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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
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- Ocean University of China
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Dynamic spectrum nonlinear modeling of VIS & NIR band based on RBF neural network for noninvasive blood component analysis to consider the effects of scattering
摘要： Dynamic spectrum (DS) is expected to achieve non-invasive analysis of blood components by extracting the absorbance of arterial blood at multiple wavelengths. However, the nonlinearity caused by scattering of blood components is still a factor that limits the detection accuracy. According to the idea of “overlay modeling” in the “M + N” theory, theoretically, the consideration of nonlinear factor in modeling analysis can further improve the prediction accuracy of calibration model. But there is currently no recognized formula to describe this nonlinear relationship. In this paper, the ability of RBF neural network to approximate arbitrary nonlinear functions with arbitrary precision is used to approximate the nonlinear relationship between the spectrum and the component concentration from the perspective of ?tting. The calibration sets and prediction sets were randomly selected from the VIS & NIR band DS data of 231 volunteers, and 10 groups of modeling experiments were carried out. The results showed that compared with the conventional partial least squares (PLS) modeling method, the modeling indicators (correlation coe?cient (R) and root mean square error(RMSE)) of prediction set using radial basis function (RBF) neural network modeling have been signi?cantly improved. The modeling experiments suggest that the nonlinearity caused by scattering should not be ignored in DS non-invasive blood component analysis. By ?tting the nonlinear relationship, RBF neural network can better re?ect the actual mapping relationship between the spectrum and the component concentration, because it can not only consider the general part (Linear factor) in DS, but also the details (Nonlinear factor), which can e?ectively improve the accuracy of the non-invasive blood component analysis based on DS.
关键词： Scattering,Nonlinearity,RBF neural network,Dynamic spectrum (DS),Non-invasive detection
更新于2025-09-23 15:23:52
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[IEEE 2018 12th International Conference on Communications (COMM) - Bucharest (2018.6.14-2018.6.16)] 2018 International Conference on Communications (COMM) - Circularly Polarized Array Antenna with Emphasis on the Reduction of RCS by Utilizing Semi-Fractal Elements
摘要： In this paper, a unique broadband circularly polarized (CP) array antenna by modified feed network is presented. Employing wide band CP semi-fractal aperture coupled structure causes the creation of innovation at CP antenna array with emphasis on reducing of radar cross section (RCS). The 3 dB axial-ratio of the array extends to approximately 1.23 GHz with an impedance bandwidth of 24.77%. An acceptable agreement between the simulation and measured results validates the proposed design.
关键词： semi-fractal,feed network,Circularly Polarized,RCS
更新于2025-09-23 15:23:52
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[IEEE 2018 IEEE 3rd International Conference on Image, Vision and Computing (ICIVC) - Chongqing (2018.6.27-2018.6.29)] 2018 IEEE 3rd International Conference on Image, Vision and Computing (ICIVC) - Research on Single-Frame Super-Resolution Reconstruction Algorithm for Low Resolution Cell Images Based on Convolutional Neural Network
摘要： At the problem of low resolution and low contrast of cell images collected by lens-less cell imaging system, a novel cell super-resolution reconstruction network (CSRN) based on convolutional neural network is proposed. First, the cell image is collected by lens-less cell imaging system, and then the cell image is down-sampled by bicubic interpolation to obtain low-resolution cell image. Then, the low-resolution cell image is input into the CSRN network for super-resolution reconstruction. The experimental results show that the proposed CSRN network can effectively improve the resolution and contrast of cell images, and the reconstruction effect is better than that of the traditional bicubic interpolation and SRCNN network.
