Low-low Research Articles

New method of enhancement using wavelet transforms applied to SODISM telescope

PICARD is a space-based observatory hosting the Solar Diameter Imager and Surface Mapper (SODISM) telescope, which has continuously observed the Sun from July 2010 and up to March 2014. In order to study the fine structure of the solar surface, it is helpful to apply techniques that enhance the images so as to improve the visibility of solar features such as sunspots or faculae. The objective of this work is to develop an innovative technique to enhance the quality of the SODISM images in the five wavelengths monitored by the telescope at 215.0 nm, 393.37 nm, 535.7 nm, 607.1 nm and 782.2 nm. An enhancement technique using interpolation of the high-frequency sub-bands obtained by Discrete Wavelet Transforms (DWT) and the input image is applied to the SODISM images. The input images are decomposed by the DWT as well as Stationary Wavelet Transform (SWT) into four separate sub-bands in horizontal and vertical directions namely, low-low (LL), low–high (LH), high-low (HL) and high–high (HH) frequencies. The DWT high frequency sub-bands are interpolated by a factor 2. The estimated high frequency sub-bands (edges) are enhanced by introducing an intermediate stage using a Stationary Wavelet Transform (SWT), and then all these sub-bands and input image are combined and interpolated with half of the interpolation factor α/2, used to interpolate the high-frequency sub-bands, in order to reach the required size for IDWT processing. Quantitative and visual results show the superiority of the proposed technique over a bicubic image resolution enhancement technique. In addition, filling factors for sunspots are calculated from SODISM images and results are presented in this work.

CT scan contrast enhancement using singular value decomposition and adaptive gamma correction

We propose in this paper a new enhancement algorithm dedicated to dark computed tomography (CT) scan based on discrete wavelet transform with singular value decomposition (DWT–SVD) followed by adaptive gamma correction (AGC). Discrete wavelet transform (DWT) is considered to decompose the input dark CT image in four sub-bands. Singular value decomposition (SVD) is used in order to compute the corresponding singular value matrix of low–low (LL) sub-band image. The enhanced LL sub-band is determined by scaling the singular value matrix of original LL sub-band by an adequate correction factor, followed by inverse SVD. For a further contrast improvement, the new enhanced LL sub-band image is processed using an AGC algorithm. Finally, the obtained LL sub-band image undergoes inverse DWT together with the unprocessed sub-bands to generate the final enhanced image. This proposed method has the advantage of being fully automatic and could be applied for dark input images with either low or moderate contrast. Different dark CT images are considered to compare the performance of our proposed method to three other enhancement techniques using both objective and subjective assessments. Simulation results show that our proposed algorithm consistently produces good contrast enhancement, with best brightness and edges details conservation and with minimum added distortions to the enhanced CT images.

A novel image decomposition-based hybrid technique with super-resolution method for multi-focus image fusion

Multi-focus image fusion combines two or more images which have different focus values of the same scene using fusion rules. The meaningful image is named all-in-focus image which is more informative and useful for visual perception. In this paper, a novel approach for multi-focus image fusion is proposed. The method is a hybrid method with super-resolution. Firstly, super-resolution method is applied to all source images to enhance information like contrast. Thus, low-resolution source images are converted to high-resolution source images. Secondly, due to decomposing these source images, Stationary Wavelet Transform (SWT) is implemented and images are divided into four sub-bands. These sub-bands are LL (low–low), LH (low–high), HL (high–low) and HH (high–high). LL is the approximation coefficient of source images and others are the detail coefficients of source images. For all these sub-bands, Principal Component Analysis (PCA) is implemented and maximum eigenvector of each sub-band of source images is selected separately to fuse images. Then, Inverse Stationary Wavelet Transform (ISWT) is used to reconstruct the fused sub-bands. Finally, to measure quality of the proposed method objectively, fused image is resized to original source image's size using interpolation based resizing method. To measure the success of method, different metrics without reference image and with reference image, are selected. Results show that the proposed method produce clear edges, good visual perception, good clarity and very few distortion. The proposed hybrid method is applied to produce better quality fused images. Results prove success of the approach in this area. Also visual and quantitative results are very impressive.

