Stratigraphic succession of the Aravalli Supergroup from the study area. Modified after Gupta et al. [72].
\r\n\tApplied and basic studies - Field studies and lab assays of fungicides can be discussed. We also look for examples of application methods, which may include timing of application, tools for application, fungicide compatibility, phytotoxicity, etc. Field trials have to have at least two years of data;
\r\n\tAdaptation of Integrated Plant Disease Management - How the IPM practice has been adapted in the field. Application of disease risk models, or use of fungicide application aids, which can be hardware or software. The introduction of a new tool for growers can also be included;
\r\n\tNovel fungicides - In addition to the traditional chemical approach, alternative materials (enzymes, oils, extracts, etc.), biological control agents, or plant defense activators can be discussed;
\r\n\tAdaptation of new technologies - Examples will be the use of unmanned vehicles, sensor technologies, advanced sprayers, or disease forecast systems for precision agriculture;
\r\n\tFungicide resistance - Unfortunately, we cannot ignore the fact that fungicide-resistant strains are widespread. Documentation of fungicide-resistant strains, the introduction of new technologies and methods can be discussed.
Phosphonates are organophosphorus compounds characterized by a stable carbon-to-phosphorus (C—P) bond, which usually resists biochemical, thermal, and photochemical decomposition. The first phosphonate (compound
Ciliatine (2-aminoethylphosphonic acids) and its derivatives found in lipids, glycans, glycoproteins, and bile acids.
The discovery of ciliatine stimulated intensive studies on the distribution of phosphonates in nature. Despite the fact that early studies were hampered by the lack of simple and sensitive methods for the identification of the presence of carbon-to-phosphorus bond in natural samples, it was found to exist in protozoa, bacteria, coelenterates, and mollusks [6, 7, 8, 9, 10, 11]. Presumably, the unbreakable record is held by the snail
The advent of 31P NMR for the analysis of tissue extracts, body fluids, and later—whole cells provided an effective tool for tracking the forms of phosphorus and its interchanges during organism development and growth. Quite paradoxically, the availability of 31P NMR was accompanied with a significant decrease in the number of papers dealing with distribution of phosphonates in various species. Applications of this simple technique enabled the determination of the presence of C—P bond in bacteria and bacterial communities [13, 14], cyanobacteria [15], sponges [16], higher fungi [17, 18], or even human specimens [19]. However, these studies did not explain if phosphonates are synthesized
Next, gene-based methods for assessing the abundance and identity of biological phosphonate producers were applied. This approach based on knowledge regarding C—P compound biosynthesis. Thus, with a single exception [21], all the known phosphonates are derived from phosphoenolpyruvate by isomerization to phosphonopyruvate in a reaction catalyzed by the phosphoenolpyruvate mutase, followed by its fast utilization because the reaction of formation of the C—P bond is thermodynamically unfavorable (see Figure 3). Most common, decarboxylation of phosphonopyruvate by phosphonopyruvate decarboxylase to produce phosphonoacetaldehyde is the next, irreversible step [3, 22, 23, 24]. Mining in genome databases for genes related to these two enzymes, as well as their homologs, enabled to determine that 10–15% of bacterial species are able to produce phosphonates [23, 24, 25].
Discovery that phosphonates form around 10% of dissolved and particulate phosphorus in the oceans [15, 25, 26] brought the increasing recognition of the importance of these compounds in biogeochemical phosphorus cycling and an awareness of the interdependence between the global phosphorus cycle and those of the other biologically significant elements [27, 28]. It is important because phosphorus availability has been shown to be a key determinant of marine phytoplankton productivity [15]. Phosphonates are mostly concentrated in dissolved organic phosphorus (DOP), an integral and dynamic part of the marine organic matter pool. The composition of the DOP pool is complex and largely unknown, but phosphonates account for one third of its high molecular weight fraction. Thus, they seem to be an important resource of this element for aquatic organisms; however, the understanding of their utilization by eukaryotic phytoplankton is severely limited [29, 30]. They most likely occur in a form of polysaccharides esterified with methylphosphonate (compound
Up to 4% of the methane on Earth comes from the oxygen-rich waters through the cleavage of the highly unreactive carbon-to-phosphorus bond in methyl phosphonate [32]. The production of methylphosphonic acid (MPn) by cyanobacteria or marine archaea related to
Some researchers believe that phosphonates are a form of relic of evolution. Being of slightly lower formal oxidation state, they might predominate in prebiotic reductive conditions [37]. This assumption, although debatable, finds some support by finding several phosphonic acids in Murchison meteorite [39].
Ciliatine (compound
Lipids containing aminophosphonates are called phosphonolipids. There are two classes of these compounds—glycerophosphonolipids and sphingophosphonolipids (representative structures are shown in Figure 2). They have been isolated from numerous organisms including humans, mammals (sheep, goats, and rats), egg yolk, fish, insects, sea anemones, sponges, numerous species of freshwater and marine mollusks, seeds of plants, protozoa, and bacteria [3, 43, 44, 45, 46]. Usually they are a small fraction of the total lipids present, and their isolation and exact identification/characterization are difficult and cumbersome.
Representative structures of phosphonolipids, phosphonoglycans, and phosphonosteroid.
The physiologic function of phosphonolipids is still unknown, and the suggested protecting role against predators resulting from their stability toward hydrolysis by lipases and phosphatases has not been proved so far. Moreover, the distribution and abundance of phosphonolipids among organisms vary with species, tissue, or cellular location. For example, vertebrates have sphingophosphonolipids as components of nervous tissue sphingomyelin, while invertebrates frequently contain high levels of these lipids as outer membrane components.
Whereas phosphate is a common modification of polysaccharides, there are only a few examples of polysaccharides containing phosphonate moieties. Their characterization/identification was made possible as well as substantially accelerated by the development of glycomics [47]. Ciliatine and compound
Similarly as in the case of phosphonolipids, the physiological role of phosphonoglycans is not known and thus awaits determination. This might be important in the context that the glycans are essential molecules being well known to enable adaptive response to environmental changes [55]. The speculative roles of phosphonoglycans include cell-cell signaling or their action as phosphorus reservoirs in the environments of low phosphate concentration. The second assumption might be supported by the conservation of phosphonolipids at the expense of phosphodiesters in starved conditions by the oyster
It is also important to mention that the phosphonic analog of taurocholic acid was found in the gall bladders of cows [58]; however, this finding may require additional confirmation.
Biosynthesis of phosphonates starts from rearrangement of phosphoenolpyruvate (compound
Metabolic relationship between naturally occurring phosphonates.
Low-molecular antibiotics such as fosfomycin (compound
Only one of them—
The separate class is aminophosphonate antibacterial antibiotics possessing an amino group in the gamma position in relation to the phosphonic functional group, namely fosmidomycin (compound
Antibiotics structurally related to fosmidomycin.
Fosmidomycin and its homologs are potent inhibitors of 1-deoxy-d-xylulose-5-phosphate reductoisomerase, an essential enzyme of the non-mevalonate pathway of isoprenoid biosynthesis being active against a broad range of enterobacteria, but not against Gram-positive organisms or anaerobes. More importantly, they are blocking the development of isoprenoids in the parasite apicoplast, and thus, structurally modified fosmidomycin derivatives are considered as promising antimalarial agents (for representative structure, see Figure 4) [68].
Two unusual placotylene A esters [69] of ciliatine (phosphoiodyn A, compound
Half of the century after the discovery of ciliatine witnessed a slow progress in the isolation and identification of natural compounds containing the C—P bond with most of them being antibacterials. The majority of these compounds appeared to be peptides containing C-terminal phosphonic acids and mostly differ by their
Phosphonopeptide antibiotics.
Bialaphos (compound
The antibacterial activity of bialaphos is typical for all the phosphonopeptides. Peptide parts of these antibiotics usually function as a targeting unit. Thus, the peptides are efficiently transported through bacterial (or fungal) membranes and after hydrolysis release phosphonic acid, which exerts its toxic action by inhibiting parasite vital enzymes—in this case glutamine synthetase. This mechanism of action is shown schematically in Figure 6.
Representative mechanism of action of phosphonopeptides.
The following years brought the discovery of a family of antibiotics called rhizocticins (compounds
Dehydrophos (compound
Phosphonopeptides have very limited utility in human medicine because they are readily hydrolyzed in body fluids and released aminophosphonic acids that are not able to cross bacterial or fungal cell barriers and to exert antibiotic action. Additionally, they are being readily excreted through urine.
Published in 2015 work of Metcalf and van der Donk brought a significant breakthrough in studies on naturally occurring phosphonate antibiotics. By a clever combination of the mining of the genome of 10,000 of actinomycetes and selective labeling of phosphonate metabolites, they rediscovered a large number of old phosphonates and discovered 19 new compounds [24]. This opened a genetic approach in natural phosphonate chemistry and biochemistry, especially enabling the identification of metabolic pathways leading to this class of compounds. An important and instructive example here is an activation of gene cluster from
One of the examples of rediscovered compounds is fosfazinomycins A and B (compounds
The genetic approach also enabled the isolation and characterization of novel of
Intermediates and side products in the synthesis of phosphonocystoximate.
A separate group of phosphonic peptidomimetics is compounds denoted as K-26, K4, and I5B2 (compounds
Natural phosphonates might be considered as simple analogs of phosphate esters and/or carboxylic acids. The inherent stability of the C—P bond causes that they often display promising activities as enzyme inhibitors and therefore might be considered as drugs or agrochemicals. Moreover, the wide use of xenobiotics containing carbon-to-phosphorus bond has led to the spread of these compounds in the environment, which may result in their incorporation into variable metabolic pathways. All of this stimulate interest in these, still somewhat exotic, compounds. The development of 31P NMR and genomics supplemented by biochemical studies resulted in the development of new detection technologies, which enormously speed out the discovery of novel naturally occurring phosphonates, identification of their metabolic pathways (both biosynthesis and degradation), and their use as lead compounds for the design of new promising medicines. With the exception of the identification of antibacterial and antifungal antibiotics, these studies are not accompanied, however, with the determination of physiologic importance of these compounds.
This work was supported by statuary grants of Wrocław University of Science and Technology and National Science Centre, Poland (grant 2016/21/B/ST5/00115).
I declare that there is no conflict of interest that might have any bearing on research reported in this work.
Water (oceans, rivers, lakes, etc.) and land (rocky mountains, hills, peneplain, islands, etc.) are the major components of the Earth’s surface out of which only 29% are occupied by the land surfaces. This 29% land coverage included the forest, desert, mountains, islands, etc. so, a very little amount of land is reserved for geological studies.
