Abstract

The advent of the Internet of Things (IoT) has resulted in the rise of low power wide area networks such as LoRaWAN, Sigfox, and NB-IoT. Among these, LoRaWAN has garnered tremendous attention owing to the low power consumption of end nodes, long range, high resistance to multipath, low cost, and use of license-free sub-GHz bands. Consequently, LoRaWAN is gradually replacing Wi-Fi and Bluetooth in sundry IoT applications including utility metering, smart cities, and localization. Localization, in particular, has already witnessed a surge of alternatives to Global Navigation Satellite System (GNSS), based on Wi-Fi, Bluetooth, Ultra Wide Band, 5G, etc. in indoor and low power domains due to the poor indoor coverage and high power consumption of GNSS. With the need for localization only shooting up with dense IoT deployments, LoRaWAN is seen as a promising solution in this context. Indeed, many attempts employing various techniques such as Time of Arrival (ToA), Time Difference of Arrival (TDoA), and Received Signal Strength Index (RSSI) have been made to achieve localization using LoRaWAN. However, a significant drawback in this scenario is the lack of extensive data on path loss and signal propagation modeling, particularly in Indian cityscapes. Another demerit is the use of GNSS at some stage primarily for time synchronization of gateways. In this work, we attempt to nullify these two disadvantages of LoRaWAN based localization. The first part of this work presents experimental data of LoRaWAN transmissions inside a typical city building to study signal propagation and path loss. The latter part proposes a standalone GNSS-free localization approach using LoRaWAN that is achieved by applying a collaborative, TDoA-based methodology. An additional stationary node is introduced into the network to allow the synchronization of gateways without GNSS. Finally, the distribution of localization error in a triangle of gateways and the effect of timing resolution, time-on-air, and duty cycle constraints on it are investigated

Abstract

Energetically unfavorable Watson-Crick (WC)-like tautomeric forms of nucleobases are known to introduce spontaneous mutations and contribute to replication, transcription and translation errors. Recent NMR relaxation dispersion techniques were able to show that wobble (w) G•U mispair exists in equilibrium with the short-lived, low-population WC-like enolic tautomers. Presently, we have investigated the wG•U → WC-like enolic reaction pathway using various theoretical methods: quantum mechanics (QM), molecular dynamics (MD), and combined quantum mechanics/molecular mechanics (QM/MM). The previous studies on QM gas phase calculations were inconsistent with experimental data. We have performed structural and energetical characterizations of various intermediates and transition states along the reaction pathway. We have also explored the environmental effects on the reaction energies by adding explicit water. While QM-profile clearly becomes endoergic in the presence of water, QM/MM-profile remains consistently endoergic in the presence and absence of water. Hence, by including microsolvation and QM/MM calculations, experimental data can be explained. For G•U enol → G enol•U pathway, the latter appears to be energetically more favorable throughout all computational models. This study can be considered as a benchmark of various computational models of wG•U to WC-like tautomerization pathways with and without the environmental effects and may contribute to further studies of other mispairs as well. The first chapter gives the background on base mispairs. In chapter two, we give a brief introduction to the methods we have used - quantum mechanics, molecular dynamics and combined quantum mechanics/molecular mechanics. Chapter three deals with the results, analysis, and discussion. Finally, chapter four presents the conclusions of the study.

