Abstract

Sentiment analysis has emerged as a field, that has attracted a significant amount of attention over the last decade. This is the area of study to analyze people reviews, songs and attitudes from different types of data and classify whether it is a positive, negative or neutral. Recent advancement of social media which is an enormous ever-growing source has led people to share their views through various modalities such as audio, text and video. This source of information is important to automatically make out the sentiment embedded in the different types of data such as reviews and songs. In this thesis, an improved multimodal approach to detect the sentiment of product reviews and songs based on their multi-modality natures (audio and text) is proposed. The basic goal is to classify the input data as either positive or negative sentiment. Database used in this study are Spanish product reviews, Hindi product reviews and Telugu songs. Most of the existing systems for audio or speech based sentiment analysis use the conventional audio features which are extracted from entire signal, but they are not domain specific features to extract the sentiment. In this work, instead of extracting the features from entire signal, a specific regions of an audio signal have been identified and experiments are performed on these regions by extracting the relevant features. For songs data, experiments are performed over each song, and its beginning and ending regions. For all these cases, Gaussian Mixture Models (GMM) and Support Vector Machine (SVM) classifiers are built using the prosody, temporal, spectral, tempo and chroma features. Experimental results shown that the ate of detecting the sentiment of a song is high at beginning of a song compared to its ending region and over the entire duration of the song. This is because, the instruments and vocals which convey the sentiment for beginning part of the song may or may not sustain throughout the song. For the reviews data, we could not perform these experiments because in such cases the sentiment may not be present at beginning or ending regions of an utterance. So for the reviews data, the stressed and normal regions are identified using the strength of excitation. From the stressed regions, Mel Frequency Cepstral Coefficients (MFCC) features are extracted and GMM classifier is built. Further, experiments are performed by extracting the prosody (energy, pitch and duration) and relative prosody features from both the regions and from the entire audio signal and a GMM classifier is built. From the results, it is observed that, the performance at specific regions is better as compared to the entire signal. It is also observed that, relative prosody features extracted from both the regions has high accuracy of detecting the sentiment compared to the prosody and MFCC features. This is because, the natural variations present in the prosody features are reduced using the relative prosody features. Recently, neural networks have achieved good success on sentiment classification. In this work also different deep learning architectures like Deep Neural Network (DNN) and Deep Neural Network Attention Mechanism (DNNAM) are explored. Here stressed regions concept fail because of less training data. DNN performance depends on the amount of training data. The more the training data, the more accurate it is. So here the experiments are performed by combination of frames which result in better performance because each frame will not carry the sentiment. MFCC features considered are 13-dimensional, 65-dimensional and 130-dimensional feature vectors. From the studies, it is observed that DNNAM classifier gives better results compared to DNN, because the DNN approach is a frame based one where as the DNNAM approach is a utterance level classification there by efficiently making use of the context. For text based sentiment analysis, transcriptions are carried out manually from the audio signal. For song classification, SVM and Naive-Bayes classifiers are built using the textual features which are computed by Doc2Vec vectors. As in the audio, here also experiments are performed at the beginning, the ending and over the entire song. The studies shown that beginning of a song has high accuracy in detecting the sentiment compared to the ending region and over the entire song. As similar experiments could not be carried out with the reviews data, entire document is taken as an input to extract the sentiment. Support Vector Machine (SVM) and Long Short-Term Memory Recurrent Neural Network (LSTM-RNN) classifiers are used to develop a sentiment model with the textual features computed by Doc2Vec and Word2Vec. From the experimental studies, it is observed that LSTM-RNN outperforms the SVM because LSTM-RNN is able to memorize long temporal context. Finally, we combine both the modalities such as audio and text to extract the sentiment. Both the modalities are hypothesized based on

