- 标题
- 摘要
- 关键词
- 实验方案
- 产品
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Detection of Knot Defects on Coniferous Wood Surface Using Near Infrared Spectroscopy and Chemometrics
摘要： Lumber pieces usually contain defects such as knots, which strongly affect the strength and stiffness. To develop a model for rapid, accurate grading of lumbers based on knots, Douglas fir, spruce-pine-fir (SPF), Chinese hemlock, and Dragon spruce were used. The experiments explored the effects of modelling methods and spectral preprocess methods for knot detection, and investigated the feasibility of using a model built within one species to discriminate the samples from other species, using a novel variable selection method-random frog to select effective wavelengths. The results showed that least squares-support vector machines coupled with first derivative preprocessed spectra achieved best performance for both single and mixed models. Models built within Dragon spruce could be used to classify knot samples from SPF and Chinese hemlock but not Douglas fir, and vice versa. Eight effective wavelengths (1314 nm, 1358 nm, 1409 nm, 1340 nm, 1260 nm, 1586 nm, 1288 nm, and 1402 nm) were selected by RF to build effective wavelengths based models. The sensitivity, specificity, and accuracy in the validation set were 98.49%, 93.42%, and 96.30%, respectively. Good results could be obtained when using data at just eight wavelengths, as an alternative to evaluating the whole spectrum.
关键词： Coniferous wood,Knot detection,Near infrared spectroscopy (NIRS),Random frog algorithm,Least squares-support vector machines (LS-SVM)
更新于2025-09-23 15:23:52
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Automated quantification of immunomagnetic beads and leukemia cells from optical microscope images
摘要： Quanti?cation of tumor cells is crucial for early detection and monitoring the progress of cancer. Several methods have been developed for detecting tumor cells. However, automated quanti?cation of cells in the presence of immunomagnetic beads has not been studied. In this study, we developed computer vision based algorithms to quantify the leukemia cells captured and separated by micron size immunomagnetic beads. Color, size based object identi?cation and machine learning based methods were implemented to quantify targets in the images recorded by a bright ?eld microscope. Images acquired by a 40× or a 20× objective were analyzed, the immunomagnetic beads were detected with an error rate of 0.0171 and 0.0384 respectively. Our results reveal that the proposed method attains 91.6% precision for the 40× objective and 79.7% for the 20× objective. This algorithm has the potential to be the signal readout mechanism of a biochip for cell detection.
关键词： Leukemia cells,Immunomagnetic beads,Support vector machines,Bright-?eld optical microscopy,Image-processing,Machine learning
更新于2025-09-23 15:23:52
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Photomechanical organic crystals as smart materials for advanced applications
摘要： Photomechanical molecular crystals are receiving great attention due to their efficient conversion of light into mechanical work and surpassing advantages including faster response time, higher Young’s modulus and ordered structure various measured photomechanical crystals with different motions (contraction, expansion, bending, fragmentation, hopping, curling and twisting) are springing up in the forefront of smart materials research. The photomechanical motions of these single crystals during irradiation is triggered by solid-state photochemical reactions and accompanied with phase transformation. This short review intends to summarize recent developments in the growing research on photoresponsive molecular crystals. The basic mechanisms of different kinds of photomechanical materials are described in detail, the recent advances of photomechanical crystals for promising applications as smart materials are also highlighted.
关键词： crystal engineering,photomechanical crystals,photochemistry,molecular machines,photochromism
更新于2025-09-23 15:23:52
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Learning Deep Conditional Neural Network for Image Segmentation
摘要： Combining Convolutional Neural Networks (CNNs) with Conditional Random Fields (CRFs) achieve great success among recent object segmentation methods. There are two advantages by such usage. First, CNNs can extract low-level features, which are very similar to the extracted features in primates’ primary visual cortex (V1). Second, CRFs can set up the relationship between input features and output labels in a direct way. In this paper, we extend the first advantage by using CNNs for low-level feature extraction and Structured Random Forest (SRF) based border ownership detector for high-level feature extraction, which are similar to the outputs of primates secondary visual cortex (V2). Compared to the CRF model, an improved Conditional Boltzmann Machine (CBM) which has a multi-channel visible layer are proposed to model the relationship between predicted labels, local and global contexts of objects with multi-scale and multilevel features. Besides, our proposed CBM model is extended for object parsing by using multi visible branches instead of a single visible layer of CBM, which can not only segment the whole body but also the parts of the body under. These visible branches use each branch for the segmentation of the whole body or one of the body parts. All the branches share the same hidden layers of CBM and train the branches under an iterative way. By exploiting object parsing, the whole body segmentation performance of object is improved. To refine the segmentation output, two kinds of optimization algorithms are proposed. The superpixel based algorithm can re-label the overlapped regions of multi-kinds of objects. The other curve correction algorithm corrects the edges of segmented object parts by using smooth edges under a curve similarity criterion. Experiments demonstrate that our models yield competitive results for object segmentation on PASCAL VOC 2012 dataset and for object parsing on PennFudan Pedestrian Parsing dataset, Pedestrian Parsing Surveillance Scenes dataset, Horse-Cow parsing dataset, PASCAL Quadrupeds dataset.