Abstract

In this thesis, we have discussed two microfluidic sensors, microfluidic micropump, and viscosity sensors, for biomedical and industrial applications. We have done simulations, mathematical analysis, and fabrication of the mentioned devices. Micropumps are one of the most important parts of a microfluidic system. In particular, for biomedical applications such as Lab-on-Chip systems, micropumps are used to transport and manipulate test fluids in a controlled manner. In this work, a low-cost, structurally simple, piezoelectrically actuated micropump was simulated and fabricated using poly-dimethylsiloxane (PDMS). The channels in PDMS were fabricated using patterned SU-8 structures. The pump flow rate was measured to be 9.49 µL/min, 14.06 µL/min, 20.87 µL/min for applied voltages of 12 V, 14 V, 16 V respectively. Further, we report finite element analysis (FEA) simulation to confirm the operation of the micropump and compare favorably the experimentally obtained flowrate with the one predicted by simulation. By taking these flow rates as a reference, the chamber pressure was found to be 1.1 to 1.5 kPa from FEA simulations. Viscosity measurement has wide-ranging applications from the oil industry to the pharmaceutical industry. However, measuring viscosity in real-time is not a facile process. This work provides an elaborate mathematical model and study of measurement of viscosity in realtime using pressure sensors. For a given flowrate, a change in liquid viscosity gives rise to a change in pressure difference across a particular section of the pipe. Hence, by recording the pressure 7 change, viscosity can be calculated dynamically. Mathematical modeling as well as finite element analysis (FEA) modeling has been presented. A set of pressure sensors were placed at a fixed distance from each other to get the real-time pressure change. Knowing the flow rate in the channel, the viscosity has been calculated from the pressure difference. For the finite element analysis, the pressure sensors were placed 60 mm away from each other. The radius of the pipe was 19 mm. A different ratio of the mixture of water and glycerol was used to provide variable viscosity, which led to the variation in pressure-difference values.

Abstract

Motion planning is an important component of robot navigation and manipulation, and it is crucial in applications where the robot operates in full or partial autonomy. Some examples of these are multi-robot exploration, autonomous vehicles, and robot manipulation. In most of these applications, the robot’s state is not directly observable and can only be inferred from erroneous observations. Such situations require the planning algorithms to be robust to the uncertainties in actuation and perception. This thesis investigates the motion planning problem under uncertainty, with specific applications in navigation and path tracking. Applications of these kinds extensively use mathematical routines that solve constraints defined over random variables and are popularly known as chance constraints. Solving for chance constraints is extremely difficult and often intractable. This is primarily due to the fact that non-linear constraints defined over random variables result in a distribution that generally has no parametric form. In this thesis, we focus on constructing mathematical frameworks for solving such intractable optimization routines. This thesis’s key contribution is that we propose a series of formulations that can solve for chance constraints under parametric and non-parametric uncertainties. To achieve this, we construct the mathematical framework systematically over a sequence of stages, wherein each stage improvises over the limitations of the previous. For the first stage, we start with simple sampling-based approaches like the scenario approach. Though conceptually simple, this approach brings an added sample complexity in generating scenarios of the constraint functions. To overcome this limitation of the scenario approach, we proceed to the second stage, where we derive closed-form algebraic equations that would act as a surrogate to the otherwise intractable chance constraint. The novelty in this proposed approach stems from the nature of these surrogate functions, which are simple polynomial functions (mostly quadratic) of control variables and hence greatly simplify complex chance constrained optimization routines. While the proposed framework at this stage did enjoy closed-form algebraic solutions, it relied on the fact that the underlying random variables belonged to parametric forms of distributions (especially Gaussian). Hence we then proceed to the final stage where we solve for chance constraints under non-parametric uncertainties. We construct the proposed algorithm on the possibility of representing arbitrary distributions as functions in Reproducing Kernel Hilbert Space (RKHS). We systematically exploit the kernel trick to reformulate chance constrained optimization routine such that it is tractable and computationally efficient. We detail all the approaches proposed in these three stages over five chapters in this thesis. We primarily apply the proposed formulations on two challenging problems in motion planning: (i) reactive collision avoidance of mobile robots in uncertain dynamic environments and (ii) inverse dynamics based path tracking of manipulators under perception uncertainty. In all these applications, the underlying chance constraints are defined over highly non-linear and non-convex functions of the uncertain parameters, making it highly intractable. We also extend one of the proposed formulations to a multi-robot navigation scenario, where each agent is responsible for computing safe maneuvers, considering perception and ego uncertainty.

Abstract

This work proposes a multi-cloud marketplace model for Infrastructure-as-a-Service (IaaS) layer with multiple cloud providers, intermediate brokers and end users. The brokers service end users subscribed to them by aggregating resources (virtual machines) from cloud providers while maximizing their profits. Similarly cloud providers (producers) allocate their supply of virtual machines to brokers (consumers) so as to maximize their profits. The notion of social welfare in this market structure is defined and two trading schemes are studied. The first scheme involves centralized control which aims at maximizing social welfare. The problem of maximizing the social welfare is formulated as an integer linear programming problem and a greedy algorithm is proposed for the same. However, this scheme may contain unstable producer-consumer pairs who have an incentive to deviate from the current allocation. The second scheme eliminates such unstable pairs by using a generalization of stable matching algorithm but may lead to sub-optimal social welfare. The stable matching algorithm proposed in this work is a particular way of generalizing the original Gale-Shapley deferred acceptance algorithm.