Abstract

Technological advancements have made human beings dependent on machines in unprecedented ways. High precision in many tasks and a vast, tractable memory confirms their presence as an integral part of human lifestyle. Human-machine interaction, then, is the evident next step in developing smarter systems and consequently, a research topic of foremost importance. Effective communication is achieved by means of natural language and hence, language becomes an obvious necessity for human-machine communication too. The vast majority of languages spoken in a multi-ethnic society mandates the necessity of development of multilingual systems. India is one such region which is inhabited by highly diverse multicultural societies and a multitude of ethnic groups living as a nation. An effective Spoken Dialog System (SDS) developed for Indian scenario has to keep in mind the shared characteristics of languages spoken across India. This motivates the dire need to develop multilingual human-machine interfaces which can exploit this commonality. Portability and ever-increasing computing capacity have rendered mobile phones to be the most common form of communication agents. But in absence of network connectivity, they lose a lot of power residing in cloud platforms and have to rely on local resources which are limited and scarce in nature. This problem is ubiquitous because there are several locations and situations when network is not available but basic communication is crucial for information retrieval. One such situation is Healthcare for preventable diseases in rural areas. This thesis is focused to alleviate some of the problems which arise when humans move to interact with hand-held conversational machine agents in a multilingual setting such as India. With the advent of social media, code-mixing became a popular phenomena in spoken language. This work progresses to develop a spoken dialog system for the domain of Healthcare and focuses on problems of 1. recognizing the language of communication within speech segments through large-scale spoken language modeling and 2. spotting keywords in speech signal by creating a robust representation of articulatory gestures in speech. The proposed approaches are based on recurrent and convolutional architectures of neural networks and have been experimentally shown to outperform the state-of-the-art on standard datasets.

Abstract

Ring laser gyroscopes (RLG) are optical inertial rotation sensors used to measure the rate and direction of rotation. With the help of accelerometers, they provide accurate information about the position and orientation of an object. Their sensitivity depends upon the intended application: military navigation requires gyros with sensitivity of 0.01-0.1 deg/h while automobiles and handheld application need sensitivity of 1-10 deg/h. As the sensitivity of gyro is directly proportional to its size, the high performance military applications are dominated by the bulky He-Ne RLG. Over the past few decades, strong theoretical research backed up by advanced fabrication technologies have improved the performance of He-Ne RLG significantly. In the era of on-chip integrated optical devices, large size and high power requirement of He-Ne RLG limits its applications. This is where Semiconductor RLG (SRLG) provides a viable alternative. SRLG is a compact, low cost and low power inertial rotation sensor working on the principle of Sagnac effect. It offers the promise of fabricating the complete gyro system on a single Photonic Integrated Circuit (PIC). Many implementations of bulk-optic and integrated SRLG have been proposed, but their reported performance has been highly inferior to He-Ne RLG. While bulk-optic SRLG has shown sensitivity of 10 3 deg/h, the reported sensitivity of integrated SRLG has been 10 8 deg/h, which is unacceptable even for low performance applications like automobiles. While the poor performance of integrated SRLG has been attributed to phenomena like mode coupling and gain competition, a detailed performance analysis has not yet been reported. Hence, critical performance limiting parameters could not be identified and feasible practical solutions to enhance the performance could not be proposed. This has inhibited the development of SRLG technology towards high performance applications. In this thesis, SRLG has been mathematically modeled using rate equations of counter-traveling electric fields inside the gain medium and the resonant cavity. The Sagnac beat signal obtained by simulating the model is verified by rotating the experimental setup of the gyro. The sensitivity, which is found to be limited by locking of the counter-traveling fields, is enhanced by proposing few novel designs and biasing techniques. Although these techniques improve the sensitivity of SRLG, the overall performance is still very poor compared to the military navigation standards. In order to identify the critical performance limiting parameters and phenomena, every metric of SRLG such as quantum limit, angle random walk, scale factor stability, null shift and lock-in threshold have been thoroughly modeled in terms of material, geometry and environmental parameters. Moreover, effects of nonlinearities such as spatial hole burning, mode coupling, gain saturation etc. on the SRLG sensitivity have been evaluated. The critical performance affecting parameters are identified and optimized to achieve best possible performance. For integrated SRLG, lock-in due to non-linear backscattering inside the semiconductor gain medium is found to limit the sensitivity to 10 8 deg/h. Use of external coupled-cavity semiconductor ring lasers and stimulated Brillouin scattering (SBS) based ring lasers as gyroscopes are proposed for high performance military applications.

