Assessment of different genetic distances in constructing cotton core subset by genotypic values

One hundred and sixty-eight genotypes of cotton from the same growing region were used as a germplasm group to study the validity of different genetic distances in constructing cotton core subset. Mixed linear model approach was employed to unbiasedly predict genotypic values of 20 traits for eliminating the environmental effect. Six commonly used genetic distances(Euclidean,standardized Euclidean,Mahalanobis,city block,cosine and correlation distances) combining four commonly used hierarchical cluster methods(single distance,complete distance,unweighted pair-group average and Ward's methods) were used in the least distance stepwise sampling(LDSS) method for constructing different core subsets. The analyses of variance(ANOVA) of different evaluating parameters showed that the validities of cosine and correlation distances were inferior to those of Euclidean,standardized Euclidean,Mahalanobis and city block distances. Standardized Euclidean distance was slightly more effective than Euclidean,Mahalanobis and city block distances. The principal analysis validated standardized Euclidean distance in the course of constructing practical core subsets. The covariance matrix of accessions might be ill-conditioned when Mahalanobis distance was used to calculate genetic distance at low sampling percentages,which led to bias in small-sized core subset construction. The standardized Euclidean distance is recommended in core subset construction with LDSS method.

Enhancing Collaborative Filtering via Topic Model Integrated Uniform Euclidean Distance

Recommendation system can greatly alleviate the "information overload" in the big data era. Existing recommendation methods, however, typically focus on predicting missing rating values via analyzing user-item dualistic relationship, which neglect an important fact that the latent interests of users can influence their rating behaviors. Moreover, traditional recommendation methods easily suffer from the high dimensional problem and cold-start problem. To address these challenges, in this paper, we propose a PBUED(PLSA-Based Uniform Euclidean Distance) scheme, which utilizes topic model and uniform Euclidean distance to recommend the suitable items for users. The solution first employs probabilistic latent semantic analysis(PLSA) to extract users' interests, users with different interests are divided into different subgroups. Then, the uniform Euclidean distance is adopted to compute the users' similarity in the same interest subset; finally, the missing rating values of data are predicted via aggregating similar neighbors' ratings. We evaluate PBUED on two datasets and experimental results show PBUED can lead to better predicting performance and ranking performance than other approaches.

Generalized Normalized Euclidean Distance Based Fuzzy Soft Set Similarity for Data Classification

Classification is one of the data mining processes used to predict predetermined target classes with data learning accurately.This study discusses data classification using a fuzzy soft set method to predict target classes accurately.This study aims to form a data classification algorithm using the fuzzy soft set method.In this study,the fuzzy soft set was calculated based on the normalized Hamming distance.Each parameter in this method is mapped to a power set from a subset of the fuzzy set using a fuzzy approximation function.In the classification step,a generalized normalized Euclidean distance is used to determine the similarity between two sets of fuzzy soft sets.The experiments used the University of California(UCI)Machine Learning dataset to assess the accuracy of the proposed data classification method.The dataset samples were divided into training(75%of samples)and test(25%of samples)sets.Experiments were performed in MATLAB R2010a software.The experiments showed that:(1)The fastest sequence is matching function,distance measure,similarity,normalized Euclidean distance,(2)the proposed approach can improve accuracy and recall by up to 10.3436%and 6.9723%,respectively,compared with baseline techniques.Hence,the fuzzy soft set method is appropriate for classifying data.