Abstract

Natural Language Generation (NLG) is a research task which addresses the automatic generation of natural language text representative of an input non-linguistic collection of knowledge. Inputs can be of different modalities – databases, structured reports, etc. Traditional NLG systems have been categorized into one of two general approaches: Template based and Pipeline based. In this work, we explore how raw text can be leveraged to automatically extract sub-sentence level templates to generate grammatical sentences. We propose a system for generation of grammatical sentences given a partially ordered partial bagof-words which constrain the generated sentence using a template based approach. As the input, we formulate a lexical level constraint schema as the input constraints which drives the generation process. We view the task as a search problem (a problem of choice) involving combinations of smaller chunk based templates stitched together to construct a complete sentence. To achieve that, we propose a scoring function to determine syntactic correctness of a template sequence which we use in conjunction with an evolutionary algorithm as the search procedure to arrive at a potentially grammatical sentence which satisfies the input constraints.

Abstract

Growth of content and users on internet provides research opportunities in crowdsourcing, search engines and other areas that consume and process web. Lack of clarity in task definition, uncertainty in completion time, concern on data confidentiality, unavailability of workers, cost considerations and quality of output are some of the research opportunities in crowdsourcing. Quality of output is one of the primary concerns in crowdsourcing tasks, as lack of it defeats the objective of using crowd. Researchers are exploring various mechanisms that are based on statistics, machine learning and game theory for acceptable output quality. As part of this work, an online survey conducted on crowdsourcing reiterated importance of quality assurance and quality control of tasks. The online survey along with review of research literature confirmed that quality mechanisms on crowdsourcing tasks benefit from availability of credible knowledge base in a domain. To build domain specific knowledge base, this work proposes a fine grained approach to identify sub-domains in domain and extract related content, an enrichment of a knowledge base in the form of an ontology and credibility assessment based on web genres. The research outcomes on creation and enrichment of knowledge base are used to develop a domain specific search engine. Web pages on internet contain content across domains. Crawler efficiency, sub-domains representation and noise reduction are required to extract domain specific content. A systematic approach to identify sub-domains in a domain is proposed. Further, the work extends metaheuristics based Artificial Bee Colony (ABC) algorithm to extract sub-domains’ URLs. The extended ABC algorithm for crawling performed better than existing industry scale open source crawlers in terms of volume of extracted URLs and usage of compute resources. A metric SeedRel to measure precision of seed URLs based on child URLs presence and content relevance is proposed. The work measured sub-domains coverage with a baseline value of Shannon Diversity Index. In the experiment of this work, about 34,007 seed URLs and 400,726 child URLs of information security sub-domains are extracted. The measured diversity index of 2.10 conforms representation of sub-domains in a domain. The results on URL extraction, seed URL relevance and sub-domain diversity performed better than existing approaches. The extracted domain specific content is an input to build knowledge base. Ontology is a representation of knowledge and provides extensibility, interoperability and reasoning capabilities. To build knowledge base, the work proposes usage of an existing and well accepted seed ontology for enrichment so that a baseline is available, changes are incremental, auto and manual validation can be exercised. It is necessary to validate if an ontology needs to be enriched for any input to avoid unnecessary computation. Similarly, it is necessary to validate if the given ontology represents available domain content. To validate a need for enrichment, a lightweight approach to evaluate ontology sufficiency based on software requirements engineering quality principles is proposed. It is observed from research literature that existing ontology enrichment algorithms that are based on natural language processing and machine learning techniques perform poorly in contextualized extraction of concepts. This necessitated implementation of sequential deep learning architecture that traverse through dependency paths in text and extract concepts embedded in phrases and sentences based on learned path representations. The proposed fine grained ontology enrichment approach (OntoEnricher) exploits both syntactical sentence structures and distributional semantics to identify, extract concepts and instances in unstructured text leveraging Bidirectional LSTMs and pre-trained Universal Sentence Encoder transformer model. The ontology enrichment is experimented with 97,425 keyword phrases and 2.8 GB of Information Security corpus with an accuracy of 80%. The credibility of extracted content enhances usage of knowledge bases. Also, search engine results suffer from non-dominant credibility factors. After a study of existing research articles on ranking and credibility, the work proposes genre based credibility assessment of web page content. An online survey (includes crowdsourcing) is conducted to validate existence and need of web genre for credibility assessment. An exhaustive list of web page features based on surface, content and off-page are identified to validate applicability of genre for credibility assessment. Web page features are used to prepare a dataset to automate genre classification using machine learning based Gradient boosted Decision Tree algorithm with an accuracy 88.75%. An open source framework for credibility assessment of URLs is developed to automate genre classification and credibility assessme

