Minimum Variance Method Research Articles

Specifying Quality of Governance in Transition Economies with Cluster Analysis

With the collapse of Soviet Russia, transition of communist countries towards the market economy not only caused an economic transformation but also formal and informal constraints, institutional structures and legal norms in society were changed. IMF's and World Bank’s reports and governance concept containing neoliberal policies that took place in World Bank’s publications were important factors behind this transition. In this study, it is aimed to analyze quality of governance in transition economies by the year 2014. With this framework, Cluster analysis is performed by Ward (1963) Minimum Variance Method by using World Bank Governance and Freedom House dataset.

Beamforming Through Regularized Inverse Problems in Ultrasound Medical Imaging.

Beamforming (BF) in ultrasound (US) imaging has significant impact on the quality of the final image, controlling its resolution and contrast. Despite its low spatial resolution and contrast, delay-and-sum (DAS) is still extensively used nowadays in clinical applications, due to its real-time capabilities. The most common alternatives are minimum variance (MV) method and its variants, which overcome the drawbacks of DAS, at the cost of higher computational complexity that limits its utilization in real-time applications. In this paper, we propose to perform BF in US imaging through a regularized inverse problem based on a linear model relating the reflected echoes to the signal to be recovered. Our approach presents two major advantages: 1) its flexibility in the choice of statistical assumptions on the signal to be beamformed (Laplacian and Gaussian statistics are tested herein) and 2) its robustness to a reduced number of pulse emissions. The proposed framework is flexible and allows for choosing the right tradeoff between noise suppression and sharpness of the resulted image. We illustrate the performance of our approach on both simulated and experimental data, with in vivo examples of carotid and thyroid. Compared with DAS, MV, and two other recently published BF techniques, our method offers better spatial resolution, respectively contrast, when using Laplacian and Gaussian priors.

Unbiased pseudo-Cℓpower spectrum estimation with mode projection

With the steadily improving sensitivity afforded by current and future galaxy surveys, a robust extraction of two-point correlation function measurements may become increasingly hampered by the presence of astrophysical foregrounds or observational systematics. The concept of mode projection has been introduced as a means to remove contaminants for which it is possible to construct a spatial map reflecting the expected signal contribution. Owing to its computational efficiency compared to minimum-variance methods, the sub-optimal pseudo-Cl (PCL) power spectrum estimator is a popular tool for the analysis of high-resolution data sets. Here, we integrate mode projection into the framework of PCL power spectrum estimation. In contrast to results obtained with optimal estimators, we show that the uncorrected projection of template maps leads to biased power spectra. Based on analytical calculations, we find exact closed-form expressions for the expectation value of the bias and demonstrate that they can be recast in a form that allows a numerically efficient evaluation, preserving the favorable O(l_max^3) time complexity of PCL estimator algorithms. Using simulated data sets, we assess the scaling of the bias with various analysis parameters and demonstrate that it can be reliably removed. We conclude that in combination with mode projection, PCL estimators allow for a fast and robust computation of power spectra in the presence of systematic effects -- properties in high demand for the analysis of ongoing and future large scale structure surveys.

Predicting Key Phenological Stages for 17 Grapevine Cultivars ( Vitis vinifera L.)

Weather conditions have a significant impact on crops, and temperature is one of the main factors that controls plant development. Thermal time models based on temperature have been applied to predict the development of many species. To implement these models, determination of an appropriate base temperature (Tb) is required to characterize the differences among developmental stages and cultivars. The goal of this study was to determine the unique Tb and degree-days (DD) to predict budbreak, bloom, and veraison for 17 cultivars. Tb’s were estimated with the minimum variance method using phenological data collected over 23 years in Prosser, WA. Tb increased throughout grapevine development and ranged from 6.1 to 8.4°C for budbreak, from 7.2 to 10.5°C for bloom, and from 9.4 to 12.8°C for veraison. Starting DD accumulation on 1 Jan and using the Tb’s estimated for each cultivar, the duration to budbreak ranged from 78 to 180 DD, from budbreak to bloom ranged from 240 to 372 DD, and from bloom to veraison ranged from 556 to 800 DD. Errors in prediction varied between 4.8 and 7.8 days to budbreak, between 1.9 and 5.5 days to bloom, and between 7.1 and 12.4 days to veraison. Based on the errors in prediction, models that used an estimated Tb specific for a phenological stage performed better than models that had a fixed Tb of 0 and 10°C. The estimated thermal time parameters provide a simple approach for characterizing differences among cultivars and assist growers and industry in implementing management practices through simple decision support tools based on thermal time models.

