Minimizing the Localization Error in Wireless Sensor Networks Using Multi-Objective Optimization Techniques

When it comes to remote sensing applications, wireless sensor networks (WSN) are crucial. Because of their small size, low cost, and ability to communicate with one another, sensors are finding more and more applications in a wide range of wireless technologies. The sensor network is the result of the fusion of microelectronic and electromechanical technologies. Through the localization procedure, the precise location of every network node can be determined. When trying to pinpoint the precise location of a node, a mobility anchor can be used in a helpful method known as mobility-assisted localization. In addition to improving route optimization for location-aware mobile nodes, the mobile anchor can do the same for stationary ones. This system proposes a multi-objective approach to minimizing the distance between the source and target nodes by employing the Dijkstra algorithm while avoiding obstacles. Both the Improved Grasshopper Optimization Algorithm (IGOA) and the Butterfly Optimization Algorithm (BOA) have been incorporated into multi-objective models for obstacle avoidance and route planning. Accuracy in localization is enhanced by the proposed system. Further, it decreases both localization errors and computation time when compared to the existing systems

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Not AvailableFoxtail millet [Setaria italica (L.) Beauv.], also known as Italian millet, is valued as a crop of short duration, which is good as food, feed and fodder. Of late, the importance of it was recognized as diabetic food. Foxtail millet provides approximately six million tonnes of food to millions of people, mainly on poor or marginal soils in southern Europe and in temperate, subtropical and tropical Asia. At present in India, foxtail millet is cultivated on a limited area in TS, AP, KA, MH and north eastern states. There is wide genetic diversity available in foxtail millet, and characterizing these resources is a pre-requisite for the genetic improvement of its cultivars. More precise assessment of morphological diversity through characterization and identification of trait specific germplasm is essential. In this context, the present investigation was carried at Centre on Rabi sorghum (ICAR-IIMR), Solapur. A total of 138 Foxtail millet germplasm were evaluated in RCBD with two replications during kharif -2017. Huge amount of variability was observed for various agro-morphological traits. The variability found in Days to flowering (PCV=17.29), Plant height (CV=15.66), Panicle exertion (CV=41.68), Peduncle length (CV=22.71), Grain yield/plant (CV=33.55), Fodder yield/plant (CV=33.55).Qualitative traits viz., plant pigmentation, mid rib colour, leaf colour, and apical sterility was categorized into various classes. Line no. 1003, 1010, 1021, 1025, 1028, 1032, 1037 were of high yielding; Line no. 1043, 1052, 1058, 1065, 1072, 1085 and 1094 were of compact, Non-shattering and non-lodging type. The identified lines may serve as valuable genetic stocks for further crop improvement.ICA

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Not AvailableFinger millet also known as Ragi or African millet is an annual plant widely grown as an important food crop in the arid areas of Africa and south Asia. It ranks third in importance among the millets after sorghum and Pearl millet in India. Finger millet is rich in nutritional qualities with good quality protein, plentiful minerals, dietary fibres, phytochemicals and vitamins. It is the richest source of calcium providing 8-10 times more than that of rice and wheat. Finger millet is essentially a self-pollinated crop. Being a food grain crop, yield improvement is the major goal in varietal improvement. Characterization and evaluation of germplasm thus becomes important to identify the genotypes with novel traits and to break the yield plateau. In this context, the present investigation was carried out at Centre on Rabi sorghum (ICAR-IIMR), Solapur during kharif-2017 to evaluate 38 Finger millet germplasm lines. Trial was conducted in RCBD with two replications. ANOVA revealed there is a significant difference between the lines. The accession no. 5117 showed highest number of fingers/plant (59), 5114 with highest no. of productive tillers/plant (36) and finger length (17.5g), 5141 with highest panicle weight (180g). Most of the lines were of non-pigmented type except line no. 5146. Similarly most of the lines exhibited lodging character except line nos. 5121 and 5122 despite being tall. Some of the lines viz., 5141, 5140 and 5107 showed good grain and fodder yield/plant. The selected lines can be successfully utilized as donors in hybridization for better realization of superior transgressive segregants.ICA

