M Tech Dissertations
Permanent URI for this collectionhttp://drsr.daiict.ac.in/handle/123456789/3
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Item Open Access Design of metamaterial(Dhirubhai Ambani Institute of Information and Communication Technology, 2011) Roy, Santanu; Ghodgaonkar, DeepakMeta materials are artificially designed materials that exhibit very high transmission values within a frequency range. Double Negative (DNG) Meta material shows both negative permittivity and negative permeability within a certain frequency range. It is composed by an array of Split Ring Resonators (SRRs) and an array of Capacitive Loaded Strips (CLSs). This paper presents the design of a new meta material structure. As compared to conventional Double Negative Metamaterial in Reference[3], the size of the new structure is 70% reduced and hence this leads to a size miniaturization in various meta material applications specially in antenna application. It can also be used in very high frequency applications, as its operating frequency is considerably high (30-40 GHz). Both the conventional and new metamaterial structure is simulated by CST Microwave Studio. In the metamaterial structure, dimensions has been varied many times and simulated in CST to observe the behavior of meta material and also theoretical explanations have been given for the observed shift in resonance frequency.Item Open Access Mobility models and its application in ad-hoc network(Dhirubhai Ambani Institute of Information and Communication Technology, 2011) Jain, Vikas Kumar; Patil, Hemant A.; Mulherkar, JaideepThe Performance of MANET application depends on several parameters like no. of nodes, node density, communicating traffic pattern, communication range of a node, routing protocol, battery power of a node, mobility etc. Out these mobility plays an important role. Mobility model describe the mobility pattern of mobile nodes and users like how their location, velocity, direction and acceleration will change with respect to time. There are some of the mobility models like Random Way Point, Gauss Markov mobility model, Reference Point Group mobility model and Manhattan mobility model. Since simulate on plays an important role in conducting the research and to know the performance ofmany MANET applications, hence it is important to choose the appropriate mobility model. Generally, all the simulation work is done by choosing the Random Way Point mobility model because of its simplicity but it is unable to capture a real life scenario. RWP has several limitations so it cannot be applied for each MANET applications. A lot of work has been done by the researchers to design mobility models which are able to capture real life scenario. Accurate realistic modeling is a very challenging task and involves huge efforts. This work intends towards proposing a method to answer about best fit mobility model for the given trace along with confidence level and parameters values of the model. If we use best fit mobility model according to the given trace then accuracy of the results will improve. This work mainly focuses on RWP and RPGM mobility model. Also the proposed method are applied on a ad hoc wireless sensor network application called Zebra Net trace, to answer about the best fit mobility model out of RWP and RPGM.Item Open Access Super-resolution of hyperspectral images(Dhirubhai Ambani Institute of Information and Communication Technology, 2011) Bhimani, Amitkumar H.; Joshi, Manjunath V.Hyperspectral (HS) images are used for space areal application, target detection and remote sensing application. HS images are very rich in spectral resolution but at a cost of spatial resolution. HS images generated by airborne sensors like the NASA’s Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) from satellites like NASA’s Hyperion. We proposed a principal component analysis (PCA) based learning method to increase a spatial resolution of HS images. For spatial resolution enhancement of HS images we need to employ a technique to increase the resolution. We used PCA based approach by learning the details from database which consist of high spatial resolution satellite images. Super-resolution, is an ill-posed problem, and does not result to unique solution, and therefore it is necessary to regularize the solution by imposing some additional constraint to restrict the solution space. To reduce the computational complexity, minimization of the regularized cost function is done using the iterative gradient descent algorithm. In this report the effectiveness of proposed scheme is demonstrated by conducting experiments on both Multispectral (MS) and Hyperspectral real data. The HS and MS images of AVIRIS and Digital airborne Imaging spectrometer (DAIS) respectively used as input for super resolution (SR).Item Open Access Asynchronous analog to digital converter(Dhirubhai Ambani Institute of Information and Communication Technology, 2011) Patel, Vidyut A.; Parikh, Chetan D.Nowadays, asynchronous systems are becoming more popular for low power applications. Asynchronous Systems help to reduce metastability errors and clock skew errors. This thesis is about an asynchronous analog to digital converter (ADC) designed for low power. Here, a design is proposed based on successive approximation algorithm with folding circuit. The folding circuit works as a voltage mapping circuit. The proposed ADC can be used with asynchronous systems as well as conventional synchronous systems. The ADC has been implemented for 5 bit resolution for input voltage range of 0.7V to 2V in 180nm technology. It achieves maximum speed of 8MSPS and DNL of 0.5LSB. The power consumption of the ADC is 2.6mW.Item Open Access Human action recognition in video(Dhirubhai Ambani Institute of Information and Communication Technology, 2011) Kumari, Sonal; Mitra, Suman K.Action recognition is a central problem in computer vision which is also known as action recognition or object detection. Action is any meaningful movement of the human and it is used to convey information or to interact