Browsing by Author "Omkar, S.N."

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An analysis of leg muscle stretch using digital image correlation
(2009) Omkar, S.N.; Raviprakash, S.; Vishwas, S.; Kulkarni, K.P.
This experiment aims to examine the strain in the leg muscles when subjected to varied degrees of stretch using the technique of Digital Image Correlation (DIC). This test requires the subject to stand on a variable slope and images are captured at varying inclinations of the slope. The images are then analysed using a DIC software package which compares the images captured before and after the stretch. Through this comparison, strain plots are obtained that indicate how the strains in the calf act along the entire region of study. A comparison of the effects of varied inclinations is made. The experiment is then repeated for five other participants and the trends are observed. The effect of stretching on the two important superficial components of the leg muscles, namely the gastrocnemius muscles and the Achilles tendon, is studied. From the research, the strain distribution in the leg can be understood easily, which in turn helps in understanding the superficial muscle behaviour as well. It is also suggested that DIC can be an extremely effective non-contact analysis tool in biomechanics research.
An optimal fuzzy logic controller tuned with artificial immune system
(Springer Verlag service@springer.de, 2013) Omkar, S.N.; Ramaswamy, N.; Ananda, R.; Venkatesh, N.G.; Senthilnath, J.
In this paper, a method for the tuning the membership functions of a Mamdani type Fuzzy Logic Controller (FLC) using the Clonal Selection Algorithm(CSA) a model of the Artificial Immune System(AIS) paradigm is examined. FLC's are designed for two problems, firstly the linear cart centering problem and secondly the highly nonlinear inverted pendulum problem. The FLC tuned by AIS is compared with FLC tuned by GA. In order to check the robustness of the designed FLC's white noise was added to the system, further, the masses of the cart and the length and mass of the pendulum are changed. The FLC's were also tested in the presence of faulty rules. Finally, Kruskal-Wallis test was performed to compare the performance of the GA and AIS. An insight into the algorithms are also given by studying the effect of the important parameters of GA and AIS. Â© 2013 Springer.
Clustering using levy flight cuckoo search
(2013) Senthilnath, J.; Das, V.; Omkar, S.N.; Mani, V.
In this paper, a comparative study is carried using three nature-inspired algorithms namely Genetic Algorithm (GA), Particle Swarm Optimization (PSO) and Cuckoo Search (CS) on clustering problem. Cuckoo search is used with levy flight. The heavy-tail property of levy flight is exploited here. These algorithms are used on three standard benchmark datasets and one real-time multi-spectral satellite dataset. The results are tabulated and analysed using various techniques. Finally we conclude that under the given set of parameters, cuckoo search works efficiently for majority of the dataset and levy flight plays an important role. � 2013 Springer.
Clustering using levy flight cuckoo search
(Springer Verlag service@springer.de, 2013) Senthilnath, J.; Das, V.; Omkar, S.N.; Mani, V.
In this paper, a comparative study is carried using three nature-inspired algorithms namely Genetic Algorithm (GA), Particle Swarm Optimization (PSO) and Cuckoo Search (CS) on clustering problem. Cuckoo search is used with levy flight. The heavy-tail property of levy flight is exploited here. These algorithms are used on three standard benchmark datasets and one real-time multi-spectral satellite dataset. The results are tabulated and analysed using various techniques. Finally we conclude that under the given set of parameters, cuckoo search works efficiently for majority of the dataset and levy flight plays an important role. Â© 2013 Springer.
Crop stage classification of hyperspectral data using unsupervised techniques
(2013) Senthilnath, J.; Omkar, S.N.; Mani, V.; Karnwal, N.; Shreyas, P.B.
The presence of a large number of spectral bands in the hyperspectral images increases the capability to distinguish between various physical structures. However, they suffer from the high dimensionality of the data. Hence, the processing of hyperspectral images is applied in two stages: dimensionality reduction and unsupervised classification techniques. The high dimensionality of the data has been reduced with the help of Principal Component Analysis (PCA). The selected dimensions are classified using Niche Hierarchical Artificial Immune System (NHAIS). The NHAIS combines the splitting method to search for the optimal cluster centers using niching procedure and the merging method is used to group the data points based on majority voting. Results are presented for two hyperspectral images namely EO-1 Hyperion image and Indian pines image. A performance comparison of this proposed hierarchical clustering algorithm with the earlier three unsupervised algorithms is presented. From the results obtained, we deduce that the NHAIS is efficient. © 2008-2012 IEEE.