Abstract

A legal judgment is a document which provides an explanation of the court order concerning a legal case. The legal data is being digitized and is being made available in digital format for access by legal practitioners and the general public. Also, the legal data is continuously growing with the judgments being delivered. In common law based legal systems, legal practitioners refer to the previous judgments of the court to prepare the new judgment. Extracting relevant legal judgments for a given input judgment is useful to legal practitioners. The inherent complexity of a legal judgment requires effective and efficient ways to extract relevant legal judgments for a given input judgment. Legal judgments contain text and citation information which can be exploited for finding relevant legal judgments for a given input judgment. In the literature, efforts have been made to exploit the text and citation information for finding similarity between the pairs of judgments. In this thesis, we show the potential of citation information in the judgments, in finding similar judgments through cluster analysis and for ranked retrieval of relevant legal judgments. Firstly, we analyzed how the citation information could be helpful in finding similar legal judgments. Link information has been exploited extensively in the web domain for search and retrieval systems. Similarly, citation information in the legal judgments can be exploited for building efficient search systems in the legal domain. We developed a clustering approach to cluster judgments having only citation information. We have conducted experiments on real world legal judgment dataset delivered by Supreme Court of India. The clustering results indicate that the citation information is helpful in finding clusters of judgments. As the naturally available citations are sparse, we also exploit the notion of paragraph links to increase the number of citations in the judgments and reported the corresponding clustering results. Further, we have made an effort to show the potential of citation information in the ranked retrieval of relevant legal judgments for a given judgment. Efforts are being made to build efficient search systems that extract legal judgments for a given input query by extending the traditional information retrieval approaches. Intricate legal concepts are discussed at a granular level in the legal judgment documents. In addition to citation information, we exploit the paragraph-level information of the judgments for ranked retrieval of relevant legal judgments for a given input judgment. Instead of considering the entire text document for comparison, we find the aggregate relevance between judgments by computing the relevance at paragraphs of the judgments based on the Okapi retrieval model. User evaluation study on the real world legal judgment dataset shows that the citation based approach improves the performance by combining with Okapi retrieval model. Overall, we conclude that citation information available in the legal judgments can be exploited for finding similarity among legal judgments.

Abstract

Recently representation learning has been receiving much attention from both industrial and academic communities. Thanks to the advent of powerful GPUs and advanced techniques like dropout and rectified linear units, the interest in neural networks has been revived. The current state-of-the-art solution for the applications in the field of Natural Language Processing (NLP) and Information Retrieval (IR) is powered by neural network based representation learning models. In this thesis, we focus on the unsupervised representation learning models which are cheap to build (as they rely on unlabeled data) but very effective for many downstream applications. We explore two main challenges in building an unsupervised representation learning model for NLP and IR problems. Context serves as the source of knowledge for estimating the representation of data. For example, in Word2Vec, the context is the set of words surrounding a given word in a sentence. The first challenge we focus is the context insufficiency problem. For instance, consider the models used in practise to generate representations for tweets. We observe that tweets do not exist in isolation, and hence the performance of the models which work only with the content of the tweet (as context) is found to be sub-optimal. To handle this issue, we propose a better model which also captures the interactions between the tweet and its adjacent tweet (as context) in the users’ timeline. Along similar lines, we explore two more use cases where we smartly incorporate novel contexts that captures complex interactions such as scientific author and his/her paper interaction in an author collaboration network (‘Author2Vec’) and sentences interaction in a document (‘Doc2Sent2Vec’), that turned out to be advantageous in computing accurate author and document representations respectively. We conclude that if we smartly leverage the available contexts, the performance of the model improves significantly. Though the representation learning models perform well in practise, little is known about the core properties of the data encoded within the representations. Understanding these core properties would empower us in making generalizable conclusions about the quality of the representations. Hence, the second challenge we focus is the human interpretability problem with these automatically learned representations. For instance, researchers in Twitter analytics are getting interesting results by applying different representation learning models for several valuable tasks such as sentiment analysis, semantic textual similarity computation, microblog retrieval, hashtag identification and so on. In order to understand the core properties encoded in a tweet representation, we evaluate the representations to estimate the extent to which it can model each of those properties such as tweet length, presence of words, hash-tags, mentions, capitalization and so on. This is done with the help of multiple classifiers which take the representation as input. Essentially, each classifier evaluates one of the syntactic or social properties which are arguably salient for a tweet. The result is an application independent, fine-grained analysis of tweet representations generated by different representation learning models. This thesis is one of the initial work to overcome the above-mentioned challenges by proposing novel methods to improve the contexts and interpret the representations. In the growing body of representation learning research, the set of models and framework proposed in this thesis could act as the basic building blocks in the future works attempting to advance the science of building smarter NLP/IR systems.

