task. Two tasks, PolSAR image classification and clustering, are studied in this paper, where T-Net acts as a Classifier and a Clusterer, respectively. The learning procedure of Task-Oriented GAN consists of two main stages. In the first stage, G-Net and D-Net vie with each other like that in a general GAN; in the second stage, G-Net is adjusted and oriented by T-Net so that more samples, which are benefit for the task and called fake data, are generated. As a result, Task-Oriented GAN not only has the advantage of GAN (no-assumption data modeling) but also overcomes the disadvantage of GAN (task-free). After learning, fake data are employed to enrich training set and avoid overfitting; so Task-Oriented GAN performs well even if the manual-labeled data are small. To verify the effectiveness of T-Net, a visualized comparison is provided, where some fake digits generated from Task-Oriented GAN are illustrated along with that from GAN. What is more, considering that there is a great difference between PolSAR data and general data, in our PolSAR image classification and clustering tasks, the specific PolSAR information is inserted into the structure of the Task-Oriented GAN. This enables researchers to mine inherent information in PolSAR data without any data hypothesis and find ways for small sample problem at the same time. Experiment results tested on three PolSAR images show that the proposed method performs well in dealing with PolSAR image classification and clustering.
关键词： generative adversarial network (GAN),task-oriented,Clustering,PolSAR image classification
更新于2025-09-23 15:22:29
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[IEEE 2018 10th International Conference on Wireless Communications and Signal Processing (WCSP) - Hangzhou (2018.10.18-2018.10.20)] 2018 10th International Conference on Wireless Communications and Signal Processing (WCSP) - Dynamic Hand Gesture Recognition Using FMCW Radar Sensor for Driving Assistance
摘要： Dynamic hand gesture recognition is very important for human-computer interaction. In vehicles, hand gesture recognition can be used as the driver's auxiliary system to achieve remote control of the instrument. To a certain extent, this system can avoid physical buttons and touch screens causing interference to the driver. In this paper, we describe a driver-assisted dynamic gesture recognition system to classify nine hand gestures based on micro-Doppler signatures obtained by 77GHz FMCW radar using a convolutional neural network (CNN). We further explore the changes in the accuracy of same gestures in a variety of experimental scenarios to help optimize the robustness of the system.
关键词： convolutional neural network,hand gesture recognition,driver assistance system,FMCW radar sensor
更新于2025-09-23 15:22:29
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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) - Adversarial Domain Adaptation with a Domain Similarity Discriminator for Semantic Segmentation of Urban Areas
摘要： Existing semantic segmentation models of urban areas have shown to perform well in a supervised setting. However, collecting lots of annotated images from each city to train such models is time-consuming or difficult. In addition, when transferring the segmentation model from the trained city (source domain) to an unseen city (target domain), the performance will largely degrade due to the domain shift. For this reason, we propose a domain adaptation method with a domain similarity discriminator to eliminate such domain shift in the framework of adversarial learning. Contrary to the single-input adversarial network, our domain similarity discriminator, which consists of a Siamese network, is able to measure the similarity of the pairwise-input data. In this way, we can use more information about the pairwise-input to measure the similarity between different distributions so as to address the problem of domain shift. Experimental results demonstrate that our approach outperforms the competing methods on three different cities.
关键词： domain adaptation,urban areas,semantic segmentation,domain shift,Siamese network
更新于2025-09-23 15:22:29
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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) - Dense Deconvolutional Network for Semantic Segmentation
摘要： Recently, exploring multiple feature maps from different layers in fully convolutional networks (FCNs) has gained substantial attention to capture context information for semantic segmentation. This paper presents a novel encoder-decoder architecture, called dense deconvolutional network (DDN), for semantic segmentation, where the feature maps of deeper convolutional layers are densely upsampled for the shallow deconvolutional layers. The proposed DDN is trainable end-to-end, and allows us to fully investigate multiple scale context cues embedded in images. The experimental results show that our DDN outperforms previous FCNs and encoder-decoder networks (EDNs) on PASCAL VOC 2012 dataset.
关键词： FCNs,EDNs,Semantic Segmentation,Dense Deconvolutional 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) - 90-GHz Linear-Cell Systems for Public Transportation Systems
摘要： This paper reviews 90-GHz high-speed wireless links and high-resolution radars based on linear-cell systems consisting of radio-over-fibre for waveform distribution. We focus on applications for public transportation systems including airport runways, railways, etc.