Traditional mapping methods are time-consuming and require lots of effort for the preparation of lithological maps, mineral maps, structural maps, etc. But sometimes manually collected data may have errors due to inaccessibility and recording of the data which exaggerate in due course. To avoid these errors and corrections introduced therein an advanced technology came into the picture and is known as Remote Sensing. This technique helps in the mapping of the different litho-units and associated structural features with higher accuracy in a short period as compared to the traditional methods.
Remote Sensing is a tool used for the gathering of the target information without any physical/direct contact with the earth’s surface [1, 2, 3, 4, 5, 6]. It is a widely used science for the identification and mapping of the various objects/materials present on the earth’s crust. The electromagnetic wavelength ranges from 0.38 μm to 100 cm i.e. visible to microwave region [3] is utilized for capturing the information from the earth’s surface along with different sensors to capture the EM spectrum’s energy [4, 5, 7]. This technique is useful for the monitoring, protection, and management of diverse natural resources and land cover [8]. The geological studies include the demarcation of various lithologies, alteration zones, minerals, and structural features.
Multispectral Remote Sensing is utilized in the domain of geosciences for lithological mapping [9, 10, 11, 12, 13, 14, 15, 16], mineral mapping [17, 18, 19, 20, 21, 22, 23, 24], identification of the alteration zones related to the base metal mineralization [25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42], structural features as a controlling factor for mineralization [26, 28, 42, 43, 44, 45, 46] and mapping for demarcating favorable zones of mineralization [21, 47, 48]. Spectral characteristic absorption features of the rocks and minerals are utilized for the identification and mapping of lithologies and minerals like calcite, dolomite, clay, mica, and ultramafics, etc. The spectral absorption features of minerals vary with chemical composition and the resultant spectral curve varies in shape, depth, position, and asymmetry [49].
Wavelength range from 0.38 to 2.5 μm is utilized for the mapping of the various hydroxyl (Al-OH, Mg-OH), iron oxides (Fe-OH), carbonates (CO3−2), and sulphates (SO4−2) bearing minerals like clay, mica, ultramafics, hematite, limonite, dolomite, calcite, etc., due to the presence of characteristic absorption features in the VNIR and SWIR region of the EM spectrum [5, 42, 50, 51, 52, 53]. In the case of feldspar, silica-rich rocks, and discrimination between ultramafics and dolomites are possible due to spectral features associated with the TIR region in the wavelength range of 3 to 50 μm [20, 23, 54, 55, 56, 57, 58]. The dissimilarities in the spectrum in the TIR spectral-domain arise due to variation in chemical composition and molecular structure.
Geological studies are done with the help of Landsat series, ASTER, Sentinel −2, SPOT, Worldview series, GeoEye, etc. optical remote sensing satellites. They are mainly utilized for the perspective of mineral exploration by using the various methods like band ratio (BR), relative band depth (RBD), Principal Component Analysis (PCA), Independent Component Analysis (ICA), Minimum Noise Fraction (MNF), unsupervised classification (K-means, isodata, etc.), supervised classification (Spectral Angle Mapper, Spectral Feature Fitting, Mixture Tuned Matched Filtering, etc.), machine learning (support vector machine, decision tree, artificial neural network, etc.). Various BR and RBD have been used for the delineation of the different rock outcrops like dolomite, calcite, Iron rich-rocks, ultramafics, epidote, clay and mica minerals, etc. [17, 21, 22, 23, 24, 42, 58, 59, 60] and mineral prospects by the demarcation & mapping of the associated alteration zones [20, 22, 27, 28, 37, 45, 61]. Lithological mapping of the exposed outcrops and their associated features are demarcated with the help of PCA, ICA, and MNF analyses to govern the mineral potentiality of the outcrops [9, 17, 40, 42, 44, 62, 63]. Different supervised, unsupervised, machine learning and prospectivity mapping algorithms were applied to the optical datasets to prepare the mineralogical and prospective zone maps of the region and these maps contain the information about the mineral potential zones which were utilized for the perspective of mineral explorations [17, 20, 21, 31, 42, 47, 48, 64, 65, 66, 67, 68, 69, 70, 71].
This chapter explains the use of SWIR and TIR spectral bands for the demarcation of the different minerals and lithologies present in the region. The importance of integrated datasets from SWIR and TIR-derived outcomes and the utility of the integrated dataset for the demarcation of the various litho-units has also been explained.
The coverage of the study area extends between latitude 23°51′35.45″ to 24°18′34.14″ in the North and longitude 73°28′43.95″ to 73°49′34.24″ in the East and occupies the region in Udaipur and Dungarpur districts of Rajasthan, India (Figure 1).
Location of the study area in inset maps of India and Rajasthan. Lithological map of the study area. Modified after Gupta et al. [
Geologically the study area falls in the Udaipur sector exposes various litho-stratigraphic units of the Archean and Palaeoproterozoic age [72, 73, 74] (Figure 1, Table 1). The basement rocks are the Banded Gneissic Complex (BGC) [73, 75] or the Bhilwara Supergroup (BSG) [72, 74]. They are overlain by the rocks of the Aravalli Supergroup through an erosional unconformity. The Aravalli Supergroup has been categorized into Debari, Udaipur, Bari Lake, Jharol, Dovda, Nathdwara, Lunavada Groups [72]. It has also been subdivided into Lower, Middle, and Upper Aravalli Groups [73].
Era | Supergroup | Group | Formation | |
---|---|---|---|---|
Paleoproterozoic | Aravalli | Synorogenic Granite and Gneiss (intrusion) Rakhabdev Ultramafic Suite (intrusion) | ||
Jharol | Samlaji | |||
Goran | ||||
Udaipur | Tiri Sub-group | Zawar | ||
Balicha/ Baroi Magra | ||||
Mandli | ||||
Debari | Kathalia | |||
Sisa Magra | ||||
----Unconformity---- | ||||
Natharia ki Pal | ||||
Gurali/ Basal | ||||
----------------Unconformity---------------- | ||||
Archean | Banded Gneissic Complex | Mangalwar Complex | Mando ki Pal | |
Sarada |
Stratigraphic succession of the Aravalli Supergroup from the study area. Modified after Gupta et al. [72].
Majority of the pristine Archean features of the basement rocks have diminished due to tectono-thermal reconstruction of the basement [73]. Basement rocks from Mangalwar Complex are composed of heterogeneous rocks of amphibolite-facies metamorphites [75] or granite-greenstone belt [74]. Gneisses, metabasics, migmatites, and schists constitute the basement while greywacke, chert, marble, dolomite, quartzite, fuchsite quartzite, and mica schist represents the metasediments within the basement [74, 76]. Biotite schist, garnets, and staurolites are present in the Sarara ki Pal inlier [73, 77] and the presence of chlorite and chloritoid represents the retrogression mechanism [78].
The base of the Aravalli Supergroup is having thin bands of quartzites and pebbly oligomictic conglomerate. The continuity of quartzite is interrupted by the ESE-WNW, NE–SW, and ENE-WSW faults. In the majority of the study area phyllites and mica-schists are exposed. Graded bedded greywacke occurs within the phyllite [79]. Poddar & Mathur [80] mentioned the characteristic repetition of graded bedded and slaty phyllite. Different varieties of dolomites are exposed in the Zawar region with gradational contact with greywacke. They are pure to siliceous and massive to gritty nature. Lead-zinc mineralization is confined in the siliceous dolomites [72, 73, 74, 75, 77, 81, 82, 83, 84, 85, 86]. The lithological and chemical control of the metallogenesis in the region is supporting the concept of the syngenetic origin of lead-zinc sulphides [72]. The Rakhabdev-Dungarpur area consists of ultramafic rocks as linear belts which are serpentinized and are metasomatically altered [73, 87, 88, 89, 90]. The ultramafic rocks occur along a prominent lineament named Rakhabdev lineament which passes through the Aravalli fold belt [77, 89]. Thicker ultramafic outcrops are more massive and fractures are developed in an irregular manner [91].
The present study uses the Advanced Spaceborne Thermal Emission and Reflection Radiometer Level-1 Precision Terrain Corrected Registered At-Sensor Radiance (ASTER L1T) dataset. ASTER sensor carries the VNIR, SWIR, and TIR scanners which have 3 (1–3), 6 (4–9), and 5 (10–14) bands respectively, and its technical specifications mentioned in Table 2. The ASTER L1T imagery is already geometrically corrected, georeferenced (WGS-1984) and UTM projected (UTM zone 43 N) [92].
Granule ID | Sensor-scanner | Band number | Spectral width (μm) | Spatial resolution (mtr) | Radiometric resolution | Valid range |
---|---|---|---|---|---|---|
AST_L1T_00304222003055021_20150428031510_40583 | ASTER-VNIR | 0.520–0.60 | 15 | 8-bits | 0–255 | |
2 | 0.630–0.690 | 15 | 8-bits | 0–255 | ||
3 | 0.760–0.860 | 15 | 8-bits | 0–255 | ||
ASTER-SWIR | 4 | 1.600–1.700 | 30 | 8-bits | 0–255 | |
5 | 2.145–2.185 | 30 | 8-bits | 0–255 | ||
6 | 2.185–2.225 | 30 | 8-bits | 0–255 | ||
7 | 2.235–2.285 | 30 | 8-bits | 0–255 | ||
8 | 2.295–2.365 | 30 | 8-bits | 0–255 | ||
9 | 2.360–2.430 | 30 | 8-bits | 0–255 | ||
ASTER-TIR | 10 | 8.125–8.475 | 90 | 12-bits | 0–65535 | |
11 | 8.475–8.825 | 90 | 12-bits | 0–65535 | ||
12 | 8.925–9.275 | 90 | 12-bits | 0–65535 | ||
13 | 10.25–10.95 | 90 | 12-bits | 0–65535 | ||
14 | 10.95–11.65 | 90 | 12-bits | 0–65535 |
Technical specifications of the ASTER L1T dataset. Source: [92].
The overall methodology flowchart for the delineation of the various litho-units is depicted in Figure 2. The different litho-units were traced out with the help of the Feature-Oriented Principal Component Selection (FPCS) method which uses the various derived outcomes of band ratios, relative band depths, and mineral indices from SWIR and TIR datasets through an integrated approach.
Overall methodology for the derivation of the lithological map.