Abstract

In the era of globalization, vast amount of information is available on World Wide Web. This information is available in various natural languages. In order to access this information, the speakers of different languages have to rely on translation or learn the other language. Learning multiple languages is not feasible for everyone, hence the need of translation arises. In such a scenario, machine translation emerges as an important discipline in the field of computational linguistics that can give access to the vast information within a short time and at low cost. Translating natural languages automatically is one of the most complex and comprehensive applications of computational linguistics [63], and the dream of a perfect fullscale machine translation system still remains an intellectually challenging task. Several approaches, such as rule based, statistical and hybrid, have been tried out to build linguistic tools. All these approaches have their own merits as well as demerits. Pān.inian Grammar, universally admired for its insightful analysis of Sanskrit [74], offers a perspective to language analysis. The main focus of Pān.inian Grammar is to explain how, where and how much information a language encodes that helps in capturing information dynamics in a language [3]. In order to use Pān.inian theory to analyse other languages, it is necessary to model these languages in terms of Pān.inian primitives. We have selected the primary basic notions such as nominal inflections (sup), verbal inflections (tiṅ), basic grammatical constructions (pada), compound constructions (samasta-pada), etc., to analyse the syntax and semantics of natural language sentences. In our research, we have used these concepts to analyse English from Pān.inian perspective and then map English structures into Hindi for handling three major linguistic tasks for English-Hindi machine translation as follows: 1. Finding Out the Meaningful Linguistic Units That Can Participate in a Sentence: This step theoretically accounts for English sentence in terms of Pān.inian primitives such as sup, tiṅ, pada and samasta-pada. Here we find out the syntactic and semantic units called padas and samasta-padas and the grammatical markings that connect these units, the padas and samasta-padas with the other meaningful units in a sentence. Such analysis looks at the words in a sentence as a combination of content words and grammatical markings [125]. Thus, this type of syntactic analysis helps in capturing the flow of information and gives us the shallow parse of the sentences. It is also helpful in various other linguistic tasks such as dependency parsing, word ordering and case marking. 2. Mapping Dependency Parsers’ Output into a Uniform Notation: Parsers play an important role in analyzing the exact meaning of a sentence. The parse tree produced by the parsers formally represents syntactic and semantic information among the words in a sentence. In a machine translation system, various modules like Gender Number Person (GNP) agreement, Word Sense Disambiguation (WSD), reordering of the source language sentence according to target language sentence, etc. are some of the important tasks which require dependency parse as input [89]. Several dependency parsers are available for English viz. Stanford Parser, Link Grammar Parser, CLEAR Dependency Parser, Minipar, Malt and many more [119, 47, 36, 85]. However, no two dependency parse output formats match with each other. These parsers differ on the names of the dependency labels, their representation style and the number of dependency labels [17]. Since the parsers are based on different approaches, and each approach has its own limitations, the parsers produce different degrees of output quality for different syntactic constructions. In order to obtain maximum benefit out of these parsers, one has to combine the strengths of the parsers [24]. Hence arises the need of a common representation schema. Also, the use of computers as an information processing device requires a sophisticated theory for processing the information in a language. Bringing the parsers’ output into one uniform notation provides the system an ability to plug-in any of these parsers without modifying other translation modules, thereby avoiding large amount of manual work that is costly as well as time consuming. 3. Framing Transfer Grammar Rules: Word ordering plays an important role in the translation process between languages. Using the concept of pada and samasta-pada, the source language sentence is manipulated into target language sentence for natural translation. Since the notion of pada overtly identifies the grammatical markers and their respective content words as syntactic units of a sentence, we get a little more freedom for word ordering as an advantage due to the relatively free word order nature of the target language Hindi [23]. The concept of pada also helps in generating direct

