Doctor of Philosophy

dissertationIn wireless sensor networks, knowing the location of the wireless sensors is critical in many remote sensing and location-based applications, from asset tracking, and structural monitoring to geographical routing. For a majority of these applications, received signal strength (RSS)-based localization algorithms are a cost effective and viable solution. However, RSS measurements vary unpredictably because of fading, the shadowing caused by presence of walls and obstacles in the path, and non-isotropic antenna gain patterns, which affect the performance of the RSS-based localization algorithms. This dissertation aims to provide efficient models for the measured RSS and use the lessons learned from these models to develop and evaluate efficient localization algorithms. The first contribution of this dissertation is to model the correlation in shadowing across link pairs. We propose a non-site specific statistical joint path loss model between a set of static nodes. Radio links that are geographically proximate often experience similar environmental shadowing effects and thus have correlated shadowing. Using a large number of multi-hop network measurements in an ensemble of indoor and outdoor environments, we show statistically significant correlations among shadowing experienced on different links in the network. Finally, we analyze multihop paths in three and four node networks using both correlated and independent shadowing models and show that independent shadowing models can underestimate the probability of route failure by a factor of two or greater. Second, we study a special class of algorithms, called kernel-based localization algorithms, that use kernel methods as a tool for learning correlation between the RSS measurements. Kernel methods simplify RSS-based localization algorithms by providing a means to learn the complicated relationship between RSS measurements and position. We present a common mathematical framework for kernel-based localization algorithms to study and compare the performance of four different kernel-based localization algorithms from the literature. We show via simulations and an extensive measurement data set that kernel-based localization algorithms can perform better than model-based algorithms. Results show that kernel methods can achieve an RMSE up to 55% lower than a model-based algorithm. Finally, we propose a novel distance estimator for estimating the distance between two nodes a and b using indirect link measurements, which are the measurements made between a and k, for k ? b and b and k, for k ? a. Traditionally, distance estimators use only direct link measurement, which is the pairwise measurement between the nodes a and b. The results show that the estimator that uses indirect link measurements enables better distance estimation than the estimator that uses direct link measurements

Can humans help BERT gain "confidence"?

The advancements in artificial intelligence over the last decade have opened a multitude of avenues for interdisciplinary research. Since the idea of artificial intelligence was inspired by the working of neurons in the brain, it seems pretty practical to combine the two fields and take the help of cognitive data to train AI models. Not only it will help to get a deeper understanding of the technology, but of the brain as well. In this thesis, I conduct novel experiments to integrate cognitive features from the Zurich Cognitive Corpus (ZuCo) (Hollenstein et al., 2018) with a transformer-based encoder model called BERT. I show how EEG and eye-tracking features from ZuCo can help to increase the performance of the NLP model. I confirm the performance increase with the help of a robustness-checking pipeline and derive a word-EEG lexicon to use in benchmarking on an external dataset that does not have any cognitive features associated with it. Further, I analyze the internal working mechanism of BERT and explore a potential method for model explainability by correlating it with a popular model-agnostic explainability framework called LIME (Ribeiro et al., 2016). Finally, I discuss the possible directions to take this research forward.Comment: Masters thesi

Real-time Contextual Searches to Assist Speakers

Persons who speak professionally to audiences, e.g., teachers, typically spend substantial time and effort to prepare presentation or lecture materials. Answers to follow-up questions from the audience can become more meaningful if the speaker has access to relevant backup material, which, however, may not always be on hand. This disclosure describes techniques to assist speakers in a dynamic, real-time, and automatic manner. Speaker presentation materials and spoken statements are obtained with permission and are used to formulate contextual queries. Results of the queries that include contextually relevant content are grouped by topic and are displayed in a user interface, e.g., as a sidebar that is viewable by the speaker. The results can be displayed in a tabbed user interface that separates content based on content type

Automatic Generation of Push Notification Alerts of Approaching Emergency Vehicles

In congested cities, it can be difficult for an ambulance or other emergency vehicle to reach a location from which the vehicle is requested. Delay in the arrival of such vehicles is a waste of crucial time due to the emergency nature of the task for such vehicles. This disclosure describes techniques to address the above deficiencies through an alert notification system in a navigation or maps application (app). Vehicles along the route of the emergency vehicle are identified automatically, e.g., based on location information shared from such vehicles via their navigation app. Appropriate notifications are generated and sent to such vehicles. The notifications can include information that an emergency vehicle is approaching and optionally, a suggested action for the vehicle. Vehicles that cannot directly receive push notifications can be notified through vehicle-to-vehicle communication or via on-board sensors

Prediction of Antimicrobial Potential of a Chemically Modified Peptide From Its Tertiary Structure

