text:
Comparing estimated versus the breeding value [1].
\r\n\tThis book will cover the most recent research studies done in three fields (i) Evolution and phylogenetic diversity of extremophilic microbes including Archaea, Bacteria, and fungi (ii) Enzymatic and metabolic diversity (iii) Biotechnological applications of enzymes or metabolites. On the other hand, this book will be a useful reference for finding clues to the origin of life and for exploring the biotechnology potential of these fascinating organisms.",isbn:null,printIsbn:null,pdfIsbn:null,doi:null,price:0,priceEur:0,priceUsd:0,slug:null,numberOfPages:0,isOpenForSubmission:!1,hash:"93e0321bc93b89ff73730157738f8f97",bookSignature:"Dr. Afef Najjari, Dr. Ameur Cherif, Dr. Haitham Sghaier and Dr. Hadda Imene Ouzari",publishedDate:null,coverURL:"https://cdn.intechopen.com/books/images_new/8415.jpg",keywords:"Extremophiles, Halophiles, Thermophiles, Barophiles, Psychrophiles, Alkaliphiles, Acidophiles-Archaea, Bacteria, Fungi, Microbial diversity, phylogeny, phylogenomics, metagenomics, Bacterial community, Archaeal community, enzymatic profiles, Metabolites, Secondary metabolites, Enzymes, microbial secondary metabolites, Bioprospecting of metabolites, Biotechnological applications, pharmaceutical applications",numberOfDownloads:1574,numberOfWosCitations:0,numberOfCrossrefCitations:1,numberOfDimensionsCitations:6,numberOfTotalCitations:7,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"May 17th 2018",dateEndSecondStepPublish:"July 6th 2018",dateEndThirdStepPublish:"September 4th 2018",dateEndFourthStepPublish:"November 23rd 2018",dateEndFifthStepPublish:"January 22nd 2019",remainingDaysToSecondStep:"2 years",secondStepPassed:!0,currentStepOfPublishingProcess:5,editedByType:null,kuFlag:!1,editors:[{id:"196823",title:"Dr.",name:"Afef",middleName:null,surname:"Najjari",slug:"afef-najjari",fullName:"Afef Najjari",profilePictureURL:"https://mts.intechopen.com/storage/users/196823/images/system/196823.jpeg",biography:"Dr. Afef Najjari is an assistant professor in Bioinformatics at the Faculty of Sciences of Tunisia, University of Tunis El Manar. Dr. Afef worked on several topics including genetic and enzymatic diversity of bacteria and microbial diversity in arid and saline ecosystems and mainly on archaeal groups. These works were funded by national and international projects. Currently, she is interested on metagenomic analysis, genome assemblies and annotations, transcriptomic data analysis (microarrays), and biological databases development.",institutionString:"University of Tunis El Manar",position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"0",totalChapterViews:"0",totalEditedBooks:"0",institution:null}],coeditorOne:{id:"223936",title:"Dr.",name:"Ameur",middleName:null,surname:"Cherif",slug:"ameur-cherif",fullName:"Ameur Cherif",profilePictureURL:"https://mts.intechopen.com/storage/users/223936/images/system/223936.jpg",biography:"Prof. Ameur CHERIF graduated in Natural Sciences (1995) and later obtained his PhD degree in Microbiology at the Faculty of Sciences of Tunis (FST), University of Tunis El Manar in 2001. He is the head of a research laboratory 'Biotechnology and Bio-Geo Resources valorization" at the University of Manouba. His research activities focuses on: (i) Phylogeny and ecology of extremophilic microbes; (ii) Microbial ecology of symbionts, commensal and pathogen microbiota in different hosts, (iii) biotechnological applications of active biomolecules, plant growth promotion and bioremediation. He participated in European Cost Action FA 0701, partner member in several EU FP7, Tempus, Erasmus, H2020 and bilateral projects, and serves on various review and editorial committees. Prof. Cherif has published over 80 peer reviewed articles and book chapters (h-index 21, Scopus, May 2018).",institutionString:"Manouba University",position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"1",totalChapterViews:"0",totalEditedBooks:"0",institution:null},coeditorTwo:{id:"47210",title:"Dr.",name:"Haitham",middleName:null,surname:"Sghaier",slug:"haitham-sghaier",fullName:"Haitham Sghaier",profilePictureURL:"https://mts.intechopen.com/storage/users/47210/images/system/47210.jpg",biography:"Dr. Ir. Haïtham SGHAIER (TUNAC ISO 9001/17025/15189 Lead/Technical Assessor), is an Engineer in Biotechnology and Professor (Associate) of Radiation and Computational Microbiology at the National Center for Nuclear Sciences and Technology (CNSTN) in Tunisia.\r\nHe was educated at Technical School (Kebili, Tunisia), INAT (Tunis, Tunisia) and ENIS (Sfax, Tunisia). He obtained a Master Degree from Kiryu Faculty of Engineering (Japan) and a Ph.D. following his research done at the Japan Atomic Energy Agency (JAEA, Japan). Being a glossophile, he won the 2002 best prize of Gunma University of the Third Speech Contest, in Japanese about peace, for foreign students.\r\n In 2007, he joined the CNSTN. He won the 2016 TWAS Young Arab Scientist (YAS) Prize. Basic research of Dr. Ir. Haïtham SGHAIER is related to the study of microbial resistomes, (oxidative) stress tolerance and interactions among prokaryotes/eukaryotes through computational biology and omics tools (http://orcid.org/0000-0002-8210-5345). \r\nHe has published on the evolution of ionizing-radiation-resistant prokaryotes (IRRP). In 2007, he draw attention to major discrepancy that has emerged between the ''Desiccation Adaptation Hypothesis'' and recent findings in computational biology, experimental research and terrestrial subsurface surveys.\r\n He proposed an alternative hypothesis, suggesting that desiccation tolerance could be a consequence of bacterial adaptation to ionizing radiation (Radiation Adaptation Hypothesis). \r\nAlso, he has published on bioremediation issues, particularly on bioremediation of radioactive waste using IRRP. Currently, he is implicated in various ongoing projects: multi-omics analyses (https://www.nature.com/articles/s41598-017-18839-0; https://www.nature.com/articles/ismej2015108), databases (http://home.isima.fr/irrb/; http://www.radiop.org.tn/; etc.) about IRRP, extremophiles, biotechnologically-relevant or disease-associated microbes, certification and accreditation of laboratories (ISO 11137, etc.).",institutionString:"National Center for Nuclear Sciences and Technology",position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"4",totalChapterViews:"0",totalEditedBooks:"1",institution:null},coeditorThree:{id:"259498",title:"Dr.",name:"Hadda Imene",middleName:null,surname:"Ouzari",slug:"hadda-imene-ouzari",fullName:"Hadda Imene Ouzari",profilePictureURL:"https://mts.intechopen.com/storage/users/259498/images/system/259498.png",biography:"Ouzari Hadda-Imene is graduated in Natural Sciences in 1995 and completed her PhD in Microbiology at the Faculty of Sciences of Tunis in 2002. Getting her post doctoral qualification (Habilitation) in 2008, she passed as Associate Professor and promoted as Professor in 2013 at the Faculty of Sciences of Tunis. she is teaching different courses in Molecular biology, Food Microbiology and Microbial Ecology and she is currently coordinating the conduct of a research Master "Microbiology and Molecular Epidemiology" in the FST and a principal investigator in the Laboratory of "Microorganisms and Active Biomolecules". The main research activities of Prof. Ouzari Hadda-Imene are focused on several aspects of Microbial Ecology and Food Microbiology including (i) Plant-Microbes interactions and Plant Growth Promoting Rhizobacteria selection, (ii) Biotechnological potentialities and microbial application in bioremediation, (iii) Lactic acid Bacteria characterization and microbial food security. She published about 56 peer reviewed papers and more than 600 nucleotidic sequences.",institutionString:"University of Tunis El Manar",position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"0",totalChapterViews:"0",totalEditedBooks:"0",institution:null},coeditorFour:null,coeditorFive:null,topics:[{id:"6",title:"Biochemistry, Genetics and Molecular Biology",slug:"biochemistry-genetics-and-molecular-biology"}],chapters:[{id:"62301",title:"Predator-Prey Interactions in Ciliated Protists",slug:"predator-prey-interactions-in-ciliated-protists",totalDownloads:553,totalCrossrefCites:1,authors:[null]},{id:"64400",title:"Microbial Ecology in the Atmosphere: The Last Extreme Environment",slug:"microbial-ecology-in-the-atmosphere-the-last-extreme-environment",totalDownloads:503,totalCrossrefCites:0,authors:[null]},{id:"65778",title:"Methanogenic Diversity and Taxonomy in the Gastro Intestinal Tract of Ruminants",slug:"methanogenic-diversity-and-taxonomy-in-the-gastro-intestinal-tract-of-ruminants",totalDownloads:229,totalCrossrefCites:0,authors:[null]},{id:"72010",title:"A Review on Electro-Rheological Fluid (ER) and Its Various Technological Applications",slug:"a-review-on-electro-rheological-fluid-er-and-its-various-technological-applications",totalDownloads:29,totalCrossrefCites:0,authors:[{id:"304764",title:"Dr.",name:"Sudipto",surname:"Datta",slug:"sudipto-datta",fullName:"Sudipto Datta"},{id:"304768",title:"Mr.",name:"Ranjit",surname:"Barua",slug:"ranjit-barua",fullName:"Ranjit Barua"},{id:"312785",title:"Dr.",name:"Jonali",surname:"Das",slug:"jonali-das",fullName:"Jonali Das"}]},{id:"64796",title:"Tree Species and Precipitation Effect on the Soil Microbial Community Structure and Enzyme Activities in a Tropical Dry Forest Reserve",slug:"tree-species-and-precipitation-effect-on-the-soil-microbial-community-structure-and-enzyme-activitie",totalDownloads:260,totalCrossrefCites:0,authors:[null]}],productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"},personalPublishingAssistant:{id:"280415",firstName:"Josip",lastName:"Knapic",middleName:null,title:"Mr.",imageUrl:"https://mts.intechopen.com/storage/users/280415/images/8050_n.jpg",email:"firstname.lastname@example.com",biography:"As an Author Service Manager my responsibilities include monitoring and facilitating all publishing activities for authors and editors. 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Quantitative genetics result from the (connection) combination of statistics and the principles of animal and plant breeding. In quantitative genetics, selection for economically important traits refers to use of phenotypic values of the individual and pedigree information. Genomic is based on the use of dense markers through the whole genome to predict the breeding value of the individuals [1]. Linear models (univariate and multivariate) are of fundamental importance in applied and theoretical quantitative genetics [2]. In animal breeding, two major methods were particularly applied, restricted maximum likelihood (REML) and Bayesian methods. REML has emerged as the method of choice in animal breeding for variance component estimation [3]. Bayesian analysis is gaining popularity because of its more comprehensive assumptions than those of classical approaches and its flexibility in resolving a wide range of biological problems [4, 5]. In the Bayesian approach, the idea is to combine what is known about the statistical ensemble before the data are observed (prior probability distributions) with the information coming from the data, to obtain a posterior distribution from which inferences are made using the standard probability calculus techniques [2, 6]. In recent years, Bayesian methods were broadly used to solve many of the difficulties faced by conventional statistical methods and extend the applicability of statistics on animal and plant breeding data [7]. Furthermore, Markov chain Monte Carlo (MCMC) has an important impact in applied statistics, especially from Bayesian perspective for the estimation of genetic parameters in the linear mixed effect model [2, 5]. The specific objective of this chapter was to illustrate applications of Bayesian inference in quantitative genetics and genomics. First, Bayesian models in the quantitative genetics theory are examined. Second, and in the context of the genomic selection, we presented the details of statistical modeling, using BLUP and Bayesian analyses. Third, a critical review with a focus on the prior distributions is illustrated. Finally, genomic predictions from several methods used in many countries are discussed.\n