RGB and multispectral UAV image fusion for Gramineae weed detection in rice fields

In this paper, a new method to fuse low resolution multispectral and high resolution RGB images is introduced, in order to detect Gramineae weed in rice fields with plants at 50 days after emergence (DAE).The images are taken from a fixed-wing unmanned aerial vehicle (UAV) at 60 and 70 m altitude. The proposed method combines the texture information given by a high resolution red–green–blue (RGB) image and the reflectance information given by a low resolution multispectral (MS) image, to obtain a fused RGB-MS image with better weed discrimination features. After analyzing the normalized difference vegetation index (NDVI) and normalized green red difference index (NGRDI) for weed detection, it was found that NGRDI presents better features. The fusion method consists of decomposing the RGB image using the intensity, hue and saturation (IHS) transformation, then, a second order Haar wavelet transformation is applied to the intensity layer (I) and the NGRDI image. From this transformation, the low–low (LL) coefficients of the NGRDI image are replaced by the LL coefficients of the I layer. Finally, the fused image is obtained by transforming the new wavelet coefficients to RGB space. To test the method, a one hectare experimental plot with rice plants at 50 DAE with Gramineae weeds was selected. Additionally, to compare the performance of the method, two indices were used, specifically, the M/MGT index which is the percentage of detected weed area, and the MP index which indicates the precision of weed detection. These indices were evaluated in four validation zones using three Neural Networks (NN) detection systems based on three types of images; namely, RGB, RGB + NGRDI, and fused RGB-NGRDI. The best weed detection performance was obtained by the NN with the fused image, with M/MGT index between 80 and 108% and MP between 70 and 85%.

A New Adaptive Gamma Correction Based Algorithm Using DWT-SVD for Non-Contrast CT Image Enhancement.

The performances of medical image processing techniques, in particular CT scans, are usually affected by poor contrast quality introduced by some medical imaging devices. This suggests the use of contrast enhancement methods as a solution to adjust the intensity distribution of the dark image. In this paper, an advanced adaptive and simple algorithm for dark medical image enhancement is proposed. This approach is principally based on adaptive gamma correction using discrete wavelet transform with singular-value decomposition (DWT-SVD). In a first step, the technique decomposes the input medical image into four frequency sub-bands by using DWT and then estimates the singular-value matrix of the low-low (LL) sub-band image. In a second step, an enhanced LL component is generated using an adequate correction factor and inverse singular value decomposition (SVD). In a third step, for an additional improvement of LL component, obtained LL sub-band image from SVD enhancement stage is classified into two main classes (low contrast and moderate contrast classes) based on their statistical information and therefore processed using an adaptive dynamic gamma correction function. In fact, an adaptive gamma correction factor is calculated for each image according to its class. Finally, the obtained LL sub-band image undergoes inverse DWT together with the unprocessed low-high (LH), high-low (HL), and high-high (HH) sub-bands for enhanced image generation. Different types of non-contrast CT medical images are considered for performance evaluation of the proposed contrast enhancement algorithm based on adaptive gamma correction using DWT-SVD (DWT-SVD-AGC). Results show that our proposed algorithm performs better than other state-of-the-art techniques.