naturally without any mechanical devices. It is of utmost importance in designing an intelligent and efficient human–computer interface. The applications of action recognition are manifold, ranging from sign language through medical rehabilitation to virtual reality. Human action recognition is motivated by some of the applications such as video retrieval, Human robot interaction, to interact with deaf and dumb people etc. In any Action Recognition System, a video stream can be captured by using a fixed camera, which may be mounted on the computer or somewhere else. Then some preprocessing steps are done for removing the noise caused because of illumination effects, blurring, false contour etc. Background subtraction is done to remove the static or slowly varying background. In this thesis, multiple background subtraction algorithms are tested and then one of them selected for action recognition system. Background subtraction is also known as foreground/background segmentation or background segmentation or foreground extraction. These terms are frequently used interchangeably in this thesis. The selection of background segmentation algorithm is done on the basis of result of these algorithms on the action database. Good background segmentation result provides a more robust basis for object class recognition. The following four methods for extracting the foreground which are tested: (1) Frame Difference, (2) Background Subtraction, (3) Adaptive Gaussian Mixture Model (Adaptive GMM) [25], and (4) Improved Adaptive Gaussian Mixture Model (Improved Adaptive GMM) [26] in which the last one gives the best result. Now the action region can be extracted in the original video sequences with the help of extracted foreground object. The next step is the feature extraction which deals with the extraction of the important feature (like corner points, optical flow, shape, motion vectors etc.) from the image frame which can be used for tracking in the video frame sequences. Feature reduction is an optional step which basically reduces the dimension of the feature vector. In order to recognize actions, any learning and classification algorithm can be employed. The System is trained by using a training dataset. Then, a new video can be classified according to the action occurring in the video. Following three features are applied for the action recognition task: (1) distance between centroid and corner point, (2) optical flow motion estimation [28, 29], (3) discrete Fourier transform (DFT) of the image block. Among these the proposed DFT feature plays very important role in uniquely identifying any specific action from the database. The proposed novel action recognition model uses discrete Fourier transform (DFT) of the small image block.For the experimentation, MuHAVi data [33] and DA-IICT data are used which includes various kinds of actions of various actors. Following two supervised recognition techniques are used: K-nearest neighbor (KNN) and the classifier using Mahalanobis metric. KNN is parameterized classification techniques where K parameter is to be optimized. Mahalanobis Classifier is non-parameterized classification technique, so no need to worry about parameter optimization. To check the accuracy of the proposed algorithm, Sensitivity and False alarm rate test is performed. The results of this tests show that the proposed algorithm proves to be quite accurate in action recognition in video. And to compare result of the recognition system confusion matrices are created and then compared with other recognition techniques. All the experiments are performed in MATLAB®.
Item Open Access Depth from defocus(Dhirubhai Ambani Institute of Information and Communication Technology, 2010) Khatri, Nilay; Banerjee, AsimWith the recent innovations in 3D technology accurate estimation of depth is very fascinating and challenging problem. In this thesis, a depth estimation algorithm, utilizing Singular Value Decomposition to compute orthogonal operators, has been implemented to test the algorithm on a variety of image database. Due to the difficulty in obtaining the database, an algorithm is implemented, that attempts to generate various synthetic image database of a scene from two defocused images by varying camera parameters. Thus, providing a researcher with more databases to work upon.Item Open Access Ant colony optimization in routing algorithms of mobile ad hoc networks(Dhirubhai Ambani Institute of Information and Communication Technology, 2010) Agarwal, Navneet; Srivastava, Sanjay; Sunitha, V.The study on performance of On-demand Ant Routing Algorithm for Mobile Ad Hoc Network is done. An ant routing algorithm based on swarm intelligence and especially on Ant Colony Optimization (ACO). It describes a noval on demand Ant colony algorithm for MANETs. This algorithm tries to minimize complexity at nodes and this is achived at exoences of optimality of routing path. Inextensive set of simulation experiment, We try to set parameter of ant routing algorithms and compare Proposed algorithm with DAR,a pre existing on demand ant routing algorithms and with AODV,a reference algorithm in MANETs. The comparition base on optimal path length with respect to control overhead.Item Open Access Multiresolution fusion of satellite images and super-resolution of hyper-spectral images(Dhirubhai Ambani Institute of Information and Communication Technology, 2010) Shripat, Abhishek Kumar; Joshi, Manjunath V.This thesis presents a model based approach for multi-resolution fusion of the satellite images. Given a high resolution panchromatic (Pan) image and a low spatial but high spectral resolution multi spectral (MS) image acquired over the same geographical area, the objective is to obtain a high spatial resolution MS image. To solve this problem, maximum a posteriori (MAP) - Markov random field (MRF) based approach is used. Each of the low spatial resolution MS images are modeled as the aliased and noisy versions of their high resolution versions. The high spatial resolution MS images to be estimated are modeled separately as