关键词： radio-wave communications,optical network,radio-over-fibre,optical fibre,high-speed train
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 - Fully Convolutional Network with Polarimetric Manifold for SAR Imagery Classification
摘要： Image classification performance depends on the understanding of image features and classifier selection. Owing to the special imaging mechanism, achieving precise classification for remote sensing imagery is still quite challenging. In this paper, a fully convolutional network with polarimetric manifold, is proposed for Synthetic Aperture Radar (SAR) image classification. First, the polarimetric features are extracted to describe the target information; then the feature points in high-dimension are mapped to low-dimension through the manifold structure. In this way, the effect of single manifold is equal to that of multi-layer convolution. The experimental results on SAR image data indicate that the presented manifold network can effectively separate the polarimetric features and improve the classification accuracy.
关键词： manifold structure,Synthetic Aperture Radar (SAR),image classification,convolution network
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 - Reconstruction of Full-Pol SAR Data from Partialpol Data Using Deep Neural Networks
摘要： We propose a deep neural networks based method to reconstruct full polarimetric (full-pol) information from single polarimetric (single-pol) SAR data. It consists of two parts: feature extractor which is used to obtain multi-scale multi-layer features of targets in single-pol gray image, and feature translator that converts the geometric features to defined polarimetric feature space. The proposed method is demonstrated on L-band UAVSAR of NASA/JPL images over San Diego, CA, and New Orleans LA, USA. Both qualitative and quantitative results show the reconstructed full-pol images agree well with true full-pol images, the proposed networks have a good spatial robustness. Model-based target decomposition and unsupervised classification can be used directly on constructed full-pol images.
关键词： Deep Neural Network,unsupervised classification,Polarimetric Synthetic Aperture Radar (PolSAR),SAR image colorization
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) - Studying an Optimal Approach to Design Combined Fiber-Wireless Telecom Systems
摘要： We proposed a new approach to design interface hardware for next-generation telecom systems of combined fiber-wireless architecture based on nonlinear behavioral modeling in widespread off-the-shelf CAD tool NI AWRDE. To validate the approach, we research in detail a prospective base station operating in EU-assigned band (3.4-3.8 GHz) of 5G networks, which has easy-to-configure layout using the same cost- and power-efficient LW-VCSEL in downlink and uplink channels and direct modulation of LW-VCSEL in period-doubling state.
关键词： fronthaul fiber-wireless architecture,vertical cavity surface-emitting laser,base station,fifth-generation telecom network,computer-aided design
更新于2025-09-23 15:22:29
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Microfluidic Sensors with Impregnated Fluorophores for Simultaneous Imaging of Spatial Structure and Chemical Oxygen Gradients
摘要： Interior surfaces of polystyrene microfluidic structures were impregnated with the oxygen sensing dye Pt(II) tetra(pentafluorophenyl)porphyrin (PtTFPP) using a solvent-induced fluorophore impregnation (SIFI) method. Using this technique, microfluidic oxygen sensors are obtained that enable simultaneous imaging of both chemical oxygen gradients and the physical structure of the microfluidic interior. A gentle method of fluorophore impregnation using acetonitrile solutions of PtTFPP at 50oC was developed leading to a 10-μm-deep region containing fluorophore. This region is localized at the surface to sense oxygen in the interior fluid during use. Regions of the device that do not contact the interior fluid pathways lack fluorophores and are dark in fluorescent imaging. The technique was demonstrated on straight microchannel and pore network devices, the latter having pillars of 300 μm diameter spaced center to center at 340 μm providing pore throats of 40 μm. Sensing within channels or pores, and imaging across the pore network devices were performed using a Lambert LIFA-P frequency domain fluorescence lifetime imaging system on a Leica microscope platform. Calibrations of different devices prepared by the SIFI method were indistinguishable. Gradient imaging showed fluorescent regions corresponding to the fluid pore network, dark pillars, and fluorescent lifetime varying across the gradient, thus providing both physical and chemical imaging. More generally, the SIFI technique can impregnate the interior surfaces of other polystyrene containers, such as cuvettes or cell and tissue culture containers, to enable sensing of interior conditions.
关键词： Oxygen,sensor,impregnation,fluorophore,chemical imaging,pore network,polystyrene,microfluidic
更新于2025-09-23 15:22:29