Abstract

Healthy heart is a key for healthy life. Unfortunately in many cases the heart function deteriorates over a person’s lifetime, often with tragic consequences. A class of diseases involving heart functionality is termed cardiovascular disease. Among a number of such cardiovascular diseases, cardiac arrhythmia and coronary artery disease (CAD) are two of the most common and potentially life threatening diseases and are the our work is focussed around them. A primary reason for a high casualty figures caused by these diseases can be attributed to the fact that the diagnosis is not done on time. Often, the symptoms are noticeable only at a stage where the person needs immediate medical attention. Accurate diagnosis can only be done at specialized healthcare facilities by trained personnel. The diagnosis is also expensive. This limits its accessibility for major portion of population especially in rural areas. State of the art diagnosis for coronary artery disease typically involves an invasive procedure. Though this procedure is conclusive, it is still painful and risky. Assessing these challenges, there is a need for a widely accessible, inexpensive and preferably non invasive alternative. While looking for alternatives, electrocardiography or ECG has advantages of being accessible, non-invasive, and inexpensive. It is used for diagnosing cardiac arrhythmia, and as a screening test for coronary artery disease. However this screening is inconclusive and other techniques are recommended. Another non invasive technique, phonocardiography or PCG suffers from complex design and sensitive equipment. Which brings us to yet another well known technique, most commonly used for pulse oximetry, the photoplethysmography (PPG). Photoplethysmography can be defined as study of blood volume changes through light. It is non invasive, inexpensive and can easily be made portable thus making an ideal candidate for further investigation. PPG is also very suitable for use in wearable devices technology and such devices are used for ambulatory monitoring. Having all the desired characteristics, PPG is further investigated towards diagnosing cardiac diseases. As PPG as a technique already exists and is widely used, this work is aimed at developing techniques to analyse the PPG data towards possible diagnosis of cardiac diseases. Phtoplethysmography has its own challenges. It has high sensitivity and susceptibility to motion. This is particularly concerning for detecting arrhythmia and coronary artery disease alike as it wrongly affects heart beat detection as well as degrades the natural characteristics of the signal. First contribution of this work is a technique to estimate heart rate in presence of motion induced artifcats using fuzzy entropy of the signal. Once accurate heart rate is estimated, the input signal is tested for possible cardiac arrhythmias such as tachycardia, bradycardia, asystole, ventricular tachycardia and ventricular fibrillation. The fuzzy entropy based method poses some implementation limitations as it is computationally inefficient and requires initial user input. Another approach is developed to overcome these limitations. A quality index is assigned to each pulse. The artifact portion is removed using an appropriate threshold. The remaining clean portion of the signal is used for arrhythmia detection. This technique is faster and more accurate than fuzzy entropy based technique and much more useful for arrhythmia detection. Another important contribution of this work is to explore PPG for detecting coronary artery disease. This involves analysing the morphology of the PPG signal and its second derivative. Characteristic points are obtained from the PPG waveform and its second derivative. Temporal position of these characteristic points is used as a distinguishing feature to classify between healthy subjects and CAD patients. This analysis is carried out using invasive arterial blood pressure (ABP) signal as well. Supervised classification using support vector machine is employed for classifying healthy subjects and CAD patients. It is observed that higher classification accuracy was obtained using PPG signal compared to ABP. Despite certain limitations, we present proof to suggest that PPG can be effective in detecting certain cardiac arrhythmias and coronary artery disease.