In Bayesian inference, the idea is to combine what is known about the statistical ensemble before the data are observed (prior probability distributions) with the information coming from the data, to obtain a posterior distribution from which inferences are made using the standard probability calculus techniques.\n
P(θ) is the prior distribution, which reflects the relative uncertainty about the possible values of θ before the data are seen. P(y/θ) is the likelihood function of observing the data given the parameter which represents the contribution of y to knowledge about the parameter θ. P(θ/y) is the posterior distribution of the parameter θ given the previous information on the data.\n
The mixed linear model is of great importance in genetics and is one of the most used statistical models. Arguably, variance components and genetic parameters are important because they give an indication of the ability of species to respond to selection and thus the potential of that species to evolve. Mixed linear model is the simplest method for estimating the variance components for quantitative traits in population. In the "frequentist" view, mixed linear model is one included linearly the fixed and random effects. In the Bayesian context, there is no distinction between fixed and random effects. Detailed Bayesian analyses of models with two or more component variances will be discussed.\n
The mixed linear model is one that includes fixed and random effects.\n
Consider the linear model:\n
y is a n×1 vector of records on a trait; β is the vector of fixed effects affecting records; a is the vector of additive genetic effects; e is a vector of residual effects. X and Z are incidence matrices relating records to fixed effects and additive genetic effects, respectively. Data are assumed to be generated from the following distribution:\n
where, I is an identity matrix of order n×n and \n\n is the residual variance. Independence of various effects was assumed for the sake of simplicity in implementation. We assume a genetic model in which genes act additively within and between loci, and there are effectively an infinite number of loci. Under this infinitesimal model, and assuming further initial Hardy-Weinberg and linkage equilibrium, the distribution of additive genetic values conditional on the additive genetic covariance is multivariate normal.\n
where A is the numerator relationship matrix of order q×q; β is assumed to have a uniform distribution with bounds βmin and βmax.\n
where \n\n are interpreted as degrees of belief and a priori values for residual and additive genetic covariances. Posterior conditional distributions derived from the likelihood and the prior distributions for these parameters are,\n
bi| b-i, a, \n\n, \n\n, \n\n, with (x′ixi) is the ith element of the diagonal of X′X\n
The model equation [8] used to estimate genetic parameters and genetic breeding value for milk yield was as follows:\n
where y is the vector of milk yield, b is the vector of fixed effects, a is the vector of additive genetic effects, g is the vector of genetic group effects, p is the vector of random permanent environmental effects, and e is the vector of residual effects. X, Z, W, and ZQ are incidence matrices relating a record to fixed environmental effects in b, to a random animal effects in a, to a random permanent environment effects in p, and to genetic groups in g, respectively. g* is the vector of genetic group effects, â is a vector of breeding values. A is the numerator relationship matrix. where \n\n.\n
The conditional distribution of observed yield is defined by:\n
with the assumption of P(b) being a constant; a*|A*, \n\n;\n
where \n\n are prior values for the variances, \n\n are inverted chi-square distributions, and νi are degrees of freedom of parameters.\n
The distribution of a fixed effect is:\n
with (x′i xi) \n\n\n
where (x′i xi) is the ith element of the diagonal of X′X\n
The distribution of a permanent effect is:\n
with \n\n\n
where w′i wi is the ith element of the diagonal of W′W.\n
The distribution of a breeding value is:\n
with \n\n,\n
where z′i zi is the i th element of the diagonal of Z′Z.\n
The additive genetic variance is defined by\n
with \n\n, \n\n, and np is the number of animals being evaluated.\n
The variance of permanent environmental effects is given by:\n
with \n\n, \n\n, and np is the number of animals being evaluated.\n
Residual variance:\n
with \n\n,\n
and ne is the total number of records.\n
Comparing genetic value predictions based on polygenic model in Tunisian Holstein Population using BLUP and Bayesian analyses, Ref. [8] reported that the rankings of animals with Bayesian methods are similar to those obtained by BLUP method. Spearman's rank correlation between genetic values estimated from Bayesian procedures and genetic values estimated from BLUP methods were high (0.99). Again, Bayesian and best linear unbiased estimator (BLUE) solutions of fixed effects (month of calving, herd-year, and age-parity) showed the same patterns. The same result is reported by Ref. [9]. However, Ref. [8] illustrated different correlation estimates between two methods (Bayesian and BLUP) for cow's and bull's breeding value.\n
A massive quantity of genomic data is now available in animal and plant breeding with the revolutionary development in sequencing and genotyping. The cost of genotyping is dramatically reduced. Consequently, practices of genomic selection are nowadays possible with the high number of single nucleotide polymorphism (SNP) markers available. Therefore, it is feasible to perform analysis of the genome at a level that was not possible before [10–13]. The concept of genomic selection was introduced by Ref. [1]. The latter suggested that a set of markers covering the whole genome explain the all genetic variances and each marker is likely to be associated with a quantitative trait locus (QTL), and each QTL is in linkage disequilibrium with the markers. The number of effects per QTL to be estimated is very small. The estimated effects of all markers are summed in order to obtain the genetic value of the individual. Using simulation, Ref. [1] showed in simulation that with a high-density SNP marker, it is possible to predict the breeding value with an accuracy of 0.85 (where accuracy is the correlation between the estimated breeding value and true breeding value). The challenge in genomic evaluation is to find the best prediction method to obtain accurate genetic values of candidates. Many genomic evaluation methods have been proposed [14, 15]. The main objective of this section is to compare Bayesian methods to other methods used in genomic selection based on their predictive abilities. The study reported by Ref. [1] was considered an influential paper on dairy cattle breeding programs. First, the methods suggested correspond well to the data structures where the number of SNPs substantially exceeds the number of observations. Second, the methods of Ref. [1] constitute a logical evolution of the BLUP methodology, which is the reference method in animal genetics by considering specific variances of SNPs in the different loci. Third, the Bayesian approaches used in Ref. [1] that take into account unknown effects (measuring prior uncertainty) in a model, and combined with the ability of the Monte Carlo Markov chain, can be used in the majority of parametric statistical models.\n
The GBLUP method assumes that effects of all SNPs are sampled from the same normal distribution; the effects of all markers are assumed to be small with equal variance. Genomic BLUP was defined by the model:\n
where y is the data vector; μ is the overall mean; 1 is a vector of n ones; Z is a matrix of incidence, allocating records to the markers' effects; g is a vector of SNP effects assumed to be normally distributed \n\n, where \n\n is the additive genetic variance and G is the genomic relationship matrix; e is the vector of normal error, \n\n where \n\n is the error variance. The genomic relationship matrix was defined as \n\n, where X is matrix for specified SNP genotype coefficient at each locus, pi is the rare allele frequency for SNPi.\n
In Bayesian estimation, the information from the data is combined with the information from the prior distribution of the variances of the markers. Several Bayesian statistical analyses have been used in genomic evaluation, which differ in the hypotheses of distributions of marker effects. At the level of the modeling of the variances of the effects of the markers, Meuwissen et al. [1] proposed different distributions a priori between the Bayes A and Bayes B methods.\n
Bayes A method assumes that variance of marker effects differ among loci (e.g., \n\n is different across the j) [16]. The variances are modeled according to the scaled inverted chi-square distribution: The a priori distribution of the variances of the SNP effects is written:\n
\n\n\n, where S is the scale parameter and ν is the number of degrees of freedom. This has the advantage, if we consider a normal distribution of the data, to lead to an a posteriori conditional distribution of χ−2.\n
where, nj is the number of marker effects at segment j. The posterior distribution combines both the information provided by the data and the a priori distribution.\n
In a genomic evaluation context, Bayes B method [1, 17] assumes different variances of SNP effects, with many SNP contribute per zero effects, and a few contribute per a large effects on the trait. Meuwissen et al. [1] propose a model in which a proportion π (arbitrarily fixed at 0.95) of the markers having zero effect. The a priori distribution of the variances of the effects to the markers is then written:\n
\n\n\n with a probability π, \n\n with a probability (1 − π), Gibbs sampling cannot be used to estimate the effects and variances of the Bayes B model because of the high probability on some markers of being of zero variance. We therefore use a Metropolis-Hastings algorithm which allows the simultaneous estimation of \n\n and gj. On the basis of the results of Ref. [1] and many subsequent works, the Bayes B method is often considered the "benchmark" in terms of genomic prediction efficiency, but it is extremely costly in computational time. However, Meuwissen [18] propose an alternative to the Bayes B method which relies on a fast algorithm.\n
Legarra et al. [19] proposed a model of Bayesian lasso (BL) with different variances for residual and SNP effects which they termed BL2Var. It is therefore assumed that a large number of SNPs have an effect practically zero and that very few have large effects. Tibshirani [20] showed that the distribution of the lasso estimators can be written:\n
He suggests that the lasso estimators can be interpreted as an a posteriori mode of a model in which the regression parameters would be independent and identically distributed according to a prior double exponential distribution. Park and Casella [21] propose to use a complete Bayesian approach by assuming an a priori distribution of regression coefficients such as:\n