中国水生态足迹广度、深度评价及空间格局

PDF HTML阅读 XML下载 导出引用 引用提醒 中国水生态足迹广度、深度评价及空间格局 DOI: 10.5846/stxb201608101640 作者: 作者单位: 作者简介: 通讯作者: 中图分类号: 基金项目: 国家社会科学基金重点项目（16AJY009） Assessment of water ecological footprint size, depth, and spatial pattern in China Author: Affiliation: Fund Project: 摘要 | 图/表 | 访问统计 | 参考文献 | 相似文献 | 引证文献 | 资源附件 | 文章评论 摘要:运用生态足迹方法对水资源进行流量资本和存量资本区分，测算分析了中国31个省市1997-2014年的水生态足迹广度与深度。结果显示：①中国的水生态足迹广度受年际水资源量丰枯影响，总体呈波动趋势；各省市的水生态足迹广度存在着明显差异，南方地区水生态足迹广度普遍大于北方地区；②研究期内，中国的水生态足迹深度只有1998年为1，其余年份的水资源流量资本已不能满足人类生产生活的需求，需要消耗水资源存量资本；各省市之间水生态足迹深度相差较大，整体上北方高南方低，其中14个省份18年的平均水生态足迹深度为1，平均水生态足迹深度最高的地区是宁夏的308.12；③运用空间自相关方法对31个省市的水生态足迹广度和深度进行分析得出，中国省际水生态足迹广度与深度均存在明显的空间集聚现象。水生态足迹广度H-H集聚地区主要集中在中国南方地区，水生态足迹深度H-H集聚地区主要集中在中国北方地区。通过对全国水生态足迹广度与深度的测度分析为水生态足迹分析提供新的研究方法，同时也为区域水资源可持续利用提供理论依据。 Abstract:Water is an important resource for our life and production, and the development of human beings and society are closely related. The accelerating growth in water consumption and exploration is creating serious water scarcity problems in China. Ecological economist William Rees first formally presented the concept of the Ecological Footprint in the 1990s. With the development of this research, scholars attempt to introduce new methods or indicators to improve research methods into the ecological footprint. In particular, Niccolucci and Wackernagel put forward two indicators of a 3D model footprint depth and size, and the model is a temporal-spatial approach that more clearly explains the differences between human demand for natural capital flows and stocks. The water ecological footprint was derived from the ecological footprint concept, and represents the amount of water resources productive land areas still need, in terms of a the water resources account. In this paper, we applied footprint size and depth to the sustainable use of water resources. Using the ecological footprint method to distinguish between flow and the stock of water resources, this method was used to analyze water ecological footprint size and depth among 31 provinces in China during the period from 1997 to 2014. The results showed the following three points: 1) The depth of the water ecological footprint was affected by water resource quantity change between continuous high flow years and low flow years. They exhibit a fluctuating trend in the interior nationwide, and the water ecological footprint size of different provinces differ with footprint size higher in the south than in the north. 2) During 1997-2014, the water ecological footprint depth was 1 in 1998, and in other years flow capital could not meet people's needs, and an increase in the stock capital of water was needed. The water ecological footprint depth exhibited a large difference between countries, but it was obvious that it was lower in the south than in the north. Among these, the average size of the water ecological footprint was 1 in the other 14 provinces and this lasted for 18 years, and included Zhejiang, Fujian, Jiangxi, Hubei, Hunan, Guangdong, Guangxi, Hainan, Chongqing, Sichuan, Guizhou, Yunnan, Xizang, and Qinghai, with the highest of in Ningxia, being 308.12. 3) We analyzed the water ecological footprint size for 31 provinces using the global spatial autocorrelation method. In China, the water ecological footprint size and depth existed as an obvious spatial agglomeration spatial cluster feature. The water ecological footprint size had a H-H (high-high) correlation in provinces mainly in southern China, and in L-L (low-low) correlation provinces in the north of China. The water ecological footprint depth was in H-H correlation in provinces mainly in the north of China, and in L-L correlation mainly in the south of China. This provided a new method to analyze the water ecological footprint through analyzing the water ecological footprint size and depth, and at the same time, it provided a theoretical basis for regional sustainable utilization of water resources. 参考文献 相似文献 引证文献

Image Resolution Enhancement Using Wavelet Domain Transformation and Sparse Signal Representation

Image resolution enhancement or super-resolution (SR) problem generates a high resolution (HR) image from one or a set of low resolution (LR) images. In the past two decades, a wide variety of resolution enhancement algorithms have been proposed. These methods are confined to small scaling factors. This paper presents a novel single image resolution enhancement algorithm in wavelet domain which operates at high scaling factors. First, we perform subband decomposition on the input LR image by using discrete wavelet transform (DWT). It decomposes the LR image into different frequency subbands namely low-low (LL), low-high (LH), high-low (HL) and high-high (HH). In parallel we apply sparse representation based interpolation method on the LR image. Next, we process the three high frequency subbands in wavelet domain by applying bicubic interpolation. Finally, the interpolated high frequency subbands in addition to the sparse recovered solution are combined to produce a HR image using inverse discrete wavelet transform (IDWT). Experiments on different LR test images demonstrate that our approach produces relatively less artifacts compared to the existing methods.