Development of polarimetric decomposition techniques for land use and land cover mapping using RISAT-1 radar satellite sensor data
(2015) Sridhar, J.; Mahadev, S.; Vanjare, A.; Omkar, S.N.; Diwakar, P.G.
In this paper, we are examines polarimetric decomposition techniques like on Pauli decomposition and Sphere Di-Plane Helix (SDH) decomposition of RISAT-1 satellite image for land use and land cover mapping. The data processing methods adopted are 1) Pre-processing, antenna pattern correction, sigma nought calibration, Speckle Reduction, 2) Polarimetric Decomposition and 3) Polarimetric Classification. We have used RISAT-1 satellite image datasets of Mysore-Mandya region of Karnataka, India for classifying five classes - agricultural lands, urban area, forest land, water land and barren land. Polarimetric SAR data possess a high potential because it captures earth land surface features. After applying the polarimetric classification techniques, post-classification techniques is applied in order to access the classification accuracy. The Post-classification step gives the over-all accuracy was observed to be higher in the SDH decomposed image, as it operates on individual pixels on a coherent basis and utilises the complete intrinsic coherent nature of polarimetric SAR data as compared to the ground truth collected through GPS measurements and maps. Thereby, making SDH decomposition particularly suited for analysis of high-resolution SAR data. The Pauli Decomposition represents all the polarimetric information in a single SAR image however interpretation of the resulting image in less accuracy. The SDH decomposition technique seems to produce better results and interpretation as compared to Pauli Decomposition however more quantification and further analysis are being done in this area of research. The comparison of the Polarimetric decomposition techniques will be the scope of the paper. Polarimetric decomposition techniques helps in better understanding earth land surface features.
Development of polarimetric decomposition techniques for land use and land cover mapping using RISAT-1 radar satellite sensor data
(Asian Association on Remote Sensing Sh1939murai@nifty.com, 2015) Sridhar, J.; Mahadev, S.; Vanjare, A.; Omkar, S.N.; Diwakar, P.G.
In this paper, we are examines polarimetric decomposition techniques like on Pauli decomposition and Sphere Di-Plane Helix (SDH) decomposition of RISAT-1 satellite image for land use and land cover mapping. The data processing methods adopted are 1) Pre-processing, antenna pattern correction, sigma nought calibration, Speckle Reduction, 2) Polarimetric Decomposition and 3) Polarimetric Classification. We have used RISAT-1 satellite image datasets of Mysore-Mandya region of Karnataka, India for classifying five classes - agricultural lands, urban area, forest land, water land and barren land. Polarimetric SAR data possess a high potential because it captures earth land surface features. After applying the polarimetric classification techniques, post-classification techniques is applied in order to access the classification accuracy. The Post-classification step gives the over-all accuracy was observed to be higher in the SDH decomposed image, as it operates on individual pixels on a coherent basis and utilises the complete intrinsic coherent nature of polarimetric SAR data as compared to the ground truth collected through GPS measurements and maps. Thereby, making SDH decomposition particularly suited for analysis of high-resolution SAR data. The Pauli Decomposition represents all the polarimetric information in a single SAR image however interpretation of the resulting image in less accuracy. The SDH decomposition technique seems to produce better results and interpretation as compared to Pauli Decomposition however more quantification and further analysis are being done in this area of research. The comparison of the Polarimetric decomposition techniques will be the scope of the paper. Polarimetric decomposition techniques helps in better understanding earth land surface features.
Hierarchical clustering algorithm for land cover mapping using satellite images
(2012) Senthilnath, J.; Omkar, S.N.; Mani, V.; Tejovanth, N.; Diwakar, P.G.; Archana Shenoy, B.