Vegetation and water bodies are present in the region which creates a hindrance in geological mapping therefore, these land features were masked from the derived outcome. Vegetation coverage was calculated using the Normalized Difference Vegetation Index (NDVI) and the values ranging greater than 0.2 were used for the preparation of the vegetation mask. Water bodies were masked using band 1. The DN values ranging from 0 to 100 were selected to prepare the water mask. Both of the masks were applied on the derived outcomes to eliminate the vegetative lands and water bodies from the mineral and lithological map of the region.
The ASTER SWIR dataset has the spillover of the energy from band 4 to band 5 and band 9 which is known as crosstalk effects [17, 41, 45, 47, 93, 94]. Crosstalk correction was applied for the removal of effects from the dataset and to enhances the spectral signatures of the minerals/rocks. A semi-empirical atmospheric correction, QUick Atmospheric Correction (QUAC), was applied to retrieve the surface reflection from the sensor radiance [95, 96, 97, 98, 99, 100].
The ASTER TIR datasets were converted into the calibrated radiance from the digital number using Eq. (1) [46, 55, 58, 101, 102].
where:
cof10 | cof11 | cof12 | cof13 | cof14 |
---|---|---|---|---|
0.006882 | 0.006780 | 0.006590 | 0.005693 | 0.005224 |
Band Ratio (BR) is a method in which one band is divided by another band (Eq. (2)) to get the better delineation of the rocks/minerals instead of using a single band and combined with enhancement of spectral properties.
where: BR = Output Band Ratio image; B1 and B2 = Brightness value of selected bands.
Relative Band Depth (RBD) is another technique for mineral mapping in which the position and depth of the mineral spectrum were considered for calculation [103]. The RBD governs better discrimination of minerals than BR because it considers the characteristic absorption features and normalizes the effects generated due to topography and albedo [32, 37, 104]. The bands acquired the shoulder position on absorption spectrum are summed up as (S1 and S2) and divided by the band having minimal absorption value (T; Eq. (3); Figure 3).
Artistic sketch for interpretation of the RBD for any mineral.
Mineral Indices (MI) is also a mathematical expression derived for mapping of the minerals by using the band math operators with different logics in the TIR wavelength region. The TIR part of the EM spectrum is utilized for the mapping of the feldspars, silicates, carbonates, and ultramafic minerals.
BR and RBD were applied on the atmospherically corrected SWIR datasets and MI was applied on the calibrated radiance TIR datasets (Table 3). Al-OH consisting of minerals like mica and clay minerals were delineated with the help of different BRs and RBD6 (Table 3). Spectral absorption minima were recorded at band 6 of ASTER at 2.205 μm which highly suitable for the mapping of clay and mica minerals [12, 34, 37, 42, 105]. Similarly, Mg-OH and CO3−2 containing minerals showed the absorption minima at band 8 of ASTER at 2.336 μm which was used in the RBD8 for mapping of carbonates and ultramafics [34, 37, 106, 107]. The SiO2 containing minerals/rocks showed the emissivity minima at the band 12 of ASTER at 9.075 μm due to vibrational energy along the Si-O bond. The CO3−2 bearing minerals showed the emissivity minima at 11.318 μm which is represented by the band 14. The Mg-OH bearing minerals of ultramafics recorded the emissivity minima at band 13 at 10.657 μm.
S. no. | Mineral composition | Indicator minerals | Formula | Absorption band: wavelength (μm) | References |
---|---|---|---|---|---|
SWIR bands Band Ratio (BR) | |||||
1. | Al-OH | Sericite, smectite, muscovite, and illite | B7/B6 | 6: 2.205 | [106] |
2. | B4/B6 | [8] | |||
3. | Alunite and kaolinite | B7/B5 | 5: 2.167 | [106] | |
Relative Band Depth (RBD) | |||||
4. | Al-OH (RBD6) | Sericite, smectite, and illite | (B5 + B8) /B6 | 6: 2.205 | [12, 42] |
5. | Mg-OH and CO3−2 (RBD8) | Carbonates and ultramafics | (B6 + B9) /B8 | 8: 2.336 | [37, 106] |
TIR bands Mineral Indices (MI) | |||||
6. | Mg-OH | Ultramafics | (B12/B13) × (B14/B13) | 13: 10.657 | [23] |
7. | SiO2 | Silica-rich | (B11/(B10 + B12)) × (B13/B12) | 12: 9.075 | [56] |
8. | CO3−2 | Carbonates | B13/B14 | 14: 11.318 | [54, 101] |
BR, RBD, and MI used for the derivation of the mineral maps from ASTER SWIR and TIR bands.
Principal Component Analysis (PCA) is widely used for the identification and delineation of the litho-units and hydrothermal alteration minerals using the spectral bands generated from different sensors [9, 17, 28, 45, 48, 108]. The PCA uses the statistical mechanism for the transformation of the variables into several linear variables which are not having a correlation with each other, known as Principal Components (PCs). It is implemented on the symmetric matrix which is based on the either correlation matrix or covariance matrix (Eq. (4)). The PCs have the information related to the specific mineral which can be retrieved with the help of loadings of eigenvector (Eq. (5)). The strong eigenvector loadings of the PCs were utilized for the demarcation of the various mineral/groups through its PC indicative bands (Eq. (6)) which showed bright and dark pixels for the specific minerals in the PC image [108]. The present work uses the Feature-Oriented Principal Component Selection (FPCS) on the integrated data derived from BR, RBD, and MI from the ASTER SWIR and TIR data for mapping of the lithological units present in the region. The FPCS was used to achieve the desired goal by the combination of SWIR and TIR-derived outcomes. The phyllite and mica-schist can be marked with the help of BRs and RBD6, carbonates and ultramafics gave a similar tone by the use of RBD8 of SWIR region while MI has the capabilities to distinguish these two minerals/groups. Quartzites were not mapped in the SWIR EM region due to lack of the characteristic absorption band while TIR EM regions have these capabilities. Mineral/rock identification was not possible through the single kind of dataset like only by SWIR only by TIR so, integrated approach was required to delineate all lithological units existed in the study area. Therefore, FPCS was implemented on the basis of covariance matrix of the integrated outcomes of ASTER SWIR and TIR. The derived eigenvector matrix is tabulated in Table 4.
Eigenvectors | MI CO3−2 | MI SiO2 | MI Mg-OH | BR 7/5 | RBD6 | RBD8 | BR 4/6 | BR 7/6 |
---|---|---|---|---|---|---|---|---|
Band 1 | 0.006692 | −0.02224 | 0.056749 | −0.18058 | −0.50483 | 0.772556 | −0.11678 | −0.31352 |
Band 2 | −0.02898 | −0.01231 | −0.04299 | −0.08242 | −0.4053 | −0.48972 | −0.72584 | −0.24381 |
Band 3 | −0.69819 | −0.33987 | −0.62711 | −0.0019 | 0.001132 | 0.049081 | 0.036474 | 0.002323 |
Band 4 | −0.006 | −0.01368 | 0.020563 | −0.26053 | −0.48361 | −0.39945 | 0.669243 | −0.30025 |
Band 5 | 0.015369 | 0.017849 | −0.02398 | 0.807885 | −0.49943 | −0.00099 | 0.088098 | 0.298333 |
Band 6 | −0.0354 | −0.00343 | 0.042673 | −0.48543 | −0.31084 | 0.003489 | −0.04809 | 0.813837 |
Band 7 | 0.216406 | 0.736543 | −0.6363 | −0.05878 | −0.02868 | 0.036048 | 0.014719 | 0.000581 |
Band 8 | 0.680651 | −0.58381 | −0.44041 | −0.03078 | −0.01117 | 0.007118 | 0.004385 | 0.027843 |
Eigenvector matrix generated from the integrated derived mineral maps for FPCS.
where:
cov(X, Y) = Covariance matrix; X and Y = Variables;
A
y = Final outcome; W′ = transpose of scaler data; x = Feature vector.
Clay (kaolinite, illite, montmorillonite) and mica (sericite and muscovite) minerals consist of the Al-OH in their chemical composition. These minerals especially illite, montmorillonite, muscovite, and sericite exhibited characteristic spectral absorption features at a wavelength of 2.205 μm which is detectable with band 6 (2.185–2.225 μm) of the ASTER sensor. Kaolinite mineral also showed minor absorption at 2.165 μm for that band 5 (2.145–2.185 μm). The Al-OH consisting of minerals were mapped in the quartzites south of Rakhabdev and near the granitic outcrop of Kherwara inlier [89]. Phyllite and mica-schist also depicted higher values for Al-OH containing minerals by using BR 7/6 (Figure 4A). The granitoids of the basement, granites, and quartzites have high values for Al-OH by using BR 4/6 (Figure 4B). On applying BR 7/5, almost the entire region depicted higher values for kaolinite (Al-OH) which is indicative of a poor interpretation (Figure 4C). The RBD (5 + 8)/6 gave a similar kind of result like BR 7/6. It depicted higher values of the Al-OH consisting of rocks/minerals for the quartzites, phyllites, mica-schists, conglomerate, and arkose litho-units (Figure 4D). Ultramafics and carbonates (dolomite) have very low values from BRs 7/6, 4/5, and RBD6 due to the absence of Al-OH minerals.
Results from the SWIR data for Al-OH bearing minerals using the techniques of BRs and RBDs. (A) BR 7/6 for mica and clay minerals. (B) BR 4/6 for clay minerals. (C) BR 7/5 for kaolinite. (D) RBD6 (5 + 8)/6 for clay and mica minerals.
Ultramafics are having the Mg-OH while dolomites are having both Mg-OH and CO3−2 constituents in their composition. These minerals have the characteristic absorption feature at 2.33 μm, which occurs at band 8 (2.295–2.365 μm). The RBD (6 + 9)/8 was applied for mapping of the Mg-OH and CO3−2 consisting of minerals. Ultramafics depicted extremely high values while dolomites have moderate values (Figure 5A). The regions of Zawarmala and Hati Magra are dominated by dolomite exposures but a poor carbonate map as an outcome may be due to the presence of extreme vegetation on the hills.
Results from the SWIR data. (A) RBD8 (6 + 9)/8 for the mapping of ultramafics and carbonates. (B) Lithological map using the RBD6, RBD8, and BR4/6 in the RGB channels.
A lithological map has also been prepared from RBD6, RBD8, and BR4/6 in RGB channels respectively (Figure 5B). Basement rocks are depicted as pinkish-blue, phyllite, and mica-schist as reddish color. Dolomite is depicted as dark green while ultramafics are as green color. Quartzites are light pink in color. The resultant map discriminates the various lithologies present in the area and is comparable with the published geological maps.