Soil respiration, microbial biomass and nutrient availability after the second amendment are influenced by legacy effects of prior residue addition

It is well-known that plant residue composition (e.g. C/nutrient ratio, organic C composition) and environmental factors influence decomposition rate. Nevertheless, the magnitude and timing of nutrient availability after residue addition often differ from predictions indicating other factors may also play a role. One such factor could be the legacy effect which we define as the influence of previous plant residue C/nutrient ratio on the microbial activity, biomass and nutrient availability after addition of a second residue with the same or different C/nutrient ratio. To a loam soil, a high (C/N 60) or a low (C/N 20) C/N ratio residue were added at 10 g kg−1. After 20 days, either high or low C/N residue was added at the same rate, resulting in four residue treatments: high–high (HH), high-low (HL), low–low (LL) or low–high (LH). Unamended soil served as control. Soil respiration was measured continuously over 42 days. Soils were destructively sampled on day 0 (after first residue addition), 7, 20 (before second addition), 28 and 42 (after second addition) and analysed for microbial biomass C, N and P as well as available N and P. After the first residue addition, soil respiration, available N and P and microbial biomass C, N and P were higher with low C/N than high C/N residue. The previously added low C/N residue (treatment LH) stimulated soil respiration, microbial growth and nutrient availability compared to high C/N residue following high C/N residue (HH). Compared to LL, the previously added high C/N residue delayed the peak of respiration rate after the addition of low C/N residue and reduced MBC, MBN and MBP concentrations by about 25% on day 28. There were only small and mainly transient differences in the measured parameters between HL and LH. This study showed that the C/N ratio of the previously added residue influences microbial activity and nutrient availability. In this short term study, a large proportion of the residue added initially remained in the soil at the time of the second addition (70–90% of added) which would be decomposed together with the residue added on day 21. The observed legacy effect is likely due to nutrient availability and amount of previously added residue still present in soil at the time when the second residue is added.

Inert gas narcosis disrupts encoding but not retrieval of long term memory

Exposure to increased ambient pressure causes inert gas narcosis of which one symptom is long-term memory (LTM) impairment. Narcosis is posited to impair LTM by disrupting information encoding, retrieval (self-guided search), or both. The effect of narcosis on the encoding and retrieval of LTM was investigated by testing the effect of learning–recall pressure and levels of processing (LoP) on the free-recall of word lists in divers underwater. All participants (n=60) took part in four conditions in which words were learnt and then recalled at either low pressure (1.4–1.9atm/4–9msw) or high pressure (4.4–5.0atm/34–40msw), as manipulated by changes in depth underwater: low–low (LL), low–high(LH), high–high (HH), and high–low (HL). In addition, participants were assigned to either a deep or shallow processing condition, using LoP methodology. Free-recall memory ability was significantly impaired only when words were initially learned at high pressure (HH & HL conditions). When words were learned at low pressure and then recalled at low pressure (LL condition) or high pressure (LH condition) free-recall was not impaired. Although numerically superior in several conditions, deeper processing failed to significantly improve free-recall ability in any of the learning–recall conditions. This pattern of results support the hypothesis that narcosis disrupts encoding of information into LTM, while retrieval appears to be unaffected. These findings are discussed in relation to similar effects reported by some memory impairing drugs and the practical implications for workers in pressurised environments.

Robust image watermarking scheme in lifting wavelet domain using GA-LSVR hybridization

This paper presents an imperceptible, robust, secure and efficient image watermarking scheme in lifting wavelet domain using combination of genetic algorithm (GA) and Lagrangian support vector regression (LSVR). First, four subbands low–low (LL), low–high (LH), high–low (HL) and high–high (HH) are obtained by decomposing the host image from spatial domain to frequency domain using one level lifting wavelet transform. Second, the approximate image (LL subband) is divided into non overlapping blocks and the selected blocks based on the fuzzy entropy are used to embed the binary watermark. Third, based on the correlation property of each transformed selected block, significant lifting wavelet coefficient act as target to LSVR and its neighboring coefficients (called feature vector) are set as input to LSVR to find optimal regression function. This optimal regression function is used to embed and extract the scrambled watermark. In the proposed scheme, GA is used to solve the problem of optimal watermark embedding strength, based on the noise sensitivity of each selected block, in order to increase the imperceptibility of the watermark. Due to the good learning capability and high generalization property of LSVR against noisy datasets, high degree of robustness is achieved and is well suited for copyright protection applications. Experimental results on standard and real world images show that proposed scheme not only efficient in terms of computational cost and memory requirement but also achieve good imperceptibility and robustness against geometric and non geometric attacks like JPEG compression, median filtering, average filtering, addition of noise, sharpening, scaling, cropping and rotation compared with the state-of-art techniques.