Toward autonomous surface-based infrared remote sensing of polar clouds: cloud-height retrievals

Abstract. Polar regions are characterized by their remoteness, making measurements challenging, but an improved knowledge of clouds and radiation is necessary to understand polar climate change. Infrared radiance spectrometers can operate continuously from the surface and have low power requirements relative to active sensors. Here we explore the feasibility of retrieving cloud height with an infrared spectrometer that would be designed for use in remote polar locations. Using a wide variety of simulated spectra of mixed-phase polar clouds at varying instrument resolutions, retrieval accuracy is explored using the CO2 slicing/sorting and the minimum local emissivity variance (MLEV) methods. In the absence of imposed errors and for clouds with optical depths greater than ∼ 0.3, cloud-height retrievals from simulated spectra using CO2 slicing/sorting and MLEV are found to have roughly equivalent high accuracies: at an instrument resolution of 0.5 cm−1, mean biases are found to be ∼ 0.2 km for clouds with bases below 2 and −0.2 km for higher clouds. Accuracy is found to decrease with coarsening resolution and become worse overall for MLEV than for CO2 slicing/sorting; however, the two methods have differing sensitivity to different sources of error, suggesting an approach that combines them. For expected errors in the atmospheric state as well as both instrument noise and bias of 0.2 mW/(m2 sr cm−1), at a resolution of 4 cm−1, average retrieval errors are found to be less than ∼ 0.5 km for cloud bases within 1 km of the surface, increasing to ∼ 1.5 km at 4 km. This sensitivity indicates that a portable, surface-based infrared radiance spectrometer could provide an important complement in remote locations to satellite-based measurements, for which retrievals of low-level cloud are challenging.

Identification of five clusters of comorbidities in a longitudinal Japanese chronic obstructive pulmonary disease cohort

Background and objectivesPatients with chronic obstructive pulmonary disease (COPD) frequently suffer from various comorbidities. Recently, cluster analysis has been proposed to examine the phenotypic heterogeneity in COPD. In order to comprehensively understand the comorbidities of COPD in Japan, we conducted multicenter, longitudinal cohort study, called the Keio COPD Comorbidity Research (K-CCR). In this cohort, comorbid diagnoses were established by both objective examination and review of clinical records, in addition to self-report. We aimed to investigate the clustering of nineteen clinically relevant comorbidities and the meaningful outcomes of the clusters over a two-year follow-up period. Material and MethodsThe present study analyzed data from COPD patients whose data of comorbidities were completed (n = 311). Cluster analysis was performed using Ward’s minimum-variance method. ResultsFive comorbidity clusters were identified: less comorbidity; malignancy; metabolic and cardiovascular; gastroesophageal reflux disease (GERD) and psychological; and underweight and anemic. FEV1 did not differ among the clusters. GERD and psychological cluster had worse COPD assessment test (CAT) and Saint George’s respiratory questionnaire (SGRQ) at baseline compared to the other clusters (CAT: p = 0.0003 and SGRQ: p = 0.00046). The rate of change in these scores did not differ within 2 years. The underweight and anemic cluster included subjects with lower baseline ratio of predicted diffusing capacity (DLco/VA) compared to the malignancy cluster (p = 0.036). ConclusionsFive clusters of comorbidities were identified in Japanese COPD patients. The clinical characteristics and health-related quality of life were different among these clusters during a follow-up of two years.