where σ2 represents the variance of residual effects of the model and the variance of the SNP effects. Applications of the Bayesian lasso to the genomic selection proposed by Refs. [22, 23] use the same variance σ2 to model both the distribution of effects of SNPs and residuals. De los Campos et al. [22] showed that the Bayesian lasso is close in terms of precision of prediction to the Bayes B method but with a significant reduction in the complexity of the calculations. In addition, these authors suggested using Bayesian lasso against the large number of markers included in regression models, which is typically larger than the number of records.\n
Bayesian methods such as Bayes A and Bayes B [1] have been widely used for genomic evaluation. Similar methods exist, with similar performances, developed in order to reduce computation times and to simplify statistical modeling. The Bayes C method [24] differs from Bayes B by assuming the variance associated with SNPs common to all markers. In Bayes C, as in Bayes B, the probability π that an SNP has a nonzero effect is assumed to be known. The model is similar to the Bayes B model but for a homogeneous variance of effects on all loci: \n\n with a probability 1 − π;\n\n. The main problem with the Bayes C method is that SNPs with a nonzero effect is assumed to be known. With the Bayes A method, the parameter π is equal to 1, which implies that all the markers have an effect. For the Bayes B method, π is strictly less than 1 in order to take into account the hypothesis that some SNPs may have a zero effect but is fixed arbitrarily while the intensity of the selection of variables is controlled by this parameter. Habier et al. [25] propose to modify the Bayes C method by estimating the parameter π: the parameter π is assumed to be unknown. Thus, the a priori distribution of π becomes uniform over [0, 1]. SNP modeling is the same as with Bayes C. \n\n with a probability 1 − π; \n\n where \n\n with a probability π. The various parameters of this model are estimated by MCMC methods, Markov Chain Monte Carlo [6, 26] as proposed by Ref. [25]. It is written as a function of the additive genetic variance \n\n. \n\n, where pj is the allelic frequency of SNP j.\n
The extreme speed with which events are running handicaps the process of linking new development to extant theory, and the understanding of statistical models suggested up until now [27]. The latter authors criticize the theoretical and statistical concepts followed by Ref. [1] in three levels. The first is the connection between parameters (additive genetic variances with Bayesian view) from infinitesimal models with those from marker-based models. The second is the relationship between molecular marker genotypes and similarity between relatives. The third is the connection between infinitesimal genetic models and marker-based regression models. Gianola et al. [27] argued that the methods Bayes A and Bayes B proposed by Ref. [18] require specifying parameters. The latter used formulas for obtaining the variance of SNP effects, based on some knowledge of the additive genetic variance in the population. Their development begins on the assumption that the effects of the markers are fixed and in other development, they consider them as random without a clear demonstration. Meuwissen et al. [1] explained that affecting a priori a value \n\n with a probability π means that the specific SNP does not have an effect on the trait. By contrast, Ref. [27] illustrated that a parameter having zero variance does not obligatory imply that the parameter takes zero value. The parameter could have any value, but with certainty. Gianola et al. [27] suggested the use of a nonparametric method as developed by Refs. [22, 28] because these methods do not impose hypotheses about mode of inheritance as Bayesian A and Bayesian B methods.\n
Major dairy breeding countries are now using genomic evaluation [27]. Several results have been reported around the world. Several authors reported that the reliabilities of genomic estimated breeding values (GEBV) were substantially greater than breeding values from estimated breeding values (EBV) based on pedigree information [29]. The accuracy of selection was different between countries [12]. The accuracy was dependent on the size of reference population, the heritability of the trait studied, the statistical models and approaches used for prediction of genetic values for quantitative traits, and the method achieved to estimate the accuracy [12, 27, 29]. Ref. [14] found the reliability of GEBV bulls of the Canadian and American Holstein population. A genotyping of 39,416 molecular markers of 3576 Holstein bulls was used to establish the prediction equations.\n
The prediction methods contained a linear model, in which marker effects are assumed to be normal, and a nonlinear model with a heavier tailed prior distribution to account for major genes as described by [1]. VanRaden et al. [14] reported that the combination of the polygenic effects based on pedigree information with the genomic predictions can improve the reliability to 23% greater than the reliability of polygenic effects only. The same study showed that the nonlinear model had a little advantage in reliability over the linear model for all traits except for fat and protein percentages. Genomic breeding values of 25 traits in New Zealand dairy cattle were estimated by Ref. [30]. The reference population consisted of 4500 bulls genotyped using the BovineSNP50Beadchip, containing 44,146 SNPs. Harris and Johnson [31] reported an increase in accuracy was found by using Bayesian approaches compared to BLUP methods. In Ref. [31], genomic breeding values (GBVs) for young bulls with no daughter information had accuracies ranging from 50 to 67% for milk traits, live weight, fertility, somatic cell, and longevity, versus an average 34% for progeny test. Meuwissen et al. [1] compared least squares method with BLUP and two Bayesian methods (Bayesian A and Bayesian B). The latter authors estimated the effects of 50,000 marker haplotypes from a limited number of observations (2200). Using least squares method, it is not possible to estimate all effects simultaneously. For this reason, different steps have been adopted to incorporate the effects of markers. First, they performed regression on markers for every segment of 1 cm each. Second, they calculated a Log-likelihood, which assumed to be normal at every segment of chromosome. Third, they summed all segments corresponding to a likelihood peak into multiple regression models. Using BLUP analyses, Ref. [1] considered that all SNP effects were independent and identically distributed with a known variance. Bayes A method was as BLUP at the level of the data, but differs in the variance of the chromosome segments, which assumed to have an inverted chi-square distribution. A mixture prior distribution of genetic variances was used in Bayes B method. Table 1 shows the accuracy of selection obtained by Ref. [1] from the GBLUP methods, the least squares regression and the Bayes A and Bayes B approaches. The predictive abilities of the different methods are estimated by calculating the correlation (ρ) between true and estimated breeding values and the regression (b) of true on estimated breeding value.\n
|Least squares\n
|Bayes A\n
|Bayes B\n
The least squares method is the least efficient because it overestimates effects on QTL [32]. The Bayes B approach is the most accurate both in terms of correlation and regression. However, the regression coefficient obtained by the Bayesian methods was still less than 1, and probably due to the hypothesis of a priori distribution χ−2 for Bayes A and Bayes B being different from the simulated distribution of the variances. Goddard and Hayes [11] compared the correlation of 0.85 as reported by Ref. [1] to results obtained on real data by Refs. [14, 33, 34]. VanRaden et al. [14] produced a mean correlation over several characters of 0.71 from a reference population of more than 3500 bulls. Studies have shown the superiority of genomic evaluation [35] or marker-assisted selection in France [36] on classical infinitesimal model of quantitative genetics. Several authors have applied the first genomic evaluation methods described by Ref. [1] or their derived methods on real data. The Bayes A and Bayes B approaches have found results that are often similar or slightly superior to GBLUP in terms of accuracy of genetic value prediction for the Australian Holstein-Friesian cattle breed (+0.02 to +0.07 of correlation gain between predicted and observed values), for example [12] and New Zealand (+2% correlation gain, [31]). However, the GBLUP method required less computing time than the Bayes A method [32, 37]. Gredler et al. [38] demonstrated the superiority of the Bayes B method, in terms of the accuracy of genomic estimates, on a modified Bayes A method for integrating a polygenic effect [39]. Thus, although the Bayes B method seems slightly more efficient than the Bayes A method, numerous studies showed that the Bayes B method is not so much better in terms of accuracy of the genomic estimates than a GBLUP model [40]. Again, all researches indicate that the Bayesian approaches, which assume an a priori distribution of SNPs, increase the reliability of breeding values over traditional BLUP methods [1, 12, 14]. A common conclusion is that for most quantitative traits, the hypothesis of the traditional BLUP method, that all markers are associated with equal variances, is far from reality. By comparing the results obtained in the various populations around the world, clearly, the accuracies of GEBVs were greater than breeding values estimated from progeny test based on pedigree information. Several researches suggested combining the progeny test based on pedigree information with the breeding value from genomic to calculate the final GEBV [5, 25]. Accuracy based on modeling molecular marker and pedigree information was generally superior to that of the model including only genomic or pedigree information. Hayes et al. [12] reported that a main advantage of using the both sources of information coming from polygenic breeding values and genomic information is that any QTL not detected by the marker effects may be detected by the progeny test based on pedigree information. A significant reduction in posterior mean of residual variance component was reported by Ref. [22] when pedigree and markers were considered jointly compared to pedigree-based model. In the same study, Spearman's rank correlation of estimated breeding value between model including marker information and pedigree-based model was close to 1.\n
Standard quantitative genetic model based on phenotypic and pedigree information has been very successful in term of genetic value prediction. Also, the availability of genome-wide dense markers leads researchers to be able to perform advanced genetic evaluation of quantitative traits with a high accuracy of prediction of genetic value. However, a main problem is how this information should be included into statistical genetic models. Bayesian MCMC methods appear to be convenient for genetic value prediction with a focus on the precision of the choice of prior distribution for the different parameters.\n
Vision-based SLAM systems are much appreciated nowadays in a broad spectrum of applications in robotics, autonomous navigation, and guidance systems, in airborne, underwater, and terrestrial environments [1, 2, 3, 4]. A global comparison shows that SLAM applications underwater are less abundant (see some state-of-the-art works) [5, 6, 7, 8, 9, 10].
