Faculty Publications
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Item Evaluation of ultrasonic sensor in robot mapping(Institute of Electrical and Electronics Engineers Inc., 2019) Nair, S.K.A.; Joladarashi, S.; Ganesh, N.This paper presents the simulation and experimental investigation on mapping done using ultrasonic sensor(HC-SR04). Issues related to exploration, mapping and localization are discussed. Kobuki base with three ultrasonic sensors was kept as robot mobile base platform. A 2D occupancy grid map is updated by the robot when it moves and collects information from ultrasonic sensor. The sensor being noisy in nature generates unwanted ghost points. This results in outliers in the map especially in corners of the environment due to specular reflections. The issues like outliers are dealt with image processing techniques. © 2019 IEEE.Item Geometrical Mapping of an Initially Unknown Region by a Mobile Robot(Institute of Electrical and Electronics Engineers Inc., 2019) Marpally, S.R.; Nagarakshith, M.S.; Sadananda, A.; Guruprasad, K.R.In this paper, we address a problem of mapping an unknown region of interest by a mobile robot. Unlike the conventional exploration and mapping techniques where the occupancy map of a spatially discretized environment is obtained, in the proposed Geometric Mapping (G-Mapping) strategy, the map is obtained in the form of geometric models of the obstacles, in a continuous space. For simplicity, we consider convex polygonal obstacles within a convex region. The proposed exploration strategy is implemented using MATLAB. The simulation results are presented to illustrate and demonstrate the G-Mapping strategy. © 2019 IEEE.Item Automatizing the Khasra Maps Generation Process Using Open Source Software: QGIS and Python Coding Language(Springer Science and Business Media Deutschland GmbH, 2022) Sharma, R.; Beg, M.K.; Bhojaraja, B.E.; Umesh, P.Humans are trying to acquire a piece of land from the time they have come into existence. In modern era, the management of land and its ownership is taken up by the Land and Revenue Department of the State. In order to do that, they need maps with specific objectives, so that even a laymen can understand and use it. The process explained in this paper automate the process of map making after getting the digitized shapefile of the khasra (property identification number), as a single village is divided into numerous grids and it is a tedious work and can have lots of errors while doing it manually. So in order to do the process in swift manner and without having any errors, the process was developed using the Quantum Geographic Information System (QGIS) and Python. The proposed method involves making the use of models built in QGIS along with the Python console. It helps to run the whole process on its own with taking the required input parameters and storing the outputs in a specific folder designed for them. The requirement of the project was to do the same operations on a village file and to get the final khasra map from the village polygon file. Depending upon the village area and its dimensions, the numbers of grids for a particular village is decided and the same GIS tools need to be run on each grid files which make this process a tedious work and more prone to errors. By making use of the method suggested in the paper, all the work can be done error proof with the use of Python. The use of Python code helps to do work in just couple of seconds which would have taken days to complete. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.Item Comparison of Hyperspectral Atmospheric Correction Algorithms for Precise Mapping of Rice Crop(Springer Science and Business Media Deutschland GmbH, 2022) Vivek, B.; Bhojaraja, B.E.; Shetty, A.For millions of people, rice means life, and therefore, it is harvested in many regions of the world. Two rice species are primarily cultivated in the world, namely Asian and African rice. It grows primarily in major river deltas, such as Asia and Southeast Asia. Conventional method of mapping rice crop area is tedious and time-consuming job and more often subjected to erroneous results. In this study advanced remote sensing technique is used for mapping, to map precisely hyperspectral remote sensing with different atmospheric algorithms were compared for better accuracy. Also different supervised classification techniques were compared for the accurate area mapping of rice crop. The ASD field spec Pro hand held spectroradiometer is used for reference spectra collection. And high accuracy GPS device is used to collect ground truth information. Results show that both FLAASH and HAC algorithms produce a good spectrum with respect to the rice spectra obtained from ASD handheld spectroradiometer. SAM image classification and Parallelepiped classifier were used for classification of imagery. From the accuracy assessment performed, accuracy of 88% by using SAM and 84% obtained using Parallelepiped classifier for Hooghly region and 93% using SAM and 87% using Parallelepiped for West Godavari region. From the study, it was found that the best approach for rice crop mapping in Hooghly and West Godavari is SAM classification. The study helps to map the rice crop area accurately; it can be used for yield estimation, indirectly which is helpful for policy makers and to estimate the export, import potential. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.Item An intelligent system for squeeze casting process—soft computing based approach(Springer London, 2016) Gowdru Chandrashekarappa, G.C.; Krishna, P.; Parappagoudar, M.B.The present work deals with the forward and reverse modelling of squeeze casting process by utilizing the neural network-based approaches. The important quality characteristics in squeeze casting, namely surface roughness and tensile strength, are significantly influenced by its process variables like pressure duration, squeeze pressure, and pouring and die temperatures. The process variables are considered as input and output to neural network in forward and reverse mapping, respectively. Forward and reverse mappings are carried out utilizing back propagation neural network and genetic algorithm neural network. For both supervised learning networks, batch training is employed using huge training data (input-output data). The input-output data required for training is generated artificially at random by varying process variables between their respective levels. Further, the developed model prediction performances are compared for 15 random test cases. Results have shown that both models are capable to make better predictions, and the models can be used by any novice user without knowing much about the mechanics of materials and the process. However, the genetic algorithm tuned neural network (GA-NN) model prediction