The ultramafic map was developed using the mafic index defined by Guha & Vinod Kumar [23]. The derived map of the mafic index mapped the outcrops of the ultramafics with very higher values near the Rakhabdev region and along other thin belts of the ultramafic outcrops south of Rakhabdev and on the west of Kherwara (Figure 6A). The dolomites were suppressed and showed their uniqueness to distinguish by ultramafics.
Results derived from the TIR data. (A) Ultramafic map derived using the Guha and Vinod Kumar index [
Silica-rich rocks were marked by using the silica index of Rockwell & Hofstra [56] (Figure 6B). Quartzites present on the outer periphery of basement rocks were precisely demarcated through the silica index. Silica index also mapped the quartzites present adjacent to the dolomites in the Zawar region. The derived mineral map showed very low values for the ultramafics and dolomites.
Ninomiya et al. [54] defined the mathematical expression for the mapping of carbonate rocks and the derived mineral map showed dense noise, consequently, identification of the carbonate outcrops was not precisely obtained (Figure 6C). The presence of stripping noise and poor signal at band 14 hinders the demarcation of carbonate outcrops [20, 109, 110]. The ultramafics and quartzitic outcrops were depicting very low values in the carbonate map.
A lithological map was prepared using the silica, mafic, and carbonate indices in RGB channels respectively (Figure 6D). Dolomites of the region were marked by bluish-green color but the majority of the region was marked as bluish-green color which is a poor identification for dolomites, quartzites are depicting the maroon color and ultramafics as bright green color. The yellow color at the tips of ultramafics and within the massive outcrops of ultramafics are identified as talc.
The FPCS technique was implemented to the integrated derived outcomes from BRs, RBDs, and MIs of the ASTER SWIR and TIR bands for discrimination of the different litho-units present in the study area. The generated eigenvector matrix from the FPCS from the integrated derived mineral maps is shown in Table 4. The PC1 shows the extreme values for the ultramafics and moderate values for the dolomites. Quartzites showed very low values and represented dark pixels (Figure 7). The PC1 shows the combined outcome from the RBD8 and MI Mg-OH because RBD8 highlighted ultramafics and dolomites of the region while MI Mg-OH mapped the ultramafics and suppressed the quartzites of the region. The PC2 showed extremely high values for phyllite and mica-schists and dark pixels for the quartzites of the region. The ultramafics & dolomites of the region are depicting the low values (Figure 7). The PC2 showed the combination of BR 4/6, BR 7/5, and RBD6 in which BR 4/6 highlighted the silica-rich rocks. The Al-OH consisting minerals are suppressed in the PC2 while RBD6 highlighted the phyllite and mica-schists as Al-OH consisting rocks and BR 7/5 also highlighted the Al-OH consisting minerals. The PC3 depicted very high values for quartzites i.e. silica-rich rocks as by MI SiO2 but on the south of Rakhabdev the distribution of silica-rich rocks is not showing the vague distribution like MI SiO2, and a clear delineation of quartzites are obtained (Figure 7). The PC4 depicted a similar kind of pattern as BR 4/6 but the extremity of the pixel values gets lower down and appearance gets noisy (Figure 7). The PC6 depicted higher values for the ultramafics and silica enriched rocks of the granitoids & migmatites from the basement, quartzites, and phyllite, and mica-schist while carbonates are depicted moderate values (Figure 7). The PC6 is helpful for the delineation of the litho-units of the region but the band showed an association of the moderate amount of noise. The PC5, PC7, and PC8 are not useful for the discrimination of the geological units due to the presence of a high amount of noise with them (Figure 7).
FPCS components generated using the PCA technique on integrated BRs, RBDs, and MIs from the SWIR and TIR bands.
FCC was been prepared using the combinations of bands PC1, PC3, and PC6 respectively for delineation of different litho-units (Figure 8). Granitoids and migmatites appeared as greenish-blue colors while quartzites as light green with a mixture of cyan color. Phyllite and mica-schist appeared as dark blue to greenish-brown color. Conglomerate and meta-arkose gave shades of green color. Dolomites appeared as purplish colors and ultramafics as pinkish colors. Light yellow color on the tips of ultramafics and within the ultramafics which showed the presence of talc.
Lithological map of the study area prepared by the combination of FPCS1, FPCS3, and FPCS6 in the RGB channels respectively.
To verify the different litho-units of the region various field reconnaissance was conducted and for the estimation of the overall accuracy of the generated lithological map, GPS surveys were carried out. Various traverses were conducted along the major litho-units of the region and some traverses were conducted for the verification of changes observed in the generated lithological map. Field photographs and rock samples were collected for the determination of accurate locations and associated lithology/ies if present at the contact zone. Pink colored granites from the Kherwara Inlier were observed (Figure 9A) and deformed quartzites were present with the contact of it (Figure 9B). Serpentinites of the Rakhabdev showed the variation all along the belt-like massive to fibrous nature and open mining pits of the serpentinites in the massive variants (Figure 9C, D, G and I). In the field, outcrops of the different dolomites were observed (Figure 9E) associated with the quartzites, metagraywacke, phyllite, and mica-schist (Figure 9F). Contact between the phyllite & mica-schist and quartzites was also observed (Figure 9H). Talc was also observed in the field and it is mainly in the region of deformation (Figure 9G). The isolated patch of the serpentinite near the Parbeela region shows the contact with the granites of Kherwara Inlier. Furthermore, the accuracy assessment was carried out between the generated lithological map and field-collected information using the GPS (Table 5). The derived confusion matrix shows the overall accuracy and kappa coefficient as 85.25% and 0.8164 respectively for the lithological map.
Field validation of the different litho-units. (A) Granites from the Kherwara inlier. (B) Mullions of quartzites from Kherwara inlier. (C) Talc mineralization along the serpentinites from southeast of Rakhabdev. (D) Massive serpentinites from Rakhabdev. (E) Dolomites from the northeast of Rakhabdev. (F) Massive quartzites from the east of Rakhabdev. (G) Mining activities for talc from the hinges of serpentinites outcrops from south of Rakhabdev. (H) Contact between the quartzites and phyllites from north of Rakhabdev. (I) Serpetninites and its alteration products from south of Rakhabdev.
Class | Dolomite | Serpentinite | Quartzite | Talc | Phyllite | Granite | User’s Accuracy |
---|---|---|---|---|---|---|---|
Dolomite | 34 | 1 | 0 | 2 | 0 | 1 | 89.47% |
Serpentinite | 2 | 16 | 0 | 1 | 0 | 0 | 84.21% |
Quartzite | 0 | 1 | 10 | 0 | 2 | 0 | 76.92% |
Talc | 2 | 1 | 0 | 14 | 1 | 0 | 77.78% |
Phyllite | 1 | 0 | 2 | 0 | 17 | 1 | 80.95% |
Granite | 0 | 0 | 0 | 0 | 0 | 13 | 100.00% |
Producer’s Accuracy | 87.18% | 84.21% | 83.33% | 82.35% | 85.00% | 86.67% | |
Overall Accuracy | 85.25% | ||||||
Kappa Coefficient | 0.8164 |
Accuracy assessment of the derived lithological map using the GPS survey collected localities.
The study area belongs to the Aravalli Orogeny and several deformational histories were recorded [72, 73, 74, 75, 77, 81, 84, 85, 86, 89, 111, 112, 113]. The remote sensing technique is widely used for mineral mapping and lithological mapping around the study area [9, 58, 102, 114]. In the present research, different BR, RBD, & MI and their combinations were generated using the ASTER SWIR and TIR bands for the demarcation of the different litho-units present in the region. The BRs and RBDs were used to derive the Al-OH and Mg-OH & CO3−2 consisting minerals [8, 12, 35, 37, 42, 106] like phyllite & mica-schist, carbonates, and ultramafics. And their FCC combinations helped to delineate granitoids, granites, phyllite, mica-schist, quartzites, dolomite, and ultramafics (Figure 5B). The MIs were used to derive SiO2, Mg-OH, and CO3−2 mineral maps of the region [23, 54, 55, 56, 58, 101], and quartzites, ultramafics, and carbonates were delineated. The FCC also helped a lot to determine the lithologies of the region but were limited to the quartzites, ultramafics, and carbonates (Figure 6D).
FPCS technique was utilized for the delineation of litho-units present in the region on the basis of the combined results derived from the BRs, RBDs, and MIs from the ASTER SWIR and TIR bands. The PC1, PC3, and PC6 have capabilities to discriminate the ultramafics, carbonates, quartzites, phyllite, mica-schist, and granitoids. The prepared FCC using the combinations of these PCs demarcated the granitoids, granites, phyllite, mica-schist, quartzites, conglomerate, meta-arkose, dolomites, ultramafics (Figure 8). The integrated approach from the ASTER SWIR and TIR proved its potentials for lithological mapping. The generated confusion matrix showed the overall accuracy as 85.25% and kappa coefficient as 0.8164 in which maximum producer’s accuracy (%) was attained by dolomite while user’s accuracy (%) by granite. Field validation was performed on the generated lithological map by observing the various litho-units present on the surficial exposures and gathered the location information by the use of a GPS survey. Association of quartzites with dolomites and serpentinites with dolomites was observed in the field. Talc is found with the serpentinites and was produced due to the process of serpentinization and mainly formed at the deformational zones [115].
Lithological and mineral mapping of the region can be done with the help of various BR, RBD, & MI and by the combinations of these but discrimination between every single litho-unit is not possible with it.
The data integration (combination of derived mineral maps from ASTER SWIR and TIR bands) approach played an important role to obtain the desired research goal.
PCA is a statistical technique that is utilized for the demarcation of the various litho-units using the original bands but, in this research, FPCS was utilized on the integrated data which shows its capabilities towards discrimination of every single litho-unit.
PC1, PC3, and PC6 from the FPCS were utilized for the discrimination of various litho-units in which talc is also identified within ultramafics which is an alteration product of serpentinization.
The overall accuracy and kappa coefficient of the generated lithological map are 85.25% and 0.8164 respectively and calculated with the help of GPS surveys.
The author is thankful to USGS Earth Explorer to provide the ASTER dataset under the open distribution policy. Thanks to the Heads of the Department of Geology for providing the necessary facilities for conducting the research. Some part of this chapter has been taken from the Ph.D. thesis of the author.
The author declares no conflict of interest.