In particular, underwater navigation generally excludes any form of global positioning system such as in other environments, so vision is a sound alternative, provided visibility is good enough. On the other hand, in turbid waters it is possible, to some extent, to navigate at low altitude on the seabed. However, in these extreme cases, the texture of the seabed may appear voluminous, which implies visual occlusions and collision probabilities. Conversely, shallow waters, rapid changes in the natural illumination, for example, due to the sunlight caustic on the floor, shadows, and flashes, can seriously damage the photometric properties of the images. In such extreme cases, the estimation of the vehicle position can be so impaired with the possibility of vehicle loss.
These characteristics of the underwater environment pose major challenges to the success of navigation, especially if it takes place in unknown regions. These challenges relate not only to robust estimations of the vehicle position and surrounding cartography [11] but also to the ability of the guidance system to avoid possible collisions at low altitudes.
In this work we will present an approach for the autonomous exploration of unknown regions of the seabed by means of a navigating system connected to a decision-making process hosted in the submarine. This vision-based approach is called Corridor SLAM (C-SLAM), because it combines SLAM techniques with a strategy to build a corridor over the seabed. The original concept was described in [12]. This document presents a generalization of previous work centered on a robust network of corridors. A variant of the basic concept uses active SLAM for exploring and building a grid map (see recent works [13, 14, 15]), but they do not enclose in any way the concept of path optimality as here, which proposes continuous paths with viewpoints associated.
A relatively close idea to the concept of corridor can be found in the "teach and repeat" method, [16], to follow a path in unfamiliar outdoor environment, such as in the exploration on the surface of Mars by a rover. However, the basic method does not include an autonomous exploration, as the first path construction is given remotely by a human operator with the aid of a camera at the rover front. Once an a priori feasible path has been defined, the vehicle can "repeat" the path that was previously "learned."
The main objective of our approach is to make the right decisions to build robust pathways to explore and configure them in a network. At the same time, the system is able to bring the vehicle back to the starting point from any position in the explored area. The robustness properties of the network are conceived in the sense that there are preferential directions to explore and are such that one allows a successful return.
Figure 1 illustrates the complete structure of the proposal for an autonomous vision-based system. It consists of three nested feedback loops, called adaptive control loop, reference adjustment loop, and dense mapping loop. They are described in details below.
The adaptive control loop generates the control actions u necessary to achieve the prescribed reference path ref and the reference speed ref. Both are provided by the guidance system. The path following will attenuate the influence of sea currents that push the vehicle off the corridor. The adaptive law circumvents the requirement to make available an AUV model which is generally complex due to its 6 degrees of freedom (see [17, 18]). Additionally, the action of marine currents and a variable speed of the AUV make hydrodynamic resistance forces very difficult to estimate and include in the dynamic model. For these reasons, controllers with self-adjustment of coefficients and dynamics adaptation seem to be much more effective than fixed controllers.
In the adaptive control loop feedback, there is the vision system that basically takes images of the seabed with the vehicle in motion to estimate the position and rate of the AUV. The estimates are termed and , respectively. The monocular camera onboard takes images in a sequence called I. The line of vision is oriented forward with a certain inclination towards the ground. In case of sunlight caustics on the seafloor [19], the quality of the images may be significantly improved by an online deflickering filter [20, 21, 22]. The filter attempts to retrieve the original photometric properties of the frame. Its output is the frame sequence Id. A feature-based SLAM algorithm estimates the position of the vehicle and a sparse depth map MS of the floor. Here, texture characteristics of the ground like self-similarity and water turbidity will generally difficult the self-localization and map estimation. Therefore they are considered as disturbances [23, 24].
The reference adjustment loop has the function of reformulating the references ref and ref according to a new scale factor of the estimated relief. This is necessary due to the variability of the estimated sparse map when revisiting the corridor site.
The outer loop of the C-SLAM, namely, the dense mapping loop, is dedicated to the construction of the corridor network, providing alternatives for bifurcating the path during the exploration. The feedback consists of the navigation system, a block for estimating a dense mapping MD of the terrain, and a block for path planning and bifurcation managing. These blocks will process the odometry information provided by the inner feedback in order to update the topography of the corridors. The dense map helps the guidance system to implement the future path to follow and to avoid collisions.
A typical navigation route consists in part of stretches between relevant points or landmarks. For example, a stretch that links a starting point with a bifurcation point, or one that covers an entire branch, or one between a bifurcation point and a point of no return. During the exploration, the path is created step by step, in which the vehicle direction is regularly changed towards preferential points that should ensure the continuity of the future path.
According to the main objectives of the exploration, C-SLAM should allow a successful return of the vehicle to the starting point from any distant point of the network. The farthest points are called no-return points. In this case, it is assumed that when the vehicle reaches a point of no return, it possesses at least half of its energy autonomy [25, 26, 27]. It is therefore that C-SLAM must permanently estimate points of no return and also ensure that there exists at least one way for the comeback before losing the energy autonomy.
The network of submarine corridors is the composition of interconnected strips on the seabed, which covers a certain explored region and allows navigation between different points of it. Topographically the strips are sequences of recognizable sites on MS and topologically by a branched network that is made up of nodes and bifurcation points.
A node is characterized by a cluster of seabed characteristics. It has a position and a viewpoint associated with the local characteristics. These characteristics are represented by keypoints of the local scene. The position of the cluster is determined by its centroid.
In the exploration, C-SLAM has to decide at each moment the next step of movement that starts from the current node. Each step consists of the identification of the next node, the direction of its centroid, and the speed of navigation to bridge the gap between nodes. The detection and identification of a node is a process that can involve many steps, during which a node is tracked and classified among other nodes in a merit order list.
In order to choose a suitable node among many possible ones, the vision system evaluates the most outstanding characteristics of the scene and then decides the optimal direction towards the corresponding node. The optimization of this direction results from the maximization of a cost function of the quantifiable requirements. Similarly, the rate is properly determined based on a previously specified vehicle cruising speed. Thus, the speed can be regulated to go up and down according to the curvature of the path. This construction procedure is repeated in the next step permanently during the exploration.
Cluster tracking involves localization on both a scattered local map and a dense map. The estimation of distances between nodes of any network branch is decisive for the planning of revisits according to the available margins of autonomy of the vehicle.
It is worth noting that estimated distances on the maps are simply evaluated on the particular scale of the monocular SLAM method, which does not necessarily coincide with the actual scale. This would not represent an obstacle to C-SLAM as long as the estimated distance between the starting point and any point of no return on the network is exactly related to half the energy autonomy of the vehicle. Some approaches define another type of scaled topology, achieved by means of stereo vision or scanner [8, 10, 27].
Bifurcation points are commonly distributed along each branch. There are two different sets of bifurcation points. The first set includes divergent points at which the paths physically bifurcate. The second set covers the so-called open bifurcation points, which were identified and ranked in a list of suitable landmarks for a future deviation of the path. The ranking position is issued in accordance to a criterion of robustness.
While each new corridor is simply added to the network as a new branch, existing corridors are updated in each new revisiting. Each round trip implies that the vehicle starts with plenty of energy autonomy. This indicates the dimensions of the explored region. Clearly, with the deployment of multi-robots, the scan will be faster [29, 30] but will not necessarily cover a larger region than with this proposal.
Since the camera is monocular, safe return is practically implemented by guiding the vehicle in reverse. This is because the system might not identify all the nodes in the 3D landscape in the way back, i.e., when navigating from the opposite direction. However, with the camera in reverse, the system is able to recognize the seabed features on the path straightforwardly. In reverse motion, all nodes are revisited but from back to front.
Before starting a round trip in the network of corridors, C-SLAM must make the decision to advance the exploration or revisit explored branches of the network. Commonly, both decisions are involved with the intent to expand the explored region. In addition, the system defines a suitable ground altitude for navigation with the end of achieving good visibility conditions.
For the next discussion, Figure 2 is taken as support.
The first corridor and each new section of the corridor network are constructed according to an optimum criterion. This criterion takes into account the most outstanding characteristics of the seabed in the form of keypoints (shortened KP), which can be categorically recognized over time and preferably from different points of view.
