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- 2018
- Conditional Random Fields (CRF)
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[IEEE 2018 Eleventh International Conference "Management of large-scale system development" (MLSD 2018) - Moscow (2018.10.1-2018.10.3)] 2018 Eleventh International Conference "Management of large-scale system development" (MLSD - Optimization of the Structure and Parameters of the Light Cycle Aimed at Improving Traffic Safety at an Intersection
摘要： Formulas for evaluating structural safety for an arbitrary scheme of the road network segment are proposed. On their basis the problem of structural optimization of the traffic light cycle aimed at improving traffic safety is set. The similar optimization problem with respect to traffic intensity is set as well. Approaches to their solution are offered.
关键词： scheme of phase separation,microscopic models of traffic flows,optimization,safety of the road network segment,controlled intersection,computational experiments
更新于2025-09-23 15:22:29
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[IEEE 2018 OCEANS - MTS/IEEE Kobe Techno-Ocean (OTO) - Kobe (2018.5.28-2018.5.31)] 2018 OCEANS - MTS/IEEE Kobe Techno-Oceans (OTO) - DEMON Spectrum Extraction Method Using Empirical Mode Decomposition
摘要： The noise radiated by a ship is modulated at a rate dictated by some parameters of the propeller and engine (number of blades, rotational speed). Evaluation of that modulation provides information on the ship, such as the shaft rotation frequency, that can be used for ship classification. The method for estimation of the envelope modulation is known as DEMON (Detection of Envelope Modulation on Noise). Traditionally, the ship noise is bandpass filtered in different frequency bands before the envelope analysis. The bandwidth and the number of the bandpass filters is not known. In this paper a new DEMON spectrum extraction method is proposed using empirical mode decomposition (EMD), in which the band number and width are automatically determined. In performance test, a feedforward neural network is used for 5 kinds ship noise classification, and the percentage of correct classification reaches 91.6%.
关键词： DEMON,empirical mode decomposition,Detection of envelope modulation on noise,feedforward neural network,EMD
更新于2025-09-23 15:22:29
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Fully automated detection of retinal disorders by image-based deep learning
摘要： Purpose With the aging population and the global diabetes epidemic, the prevalence of age-related macular degeneration (AMD) and diabetic macular edema (DME) diseases which are the leading causes of blindness is further increasing. Intravitreal injections with anti-vascular endothelial growth factor (anti-VEGF) medications are the standard of care for their indications. Optical coherence tomography (OCT), as a noninvasive imaging modality, plays a major part in guiding the administration of anti-VEGF therapy by providing detailed cross-sectional scans of the retina pathology. Fully automating OCT image detection can significantly decrease the tedious clinician labor and obtain a faithful pre-diagnosis from the analysis of the structural elements of the retina. Thereby, we explore the use of deep transfer learning method based on the visual geometry group 16 (VGG-16) network for classifying AMD and DME in OCT images accurately and automatically. Method A total of 207,130 retinal OCT images between 2013 and 2017 were selected from retrospective cohorts of 5319 adult patients from the Shiley Eye Institute of the University of California San Diego, the California Retinal Research Foundation, Medical Center Ophthalmology Associates, the Shanghai First People’s Hospital, and the Beijing Tongren Eye Center, with 109,312 images (37,456 with choroidal neovascularization, 11,599 with diabetic macular edema, 8867 with drusen, and 51,390 normal) for the experiment. After images preprocessing, 1000 images (250 images from each category) from 633 patients were selected as validation dataset while the rest images from another 4686 patients were used as training dataset. We used deep transfer learning method to fine-tune the VGG-16 network pre-trained on the ImageNet dataset, and evaluated its performance on the validation dataset. Then, prediction accuracy, sensitivity, specificity, and receiver-operating characteristic (ROC) were calculated. Results Experimental results proved that the proposed approach had manifested superior performance in retinal OCT images detection, which achieved a prediction accuracy of 98.6%, with a sensitivity of 97.8%, a specificity of 99.4%, and introduced an area under the ROC curve of 100%. Conclusion Deep transfer learning method based on the VGG-16 network shows significant effectiveness on classification of retinal OCT images with a relatively small dataset, which can provide assistant support for medical decision-making. Moreover, the performance of the proposed approach is comparable to that of human experts with significant clinical experience. Thereby, it will find promising applications in an automatic diagnosis and classification of common retinal diseases.
关键词： Diabetic macular edema,Visual geometry group 16 network,Age-related macular degeneration,Optical coherence tomography,Deep transfer learning
更新于2025-09-23 15:22:29
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[IEEE 2018 International Conference on Radar (RADAR) - Brisbane, Australia (2018.8.27-2018.8.31)] 2018 International Conference on Radar (RADAR) - Improved Tx-to-Rx Isolation of Radar Transceivers Using Integrated Full Duplexer with PLL
摘要： The isolation between transmitter and receiver for the radars is ultimately important since the transmitting radar signals can be penetrated the receiver directly in case a bad isolation is formed. This paper describes the isolation between the transceiver and the design of an integrated full duplexer using the phase locked loop (PLL). Although the antenna impedance varies arbitrarily, the PLL tracks the impedance variation in real time, leading to improvement of isolation between the transmitter and the receiver of radars. The full duplexer reduces the transmitter leakage up to 45 dB using the balance network along with the PLL in measurement.