This paper presents hierarchical clustering algorithms for land cover mapping problem using multi-spectral satellite images. In unsupervised techniques, the automatic generation of number of clusters and its centers for a huge database is not exploited to their full potential. Hence, a hierarchical clustering algorithm that uses splitting and merging techniques is proposed. Initially, the splitting method is used to search for the best possible number of clusters and its centers using Mean Shift Clustering (MSC), Niche Particle Swarm Optimization (NPSO) and Glowworm Swarm Optimization (GSO). Using these clusters and its centers, the merging method is used to group the data points based on a parametric method (k-means algorithm). A performance comparison of the proposed hierarchical clustering algorithms (MSC, NPSO and GSO) is presented using two typical multi-spectral satellite images - Landsat 7 thematic mapper and QuickBird. From the results obtained, we conclude that the proposed GSO based hierarchical clustering algorithm is more accurate and robust. © 2012 IEEE.
Integration of speckle de-noising and image segmentation using Synthetic Aperture Radar image for flood extent extraction
(Indian Academy of Sciences, 2013) Senthilnath, J.; Handiru, H.V.; Rajendra, R.; Omkar, S.N.; Mani, V.; Diwakar, P.G.
Flood is one of the detrimental hydro-meteorological threats to mankind. This compels very efficient flood assessment models. In this paper, we propose remote sensing based flood assessment using Synthetic Aperture Radar (SAR) image because of its imperviousness to unfavourable weather conditions. However, they suffer from the speckle noise. Hence, the processing of SAR image is applied in two stages: speckle removal filters and image segmentation methods for flood mapping. The speckle noise has been reduced with the help of Lee, Frost and Gamma MAP filters. A performance comparison of these speckle removal filters is presented. From the results obtained, we deduce that the Gamma MAP is reliable. The selected Gamma MAP filtered image is segmented using Gray Level Co-occurrence Matrix (GLCM) and Mean Shift Segmentation (MSS). The GLCM is a texture analysis method that separates the image pixels into water and non-water groups based on their spectral feature whereas MSS is a gradient ascent method, here segmentation is carried out using spectral and spatial information. As test case, Kosi river flood is considered in our study. From the segmentation result of both these methods are comprehensively analysed and concluded that the MSS is efficient for flood mapping. © Indian Academy of Sciences.
Location management in mobile computing using swarm intelligence techniques
(2014) Goel, N.; Senthilnath, J.; Omkar, S.N.; Mani, V.
Location management is an important and complex issue in mobile computing. Location management problem can be solved by partitioning the network into location areas such that the total cost, i.e., sum of handoff (update) cost and paging cost is minimum. Finding the optimal number of location areas and the corresponding configuration of the partitioned network is NP-complete problem. In this paper, we present two swarm intelligence algorithms namely genetic algorithm (GA) and artificial bee colony (ABC) to obtain minimum cost in the location management problem. We compare the performance of the swarm intelligence algorithms and the results show that ABC give better optimal solution to locate the optimal solution. � Springer India 2014.
Location management in mobile computing using swarm intelligence techniques
(Springer Verlag service@springer.de, 2014) Goel, N.; Senthilnath, J.; Omkar, S.N.; Mani, V.
Location management is an important and complex issue in mobile computing. Location management problem can be solved by partitioning the network into location areas such that the total cost, i.e., sum of handoff (update) cost and paging cost is minimum. Finding the optimal number of location areas and the corresponding configuration of the partitioned network is NP-complete problem. In this paper, we present two swarm intelligence algorithms namely genetic algorithm (GA) and artificial bee colony (ABC) to obtain minimum cost in the location management problem. We compare the performance of the swarm intelligence algorithms and the results show that ABC give better optimal solution to locate the optimal solution. Â© Springer India 2014.
MPI-based parallel synchronous vector evaluated particle swarm optimization for multi-objective design optimization of composite structures
(2012) Omkar, S.N.; Venkatesh, A.; Mudigere, M.
