Abstract

Glaucoma is an eye disease characterized by weakening of nerve cells often resulting in a permanent loss of vision. Glaucoma progression can occur without any physical indication to patients. Hence, early diagnosis of Glaucoma is recommended for preventing the permanent damage to vision. Early Glaucoma is often characterized by thinning of the Retinal Nerve Fiber Layer which is commonly called as RNFL defect (RNFLD). Computer-aided diagnosis (CAD) of eye diseases is popular and is based on automated analysis of fundus images. CAD solutions for diabetic retinopathy have reached more maturity than for glaucoma as the latter is more difficult to detect from fundus images. This is due to the fact that nerve damage appears in the form of subtle change in the background around optic disc. SD-OCT (Spectral Domain - Optical Coherence Tomography), a recently introduced modality, helps to capture 3D information of retina. Hence, it is more reliable for detecting nerve damage in retina compared with fundus imaging. However, a wide usage of OCT is limited due to cost per scan, time and ease of acquisition. This thesis focuses on integrating information from multiple modalities (OCT and fundus) for improving retinal nerve fibre layer defect or detection of RNFLD from fundus images. We examine two key problems in the context of CAD development: i) spatial alignment or registration of two modalities of imaging, namely, 2D fundus and 3D OCT volume images. This can pave way to integrate information across the modalities. Multimodal registration is challenging because of the varied Field of View and noise levels across the modalities. We propose a computationally efficient registration algorithm which is capable of handling complementary nature of modalities. Extensive qualitative and quantitative evaluations are performed to show the robustness of proposed method. ii) Detection of RNFLD from fundus images with good accuracy. We propose a novel CAD solution which utilises information from the 2 modalities for learning a model and uses it to predict the presence of RNFLD from fundus images. The problem is posed as learning from 2 modalities (fundus, OCT images) and predicting from only one (fundus images) with the other (OCT) as missing data. Our solution consists of a deep neural network architecture which learn modality independent representations. In the final part of the thesis we explore the scope of a new imaging modality angiography-Optical Coherence Tomography (A-OCT) in diagnosing Glaucoma. Two case studies are reported which help in understanding the progression of Retinal Nerve Fiber Layer thickness, Capillary Density in normaland glaucoma effected patients. The experiments on new modality has shown potential for considering it as a reliable biomarker along with existing modalities.

Abstract

This study aims to investigate different aspects of sequencing. Sequencing and timing the elements of a sequence are fundamental to animal behaviour and in fact, even basic animal sustenance. Sequencing refers to a set of related events rolling out in time. Animals need to adopt mechanisms to execute several facets of sequencing in their day-to-day life, including the perception of, recognition of and response to sequences. In this thesis, we specifically explore the aspects of duration and order. To investigate duration estimation in humans, we conduct a behavioural study, using the paradigm of the temporal bisection task, and assess the influence of a neuromodulator, caffeine. The task requires candidates to form memory templates of two reference durations, and then, judge which standard a particular stimulus duration is closer to. When tested on a between-subject setting, caffeine administration was observed to lead to an accelerated perception of time. A given probe duration was perceived to be longer by subjects, under the influence of caffeine, than by participants in the control group. We argue that caffeine plausibly modulates memory representation of time, as well as, the internal pacemaker, on the basis of the scalar expectancy theory. We, additionally, conducted a computational investigation to explore the effect of caffeine on duration judgement. We constructed two models, one based on the Gaussian representation of time and one anchored in reinforcement learning. While one model gives us a quantitative account of the empirical data obtained above, the other offers a qualitative understanding of the circuitry involved in the perception of duration. In the second part of the thesis, we investigate the order aspect of timing using the paradigm of vocal learning in songbirds. Song is a sequence of syllables, i.e. motor commands. We attempt to model the HVc, i.e. an avian brain region involved in forming a representation of the conspecific song that the bird tries to imitate, using a biologically realistic adaptation of the pyramidical neurons found in the neocortex. In addition, electrophysiology experiments show that lesions to the basal ganglia homologue in birds, Area X, fatally affects vocal learning, while lesions made after crystallisation of song, has a negligible effect. Such experiments motivated us to set out to explore the circuitry underlying the learning and issuing of the motor commands responsible for song synthesis. We build a biologically realistic insightful model of vocal learning via the anterior frontal pathway, consisting of the Area X, and the eventual consolidation of this skill in the song motor pathway. The thesis concludes with a summary of the work, and a discussion on its limitations and future directions.

