Orthogonal Polynomial Model Research Articles

Automated Estimation of Sub-Canopy Topography Combined with Single-Baseline Single-Polarization TanDEM-X InSAR and ICESat-2 Data

TanDEM-X bistatic interferometric system successfully generated a high-precision, high-resolution global digital elevation model (DEM). However, in forested areas, two core problems make it difficult to obtain sub-canopy topography: (1) the penetrability of short-wave signals is limited, and the DEM obtained in dense forest areas contains a significant forest signal, that is, the scattering phase center (SPC) height; and (2) the single-baseline and single-polarization TanDEM-X interferometric synthetic aperture radar (InSAR) data cannot provide sufficient observations to make the existing physical model reversible for estimating the real surface phase, whereas the introduction of optical data makes it difficult to ensure data synchronization and availability of cloud-free data. To overcome these problems in accurately estimating sub-canopy topography from TanDEM-X InSAR data, this study proposes a practical method of sub-canopy topography estimation based on the following innovations: (1) An orthogonal polynomial model was established using TanDEM-X interferometric coherence and slope to estimate the SPC height. Interferometric coherence records forest height and dielectric property information from an InSAR perspective and has spatiotemporal consistency with the InSAR-derived DEM. (2) Introduce Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) data to provide more observational information and automatically screen ICESat-2 control points with similar forest and slope conditions in the local area to suppress forest spatial heterogeneity. (3) A weighted least squares criterion was used to solve this model to estimate the SPC height. The results were validated at four test sites using high-precision airborne light detection and ranging (LiDAR) data as a reference. Compared to the InSAR-derived DEM, the accuracy of the sub-canopy topography was improved by nearly 60%, on average. Furthermore, we investigated the necessity of local modeling, confirming the potential of the proposed method for estimating sub-canopy topography by relying only on TanDEM-X and ICESat-2 data.

Orthogonal Expanded Memory Polynomial Model for Circular Complex Gaussian Processes

Orthogonal Expanded Memory Polynomial Model for Circular Complex Gaussian Processes

2-D orthogonal polynomial model for concurrent dual-band digital predistortion based on complex Gaussian assumption

Two-dimensional (2-D) polynomial models are widely used in digital predistortion (DPD) design for dual-band power amplifier (PA) linearization. However, the conventional polynomial model exhibits numerical instabilities when high-order nonlinearities are included. To solve this problem, a closed-form 2-D orthogonal polynomial DPD model is deduced under the assumption of complex Gaussian processes. Compared with the Legendre orthogonal polynomial for uniform distribution, the complex Gaussian assumption is more coincident with practical communication signals. Cutting down the condition number of the input correlation matrix is the most important objective to deduce the new model, and the experiment results show that the condition number of the new model can be significantly decreased both for the Gaussian signal and the practical OFDM signal. The predistortion performance of the OFDM signal is also investigated. In the presence of 32-bit floating point single precision processing, the proposed model is more stable in the least squares (LS) parameter estimation and yield better adjacent channel power ratio (ACPR) and normalized mean square error (NMSE) improvement when compared with the conventional model and the Legendre model. At the same time, the proposed model has fewer polynomial coefficients compared with the Legendre model and accordingly reduce the complexity of coefficient estimation.

Optimal design of metallic corrugated sandwich panels with polyurea-metal laminate face sheets for simultaneous vibration attenuation and structural stiffness

With attributes such as high stiffness, high damping and lightweight, laser-welded corrugated-core (LASCOR) sandwich panels with polyurea-metal laminate (PML) face sheets were envisioned as multifunctional sandwich constructions to meet the growing needs of loading bearing and vibration/noise suppression. The sensitivity of vibration damping characteristics of these novel sandwich panels was systematically investigated using a combined finite element-modal strain energy (FE-MSE) method, and their superiority over monolithic panels having equal mass was highlighted. Subsequently, the fidelity of using the surrogate modeling technique to approximate the damping loss factor of the sandwich panel was analyzed. Under the principles of cross-validation, the orthogonal polynomial model was found to provide the most accurate predictions among four widely used surrogate models. A high-efficiency optimization procedure factoring structural stiffness, damping loss, and weight of the sandwich panel was proposed by coupling the surrogate model and an optimization algorithm. For single-objective optimization, the total weight of the optimal sandwich panel decreased by around 7% compared with that of preliminary design. Meanwhile, the Pareto fronts obtained from multi-objective optimizations revealed significant enhancements of both damping loss factor/structural stiffness and specific damping loss factor/structural stiffness.

