- 标题
- 摘要
- 关键词
- 实验方案
- 产品
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D-Optimal Design and PARAFAC as Useful Tools for the Optimisation of Signals from Fluorescence Spectroscopy Prior to the Characterisation of Green Tea Samples
摘要： A procedure based on a D-optimal design coupled with PARAFAC was proposed to optimise signals from molecular fluorescence spectroscopy to obtain the best experimental conditions for the achievement of the best fluorescence signal of green tea samples. Excitation-emission signals (EEMs) were used to analyse the liquid samples (tea infusions), whereas front-face fluorescence excitation-emission matrices (FFEEMs) were recorded for the solid samples (raw or powder tea leaves). The experimental effort was reduced considerably in both cases thanks to the D-optimal design. Once the optimal conditions have been found, the characterisation of green tea was carried out and the sensitivity and specificity were evaluated. The projection of the principal component analysis (PCA) scores enabled to differentiate among the types of liquid green tea (Chinese tea, Chinese tea with lemon and Indian tea with and without theine). The discrimination of solid green tea according to its geographical origin (Chinese, Indian and Japanese) was also carried out through PCA. In addition, the discrimination between the most expensive Japanese tea and the cheapest one was possible. The sensitivity of the models built with SIMCA was 100% and the specificity of the models for the Chinese tea with respect to the Japanese tea was also high.
关键词： Characterisation,Green tea,PCA,D-optimal design,Front-face fluorescence spectroscopy,PARAFAC
更新于2025-09-10 09:29:36
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Automatic Image Alignment Using Principal Component Analysis
摘要： We present an automatic technique for image alignment using principal component analysis (PCA) that broadly consists of two steps. The first step is the segmentation of the region of interest by thresholding. In the second step, PCA is applied on nonzero pixels in the binary image to determine the object rotation about the mean of the object pixels. Existing PCA-based techniques align the data in their principal spread; however, they have a critical problem of 180? rotation in their principal axes. The current paper provides an automatic solution to address this problem. The algorithm is based on assignment rules inferred from the eigenvectors given by PCA. We applied the proposed algorithm to different datasets including handwritten images of digits, a rotated fingerprint image dataset, and a dataset of magnetic resonance brain images, and confirmed that the proposed method aligns the data efficiently and accurately. In addition to alignment, the algorithm proposes two standard orientations for automatically assessing the true side (upside) of an object.
关键词： fingerprint alignment,digit alignment,Image alignment,PCA,image registration
更新于2025-09-10 09:29:36
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A Local Metric for Defocus Blur Detection Based on CNN Feature Learning
摘要： Defocus blur detection is an important and challenging task in computer vision and digital imaging fields. Previous work on defocus blur detection has put a lot of effort into designing local sharpness metric maps. This paper presents a simple yet effective method to automatically obtain the local metric map for defocus blur detection, which based on the feature learning of multiple convolutional neural networks (ConvNets). The ConvNets automatically learn the most locally relevant features at the super-pixel level of the image in a supervised manner. By extracting convolution kernels from the trained neural network structures and processing it with principal component analysis, we can automatically obtain the local sharpness metric by reshaping the principal component vector. Meanwhile, an effective iterative updating mechanism is proposed to refine the defocus blur detection result from coarse to fine by exploiting the intrinsic peculiarity of the hyperbolic tangent function. The experimental results demonstrate that our proposed method consistently performed better than previous state-of-the-art methods.
关键词： Defocus blur,PCA,local sharpness metric,ConvNets,feature learning
更新于2025-09-10 09:29:36
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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 - Low-Complexity Hyperspectral Image Compression Using Folded PCA and JPEG2000
摘要： Hyperspectral image compression by PCA and JPEG2000 can provide excellent rate distortion performance while preserving essential information for a successive application, e.g., classification tasks. However, for onboard applications, PCA suffers from high computational complexity and large memory requirements due to the eigen-analysis of high-dimensional covariance matrix. Therefore, a computationally more efficient analysis, namely Folded Principal Component Analysis (FPCA) is adopted to perform dimension reduction and combined with JPEG2000 for compression. In FPCA, the spectral vector of hyperspectral pixels is folded into a matrix to compute covariance matrix, by which the dimension of covariance matrix is highly reduced. As a result, both computational complexity and memory requirement in subsequent eigen-analysis is reduced. Experimental results demonstrate that the proposed FPCA+JPEG2000 based compression scheme outperforms existing PCA+JPEG2000 in terms of rate distortion and classification after de-compression.