Abstract

Metal clusters with ultra-small size offer many novel properties such as molecule-like optical transitions, photoluminescence, quantized charging, chirality, and so on. Ligand-protected metal nanoclusters with precise compositions appear to be promising building units of various functional materials. Novel properties and promising applications make metal clusters a prolific target of fundamental and applied research. The major challenges of the nanocluster research are developing synthesis protocols for precise control of cluster size, achieving tunability of nanocluster properties, and imparting stability and desired functionality to the clusters particularly in aqueous and biological media. Cluster synthesis often involves multi-step, two-phase methods and results in mixed-sized products that require time-consuming post-synthesis size-separation and surface-modification steps. Therefore, we need to develop straightforward synthesis protocols that can directly produce stable, atomically precise metal nanoclusters in high yields. Further, we need to develop guiding principles for the rational design of desired nanoclusters and tailoring the properties of the nanoclusters. The thesis work attempts to address some of these challenges by taking the cases of two representative nanoclusters, Au18(SCH2CH2COOH)14 and Au25(SCH2CH2COOH)18 as examples. We have developed straightforward direct synthesis methods capable of producing atomically precise gold nanoclusters in high yield in an aqueous medium. We have synthesized two representative nanoclusters, Au18(SCH2CH2COOH)14 and Au25(SCH2CH2COOH)18. Among various gold nanoclusters, Au25L18 (where L= ligand) is a ubiquitous nanocluster because of its extraordinarily high stability, unlike Au18L14. Our synthesis of atomically-precise water-soluble gold nanoclusters involved a common reducing agent, NaBH4, and a short carbon chain bifunctional ligand, HSCH2CH2COOH (3-mercaptopropionic acid, MPA) under ambient conditions. We have explored the essential factors that influence the size focusing and stability of water-soluble gold clusters. The synthesis methods do not require maintenance of any special reaction conditions, such as pH, temperatures (such as high or low temperatures), special reagent, etc. By our choice of the bifunctional capping ligand, MPA, we were able to impart water solubility to the gold clusters. Further, we have demonstrated that this kind of capping ligand allows tailoring of nanocluster stability and properties such as photoluminescence, Raman scattering, catalytic properties, etc. over a wide range in aqueous media simply via the variation of the pH of the media. It also allows transfer of the clusters into an organic solvent such as toluene by using a phase transfer reagent such as tetraoctylammonium bromide (TOAB). We have demonstrated that relative quantities of capping agent to gold precursor ratio ([thiol/Au]) and the gold precursor to reducing agent ([Au]/[NaBH4]) ratios play decisive roles in obtaining size-focused atomically-precise gold nanoclusters. The work will open up new avenues for the rational synthesis of definite sized clusters and tuning of their properties in aqueous media.

Abstract

The integration density achievable in Complementary Metal-Oxide-Semiconductor (CMOS) technology is limited by the leakage power seen in nanoscale transistors. Efforts to overcome the bottlenecks of power dissipation set by classical Boltzmann physics, resulted in exploration of new transistor architectures having novel transport mechanisms in channel. Ferroelectric FETs use the negative capacitance phenomenon to achieve low subthreshold slopes and have good potential to replace the classical Metal Oxide Semiconductor Field Effect Transistors (MOSFETs). The transport mechanism of the channel remains unmodified and these novel transistors are fabricated by depositing a ferroelectric material on top of the gate oxide of a conventional transistors. The subthreshold slope (SS) in a ferroelectric device can go below the Boltzmann limit of 60 mV/dec, resulting in low leakage power in FeFET devices. Further, the current boosting observed in these transistors around the threshold region, aids in improving the speed of operation of these devices. In this thesis, we present hysteresis free operation conditions for Ferroelectric FETs, and derive the core compact models of Symmetric Double-Gate Ferroelectric Field Effect Transistors (SDG-FeFET) and Pillar Gate ferroelectric Field Effect Transistors (Pillar-FeFET). The quasi-static terminal-charge models proposed are shown to match very well with exact numerical terminal charge integrals, for all bias conditions and across various device parmeters. The proposed models are integrated into a professional SPICE simulators using the Verilog-A interface. Different combinational, sequential and SRAM circuits are built using SDG-FeFET and Pillar-FeFET transistors and improvement in their switching behavior is demonstrated through SPICE simulations.