A Video Retrieval System with Orthogonal Polynomials Model based Shot Detection, Key Frame Selection and Color Feature Extraction

A Video Retrieval System with Orthogonal Polynomials Model based Shot Detection, Key Frame Selection and Color Feature Extraction

Optimization on Pulp Delignification from Nypa Palm (Nypa fruticans) Petioles Fibre of Chemical and Microbiological Methods

Demand for handycraftpaper is getting higher lately. This paper is commonly utilized as decorative paper, interior decoration, frames, and many more. Handycraftpaper making was one alternative to waste treatment and to reduce the use of wood fiber as paper raw material. Nypa palm (Nypa fruticans) petioles which is usually thrown away has the potency to be processed as pulp because it has 42.22% of cellulose and 19.85% of lignin. Delignification with chemical and microbiological methods has been executed to find out the pulp with the lowest lignin, highest cellulose and highest yield. The chemical method used NaOH 1 N (with concentration of 5, 10, 15, and 20%) and H2O2, while the microbiological method used effective microorganisms 4 (EM4) which was economical and easy to find. Fermentation time (5, 6, 7 and 8 days) was implemented as treatment. For chemical method, the treatment that produced the optimum response of lignin and cellulose was 20% of NaOH concentration, while for the microbiological method was 8 days of fermentation. Orthogonal polynomial model showed that both chemical and microbiological methods produce cellulose had linier model that P-Value was less than 0.05. For lignin response, the chemical method obtained linier model, meanwhile the microbiological method obtained quadratic linier model where P-Value was less than 0.05.

A Simple Transform Domain Based Low Level Primitives Preserving Texture Synthesis

In this work, a new patch-based texture synthesis scheme with orthogonal polynomials model coefficients is presented. The proposed scheme has four phases. In the first phase, a block matching technique that identifies a best match, to synthesis in the output image of bigger size is designed in terms of ordered orthogonal polynomials model coefficients. In case of successful match of block, called patch-hit, the proposed scheme finds candidate blocks with triangular search, in the next phase. In the patch selection phase, the proposed scheme considers a subset of orthogonal polynomials model coefficients among the blocks, for the purpose of synthesis which consumes less memory and time. This synthesised output is smoothened in the final phase, by preserving the low level contents between the synthesised patches. The performance of the proposed scheme is measured with energy, contrast, correlation, homogeneity and entropy between the original and synthesised images and is also compared with existing texture synthesis schemes. The results are encouraging.

A Systematic Analysis of Transform Coefficients and Block Decomposition for Texture Enhancement With Orthogonal Polynomials Model

Although numerous image enhancement techniques have been reported in the literature, enhancing a specific feature of an image is a challenging task and requires careful study. With that said, a novel threefold texture enhancement technique in the orthogonal polynomials transformed domain is proposed in this paper, by identifying the contribution of coefficients toward particular low-level characteristics. In the first fold, a systematic semantic analysis of orthogonal polynomials transformed coefficients is carried out in terms of the response of the basis operators and the directional properties of the coefficients are identified. A block decomposition scheme is then proposed with Meyer's model and blocks with different low-level characteristics are identified. In the second fold, an adaptive brightness adjustment scheme is proposed for nontextured blocks. In the case of texture blocks, besides brightness adjustments, a texture sharpening scheme based on the modification of the ac transform coefficients is proposed as the third fold. The proposed texture enhancement scheme is experimented on the standard benchmark University of Illinois at Urbana-Champaign images. The performance of the proposed texture enhancement scheme is measured with standard measures and compared with existing schemes. The results are encouraging.

