Spectral imaging technology have been used mostly in remote sensing, but have recently been extended to new area requiring high fidelity color reproductions like telemedicine, e-commerce, etc. These spectral imaging systems are important because they offer improved color reproduction quality not only for a standard observer under a particular illuminantion, but for any other individual exhibiting normal color vision capability under another illuminantion. A possibility for browsing of the archives is needed. In this paper, the authors present a new spectral image browsing architecture. The architecture for browsing is expressed as follow: (1) The spectral domain of the spectral image is reduced with the PCA transform. As a result of the PCA transform the eigenvectors and the eigenimages are obtained. (2) We quantize the eigenimages with the original bit depth of spectral image (e.g. if spectral image is originally 8bit, then quantize eigenimage to 8bit), and use 32bit floating numbers for the eigenvectors. (3) The first eigenimage is lossless compressed by JPEG-LS, the other eigenimages were lossy compressed by wavelet based SPIHT algorithm. For experimental evalution, the following measures were used. We used PSNR as the measurement for spectral accuracy. And for the evaluation of color reproducibility, E was used.here standard D65 was used as a light source. To test the proposed method, we used FOREST and CORAL spectral image databases contrain 12 and 10 spectral images, respectively. The images were acquired in the range of 403-696nm. The size of the images were 128*128, the number of bands was 40 and the resolution was 8 bits per sample. Our experiments show the proposed compression method is suitable for browsing, i.e., for visual purpose.
The development of statistical software has enabled food scientists to perform a wide variety of mathematical/statistical analyses and solve problems. Therefore, not only sophisticated analytical methods but also the application of multivariate statistical methods have increased considerably. Herein, principal component analysis (PCA) and hierarchical cluster analysis (HCA) are the most widely used tools to explore similarities and hidden patterns among samples where relationship on data and grouping are until unclear. Usually, larger chemical data sets, bioactive compounds and functional properties are the target of these methodologies. In this article, we criticize these methods when correlation analysis should be calculated and results analyzed. The use of PCA and HCA in food chemistry studies has increased because the results are easy to interpret and discuss. However, their indiscriminate use to assess the association between bioactive compounds and functional properties is criticized as they provide a qualitative view of the data. When appropriate, one should bear in mind that the correlation between the content of chemical compounds and bioactivity could be duly discussed using correlation coefficients.
Modeling of textures in natural images is an important task to make a microscopic model of natural images. Portilla and Simoncelli proposed a generative texture model, which is based on the mechanism of visual systems in brains, with a set of texture features and a feature matching. On the other hand, the texture features, used in Portillas' model, have redundancy between its components came from typical natural textures. In this paper, we propose a contracted texture model which provides a dimension reduction for the Portillas' feature. This model is based on a hierarchical principal components analysis using known group structure of the feature. In the experiment, we reveal effective dimensions to descrive texture is fewer than the original description. Moreover, we also demonstrate how well the textures can be synthesized from the contracted texture representations.
Although high As groundwater has been observed in shallow groundwater of the Hetao basin, little is known about As distribution in deep groundwater. Quantitative investigations into relationships among chemical properties and among samples in different areas were carried out. Ninety groundwater samples were collected from deep aquifers of the northwest of the basin. Twenty-two physicochemical parameters were obtained for each sample. Statistical methods, including principal component analysis (PCA) and hierarchical cluster analysis (HCA), were used to analyze those data. Results show that As species were highly correlated with Fe species, NH -N and pH. Furthermore, result of PCA indicates that high As groundwater was controlled by geological, reducing and oxic factors. The samples are classified into three clusters in HCA, which corresponded to the alluvial fans, the distal zone and the flat plain. Moreover, the combination of PCA with HCA shows the different dominant factors in different areas. In the alluvial fans, groundwater is influenced by oxic factors, and low As concentrations are observed. In the distal zone, groundwater is under suboxic conditions, which is dominated by reducing and geological factors. In the flat plain, groundwater is characterized by reducing conditions and high As concentrations, which is dominated by the reducing factor. This investigations indicate that deep groundwater in the alluvial fans mostly contains low As concentrations but high NO and U concentrations, and needs to be carefully checked prior to being used for drinking water sources.
In tropical environments, the design of bioclimatic houses adapted to their environment represents a crucial issue when considering thermal comfort and limiting energy consumption. A preliminary aspect of such design endeavors is the acquisition of accurate knowledge regarding the climatic conditions in each region of the studied territory. The objective of this paper is to propose a climatic zoning for Madagascar from a database of 47 meteorological stations by performing hierarchical clustering on principal components (HCPC). The results are then combined with spatial interpolation using geographic information system (GIS) tools, enabling us to define three climate zones corresponding to dry, humid and highland areas. These results make it possible to define standard meteorological files to evaluate the thermal performance of traditional Malagasy houses. Regardless of the type of house and the areas considered, the percentage of thermal comfort according to the Givoni bioclimatic chart varies from average values of 20% without ventilation to 70% with an air velocity of 1 m/s. In summary, Madagascar's traditional habitat typologies have adapted over time to the constraints of their environment.
Traditional monitoring algorithms use the normal data for modeling, which are universal for different types of faults. However, these algorithms may perform poorly sometimes because of the lack of fault information. In order to further increase the fault detection rate while preserving the universality of the algorithm, a novel dynamic weight principal component analysis (DWPCA) algorithm and a hierarchical monitoring strategy are proposed. In the first layer, the dynamic PCA is used for fault detection and diagnosis, if no fault is detected, the following DWPCA based second layer monitoring will be triggered. In the second layer, the principal components (PCs) are weighted according to its ability in distinguishing between the normal and fault conditions, then the PCs which own larger weight are selected to construct the monitoring model. Compared to the DPCA method, the proposed DWPCA algorithm establishes the monitoring model by combining the information of fault. Afterwards, the DWPCA based variable relative contribution and a novel control limit for the variable relative contribution are presented for the fault diagnosis. Finally, the superiority of the proposed method is demonstrated by a numerical case and the Tennessee Eastman (TE) process.