关键词： Transceiver,Impedance tracking,PLL,Balanced network,CMOS process,Integrated Full Duplexer
更新于2025-09-23 15:22:29
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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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60-GHz 2-D Scanning Multibeam Cavity Backed Patch Array fed by Compact SIW Beam-Forming Network for 5G Applications
摘要： This paper presents a 2x2 multibeam array employing a novel wideband linearly polarized cavity-backed patch antenna for fifth generation (5G) wireless communication technology. The antenna array comprises four layers stacked on top of one another. The proposed antenna element is composed of three substrate layers and excited using a substrate integrated waveguide (SIW) aperture-coupled feed. The antenna element exhibits -10-dB impedance bandwidth of 36.2% from 53 to 76.4 GHz with very flat gain and excellent radiation characteristics. A prototype is fabricated and tested. The compact 2-D scanning multibeam 2x2 antenna array is demonstrated at 60-GHz, where the proposed antenna element is utilized as a radiating element. By employing a novel compact beam forming network (BFN) in the design, the proposed array achieves a size reduction of better than 28% compared to an array fed with conventional BFN, without degradation in the array performance. A wide bandwidth larger than 27 % for |S11|<-10 dB and a peak gain of 12.4 dBi is achieved. The array shows a good symmetrical radiation pattern in the two perpendicular planes. Verified by prototype measurements, the proposed antenna element and multibeam array with features of a compact structure, low cost, wide bandwidth and superior radiation performance, would be practically attractive for 5G applications.
关键词： fifth generation (5G),linear polarization,60 GHz,SIW,beam-forming network,multibeam antenna
更新于2025-09-23 15:22:29
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Visualizing Interactions of Circulating Tumor Cell and Dendritic Cell in the Blood Circulation Using In Vivo Imaging Flow Cytometry
摘要： Objective: Visualizing cell interactions in blood circulation is of great importance in studies of anticancer immunotherapy or drugs. However, the lack of a suitable imaging system hampers progress in this field. Methods: In this work, we built a dual-channel in vivo imaging flow cytometer to visualize the interactions of circulating tumor cells (CTCs) and dendritic cells (DCs) simultaneously in the bloodstream. Two artificial neural networks were trained to identify blood vessels and cells in the acquired images. Results and Conclusion: Using this technique, single CTCs and CTC clusters were readily distinguished by their morphology. Interactions of CTCs and DCs were identified, while their moving velocities were analyzed. The CTC-DC clusters moved at a slower velocity than that of single CTCs or DCs. This may provide new insights into tumor metastasis and blood rheology. Significance: This in vivo imaging flow cytometry system holds great potential for assessing the efficiency of targeting CTCs with anticancer immune cells or drugs.
关键词： Cell Interaction,Circulating Tumor Cell,In Vivo Imaging Flow Cytometry,Artificial Neural Network,Dendritic Cell
更新于2025-09-23 15:22:29
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Multi-level Features Convolutional Neural Network for Multi-focus Image Fusion
摘要： Multi-focus image fusion is an important technique that aims to generate a single clean image by fusing multiple input images. In this paper, we propose a novel multi-level features convolutional neural network (MLFCNN) architecture for image fusion. In the MLFCNN model, all features learned from previous layers are passed to the subsequent layer. Inside every path between the previous layer and the subsequent layer, we add a 1x1 convolution module to reduce the redundancy. In our method, the source images first are fed to our pre-trained MLFCNN model to obtain the initial focus map. Then, the initial focus map is performed by morphological opening and closing operations and followed by a Gaussian filter to obtain the final decision map. Finally, the fused all-in-focus image is generated based on a weighted-sum strategy with the decision map. The experimental results demonstrate that the proposed method outperforms some state-of-the-art image fusion algorithms in terms of both qualitative and objective evaluations.
关键词： convolutional neural network,decision map,multi-focus image fusion,multi-level features
更新于2025-09-23 15:22:29
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A Novel Patch Variance Biased Convolution Neural Network for No-Reference Image Quality Assessment
摘要： Deep Convolutional Neural Networks (CNNs) have been successfully applied on no-reference image quality assessment (NR-IQA) with respect to human perception. Most of these methods deal with small image patches and use the average score of the test patches for predicting the whole image quality. We discovered that image patches from homogenous regions are unreliable for both neural network training and final image quality score estimation. In addition, image patches with complex structures have much higher chances to achieve better image quality prediction. Based on these findings, we enhanced the conventional CNN-based NR-IQA algorithm to avoid homogenous patches for the network training and quality score estimation. Moreover, we also use a variance-based weighting average to bias the final image quality score to the patches with complex structure. Experimental results show that this simple approach can achieve state-of-the-art performance as compared with well-known NR-IQA algorithms.
关键词： deep learning,no-reference image quality assessment,convolution neural network
更新于2025-09-23 15:22:29
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Robust Landmark Detection and Position Measurement Based on Monocular Vision for Autonomous Aerial Refueling of UAVs
摘要： In this paper, a position measurement system, including drogue's landmark detection and position computation for autonomous aerial refueling of unmanned aerial vehicles, is proposed. A multitask parallel deep convolution neural network (MPDCNN) is designed to detect the landmarks of the drogue target. In MPDCNN, two parallel convolution networks are used, and a fusion mechanism is proposed to accomplish the effective fusion of the drogue's two salient parts' landmark detection. Considering the drogue target's geometric constraints, a position measurement method based on monocular vision is proposed. An effective fusion strategy, which fuses the measurement results of drogue's different parts, is proposed to achieve robust position measurement. The error of landmark detection with the proposed method is 3.9%, and it is obviously lower than the errors of other methods. Experimental results on the two KUKA robots platform verify the effectiveness and robustness of the proposed position measurement system for aerial refueling.
关键词： landmark detection,multitask parallel deep convolution neural network (MPDCNN),monocular vision,position measurement,Aerial refueling
更新于2025-09-23 15:22:29