Multiagent Reinforcement Learning With Sparse Interactions by Negotiation and Knowledge Transfer.

Reinforcement learning has significant applications for multiagent systems, especially in unknown dynamic environments. However, most multiagent reinforcement learning (MARL) algorithms suffer from such problems as exponential computation complexity in the joint state-action space, which makes it difficult to scale up to realistic multiagent problems. In this paper, a novel algorithm named negotiation-based MARL with sparse interactions (NegoSIs) is presented. In contrast to traditional sparse-interaction-based MARL algorithms, NegoSI adopts the equilibrium concept and makes it possible for agents to select the nonstrict equilibrium-dominating strategy profile (nonstrict EDSP) or meta equilibrium for their joint actions. The presented NegoSI algorithm consists of four parts: 1) the equilibrium-based framework for sparse interactions; 2) the negotiation for the equilibrium set; 3) the minimum variance method for selecting one joint action; and 4) the knowledge transfer of local Q -values. In this integrated algorithm, three techniques, i.e., unshared value functions, equilibrium solutions, and sparse interactions are adopted to achieve privacy protection, better coordination and lower computational complexity, respectively. To evaluate the performance of the presented NegoSI algorithm, two groups of experiments are carried out regarding three criteria: 1) steps of each episode; 2) rewards of each episode; and 3) average runtime. The first group of experiments is conducted using six grid world games and shows fast convergence and high scalability of the presented algorithm. Then in the second group of experiments NegoSI is applied to an intelligent warehouse problem and simulated results demonstrate the effectiveness of the presented NegoSI algorithm compared with other state-of-the-art MARL algorithms.

A Novel Clustering based Feature Subset Selection Framework for Effective Data Classification

Background/Objectives: A novel feature selection framework using minimum variance method is proposed. The purpose of the proposed method is to reduce the computational complexity, reduce the number of initial features and increase the classification accuracy of the selected feature subsets. Methods/Statistical Analysis: The clusters are formed using minimum variance method. The process must be repeated for different pairs of records and voting is done on the different sets of cluster pairs. The cluster pair which has the maximum number of votes is chosen and the highest priority member is chosen from each cluster using information gain and removing the remaining attributes, thus reducing the number of attributes. Findings: The proposed feature selector is evaluated by comparing it with existing feature selection algorithms over 9 datasets from UCI and WebKb Datasets. The proposed method shows better results in terms of number of selected features, classification accuracy, and running time than most existing algorithms. Improvements/Applications: A new feature selector using minimum variance method is implemented and found that it performs better than the popular and computationally expensive traditional algorithms.

Optimal beamformer design in the reverberant environment

Most existing methods on beamformer design, such as the minimum variance distortionless response (MVDR) method and the linearly constrained minimum variance (LCMV) method, assume that the group delay is known and often heuristically preselected to be half of the filter length. However, the optimal group delay is problem-dependent in practice, and the beamformer design performance based on the optimal beamformer response can be significantly better than the one based on the preselected beamformer response in the reverberant environment. In this paper, we consider the optimal beamformer design problem, i.e., the joint beamformer response and beamformer design problem. We introduce a new variable to represent the beamformer response and formulate the joint beamformer response design and beamformer design problem as a structured constrained convex optimization (SCCO) problem. Moreover, we exploit the separable structures of the SCCO problem and propose to use the alternating direction method of multipliers (ADMM) to solve it, and give a simple proof for the analysis of convergence. Numerical simulation results show the effectiveness of the proposed method in the reverberant environment by comparing it with the LCMV method.