Abstract

High demand for reception of content from multiple users simultaneously enforces usage of intelligent transmission techniques in applications like video-on-demand services, satellite communication, interference management etc. Index coding is one such technique introduced by Birk and Kol in 1998 originally to optimize channel efficiency for satellite communication. The index coding framework consists of a source connected via a broadcast channel to multiple receivers, with each receiver storing locally partial information symbols that is available at the source (this is known as cache or side information). The receivers then demand some new information symbols from the source. An index code is an encoding of the symbols those are demanded by the receivers in such a way that everyone of the receivers can decode their own requested symbols. The goal of index coding is to minimize the number of transmitted symbols (the length or rate of the index code) while ensuring decoding by the receivers. To design the index code with reduced rate, the source makes use of the knowledge of the side-information available at the receivers. Index coding techniques received considerable attention as they can be employed in several engineering applications such as content broadcasting, device-to-device relaying,distributed caching etc. It also has interesting relationships to network coding, distributed storage and guessing games. Several approaches for solving index coding problem have been developed using graph theoretical, linear algebraic methods, interference alignment, linear programming etc. Specifically, in the graph theoretic approach, the side-information and demands of the index coding problem are captured in the form of a graph, and certain properties of the graph are used to construct an index code. Albeit the trials being done in this context, optimal solutions for index coding problems are still open. Index coding problems modelled by circular perfect graphs, which form a strict superclass of the perfect graphs, are studied in the first part of this thesis. We derive schemes that achieve optimal broadcast rate for a constrained class of index coding problems defined by complements of circular perfect graphs. We also proposed a better lower bound on the rate of a optimal solution for any index coding problem. We present lower and upper bounds on the sum and product of vector linear broadcast rates of a graph and its complement. The second part of this dissertation is in the area of coded caching, Consider a network of clients connected via a broadcast channel to one server node which has a number of files. The clients or receivers have caches which allows them to store some parts of the files at the transmitter. The caching system proceeds in two phases: Caching Phase which occurs in non-peak times, and Delivery phase which occurs in peak hours when each client demands some file from the server. By prefetching some parts of the files from the server to the clients during the non-peak hours, the remainders of the demanded files alone can be sent by the server during the peak hours. Thus, caching allows us to offload some traffic from the network during the peak hours, when each client is demanding some file from the transmitter. The paradigm of Coded Caching introduced in [1] further reduces the load during the peak hours by encoding the parts of the files to be communicated. The coded caching problem is similar to the index coding problem in this sense, and hence the goal here also is to minimize the size of the transmissions, i.e., the rate of coded caching. In this thesis, we derive a subpacketization dependent lower bound on the rate of coded caching and compare our results with prior known lower bounds through which we show that our bound performs better than existing lower bounds on the rate of coded caching.

