• Title/Summary/Keyword: Spectral methods

Search Result 1,058, Processing Time 0.039 seconds

Water-Methanol and Water-Acetonitrile Mixture Analysis using NIR Spectral Data and Iterative Target Transform Factor Analysis

  • Na, Dae-Bok;Hur, Yun-Jeong;Park, Young-Joo;Cho, Jung-Hwan
    • Proceedings of the Korean Society of Near Infrared Spectroscopy Conference
    • /
    • 2001.06a
    • /
    • pp.1289-1289
    • /
    • 2001
  • Water-methanol and water-acetonitrile mixtures are frequently used as HPLC solvent system and strong hydrogen bonding is well-known. But a detailed aspect of water-methanol and/or water-acetonitrile mixtures have not been shown with direct spectral evidence. Recently, near infrared spectroscopy and chemometric data refinery have been successfully combined in many applications. On the basis of factor analytical methods, the spectral features of water-methanol and water-acetonitrile mixtures were studied to reveal the detail of mixtures. Water-methanol and water-acetonitrile mixtures were prepared with varying concentration of each constituent and near infrared spectral data were acquired in the range of 1100-2500nm with 2-nm interval. The data matrices were analysed with ITTFA(Iterative Target Transform Factor Analysis) algorithm implemented as MATLAB codes. As a result, the concentration profiles of water, methanol and water-methanol complex were resolved and the spectra of water-methanol complexes were calculated, which cannot be acquired with pure complexes. A similar result was obtained with NIR spectral data of water-acetonitrile mixtures. Moreover, pure spectra of hydrogen-bonding complexes of water-methanol and water-acetonitrile can be computed, while any other usual physical methods cannot isolated those complexes for acquiring pure component spectra.

  • PDF

Study of Spectral Factorization using Circulant Matrix Factorization to Design the FIR/IIR Lattice Filters (FIR/IIR Lattice 필터의 설계를 위한 Circulant Matrix Factorization을 사용한 Spectral Factorization에 관한 연구)

  • 김상태;박종원
    • Journal of the Korea Institute of Information and Communication Engineering
    • /
    • v.7 no.3
    • /
    • pp.437-447
    • /
    • 2003
  • We propose the methods to design the finite impulse response (FIR) and the infinite impulse response (IIR) lattice filters using Schur algorithm through the spectral factorization of the covariance matrix by circulant matrix factorization (CMF). Circulant matrix factorization is also very powerful tool used fur spectral factorization of the covariance polynomial in matrix domain to obtain the minimum phase polynomial without the polynomial root finding problem. Schur algorithm is the method for a fast Cholesky factorization of Toeplitz matrix, which easily determines the lattice filter parameters. Examples for the case of the FIR Inter and for the case of the IIR filter are included, and performance of our method check by comparing of our method and another methods (polynomial root finding and cepstral deconvolution).

A Spectral Compensation Method for Noise Robust Speech Recognition (잡음에 강인한 음성인식을 위한 스펙트럼 보상 방법)

  • Cho, Jung-Ho
    • 전자공학회논문지 IE
    • /
    • v.49 no.2
    • /
    • pp.9-17
    • /
    • 2012
  • One of the problems on the application of the speech recognition system in the real world is the degradation of the performance by acoustical distortions. The most important source of acoustical distortion is the additive noise. This paper describes a spectral compensation technique based on a spectral peak enhancement scheme followed by an efficient noise subtraction scheme for noise robust speech recognition. The proposed methods emphasize the formant structure and compensate the spectral tilt of the speech spectrum while maintaining broad-bandwidth spectral components. The recognition experiments was conducted using noisy speech corrupted by white Gaussian noise, car noise, babble noise or subway noise. The new technique reduced the average error rate slightly under high SNR(Signal to Noise Ratio) environment, and significantly reduced the average error rate by 1/2 under low SNR(10 dB) environment when compared with the case of without spectral compensations.