Improved multiple focusing with adaptive time-reversal mirror in the ocean

The linearly constrained minimum variance (LCMV) method in adaptive signal processing is designed in such a way that it attempts to minimize the output power subject to constraints on the look directions. This adaptive method has been applied to time-reversal mirror (TRM) for simultaneous multiple focusing and its efficiency has been verified by [Kim et al., J. Acoust. Soc. Am. 109 (5), 1817–1825 (2001)]. However, the norm of the weighting vector ‖w‖ that satisfies the constraints tends to become very large when two probe sources (PS) are close to each other. It causes prominent spatial sidelobes as the weighting vector w is back-propagated in TR focusing. In this study, a relationship between ‖w‖ and PS locations is illustrated, and LCMV method is partially reformulated to calculate the weighting vector w which satisfies new constraint responses, so that sidelobes are significantly suppressed. The proposed method for improved adaptive time-reversal mirror for stable simultaneous multiple focusing is verified using numerical simulations and experimental data.

Spectral analysis comparisons of Fourier-theory-based methods and minimum variance (Capon) methods

In recent years, adaptive (data dependent) methods have been introduced into many areas where Fourier spectral analysis has traditionally been used. Although the data-dependent methods are often advanced as being superior to Fourier methods, they do require some finesse in choosing the order of the relevant filters. In performing comparisons, we have found some concerns about the mappings, particularly when related to cases involving many spectral lines or even continuous spectral signals. Using numerical simulations, several comparisons between Fourier transform procedures and minimum variance method (MVM) have been performed. For multiple frequency signals, the MVM resolves most of the frequency content only for filters that have more degrees of freedom than the number of distinct spectral lines in the signal. In the case of Gaussian spectral approximation, MVM will always underestimate the width, and can misappropriate the location of spectral line in some circumstances. Large filters can be used to improve results with multiple frequency signals, but are computationally inefficient. Significant biases can occur when using MVM to study spectral information or echo power from the atmosphere. Artifacts and artificial narrowing of turbulent layers is one such impact.

Phenotypic Diversity of Nepalese Finger Millet (Eleusine coracana (L.) Gaertn.) Accessions at IAAS, Rampur, Nepal

An experiment was conducted at Institute of Agriculture and Animal Science (IAAS), Rampur, Nepal during July 2011 to September 2011 with an objectine to determine phenotypic variability of Nepalese finger millet landraces using descriptive statistics, cluster analysis and principal component analysis. F- Value of REML procedure of mixed model analysis revealed that highly significant variation was observed in all studied 17 traits. Grain yield per plant was positive and highly significant correlated with grain yield per ear (0.769**) followed by plant height (0.529**), productive tillers number (0.473**), days to maturity (0.471**), days to heading (0.460**), days to flowering (0.457**), straw yield per plant (0.348**), finger number per ear (0.320**), thousand kernel weight (0.281**), flag leaf sheath width (0.230**) and finger length (0.211**). The ear shape (H’= 3.42) followed by grain color (H’= 3.35) showed high genetic diversity after estimated by using Shannon- Weaver index. 46% open type ear shape, 40% light brown grain color, 66% non-pigmentation, 70% absence of inflorescence branch and 54% absence of finger branch were found dominant to other on studied accessions. The distribution pattern was observed by using Ward’s minimumvariance method into five clusters. The number of accessions in a cluster was ranged from 18 in cluster IV to 1 in cluster V. The cluster V (accession no. 10996) and III (accessions no. 431 and 11024) showed early phenological traits, lowest in finger length, finger width, finger number per ear, thousand kernel weight, grain yield per ear and grain yield per plant. The cluster II and IV showed late maturity type, long finger length and width, high in finger number per ear, grain yield per ear, grain yield per plant and plant height. The first five principal components accounted for 74.9% of total variance among 50 accessions. Most of variation was contributed from phenological characters, plant height, grain yield per ear, finger length, finger width, finger number per ear and productive tillers number. These traits were positively correlated with grain yield and can be used in selection for breeding programs.Int J Appl Sci Biotechnol, Vol 3(2): 285-290 DOI: http://dx.doi.org/10.3126/ijasbt.v3i2.12413