Abstract

What is common between Veg Biryani, Ronaldo and Donald Trump? All of them pretend to be something that they are not. Not Biryani, not the GOAT (Greatest Of All Time) and not human. But again, many a true word is spoken in jest. We pretend just as we breathe. Pretense is observable all around us. Feigning sickness to avoid work, lying to get out of trouble, maintaining a persona or a child joyfully playing his doll are all instances of make-believe. Global platforms of communication have made the world smaller than ever before. With degrees of separation reducing by the day, we can now interact with numerous people almost instantly. More interactions means more dialogue among people, which means more pretense. Sometimes we even pretend about pretending, for example, when applying reverse psychology. The bottom-line is that, pretense is a very important phenomenon in our lives, which we exploit regularly. Therefore, it is only fitting that there be an account which rigorously analyses and explains pretense. No matter how grounded in reality, due to its nature, pretense always involves an element of fantasy. This role is fulfilled by imagination. It provides the necessary content and helps us in guiding an episode of pretense. The nature of this content however is a topic hotly contested in philosophy. Does it comprise of clear or fuzzy images? Or whether images form a part of it at all? Are there different forms of imagination? Moreover, can one even provide a distinction between different types of imagination? We discuss and answer all such questions in Chapter 2, and clear the air up regarding its nature and involvement in pretense. Having gathered all the information needed about imagination as an essential component of pretending, we then move on to the act itself. Pretense involves both mental as well as physical participation of our body. Therefore, an analysis of the cognitive aspects of pretense is necessary to ascertain the precise requirements for pretending. By studying, the two most definitive accounts, viz. metarepresentational account and the behavioural account of pretense we get to know about the important characteristics of pretending. Along the way, we also learn from their shortcomings to bolster our own command of the topic. After strengthening the theoretical backbone needed to properly understand the phenomenon of pretense, we apply our learning from the first three Chapters and proceed towards applying them in a computational paradigm. We are presented by the perfect problem in the shape of Humour and Sarcasm vi vii as two forms of pretense, where we can flex our knowledge and use it to improve the accuracy of existing computational systems. In Chapter 4 we work towards formulating the problem of determining different types of humor as a traditional classification task by feeding positive and negative datasets to a classifier. We develop a model to categorize various kinds of humour based on the mode of delivery, the theme of the joke and the topic of the joke. Then, we illustrate its functionality by applying it on a huge corpus one-liners. Finally, we bring closure to our work in Chapter 5. We make headway in detecting sarcasm by making use of contextual information derived from the history of the participants. Therefore, instead of treating the problem of detecting sarcasm as a mere lexical exercise, we have tried to model a system that tries to build an understanding of the whole process as a human would do.

Abstract

Building sector consumes nearly 75% of electricity sales. And with rapid population and economic expansion, electricity consumption in buildings are projected to be doubled by 2050. The building sector generates one-third of greenhouse gas, two-thirds of halo-carbon and about one-third of black-carbon emissions. This growing energy demands have raised significant concerns around the globe regarding it’s adverse effect on environment. The energy savings potential could reach as high as 30-80% with presently available building technologies. Therefore, importance on development of smart buildings, smart grids are being given which in turn can reduce carbon emissions. Accurate load forecasting is of utmost importance for the development of any modern power system. Hence, accurate building-level load forecasting can help deliver efficient building operations, thus mitigating such adverse effects. Load forecasting helps in real time building control of energy systems, including demand response, demand management, energy transactions, charging/discharging energy storage units. This work presents a robust short-term electrical load forecasting framework that can capture the variations in building operation, regardless of building type and location. Nine different hybrids of recurrent neural networks and clustering are explored. The test cases involve five commercial buildings of five different building types, i.e., academic, research laboratory, office, school and grocery store, located at five different locations in Bangkok-Thailand, Hyderabad-India, Virginia-USA, New York-USA, and Massachusetts-USA. Load forecasting results indicate that the deep learning algorithms implemented in this thesis deliver 20-45% improvement in load forecasting performance as compared to the current state-of-the-art results for both hour-ahead and 24-ahead load forecasting. With respect to sensitivity analysis, it is found that: (i) the use of hybrid deep learning algorithms can take as less as one month of data to deliver satisfactory hour-ahead load prediction, (ii) similar to the clustering technique, 15-minute resolution data, if available, delivers 30% improvement in hour-ahead load forecasting, and (iii) the formulated methods are found to be robust against weather forecasting errors. (iv) the proposed framework are found to be robust to seasonal changes. Lastly, the forecasting results across all five buildings validate the robustness of the proposed deep learning framework for the short-term building-level electrical load forecasting tasks.