PhD Theses
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Item Open Access Microwave Imaging for Breast Cancer Detection using 3D Level Set based Optimization, FDTD Method and Method of Moments(Dhirubhai Ambani Institute of Information and Communication Technology, 2019) Patel, Hardik Nayankumar; Ghodgaonkar, Deepak K.Microwave imaging is emerging as new diagnostic option for breast cancer detection because of non-ionizing nature of microwave radiation and significant contrast between dielectric properties of healthy and malignant breast tissues. Class III and IV breasts have more than 50% fibro-glandular tissues. So, it is very difficult to detect cancer in class III and IV breasts by using X-ray based mammography. Microwave imaging is very promising for cancer detection in case of dense breasts. Complex permittivity profile of breasts is reconstructed in three dimensions for microwave breast imaging. 3D level set based optimization proposed in this thesis is able to reconstruct proper shape and dielectric property values of breast tissues. Multiple frequency inverse scattering problem formulation improves computational efficiency and accuracy of microwave imaging system because complex number computations are avoided. Measurements of scattered electric fields are taken at five equally spaced frequencies in the range 0.5-2.5 GHz. Class III numerical breast phantom and Debye model are used in multiple frequency inverse scattering problem formulation. There are three unknowns per cell of numerical breast phantom due to Debye model. Linear relationships between Debye parameters are applied to get only static permittivity as unknown per cell of numerical breast phantom. Two level set functions are used to detect breast cancer in 3D level set based optimization. Pixel based reconstruction is replaced by initial guess about static permittivity solution in this modified four stage reconstruction strategy. Frequency hopping method is used to avoid local minima present at particular frequency in the 3D level set based optimization. 3D FDTD solves forward problem efficiently during each iteration of 3D level set method which leads to better reconstruction of static permittivity profile. 3D reconstruction problem is very challenging due to Ill posed system matrix and noisy scattered fields data. Tikhonov and total variation (TV) regularization schemes are used to overcome above challenges. The performance of TV regularization is better than Tikhonov regularization in 3D level set based optimization. TV regularization reconstructs shape and size of very small tumour but it fails to reconstruct exact location of very small tumour. Better 3D reconstruction is achieved by using regularized 3D level set based optimization for at least 20 dB SNR in electric field data. 3D FDTD method based electric field computation in heterogeneous numerical breast phantom is very efficient because it solves Maxwell's equations on grids by using an iterative process. Microwave imaging problem is solved with millions of cells because 3D FDTD is used. Method of moments is used to solve electric field integral equation (EFIE) which estimates complex permittivity of 2048 cell human breast model. Matrix formation and inversion time are reduced to allow large number of cells in breast model. Computational efficiency of the imaging system is improved by exploiting symmetry using group theory. Matrix formed by method of moments is ill posed due to presence of large number of buried cells in inverse scattering formulation. Ill posed system matrix and noise are two major challenges in the solution of inverse scattering problem. Levenberg-Marquardt method is used to solve above challenges.Item Open Access Distributed TDMA scheduling in tree based wireless sensor networks with multiple data attributes and multiple sinks(Dhirubhai Ambani Institute of Information and Communication Technology, 2018) Vasavada, Tejas Mukeshbhai; Srivastava, SanjayData collection is an important application of wireless sensor networks. Sensors are deployed in given region of interest. They sense physical quantity like temperature, pressure, solar radiation, speed and many others. One or more sinks are also deployed in the network along with sensor nodes. The sensor nodes send sensed data to the sink(s). This operation is known as convergecast operation. Once nodes are deployed, logical tree is formed. Every node identi es its parent node to transmit data towards sink. As TDMA (Time Division Multiple Access) completely prevents collisions, it is preferred over CSMA (Carrier Sense Multiple Access). The next step after tree formation is to assign time slot to every node of the tree. A node transmits only during the assigned slot. Once tree formation and scheduling is done, data transfer from sensors to sink takes place. Tree formation and scheduling algorithms may be implemented in centralized manner. In that case, sink node executes the algorithms and informs every node about its parent and time-slot. The alternate approach is to use distributed algorithms. In distributed approach, every node decides parent and slot on its own. Our focus is on distributed scheduling and tree formation. Most of the researchers consider scheduling and parent selection as two di erent prob- lems. Tree structure constrains e ciency of scheduling. So it is better to treat scheduling and tree formation as a single problem. One algorithm should address both in a joint manner. We use a single algorithm to perform both i.e. slot and parent selection. The main contributions of this thesis are explained in subsequent paragraphs. In the rst place, we have addressed scheduling and tree formation for single-sink heterogeneous sensor networks. In a homogeneous network, all nodes are of same type. For example, temperature sensors are deployed in given region. Many applications require use of more than one types of nodes in the same region. For example, sensors are deployed on a bridge to monitor several parameters like vibration, tilt, cracks, shocks and others. So, a network having more than one types of nodes is known as heterogeneous network. If all the nodes of network are of same type, the parent selection is trivial. A node can select the neighbor nearest to sink as parent. In heterogeneous networks, a node may receive di erent types of packets from di erent children. To maximize aggregation, appropriate parent should be selected for each outgoing packet such that packet can be aggregated at parent node. If aggregation is maximized, nodes need to forward less number of packets. So, less number of slots are required and energy consumption would be reduced. We have proposed AAJST (Attribute Aware Joint Scheduling and Tree formation) algorithm for heterogeneous networks. The objective of the algorithm is to maximize aggregation. The algorithm is evaluated using simulations. It is found that compared to traditional approach of parent selection, the proposed algorithm results in 5% to 10% smaller schedule length and 15% to 30% less energy consumption during data transfer phase. Also energy consumption during control phase is reduced by 5%. When large number of nodes are deployed in the network, it is better to use more than one sinks rather than a single sink. It provides fault tolerance and load balancing. Every sink