Analysis of CT lung images using orthogonal moment features

This paper is a study on texture analysis of the CT lung images by the segmentation methods with orthogonal moments features. The CT lung images, normal, cavitary and miliary tuberculosis are taken for study. Owing to the features of orthogonal moments are found suitable for the medical image diagnosis. The segmentation method includes histogram, Watershed and Edge based Segmentation as a base for the evaluation. The orthogonal features used here are Tchebychef, Legendre and Multi resolution enhanced orthogonal polynomial model. In order to get the features, the orthogonal moments are computed from the segmented image. The proposed work is a comparative study on performance in terms of accuracy based on orthogonal moment with segmentation methods. The accuracy is obtained through sensitivity and specificity which are computed from texture features and intensity features. The results reveal that the computation model is an efficacy for medical images.

Analysis of CT lung images using orthogonal moment features

This paper is a study on texture analysis of the CT lung images by the segmentation methods with orthogonal moments features. The CT lung images, normal, cavitary and miliary tuberculosis are taken for study. Owing to the features of orthogonal moments are found suitable for the medical image diagnosis. The segmentation method includes histogram, Watershed and Edge based Segmentation as a base for the evaluation. The orthogonal features used here are Tchebychef, Legendre and Multi resolution enhanced orthogonal polynomial model. In order to get the features, the orthogonal moments are computed from the segmented image. The proposed work is a comparative study on performance in terms of accuracy based on orthogonal moment with segmentation methods. The accuracy is obtained through sensitivity and specificity which are computed from texture features and intensity features. The results reveal that the computation model is an efficacy for medical images.

Effect of dam weight and pregnancy nutrition on average lactation performance of ewe offspring over 5 years

The foetal mammary gland is sensitive to maternal weight and nutrition during gestation, which could affect offspring milk production. It has previously been shown that ewes born to dams offered maintenance nutrition during pregnancy (day 21 to 140 of gestation) produced greater milk, lactose and CP yields in their first lactation when compared with ewes born to dams offered ad libitum nutrition. In addition, ewes born to heavier dams produced greater milk and lactose yields when compared with ewes born to lighter dams. The objective of this study was to analyse and compare the 5-year lactation performance of the previously mentioned ewes, born to heavy or light dams that were offered maintenance or ad libitum pregnancy nutrition. Ewes were milked once per week, for the first 6 weeks of their lactation, for 5 years. Using milk yield and composition data, accumulated yields were calculated over a 42-day period for each year for milk, milk fat, CP, true protein, casein and lactose using a Legendre orthogonal polynomial model. Over the 5-year period, ewes born to heavy dams produced greater average milk (P=0.04), lactose (P=0.01) and CP (P=0.04) yields than offspring born to light dams. In contrast, over the 5-year period dam nutrition during pregnancy did not affect average (P>0.05) offspring milk yields or composition, but did increase milk and lactose accumulated yield (P=0.03 and 0.01, respectively) in the first lactation. These results indicate that maternal gestational nutrition appears to only affect the first lactational performance of ewe offspring. Neither dam nutrition nor size affected grand-offspring live weight gain to, or live weight at weaning (P>0.05). Combined these data indicate that under the conditions of the present study, manipulating dam weight or nutrition in pregnancy can have some effects of offspring lactational performance, however, these effects are not large enough to alter grand-offspring growth to weaning. Therefore, such manipulations are not a viable management tool for farmers to influence lamb growth to weaning.

Unified technique for colour image retrieval based on orthogonal polynomial model and multiresolution features

This paper proposes wavelet-based orthogonal polynomial model for content-based image retrieval. The wavelet coefficients are categorised into low-frequency and high-frequency based on criteria. The criteria are adaptively determined and fixed according to the nature and structure of the image, because the wavelet-based orthogonal polynomial model spatially localises the frequency information of image. Wavelet technique is employed to derive a multiresolution pyramid structure. Features such as spectrum of energy and spatial relation among the pixels are extracted at optimum level that helps in the formation of a feature vector. The Bhattacharyya distance and orthogonal cosine similarity method is employed to find the distance value between the feature vectors of the query and target images. The proposed method is simple and suitable for fast response requirements since the features extracted at the optimum-level image contain only fewer dominant wavelet coefficients. The efficiency of the proposed feature vector is experimented with standard image databases. Also, the proposed system is compared with the other existing methods.