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Not AvailablePearl millet (Pennisetum glaucum L.) is a C4 plant with high photosynthetic efficiency and dry matter production capacity. Pearl millet is widely cultivated in Rajasthan, Maharashtra, Gujarat, Uttar Pradesh and Haryana. It is mostly grown in Rainy (Kharif) season. Its grains are highly nutritious with high levels of metabolizable energy and protein. Utilization of different kinds of germplasm and breeding material is very critical in the diversification of cultivars. Being a cross pollinated crop, it provides very good opportunity for recombination and easy pollen flow, making hybrids more rewarding to the farming community. Identification of good maintainer (B) lines and Restorer (R) lines is of utmost importance to breed for hybrids. In this context, the present investigation was carried out at Centre on Rabi sorghum (ICAR-IIMR), Solapur during Kharif 2017 and the study consists of two sets of material viz., 56 B lines and 65 R lines. These lines were evaluated in a Randomized Block Design with two replications. To utilize large number of lines is difficult hence to bring the number to utilizable size cluster analysis is very useful. The variability in six quantitative traits such as Days to 50% flowering, Plant height, Panicle length, Panicle thickness, Grain yield/plant, Fodder yield/plant was studied. Maximum variation was found in Panicle exertion (B; CV=43.84, R; CV=38.48) followed by fodder yield per plant (B;CV=37.24, R; CV=37.54). All the six traits were used as variables in cluster analysis. In the present study four clusters were assigned a priori in both B and R lines. The frequency classes in the qualitative traits were studied to identify the common and rare types in total set. It was found that 75% of lines were of intermediate Spikelet density and synchrony of maturity, 85.3% were with good Seed Yield Potential and 50% with good Fodder Yield Potential. The promising lines belonging to different clusters are to be crossed to broaden the genetic base. Superior ones are hybridized with their counter A linesICA

COMBINED EFFECT OF EXHAUST GAS RECIRCULATION (EGR) AND FUEL INJECTION PRESSURE ON CRDI ENGINE OPERATING WITH JATROPHA CURCAS BIODIESEL BLENDS

This work investigates the influence of Exhaust gas recirculation (EGR) and injection pressure on the performance and emissions of CRDI engine using Jatropha curcas biodiesel blends of 10% and 20% (B10 and B20). Experiments were carried out for three fuel injection pressures (FIP) of 300, 400 and 500 bar with 15% and 20% EGR rate at constant speed of 2000 rpm and standard injection timing of 150 BTDC. Parameters like brake thermal efficiency and emission characteristics such as smoke opacity, oxides of nitrogen (NOx), hydrocarbon (HC) and carbon mono-oxide (CO) were measured and analysed. The results showed improvement of performance in terms of brake thermal efficiency for blends B10, B20 and with 15%EGR rate. Smoke, HC and CO decreased while slightly increasing NOx emissions when working with biodiesel. In summary, it is optimized that engine running with combination of B20 blend and 15% EGR rate culminates into NOx reductions without affecting engine efficiency and other emissions like smoke opacity, hydrocarbon and carbon mono-oxide

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Not AvailableHeterosis and combining ability for yield and yield attributing characters were studied in finger millet through Line x Tester mating design using four lines and four testers. Combining ability analysis showed the predominant role of non-additive gene action for all the characters studied. The lines GE 4596 and GPU 28 and the testers L 5 and GPU 69 had recorded high per se and gca for yield and most of the yield contributing characters studied. The hybrid GE 4596 x L 5 and GE 4596 x GPU 69 had significant and superior per se performance for grain yield per plant, straw yield per plant, finger length, peduncle length, number of fingers per ear, culm thickness and number of productive tillers per plant. The hybrid GE 4596 x L 5 exhibited high sca effect for plant height , number of productive tillers per plant, finger length, peduncle length, days to 50per cent flowering, straw yield per plant, grain yield per plant and test weight. Studies on specific combining ability effects, indicates that the crosses viz., GE 4596 x L 5, GE 6216 x GPU 48 and GE 4906 x GPU 48 have significant sca effects for most of the characters. The hybrids, GE 4596 x L 5, GE 4596 x GPU 69 and GPU 28 x L 5 were from parents with high x high gca and GE 4596 x GE 5095, GE 6216 x GPU 69, GE 4906 x GPU 69 and GPU 28 x GE 5095 were from parents with high x low gca combinations. Thus, six crosses are suggested for realization of transgressive segregants in F2 and subsequent generations. The five hybrids viz., GE 4596 x L 5, GE 4596 x GE 5095, GPU 28 x L 5, GPU 28 x GE 5095 and GE 4906 x GE 5095 showed significant heterosis for most of the traits over their parentsNot Availabl

A comparative analysis of paddy crop biotic stress classification using pre-trained deep neural networks

The agriculture sector is no exception to the widespread usage of deep learning tools and techniques. In this paper, an automated detection method on the basis of pre-trained Convolutional Neural Network (CNN) models is proposed to identify and classify paddy crop biotic stresses from the field images. The proposed work also provides the empirical comparison among the leading CNN models with transfer learning from the ImageNet weights namely, Inception-V3, VGG-16, ResNet-50, DenseNet-121 and MobileNet-28. Brown spot, hispa, and leaf blast, three of the most common and destructive paddy crop biotic stresses that occur during the flowering and ripening growth stages are considered for the experimentation. The experimental results reveal that the ResNet-50 model achieves the highest average paddy crop stress classification accuracy of 92.61% outperforming the other considered CNN models. The study explores the feasibility of CNN models for the paddy crop stress identification as well as the applicability of automated methods to non-experts