Abstract

Change is inevitable and everywhere. Resources, ecosystem, biophysical environment, and land use/cover on the surface of the earth undergo continuous change over time and space. Land cover is the layer of soil and biomass, including natural vegetation, crops and man made infrastructures that cover the land surface. Land use is the purpose for which human exploit land cover [Verburg et al., 2000]. LULC change is probably the most important form of global environment changes due to its direct relevance with human existence and spatio-temporal nature. The land use changes also have significant implications in climatic changes as well [Scavia et al., 2002], which in turn brings the modification in future land use change. There is also a considerable impact of human intervention on ecology and thus land use land cover change, studying these changes over a considerable time frame reveals the complex yet predictable nature of the change. Modelling of these land use changes can prove to be very much useful in order to understand and predict future scenarios. Land use modelling requires analysing the biophysical and socio-economic drivers for land use change and then incorporating these to study the impact of human dimensions on LULC dynamics. The models help us understand the LULC changes for different socio-economic scenarios within its defined biophysical context. This thesis aims at the study, analyse and model the LULC changes taking place in the Godavari river basin in the country of India, and to predict the LULC map of 2025 for the basin. The whole process involved creation of Land Use, demographic, socio-economic, topographic, climatic, infrastructure and biophysical database, Identifying the various change inducing drivers and processes for each class and then modelling the database using both agent based and statistical techniques. The Godavari river basin encompasses an area of 3,13,346 km square across the States of Maharashtra, Andhra Pradesh, Madhya Pradesh, Orrisa, Chhattisgarh and a small part of Karnataka. The major LULC category in the basin is cropland, which forms almost 57% of the geographical area of the basin. Forests occupy about 35% of the basin area and area are located in the central regions in the states of Maharashtra, M.P. and in the eastern parts of Orrisa. Extensive shrub land areas are found in the central and western part of the basin particularly in the undulating and hilly areas and upland areas of rainfed central region. Satellite data of 1985, 1995 and 2005 was analysed to generate LULC maps at 1: 250,000 scale. Two models were then modified for the purpose of analysing and studying the LULC changes: a process based agent simulation model, Agent-LUC [Rajan and Shibasaki, 2001], , and an empirically fitted statistical regression model ILULCDMP [Ashutosh Kumar Jha, 2015]. In Agent-LUC, land cover change agents are land owners, farmers, migrants and/or policy making bodies, all of whom make decisions or take actions that affect land-use patterns and processes. By simulating the individual actions of many diverse actors, and measuring the resulting system behaviour and outcomes over time (e.g., the changes in patterns of land cover), Agent-LUC can be useful for studying the effects in land-use land cover processes. The model is designed and implemented in C++ along with QT libraries for user interface. Whereas, ILULC-DMP, calculates the correlations between the land use changes happening spatio-temporally with the driver datasets and predicts the future land-use change, by applying statistical regressions on those correlations on the current land-use and driver datasets. The model is designed and implemented in stastical modelling language R. The models first predicted the land use map of 2005 from the database of 1985 and 1995, with accuracies of .96 and .88 respectively for Agent-LUC and ILULC-DMP model when validated with the satellite derived land-use data of 2005. Then the models were run on the 2005 data to predict the land-use map for 2025. As per the further runs of the Agent-LUC model, by 2015 there will be an urban expansion of 404 square kilometres and by 2025, around 818 sq. kms of more urban area would be there in Godavari basin as compared to 2005 data. The agricultural expansion will be of 1345 sq. kms and 2752 sq. kms., both expansions will cause the deciduous forests and shrublands to decrease. The ILULC-DMP predicted map of 2025 shows an increase in built up area by 1840 sq. Kms, cropland by 4900 sq kms, decrease in forest and shrub land by 4422 sq. kms and 4290 sq. Kms. And an increase in waterbody area by 1875 sq. Kms.