Fusion Framework for Robust and Secured Watermarking

This paper presents a robust and secure watermarking technique for digital image. To implement the technique, Discrete Wavelet Transform (DWT) is applied on cover image. Further on Low-Low (LL) sub-band of DWT, Discrete Cosine Transform (DCT) is applied which is followed by Singular Value Decomposition (SVD). To introduce the secure watermarking, watermark is secured using Arnold Transformation and embedded in the cover image. Parameters such as Peak Signal to Noise Ratio (PSNR) and Normalized Correlation (NC) are used for checking the reliability of the proposed technique. Different attacks like noise, filtering, rotation, cropping, flipping, and compression are applied on watermarked image to check the robustness of the proposed approach.

Space–time indicators in interdependent urban–environmental systems: A study on the Huai River Basin in China

Cities in the Huai River Basin are experiencing rapid urbanization, which has resulted in many challenges. Based on the concept of ‘coupling’ – the interaction between the man-made urban environment and the ecological environment – this paper presents a comprehensive index system and an interlinked planning model which focuses on the nature of policy coordination between urbanization and the environment. This model is operationalized using statistical data collected from 2003, 2006, and 2009 to assess the coupling relationship between urban and environmental subsystems. Furthermore, Exploratory Spatial Data Analysis (ESDA) is applied to examine dynamic spatial patterns of impacts of both coupling and policy coordination. The results show that the degree of interdependency of the urbanization–environment system in the Huai River Basin has gradually increased over the years 2003–2006, reaching a maximum value in 2006, but decreasing thereafter. Both the degree of coupling and the degree of policy coordination appear to fluctuate over time. In the period 2003–2009, the urbanization–environment system showed always antagonistic and low coupling coordination features. The degree of both coupling and policy coordination of all cities in the area shows a positive spatial autocorrelation and similar characteristics of spatial agglomeration. This agglomeration trend is strengthening, as time goes on. In the period 2003–2009, cities of type Low–Low (LL) and type High–High (HH) occupied the dominant position in terms of number and area. Cities of type LL are mainly found in the southwest of the Huai River Basin, and cities of type HH mainly in the east and northeast. However, the difference between such cities and their neighbours is tending to decrease. In addition, there is a low number of cities of type high–low (HL) and type low–high (LH) in the Huai River Basin.

Image Resolution Enhancement in Satellite Based On Discrete Wavelet Transform

Satellite images are being used in many fields of research. One of the major issues of these types of images is their resolution. In this project, I propose a new satellite image resolution enhancement technique based on the interpolation of the high-frequency sub bands obtained by discrete wavelet transform (DWT) and the input image. Resolution of an image has been always an important issue in many imageand videoprocessing applications, such as video resolution enhancement, feature extraction, and satellite image resolution enhancement. Interpolation in image processing is a method to increase the number of pixels in a digital image. Interpolation has been widely used in many image processing applications, such as facial reconstruction,. The DWT operation results in four decomposed sub band images referred to lowlow (LL), low-high (LH), high-low (HL), and high-high (HH). The frequency components of those sub bands cover the full frequency spectrum of the original image. The proposed resolution enhancement technique uses DWT to decompose the input image into different sub bands. Then, the high-frequency sub band images and the input low-resolution image have been interpolated, followed by combining all these images to generate a new resolution enhanced image by using inverse DWT. In order to achieve a sharper image, an intermediate stage for estimating the high frequency sub bands has been proposed.

SU‐E‐J‐108: Texture Segmentation in Magnetic Resonance Images Using Discrete Wavelet Transform Combined with Gabor Wavelets

Purpose: Edge detection improves image readability and plays an important role in images preprocessing aimed to their segmentation and automatic recognition of their contents. The purpose of this study was to describe methods of edge detection in magnetic resonance images, with the emphasis on the use of discrete wavelet transform (DWT) combined with Gabor wavelets. Methods: Modulus maxima method by Mallat S (A Wavelet Tour of Signal Processing. Academic Press, 1998), provides the method for edge detection using wavelet transform. This method is based on finding local maxima of horizontal and vertical wavelet coefficients in the first level of wavelet decomposition because it is supposed that this level represents edges. This method was tested with various wavelet functions both on simulated and real medical images. A multiresolution approach using undecimated wavelet transform is also employed which allows the low‐low (LL), low‐high (LH), high‐low (HL), and high‐high (HH) sub‐bands to remain at full size. A simple peak finding algorithm is used to determine the peaks out of array of these texture features. Results: Using wavelet transform method, the decomposition was performed up to two levels. Gabor filters are then applied to the wavelet approximations at all levels to obtain the characteristic texture features such as entropy, second to fourth central moments and coefficient of variation. High values of the second central variance and fourth central variance signify images in which regions can be clearly differentiated. The corresponding filter outputs are compared to obtain an image containing minimum pixel values. Conclusion: A complex wavelet function could help to improve results of edge detection in real images. A comparison of basic edge detection methods including simple gradient operators and Gabor wavelets, and their combination with wavelet transform was presented. Mathematical principals were studied, as well as application of these methods.