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Shamshiri"}]},{id:"850",doi:"10.5772/5452",title:"Real-Time Evolutionary Algorithms for Constrained Predictive Control",slug:"real-time_evolutionary_algorithms_for_constrained_predictive_control",totalDownloads:2325,totalCrossrefCites:0,totalDimensionsCites:5,abstract:null,book:{id:"3600",slug:"frontiers_in_evolutionary_robotics",title:"Frontiers in Evolutionary Robotics",fullTitle:"Frontiers in Evolutionary Robotics"},signatures:"Mario Luca Fravolini, Antonio Ficola and Michele La Cava",authors:null},{id:"847",doi:"10.5772/5449",title:"Cellular Non-Linear Networks as a New Paradigm for Evolutionary Robotics",slug:"cellular_non-linear_networks_as_a_new_paradigm_for_evolutionary_robotics",totalDownloads:2496,totalCrossrefCites:2,totalDimensionsCites:5,abstract:null,book:{id:"3600",slug:"frontiers_in_evolutionary_robotics",title:"Frontiers in Evolutionary Robotics",fullTitle:"Frontiers in Evolutionary Robotics"},signatures:"Eleonora Bilotta and Pietro Pantano",authors:null},{id:"871",doi:"10.5772/5473",title:"Evolutionary Motion Design for Humanoid Robots",slug:"evolutionary_motion_design_for_humanoid_robots",totalDownloads:2515,totalCrossrefCites:2,totalDimensionsCites:4,abstract:null,book:{id:"3600",slug:"frontiers_in_evolutionary_robotics",title:"Frontiers in Evolutionary Robotics",fullTitle:"Frontiers in Evolutionary Robotics"},signatures:"Toshihiko Yanase and Hitoshi Iba",authors:null}],mostDownloadedChaptersLast30Days:[{id:"63775",title:"Fundamental Research on Unmanned Aerial Vehicles to Support Precision Agriculture in Oil Palm Plantations",slug:"fundamental-research-on-unmanned-aerial-vehicles-to-support-precision-agriculture-in-oil-palm-planta",totalDownloads:2910,totalCrossrefCites:3,totalDimensionsCites:24,abstract:"Unmanned aerial vehicles carrying multimodal sensors for precision agriculture (PA) applications face adaptation challenges to satisfy reliability, accuracy, and timeliness. Unlike ground platforms, UAV/drones are subjected to additional considerations such as payload, flight time, stabilization, autonomous missions, and external disturbances. For instance, in oil palm plantations (OPP), accruing high resolution images to generate multidimensional maps necessitates lower altitude mission flights with greater stability. This chapter addresses various UAV-based smart farming and PA solutions for OPP including health assessment and disease detection, pest monitoring, yield estimation, creation of virtual plantations, and dynamic Web-mapping. Stabilization of UAVs was discussed as one of the key factors for acquiring high quality aerial images. For this purpose, a case study was presented on stabilizing a fixed-wing Osprey drone crop surveillance that can be adapted as a remote sensing research platform. The objective was to design three controllers (including PID, LQR with full state feedback, and LQR plus observer) to improve the automatic flight mission. 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Balasundram, Desa Ahmad, Cornelia Weltzien and Muhammad Yamin",authors:[{id:"182449",title:"Prof.",name:"Ibrahim",middleName:"A.",surname:"Hameed",slug:"ibrahim-hameed",fullName:"Ibrahim Hameed"},{id:"203413",title:"Dr.",name:"Redmond R.",middleName:null,surname:"Shamshiri",slug:"redmond-r.-shamshiri",fullName:"Redmond R. Shamshiri"}]},{id:"869",title:"Emotional Intervention on Stigmergy Based Foraging Behaviour of Immune Network Driven Mobile Robots",slug:"emotional_intervention_on_stigmergy_based_foraging_behaviour_of_immune_network_driven_mobile_robots",totalDownloads:2017,totalCrossrefCites:0,totalDimensionsCites:0,abstract:null,book:{id:"3600",slug:"frontiers_in_evolutionary_robotics",title:"Frontiers in Evolutionary Robotics",fullTitle:"Frontiers in Evolutionary Robotics"},signatures:"Diana Tsankova",authors:null},{id:"64615",title:"Multimodal Classification of Mangoes",slug:"multimodal-classification-of-mangoes",totalDownloads:1001,totalCrossrefCites:0,totalDimensionsCites:0,abstract:"Grading, sorting, and classification of agricultural products are important steps to ensure a profitable and sustainable food industry. Human-intensive labors are replaced with better devices/machines that can be used in-line and generate sufficiently fast measurements for a high production volume. Most previous works focused on only one of the external quality parameters, such as color, size, mass, shape, and defects. In this work, we proposed an integrated machine vision system that can grade, sort, and classify mangoes using multiple features including weight, size, and external defects. We found that weight estimation using our proposed algorithm based on visual information was not statistically different from that of a conventional weight measurement using a static digital load cell; the estimation error is relatively small (4–5%). We also constructed an artificial neural network model to classify mango having multiple types of external defect; the classification error is less than 8% for the worst possible case. The results indicate that our system shows a great potential to be used in a real industrial setting. Future work will aim to investigate other features such as ripeness and bruises to increase the effectiveness and practicality of the system.",book:{id:"7270",slug:"agricultural-robots-fundamentals-and-applications",title:"Agricultural Robots",fullTitle:"Agricultural Robots - Fundamentals and Applications"},signatures:"Son V.T. Dao",authors:[{id:"252669",title:"Dr.",name:"Vu Truong Son",middleName:null,surname:"Dao",slug:"vu-truong-son-dao",fullName:"Vu Truong Son Dao"}]},{id:"62785",title:"Hybrid-Powered Autonomous Robots for Reducing Both Fuel Consumption and Pollution in Precision Agriculture Tasks",slug:"hybrid-powered-autonomous-robots-for-reducing-both-fuel-consumption-and-pollution-in-precision-agric",totalDownloads:1038,totalCrossrefCites:1,totalDimensionsCites:2,abstract:"Environmental contamination and the resulting climate change are major concerns worldwide. Agricultural vehicles that use fossil fuels emit significant amounts of atmospheric pollutants. Thus, this study investigates techniques to reduce fuel consumption in robotic vehicles used for agricultural tasks and therefore reduce atmospheric emissions from these automated systems. A hybrid energy system for autonomous robots devoted to weed and pest control in agriculture is modeled and evaluated, and its exhaust emissions are compared with those of an internal combustion engine-powered system. Agricultural implements require power for hydraulic pumps and fans; this energy is conventionally provided by power take-off (PTO) systems, which waste substantial amounts of energy. In this work, we examine a solution by designing and assessing a hybrid energy system that omits the alternators from the original vehicle and modifies the agricultural implements to replace the PTO power with electrical power. The hybrid energy system uses the original combustion engine of the tractor in combination with a new electrical energy system based on a hydrogen fuel cell. We analyze and compare the exhaust gases resulting from the use of (1) an internal combustion engine as the single power source and (2) the hybrid energy system. The results demonstrate that the hybrid energy system reduced emissions by up to approximately 50%.",book:{id:"7270",slug:"agricultural-robots-fundamentals-and-applications",title:"Agricultural Robots",fullTitle:"Agricultural Robots - Fundamentals and Applications"},signatures:"Mariano Gonzalez-de-Soto, Luis Emmi and Pablo Gonzalez-de-Santos",authors:[{id:"252783",title:"Prof.",name:"Pablo",middleName:null,surname:"Gonzalez-De-Santos",slug:"pablo-gonzalez-de-santos",fullName:"Pablo Gonzalez-De-Santos"},{id:"252784",title:"Dr.",name:"Mariano",middleName:null,surname:"Gonzalez-De-Soto",slug:"mariano-gonzalez-de-soto",fullName:"Mariano Gonzalez-De-Soto"},{id:"252785",title:"Dr.",name:"Luis",middleName:null,surname:"Emmi",slug:"luis-emmi",fullName:"Luis Emmi"}]},{id:"62821",title:"An Evaluation of Three Different Infield Navigation Algorithms",slug:"an-evaluation-of-three-different-infield-navigation-algorithms",totalDownloads:1095,totalCrossrefCites:2,totalDimensionsCites:2,abstract:"In this chapter, we present and evaluate three different infield navigation algorithms, based on the readings from a LIDAR sensor. All three algorithms are tested on a small field robot and used to autonomously drive the robot between the two adjacent rows of maze plants. The first algorithm is the simplest one and just takes distance readings from the left and right side. If robot is not in the center of the mid-row space, it adjusts its course by turning the robot in the right direction accordingly. The second approach groups the left and right readings into two vertical lines by using least-square fit approach. According to the calculated distance and orientation to both lines, it adjusts the course of the robot. The third approach tries to fit an optimal triangle between the robot and the plants, revealing the most optimal one. Based on its shape, the course of the robot is adjusted. All three algorithms are tested in a simulated (ROS stage) and then in an outdoor (maze test field) environment comparing the optimal line with the actual calculated position of the robot. The tests prove that all three approaches work with an error of 0.041 ± 0.034 m for the first algorithm, 0.07 ± 0.059 m for the second, and 0.078 ± 0.055 m error for the third.",book:{id:"7270",slug:"agricultural-robots-fundamentals-and-applications",title:"Agricultural Robots",fullTitle:"Agricultural Robots - Fundamentals and Applications"},signatures:"Peter Bernad, Peter Lepej, Črtomir Rozman, Karmen Pažek and Jurij Rakun",authors:[{id:"179642",title:"Prof.",name:"Karmen",middleName:null,surname:"Pažek",slug:"karmen-pazek",fullName:"Karmen Pažek"},{id:"188886",title:"Prof.",name:"Črtomir",middleName:null,surname:"Rozman",slug:"crtomir-rozman",fullName:"Črtomir Rozman"},{id:"255090",title:"Dr.",name:"Jurij",middleName:null,surname:"Rakun",slug:"jurij-rakun",fullName:"Jurij Rakun"},{id:"255091",title:"Dr.",name:"Peter",middleName:null,surname:"Lepej",slug:"peter-lepej",fullName:"Peter Lepej"},{id:"255092",title:"BSc.",name:"Peter",middleName:null,surname:"Bernard",slug:"peter-bernard",fullName:"Peter Bernard"}]}],onlineFirstChaptersFilter:{topicId:"1283",limit:6,offset:0},onlineFirstChaptersCollection:[],onlineFirstChaptersTotal:0},preDownload:{success:null,errors:{}},subscriptionForm:{success:null,errors:{}},aboutIntechopen:{},privacyPolicy:{},peerReviewing:{},howOpenAccessPublishingWithIntechopenWorks:{},sponsorshipBooks:{sponsorshipBooks:[],offset:8,limit:8,total:0},allSeries:{pteSeriesList:[{id:"14",title:"Artificial Intelligence",numberOfPublishedBooks:9,numberOfPublishedChapters:87,numberOfOpenTopics:6,numberOfUpcomingTopics:0,issn:"2633-1403",doi:"10.5772/intechopen.79920",isOpenForSubmission:!0},{id:"7",title:"Biomedical