关键词： Principal Component Analysis (PCA),Hyperspectral Images (HSI),Folded Principal Component Analysis (FPCA),Compression
更新于2025-09-10 09:29:36
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[IEEE 2018 37th Chinese Control Conference (CCC) - Wuhan (2018.7.25-2018.7.27)] 2018 37th Chinese Control Conference (CCC) - Deep Forest-Based Classification of Hyperspectral Images
摘要： The classi?cation of hyperspectral images (HSIs) is a hot topic in the ?eld of remote sensing technology. In recent years, convolutional neural network (CNN) has achieved great success for HSI classi?cation. However, CNN has to do a great effort in parameters tuning which is time-consuming. Furthermore, a large number of samples are required to train CNN, nevertheless, it is expensive to obtain enough training samples from HSIs. In this paper, we propose a novel classi?cation approach based on deep forest. To reduce the dimension of hyperspectral data, principal component analysis (PCA) is performed during the pre-processing. In contrast to the CNN, our method has fewer hyper-parameters and faster training speed. To the best of our knowledge, this is among the ?rst deep forest-based hyperspectral spectral information classi?cation. Extensive experiments are conducted on two real-world HSI datasets to show the proposed method is signi?cantly superior to the state-of-the-art methods.
关键词： Deep Neural Network(DNN),Hyperspectral Image (HSI),Principal Component Analysis (PCA),Deep Forest
更新于2025-09-10 09:29:36
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Combination of High-Resolution Optical Coherence Tomography and Raman Spectroscopy for Improved Staging and Grading in Bladder Cancer
摘要： We present a combination of optical coherence tomography (OCT) and Raman spectroscopy (RS) for improved diagnosis and discrimination of different stages and grades of bladder cancer ex vivo by linking the complementary information provided by these two techniques. Bladder samples were obtained from biopsies dissected via transurethral resection of the bladder tumor (TURBT). As OCT provides structural information rapidly, it was used as a red-flag technology to scan the bladder wall for suspicious lesions with the ability to discriminate malignant tissue from healthy urothelium. Upon identification of degenerated tissue via OCT, RS was implemented to determine the molecular characteristics via point measurements at suspicious sites. Combining the complementary information of both modalities allows not only for staging, but also for differentiation of low-grade and high-grade cancer based on a multivariate statistical analysis. OCT was able to clearly differentiate between healthy and malignant tissue by tomogram inspection and achieved an accuracy of 71% in the staging of the tumor, from pTa to pT2, through texture analysis followed by k-nearest neighbor classification. RS yielded an accuracy of 93% in discriminating low-grade from high-grade lesions via principal component analysis followed by k-nearest neighbor classification. In this study, we show the potential of a multi-modal approach with OCT for fast pre-screening and staging of cancerous lesions followed by RS for enhanced discrimination of low-grade and high-grade bladder cancer in a non-destructive, label-free and non-invasive way.
关键词： principal component analysis (PCA),Raman spectroscopy (RS),k-nearest neighbor classification (kNN),bladder cancer,optical coherence tomography (OCT)
更新于2025-09-10 09:29:36
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Partial Least Squares Identification of Multi Look-Up Table Digital Predistorters for Concurrent Dual-Band Envelope Tracking Power Amplifiers
摘要： This paper presents a technique to estimate the coefficients of a multiple-look-up table (LUT) digital predistortion (DPD) architecture based on the partial least-squares (PLS) regression method. The proposed 3-D distributed memory LUT architecture is suitable for efficient FPGA implementation and compensates for the distortion arising in concurrent dual-band envelope tracking power amplifiers. On the one hand, a new variant of the orthogonal matching pursuit algorithm is proposed to properly select only the best LUTs of the DPD function in the forward path, and thus reduce the number of required coefficients. On the other hand, the PLS regression method is proposed to address both the regularization problem of the coefficient estimation and, at the same time, reducing the number of coefficients to be estimated in the DPD feedback identification path. Moreover, by exploiting the orthogonality of the PLS transformed matrix, the computational complexity of the parameters’ identification can be significantly simplified. Experimental results will prove how it is possible to reduce the DPD complexity (i.e., the number of coefficients) in both the forward and feedback paths while meeting the targeted linearity levels.