关键词： Convolutional Neural Networks,Conditional Boltzmann Machines,Segmentation,object parsing
更新于2025-09-23 15:23:52
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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 - Towards Joint Land Cover and Crop Type Mapping with Numerous Classes
摘要： The detailed, accurate and frequent land cover and crop-type mapping emerge as essential for several scientific communities and geospatial applications. This paper presents a methodology for the semi-automatic production of land cover and crop type maps using a highly analytic nomenclature of more than 40 classes. An intensive manual annotation procedure was carried out for the production of reference data. A class nomenclature based on CORINE land cover Level-3 was employed along with several additional crop-type classes. Multitemporal surface reflectance Landsat-8 data for the year of 2016 were used for all classification experiments with a linear SVM classifier. Quantitative and qualitative evaluation highlighted the efficiency of the proposed approach achieving high accuracy rates. Further analysis on individual classes’ performance highlighted the challenges in the proposed classification scheme as well as important outcomes regarding the spectral behavior of the considered categories.
关键词： support vector machines,CORINE Land Cover,Landsat-8,classification
更新于2025-09-23 15:23:52
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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
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HCKBoost: Hybridized composite kernel boosting with extreme learning machines for hyperspectral image classification
摘要： Utilization of contextual information on the hyperspectral image (HSI) analysis is an important fact. On the other hand, multiple kernels (MKs) and hybrid kernels (HKs) in connection with kernel methods have significant impact on the classification process. Activation of spatial information via composite kernels (CKs) and exploiting hidden features of the spectral information via MKs and HKs have been shown great successes on hyperspectral images separately. In this work, it is aimed to aggregate composite and hybrid kernels to obtain high classification success with a boosting based community learner. Spatial and spectral hybrid kernels are constructed using weighted convex combination approach with respect to individual success of the predefined kernels. Composite kernel formation is realized with certain proportions of the obtained spatial and spectral HKs. Computationally fast and effective extreme learning machine (ELM) classification algorithm is adopted. Since, main objective is to obtain optimal kernel during ensemble formation operation, unlike the standard MKL methods, proposed method disposes off the complex optimization processes and allows multi-class classification. Pavia University, Indian Pines, and Salinas hyperspectral scenes that have ground truth information are used for simulations. Hybridized composite kernels (HCK) are constructed using Gaussian, polynomial, and logarithmic kernel functions with various parameters and then obtained results are presented comparatively along with the state-of-the-art MKL, CK, sparse representation, and single kernel based methods.
关键词： Hyperspectral images,Composite kernels,Adaptive boosting,Extreme learning machines,Hybrid kernels
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, Spain (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Very High Resolution Optical Image Classification Using Watershed Segmentation and a Region-Based Kernel
摘要： In this paper, the problem of the spatial-spectral classification of very high-resolution optical images is addressed using a kernel- and region-based approach. A novel method based on integrating region-based or object-based information into a kernel machine is developed. A Gaussian process model is used to characterize each segment in a segmentation map and to define a region-based admissible kernel accordingly. This kernel is combined with a marker-controlled watershed segmentation that incorporates scale adaptivity. Spatial-spectral fusion capabilities are also ensured by combining the resulting classification method with composite kernels.
关键词： watershed segmentation,region-based classification,Kernel machines,geospatial object-based image analysis (GEOBIA)
更新于2025-09-23 15:21:21
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[IEEE 2018 31st International Vacuum Nanoelectronics Conference (IVNC) - Kyoto, Japan (2018.7.9-2018.7.13)] 2018 31st International Vacuum Nanoelectronics Conference (IVNC) - Nanogranular Compound Material Layers Serve as Storage for Infra-red to Ultra-Violet Photons for the Energy Supply for Electric Machines
摘要： Hyper-Giant conducting nanogranular compound materials, which serve as IR photon detectors allow to use BOSON-current transport at room temperature with GA/cm2 current carrying capability for Energy applications
关键词： Room temperature,Bosons having parallel spin,Boson-current transport,Energy supply for electric machines
更新于2025-09-23 15:21:21
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[IEEE 2019 IEEE SENSORS - Montreal, QC, Canada (2019.10.27-2019.10.30)] 2019 IEEE SENSORS - Single Particle Detector Using the Evanescent Field of a Silicon Nitride Waveguide
摘要： Cloud computing technology has become an integral trend in the market of information technology. Cloud computing virtualization and its Internet-based lead to various types of failures to occur and thus the need for reliability and availability has become a crucial issue. To ensure cloud reliability and availability, a fault tolerance strategy should be developed and implemented. Most of the early fault tolerant strategies focused on using only one method to tolerate faults. This paper presents an adaptive framework to cope with the problem of fault tolerance in cloud computing environments. The framework employs both replication and checkpointing methods in order to obtain a reliable platform for carrying out customer requests. Also, the algorithm determines the most appropriate fault tolerance method for each selected virtual machine. Simulation experiments are carried out to evaluate the framework’s performance. The results of the experiments show that the proposed framework improves the performance of the cloud in terms of throughput, overheads, monetary cost, and availability.
关键词： replication,checkpointing,virtual machines,Fault tolerance,cloud computing
更新于2025-09-23 15:21:01