Abstract

Sindhi is an Indo-Aryan language, which is spoken by about 53 million people in Pakistan and about 5.8 million people in India. Sindhi is also one of the 22 official languages in India. Despite all these statistics showing how widely spoken Sindhi is, it is still a computationally resource poor language. Development of natural language applications for any language is possible with the help of linguistic resources and computational tools for that language. In this work, we have developed some fundamental resources and tools that shall help natural language processing of Sindhi language. We have developed raw and part-of-speech (POS) annotated corpus for Sindhi Devanagari and subsequently created a Conditional Random Fields (CRF) based automatic POS Tagger that yields an accuracy of 91.78%. We have also built a paradigm based finite-state morphological analyser for Sindhi Perso-Arabic using Apertium’s lttoolbox. This morphological analyser currently has about 3500 entries and a coverage of more than 81% on Sindhi Wikipedia consisting of 341.5k tokens. We worked on Sindhi Perso-Arabic because the corpus of Sindhi Devanagari was very small and we needed very large corpus for good coverage of the vocabulary of the language, which was available in Perso-Arabic. To diminish the script barrier, we also worked on transliteration. We developed a rule-based transliteration system between Sindhi Devanagari and Sindhi Perso-Arabic which yields 91.33% accuracy. We have also conducted experiments to demonstrate resources leveraging and sharing among the scripts through transliteration. These include, generating more data for Sindhi Devanagari to bootsrap POS tagger and building POS tagger for Sindhi Perso-Arabic.

Abstract

Geovisualization or geographic visualization makes patterns become apparent which otherwise would not have been deciphered. It prompts users into thinking and developing new hypothesis which leads them to make better decisions based on the visualization. It can be 2D or 3D visualization generated using real or simulated data. Moreover, both static as well as dynamic processes can be visualized using geovisualization softwares and tools. The visualizations are graphical representations of properties and relationships between objects in space and time. Today, a large variety of geovisualization tools are available. Each of these have a different set of features. Study of current geovisualization tools was done to identify set of unique features. Based on this study, features of these tools were divided into data modelling, data analysis and data visualization. Data visualization helps us explore data, build and validate hypothesis. To gather further insights and identify patterns in this data, analytical functions are required. In some cases, you will have to model the data. Modelling is required to convert input data and develop a more sensible visualization. Different combinations of these features exist in various tools. Along with this, it is also important to understand other aspects such as whether the tool is map-based or graph-based, whether is open source or proprietary, what kind of data input format they have, whether it is interactive or not among others. This work involves the design and development of LSI-STAT, a web-based spatio-temporal interactive analytical platform. It is a visualization platform which captures both spatial and temporal trends in map along with neighborhood. It is a SOLAP platform which extends OLAP capabilities to Spatial. It provides user with some basic querying capabilities based on OLAP principle. The platform models on user-given data. It computes data aggregation over field names based on hierarchy. This hierarchy is user-defined. It enables users to do an in-depth visual analysis of data to discover hidden insights and patterns in data. With this platform, it is expected that users can visualize charts in combination with maps spatially distributed over the area of interest thus providing for a more enhanced integrated spatio-temporal experience. It allows user to configure various parameters and define hierarchy and time-intervals as required. A large number of visualizations can be generated by the user with the help of this platform using location, time and attribute combinations. The developed platform allows user to select multiple data attributes and generate chart for geovisualization. With the toggle basemap option, user can change the basemap used in geovisualization. As geo-spatial extent of the data can change the perspective of looking at the data at multiple geo-levels, the information is tagged to these zoom levels. Also, we anticipate that as the usage of this tool builds, there can be some pre-defined functions and some new toolsets may need to be incorporated. Hence, the platform has been entirely developed using a suite of open-source technologies. To demonstrate the utility and functionalities of the developed tool and to provide for a good understanding of the value that it provides, two case studies have been presented in this thesis. To cover the range, 2 extremely different levels of case studies have been taken - local and global level. Additionally, both of them have different number of parameters. One of them is a GeoBI case study which was done on a large online retail data of three products sold in different countries during January 2009. The combined view of the temporal trends over the regions helps provide insights into ’what sells more where’, ’is the growth patterns similar or not’, etc. It is apparent that this approach helps overcome the fragmented view that the separate views of space and time using the earlier paradigm provided. Another case study that was done using this tool was on Rajiv Aarogyasri health insurance data of Khammam district in Telangana for the years 2013 to 2015. Over the three year analysis which was done using this tool, it was seen that the map showed a lack of access to a large population. Results can help a policy maker to find places that are inaccessible, have limited access to healthcare facilities, lack certain healthcare services or have fewer number of healthcare facilities. Further, they can find the geographical extents of the population that are actually utilizing different healthcare facilities. By analyzing the geographical coverage and spatial distribution of existing healthcare facilities, they can take decisions for scaling up the existing healthcare facility network. It helps them to easily locate places to setup new healthcare facilities and identify services that need to be added to the healthcare facility. Also, capturing the tempora