关键词： principal component analysis (PCA),look-up tables (LUTs),power amplifier (PA),envelope tracking (ET),partial least squares (PLS),Digital predistortion (DPD)
更新于2025-09-09 09:28:46
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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 - Wind Field Retrieving for SCAT Onboard CFOSAT Based on PCA Method
摘要： The scatterometer (SCAT) onboard China France Oceanography Satellite (CFOSAT) will be the first scanning scatterometer with rotating fan-beams. Wind retrieving methods for existed scatterometers, which are with rotating pencil beams or fixed fan beams, are needed to be modified considering efficiency in information extraction from the numerous observations acquired in this working mode. This was achieved in the research of this paper by applying Principle Component Analysis (PCA) method. Firstly, vectors for applying PCA are composed in two different ways for analysis of the effectiveness of information extraction for the SCAT data and for wind retrieving respectively. Then the description of simulated SCAT data considering observing geometry and SCAT working mode was given. Then experiments of PCA based wind retrieving method was carried out with conclusion that it was effective in information extraction for preparing data sets for wind retrieving. Finally, further research has been discussed.
关键词： wind retrieval,principle component analysis (PCA),China-France Oceanography Satellite (CFOSAT),Scatterometer (SCAT),simulated data
更新于2025-09-09 09:28:46
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Direct nanocrystallite size investigation in microstrained mixed phase TiO2 nanoparticles by PCA of Raman spectra
摘要： Mixed phase anatase and rutile TiO2 nanoparticles (30-67 % R/A) have been synthesized by single step laser pyrolysis. Parallel studies by XRD and Raman spectroscopy suggested possible deforming microstress inside certain samples, due to boundary interactions between neighbouring nanocrystallites in nanoparticles, phase instabilities and/or O/C contents. Microstress in nanoparticles supports anatase phase stability in competition with rutile phase. Williamson-Hall plot was used to evaluate crystallite size and strain. A tensile global microstrain in anatase crystallites, was observed for elevated C content titania nanoparticles, together with compressive microstrain in crystallites within O deficient TiO2 nanoparticles. XPS and TEM characterization opens insights into the processes behind this type of behaviour. Principal Component Analysis of Raman spectra was applied for batch auto-characterization of mixed phase nanoparticles, with emphasis upon the method ability to simultaneously appreciate crystallite size for both anatase and rutile phase. The method focuses on covariational matrix of multiple samples Raman spectra. It provides results in good agreement with XRD calculated crystallite dimensions for unstrained titania, while for samples with microstrains it returns the size closer to the one predicted by Williamson-Hall plot.
关键词： Microstrain,Anatase,Principal Component Analysis (PCA),Raman Spectroscopy,Williamson-Hall,Rutile,TiO2 nanoparticles
更新于2025-09-09 09:28:46
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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 - GPU Acceleration of UAV Image Splicing Using Oriented Fast and Rotated Brief Combined with PCA
摘要： In this study, an accelerating method of oriented FAST and rotated BRIEF combined with principal component analysis (ORB/PCA) is proposed for splicing detection of unmanned aerial vehicle (UAV) images. Compared to traditional scale-invariant feature transform (SIFT) and speeded up robust features (SURF) methods, the proposed ORB/PCA can not only be faster but also produce more accurate. Moreover, in order for the proposed ORB to be effective for image stitching process in near real-time, the Compute Unified Device Architecture (CUDA) application programming interface of graphics processing unit (GPU) is cooperated to speed up the proposed method. Experimental results show that the proposed GPU based ORB/PCA framework is suitable for splicing detection of UAV images in Earth remote sensing. It can improve the image stitching process both in time and accuracy compared to conventional methods.
关键词： oriented FAST and rotated BRIEF (ORB),principal component analysis (PCA),unmanned aerial vehicle (UAV),graphics processing unit (GPU)
更新于2025-09-09 09:28:46