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- 摘要
- 关键词
- 实验方案
- 产品
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Identification performance of two luminescent lanthanide–organic frameworks
摘要： By the reaction of organic ligands, 2-(3,4-dimethylphenyl)-1H-imidazole-4,5-dicarboxylic acid (H3DMPhIDC) and terephthalic acid (H2DCB) with Eu3+ or Tb3+ ions, respectively, two luminescent metal-organic frameworks (MOFs) [Ln(H2DMPhIDC)(DCB)]n [Ln = Eu (1); Tb (2)] have been constructed. Both MOFs 1 and 2 are isostructural and show two-dimensional structures, which were fully characterized by single crystal X-ray diffraction, elemental analysis, and infrared spectroscopy. Both MOFs have excellent thermal and water stability, and indicate characteristic lanthanide metallic luminescence. Importantly, they can significantly recognize Fe3+ cation in aqoues solutions. Their sensing mechanisms have been suggested according to structural analyses, PXRD data and UV-Vis determinations.
关键词： recognition,crystal structure,luminescent MOFs,fluorescence quenching
更新于2025-09-23 15:23:52
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[Lecture Notes in Computer Science] Advances in Soft Computing Volume 10632 (16th Mexican International Conference on Artificial Intelligence, MICAI 2017, Enseneda, Mexico, October 23-28, 2017, Proceedings, Part I) || A Survey of Machine Learning Approaches for Age Related Macular Degeneration Diagnosis and Prediction
摘要： Age Related Macular Degeneration (AMD) is a complex disease caused by the interaction of multiple genes and environmental factors. AMD is the leading cause of visual dysfunction and blindness in developed countries, and a rising cause in underdeveloped countries. Currently, retinal images are studied in order to identify drusen in the retina. The classification of these images allows to support the medical diagnosis. Likewise, genetic variants and risk factors are studied in order to make predictive studies of the disease, which are carried out with the support of statistical tools and, recently, with Machine Learning (ML) methods. In this paper, we present a survey of studies performed in complex diseases under both approaches, especially for the case of AMD. We emphasize the approach based on the genetic variants of individuals, as it is a support tool for the prevention of AMD. According to the vision of personalized medicine, disease prevention is a priority to improve the quality of life of people and their families, as well as to avoid the inherent health burden.
关键词： Predictive diagnosis,Machine Learning,Classification,Automated diagnosis,Pattern recognition,AMD
更新于2025-09-23 15:23:52
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FRED-Net: Fully Residual Encoder-Decoder Network for Accurate Iris Segmentation
摘要： Iris recognition is now developed enough to recognize a person from a distance. The process of iris segmentation plays a vital role in maintaining the accuracy of the iris-based recognition systems by limiting the errors at the current stage. However, its performance is affected by non-ideal situations created by environmental light noise and user non-cooperation. The existing local feature-based segmentation methods are unable to find the true iris boundary in these non-ideal situations, and the error created at the segmentation stage traverses to all the subsequent stages, which results in reduced accuracy and reliability. In addition, it is necessary to segment the true iris boundary without the extra cost of denoising as preprocessing. To overcome these challenging issues during iris segmentation, a deep learning-based fully residual encoder-decoder network (FRED-Net) is proposed to determine the true iris region with the flow of high-frequency information from the preceding layers via residual skip connection. The main four impacts and significances of this study are as follows. First, FRED-Net is an end-to-end semantic segmentation network that does not use conventional image processing schemes, and does not have a preprocessing overhead. It is a standalone network in which eyelid, eyelash, and glint detections are not required to obtain the true iris boundary. Second, the proposed FRED-Net is the final resultant structure of a step-by-step development, and in each step, a new complete variant network is created for semantic segmentation considering the detailed descriptions of the networks. Third, FRED-Net uses the residual connectivity between convolutional layers by the residual shortcut for both encoder and decoder, which enables a high-frequency component to flow through the network and achieve higher accuracy with few layers. Fourth, the performance of the proposed FRED-Net is tested with five different iris datasets under visible and NIR light environments, and two general road scene segmentation datasets. To achieve fair comparisons with other studies, our trained FRED-Net models, along with the algorithms, are made publicly available through our website (Dongguk FRED-Net Model with Algorithm. accessed on 16 May 2018). The experiments include two datasets: Noisy Iris Challenge Evaluation - Part II (NICE-II) selected from the UBIRIS.v2 database and Mobile Iris Challenge Evaluation (MICHE-I), for the visible light environment and three datasets: Institute of Automation, Chinese Academy of Sciences (CASIA) v4.0 interval, v4.0 distance, and IIT Delhi v1.0, for the near-infrared (NIR) light environment. Moreover, to evaluate the performance of the proposed network in general segmentation, experiments with two famous road scene segmentation datasets: Cambridge-driving Labeled Video Database (CamVid) and Karlsruhe Institute of Technology and Toyota Technological Institute at Chicago (KITTI), are included. The experimental results showed the optimum performance of the proposed FRED-Net on the above-mentioned seven datasets of iris and general road scene segmentation.
关键词： iris segmentation,full residual encoder-decoder network,Iris recognition,semantic segmentation
更新于2025-09-23 15:23:52
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An automated shearography system for cylindrical surface inspection
摘要： A shearography system has been developed to inspect the external heat proof coating bonded to a cylinder. The system consists of a shearography device integrated with thermal excitation, a mechanical translation and rotation device, and a central control unit. The translation and the rotation are driven with 2 servo motors. The combination of these two movements enables full inspection of the entire surface of the cylinder. The inspection sequence is automatically scheduled by inputting the geometry of the sample. Artificial intelligence (AI) has been first introduced to aid defect recognition from the resulted phase shifting fringe patterns. A recognition algorithm based on deep learning has been developed using Faster R-CNN model for recognition of bonding defects. By training the system using typical butterfly fringe patterns which are captured from bonding samples, the system can accurately identify the bonding defects on the cylindrical surface at a high success rate.