Engineering",numberOfPublishedBooks:12,numberOfPublishedChapters:98,numberOfOpenTopics:3,numberOfUpcomingTopics:0,issn:"2631-5343",doi:"10.5772/intechopen.71985",isOpenForSubmission:!0}],lsSeriesList:[{id:"11",title:"Biochemistry",numberOfPublishedBooks:27,numberOfPublishedChapters:288,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2632-0983",doi:"10.5772/intechopen.72877",isOpenForSubmission:!0},{id:"25",title:"Environmental Sciences",numberOfPublishedBooks:1,numberOfPublishedChapters:9,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2754-6713",doi:"10.5772/intechopen.100362",isOpenForSubmission:!0},{id:"10",title:"Physiology",numberOfPublishedBooks:11,numberOfPublishedChapters:139,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2631-8261",doi:"10.5772/intechopen.72796",isOpenForSubmission:!0}],hsSeriesList:[{id:"3",title:"Dentistry",numberOfPublishedBooks:8,numberOfPublishedChapters:129,numberOfOpenTopics:0,numberOfUpcomingTopics:2,issn:"2631-6218",doi:"10.5772/intechopen.71199",isOpenForSubmission:!1},{id:"6",title:"Infectious Diseases",numberOfPublishedBooks:13,numberOfPublishedChapters:107,numberOfOpenTopics:3,numberOfUpcomingTopics:1,issn:"2631-6188",doi:"10.5772/intechopen.71852",isOpenForSubmission:!0},{id:"13",title:"Veterinary Medicine and Science",numberOfPublishedBooks:10,numberOfPublishedChapters:103,numberOfOpenTopics:3,numberOfUpcomingTopics:0,issn:"2632-0517",doi:"10.5772/intechopen.73681",isOpenForSubmission:!0}],sshSeriesList:[{id:"22",title:"Business, Management and Economics",numberOfPublishedBooks:1,numberOfPublishedChapters:12,numberOfOpenTopics:2,numberOfUpcomingTopics:1,issn:"2753-894X",doi:"10.5772/intechopen.100359",isOpenForSubmission:!0},{id:"23",title:"Education and Human Development",numberOfPublishedBooks:0,numberOfPublishedChapters:0,numberOfOpenTopics:2,numberOfUpcomingTopics:0,issn:null,doi:"10.5772/intechopen.100360",isOpenForSubmission:!1},{id:"24",title:"Sustainable Development",numberOfPublishedBooks:0,numberOfPublishedChapters:11,numberOfOpenTopics:4,numberOfUpcomingTopics:1,issn:null,doi:"10.5772/intechopen.100361",isOpenForSubmission:!0}],testimonialsList:[{id:"6",text:"It is great to work with the IntechOpen to produce a worthwhile collection of research that also becomes a great educational resource and guide for future research endeavors.",author:{id:"259298",name:"Edward",surname:"Narayan",institutionString:null,profilePictureURL:"https://mts.intechopen.com/storage/users/259298/images/system/259298.jpeg",slug:"edward-narayan",institution:{id:"3",name:"University of Queensland",country:{id:null,name:"Australia"}}}},{id:"13",text:"The collaboration with and support of the technical staff of IntechOpen is fantastic. The whole process of submitting an article and editing of the submitted article goes extremely smooth and fast, the number of reads and downloads of chapters is high, and the contributions are also frequently cited.",author:{id:"55578",name:"Antonio",surname:"Jurado-Navas",institutionString:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRisIQAS/Profile_Picture_1626166543950",slug:"antonio-jurado-navas",institution:{id:"720",name:"University of Malaga",country:{id:null,name:"Spain"}}}}]},series:{item:{id:"13",title:"Veterinary Medicine and Science",doi:"10.5772/intechopen.73681",issn:"2632-0517",scope:"Paralleling similar advances in the medical field, astounding advances occurred in Veterinary Medicine and Science in recent decades. These advances have helped foster better support for animal health, more humane animal production, and a better understanding of the physiology of endangered species to improve the assisted reproductive technologies or the pathogenesis of certain diseases, where animals can be used as models for human diseases (like cancer, degenerative diseases or fertility), and even as a guarantee of public health. Bridging Human, Animal, and Environmental health, the holistic and integrative “One Health” concept intimately associates the developments within those fields, projecting its advancements into practice. This book series aims to tackle various animal-related medicine and sciences fields, providing thematic volumes consisting of high-quality significant research directed to researchers and postgraduates. It aims to give us a glimpse into the new accomplishments in the Veterinary Medicine and Science field. 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After almost 32 years of teaching at the University of Trás-os-Montes and Alto Douro, she recently moved to the University of Évora, Department of Veterinary Medicine, where she teaches in the field of Animal Reproduction and Clinics. Her primary research areas include the molecular markers of the endometrial cycle and the embryo–maternal interaction, including oxidative stress and the reproductive physiology and disorders of sexual development, besides the molecular determinants of male and female fertility. She often supervises students preparing their master's or doctoral theses. She is also a frequent referee for various journals.",institutionString:null,institution:{name:"University of Évora",institutionURL:null,country:{name:"Portugal"}}},editorTwo:null,editorThree:null},subseries:{paginationCount:5,paginationItems:[{id:"19",title:"Animal Science",coverUrl:"https://cdn.intechopen.com/series_topics/covers/19.jpg",editor:{id:"259298",title:"Dr.",name:"Edward",middleName:null,surname:"Narayan",slug:"edward-narayan",fullName:"Edward Narayan",profilePictureURL:"https://mts.intechopen.com/storage/users/259298/images/system/259298.jpeg",biography:"Dr. Edward Narayan graduated with Ph.D. degree in Biology from the University of the South Pacific and pioneered non-invasive reproductive and stress endocrinology tools for amphibians - the novel development and validation of non-invasive enzyme immunoassays for the evaluation of reproductive hormonal cycle and stress hormone responses to environmental stressors. \nDr. Narayan leads the Stress Lab (Comparative Physiology and Endocrinology) at the University of Queensland. A dynamic career research platform which is based on the thematic areas of comparative vertebrate physiology, stress endocrinology, reproductive endocrinology, animal health and welfare, and conservation biology. \nEdward has supervised 40 research students and published over 60 peer reviewed research.",institutionString:null,institution:{name:"University of Queensland",institutionURL:null,country:{name:"Australia"}}},editorTwo:null,editorThree:null,editorialBoard:[{id:"258334",title:"Dr.",name:"Carlos Eduardo",middleName:null,surname:"Fonseca-Alves",slug:"carlos-eduardo-fonseca-alves",fullName:"Carlos Eduardo Fonseca-Alves",profilePictureURL:"https://mts.intechopen.com/storage/users/258334/images/system/258334.jpg",institutionString:null,institution:{name:"Universidade Paulista",institutionURL:null,country:{name:"Brazil"}}},{id:"191123",title:"Dr.",name:"Juan José",middleName:null,surname:"Valdez-Alarcón",slug:"juan-jose-valdez-alarcon",fullName:"Juan José Valdez-Alarcón",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bSBfcQAG/Profile_Picture_1631354558068",institutionString:"Universidad Michoacana de San Nicolás de Hidalgo",institution:{name:"Universidad Michoacana de San Nicolás de Hidalgo",institutionURL:null,country:{name:"Mexico"}}},{id:"161556",title:"Dr.",name:"Maria Dos Anjos",middleName:null,surname:"Pires",slug:"maria-dos-anjos-pires",fullName:"Maria Dos Anjos Pires",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bS8q2QAC/Profile_Picture_1633432838418",institutionString:null,institution:{name:"University of Trás-os-Montes and Alto Douro",institutionURL:null,country:{name:"Portugal"}}},{id:"209839",title:"Dr.",name:"Marina",middleName:null,surname:"Spinu",slug:"marina-spinu",fullName:"Marina Spinu",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRLXpQAO/Profile_Picture_1630044895475",institutionString:null,institution:{name:"University of Agricultural Sciences and Veterinary Medicine of Cluj-Napoca",institutionURL:null,country:{name:"Romania"}}},{id:"92185",title:"Dr.",name:"Sara",middleName:null,surname:"Savic",slug:"sara-savic",fullName:"Sara Savic",profilePictureURL:"https://mts.intechopen.com/storage/users/92185/images/system/92185.jfif",institutionString:'Scientific Veterinary Institute "Novi Sad"',institution:{name:'Scientific Veterinary Institute "Novi Sad"',institutionURL:null,country:{name:"Serbia"}}}]},{id:"20",title:"Animal Nutrition",coverUrl:"https://cdn.intechopen.com/series_topics/covers/20.jpg",editor:{id:"175967",title:"Dr.",name:"Manuel",middleName:null,surname:"Gonzalez Ronquillo",slug:"manuel-gonzalez-ronquillo",fullName:"Manuel Gonzalez Ronquillo",profilePictureURL:"https://mts.intechopen.com/storage/users/175967/images/system/175967.png",biography:"Dr. Manuel González Ronquillo obtained his doctorate degree from the University of Zaragoza, Spain, in 2001. He is a research professor at the Faculty of Veterinary Medicine and Animal Husbandry, Autonomous University of the State of Mexico. He is also a level-2 researcher. He received a Fulbright-Garcia Robles fellowship for a postdoctoral stay at the US Dairy Forage Research Center, Madison, Wisconsin, USA in 2008–2009. He received grants from Alianza del Pacifico for a stay at the University of Magallanes, Chile, in 2014, and from Consejo Nacional de Ciencia y Tecnología (CONACyT) to work in the Food and Agriculture Organization’s Animal Production and Health Division (AGA), Rome, Italy, in 2014–2015. He has collaborated with researchers from different countries and published ninety-eight journal articles. 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He has more than 200 publications in reputed international journals, refereed conference proceedings, and 20 book chapters in books published by internationally renowned publishing houses, such as Springer, CRC press, IGI Global, etc. Currently, he is serving on the editorial board of the prestigious journal Frontiers in Communications and Networks and in the technical program committees of a number of high-ranked international conferences organized by the IEEE, USA, and the ACM, USA. 