Recursos de colección

ETD at Indian Institute of Science (3.494 recursos)

Repository of Theses and Dissertations of Indian Institute of Science, Bangalore, India. The repository has been developed to capture, disseminate and preserve research theses of Indian Institute of Science.

Department of Computational and Data Sciences (cds)

Mostrando recursos 1 - 14 de 14

  1. Projection based Variational Multiscale Methods for Incompressible Navier-Stokes Equations to Model Turbulent Flows in Time-dependent Domains

    Pal, Birupaksha
    Numerical solution of differential equations having multitude of scales in the solution field is one of the most challenging research areas, but highly demanded in scientific and industrial applications. One of the natural approaches for handling such problems is to separate the scales and approximate the solution of the segregated scales with appropriate numerical method. Variational multiscale method (VMS) is a predominant method in the paradigm of scale separation schemes. In our work we have used the VMS technique to develop a numerical scheme for computations of turbulent flows in time-dependent domains. VMS allows separation of the entire range of...

  2. Numerical Studies of Axially Symmetric Ion Trap Mass Analysers

    Kotana, Appala Naidu
    In this thesis we have focussed on two types of axially symmetric ion trap mass analysers viz., the quadrupole ion trap mass analyser and the toroidal ion trap mass analyser. We have undertaken three numerical studies in this thesis, one study is on the quadrupole ion trap mass analysers and two studies are on the toroidal ion trap mass analysers. The first study is related to improvement of the sensitivity of quadrupole ion trap mass analysers operated in the resonance ejection mode. In the second study we have discussed methods to determine the multipole coefficients in the toroidal ion trap...

  3. Studies on Kernel Based Edge Detection an Hyper Parameter Selection in Image Restoration and Diffuse Optical Image Reconstruction

    Narayana Swamy, Yamuna
    Computational imaging has been playing an important role in understanding and analysing the captured images. Both image segmentation and restoration has been in-tegral parts of computational imaging. The studies performed in this thesis has been focussed toward developing novel algorithms for image segmentation and restoration. Study related to usage of Morozov Discrepancy Principle in Di use Optical Imaging was also presented here to show that hyper parameter selection could be performed with ease. The Laplacian of Gaussian (LoG) and Canny operators use Gaussian smoothing be-fore applying the derivative operator for edge detection in real images. The LoG kernel was based on second...

  4. Learning Compact Architectures for Deep Neural Networks

    Srinivas, Suraj
    Deep neural networks with millions of parameters are at the heart of many state of the art computer vision models. However, recent works have shown that models with much smaller number of parameters can often perform just as well. A smaller model has the advantage of being faster to evaluate and easier to store - both of which are crucial for real-time and embedded applications. While prior work on compressing neural networks have looked at methods based on sparsity, quantization and factorization of neural network layers, we look at the alternate approach of pruning neurons. Training Neural Networks is often described...

  5. Spectrum Sensing Receivers for Cognitive Radio

    Khatri, Vishal
    Cognitive radios require spectral occupancy information in a given location, to avoid any interference with the existing licensed users. This is achieved by spectrum sensing. Existing narrowband, serial spectrum sensors are spectrally inefficient and power hungry. Wideband spectrum sensing increases the number of probable fre-quency candidates for cognitive radio. Wideband RF systems cannot use analog to digital converters (ADCs) for spectrum sensing without increasing the sampling rate and power consumption. The use of ADCs is limited because of the dynamic range of the signals that need to be sampled and the frequency of operation. In this work, we have presented...

  6. Balancing Money and Time for OLAP Queries on Cloud Databases

    Sabih, Rafia
    Enterprise Database Management Systems (DBMSs) have to contend with resource-intensive and time-varying workloads, making them well-suited candidates for migration to cloud plat-forms { specifically, they can dynamically leverage the resource elasticity while retaining affordability through the pay-as-you-go rental interface. The current design of database engine components lays emphasis on maximizing computing efficiency, but to fully capitalize on the cloud's benefits, the outlays of these computations also need to be factored into the planning exercise. In this thesis, we investigate this contemporary problem in the context of industrial-strength deployments of relational database systems on real-world cloud platforms. Specifically, we consider how...