Demographic and Economic Changes in Remote Australia

ABSTRACTVaried distribution of resources, populations and Indigenous people result in significant socio-economic differences among statistical local areas (SLAs) in remote Australia. These differences indicate that the experience of change at the height of the resources boom will differ among SLAs in the region. Using hierarchical cluster analysis with Ward's minimum variance method, four socio-economic clusters were identified among the 197 SLAs in the region. The first was the most disadvantaged, with limited resources and human capital and the highest percentage of Indigenous people. The other three clusters improved in sequence, with the fourth having the most resources with the highest employment rate and income but least number of Indigenous people. Multivariate analysis of variance with main and interaction effects showed changes in demographics, industry structure, human capital and income over the period of investigation for the region as a whole and differences in the extent of these changes among the clusters. Policy interventions in the region are suggested for each group to match its specific needs.

Morphological variability in holy basil (Ocimum tenuiflorum L.) from India

Forty-nine accessions of cultivated holy basil (Ocimum tenuiflorum L.) representing four phytogeographical regions of India were characterized for 18 qualitative and 14 quantitative traits using minimal descriptors developed by the National Bureau of Plant Genetic Resources with minor modifications. Dendrograms were constructed using Ward’s Minimum Variance method. Analysis showed high degree of variation among studied accessions indicating rich diversity represented within the populations from different phytogeographical regions and relatedness among the morphotypes.

Analysis of agro-morphological diversity and oil content in Indian linseed germplasm

One hundred and eleven accessions of linseed germplasm belonging to 32 districts of six states (Bihar, Chhattisgarh, Himachal Pradesh, Jharkhand, Madhya Pradesh and Maharashtra) in India were characterized for five qualitative and six quantitative traits. Significant variability was observed in the agro- morphological and qualitative traits. The Shannon diversity index varied from 0.23–0.70 and the phenotypic coefficient of variation ranged from 6.0% to 37.1%. IC 345425, an accession from Surgaon, Chandrapur, Maharashtra, was identified as promising for its high oil content (41.5%). The Pearson correlation matrix generated for the quantitative traits indicated that positive and high correlation was observed among days to 50% flowering and days to 80% maturity (0.9; P value < 0.0001), plant height and seed weight (0.7; P value < 0.0001), seed weight and oil% (0.6; P value < 0.0001), seed weight and number of capsules per plant (0.5; P value < 0.0001). Two major clusters were formed when the characterization data was subjected to Ward’s minimum variance method. DIVA-GIS approaches for the analysis of the diversity in linseed germplasm were mapped for quantitative traits such as plant height, number of capsules per plant and seed oil content.

A HYBRID MULTICHANNEL PROCESSING METHOD FOR SPACEBORNE HYBRID PHASED-MIMO SAR WITH APPLICATION TO MULTI-DIRECTION SWATH IMAGING

This paper proposes a hybrid multichannel processing method for spaceborne Hybrid Phased-MIMO SAR (HPMSAR) that can achieve different applications of multi-direction swath imaging on the same platform. The method is optimal because it is a combination of two-dimension (2- D) advanced digital beamforming (DBF) technology and multichannel pre-filter technology for high- resolution wide-swath SAR signal processing. Multichannel signal processing technology for future spaceborne SAR will no longer be single and this combination may be the best choice. The proposed method could avoid spectrum aliasing caused by low pulse repetition frequency (PRF), separate the overlapped echoes caused by different subpulses corresponding to multi-direction swathes and remove the range ambiguity and azimuth ambiguity deeply. At first, we build the signal model of HPMSAR system. Furthermore, the pre-filter design is presented by using matrix inversion method. Then, we address different methods applied to 2-D DBF and propose the advanced linearly constrained minimum variance (LCMV) method. Image results on simulated distributed targets validate the proposed hybrid multichannel processing method.