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Ms. Mehtab has published seven papers in international conferences and one of her papers has been accepted for publication in a reputable international journal. She has won the best paper awards in two prestigious international conferences – BAICONF 2019, and ICADCML 2021, organized in the Indian Institute of Management, Bangalore, India in December 2019, and SOA University, Bhubaneswar, India in January 2021. Besides, Ms. Mehtab has also published two book chapters in two books. Seven of her book chapters will be published in a volume shortly in 2021 by Cambridge Scholars’ Press, UK. Currently, she is working as the joint editor of two edited volumes on Time Series Analysis and Forecasting to be published in the first half of 2021 by an international house. Currently, she is working as a Data Scientist with an MNC in Delhi, India.",institutionString:"NSHM College of Management and Technology",institution:null},{id:"226240",title:"Dr.",name:"Andri Irfan",middleName:null,surname:"Rifai",slug:"andri-irfan-rifai",fullName:"Andri Irfan Rifai",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/226240/images/7412_n.jpg",biography:"Andri IRFAN is a Senior Lecturer of Civil Engineering and Planning. He completed the PhD at the Universitas Indonesia & Universidade do Minho with Sandwich Program Scholarship from the Directorate General of Higher Education and LPDP scholarship. He has been teaching for more than 19 years and much active to applied his knowledge in the project construction in Indonesia. His research interest ranges from pavement management system to advanced data mining techniques for transportation engineering. He has published more than 50 papers in journals and 2 books.",institutionString:null,institution:{name:"Universitas Internasional Batam",country:{name:"Indonesia"}}},{id:"314576",title:"Dr.",name:"Ibai",middleName:null,surname:"Laña",slug:"ibai-lana",fullName:"Ibai Laña",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/314576/images/system/314576.jpg",biography:"Dr. Ibai Laña works at TECNALIA as a data analyst. He received his Ph.D. in Artificial Intelligence from the University of the Basque Country (UPV/EHU), Spain, in 2018. He is currently a senior researcher at TECNALIA. His research interests fall within the intersection of intelligent transportation systems, machine learning, traffic data analysis, and data science. He has dealt with urban traffic forecasting problems, applying machine learning models and evolutionary algorithms. He has experience in origin-destination matrix estimation or point of interest and trajectory detection. Working with large volumes of data has given him a good command of big data processing tools and NoSQL databases. He has also been a visiting scholar at the Knowledge Engineering and Discovery Research Institute, Auckland University of Technology.",institutionString:"TECNALIA Research & Innovation",institution:{name:"Tecnalia",country:{name:"Spain"}}},{id:"314575",title:"Dr.",name:"Jesus",middleName:null,surname:"L. Lobo",slug:"jesus-l.-lobo",fullName:"Jesus L. Lobo",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/314575/images/system/314575.png",biography:"Dr. Jesús López is currently based in Bilbao (Spain) working at TECNALIA as Artificial Intelligence Research Scientist. In most cases, a project idea or a new research line needs to be investigated to see if it is good enough to take into production or to focus on it. That is exactly what he does, diving into Machine Learning algorithms and technologies to help TECNALIA to decide whether something is great in theory or will actually impact on the product or processes of its projects. So, he is expert at framing experiments, developing hypotheses, and proving whether they’re true or not, in order to investigate fundamental problems with a longer time horizon. He is also able to design and develop PoCs and system prototypes in simulation. He has participated in several national and internacional R&D projects.\n\nAs another relevant part of his everyday research work, he usually publishes his findings in reputed scientific refereed journals and international conferences, occasionally acting as reviewer and Programme Commitee member. Concretely, since 2018 he has published 9 JCR (8 Q1) journal papers, 9 conference papers (e.g. ECML PKDD 2021), and he has co-edited a book. He is also active in popular science writing data science stories for reputed blogs (KDNuggets, TowardsDataScience, Naukas). Besides, he has recently embarked on mentoring programmes as mentor, and has also worked as data science trainer.",institutionString:"TECNALIA Research & Innovation",institution:{name:"Tecnalia",country:{name:"Spain"}}},{id:"103779",title:"Prof.",name:"Yalcin",middleName:null,surname:"Isler",slug:"yalcin-isler",fullName:"Yalcin Isler",position:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRyQ8QAK/Profile_Picture_1628834958734",biography:"Yalcin Isler (1971 - Burdur / Turkey) received the B.Sc. degree in the Department of Electrical and Electronics Engineering from Anadolu University, Eskisehir, Turkey, in 1993, the M.Sc. degree from the Department of Electronics and Communication Engineering, Suleyman Demirel University, Isparta, Turkey, in 1996, the Ph.D. degree from the Department of Electrical and Electronics Engineering, Dokuz Eylul University, Izmir, Turkey, in 2009, and the Competence of Associate Professorship from the Turkish Interuniversity Council in 2019.\n\nHe was Lecturer at Burdur Vocational School in Suleyman Demirel University (1993-2000, Burdur / Turkey), Software Engineer (2000-2002, Izmir / Turkey), Research Assistant in Bulent Ecevit University (2002-2003, Zonguldak / Turkey), Research Assistant in Dokuz Eylul University (2003-2010, Izmir / Turkey), Assistant Professor at the Department of Electrical and Electronics Engineering in Bulent Ecevit University (2010-2012, Zonguldak / Turkey), Assistant Professor at the Department of Biomedical Engineering in Izmir Katip Celebi University (2012-2019, Izmir / Turkey). He is an Associate Professor at the Department of Biomedical Engineering at Izmir Katip Celebi University, Izmir / Turkey, since 2019. In addition to academics, he has also founded Islerya Medical and Information Technologies Company, Izmir / Turkey, since 2017.\n\nHis main research interests cover biomedical signal processing, pattern recognition, medical device design, programming, and embedded systems. He has many scientific papers and participated in several projects in these study fields. He was an IEEE Student Member (2009-2011) and IEEE Member (2011-2014) and has been IEEE Senior Member since 2014.",institutionString:null,institution:{name:"Izmir Kâtip Çelebi University",country:{name:"Turkey"}}},{id:"339677",title:"Dr.",name:"Mrinmoy",middleName:null,surname:"Roy",slug:"mrinmoy-roy",fullName:"Mrinmoy Roy",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/339677/images/16768_n.jpg",biography:"An accomplished Sales & Marketing professional with 12 years of cross-functional experience in well-known organisations such as CIPLA, LUPIN, GLENMARK, ASTRAZENECA across different segment of Sales & Marketing, International Business, Institutional Business, Product Management, Strategic Marketing of HIV, Oncology, Derma, Respiratory, Anti-Diabetic, Nutraceutical & Stomatological Product Portfolio and Generic as well as Chronic Critical Care Portfolio. A First Class MBA in International Business & Strategic Marketing, B.Pharm, D.Pharm, Google Certified Digital Marketing Professional. Qualified PhD Candidate in Operations and Management with special focus on Artificial Intelligence and Machine Learning adoption, analysis and use in Healthcare, Hospital & Pharma Domain. Seasoned with diverse therapy area of Pharmaceutical Sales & Marketing ranging from generating revenue through generating prescriptions, launching new products, and making them big brands with continuous strategy execution at the Physician and Patients level. Moved from Sales to Marketing and Business Development for 3.5 years in South East Asian Market operating from Manila, Philippines. Came back to India and handled and developed Brands such as Gluconorm, Lupisulin, Supracal, Absolut Woman, Hemozink, Fabiflu (For COVID 19), and many more. In my previous assignment I used to develop and execute strategies on Sales & Marketing, Commercialization & Business Development for Institution and Corporate Hospital Business portfolio of Oncology Therapy Area for AstraZeneca Pharma India Ltd. Being a Research Scholar and Student of ‘Operations Research & Management: Artificial Intelligence’ I published several pioneer research papers and book chapters on the same in Internationally reputed journals and Books indexed in Scopus, Springer and Ei Compendex, Google Scholar etc. Currently, I am launching PGDM Pharmaceutical Management Program in IIHMR Bangalore and spearheading the course curriculum and structure of the same. I am interested in Collaboration for Healthcare Innovation, Pharma AI Innovation, Future trend in Marketing and Management with incubation on Healthcare, Healthcare IT startups, AI-ML Modelling and Healthcare Algorithm based training module development. I am also an affiliated member of the Institute of Management Consultant of India, looking forward to Healthcare, Healthcare IT and Innovation, Pharma and Hospital Management Consulting works.",institutionString:null,institution:{name:"Lovely Professional University",country:{name:"India"}}},{id:"1063",title:"Prof.",name:"Constantin",middleName:null,surname:"Volosencu",slug:"constantin-volosencu",fullName:"Constantin Volosencu",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/1063/images/system/1063.png",biography:"Prof. Dr. Constantin Voloşencu graduated as an engineer from\nPolitehnica University of Timișoara, Romania, where he also\nobtained a doctorate degree. He is currently a full professor in\nthe Department of Automation and Applied Informatics at the\nsame university. Dr. Voloşencu is the author of ten books, seven\nbook chapters, and more than 160 papers published in journals\nand conference proceedings. He has also edited twelve books and\nhas twenty-seven patents to his name. He is a manager of research grants, editor in\nchief and member of international journal editorial boards, a former plenary speaker, a member of scientific committees, and chair at international conferences. His\nresearch is in the fields of control systems, control of electric drives, fuzzy control\nsystems, neural network applications, fault detection and diagnosis, sensor network\napplications, monitoring of distributed parameter systems, and power ultrasound\napplications. He has developed automation equipment for machine tools, spooling\nmachines, high-power ultrasound processes, and more.",institutionString:"Polytechnic University of Timişoara",institution:{name:"Polytechnic University of Timişoara",country:{name:"Romania"}}},{id:"221364",title:"Dr.",name:"Eneko",middleName:null,surname:"Osaba",slug:"eneko-osaba",fullName:"Eneko Osaba",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/221364/images/system/221364.jpg",biography:"Dr. Eneko Osaba works at TECNALIA as a senior researcher. He obtained his Ph.D. in Artificial Intelligence in 2015. He has participated in more than twenty-five local and European research projects, and in the publication of more than 130 papers. He has performed several stays at universities in the United Kingdom, Italy, and Malta. Dr. Osaba has served as a program committee member in more than forty international conferences and participated in organizing activities in more than ten international conferences. He is a member of the editorial board of the International Journal of Artificial Intelligence, Data in Brief, and Journal of Advanced Transportation. He