An Application of Spatial Classification Methods for the Improvement of Classification Accuracy (분류정확도 향상을 위한 공간적 분류방법의 적용)

  • Jeong, Jae-Joon;Lee, Byoung-Kil;Kim, Hyung-Tae;Kim, Yong-Il
    • Journal of Korean Society for Geospatial Information Science
    • /
    • v.9 no.2 s.18
    • /
    • pp.37-46
    • /
    • 2001
  • Spectral pattern recognition techniques are most used in classification of remotely sensed data. Yet, in any real image, adjacent pixels are related, because imaging sensors acquire significant portions of energy from adjacent pixels. And, with the continued improvement in the spatial resolution of remote sensing systems, another spatial pattern recognition approach is must considered. In this study, we aim to show the potentiality of spatial classification methods through comparing the accuracies of spectral classification methods and those of spectral classification methods. By the comparisons between the two methods, classification accuracies of 6 different spatial classification methods are higher than that of spectral classification method by 2-6% or so. Additionally, we can show it statistically through the classification experiments with different band combinations.

  • PDF

Differentiation of Beef and Fish Meals in Animal Feeds Using Chemometric Analytic Models

  • Yang, Chun-Chieh;Garrido-Novell, Cristobal;Perez-Marin, Dolores;Guerrero-Ginel, Jose E.;Garrido-Varo, Ana;Cho, Hyunjeong;Kim, Moon S.
    • Journal of Biosystems Engineering
    • /
    • v.40 no.2
    • /
    • pp.153-158
    • /
    • 2015
  • Purpose: The research presented in this paper applied the chemometric analysis to the near-infrared spectral data from line-scanned hyperspectral images of beef and fish meals in animal feeds. The chemometric statistical models were developed to distinguish beef meals from fish ones. Methods: The meal samples of 40 fish meals and 15 beef meals were line-scanned to obtain hyperspectral images. The spectral data were retrieved from each of 3600 pixels in the Region of Interest (ROI) of every sample image. The wavebands spanning 969 nm to 1551 nm (across 176 spectral bands) were selected for chemometric analysis. The partial least squares regression (PLSR) and the principal component analysis (PCA) methods of the chemometric analysis were applied to the model development. The purpose of the models was to correctly classify as many beef pixels as possible while misclassified fish pixels in an acceptable amount. Results: The results showed that the success classification rates were 97.9% for beef samples and 99.4% for fish samples by the PLSR model, and 85.1% for beef samples and 88.2% for fish samples by the PCA model. Conclusion: The chemometric analysis-based PLSR and PCA models for the hyperspectral image analysis could differentiate beef meals from fish ones in animal feeds.

Spectral Quality Enhancement of Pan-Sharpened Satellite Image by Using Modified Induction Technique (수정된 영상 유도 기법을 통한 융합영상의 분광정보 향상 알고리즘)

  • Choi, Jae-Wan;Kim, Hyung-Tae
    • Journal of Korean Society for Geospatial Information Science
    • /
    • v.16 no.3
    • /
    • pp.15-20
    • /
    • 2008
  • High-spatial resolution remote sensing satellites (IKONOS-2, QuickBird and KOMPSAT-2) have provided low-spatial resolution multispectral images and high-spatial resolution panchromatic images. Image fusion or Pan-sharpening is a very important in that it aims at using a satellite image with various applications such as visualization and feature extraction through combining images that have a different spectral and spatial resolution. Many image fusion algorithms are proposed, most methods could not preserve the spectral information of original multispectral image after image fusion. In order to solve this problem, modified induction technique which reduce the spectral distortion of fused image is developed. The spectral distortion is adjusted by the comparison between the spatially degraded pan-sharpened image and original multispectral image and our algorithm is evaluated by QuickBird satellite imagery. In the experiment, pan-sharpened image by various methods can reduce spectral distortion when our algorithm is applied to the fused images.