Enhancement Of Satellite Image Resolution With Moving Objects

Satellite images are being used in many applications like Meteorology, Agriculture, Geology, Forestry, Landscape, Biodiversity, Planning, Education, Regional, Seismology and oceanography. The Enhanced satellite images make diagnostic details more obvious. The Image Enhancement is the main technique for improving the resolution and visual appearance of the image. One of the major issues in Image Enhancement is Wavelet Transform. The Wavelet Transform is the method which decomposes an image into a set of basic functions called Wavelets. These basis functions are limited in duration and are inherently local. A Resolution Enhancement technique is based on the Interpolation of the high-frequency subbands obtained by Discrete Wavelet Transforms (DWT). Bicubic interpolation is used as a intermediate stage for estimating high frequency components and it is more sophisticated than the nearest neighbor and bilinear techniques. The proposed technique has the advantages of superior resolution, sharper image and smoother edges. The PSNR improvement of the proposed technique is up to 7.19dB. Keywords - Image resolution, Edge detection, transforms, cycle spinning I. Introduction Image enhancement is a method of improving the definition of a video picture by a computer program, which reduces the lowest gray values to black and the highest to white, for the pictures from microscopes, surveillance cameras, and scanners. Improvement of the quality of a picture, with the aid of a computer, by giving it higher contrast or making it less blurred or less noisy. Image enhancement techniques can be divided into two broad categories are Spatial domain methods, which operate directly on pixels and frequency domain methods, which operate on the Fourier transform of an image. Interpolation in image processing is a method to increase the number of pixels in a digital image. Interpolation has been widely used in many image processing applications, such as facial reconstruction, multiple description coding, and image resolution enhancement .The interpolation-based image resolution enhancement has been used for a long time and many interpolation techniques have been developed to increase the quality of this task. Wavelets are also playing a significant role in many image processing applications. The 2-D wavelet decomposition of an image is performed by applying the 1-D discrete wavelet transform (DWT) along the rows of the image first, and then the results are decomposed along the columns. This operation results in four decomposed sub-band images referred to low-low (LL), low-high (LH), high-low (HL), and high-high (HH). The frequency components of those sub-bands cover the full frequency spectrum of the original image. A satellite image resolution enhancement technique is based on the interpolation of the high-frequency subband obtained by discrete wavelet transform (DWT) and the input image. This resolution enhancement technique uses DWT to decompose the input image into different subband. Then, the high-frequency subband images and the input low resolution image have been interpolated, followed by combining all these images to generate a new resolution enhanced image.

OC-081 Genetic polymorphisms and patterns of FGF19 response to meals in primary bile acid diarrhoea

IntroductionPrimary bile acid diarrhoea (PBAD) is associated with low median serum fibroblast growth factor 19 (FGF19) levels. Our group has suggested that PBAD results from impaired negative feedback by FGF19...

An SVD-based image watermarking in wavelet domain using SVR and PSO

The paper presents a novel blind watermarking scheme for image copyright protection, which is developed in the discrete wavelet transform (DWT) and is based on the singular value decomposition (SVD) and the support vector regression (SVR). Its embedding algorithm hides a watermark bit in the low–low (LL) subband of a target non-overlap block of the host image by modifying a coefficient of U component on SVD version of the block. A blind watermark-extraction is designed using a trained SVR to estimate original coefficients. Subsequently, the watermark bit can be computed using the watermarked coefficient and its corresponding estimate coefficient. Additionally, the particle swarm optimization (PSO) is further utilized to optimize the proposed scheme. Experimental results show the proposed scheme possesses significant improvements in both transparency and robustness, and is superior to existing methods under consideration here.