This paper presents a decentralized/peer-to-peer architecture-based parallel version of the vector evaluated particle swarm optimization (VEPSO) algorithm for multi-objective design optimization of laminated composite plates using message passing interface (MPI). The design optimization of laminated composite plates being a combinatorially explosive constrained non-linear optimization problem (CNOP), with many design variables and a vast solution space, warrants the use of non-parametric and heuristic optimization algorithms like PSO. Optimization requires minimizing both the weight and cost of these composite plates, simultaneously, which renders the problem multi-objective. Hence VEPSO, a multi-objective variant of the PSO algorithm, is used. Despite the use of such a heuristic, the application problem, being computationally intensive, suffers from long execution times due to sequential computation. Hence, a parallel version of the PSO algorithm for the problem has been developed to run on several nodes of an IBM P720 cluster. The proposed parallel algorithm, using MPI's collective communication directives, establishes a peer-to-peer relationship between the constituent parallel processes, deviating from the more common master-slave approach, in achieving reduction of computation time by factor of up to 10. Finally we show the effectiveness of the proposed parallel algorithm by comparing it with a serial implementation of VEPSO and a parallel implementation of the vector evaluated genetic algorithm (VEGA) for the same design problem. © 2012 Elsevier Ltd. All rights reserved.
Multi-objective Genetic Algorithm for efficient point matching in multi-sensor satellite image
(2012) Senthilnath, J.; Omkar, S.N.; Mani, V.; Kalro, N.P.; Diwakar, P.G.
This paper investigates a new approach for point matching in multi-sensor satellite images. The feature points are matched using multi-objective optimization (angle criterion and distance condition) based on Genetic Algorithm (GA). This optimization process is more efficient as it considers both the angle criterion and distance condition to incorporate multi-objective switching in the fitness function. This optimization process helps in matching three corresponding corner points detected in the reference and sensed image and thereby using the affine transformation, the sensed image is aligned with the reference image. From the results obtained, the performance of the image registration is evaluated and it is concluded that the proposed approach is efficient. � 2012 IEEE.
Multi-objective Genetic Algorithm for efficient point matching in multi-sensor satellite image
(2012) Senthilnath, J.; Omkar, S.N.; Mani, V.; Kalro, N.P.; Diwakar, P.G.
This paper investigates a new approach for point matching in multi-sensor satellite images. The feature points are matched using multi-objective optimization (angle criterion and distance condition) based on Genetic Algorithm (GA). This optimization process is more efficient as it considers both the angle criterion and distance condition to incorporate multi-objective switching in the fitness function. This optimization process helps in matching three corresponding corner points detected in the reference and sensed image and thereby using the affine transformation, the sensed image is aligned with the reference image. From the results obtained, the performance of the image registration is evaluated and it is concluded that the proposed approach is efficient. Â© 2012 IEEE.
Multi-objective optimization of satellite image registration using Discrete Particle Swarm Optimisation
(2011) Senthilnath, J.; Omkar, S.N.; Mani, V.; Karthikeyan, T.
A new multi-sensor image registration technique is proposed based on detecting the feature corner points using modified Harris Corner Detector (HDC). These feature points are matched using multi-objective optimization (distance condition and angle criterion) based on Discrete Particle Swarm Optimization (DPSO). This optimization process is more efficient as it considers both the distance and angle criteria to incorporate multi-objective switching in the fitness function. This optimization process helps in picking up three corresponding corner points detected in the sensed and base image and thereby using the affine transformation, the sensed image is aligned with the base image. Further, the results show that the new approach can provide a new dimension in solving multi-sensor image registration problems. From the obtained results, the performance of image registration is evaluated and is concluded that the proposed approach is efficient. � 2011 IEEE.
Multi-objective optimization of satellite image registration using Discrete Particle Swarm Optimisation
(2011) Senthilnath, J.; Omkar, S.N.; Mani, V.; Karthikeyan, T.
A new multi-sensor image registration technique is proposed based on detecting the feature corner points using modified Harris Corner Detector (HDC). These feature points are matched using multi-objective optimization (distance condition and angle criterion) based on Discrete Particle Swarm Optimization (DPSO). This optimization process is more efficient as it considers both the distance and angle criteria to incorporate multi-objective switching in the fitness function. This optimization process helps in picking up three corresponding corner points detected in the sensed and base image and thereby using the affine transformation, the sensed image is aligned with the base image. Further, the results show that the new approach can provide a new dimension in solving multi-sensor image registration problems. From the obtained results, the performance of image registration is evaluated and is concluded that the proposed approach is efficient. Â© 2011 IEEE.
Multi-sensor satellite image analysis using niche genetic algorithm for flood assessment
(2012) Senthilnath, J.; Shreyas, P.B.; Rajendra, R.; Omkar, S.N.; Mani, V.; Diwakar, P.G.