Abstract

Orthogonal frequency division multiplexing (OFDM) has been a dominant technology in several wireless standards such as LTE-A, WiMAX and WLAN. However, OFDM might be a misfit for next generation wireless communication systems (5G) and cognitive radios. OFDM suffers from drawbacks such as out of band radiation (due to high side lobe power), tight synchronization and loss in spectral efficiency. As a result of these disadvantages, OFDM is not going to be a default contender for 5G. In the recent past, many researchers have proposed filter bank multicarrier (FBMC) as a replacement for OFDM in 5G and cognitive radios. FBMC uses well designed bank of filters that has less side lobe power resulting in minimal out-of-band radiation so that the requirement of tight synchronization is relaxed in FBMC. This has a great advantage in machine-to-machine (M2M) communication and IoT. Spectrum is a very scarce resource but unfortunately it is underutilized. Cognitive radio (CR) is one of the solutions to exploit the underutilized spectrum. Spectrum sensing is an important enabler for efficient use of spectrum and interference management in 5G and CR setting. In the spectrum sensing literature, there are no detection schemes available for FBMC signal except conventional energy detector. Hence, there is a need for feature detection techniques to efficiently detect FBMC signal. In this work, we propose a spectrum sensing scheme to detect FBMC signal in the presence of noise. Autocorrelation analysis of FBMC signal is carried out at each stage of FBMC signal generation. It is found that FBMC samples are correlated at last but one stage of FBMC signal generation. However, the autocorrelation value is zero for non-zero lag values at the last stage of FBMC signal generation. A modification is proposed to the received FBMC signal to retrieve the lost autocorrelation property of FBMC signal. With this modification, autocorrelation value at the lag equal to the number of subcarriers is non-zero. This autocorrelation property is used to design a detector for FBMC signal. The distribution of the test statistic of the proposed detector is derived under noise-only scenario so that the threshold of the detector can be obtained. The performance analysis of the proposed autocorrelation detector is carried out for different non-idealities such as multipath channel, noise uncertainty and no frame-synchronization (time). Next, the performance of the proposed detector is compared with that of energy detector. Finally, the proposed detector is combined with the energy detector to increase the robustness of overall detection.

Abstract

The thesis presents a novel framework for monocular object-based simultaneous localization and mapping (SLAM) in quasi-static scenes. This presents a new paradigm for real-time object-oriented SLAM with a monocular camera. Contrary to previous approaches, that rely on object-level models, we construct category-level models from CAD collections which are now widely available. To alleviate the need for huge amounts of labeled data, we develop a rendering pipeline that enables synthesis of large data sets from a limited amount of manually labeled data. Using data thus synthesized, we learn category-level models for object deformations in 3D, as well as discriminative object features in 2D. These category models are instance-independent and aid in the design of object landmark observations that can be incorporated into a generic monocular SLAM framework. Where typical object-SLAM approaches usually solve only for object and camera poses, we also estimate object shape on-the-fly, allowing for a wide range of objects from the category to be present in the scene. Moreover, since our 2D object features are learned discriminatively, the proposed object-SLAM system succeeds in several scenarios where sparse feature-based monocular SLAM fails due to insufficient features or parallax. Also, the proposed category-models help in object instance retrieval, useful for Augmented Reality (AR) applications. We evaluate the proposed framework on multiple challenging real-world scenes and show — to the best of our knowledge — first results of an instance-independent monocular object-SLAM system and the benefits it enjoys over feature-based SLAM methods.