Daily dialysis reduces pulse wave velocity in chronic hemodialysis patients

Pulse wave velocity (PWV) is a predictor of morbidity and mortality in patients with end-stage renal disease (ESRD). Dialysis patients show cyclic changes in PWV related to their hydration status and blood pressure. Our aim is to assess the impact of daily dialysis on PWV. We performed a randomized crossover study of 60 patients who underwent standard hemodialysis (HD) three times per week for at least 6 months. Patients were classified into three groups according to their PWV values before (pre-) and after (post-) HD, with a cutoff value of 12 m s(-1), as follows: the low-low (LL) group had normal pre-HD and post-HD PWV; the high-low (HL) group had high pre-HD PWV and normal post-HD PWV; and the high-high (HH) group had high pre- and post-HD PWV. All patients continued standard HD for 2 weeks. A total of 10 patients from each group were randomly assigned to continue standard HD for 1 week and then underwent daily dialysis for 1 week. The remaining 10 patients underwent daily dialysis for 1 week and then underwent standard HD for 1 week. PWV values were measured before and 1 h after each dialysis session. With daily dialysis treatment, 2 of 20 patients (10%) moved from the PWV-HH group to the PWV-HL group, whereas 18 of 20 patients (90%) moved from the PWV-HL group to the PWV-LL group (P = 0.030). Daily dialysis reduces PWV in the ESRD patients. As PWV is a strong predictor of mortality in ESRD and has cyclic variations in patients who are on standard HD, we believe that daily dialysis may be used in patients with high PWV levels to reduce their mortality risk.

A Comparative Study of the Performance of Wavelet Decomposition of Images using Kekres Transform visavis Haar and Daubechies

In recent years Image and Signal compression have been receiving a lot of attention by scientists and researchers in order to improve storage and transmission capabilities. In this study we have compared the performance of Kekre’s wavelet with other wavelets viz. Haar and Daubechies 2 with respect to energy in the Low-Low (LL ), Low-High (LH) ,High-Low (HL) and High-High (HH) bands. The energy distribution in each band is an indicator of the performance of the transform for image compression. It is found that the percentage of energy in the low-low band is equal for Kekre’s and Haar wavelet. The daubechies 2 wavelet although offers a slightly higher energy compaction but the tradeoff is that the order of the filter being higher and therefore computational burden increases. General Terms Experimentation, Documentation, Performance, Design.

Exploratory Spatial Data Analysis of the Distribution and Evolution of Economic Growth in Loess Plateau Region during 1990-2007

Exploratory spatial data analysis(ESDA) can finely reveal the characteristics of economic growth of a region in relation to its geographical environment,and therefore is a powerful tool to help us better understand-ing the spatial-temporal dynamics of the distributions of economic factors in each region.The study of the spa-tial distribution of per capita GDP of 284 counties in the Loess Plateau region since 1990 using ESDA proved its role in investigating the rules of distribution and evolution of social or economic development.And several points can be highlighted.First,ESDA reveals significant positive global and local spatial autocorrelation of per capita GDP in the Loess Plateau region throughout the period 1990-2007.Second,the analysis of scatter plots and local indicators of spatial association(LISA) over the period indi-cates that there are four significant regional clusters persisting over time.The first is a significant High-High(HH) type of clustering,located mainly in Inner Mongolia—north Ningxia—north Shaanxi in the Loess Plateau region.The other HH forms of clustering are located in Northwest Henan and Southeast Shanxi.The largest ar-eas of low-low(LL) type of clustering are primarily located in the south of Gansu,the south of Ningxia and east of Gansu.Third,the comparative analysis of per capita GDP and average growth rates of per capita GDP suggests that the development of per capita GDP in the Loess Plateau region is mainly in a way of polarized growth in the past 20 years.And it is predictable that this trend will persist,and even get stronger in the near future.Until now there are no characteristics of β-convergence detected in the Loess Plateau region.Finally,the stable spatial patterns in Loess Plateau region indicate two kinds of economic growth modes.One is the normal economic growth pushed by industrial agglomeration,and the other is the opportunistic eco-nomic growth pulled by energy/resources exploring.The latter is far more powerful in promoting the level of per capita GDP,but always with problems of singleness of industrial type and lack of stability,sustainability and in-terference immunity during its working process in pulling economic growth.