becomes root of one tree. If ner observations are required from a region, more number of nodes are deployed there. That is, node deployment is dense. But the deployment in other regions may not be dense because application does not require the same. When trees are formed, tree passing through the dense region results in higher schedule length compared to the one passing through the sparse region. Thus schedule lengths are not balanced. For example, trees are T1 and T2. Their schedule lengths are SH1 and SH2 respec- tively. Every node in tree Ti will get its turn to transmit after SHi time-slots. If there is a large di erence between SH1 and SH2, nodes of one tree (having large value of SHi) will wait for very long time to get turn to transmit compared to the nodes of the other tree (having small value of SHi). But if SH1 and SH2 are balanced, waiting time would be almost same for all the nodes. Thus schedule lengths should be balanced. Overall sched- ule length (SH) of the network can be de ned as max(SH1,SH2). If schedule lengths are balanced, SH would also be reduced. We have proposed an algorithm known as SLBMHM (Schedule Length Balancing for Multi-sink HoMogeneous Networks). It guides every node to join a tree such that the schedule lengths of resulting trees are balanced. Through simulations, it is found that SLBMHM results 13% to 74% reduction in schedule length di erence. The overall schedule length is reduced by 9% to 24% compared to existing mechanisms. The algorithm results in 3% to 20% more energy consumption during control phase. The control phase involves transfer of control messages for schedule length balancing and for slot & parent selection. The control phase does not take place frequently. It takes place at longer intervals. So, additional energy consumption may not a ect the network lifetime much. No change in energy consumption during data transmission phase is found. The schedule lengths may be unbalanced also due to di erence in heterogeneity levels of regions. For example, in one region, two di erent types of sensors are deployed. But in the other region, four di erent types of sensors are present. When heterogeneity is high, aggregation becomes di cult. As a result, more packets ow through the network. Thus schedule length of the tree passing through region of two types of nodes will have smaller schedule length than the tree passing through the region of four types of nodes. We have proposed an algorithm known as SLBMHT (Schedule Length Balancing for Multi-sink HeTerogeneous Networks). It is an extension of SLBMHM. The proposed algorithm is capable of balancing schedule lengths no matter whether imbalance is caused due to di erence in density or di erence in heterogeneity. It is also evaluated through simulations. It is found that the SLBMHT algorithm results in maximum upto 56% reduction in schedule length di erence, maximum upto 20% reduction in overall schedule length and 2% to 17% reduction in energy consumption per TDMA frame during data transfer phase. It results in maximum 7% more energy consumption during control phase. As control phase does not take place very frequently, increase in energy consumption during control phase can be balanced by reduction in energy consumption during data phase. As a result, network lifetime is going to increase.Item Open Access From extractive to abstractive summarization: a journey(Dhirubhai Ambani Institute of Information and Communication Technology, 2018) Mehta, Parth; Majumder, PrasenjitResearch in the field of text summarisation has primarily been dominated by investigationsof various sentence extraction techniques with a significant focus towards news articles.In this thesis, we intend to look beyond generic sentence extraction and instead focuson domain-specific summarisation, methods for creating ensembles of multiple extractivesummarisation techniques and using sentence compression as the first step towardsabstractive summarisation.We start by proposing two new datasets for domain-specific summarisation. The firstcorpus is a collection of court judgements with corresponding handwritten summaries,while the second one is a collection of scientific articles from ACL anthology. The legalsummaries are recall-oriented and semi-extractive, compared to the abstracts of ACL articleswhich are more precision oriented and abstractive. Both collections have a reasonablenumber of article-summary pairs, enabling us to use data-driven techniques. Excludingnewswire corpora where the summaries are usually article headlines, the proposed collectionsare amongst the largest openly available collections of document summarisation.Next, we propose a completely data-driven technique for sentence extraction from legaland scientific articles. In both legal and ACL corpus, the summaries have a predefinedformat. Hence, it is possible to identify summary worthy sentences depending on whetherthey contain certain key phrases. Our proposed approach based on attention-based neuralnetwork learns to automatically identify these key phrases from pseudo-labelled data,without requiring any annotation or handcrafted rules. The proposed model outperformsexisting baselines and state of the art systems by a large margin.There are a large number of sentence extraction techniques, none of which guaranteebetter performance than the others. As a part of this thesis, we explore if it is possibleto leverage this variance in performance for generating an ensemble of several extractivetechniques. In the first model, we study the effect of using multiple sentence similarityscores, ranking algorithms and text representation techniques. We demonstrate that suchvariations can be used for improving Rank Aggregation. Using several sentence similaritymetrics, with any given ranking algorithm, always generates better abstracts. Next, wepropose several content-based aggregation models. Given the variation in performanceof extractive techniques across documents, the apriori knowledge about which techniquewould give the best result for a given document will drastically improve the result. Insuch case, an oracle ensemble system can be made which chose best possible summaryfor a given document. In the proposed content-based aggregation models, we estimatethe probability of a summary being good by looking at the amount of content it shareswith other candidate summaries. We present a hypothesis that a good summary will necessarilyshare more information with another good summary, but not with a bad summary.We build upon this argument to construct several content-based aggregation techniques,achieving a substantial improvement in the Rouge scores.In the end, we propose another attention based neural model for sentence compression.We use a novel context encoder, which helps the network to handle rare but informativeterms better. We compare the proposed approach to some sentence compression and abstractivetechniques that have been proposed in past few years. We present our argumentsfor and against these techniques and build a further roadmap for abstractive summarisation.In the end, we present the results on an end to end system which performs sentenceextraction using standalone summarisation systems as well as their ensembles and thenuses the sentence compression technique for generating the final abstractive summary.