Novelty of Different Distance Approach for Multi-Criteria Decision-Making Challenges Using q-Rung Vague Sets

In this article,multiple attribute decision-making problems are solved using the vague normal set(VNS).It is possible to generalize the vague set(VS)and q-rung fuzzy set(FS)into the q-rung vague set(VS).A log q-rung normal vague weighted averaging(log q-rung NVWA),a log q-rung normal vague weighted geometric(log q-rung NVWG),a log generalized q-rung normal vague weighted averaging(log Gq-rung NVWA),and a log generalized q-rungnormal vagueweightedgeometric(logGq-rungNVWG)operator are discussed in this article.Adescription is provided of the scoring function,accuracy function and operational laws of the log q-rung VS.The algorithms underlying these functions are also described.A numerical example is provided to extend the Euclidean distance and the Humming distance.Additionally,idempotency,boundedness,commutativity,and monotonicity of the log q-rung VS are examined as they facilitate recognizing the optimal alternative more quickly and help clarify conceptualization.We chose five anemia patients with four types of symptoms including seizures,emotional shock or hysteria,brain cause,and high fever,who had either retrograde amnesia,anterograde amnesia,transient global amnesia,post-traumatic amnesia,or infantile amnesia.Natural numbers q are used to express the results of the models.To demonstrate the effectiveness and accuracy of the models we are investigating,we compare several existing models with those that have been developed.

Research on Feature Matching Optimization Algorithm for Automotive Panoramic Surround View System

In response to the challenges posed by insufficient real-time performance and suboptimal matching accuracy of traditional feature matching algorithms within automotive panoramic surround view systems,this paper has proposed a high-performance dimension reduction parallel matching algorithm that integrates Principal Component Analysis(PCA)and Dual-Heap Filtering(DHF).The algorithm employs PCA to map the feature points into the lower-dimensional space and employs the square of Euclidean distance for feature matching,which significantly reduces computational complexity.To ensure the accuracy of feature matching,the algorithm utilizes Dual-Heap Filtering to filter and refine matched point pairs.To further enhance matching speed and make optimal use of computational resources,the algorithm introduces a multi-core parallel matching strategy,greatly elevating the efficiency of feature matching.Compared to Scale-Invariant Feature Transform(SIFT)and Speeded Up Robust Features(SURF),the proposed algorithm reduces matching time by 77%to 80%and concurrently enhances matching accuracy by 5%to 15%.Experimental results demonstrate that the proposed algorithmexhibits outstanding real-time matching performance and accuracy,effectivelymeeting the feature-matching requirements of automotive panoramic surround view systems.

Cloud model-clustering analysis based evaluation for ventilation system of underground metal mine in alpine region

Ventilation system is significant in underground metal mine of alpine region.Reasonable evaluation of ventilation effectiveness will lead to a practical improvement for the maintenance and management of ventilation system.However,it is difficult to make an effective evaluation of ventilation system due to the lack of classification criteria with respect to underground metal mine in alpine region.This paper proposes a novel evaluation method called the cloud model-clustering analysis(CMCA).Cloud model(CM)is utilized to process collected data of ventilation system,and they are converted into cloud descriptors by CM.Cloud similarity(CS)based Euclidean distance(ED)is proposed to make clustering analysis of assessed samples.Then the classification of assessed samples will be identified by clustering analysis results.A case study is developed based on CMCA.Evaluation results show that ventilation effectiveness can be well classified.Moreover,CM is used alone to make comparison of evaluation results obtained by CMCA.Then the availability and validity of CMCA is verified.Meanwhile,difference of CS based ED and classical ED is analyzed.Two new clustering analysis methods are introduced to make comparison with CMCA.Then the ability of proposed CMCA to meet evaluation requirements of ventilation system is verified.

Understanding the spatial representativeness of air quality monitoring network and its application to PM_(2.5)in the China's Mainland

Air pollution has seriously endangered human health and the natural ecosystem during the last decades.Air quality monitoring stations(AQMS)have played a critical role in providing valuable data sets for recording regional air pollutants.The spatial representativeness of AQMS is a critical parameter when choosing the location of stations and assessing effects on the population to long-term exposure to air pollution.In this paper,we proposed a methodological framework for assessing the spatial representativeness of the regional air quality monitoring network and applied it to ground-based PM_(2.5)observation in the mainland of China.Weighted multidimensional Euclidean distance between each pixel and the stations was used to determine the representativeness of the existing monitoring network.In addition,the K-means clustering method was adopted to improve the spatial representativeness of the existing AQMS.The results showed that there were obvious differences among the representative area of 1820 stations in the mainland of China.The monitoring stations could well represent the PM_(2.5)spatial distribution of the entire region,and the effectively represented area(i.e.the area where the Euclidean distance between the pixels and the stations was lower than the average value)accounted for 67.32%of the total area and covered 93.12%of the population.Forty additional stations were identified in the Northwest,North China,and Northeast regions,which could improve the spatial representativeness by 14.31%.