Optimized Feature Subset Selection and Relevance Feedback for Image Retrieval Based on Multiresolution Enhanced Orthogonal Polynomials Model

In this paper, a simple image retrieval method incorporating relevance feedback based on the multiresolution enhanced orthogonal polynomials model is proposed. In the proposed method, the low level image features such as texture, shape and color are extracted from the reordered orthogonal polynomials model coefficients and linearly combined to form a multifeature set. Then the dimensionality of the multifeature set is reduced by utilizing multi objective Genetic Algorithm (GA) and multiclass binary Support Vector Machine (SVM). The obtained optimized multifeature set is used for image retrieval. In order to improve the retrieval accuracy and to bridge the semantic gap, a correlation based k-Nearest Neighbor (k-NN) method for relevance feedback is also proposed. In this method, an appropriate relevance score is computed for each image in the database based on relevant and non relevant set chosen by the user with correlation based k-NN method. The experiments are carried out with Corel and Caltech database images and the retrieval rates are computed. The proposed method with correlation based k-NN for relevance feedback gives an average retrieval rate of 94.67%.

Modelling of individual lactation curves of Tunisian Holstein-Friesian cows for milk yield, fat, and protein contents using parametric, orthogonal and spline models

1. Combining different functions to describe milk, fat, and protein yield in goats using Bayesian multiple-trait random regression models1 H. R. Oliveira, F. F. Silva, O. H. G. B. D. Siqueira, N. O. Souza, V. S. Junqueira, M. D. V. Resende, R. R. A. Borquis, M. T. Rodrigues Journal of Animal Science CrossRef

Color image retrieval technique with local features based on orthogonal polynomials model and SIFT

In this paper, a new color image retrieval technique is proposed with local features given by Scale Invariant Feature Transform (SIFT) key points that are described in the integer-natured, computationally light Orthogonal Polynomials Transform (OPT) domain. The transform's point spread operators are derived from a generating function, modification to which has been proposed for alleviating computational complexity further. The expressive power of the transform coefficients has been exploited for forming the descriptors of SIFT key points of a given image. The key point descriptors, OPT-SIFT so formed have good expressive power, despite being shorter in length and having a reduced computational complexity. A retrieval technique has been proposed based on OPT-SIFT features. The proposed retrieval technique has been experimented with images from standard databases such COIL-100 and Corel and the results demonstrate the superiority of the proposed descriptors when compared to other descriptors.

Pseudo‐Zernike polynomials for numerically stable PA modeling using 3G+ signals

ABSTRACTThis article presents pseudo‐Zernike polynomials for numerically stable modeling and predistortion of high power amplifiers that, along with lesser coefficient dispersion, is capable of better model stability. Due to inherent properties of proposed basis function, orthogonality property of proposed basis function is independent of any phase rotation, which finds its use in baseband polynomial modeling. Moreover, in contrast to previously proposed orthogonal polynomial models, theoretically, presented model is independent from assumptions of Gaussian or uniform probability density function of the input signal unlike previous models. © 2014 Wiley Periodicals, Inc. Microwave Opt Technol Lett 56:2569–2572, 2014

Statistical framework for content-based medical image retrieval based on wavelet orthogonal polynomial model with multiresolution structure

This paper proposes wavelet based orthogonal polynomial coefficients model for content based image retrieval (CBIR). The coefficients are categorized into low-frequency and high-frequency based on a criteria. The criteria is adaptively determined and fixed according to the nature and structure of the image, because the wavelet based orthogonal polynomial model spatially localizes the frequency information. Wavelet packet and Daubechies-4 transforms are jointly used to construct both approximation (low-frequency) and detailed (high-frequency) multiresolution image subbands. Color feature are extracted from low-frequency subband based on color autocorrelogram, whereas texture features are extracted from high-frequency subband based on co-occurrence matrix. Based on these features, the feature vector is formed. The proposed CBIR method reduces the feature variation when different modalities of images are combined. The proposed system assessed two medical image databases and one general image database with Minkowski-form distance method. The experimental results show that the proposed method achieves comparable retrieval performance with medical dataset; moreover, it is very fast with low computational load. Further, the obtained results were compared with other recently developed methods such as highly adaptive wavelet method, Wavelet optimization method and effective CBIR techniques. The proposed method yields better results compared to that of existing methods.