Autonomous navigation begins with a commissioning phase (CP) to adjust the controller and initialize SLAM techniques for scattered and dense mapping. To achieve these objectives, the vehicle is forced to travel fast short distances, zigzagging over the bottom with a rather erratic course. In this way, images can reflect the changes in texture and parallax that are necessary for a visual odometry. This, in turn, produces a stable estimation of the vehicle localization at the beginning of the exploration.
Once odometric data and are considered reliable, the adaptation of the controller coefficients is carried out by providing persistent and rich-in-frequencies changes in the reference position ref and rate ref in the main degrees of freedom. To this end, it is convenient for ref to continue in time the previous erratic motion. Additionally, the rate ref is defined as the sum of the cruising rate and a random sequence that causes soft accelerations and brakings along the trajectory. Once the convergence of the controller coefficients has been attained, the exploration starts at the current position. This position is marked as the starting point (shortened SP) and fixed for future trips.
Figure 2 (left) illustrates the construction of a corridor path from SP up to an end point (shortened EP), for instance, the estimated no-return point. The system analyzes the texture characteristics in the image sequence in order to detect robust clusters (C). Once they are recognized, the system tracks them as they pass in front of the camera. With a set of robust clusters, the system is able to triangulate frame by frame the pose of the camera.
Usually, raw information for robust cluster detection is provided by the feature-based SLAM method employed in the vision system. Certainly, for self-localization, the SLAM method constantly defines keyframes (KF) with the most stable keypoints in the image, i.e., with those that have been followed up so far. Thus, C-SLAM has to group the robust keypoints in clusters and finally perform an evaluation of cluster set. In this way, C-SLAM can define nodes (N) and eventually bifurcation points (BP).
The topology of the corridor network usually contains a diversity of elements that are described in Figure 2 (right), shortened SP, EP, KP, KF, C, N, LM, and BP. Additionally there are trifurcation points (TF) which are created from BPs: confluence points (CO) that occur between two paths that meet at a point; outer points (OP) which are detected landmarks or nodes but due to energy reasons are unreachable; dead-end points (DP) at which the vehicle must turn by force due to the absence of Ns or LMs in the horizon; and, finally, crosspoints (CR) at which a path crosses another path and these can or cannot be detected by the vision system.
It can frequently happen that, due to the existence of COs and CRs, many possible path alternatives are established to return from any point or to join two distant points in the network. Loops can also be generated during the expansion (see Figure 2, right).
As mentioned previously, the topological space is subject to an unknown and variable cartographic scale that produces accumulative odometric errors. This means that corridors can cross others without this being reflected in the global map. However, this does not invalidate the objective of C-SLAM, to return safely to SP.
It is expected that after reiterated revisits along different corridors, the number of loop closures carried out by the basic SLAM method will increase and the network will progressively take on a more real form. Simultaneously, the scale of the map will become more uniform, and the used metric will increasingly gain in accuracy.
The construction of the network is mainly based on a criterion to define a metric in the topological space. This is discussed below.
C-SLAM sets up a node to accurately synchronize future movement steps. For example, the direction to which the vehicle is to be driven in the short term in order to reach the next node is precisely defined at the current node. In addition, the speed of the vehicle on this direction is also defined here. All this gives the reference position ref and rate ref, which are used in the adaptive control system as inputs. Many other steps are synchronized at the current node as detailed below.
When C-SLAM decides to expand the network with a new branch, it searches for a landmark in the current corridor and then deflects the path in the indicated direction.
Nodes and landmarks are gestated in a common optimization process. Optimization involves maximization of a cost function with one optimum and many suboptimal results. The optimum outcome defines the first place in a ranking list, followed orderly by the suboptimal outcomes. Nodes and landmarks are selected from this ranking list.
The direction that defines ref and ref to the next node is given by the optimal result. On the other hand, the best solution in the suboptimal set is reserved for a landmark.
Unlike a node whose next direction is defined in each step, a landmark can demand many steps. A landmark is a node whose high position on the merit list ranks it for a successful future branching. Therefore, not all the nodes become landmarks.
Finally, there is a subtle difference between a node and a keyframe generated from any SLAM technique. A node is the most robust cluster in the current keyframe, and in general its appearance frequency is generally lower than the frequency of a keyframe.
During exploration, keyframes are the basis for selecting nodes. For every few keyframes of the sequence generated by the active SLAM algorithm, C-SLAM chooses the current keyframe and defines a node. The frequency of the node appearance depends physically on the visibility conditions and is commonly fixed in the commissioning phase.
On the other side, the selection of a path direction from the current node to navigate to the next (yet not defined) node follows a particular criterion. Also the selection of a promissory landmark for a future bifurcation is subject to the same criterion.
It is assumed that many keypoint clusters are faced at the same time in the ranking process in order to establish an order of merit. When the score of a particular cluster exceeds a threshold value, it is then considered as a potential landmark. Clearly, many clusters remain in the vision field for a time, so that they may continuously be tracked during their latency periods. Some of them will become landmarks.
After a landmark is lost in the field of vision, it will remain inactive with its last score and its location on the map. While in the exploration phase landmarks are generated, in a revisiting period they are consolidated, or even separated. Eventually, a landmark becomes a bifurcation point when C-SLAM decides to branch out to explore new regions.
The proposed criterion is based on a cost that is defined as a linear combination of quantifiable requirements to be met by each cluster in its latency period. Thus
where is a cost for one keypoint cluster named , given the weights with and The requirements for are described and symbolized as
High density of keypoints in a cluster: factor
Continuous traceability of a cluster: period
Robustness against the change of points of view: span angle Δθ
Alignment of a cluster on the heading direction: factor
The cost weights will be defined previously according to the emphasis placed on some particular aspects of robust navigation. Since all requirement variables will be normalized to their expected highest values, a trivial choice is generally satisfactory. The simple election should support a continuous navigation; however in this case zigzagging on the pathway should not be ruled out.
Since some detected landmarks are far away from the vehicle position, one should check whether these will be attainable with the current autonomy range of the vehicle. If they are unreachable, they will be removed from the ranking list or marked as OPs. To this goal a metric is required for the topological space of the corridor network.
The key assumption to calculate the first parameter is that visual features are very volatile in harsh underwater environments. Therefore, dense clusters may be thought to be more resistant to long-term disappearance than dispersed clusters. The first step to determine is to group keypoints of the current keyframe in clusters. For this, for example, k-means clustering can be used in combination with an efficient initialization method. For the time k of cluster appearance, the following is calculated:
where identifies the cluster of the set and is the number of KPs in . Thus, the parameter can be determined recursively by
Since the set of selected robust keypoints in the SLAM algorithm is generally sparse, clustering operation should not be excessively time-consuming.
Statistically, the maximum expected number of can be estimated in the commissioning phase as a function of the harshness of underwater environments. For example, poor visibility, self-similarity of the ground, caustic sunlight, shadows, flares, etc. are factors that contribute to reduce the number of robust keypoints.
The second parameter is related to the traceability of . The longer is detected, the more feasible it will be to achieve continuity of navigation and a safe return. The parameter is estimated in each keyframe as follows:
where is the number of consecutive KFs in which appears approximately at the same position and almost with a similar density. To assess the similarity of the clusters, a Euclidean norm can be used. In addition, attention should be paid to changes in and cluster midpoint. is estimated by thresholding all these variables properly.
Finally, the maximum number of consecutive keyframes depends to some extent on vehicle speed, altitude, camera tilt, and field of vision, among other factors. As a reference for one can take the number of KFs required to cover a distance of 10 times the length of visibility in the line of sight.
The third parameter relates the robustness of with the variation of viewpoints. A fairly large angle would be necessary for a cluster to be unambiguously detected from different points of view. applies in these two important cases: first when clusters are aligned in front of the vehicle and second when they are aligned to one side of the vehicle (see example in Figure 2, left, for landmarks LM1 and LM2). Thus, it results in
where the symbol span means the difference between the maximum and minimum value of the sequence during the tracking of . In turn, is the angle between the line that joins the camera with the cluster midpoint and the camera optical axis.
A large value of suggests a more robust link between the nodes and, on the other hand, in the case of landmarks, a more successful bifurcation.
The maximal span of is set equal to half of the horizontal field of vision of the camera, which specifies the tracking of a cluster that is initially seen far away in front of the vehicle and disappears from the field of vision sideways.
The fourth parameter plays a key role in the construction of the corridor. When the corridor is built step by step, one would normally prefer to keep the real course along a straight line rather than change the course. This is because the control of the vehicle along a line is generally more precise than on curves. Similarly, revisits on straight stretches are more immune to loop-closing failures than on curved stretches.
Therefore, in the case of nodes, should generally produce a higher cost than in the case of landmarks that are reserved for bifurcations. Hence, landmarks are generally created on one side of the path, while nodes are rather in a straight line.
Since the cost (Eq. (1)) contains terms that must be maximized, reflects the opposite of the alienation parameter here called . Therefore, should indicate how much the transverse separation of a cluster from the route is assessed in the horizontal navigation plane. To this end, both the optical axis and the line that joins the camera with the midpoint of the cluster are projected onto the navigation plane. Between these lines, the angle is obtained. A third line is defined, which connects the camera with the point furthest to the right of the vision field. Projecting this line on the navigation plane results in the angle called with the projected optical axis.
Hence, it results in geometric mean
where N is the number of assessments j during cluster tracking.
The inclusion of a cluster in the merit order list during its tracking is subject to
where is a global threshold given for the cluster score. The merit order list is updated keyframe by keyframe. Hence, if is already in the list, its score is simply actualized.
If is set too high, many landmarks might be created, and the network could be unnecessarily too dense. The optimal number of bifurcations per corridor should satisfy a general rule that the distance between two consecutive BPs is spanned by about 20 nodes. On the other hand, if the branches are too scattered, could be too high.