Phishing Detection with Image Retrieval Based on Improved Texton Correlation Descriptor

Anti-detection is becoming as an emerging challenge for anti-phishing.This paper solves the threats of anti-detection from the threshold setting condition.Enough webpages are considered to complicate threshold setting condition when the threshold is settled.According to the common visual behavior which is easily attracted by the salient region of webpages,image retrieval methods based on texton correlation descriptor(TCD)are improved to obtain enough webpages which have similarity in the salient region for the images of webpages.There are two steps for improving TCD which has advantage of recognizing the salient region of images:(1)This paper proposed Weighted Euclidean Distance based on neighborhood location(NLW-Euclidean distance)and double cross windows,and combine them to solve the problems in TCD;(2)Space structure is introduced to map the image set to Euclid space so that similarity relation among images can be used to complicate threshold setting conditions.Experimental results show that the proposed method can improve the effectiveness of anti-phishing and make the system more stable,and significantly reduce the possibilities of being hacked to be used as mining systems for blockchain.

SERIALLY CONCATENATED CONTINUOUS PHASE MODULATION WITH REDUCED ITERATIVE DEMODULATION AND DETECTION

A reduced state Soft Input Soft Output (SISO) a posteriori probability algorithm for Seri-ally Concatenated Continuous Phase Modulation (SCCPM) is proposed in this paper. Based on the Reduced State Sequence Detection (RSSD),it has more general form compared with other reduced state SISO algorithms. The proposed algorithm can greatly reduce the state number,thus leads to the computation complexity reduction. It also minimizes the degradation in Euclidean distance with decision feedback in the reduced state trellis. Analysis and simulation results show that the perform-ance degradation is little with proper reduction scheme.

SIMPLE QUALITY ASSESSMENT FOR BINARY IMAGES

Usually image assessment methods could be classified into two categories: subjective as-sessments and objective ones. The latter are judged by the correlation coefficient with subjective quality measurement MOS (Mean Opinion Score). This paper presents an objective quality assessment algorithm special for binary images. In the algorithm, noise energy is measured by Euclidean distance between noises and signals and the structural effects caused by noise are described by Euler number change. The assessment on image quality is calculated quantitatively in terms of PSNR (Peak Signal to Noise Ratio). Our experiments show that the results of the algorithm are highly correlative with subjective MOS and the algorithm is more simple and computational saving than traditional objective assessment methods.

Land use induced blurring of forest-grassland transition in north-west Himalaya-A case study using Moving Split Window boundary detection technique

Transition zone or ecotone is a unique community sandwiched between two communities/ecosystems/biomes.These ecotones in Himalaya remained unexplored for many ecological aspects like biodiversity,phyto-sociology,boundary detection and even impact of change in land use pattern(anthropogenic activity).The most accepted and widespread technique called as Moving Split Window(MSW) technique is used for detection of vegetation and environmental boundaries at four different sites in the lesser stratum of north-west Himalaya.All the four sites were at different distances from the nearest human inhabited area.Anthropogenic activities like grazing,herb collection,wood collection etc.were common at proximal sites.Such activities have led to the change in land use pattern.In this study,we have tried to work out the impact of the change in land use pattern(human interference) on the vegetation and basic environmental parameters like soil pH,electrical conductivity and moisture on forestgrassland ecotone in north-west Himalaya.Data on mountain steepness was also collected and analyzed.The dissimilarity profile using the statistical tool Squared Euclidian Distance(SED) indicated that species turnover locations increase with the increase in distance of ecotones from human settlements.The ecotones at distant locations from human villages are characterized with blunt as well as sharp peaks for vegetation data,however,conditions are reverse in case of the proximal sites.The study also reveals that as the distance between the ecotone and human settlements increases,the complex conditions like multiple vegetation boundaries prevails on the transitions.In this regard,land use induced blurring of forest-grassland transition in north-west Himalaya is summed up in the study.