Simultaneous Control Loop Performance Assessment and Process Identification Based on Fractional Models

Control loop performance assessment procedures are established as key cornerstone of process optimization and monitoring. Unfortunately, many methods do not consider the maximum possible performance which can be achieved by the fixed structure controller installed in the loop (typically PID). In authors’ recent work such method was described. It was shown that - with respect to classical minimum variance method - the maximum performance is strongly influenced by the process normalized dead time. Consequently, the process model should be re-estimated during the plant operation. This paper describes technique which integrates continuous estimation of process model including normalized dead-time and control loop performance assessment. The process model is considered as fractional in order to fit well to distributed parameter systems appearing in chemical process control industries. All procedures are packed into advanced function blocks which are ready for real-time applications.

The Usage of the Digital Controller in Regulating Boost Converter

This paper proposes the modeling and simulation technique to analyze and design a Boost converter using generalized minimum variance method with discrete-time quasi-sliding mode to adjust the converter switch through a pulse width modulation (PWM), so as to enhance a stable output voltage. The control objective is to maintain the sensed output voltage stable, constant and equal to some constant reference voltage (8 volt) in the load resistance variation (24, 48, 240) Ω and input voltage variation (20, 24, 28) volt circumstances. This control strategy is very appropriate for the digitally controlled power converter and for the system requirement accomplishment, resulting high output voltage accuracy. The performance degradation in practical implementation can be expected due to noise, PWM nonlinearities, and components imperfection. The digital simulation using MATHLAB/Simulink is performed to validate the functionality of the system.

Assessment of Genetic Diversity in Guava (Psidium guajava) Germplasm Using Microsatellites

Although the varietal diversity is fairly rich in guava, most varieties lack one or more desirable characters. Hence, attempts were made for improving specific traits, viz., attractive pink pulp colour, soft seeds, medium fruit size, high TSS and high ascorbic acid. Genetic diversity analysis is a prerequisite for identifying potential parents in breeding programs and germplasm conservation. Molecular characterization helps discriminate closely-related genotypes, as, this technique is unaffected by environment, rendering it more reliable. In this study, 48 polymorphic SSRs screened from a total of 115 SSR markers were used for analyzing marker segregation in 72 guava accessions. Statistical analysis was done using IDENTITY1.0 and CERVUS 3.0 software. Cluster analysis was done with DARwin 5.0 software, using Wards Minimum Variance method, and weighted group neighbour joining method, to check reliability of grouping among clusters. The trend in grouping was found to be similar in both methods. Dendrograms generated showed that the hybrids clustered with their parents; exotic collections fell into two different sub-groups based on productivity; the wild species formed one group; and Navalar cultivars from Dharwad clustered together, reflecting similar origin.

2MTF – IV. A bulk flow measurement of the local Universe

Using the 2MASS near-infrared photometry and high signal-to-noise HI 21-cm data from the Arecibo, Green Bank, Nancay, and Parkes telescopes, we calculate the redshift-independent distances and peculiar velocities of 2,018 bright inclined spiral galaxies over the whole sky. This project is part of the 2MASS Tully-Fisher survey (2MTF), aiming to map the galaxy peculiar velocity field within 100 h^{-1}Mpc, with an all-sky coverage apart from Galactic latitudes |b|< 5 deg. A \chi^2 minimization method was adopted to analyze the Tully-Fisher peculiar velocity field in J, H and K bands, using a Gaussian filter. We combine information from the three wavebands, to provide bulk flow measurements of 310.9 +/- 33.9 km/s, 280.8 +/- 25.0 km/s, and 292.3 +/- 27.8 km/s at depths of 20 h^{-1}Mpc, 30 h^{-1}Mpc and 40 h^{-1}Mpc, respectively. Each of these bulk flow vectors points in a direction similar to those found by previous measurements. At each of the three depths, the bulk flow magnitude is consistent with predictions made by the $\Lambda$CDM model at the 1$\sigma$ level. The maximum likelihood and minimum variance method were also used to analyze the 2MTF samples, giving similar results.