关键词： convolutional neural network,cell image,super-resolution,lens-less imaging
更新于2025-09-23 15:23:52
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Synthesis and characterization of nanofiber-type hydrophobic organic materials as electrodes for improved performance of PVDF-based piezoelectric nanogenerators
摘要： Poly(3,4-ethylenedioxythiophene) (PEDOT) derivatives are synthesized by oxidative polymerization using sodium dodecyl sulfate (SDS) as an anionic surfactant dopant. The resulting polymeric materials featuring nanofiber-type one-dimensional (1D) structures are identified as poly(2-butyl-2,3-dihydrothieno[3,4-b][1,4]dioxine:dodecyl sulfate (PEDOT-C4:DS) and poly(2-hexyl-2,3-dihydrothieno[3,4-b][1,4]dioxine:dodecyl sulfate (PEDOT-C6:DS). The ratio of the DS anion doped into PEDOT-C4:DS and PEDOT-C6:DS is 0.16 and 0.23, respectively. The contact angle of water on the PEDOT-C4:DS and PEDOT-C6:DS films is 76.6° and 87.7°, respectively, showing hydrophobic properties similar to that with water on PVDF. It facilitated the fully uniform film formation due to excellent surface matching. Peeling force of PEDOT-C4:DS and PEDOT-C6:DS is stronger than the one of PEDOT:PSS-CNT composite. GIWAX analysis showed that PEDOT-C4:DS formed the highly ordered edge-on structure and PEDOT-C6:DS formed the bimodal orientation consisting of edge-on structure mainly and face-on structure slightly. The electrical conductivity (σPEDOT-C4:DS=50.0 S cm-1) of PEDOT-C4:DS is 41.7 times higher than that of PEDOT:PSS (σPEDOT:PSS=1.2 S cm-1). The output signals (maximum voltages/currents) of piezoelectric nanogenerators (PNGs, electrode/PVDF/electrode) using these materials as electrodes are PNG-1 (PEDOT:PSS-CNT composite) 1.25 V/128.5 nA, PNG-2 (PEDOT-C4:DS) 1.54 V/166.0 nA, and PNG-3 (PEDOT-C6:DS) 1.49 V/159.0 nA. Of these, PNG-2 & PNG-3 show maximum piezoelectric output power of 63.0 nW and 59.9 nW at 9 MΩ compared to PNG-1 (41.0 nW at 10 MΩ). They are enhanced up to 53.7%. The excellent surface matching between a piezoelectric active material and an electrode material leads to high output power.
关键词： 4-ethylenedioxythiophene) derivative,poly(3,nanofibrillar network,piezoelectric nanogenerator,nanofiber,hydrophobicity
更新于2025-09-23 15:23:52
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An image segmentation method of a modified SPCNN based on human visual system in medical images
摘要： An image segmentation method of a modified simplified pulse-coupled neural network (MSPCNN) based on human visual system (HVS) is proposed for medical images. The method successfully determines the stimulus input of the MSPCNN according to the characteristics of PCNN and HVS. In order to accomplish the goal, we attempt to deduce the sub-intensity range of central neurons firing by introducing neighboring firing matrix Q and calculating intensity distribution range based on a new MSPCNN(NMSPCNN), and then reveal the way how sub-intensity range parameter Sint generates the stimulus input Sioij closer to HVS. Besides, we try to substitute the above stimulus input into the MSPCNN to extract more suitable lesions for medical images. In contrast to prevalent PCNN models, the MSPCNN has higher segmentation accuracy rates and lower computational complexity because of the parameter setting method. Finally, the proposed method comparing with the state-of-the-art methods has a better performance, presenting the overall metric OEM with MIAS of 0.8784, DDSM of 0.8606 and gallstones of 0.8585.
关键词： Sub-intensity Range,Modified Simplified Pulse-coupled Neural Network,Image Segmentation,Stimulus Input,Human Visual System
更新于2025-09-23 15:23:52
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Triplet-Based Semantic Relation Learning for Aerial Remote Sensing Image Change Detection
摘要： This letter presents a novel supervised change detection method based on a deep siamese semantic network framework, which is trained by using improved triplet loss function for optical aerial images. The proposed framework can not only extract features directly from image pairs which include multiscale information and are more abstract as well as robust, but also enhance the interclass separability and the intraclass inseparability by learning semantic relation. The feature vectors of the pixels pair with the same label are closer, and at the same time, the feature vectors of the pixels with different labels are farther from each other. Moreover, we use the distance of the feature map to detect the changes on the difference map between the image pair. Binarized change map can be obtained by a simple threshold. Experiments on optical aerial image data set validate that the proposed approach produces comparable, even better results, favorably to the state-of-the-art methods in terms of F-measure.
关键词： triplet loss function,Change detection,semantic relation,optical aerial images,siamese semantic network
更新于2025-09-23 15:23:52
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Imbalanced Learning-Based Automatic SAR Images Change Detection by Morphologically Supervised PCA-Net
摘要： Change detection is a quite challenging task due to the imbalance between unchanged and changed class. In addition, the traditional difference map generated by log-ratio is subject to the speckle, which will reduce the accuracy. In this letter, an imbalanced learning-based change detection is proposed based on PCA network (PCA-Net), where a supervised PCA-Net is designed to obtain the robust features directly from given multitemporal synthetic aperture radar (SAR) images instead of a difference map. Furthermore, to tackle with the imbalance between changed and unchanged classes, we propose a morphologically supervised learning method, where the knowledge in the pixels near the boundary between two classes is exploited to guide network training. Finally, our proposed PCA-Net can be trained by the data sets with available reference maps and applied to a new data set, which is quite practical in change detection projects. Our proposed method is veri?ed on ?ve sets of multiple temporal SAR images. It is demonstrated from the experiment results that with the knowledge in training samples from the boundary, the learned features bene?t change detection and make the proposed method outperform than supervised methods trained by randomly drawing samples.