  7. Balancing Money and Time for OLAP Queries on Cloud Databases

    Sabih, Rafia
    Enterprise Database Management Systems (DBMSs) have to contend with resource-intensive and time-varying workloads, making them well-suited candidates for migration to cloud plat-forms { specifically, they can dynamically leverage the resource elasticity while retaining affordability through the pay-as-you-go rental interface. The current design of database engine components lays emphasis on maximizing computing efficiency, but to fully capitalize on the cloud's benefits, the outlays of these computations also need to be factored into the planning exercise. In this thesis, we investigate this contemporary problem in the context of industrial-strength deployments of relational database systems on real-world cloud platforms. Specifically, we consider how...

  8. Visual Flow Analysis and Saliency Prediction

    Srinivas, Kruthiventi S S
    Nowadays, we have millions of cameras in public places such as traffic junctions, railway stations etc., and capturing video data round the clock. This humongous data has resulted in an increased need for automation of visual surveillance. Analysis of crowd and traffic flows is an important step towards achieving this goal. In this work, we present our algorithms for identifying and segmenting dominant ows in surveillance scenarios. In the second part, we present our work aiming at predicting the visual saliency. The ability of humans to discriminate and selectively pay attention to few regions in the scene over the others...

  9. Visual Flow Analysis and Saliency Prediction

    Srinivas, Kruthiventi S S
    Nowadays, we have millions of cameras in public places such as traffic junctions, railway stations etc., and capturing video data round the clock. This humongous data has resulted in an increased need for automation of visual surveillance. Analysis of crowd and traffic flows is an important step towards achieving this goal. In this work, we present our algorithms for identifying and segmenting dominant ows in surveillance scenarios. In the second part, we present our work aiming at predicting the visual saliency. The ability of humans to discriminate and selectively pay attention to few regions in the scene over the others...

  10. Error Estimation for Solutions of Linear Systems in Bi-Conjugate Gradient Algorithm

    Jain, Puneet

  11. Image Representation using Attribute-Graphs

    Prabhu, Nikita
    In a digital world of Flickr, Picasa and Google Images, developing a semantic image represen-tation has become a vital problem. Image processing and computer vision researchers to date, have used several di erent representations for images. They vary from low level features such as SIFT, HOG, GIST etc. to high level concepts such as objects and people. When asked to describe an object or a scene, people usually resort to mid-level features such as size, appearance, feel, use, behaviour etc. Such descriptions are commonly referred to as the attributes of the object or scene. These human understandable, machine detectable attributes...

  12. Efficient betweenness Centrality Computations on Hybrid CPU-GPU Systems

    Mishra, Ashirbad
    Analysis of networks is quite interesting, because they can be interpreted for several purposes. Various features require different metrics to measure and interpret them. Measuring the relative importance of each vertex in a network is one of the most fundamental building blocks in network analysis. Between’s Centrality (BC) is one such metric that plays a key role in many real world applications. BC is an important graph analytics application for large-scale graphs. However it is one of the most computationally intensive kernels to execute, and measuring centrality in billion-scale graphs is quite challenging. While there are several existing e orts towards...

  13. Similarity between Scalar Fields

    Narayanan, Vidya
    Scientific phenomena are often studied through collections of related scalar fields such as data generated by simulation experiments that are parameter or time dependent . Exploration of such data requires robust measures to compare them in a feature aware and intuitive manner. Topological data analysis is a growing area that has had success in analyzing and visualizing scalar fields in a feature aware manner based on the topological features. Various data structures such as contour and merge trees, Morse-Smale complexes and extremum graphs have been developed to study scalar fields. The extremum graph is a topological data structure based on either...

  14. Similarity between Scalar Fields

    Narayanan, Vidya
    Scientific phenomena are often studied through collections of related scalar fields such as data generated by simulation experiments that are parameter or time dependent . Exploration of such data requires robust measures to compare them in a feature aware and intuitive manner. Topological data analysis is a growing area that has had success in analyzing and visualizing scalar fields in a feature aware manner based on the topological features. Various data structures such as contour and merge trees, Morse-Smale complexes and extremum graphs have been developed to study scalar fields. The extremum graph is a topological data structure based on either...

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