Novel Mapping Design Criterion for BICM-ID with Square QAM Constellations

Mapping design criteria of bit-interleaved coded modulation with iterative decoding (BICM-ID) with square 16QAM are analyzed. Three of the existing criteria are analyzed and compared with each other. Through the comparison, two main characters of the mapping design criteria are found. They are the harmonic mean of the minimum squared Euclidean distance and the average of Hamming distances with the nearest Euclidean distance. Based on these two characters, a novel mapping design criterion is proposed and a label mapping named mixed mapping is searched according to it. Simulation results show that mixed mapping performs better than the other mappings in BICM-ID system.

Research on the Algorithm of Image Matching based on Improved SIFT

The paper analyze and improve the SIFT optimized algorithm, and proposes an image matching method for SIFT algorithm based on quasi Euclidean distance and KD-tree. Experiments show that this algorithm has matching more points, high matching accuracy, no repealed points and higher advantage of matching efficiency based on keeping the basic characteristics of SIFT algorithm unchanged, and provides precise matching point to generate precise image stitching and other related fields of the follow-up product. At the same time, this method was applied to the layout optimization and achieved good results.

A 3-D Polarization Quadrature Amplitude Modulation Method and Constellation Mapping

As a new three-dimensional(3-D)modulation,Polarization Quadrature Amplitude Modulation(PQAM) can be regarded as the combination of Pulse amplitude modulation(PAM) and Quadrature Amplitude Modulation(QAM) Modulation.It can better improve the digital communication efficiency and reduce the Symbol error rate(SER) of the system than one-dimensional or two-dimensional modulation scheme.How to design a feasible constellation is the most concerned problem of PQAM currently.This paper first studies the relationship between the SER theoretical value of PQAM and the distribution of M and N,proposes a new M,N allocation scheme.Secondly,a new and straightforward design method of constructing higher-level 3-D signal constellations,which can be matched with the PQAM,and the constellation can divided into three different structures according to the ary for PQAM.Finally,the simulation results show that:in PQAM system,the modulation scheme and the constellation mapping scheme are proposed in this paper which can effectively reduce the system SER and improve the anti-noise performance of the system.

The Relation between Genetic Difference for Parents of Hybrid Rice and Heterosis

Exploring the indica-japonica differentiation in parents of hybridization can provide theoretical bases for utilizing inter-subspecific heterosis. In this study, 5 sterile lines and 18 self-bred restorer lines were used as female parents and male parents respectively. Then 90 combinations were constructed by incomplete diallel cross followed by relationship analysis between parental Cheng’s index difference value and Euclidean distance and heterosis. The results showed a significant correlation between several phenotype values, super male parent heterosis and control heterosis and Euclidean distance or Cheng’s index difference value. However, it was no significant correlation for yield. Further analysis found a common interval, 3.41 - 3.46 for Euclidean distance and 3 - 4 for cheng’s index difference value of parents, which was significant or high significant positive correlated with phenotype value, super male parent and control heterosis of main yield traits. This illustrates that the larger the genetic difference of parents was, the stronger the heterosis combinations were, when the genetic differences of parents were in an appropriate range.

A Kernel-Based Nonlinear Representor with Application to Eigenface Classification

This paper presents a classifier named kernel-based nonlinear representor (KNR) for optimal representation of pattern features. Adopting the Gaussian kernel, with the kernel width adaptively estimated by a simple technique, it is applied to eigenface classification. Experimental results on the ORL face database show that it improves performance by around 6 points, in classification rate, over the Euclidean distance classifier.