Designing novel antimicrobial peptides is a hot area of research in the field of therapeutics especially after the emergence of resistant strains against the conventional antibiotics. In the past number of in silico methods have been developed for predicting the antimicrobial property of the peptide containing natural residues. This study describes models developed for predicting the antimicrobial property of a chemically modified peptide. Our models have been trained, tested and evaluated on a dataset that contains 948 antimicrobial and 931 non-antimicrobial peptides, containing chemically modified and natural residues. Firstly, the tertiary structure of all peptides has been predicted using software PEPstrMOD. Structure analysis indicates that certain type of modifications enhance the antimicrobial property of peptides. Secondly, a wide range of features was computed from the structure of these peptides using software PaDEL. Finally, models were developed for predicting the antimicrobial potential of chemically modified peptides using a wide range of structural features of these peptides. Our best model based on support vector machine achieve maximum MCC of 0.84 with an accuracy of 91.62% on training dataset and MCC of 0.80 with an accuracy of 89.89% on validation dataset. To assist the scientific community, we have developed a web server called “AntiMPmod” which predicts the antimicrobial property of the chemically modified peptide. The web server is present at the following link (http://webs.iiitd.edu.in/raghava/antimpmod/)

Methanol stream reforming on plate reactor over catalyst surface supported by copper metal foam

by Piyush AgrawalM.Tech

Characterization at the Atomistic Level of Defective Structures in Complex Materials:A Theoretical Study

Defects are key to enhance or deploy particular materials properties. In this thesis I present analyses of the impact of defects on the electronic structure of materials using combined experimental and theoretical Electron energy loss spectroscopy (EELS) in (Scanning) Transmission Electron Microscopy. The energy loss near-edge structure (ELNES) in EELS reflects the element-specific electronic structure providing insights into bonding characteristics of individual atomic species. New electron optical devices have boosted the analytical capabilities by which materials can be investigated with atomic resolution and single atom sensitivity using (scanning) transmission electron spectroscopy (STEM/TEM).With the help of aberration correctors for forming small electron probes, high intensity electron beams can nowadays be focused to clearly less than 100 pm which has enhanced the resolution and sensitivity in analytical scanning transmission electron microscopy (STEM). Various kinds of defects in different complex oxides were studied: point defects like oxygen vacancies in BiVO4 and SrMnO3, edge-dislocations in BiFeO3, and planar defects in GaAs. By comparison with experimental data, structures for these systems were proposed based on all-electron density functional theory (DFT) code Wien2k. By comparing theoretical calculations and experimental data, a pronounced surface reduction in the oxidation state of vanadium in BiVO4 from +5 to +4 was unveiled, which is due to a high density of oxygen vacancies, and its importance in potential application of BiVO4 in photoelectrochemical energy conversion. A similar study was performed on a series of SrMnO3 thin films with different epitaxial strain where theoretical investigations revealed the impact of oxygen non-stoichiometry and strain on the O-K ELNES. In the next study, molecular dynamics simulations were combined with FEFF-based EELS calculations and its comparison with experiments was helpful for the correct prediction of the edge dislocation core structure in BiFeO3. This study also confirmed the presence of Fe atoms in the core of the edge dislocation which possibly makes these defects ferromagnetic whereas the bulk structure is known to be antiferromagnetic. This thesis has established methodologies for utilizing different codes, illustrating how links between experimental and theoretical ELNES can be used in revealing structural information around defects and how defects affect materials properties. This tandem methodology of theory and experiments is applicable to various future materials where the reliable interpretation of EELS data is pivotal in unfolding mysteries of such technologically important materials

Functional Consistency across Retail Central Bank Digital Currency and Commercial Bank Money

Central banks are actively exploring central bank digital currencies (CBDCs) by conducting research, proofs of concept and pilots. However, adoption of a retail CBDC can risk fragmenting both payments markets and retail deposits if the retail CBDC and commercial bank money do not have common operational characteristics. In this paper, we focus on a potential UK retail CBDC, the 'digital pound', and the Bank of England's 'platform model'. We first explore how the concept of functional consistency could mitigate the risk of fragmentation. We next identify the common operational characteristics that are required to achieve functional consistency across all forms of regulated retail digital money. We identify four design options based on the provision of these common operational characteristics by the central bank, payment interface providers (PIPs), technical service providers (TSPs) or a financial market infrastructure (FMI). We next identify architecturally-significant use cases and select key capabilities that support these use cases and the common operational characteristics. We evaluate the suitability of the design options to provide these key capabilities and draw insights. We conclude that no single design option could provide functional consistency across digital pounds and commercial bank money and, instead, a complete solution would need to combine the suitable design option(s) for each key capability and include common ecosystem services provided by an FMI and TSPs.Comment: 24 pages, 3 figures, 3 table

Aneurysmal bone cyst of proximal fibula treated with en-bloc excision: a rare case report

Aneurysmal bone cysts (ABCs) are benign but locally destructive, blood filled reactive lesions of the bone. Although a wider age group may be affected, most commonly they are seen in patients younger than 20 years of age, with a slight female preponderance. Most common sites include metaphysis of femur followed by tibia and then humerus. Vertebral lesions involving the posterior elements are common.Aneurysmal bone cyst of proximal fibula is a rare and uncommon. Here, we report a case of 13 year old female with classic histologic, clinical, and radiographic findings that was treated by en bloc resection.