  • PDF

A study on Reliability Analysis for Prediction Technology of Water Content in the Ground using Hyperspectral Informations (초분광정보를 이용한 지반의 함수비 예측 기술의 신뢰성 분석 연구)

  • Lee, Kicheol;Ahn, Heechul;Park, Jeong-Jun;Cho, Jinwoo;You, Seung-Kyong;Hong, Gigwon
    • Journal of the Korean Geosynthetics Society
    • /
    • v.20 no.4
    • /
    • pp.141-149
    • /
    • 2021
  • In this study, an laboratory experiment was performed for prediction technology of water content in the ground using hyperspectral information. And the spectral reflectance with a specific wavelength band was obtained according to the fine and water content. Through it, the spectral information was normalized with the spectral index of the existing literature, and the relationship with the fine and water contents and the reliability of the prediction technology were analyzed. As a result of analysis, the spectral reflectance is decreased when the water and fine contents are increased under the high water contents. In addition, the reliability of prediction technology of water content was evaluated by examining 7 different spectral index calculation methods. Among them, DVI showed relatively high prediction reliability and was superior to other calculation methods in terms of sensitivity.

Image Fusion Methods for Multispectral and Panchromatic Images of Pleiades and KOMPSAT 3 Satellites

  • Kim, Yeji;Choi, Jaewan;Kim, Yongil
    • Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
    • /
    • v.36 no.5
    • /
    • pp.413-422
    • /
    • 2018
  • Many applications using satellite data from high-resolution multispectral sensors require an image fusion step, known as pansharpening, before processing and analyzing the multispectral images when spatial fidelity is crucial. Image fusion methods are to improve images with higher spatial and spectral resolutions by reducing spectral distortion, which occurs on image fusion processing. The image fusion methods can be classified into MRA (Multi-Resolution Analysis) and CSA (Component Substitution Analysis) approaches. To suggest the efficient image fusion method for Pleiades and KOMPSAT (Korea Multi-Purpose Satellite) 3 satellites, this study will evaluate image fusion methods for multispectral and panchromatic images. HPF (High-Pass Filtering), SFIM (Smoothing Filter-based Intensity Modulation), GS (Gram Schmidt), and GSA (Adoptive GS) were selected for MRA and CSA based image fusion methods and applied on multispectral and panchromatic images. Their performances were evaluated using visual and quality index analysis. HPF and SFIM fusion results presented low performance of spatial details. GS and GSA fusion results had enhanced spatial information closer to panchromatic images, but GS produced more spectral distortions on urban structures. This study presented that GSA was effective to improve spatial resolution of multispectral images from Pleiades 1A and KOMPSAT 3.

SPECTRAL APPROXIMATIONS OF ATTRACTORS FOR CONVECTIVE CAHN-HILLIARD EQUATION IN TWO DIMENSIONS

  • ZHAO, XIAOPENG
    • Bulletin of the Korean Mathematical Society
    • /
    • v.52 no.5
    • /
    • pp.1445-1465
    • /
    • 2015
  • In this paper, the long time behavior of the convective Cahn-Hilliard equation in two dimensions is considered, semidiscrete and completely discrete spectral approximations are constructed, error estimates of optimal order that hold uniformly on the unbounded time interval $0{\leq}t<{\infty}$ are obtained.

Application of Spectral Mixture Analysis to Geological Mapping using LANDSAT 7 ETM+ and ASTER Images: Mineral Potential Mapping of Mongolian Plateau

  • Kim Seung Tae;Lee Kiwon
    • Proceedings of the KSRS Conference
    • /
    • 2004.10a
    • /
    • pp.425-427
    • /
    • 2004
  • Motivation of this study is based on these two aspects: geologic uses of ASTER and application scheme of Spectral Mixture Analysis. This study aims at geologic mapping for mineral exploration using ASTER and LANDSAT 7 ETM+ at Mongolian plateau region by SMA. After basic pre-processing such as the normalization, geometric corrections and calibration of reflectance, related to endmembers selection and spectral signature deviation, both methods using spectral library and using PPI(Pixel Purity Index) are performed and compared on a given task. Based on these schemes, SMA is performed using LANDSAT 7 ETM+ and ASTER image. As the results, fraction map showing geologic rock types are enough to meet purposes such as geologic mapping and mineral potential mapping in the case of both uses of these different types of remotely sensed images. It concluded that this approach based on SMA with LANDSAT and ASTER is regarded as one of effective schemes for geologic remote sensing.

  • PDF