In this paper, cluster splitting and merging algorithms are used for flood assessment using LISS-III (before flood) and SAR (during flood) images. Bayesian Information Criteria (BIC) is used to determine the optimal number of clusters. Keeping this constraint, the cluster centers are generated using the cluster splitting techniques, namely Mean Shift Clustering (MSC), and Niche Genetic Algorithm (NGA). The merging method is used to group the data points into their respective classes, using the cluster centers obtained from the above techniques. These techniques are applied on the LISS-III and SAR image. Further, the resultant images are overlaid to analyze the extent of the flood in individual land classes. A performance comparison of these techniques (MSC and NGA) is presented. From the results obtained, we deduce that the NGA is efficient. � 2012 Springer-Verlag.
Multi-sensor satellite image analysis using niche genetic algorithm for flood assessment
(2012) Senthilnath, J.; Shreyas, P.B.; Rajendra, R.; Omkar, S.N.; Mani, V.; Diwakar, P.G.
In this paper, cluster splitting and merging algorithms are used for flood assessment using LISS-III (before flood) and SAR (during flood) images. Bayesian Information Criteria (BIC) is used to determine the optimal number of clusters. Keeping this constraint, the cluster centers are generated using the cluster splitting techniques, namely Mean Shift Clustering (MSC), and Niche Genetic Algorithm (NGA). The merging method is used to group the data points into their respective classes, using the cluster centers obtained from the above techniques. These techniques are applied on the LISS-III and SAR image. Further, the resultant images are overlaid to analyze the extent of the flood in individual land classes. A performance comparison of these techniques (MSC and NGA) is presented. From the results obtained, we deduce that the NGA is efficient. Â© 2012 Springer-Verlag.
Multi-sensor satellite remote sensing images for flood assessment using swarm intelligence
(2015) Senthilnath, J.; Omkar, S.N.; Mani, V.; Prasad, R.; Rajendra, R.; Shreyas, P.B.
This paper investigates a new approach for flood evaluation based on multi-sensor satellite images utilizing swarm intelligence techniques. The swarm intelligence techniques used are Genetic Algorithm (GA) for image registration and Niche Particle Swarm Optimization (NPSO) for image clustering. Analysis of satellite images are applied in two stages: Linear Imaging Self Scanning Sensor (LISS-III) image acquired before-flood and Synthetic Aperture Radar (SAR) image acquired during-flood. In the first step, SAR image is aligned with LISS-III image using GA. The aligned SAR image (during-flood) is used to extract flooded and non-flooded regions where as LISS-III image (before-flood) is used to classify various land cover regions. For this image clustering is carried out where cluster centers are generated using the cluster splitting technique such as NPSO. The data points are grouped into their respective classes using the merging method. Further, the resultant images are overlaid to analyze the extent of the flood in individual land classes. The performance comparisons of these swarm intelligence techniques with conventional methods are presented. � 2015 IEEE.
Multi-sensor satellite remote sensing images for flood assessment using swarm intelligence
(Institute of Electrical and Electronics Engineers Inc., 2015) Senthilnath, J.; Omkar, S.N.; Mani, V.; Prasad, R.; Rajendra, R.; Shreyas, P.B.
This paper investigates a new approach for flood evaluation based on multi-sensor satellite images utilizing swarm intelligence techniques. The swarm intelligence techniques used are Genetic Algorithm (GA) for image registration and Niche Particle Swarm Optimization (NPSO) for image clustering. Analysis of satellite images are applied in two stages: Linear Imaging Self Scanning Sensor (LISS-III) image acquired before-flood and Synthetic Aperture Radar (SAR) image acquired during-flood. In the first step, SAR image is aligned with LISS-III image using GA. The aligned SAR image (during-flood) is used to extract flooded and non-flooded regions where as LISS-III image (before-flood) is used to classify various land cover regions. For this image clustering is carried out where cluster centers are generated using the cluster splitting technique such as NPSO. The data points are grouped into their respective classes using the merging method. Further, the resultant images are overlaid to analyze the extent of the flood in individual land classes. The performance comparisons of these swarm intelligence techniques with conventional methods are presented. Â© 2015 IEEE.