discontinuity preserving MRF that serve as prior information. The MRF parameters are estimated from the available high resolution Pan image using homotopy continuation method. The proposed approach has the advantage of having minimum spectral distortion in the fused image as it does not directly operate on the Pan digital numbers. This method does not require registration of MS and Pan images. Also the number of MRF parameters to be estimated from the Pan image is limited as homogeneous MRF is used. The time complexity of the approach is reduced by using the particle swarm optimization (PSO) in order to minimize the final cost function. The effectiveness of the approach is demonstrated by conducting experiments on real image captured by Landsat-7 ETM+ Satellite.This thesis also presents the Super-resolution of Hyper-spectral satellite images using Discrete Wavelet Transform based (DWT) learning. Given low resolution hyper spectral images and a data base consisting of sets of LR and HR textured images and satellite images; super-resolution of the hyper spectral image is obtained. Four hyper spectral test images are selected from 224 bands of hyper-spectral images through principal component analysis (PCA) technique. Using minimum absolute difference (MAD) criterion the best match wavelet coefficients are obtained. The finer details of test image are learned from the high resolution wavelet coefficients of the training data set. The inverse wavelet transform gives super resolved image corresponding to the test image. The effectiveness of above approach is demonstrated by conducting experiments on real Hyper-spectral images captured by Airborne Visible Infrared Imaging Spectrometer (AVIRIS).
Item Open Access Moment based image segmentation(Dhirubhai Ambani Institute of Information and Communication Technology, 2010) Chawla, Charu; Mitra, Suman K.Usually, digital image of scene is not same as actual; it may degrade because of environment, camera focus, lightening conditions, etc. Segmentation is the key step before performing other operations like description, recognition, scene understanding, indexing, etc. Image segmentation is the identification of homogeneous regions in the image. This is accomplished by segmenting an image into subsets and later assigning the individual pixels to classes. There are various approaches for segmentation to identify the object and its spatial information. These approaches employ some features of the input image(s). The concept of feature is used to denote a piece of information which is relevant for solving the computational task related to a certain application. The moment is an invariant feature used in the pattern recognition field to recognize the test object from the database. The key point of using moment is to provide a unique identification for each object irrespective of its transformations. The moment is the weighted average intensity of pixels. It is used for object recognition so far. Now the idea is to use moment in object classification field. The propose method is to compute Set of Moments as a feature for each pixel to get information of the image. This information can be used further in its detail analysis or decision making systems by classification techniques. Moment requires an area to compute it. Hence, window based method is used for each pixel in the image. All possible windows have been defined in which current pixel is placed at different positions and moment is computed for each window representation. The moments define a relationship of that pixel with its neighbors. The set of moments computed will be feature vector of that pixel. After obtaining the feature vector of pixels, k-means classification technique is used to classify these vectors in k number of classes. The different types of moments are used to classify the images namely: Statistical, Geometric, Legendre moments. Experiments are performed using moments with different window sizes to analyze their effect on execution time and other features. The comparative study is performed on various moments using different window sizes. The comparison is done using mismatching between moments, window sizes and their computation time. The implementation is also performed on noisy images. The results conclude that the proposed method probably gives better result than pixel based classification. The Statistical moment gives better result as compared to Geometric and Legendry moment. Its computation time is also less because it does not involve polynomial function in computation. The window size also affects the segmentation. The small window size preserves edge information in segmented image. The computation time and noise tolerance of proposed algorithm also increases as window size increases. Hence, the selections of window size have trade between computation time and image quality. All the experiments have been performed on both gray and colour scale images in MATLAB(R).Item Open Access New learning based super resolution using contourlet transform(Dhirubhai Ambani Institute of Information and Communication Technology, 2009) Singh, Vineet P.; Joshi, Manjunath V.new learning based super-resolution reconstruction using contourlet transforms is proposed. contourlet transform provides high degree of directionality. It captures geometrical smoothness along multiple directions and learns the edges present in an image normal to the contour. For learning purpose, training set of low resolution (LR) and high resolution (HR) images, all captured using the same camera, are used. Here two and three level contourlet decomposition for LR images (test image and training image dataset) and HR training images respectively. The comparison of contourlet coeffcients of LR test image from the LR training set using minimum absolute difference (MAD) criterion to obtain the best match contourlet coeffcient. The finer details of test image are learned from the high resolution contourlet coefficients of the training data set. The inverse contourlet transform gives super resolved image corresponding to the test image.