关键词： Change detection,imbalance learning,synthetic aperture radar (SAR) images,PCA network (PCA-Net)
更新于2025-09-23 15:23:52
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Neural network-based hybrid signal processing approach for resolving thin marine protective coating by terahertz pulsed imaging
摘要： A novel approach was presented to enhance the capability of resolving thin coating layers using terahertz pulsed imaging (TPI) based on a neural network-based hybrid signal procession method, which is of great significance for in-line painting applications. In the present work, Terahertz detected signals were obtained by numerical simulation using finite difference time domain (FDTD) method. Models of marine protective coatings with different coating structures were calculated and analyzed. Different signal pre-processing techniques, including Fourier deconvolution, Fast Fourier Transform and wavelet analysis, were employed on the terahertz signals respectively to obtain various signal features. The processed signal was subsequently adopted as the input vectors for a neural network (NN). The optimization procedure for determining the architecture of neural network was investigated and the evaluated results obtained by the different networks were compared. Furthermore, the predicted results of thinner coating layer obtained by multiple-regression analysis method and BP network prediction method respectively were compared. The analysis demonstrated that the best prediction performance was achieved by neural network technique combined with wavelet analysis. Therefore, the hybrid signal processing approach could be recommended for terahertz non-destructive testing applications of marine protective coating.
关键词： Terahertz pulsed imaging,Non-destructive testing,Neural network,Thin marine protective coating
更新于2025-09-23 15:23:52
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[IEEE 2018 IEEE 3rd Optoelectronics Global Conference (OGC) - Shenzhen (2018.9.4-2018.9.7)] 2018 IEEE 3rd Optoelectronics Global Conference (OGC) - High Speed Novel Hybrid Modulation Technique of Visible Light Communication Based on Artificial Neural Network Equalizer
摘要： Visible light communication (VLC) which realizes data transmission and universal illumination simultaneously has attracted much attention recently. However, the transmission rate of the VLC remains low due to the low bandwidth performance and inter-symbol interference (ISI). Therefore, a hybrid approach using pulse amplitude modulation and pulse width modulation in conjunction with an artificial neural network (ANN) equalizer is proposed, which can theoretically increase the transmission rate by 4 times compared with the traditional way, and provide variable brightness to realize the integration of data transmission and illumination control. In addition, an artificial neural network equalizer is proposed to undo the effects of ISI, considering that the bandwidth of the LED is only 3MHz. Without the ANN equalizer, the maximum transmission rate of the proposed hybrid modulation link only reaches 36 Mbps under the condition of no signal processing; however, with the ANN equalizer, the transmission speed can up to 2.6 Gbps. The proposed system not only achieves a genuine combination of data transmission and control illumination levels, but also realizes a high data rate with less complexity.
关键词： code division multiple access,pulse amplitude modulation,visible light communication,artificial neural network equalizer
更新于2025-09-23 15:23:52
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SCN: Switchable Context Network for Semantic Segmentation of RGB-D Images
摘要： Context representations have been widely used to profit semantic image segmentation. The emergence of depth data provides additional information to construct more discriminating context representations. Depth data preserves the geometric relationship of objects in a scene, which is generally hard to be inferred from RGB images. While deep convolutional neural networks (CNNs) have been successful in solving semantic segmentation, we encounter the problem of optimizing CNN training for the informative context using depth data to enhance the segmentation accuracy. In this paper, we present a novel switchable context network (SCN) to facilitate semantic segmentation of RGB-D images. Depth data is used to identify objects existing in multiple image regions. The network analyzes the information in the image regions to identify different characteristics, which are then used selectively through switching network branches. With the content extracted from the inherent image structure, we are able to generate effective context representations that are aware of both image structures and object relationships, leading to a more coherent learning of semantic segmentation network. We demonstrate that our SCN outperforms state-of-the-art methods on two public datasets.
关键词： Context representation,convolutional neural network (CNN),RGB-D images,semantic segmentation
更新于2025-09-23 15:23:52