Statistical framework for image retrieval based on multiresolution features and similarity method

The advent of large scale digital image database leads to great challenges in content-based image retrieval (CBIR) method. The CBIR is considered an active area of research; however, it comprises a strong backdrop for new methodologies and system implementations. Hence, many research contributions focus on these techniques to enable higher image retrieval accuracy while preserving the low level computational complexity. This paper proposes a CBIR method, which is based on an efficient combination of multiresolution based color and texture features. This paper considers color autocorrelogram of the hue(H) and saturation(s) components of HSV color space for color features, and value(V) component of HSV color space for texture features. These two image features are extracted by computing co-occurrence matrix at optimum level image, which is the basis for the formation of feature vector. Though the optimum level is constructed based on wavelet transform, which contains a few dominant wavelet coefficients. The efficiency of the proposed system is tested with standard image databases, and the experimental results show that the proposed method achieves better retrieval accuracy at optimum level; moreover, the proposed method is very fast with low computational load. The obtained results are compared with existing techniques such as orthogonal polynomial model, multiresolution with BDIP-BVLC method and GLCM based system, and results reveal that the proposed method outperforms the existing methods.

Estimation of crossover DPD using orthogonal polynomials in fixed point arithmetic

This paper deals with the fixed point implementation of crossover digital predistortion (DPD). The implementation of digital predistortion linearization technique on DSPs poses major challenges, regarding cost, power consumption, speed, precision and volume. Depending on resource availability and design restrictions, fixed-point DSPs may be considered as a suitable solution. Least squares estimation of crossover DPD for multiple-input, multiple-output (MIMO) applications using conventional polynomial models face numerical instability for fixed-point processing. Orthogonal polynomials on the other hand are robust to matrix inversion. Fixed point matrix inversion was implemented using LU decomposition and triangular matrix inversion. This paper examines crossover DPD design for MIMO applications, while using fixed-point arithmetic. It also compares the linearization of 2×2 MIMO transmitters in the presence of radio frequency crosstalk using both orthogonal and memory polynomial models. The performance of the two crossover DPDs has been evaluated using a 3GPP standard: orthogonal crossover DPD decreased the measured adjacent channel power ratio of the signal from −42dBc to −52dBc.

The mathematical modeling of the lactation curve for dairy traits of the donkey (Equus asinus)

In recent years, an increase in the number of donkeys farmed in Italy as a consequence of the growing demand for donkey milk for direct consumption has been observed. Some research has been carried out on jenny milk composition and on its nutritional properties, whereas milk production features are scarcely described for this species. In this work, the lactation curve shape of donkeys for milk yield and composition was investigated. A total of 453 test-day records for milk yield, fat and protein percentage, and somatic cell count of 62 lactations measured on 46 multiparous jennies of the Ragusano breed were considered. Effects of herd, age, and foaling season were assessed by using a mixed model analysis. Average and individual lactation curves were fitted using the Wood incomplete gamma function, the Cappio-Borlino modified gamma, and a third-order Legendre orthogonal polynomial model. Donkeys foaling between 6- and 10-yr-old had the highest test-day milk yield (about 1.85kg/d). Donkeys foaling in winter and autumn had a higher daily milk yield compared with those foaling in summer and spring. Less defined results were obtained for composition traits. The general pattern of the donkey lactation curve is similar to the standard shape reported for the main dairy ruminant species, with a peak yield occurring at about 5wk from parturition. Younger jennies tended to have lower production peaks and higher lactation persistency. Similarly to what is reported for dairy cattle, a large variability in individual patterns has been observed. No differences in goodness of fit have been observed between the models in the case of average lactation curves, whereas orthogonal polynomials were more efficient in fitting individual patterns.