Abstract

Gaze and visual attention are very related. Analysis of gaze and attention can be used for behavior analysis, anticipation or to predict the engagement level of a person. Collectively all these problems fall into the space of cognitive vision systems. As the name suggests it is the intersection of two areas: computer vision and cognition. The goal of the cognitive vision system is to understand the principles of human vision and use them as inspiration to improve machine vision systems. In this thesis, we have focused on Eye gaze and Electroencephalogram (EEG) data to understand and analyze the attention, cognitive workload and demonstrated a few applications like engagement analysis and image annotation. With the presence of ubiquitous devices in our daily lives, effectively capturing and managing user attention becomes a critical device requirement. Gaze-lock detection to sense eye-contact with a device is a useful technique to track user’s interaction with the device. We propose an eye contact detection using a convolutional neural network (CNN) architecture, which achieves superior eye-contact detection performance as compared to state of the art methods with minimal data pre-processing; our algorithm is furthermore validated on multiple datasets, Gaze-lock detection is improved by combining head pose and eye-gaze information consistent with social attention literature. Further, we extend our work to analyze the engagement level in the person with dementia via visual attention. Engagement in dementia is typically measured using behavior observational scales (BOS) that are tedious and involve intensive manual labor to annotate, and are therefore not easily scalable. We propose AVEID, a low-cost and easy to use video-based engagement measurement tool to determine the level of engagement of a person with dementia (PwD) when interacting with a target object. We show that the objective behavioral measures computed via AVEID correlate well with subjective expert impressions for the popular MPES and OME BOS, confirming its viability and effectiveness. Moreover, AVEID measures can be obtained for a variety of engagement designs, thereby facilitating large-scale studies with PwD populations. Analysis of Cognitive load for a given user interface is an important measure of effectiveness or usability. We examine whether EEG-based cognitive load estimation is generalizable across the character, spatial pattern, bar graph and pie chart-based visualizations for the n-back task. Cognitive load is estimated via two recent approaches: (1) Deep convolutional neural network and Proximal support vector machines. Experiments reveal that cognitive load estimation suffers across visualizations suggesting that (a) they may inherently induce varied cognitive processes in users, and (b) effective adaptation techniques are needed to benchmark visual interfaces for usability given pre-defined tasks. Finally, the success of deep learning in computer vision has greatly increased the need for annotated image datasets. We propose an EEG (Electroencephalogram)-based image annotation system. While humans can recognize objects in 20-200 milliseconds, the need to manually label images results in a low annotation throughput. Our system employs brain signals captured via a consumer EEG device to achieve an annotation rate of up to 10 images per second. We exploit the P300 event-related potential (ERP) signature to identify target images during a rapid serial visual presentation (RSVP) task. We further perform unsupervised outlier removal to achieve an F1-score of 0.88 on the test set. The proposed system does not depend on category-specific EEG signatures enabling the annotation of any new image category without any model pre-training.

Abstract

Our news are saturated with various statements pertaining to future actions, employment, stock market, economic growth projections, global financial crisis, policies, globalization, climate changes, technologies, etc. Such future trends could have potential impact on our lives, business, etc. In this work, we consider a set of articles or reports by journalists or others, wherein they predict or promise something about future. The problem we address is to determine the credibility of the authors based on the predictions coming out to be true. The two specific problems we address in this thesis are automatically extracting the predictions from the articles and annotating with various prediction attributes. And then, we determine the truth of these predictions, using Wikipedia as a credible source to retrieve relevant facts which can ascertain the validity of the predictions. We estimate that large number of news articles contain references to future. The reference is detected through the notion of predictive statements (phrases). Distinguishing such predictive statements from factual statements in news articles is important for most applications such as fact checking, opinion mining, future trend analysis, etc. In this thesis, we present our approach to the problem of automatically extracting future-related information by solving two sub-problems. The first sub-problem is labeling a sentence as predictive or factual. In addition to extracting the predictions, we address the tasks of clausal scope resolution and dis-embedding linguistic peripheral clauses with respect to the predictive clause in a sentence. To solve these problems, we extract all the clauses of a given sentence and classify each of the clauses as predictive or factual. We then use a machine learning based approach to disambiguate the clause labels by using the clausal dependency relations and label the sentence. ’. We present the results obtained by our predictions extraction methods on two datasets collected and annotated from economics, politics and sports domain. Our system attained an accuracy of (0.85-0.89) on both the datasets. In addition to extracting predictive statements, we further attempted to validate the truth of these predictions. We proposed an architecture to integrate various unscaled Natural Language Processing resources to incorporate background knowledge into our modules, and built an end to end system for automated predictions validation(APV) by extracting future speculations and predictions from news articles and social media. We proposed heuristics to identify core prediction triplets and a retrieval model to extract facts relevant to the prediction. We demonstrated relation alignment based entailment methods for validating the truth of predictions against relevant facts obtained from credible sources(Wikipedia). We experimented and validated our methods using two datasets for the predictions validation task. Dataset 1: Rio Olympics dataset from which we considered 28 news articles from 6 different sources to extract 97 predictions from these articles and the range of credibility scores(F-scores) for these articles are (0.57-0.71). Dataset 2: Obama Promises dataset where we collected 300 campaign promises of Barack Obama. To make our case, we compared the trustworthiness of a person based on their promises made and the promises kept. We worked on Barack Obama’s campaign promises dataset and credibility identified by our system is that 137 out of 300 promises are kept, compared to the actual result(manual) of 204 out of 300 promises identified as kept by Politifact website.