关键词： Defect recognition,Automatic inspection,Shearography,NDT
更新于2025-09-23 15:23:52
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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) - Multiband Polarimetric SAR in Arctic Scenarios
摘要： In relation to a working group on future capabilities for applications in the Arctic region, the Danish Defence Acquisition and Logistics Organization (DALO) has conducted test campaigns with the multi-band, fully polarimetric F-SAR system owned by the German Aerospace Center (DLR) in order to explore the possibilities that advanced synthetic aperture radar (SAR) systems provide for surveillance, change detection, moving target identification and high resolution imaging. Examples of results and some preliminary conclusions are presented in this paper.
关键词： detection,SAR,polarimetry,surveillance,Arctic,recognition
更新于2025-09-23 15:23:52
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Human gait recognition based on deterministic learning and data stream of Microsoft Kinect
摘要： Gait is an important biometric technology for human identification at a distance. This study focuses on gait features obtained by Kinect and proposes a new model-based gait recognition method by combining deterministic learning theory and data stream of Microsoft Kinect. Deterministic learning theory is employed to capture the gait dynamics underlying Kinect-based gait parameters. Spatial-temporal gait features can be represented as the gait dynamics underlying the trajectories of spatial-temporal parameters, which can implicitly reflect the temporal changes of silhouette shape. Kinematic gait features can be represented as the gait dynamics underlying the trajectories of kinematic parameters, which can represent the temporal changes of body structure and dynamics. Both spatial-temporal and kinematic cues can be used separately for gait recognition using smallest error principle. They are fused on the decision level to improve the gait recognition performance. Additionally, we discuss how to eliminate the effect of view angle on the proposed method. Experimental results indicate that encouraging recognition accuracy can be achieved.
关键词： deterministic learning,Kinect-based gait features,Gait recognition,biometrics
更新于2025-09-23 15:23:52
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Adaptive Fuzzy Switching Noise Reduction Filter for Iris Pattern Recognition
摘要： Noise reduction is a necessary procedure for the iris recognition systems. This paper proposes an adaptive fuzzy switching noise reduction (AFSNR) filter to reduce noise for iris pattern recognition. The proposed low complexity AFSNR filter removes noise pixels by fuzzy switching between an adaptive median filter and the filling method. The threshold values of AFSNR filter are calculated on the basis of the histogram statistics of eyelashes, pupils, eyelids, and light illumination. The experimental results on the CASIA V3.0 iris database, with genuine acceptance rate equals 99.72%, show the success of the proposed method.
关键词： fuzzy switching median,iris normalization,eyelash detection,fuzzy weighted median,noise reduction,Iris pattern recognition
更新于2025-09-23 15:23:52
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Imaging analysis of chlorophyll fluorescence induction for monitoring plant water and nitrogen treatments
摘要： The objective of this study was to check whether different water and nitrogen treatments and, even the water-nitrogen coupling effect of plants could be correctly differentiated via chlorophyll a fluorescence image. We developed a classification method using the imaging analysis of chlorophyll a fluorescence induction based on Artificial Neural Network. The measurements were carried out on scheffera octophylla (Lour.) Harms, and the images were recorded at 690 nm with a high-resolution imaging device consisting of LEDs for an excitation at 460 nm and an Electron-Multiplying CCD camera. The effect of three different water and three different nitrogen treatments on the fluorescence parameters were obtained by hundreds of time-resolved fluorescence images. We used a Radial Basis Function neural network to model and test the sample data. The results showed that the different water and nitrogen statuses of plants were identified by the chlorophyll a fluorescence images and showed a high recognition accuracy. Compared with nitrogen, water had more of an influence on chlorophyll a fluorescence and was easier to identify. However, because the water and nitrogen restrict and promote each other, studying the coupling effect of water and nitrogen is necessary. Nine levels of water-nitrogen coupling plants were tested and classified. We discovered that a significant decrease on the classified accuracy was observed for the high nitrogen and low nitrogen treatments, while under a medium N-supply, the recognition rate was high. The method in this paper allowed plants to be classified under different water and nitrogen treatments, and has the potential to monitor the water and nitrogen coupling effect of plants in situ.
关键词： Artificial Neural Network,Classification,Recognition,Chlorophyll a Fluorescence
更新于2025-09-23 15:23:52
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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 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 - Recognition of Windmills in Remote Sensing Image By SVM and Morphological Attribute Filters
摘要： Windmills have the characteristics of small area and small quantity in remote sensing images, so the traditional methods of object classification and recognition are not suitable for the recognition of windmills. In this paper, we analyzed the spectral information and shape characteristics of windmill, and proposed a technique of recognition windmills in remote sensing images based on SVM (support vector machines) and morphological attribute filters. The main idea of technique can be parted into two steps: the remote sensing image are divided into windmill and windmill-like areas, using morphological attribute filters to filter out the windmill-like areas. In addition, we have recognized the distributed windmills group in the images of four regions, and verify the accuracy of the recognition technique.
关键词： morphological attribute filters,windmills,support vector machines,target recognition
更新于2025-09-23 15:22:29