is also a guest editor for the Journal of Computational Science, Neurocomputing, Swarm, and Evolutionary Computation and IEEE ITS Magazine.",institutionString:"TECNALIA Research & Innovation",institution:{name:"Tecnalia",country:{name:"Spain"}}},{id:"275829",title:"Dr.",name:"Esther",middleName:null,surname:"Villar-Rodriguez",slug:"esther-villar-rodriguez",fullName:"Esther Villar-Rodriguez",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/275829/images/system/275829.jpg",biography:"Dr. Esther Villar obtained a Ph.D. in Information and Communication Technologies from the University of Alcalá, Spain, in 2015. She obtained a degree in Computer Science from the University of Deusto, Spain, in 2010, and an MSc in Computer Languages and Systems from the National University of Distance Education, Spain, in 2012. Her areas of interest and knowledge include natural language processing (NLP), detection of impersonation in social networks, semantic web, and machine learning. Dr. Esther Villar made several contributions at conferences and publishing in various journals in those fields. Currently, she is working within the OPTIMA (Optimization Modeling & Analytics) business of TECNALIA’s ICT Division as a data scientist in projects related to the prediction and optimization of management and industrial processes (resource planning, energy efficiency, etc).",institutionString:"TECNALIA Research & Innovation",institution:{name:"Tecnalia",country:{name:"Spain"}}},{id:"49813",title:"Dr.",name:"Javier",middleName:null,surname:"Del Ser",slug:"javier-del-ser",fullName:"Javier Del Ser",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/49813/images/system/49813.png",biography:"Prof. Dr. Javier Del Ser received his first PhD in Telecommunication Engineering (Cum Laude) from the University of Navarra, Spain, in 2006, and a second PhD in Computational Intelligence (Summa Cum Laude) from the University of Alcala, Spain, in 2013. He is currently a principal researcher in data analytics and optimisation at TECNALIA (Spain), a visiting fellow at the Basque Center for Applied Mathematics (BCAM) and a part-time lecturer at the University of the Basque Country (UPV/EHU). His research interests gravitate on the use of descriptive, prescriptive and predictive algorithms for data mining and optimization in a diverse range of application fields such as Energy, Transport, Telecommunications, Health and Industry, among others. In these fields he has published more than 240 articles, co-supervised 8 Ph.D. theses, edited 6 books, coauthored 7 patents and participated/led more than 40 research projects. He is a Senior Member of the IEEE, and a recipient of the Biscay Talent prize for his academic career.",institutionString:"Tecnalia Research & Innovation",institution:null},{id:"278948",title:"Dr.",name:"Carlos Pedro",middleName:null,surname:"Gonçalves",slug:"carlos-pedro-goncalves",fullName:"Carlos Pedro Gonçalves",position:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRcmyQAC/Profile_Picture_1564224512145",biography:'Carlos Pedro Gonçalves (PhD) is an Associate Professor at Lusophone University of Humanities and Technologies and a researcher on Complexity Sciences, Quantum Technologies, Artificial Intelligence, Strategic Studies, Studies in Intelligence and Security, FinTech and Financial Risk Modeling. He is also a progammer with programming experience in:\n\nA) Quantum Computing using Qiskit Python module and IBM Quantum Experience Platform, with software developed on the simulation of Quantum Artificial Neural Networks and Quantum Cybersecurity;\n\nB) Artificial Intelligence and Machine learning programming in Python;\n\nC) Artificial Intelligence, Multiagent Systems Modeling and System Dynamics Modeling in Netlogo, with models developed in the areas of Chaos Theory, Econophysics, Artificial Intelligence, Classical and Quantum Complex Systems Science, with the Econophysics models having been cited worldwide and incorporated in PhD programs by different Universities.\n\nReceived an Arctic Code Vault Contributor status by GitHub, due to having developed open source software preserved in the \\"Arctic Code Vault\\" for future generations (https://archiveprogram.github.com/arctic-vault/), with the Strategy Analyzer A.I. module for decision making support (based on his PhD thesis, used in his Classes on Decision Making and in Strategic Intelligence Consulting Activities) and QNeural Python Quantum Neural Network simulator also preserved in the \\"Arctic Code Vault\\", for access to these software modules see: https://github.com/cpgoncalves. He is also a peer reviewer with outsanding review status from Elsevier journals, including Physica A, Neurocomputing and Engineering Applications of Artificial Intelligence. Science CV available at: https://www.cienciavitae.pt//pt/8E1C-A8B3-78C5 and ORCID: https://orcid.org/0000-0002-0298-3974',institutionString:"University of Lisbon",institution:{name:"Universidade Lusófona",country:{name:"Portugal"}}},{id:"241400",title:"Prof.",name:"Mohammed",middleName:null,surname:"Bsiss",slug:"mohammed-bsiss",fullName:"Mohammed Bsiss",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/241400/images/8062_n.jpg",biography:null,institutionString:null,institution:null},{id:"276128",title:"Dr.",name:"Hira",middleName:null,surname:"Fatima",slug:"hira-fatima",fullName:"Hira Fatima",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/276128/images/14420_n.jpg",biography:"Dr. Hira Fatima\nAssistant Professor\nDepartment of Mathematics\nInstitute of Applied Science\nMangalayatan University, Aligarh\nMobile: no : 8532041179\nhirafatima2014@gmal.com\n\nDr. Hira Fatima has received his Ph.D. degree in pure Mathematics from Aligarh Muslim University, Aligarh India. Currently working as an Assistant Professor in the Department of Mathematics, Institute of Applied Science, Mangalayatan University, Aligarh. She taught so many courses of Mathematics of UG and PG level. Her research Area of Expertise is Functional Analysis & Sequence Spaces. She has been working on Ideal Convergence of double sequence. She has published 17 research papers in National and International Journals including Cogent Mathematics, Filomat, Journal of Intelligent and Fuzzy Systems, Advances in Difference Equations, Journal of Mathematical Analysis, Journal of Mathematical & Computer Science etc. She has also reviewed few research papers for the and international journals. She is a member of Indian Mathematical Society.",institutionString:null,institution:null},{id:"414880",title:"Dr.",name:"Maryam",middleName:null,surname:"Vatankhah",slug:"maryam-vatankhah",fullName:"Maryam Vatankhah",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Borough of Manhattan Community College",country:{name:"United States of America"}}},{id:"414879",title:"Prof.",name:"Mohammad-Reza",middleName:null,surname:"Akbarzadeh-Totonchi",slug:"mohammad-reza-akbarzadeh-totonchi",fullName:"Mohammad-Reza Akbarzadeh-Totonchi",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Ferdowsi University of Mashhad",country:{name:"Iran"}}},{id:"414878",title:"Prof.",name:"Reza",middleName:null,surname:"Fazel-Rezai",slug:"reza-fazel-rezai",fullName:"Reza Fazel-Rezai",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"American Public University System",country:{name:"United States of America"}}},{id:"302698",title:"Dr.",name:"Yao",middleName:null,surname:"Shan",slug:"yao-shan",fullName:"Yao Shan",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Dalian University of Technology",country:{name:"China"}}},{id:"125911",title:"Prof.",name:"Jia-Ching",middleName:null,surname:"Wang",slug:"jia-ching-wang",fullName:"Jia-Ching Wang",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"National Central University",country:{name:"Taiwan"}}},{id:"357085",title:"Mr.",name:"P. Mohan",middleName:null,surname:"Anand",slug:"p.-mohan-anand",fullName:"P. Mohan Anand",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Indian Institute of Technology Kanpur",country:{name:"India"}}},{id:"356696",title:"Ph.D. Student",name:"P.V.",middleName:null,surname:"Sai Charan",slug:"p.v.-sai-charan",fullName:"P.V. Sai Charan",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Indian Institute of Technology Kanpur",country:{name:"India"}}},{id:"357086",title:"Prof.",name:"Sandeep K.",middleName:null,surname:"Shukla",slug:"sandeep-k.-shukla",fullName:"Sandeep K. Shukla",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Indian Institute of Technology Kanpur",country:{name:"India"}}},{id:"356823",title:"MSc.",name:"Seonghee",middleName:null,surname:"Min",slug:"seonghee-min",fullName:"Seonghee Min",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Daegu University",country:{name:"Korea, South"}}},{id:"353307",title:"Prof.",name:"Yoosoo",middleName:null,surname:"Oh",slug:"yoosoo-oh",fullName:"Yoosoo Oh",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:"Yoosoo Oh received his Bachelor's degree in the Department of Electronics and Engineering from Kyungpook National University in 2002. He obtained his Master’s degree in the Department of Information and Communications from Gwangju Institute of Science and Technology (GIST) in 2003. In 2010, he received his Ph.D. degree in the School of Information and Mechatronics from GIST. In the meantime, he was an executed team leader at Culture Technology Institute, GIST, 2010-2012. In 2011, he worked at Lancaster University, the UK as a visiting scholar. In September 2012, he joined Daegu University, where he is currently an associate professor in the School of ICT Conver, Daegu University. Also, he served as the Board of Directors of KSIIS since 2019, and HCI Korea since 2016. From 2017~2019, he worked as a center director of the Mixed Reality Convergence Research Center at Daegu University. From 2015-2017, He worked as a director in the Enterprise Supporting Office of LINC Project Group, Daegu University. His research interests include Activity Fusion & Reasoning, Machine Learning, Context-aware Middleware, Human-Computer Interaction, etc.",institutionString:null,institution:{name:"Daegu Gyeongbuk Institute of Science and Technology",country:{name:"Korea, South"}}},{id:"262719",title:"Dr.",name:"Esma",middleName:null,surname:"Ergüner Özkoç",slug:"esma-erguner-ozkoc",fullName:"Esma Ergüner Özkoç",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Başkent University",country:{name:"Turkey"}}},{id:"346530",title:"Dr.",name:"Ibrahim",middleName:null,surname:"Kaya",slug:"ibrahim-kaya",fullName:"Ibrahim Kaya",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Izmir Kâtip Çelebi University",country:{name:"Turkey"}}},{id:"419199",title:"Dr.",name:"Qun",middleName:null,surname:"Yang",slug:"qun-yang",fullName:"Qun Yang",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"University of Auckland",country:{name:"New Zealand"}}},{id:"351158",title:"Prof.",name:"David W.",middleName:null,surname:"Anderson",slug:"david-w.-anderson",fullName:"David W. 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