A Normalization Method of Moment Invariants for 3D Objects on Different Manifolds

3D objects can be stored in computer of different describing ways, such as point set, polyline, polygonal surface and Euclidean distance map. Moment invariants of different orders may have the different magnitude. A method for normalizing moments of 3D objects is proposed, which can set the values of moments of different orders roughly in the same range and be applied to different 3D data formats universally. Then accurate computation of moments for several objects is presented and experiments show that this kind of normalization is very useful for moment invariants in 3D objects analysis and recognition.

Phenotypic and molecular diversity-based prediction of heterosis in pearl millet(Pennisetum glaucum L.(R.)Br.)

Genetic distances between hybrid parents based on phenotypic traits and molecular markers were investigated to assess their relationship with heterosis for grain and stover yield and other traits in pearl millet(Pennisetum glaucum [L.] R. Br.). Fifty-one hybrids developed using 101 hybrid parents(B and R lines) and showing a wide range of genetic distance between their parents based on eight phenotypic traits and 28–38 SSRs were evaluated in two sets for two seasons. The correlation between Euclidean distance(phenotypic distance, ED) and simple matching distance(molecular distance, SM) for parents of both sets was low but positive and significant(r = 0.2, P < 0.001).The correlation of ED in parents with better-parent heterosis for grain yield was similar in both sets(r =0.38, P < 0.05). SM was not correlated with heterosis for grain yield in either set of hybrids.The results showed that phenotypic distance could be a better predictor of heterosis than molecular distance. The correlation between phenotypic distance and heterosis was not strong enough to permit the use of phenotypic diversity among parents as a major selection criterion for selection of parental lines displaying high levels of heterosis for grain and stover yield in pearl millet.

An automated detection of glaucoma using histogram features

Glaucoma is a chronic and progressive optic neurodegenerative disease leading to vision deterioration and in most cases produce increased pressure within the eye. This is due to the backup of fluid in the eye; it causes damage to the optic nerve. Hence, early detection diagnosis and treatment of an eye help to prevent the loss of vision. In this paper, a novel method is proposed for the early detection of glaucoma using a combination of magnitude and phase features from the digital fundus images. Local binary patterns(LBP) and Daugman’s algorithm are used to perform the feature set extraction.The histogram features are computed for both the magnitude and phase components. The Euclidean distance between the feature vectors are analyzed to predict glaucoma. The performance of the proposed method is compared with the higher order spectra(HOS)features in terms of sensitivity, specificity, classification accuracy and execution time. The proposed system results 95.45% output for sensitivity, specificity and classification. Also, the execution time for the proposed method takes lesser time than the existing method which is based on HOS features. Hence, the proposed system is accurate, reliable and robust than the existing approach to predict the glaucoma features.

Prospect Theory Based Hesitant Fuzzy Multi-Criteria Decision Making for Low Sulphur Fuel of Maritime Transportation

The environmental impact of maritime transport has now become a relevant issue in sustainable policy formulation and has attracted increasing interest from academia.For the sustainable development of maritime transport,International Maritime Organization stipulates that the sulfur content of ship emissions will reach 0.5 from 2020.With the approaching of the stipulated implementation date,shipowners need to adopt scientific methods to make decision on low sulfur fuel.In this study,we applied a prospect theory based hesitant fuzzy multi-criteria decision-making model to obtain the optimal decision of low Sulphur marine fuel.For this purpose,the hesitant fuzzy decision matrix is established to collect expert opinions,the maximizing deviation method is adopted to determine criteria weights.According to calculate the Euclidean distance from the reference points,we obtain the comprehensive prospect values of alternatives.Lastly,a case study is carried out to illustrate the significance and effectiveness of the proposed methodology.The innovation of this study is that it is the first-time adopting prospect theory and hesitate fuzzy sets to multi-criteria decision making for low Sulphur marine fuel,which provides an effective decision model for shipping companies under Low Sulphur regulations,and can also be extended to other industries.