When a new node is created, C-SLAM immediately evaluates the next most promising direction to lead the vehicle into the unknown environment with the goal of exploration. Therefore, the highest score refers to the cluster with which the reference path ref should be continued.
In addition, C-SLAM checks whether the second highest score corresponds to an active cluster and in which case decides to set a landmark. Another more cautious strategy is to set a landmark when the second highest cluster just disappears from the list.
Landmarks can be also consolidated in the return and in every passage on any explored corridor. On the other side, the landmark cost may decrease so much that it is to be removed from the list. In this way, landmarks are permanently evaluated up to the moment they are employed to bifurcate into the corridor. At this moment, they change their status from LM to BP and disappear from the list forever.
In a changing environment, the adaptation of Ns, LMs, and BPs after a long period is necessary. This allows a renewal of the corridor network as needed (cf. [7, 31]).
During assiduous navigation, the topology of the network is outlined like a tree of branches. Each branch contains its identification in the tree, the sequence of nodes arranged in one direction, the landmarks, the bifurcation points, and any other particular element that should be important for decision-making.
Extending the boundaries of the corridor network requires C-SLAM to implement certain policy-makings, which goes beyond the corridor network building.
A first policy implies the definition of an appropriate metric to extend the lengths of the corridor network as much as possible according to the available energy autonomy.
A second policy supports the decision-making to choose bifurcations in order to multiply the number of branches. In this case, revisitings are of secondary importance as they take place only when they are needed as bridges to create new branches.
A third and final policy concerns the optimal scheduling of paths between two points of the network. In a situation with many alternatives to get a connection between sites, the system will search for the one with the best energy efficiency.
The metric space represented by the corridor network depends on an unknown scale which also changes over time due to cumulative odometric errors. This will affect, above all, the global map, which is the composition of many local maps with their own scale. As scale variations are generally small from map to map, the metrics are similar.
In order to maximize the length of a corridor without compromising the safety of the way back, it is essential to have a reliable metric. Since safety and energy autonomy are closely related, the metric must express the energy margin that the vehicle possesses at any time and position in the network (cf. [24, 25]).
It is clear that due to the challenging environment, the energy used to connect two points is often not the same in both directions. This will undoubtedly depend on sea currents, vehicle speed changes, travel breaks, and zigzagging on the reference route. Thus, for example, real-time detection of the no-return points based on the battery state of charge may not be reliable enough in unforeseeable circumstances. For this reason, a more powerful detection of no-return points is developed here on an empirical basis.
To this end, a function for the estimation of the energy margin is proposed. It adds up the energy consumption until the full autonomy is completed. The function can be evaluated at any time, especially in the decision to return.
The idea behind the proposal is that almost all the energy available in navigation is intended to move the vehicle. Therefore, an approach that is based solely on the energy of motion along the travelled path seems to be quite rational.
The first one defines the consumption of energy as a set of possible cases:
where is a symbol for a set of cases and describes a curvilinear integral along the path i that spans the way from the SP up to the point where the autonomy is completed (see Figure 3 to support the concept). Similarly, the distance covered by the vehicle on this path i until all available energy is consumed is defined as a set of cases:
It should be noted that all routes cover each shape, both straight and curved ones.
Figure 3 shows numerical simulation results wherein the distance travelled by a vehicle over different paths is computed after accomplishing full autonomy. In the experiments the vehicle acquires a cruise velocity, but this is slightly changed around this value at random. The resulting ground truth statistics must be available before applying C-SLAM but are updated during the construction of the corridor network.
In order for C-SLAM to decide the time point for the comeback on a new corridor, a norm based on the average on the sets in Eqs. (8) and (9) is applied. The Euclidean norm can be used to obtain the energy limits called and travelled distance :
subject to the conditions for safe navigating on the path i
where the equality of either of the two equations will mean the identification of the end point EPi for this path. N is the number of trips in the statistics that cover a distance di for any route from SP. It is very important to frame the confidence of the norm in the risk of loss of the vehicle. In this sense, the most unfavorable case might be more appropriated. In this case, it is valid
and so the conditions for the detection of a no-return point are as follows:
where again the equality of either of the two equations will mean the identification of the end point EPi for this path i.
In order to continuously adapt the norm to the environment, the set (Eqs. (8) and (9)) must be updated on each round trip. It can be noticed that the update can be applied both in a new corridor and in a revisiting case as well, regardless of the path chosen.
The way in which C-SLAM progressively explores and increases branches can be supported by different criteria.
The first method is based on the criterion that for fast expansion of new branches, occurrences of revisitings of old branches should be reduced as much as possible. To this end, the number of revisited stretches in old branches and their distances to SP should be minimized during the sequence of round trips. Here, the rectification of the path is employed. It is evident that to reach a certain BP on an old branch, the vehicle must unfailingly revisit some first stretches on this branch.
Figure 4, right and middle, illustrates a case study of an expanded network in two different forms.
Once the trunk corridor is created, the proposed method starts from SP and searches for the closest LM and splits into a new branch until it culminates in an EP. At this stage, this LM becomes a BP. In doing so, the approach minimizes the length of all revisited stretches. Any other branch generation is suboptimal. The worst suboptimal generation, on the contrary, expands the network by starting each round trip from SP to the most distant LM. In fact, this option produces a maximization of the revisited stretches.
Even when the optimal branch generation expands the network faster than other options, all methods end up with the same number of revisited stretches. This fact is reflected in Figure 4 (right). Therefore, from the point of view of the total energy consumed, the difference is short and medium term only.
The optimal solution described above has a theoretical rather than a practical value. The disadvantage of method I is that the way BPs are used has no connection with the merit order list. For that reason, vehicle safety might be compromised. When making decisions about where to branch, trust in the list is the only support for secure expansion.
Therefore, the main focus of the second method is to build up a solid network in which the path from every EPi to SP maximizes the trust on the BPs on the pathway. This means that the LMs chosen for bifurcation have the maximal score on this pathway. The number of revisited stretches over time is described by a curve that lies between the two extreme curves in Figure 4 (right). This means that in the long term, the new curve will also converge to the same extreme value.
Many other strategies can be applied besides the other two methods. For instance, one could be specifically interested in developing the branches to the left (or to the right) of the trunk line or in creating a solid statistic for the newly explored branch before continuing with the exploration.
Starting from the conception of a fully interconnected corridor network, multiple alternatives can be considered to obtain a connection between two distant points of the network (see, for example, in Figure 3, right, the bypass shaped by the points BP-CO and BP-TP-CO). In such situations, C-SLAM should be able to seek that path that involves the best energy efficiency. For that purpose C-SLAM must count with odometric information of each stretch and the energy consumption cost per unit of length.
Once the network has converged in its expansion and especially after a large number of revisits, it is common to count with the presence of bypasses and loops. Thereupon these elements will become part of the topology of the network.
In the case of one-way corridors, the existence of a bypass can only occur in the presence of COs or CRs (see Figure 5, left). On the other hand, the generation of a loop needs indefectibly at least one CR (see Figure 5, right). It is worth noticing that a loop represents a dummy alternative to connect a point with oneself through the loop as illustrated in Figure 5 (right).
As topological elements of the network, loops are marked to avoid unnecessary energy consumption. For example, in Figure 5 (right) when a loop is encountered, the path entering the loop is avoided unless the destination is a BP allowing exit from the loop. For the optimization of paths, the criterion consists in searching for the shortest path between two points or for the path that minimizes the energy consumption, or a combination of both. If safety aspects are emphasized, the energy approach is the most suitable for C-SLAM. This option is described in the following.
To find the optimal path, the following cost functional is minimized for a path starting at node and ending at node :
where is one of the paths that connects and and the pair and is the terminal nodes of the stretch in . The energy information on the stretch from to is the result of an average each time that the vehicle passes on this section. For the minimization in Eq. (16), BPs and COs, even dead-end points, are taken into account along with all nodes. Thus, the minimal value of the sum on every stretch that links and of represents the optimal path denoted by .
To assist Eq. (16), the A* search algorithm has been chosen in this work over other tree search algorithms because of its optimal efficiency [32]. Therefore, the cost is
where is the cost of the path from to and is a heuristic function that estimates the cost of the cheapest path from to . A* ends when the goal is reached or when there are no paths eligible to be extended. In this work, the cost functions and are selected according to the following two terms:
The heuristic function is admissible provided that paths with one or more sections travelled in both directions are excluded from the minimization. Thus, branches that do not represent a bypass to the branch on which is located are discarded. Loops are also avoided when used to connect a node to itself.
This section shows some experimental results which are selected to illustrate the viability of the proposal for autonomous navigation based on the proposal. Many of the functions of the approach are tested together according to the C-SLAM structure in Figure 1. It is important to note that the case studies are carried out in limited spaces with simulation of light effects and seabed textures. However, the staged environments reflect quite well the difficulties encountered in image processing in real underwater scenarios.
In order to provide a ground truth for testing and to achieve acceptable reproducibility of results, many scenarios were set up in a basin with a staged underwater landscape that closely resembled the natural seabed. In this scenario, rocks, gravel, sandbanks, and benthos, among others, predominated wherein a variety of underwater visual effects could be obtained [19, 23] (see Figure 6).
The bioactivity of microorganisms was cyclically changing the characteristics of soil texture and water transparency. The scenarios were illuminated by direct and indirect sunlight. However, light disturbances and turbidity were controlled for the range of tests. For instance, strong or weak sunlight flickers on the ground were generated by agitating the water in two orthogonal directions. In addition, visibility was reduced by discharging silt particles onto the surface that remained suspended for a period of time.