performance is found marginally better in forward mapping, whereas BPNN produced better results in reverse mapping. © 2016, Springer-Verlag London.Item A nonlocal maximum likelihood estimation method for enhancing magnetic resonance phase maps(Springer London, 2017) Sudeep, P.V.; Ponnusamy, P.; Kesavadas, C.; Sijbers, J.; den Dekker, A.J.; Rajan, J.A phase map can be obtained from the real and imaginary components of a complex valued magnetic resonance (MR) image. Many applications, such as MR phase velocity mapping and susceptibility mapping, make use of the information contained in the MR phase maps. Unfortunately, noise in the complex MR signal affects the measurement of parameters related to phase (e.g, the phase velocity). In this paper, we propose a nonlocal maximum likelihood (NLML) estimation method for enhancing phase maps. The proposed method estimates the true underlying phase map from a noisy MR phase map. Experiments on both simulated and real data sets indicate that the proposed NLML method has a better performance in terms of qualitative and quantitative evaluations when compared to state-of-the-art methods. © 2016, Springer-Verlag London.Item Back propagation genetic and recurrent neural network applications in modelling and analysis of squeeze casting process(Elsevier Ltd, 2017) Gowdru Chandrashekarappa, M.; Shettigar, A.K.; Krishna, P.; Parappagoudar, M.B.Today, in competitive manufacturing environment reducing casting defects with improved mechanical properties is of industrial relevance. This led the present work to deal with developing the input-output relationship in squeeze casting process utilizing the neural network based forward and reverse mapping. Forward mapping is aimed to predict the casting quality (such as density, hardness and secondary dendrite arm spacing) for the known combination of casting variables (that is, squeeze pressure, pressure duration, die and pouring temperature). Conversely, attempt is also made to determine the appropriate set of casting variables for the required casting quality (that is, reverse mapping). Forward and reverse mapping tasks are carried out utilizing back propagation, recurrent and genetic algorithm tuned neural networks. Parameter study has been conducted to adjust and optimize the neural network parameters utilizing the batch mode of training. Since, batch mode of training requires huge data, the training data is generated artificially using response equations. Furthermore, neural network prediction performances are compared among themselves (reverse mapping) and with those of statistical regression models (forward mapping) with the help of test cases. The results shown all developed neural network models in both forward and reverse mappings are capable of making effective predictions. The results obtained will help the foundry personnel to automate and précised control of squeeze casting process. © 2017Item Visibility enhancement of images degraded by hazy weather conditions using modified non-local approach(Elsevier GmbH journals@elsevier.com, 2018) Pal, N.S.; Lal, S.; Shinghal, K.Natural images captured under bad weather situations suffer from poor visibility and contrast problems. Object tracking and recognition under hazy bad weather conditions is a very difficult task for real time applications. Therefore, in this paper, we have proposed an efficient dehazy algorithm for visibility and contrast enhancement of color hazy images. The proposed algorithm works in two phases. In the first phase, a non local approach is applied in the hazy model, which is a pixel based approach not a patch based. Pixels are spread over the entire image plane and positioned at different distance from the sensor, so this approach is called non local. Degradation is different for every pixel therefore; estimation of the transmission map for every pixel through the haze line is the essential step. After the first phase, the image becomes unnatural and dimmed, therefore to proper tone mapping and improving the visual quality of the image, we applied the S-shaped mapping function in the second phase. The quantitative results of the proposed algorithm and other existing dehazy algorithms for color hazy images are obtained in terms of Hazy Reduction factor(HRF), and measure of enhancement factor(EMF) on different hazy image databases. Qualitative results reveal that the visual quality of the proposed algorithm is better than other existing de-hazy algorithms. Simulation results demonstrate that the proposed algorithm provides better results as compared to other existing dehazy algorithms for color hazy images. Proposed algorithm is highly efficient as compare to other latest dehazy algorithms. © 2018 Elsevier GmbHItem Performance analysis of vertically offset overlapped propulsion system based quadrotor in an aerial mapping mission(SAGE Publications Inc. claims@sagepub.com, 2018) Nandakumar, G.; Saphal, R.; Joishy, A.; Thondiyath, A.In this paper, the authors present the performance analysis of a Vertically Offset Overlapped Propulsion System (VOOPS)-based quadrotor in an aerial mapping mission. The dynamic model of the VOOPS quadrotor with the effect of overlapping propellers and the profile drag has been derived and simulated. A path-tracking mission is taken as an example for aerial survey. The controller used for this task is presented, followed by the response study of the attitude and the position controller with standard test inputs. A graphical interface has been built to select the area to be mapped by defining a polygon around it, and waypoints for lawn-mower type survey grid were generated based on the direction of wind. The path-tracking algorithm is presented along with course correction and simulations were performed with both conventional and VOOPS quadrotor. An experimental vehicle based on the proposed VOOPS concept has been built, tested on the same path, and the results are discussed. The results show that the VOOPS quadrotor is capable of performing the aerial mapping mission with quick response and good accuracy. © The Author(s) 2018.Item Extended Newton-type iteration for nonlinear ill-posed equations in Banach space(Springer Verlag service@springer.de, 2019) Sreedeep, C.D.; George, S.; Argyros, I.K.In this paper, we study nonlinear ill-posed equations involving m-accretive mappings in Banach spaces. We produce an extended Newton-type iterative scheme that converges cubically to the solution which uses assumptions only on the first Fréchet derivative of the operator. Using general Hölder type source condition we obtain an error estimate. We also use the adaptive parameter choice strategy proposed by Pereverzev and Schock (SIAM J Numer Anal 43(5):2060–2076, 2005) for choosing the regularization parameter. © 2018, Korean Society for Computational and Applied Mathematics.