Therefore, a wide variety of case studies could be faithfully reproduced, such as rapid changes in luminance, transition from dark to bright scenarios, blurriness, lens flares, motion blur, self-similarities, glare, and bubbles (see Figure 6).
An ROV (model OpenROV v2.8) was used as the platform for the experiments, although this was hydrodynamically reformed with side fins to reduce the motion blur.
Two independent cameras were installed on board, a high-resolution, wide-angle vehicle camera (Genius f100) and a high-performance camera (GoPro Session H4). Both cameras are rolling shutter and operate at different frame rates. To attenuate undesired effects of the rolling shutter mechanism, especially in photometric-based algorithms, a high speed of 120 fps with an image size of 848 × 480 pixels was used for dense mapping, albeit off-line at the end of every round trip.
The vehicle was completely steered by C-SLAM that was programmed and installed in a notebook with GPGPU technology. The bi-directional flow of video and control signals was implemented via cable. The altitude was conveniently fixed in advance to adapt to the visibility of the environment. Altitude control was carried out independently of the adaptive controller by a PI controller. An adaptive speed-gradient controller [17] was used to self-adjust the controller coefficients for the main dynamics of the ROV.
Among other functions, C-SLAM uses state-of-the-art free software to localization and mapping. As shown in Figure 1, the block for feature-based SLAM was implemented with the algorithm ORB-SLAM [33]. On the other hand, the block for dense mapping was implemented with the photometric-based DSO-SLAM [34] and sometimes with LSD-SLAM [35, 36]. However, there was a significant modification in the implementation, namely, DSO (or LSD-SLAM) was supported with the estimated camera position provided by ORB. This was necessary in general for improving the stability of the mapping in very harsh environments, especially due to the presence of strong sunlight caustics or/and poor visibility [23]. The deflickering filter in this work is based on estimations of sunlight caustic fringes using a feedback of predicted images [20], which employs very high accuracy velocity estimation [38]. Software for guidance, control, and corridor construction was developed specially for C-SLAM.
One begins with Figure 7 which illustrates the role deflickering filter plays in the improvement of dense mapping in environments with strong sunlight caustics. Generally, spatiotemporal light changes affect the performance of photometric-based methods seriously. As seen in the heat maps, the camera depths are coherent with the physical environment. Thus, the photometric consistence is preserved after image deflickering.
The situation is totally different in the case of ORB-SLAM, where light disturbances do not affect ORB to the same degree as DSO (or LSD-SLAM) (see Figure 8). However, from several experiments, it was concluded that in turbid waters, robust texture features decrease substantially, although the overall performance of ORB does not degrade as much as in the case of DSO (see [8]). Therefore, the filtering of caustic sunlight waves can be omitted in the case of ORB, but not in the case of DSO (or LSD-SLAM). For this reason, the deflickering filter in the C-SLAM structure in Figure 1 is necessary only for dense mapping ends. Another conclusion was drawn from navigation in self-similar terrains staged in the basin like in the third picture to the right in Figure 8.
These terrains provide commonly numerous features in a similar cluster pattern, but ORB-SLAM often losses the track as it is unable to deal with nearby similarities.
An important instance at the beginning of C-SLAM navigation is the start-up phase for initializing SLAM algorithms and adaptive controller parameters. To this end, in the study, the vehicle movements were performed manually providing a zigzag path of the camera. Figure 9 illustrates the initial process of constituting a dense mapping of the environment with an adequate camera trajectory. From there, the start of the exploration was supplied with good estimates of vehicle position and speed, which in turn, allow the adaptive controller to adjust its coefficients.
Dense methods suffer, more than any other class, from odometric errors [8], which are minimized in the particular case of DSO through a very cumbersome and thorough camera calibration process. In these trials, the combination of direct methods for mapping with ORB-SLAM for tracking increases the accuracy of the global map of the corridor network, even in the case of normal rolling shutter cameras as used here. Figure 10 illustrates these results in one experiment with good textured floor.
The limited space and relatively short time span for the experiments prevented the use of an energy autonomy metric beyond the context of numerical computer simulations. In this sense, end points located sideways must be at a certain limited distance from the trunk corridor. The frequency of occurrence of nodes was synchronized with the keyframe generation according to the simple strategy, namely, "one keyframe, one node." The direction to the next node was optimized primarily by the maximizing the density of cluster keypoints near the front line of vision to avoid zigzagging of the vehicle. In this way, branches could be extended to a relatively significant length.
An important feature of the monocular C-SLAM is the vehicle return through the corridor in reverse motion as seen in the display provided by ORB-SLAM by means of the symbol "
Reverse movements often caused motion blur due to pulling and cable drag on the floor [37]. Besides, the drag of the ROV backside is much more pronounced than in the forward displacement. All this made it necessary to reduce the cruise velocity to minimize large heading perturbations that would cause track loss. In the following, two case studies illustrate the C-SLAM performance under these circumstances.
Figure 11 shows a corridor network that was constructed under the criterion of fast expansion of new branches, i.e., minimizing of revisits in short and medium terms. Here, three bifurcation points were implemented in four steps. First, the system creates the trunk corridor and returns to SP. Accordingly, in the next round trip, the vehicle is led to the nearest BP and forced to bifurcate to create the first branch, and thereafter it returns to SP again. This routine was repeated for the second and third BP.
It is notorious that the way back through the trunk corridor from the BP3 to the SP does not completely agree with the way out. However the position track was never lost, which demonstrates the robustness of the system against changes of points of views. The differences in trajectories were due to many causes, including control tracking errors, cable tugs, and drag disturbances [37].
The diversity of terrain texture shown in Figure 11 (bottom) and their impact in the performance and robustness of C-SLAM are noticeable. For instance, at BP1, the terrain is bulky, so the contrast is high and the keypoints are robust. On the other hand, in BP2 and BP3, the terrain was acquiring an increasingly self-similar appearance, and the keypoint clusters were becoming increasingly volatile. In some trips in reverse through this self-similar zone, the vehicle briefly lost its tracked position.
Figure 12 illustrates a more sophisticated experiment in which branches were allowed to occur on both sides of the trunk corridor. Over time, in addition to the formation of branches, confluence of paths and way crossings were also appearing. The picture on the left shows the network from above and the interconnection of nodes. On the other side, the picture on the right displays the network nodes and keypoints produced during the multiple round trips. It is observed that the density of keypoints is significantly higher in the right zone than in the left zone of the explored area. This is due to the marked self-similarity of the terrain that dispersed the keypoints over the terrain, while in the left zone, the bulky elements concentrate the keypoints around their peripheries.
The trunk corridor was long enough to encompass areas of different textures. Light disturbances were moderate; most of them transitions from dim to shining scenarios.
The sequence of the round trips can be followed by the order of occurrence of EPs and BPs. The strategy of expanding the network is the same as in the case before, i.e., create the trunk corridor, return to SP, annex a new branch, and begin a new round trip.
The most remarkable instance in this experiment occurred on a stretch in front of EP1, in the self-similar zone, where the C-SLAM briefly loses the vehicle location. Afterwards the vehicle was led in the direction of EP1 up to cross the trunk corridor. This CR was recognized by the system and aggregated to the network. From this point, the vehicle could return in reverse to a point that was also recognized by the system as a CO. Finally, after the sixth branch, C-SLAM completed the exploration by providing a global map of the corridor network.
This work deals with the autonomous navigation of underwater vehicles with the aim of achieving a broad exploration of the seabed. Unlike autonomous navigation systems in aerial, terrestrial, and space applications, for which the main source for localization is a GPS system, this approach basically uses only a monocular camera as sensor.
To establish the position of the vehicle, the proposed vision system takes advantage of the characteristics of texture of the seabed. Since underwater scenarios are generally very diverse and harsh due to water turbidity and light disturbances, vision-supported navigation poses a huge challenge for safe autonomous exploration. In particular, the lack of transparency in water forces the control system to lower the altitude to the seabed, which in turns demands a high degree of maneuverability to avoid collisions.
This work describes a vision-based system named C-SLAM that provides a nested loop structure to integrate control, guidance, navigation, and route planning systems into one. To explore the seabed, C-SLAM implements a strategy based on active SLAM. In contrast to many methods in the literature that employs topological models of the environment like feature graphs [13], Bayes tree [14], or grid maps [15], C-SLAM presents the environment as a network of interconnected corridors made up nodes and bifurcation points. It is claimed in this work that this simple topology is highly adequate and robust for harsh underwater environments with self-similarities, strong spatiotemporal light perturbations, and lack of water transparency [23].
Compared to other optimization techniques proposed for underwater applications such as random trees [28], particle swarming [38], octo-trees [39], level setting [40], and genetic background [41], among others, this proposal has a significant level of novelty. It radially searches in the field of vision for the optimal direction to explore. This direction is subject to satisfy quantifiable requirements for the navigation. The requirements are integrated in a weighted linear combination that is maximized step by step. This produces an active scoring list of promising points for selection of nodes and future bifurcation points providing adaptation to the environment and robustness of the node sequence.
The heuristic of the proposal have similitudes with the visual teach and repeat method for long-range autonomy [16]. Therein a pathway is created in an unfamiliar outdoor environment, at first by teleoperation and then followed by a rover. However, C-SLAM is essentially designed for autonomous exploration underwater and deals with a dense exploration in 2D. Besides, the submarine navigates basically on a plane over the seabed contemplating topographic features from above, not over the terrain. So, comparatively, C-SLAM has the advantage to choose the optimal route in order to be able to safely return to the start point. Another difference is that the C-SLAM can cope with the lack of map scale and odometric errors and even so ensure the vehicle return.
In order to expand the explored region as far as possible, a suitable metric in the non-scaled topological space is defined in relation to the vehicle energy autonomy. The key idea is to lead the vehicle by a corridor just up to the no-return point in order to make the return trip safe. In contrast to other approaches that solve the problem in a rather complex form, for instance, employing dynamic programming onto bathymetric and sea current maps [26, 27], C-SLAM proposes a statistic-based connection of odometric information and energy consumption.
The approach was experimentally tested in reduced-scale in a basin, wherein subaquatic environments with a good resemblance to a real seabed were staged. Experimental trials have demonstrated the feasibility of the approach in future applications where an autonomous underwater vehicle can host the C-SLAM vision system for large-scale underwater exploration. With the exception of extremely turbid environments, practically in all other cases, C-SLAM was able to make correct decisions to create and expand the underwater corridor network in a stable manner.
I thank E. Trabes for the valuable assistance in implementing the concepts and ideas elaborated in this work. I also thank J.L. Bustamante, J.L. Bonitatibus, G.D. Van Waarde, and L. Nuciari for their support in the experiments carried out at IADO. This work was financially supported by CONICET, CCT-Bahía Blanca, Argentine.
IntechOpen is the first native scientific publisher of Open Access books, with more than 116,000 authors worldwide, ranging from globally-renowned Nobel Prize winners to up-and-coming researchers at the cutting edge of scientific discovery. Established in Europe with the new headquarters based in London, and with plans for international growth, IntechOpen is the leading publisher of Open Access scientific books. The values of our business are based on the same ones that any scientist applies to their research -- we have created a culture of respect, collegiality and collaboration within an atmosphere that's relaxed, friendly and progressive.",metaTitle:"Social Media Community Manager and Marketing Assistant",metaDescription:"We are looking to add further talent to our team in The Shard office in London with a full-time Marketing and Communications Specialist position. The candidate will bring with them a creative and enthusiastic mindset, high level problem-solving skills, the latest marketing and social media platforms skills and strong involvement in community-best practices to engage with researchers and scholars online. The ideal candidate will be a dynamic, forward thinking, approachable team player, able to communicate with all in the global, growing company, with an ability to understand and build a rapport within the research community.",metaKeywords:null,canonicalURL:null,contentRaw:'[{"type":"htmlEditorComponent","content":"
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We are looking to add further talent to our team in The Shard office in London with a full-time Social Media Community Manager and Marketing Assistant position. The candidate will bring with them a creative and enthusiastic mindset, high level problem-solving skills, the latest marketing and social media platforms skills and strong involvement in community-best practices to engage with researchers and scholars online. The ideal candidate wll be a dynamic, forward thinking, approachable team player, able to communicate with all in the global, growing company, with an ability to understand and build a rapport within the research community.\n\n
The Social Media Community Manager and Marketing Assistant will report to the Senior Marketing Manager. They will work alongside the Marketing and Corporate Communications team, supporting the preparation of all marketing programs, assisting in the development of scientific marketing and communication deliverables, and creating content for social media outlets, as well as managing international social communities.\n\n
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The teams of researchers are working very hard to bring novel results in this field. I am also a member of the team in charge for the supervision of Ph.D. students in the fields of development of silicon based planar waveguide sensor devices, study of inelastic electron tunnelling in planar tunnelling nanostructures for sensing applications and development of organotellurium(IV) compounds for semiconductor applications. I am a specialist in data analysis techniques and nanosurface structure. 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After obtaining a Master's degree in Mechanical Engineering, he continued his PhD studies in Robotics at the Vienna University of Technology. Here he worked as a robotic researcher with the university's Intelligent Manufacturing Systems Group as well as a guest researcher at various European universities, including the Swiss Federal Institute of Technology Lausanne (EPFL). During this time he published more than 20 scientific papers, gave presentations, served as a reviewer for major robotic journals and conferences and most importantly he co-founded and built the International Journal of Advanced Robotic Systems- world's first Open Access journal in the field of robotics. Starting this journal was a pivotal point in his career, since it was a pathway to founding IntechOpen - Open Access publisher focused on addressing academic researchers needs. Alex is a personification of IntechOpen key values being trusted, open and entrepreneurial. Today his focus is on defining the growth and development strategy for the company.",institutionString:null,institution:{name:"TU Wien",country:{name:"Austria"}}},{id:"19816",title:"Prof.",name:"Alexander",middleName:null,surname:"Kokorin",slug:"alexander-kokorin",fullName:"Alexander Kokorin",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/19816/images/1607_n.jpg",biography:"Alexander I. Kokorin: born: 1947, Moscow; DSc., PhD; Principal Research Fellow (Research Professor) of Department of Kinetics and Catalysis, N. Semenov Institute of Chemical Physics, Russian Academy of Sciences, Moscow.\r\nArea of research interests: physical chemistry of complex-organized molecular and nanosized systems, including polymer-metal complexes; the surface of doped oxide semiconductors. He is an expert in structural, absorptive, catalytic and photocatalytic properties, in structural organization and dynamic features of ionic liquids, in magnetic interactions between paramagnetic centers. The author or co-author of 3 books, over 200 articles and reviews in scientific journals and books. He is an actual member of the International EPR/ESR Society, European Society on Quantum Solar Energy Conversion, Moscow House of Scientists, of the Board of Moscow Physical Society.",institutionString:null,institution:{name:"Semenov Institute of Chemical Physics",country:{name:"Russia"}}},{id:"62389",title:"PhD.",name:"Ali Demir",middleName:null,surname:"Sezer",slug:"ali-demir-sezer",fullName:"Ali Demir Sezer",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/62389/images/3413_n.jpg",biography:"Dr. Ali Demir Sezer has a Ph.D. from Pharmaceutical Biotechnology at the Faculty of Pharmacy, University of Marmara (Turkey). He is the member of many Pharmaceutical Associations and acts as a reviewer of scientific journals and European projects under different research areas such as: drug delivery systems, nanotechnology and pharmaceutical biotechnology. Dr. Sezer is the author of many scientific publications in peer-reviewed journals and poster communications. Focus of his research activity is drug delivery, physico-chemical characterization and biological evaluation of biopolymers micro and nanoparticles as modified drug delivery system, and colloidal drug carriers (liposomes, nanoparticles etc.).",institutionString:null,institution:{name:"Marmara University",country:{name:"Turkey"}}},{id:"61051",title:"Prof.",name:"Andrea",middleName:null,surname:"Natale",slug:"andrea-natale",fullName:"Andrea Natale",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:null},{id:"100762",title:"Prof.",name:"Andrea",middleName:null,surname:"Natale",slug:"andrea-natale",fullName:"Andrea Natale",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"St David's Medical Center",country:{name:"United States of America"}}},{id:"107416",title:"Dr.",name:"Andrea",middleName:null,surname:"Natale",slug:"andrea-natale",fullName:"Andrea Natale",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Texas Cardiac Arrhythmia",country:{name:"United States of America"}}},{id:"64434",title:"Dr.",name:"Angkoon",middleName:null,surname:"Phinyomark",slug:"angkoon-phinyomark",fullName:"Angkoon Phinyomark",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/64434/images/2619_n.jpg",biography:"My name is Angkoon Phinyomark. I received a B.Eng. degree in Computer Engineering with First Class Honors in 2008 from Prince of Songkla University, Songkhla, Thailand, where I received a Ph.D. degree in Electrical Engineering. My research interests are primarily in the area of biomedical signal processing and classification notably EMG (electromyography signal), EOG (electrooculography signal), and EEG (electroencephalography signal), image analysis notably breast cancer analysis and optical coherence tomography, and rehabilitation engineering. I became a student member of IEEE in 2008. During October 2011-March 2012, I had worked at School of Computer Science and Electronic Engineering, University of Essex, Colchester, Essex, United Kingdom. In addition, during a B.Eng. I had been a visiting research student at Faculty of Computer Science, University of Murcia, Murcia, Spain for three months.\n\nI have published over 40 papers during 5 years in refereed journals, books, and conference proceedings in the areas of electro-physiological signals processing and classification, notably EMG and EOG signals, fractal analysis, wavelet analysis, texture analysis, feature extraction and machine learning algorithms, and assistive and rehabilitative devices. I have several computer programming language certificates, i.e. Sun Certified Programmer for the Java 2 Platform 1.4 (SCJP), Microsoft Certified Professional Developer, Web Developer (MCPD), Microsoft Certified Technology Specialist, .NET Framework 2.0 Web (MCTS). I am a Reviewer for several refereed journals and international conferences, such as IEEE Transactions on Biomedical Engineering, IEEE Transactions on Industrial Electronics, Optic Letters, Measurement Science Review, and also a member of the International Advisory Committee for 2012 IEEE Business Engineering and Industrial Applications and 2012 IEEE Symposium on Business, Engineering and Industrial Applications.",institutionString:null,institution:{name:"Joseph Fourier University",country:{name:"France"}}},{id:"55578",title:"Dr.",name:"Antonio",middleName:null,surname:"Jurado-Navas",slug:"antonio-jurado-navas",fullName:"Antonio Jurado-Navas",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/55578/images/4574_n.png",biography:"Antonio Jurado-Navas received the M.S. degree (2002) and the Ph.D. degree (2009) in Telecommunication Engineering, both from the University of Málaga (Spain). He first worked as a consultant at Vodafone-Spain. From 2004 to 2011, he was a Research Assistant with the Communications Engineering Department at the University of Málaga. In 2011, he became an Assistant Professor in the same department. From 2012 to 2015, he was with Ericsson Spain, where he was working on geo-location\ntools for third generation mobile networks. Since 2015, he is a Marie-Curie fellow at the Denmark Technical University. 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