Abstract

Batteries are electrochemical devices that convert chemical energy into electrical energy. An electrochemical cell of a battery composed of three major components: an anode, a cathode and an electrolyte. The electrolyte is an ionic conductor that provides a medium for transfer of ions between the electrodes of a cell. The amount of energy stored in a battery depends critically on the nature of electrochemical reactions at the electrode-electrolyte interfaces and the choice of the electrolyte. Solid polymer electrolytes (SPEs) consisting of alkali salts dissolved in a solid polymer matrix of poly(ethylene oxide) (PEO) are novel ion transporters that offer several advantages over other conventional electrolytes for use in all-solid-state rechargeable batteries. In order to fully exploit the advantages of SPEs and for rational design of novel SPEs with higher conductivity and higher energy density for future applications, it is necessary to understand the molecular-level details of the mechanism and the critical factors governing the ion conduction in these materials. Direct observation of migration pathways of ions and a quantitative dissection of their en- ergetics in solid polymer electrolytes (SPEs) are essential to understand the molecular origins of barriers limiting the conductivity of these novel materials. Depending upon the interplay between molecular packing and dynamics, SPEs exhibit a wide range of conductivity (10−9 - 10−4 S/cm) at room temperature despite their common polymer matrix. The principal driving forces controlling the degree of conductivity of SPEs including the nature of macromolecular packing, ion-polymer interactions, ion coordination structure, conformational order and mo- bility of polymers are significantly different among the SPEs. However, the precise correla- tions between these molecular factors and the ion transport in SPEs are not well established. It remains unclear whether disordered amorphous or ordered crystalline structures promote conduction of ions in SPEs. The other important challenges that remain in SPE-related re- search are the identification of low-energy ion conduction pathways, characterization of the free energy profiles and the structural and dynamical contributions to the favorable (stable energy wells) and unfavorable (activation energy barriers) ion sites along these conduction pathways. ix Detailed molecular studies are needed to establish a precise correlation between the na- ture of polymer packing, dynamics, energetics, and ion conduction for rational design of SPE-based fast ion conductors. In this regard, molecular dynamics (MD) simulations and enhanced sampling free energy methods play an important role in elucidating the atomistic details of ion transport and the critical ion-ion and ion-polymer interactions in SPEs. In the present thesis, we have employed molecular dynamics (MD) simulation, enhanced sampling methods (for example, the adaptive biasing force (ABF) method, well-tempered metadynam- ics (WTmetaD) simulation, and nudged elastic band (NEB) method) and normal mode analy- sis (NMA) to investigate the structure, dynamics and energetics of crystalline and amorphous phases of poly(ethylene oxide) (PEO) and PEO-based SPEs. Thermal phase behavior of crystalline poly(ethylene oxide) explored using MD simula- tions has shown the possibility of a premelting transition well below the equilibrium melting of the crystal. At temperatures just above the premelting transition temperature, molecules are able to rotate about their long axes, and diffuse along the chain-axis of the crystal. To under- stand the nature of coupling between polymer dynamics and ion transport in SPEs, all-atom molecular dynamics simulations of crystalline PEO3:NaI were carried out and polymer con- formational dynamics, energetics and migration pathways of ions were characterized . The results reveal that in crystalline PEO3:NaI both anions and cations follow helical pathways mirroring the helical symmetry of the polymer. The normal mode analysis (NMA) and the power spectral analysis of MD-derived velocity autocorrelation functions (VACF) have eluci- dated the important roles of backbone twisting of PEO helical chains about their long axes and softening of a few low-frequency collective modes in ion transport in SPEs. The calculated potentials of mean force for cations reveal the existence multiple isoenergetic coordination states separated by low energy barriers in the amorphous SPEs indicating that ion transport is facile and the mechanism of ion conduction is different in the amorphous SPEs than those in the crystalline SPEs.

Abstract

Indoor positioning is gaining momentum for its various applications. As we know that Global Positioning System (GPS) does not work well indoors, while todays more sensitive GPS chips can sometimes get a location x (via receiving signals from enough satellites to determine a location) inside a building. The re- sulting location is typically not accurate enough to be useful. The signals from the satellites are attenuated and scattered by roofs, walls and other objects. Besides, the error range of many GPS chips (tennis court) can be larger than the indoor space itself (small grocery store)! Some indoor positioning solutions work similar to GPS. Many companies tap into Wi-Fi signals that are all around us - including when we are indoors. With a good map of the locations of the access points, a Wi-Fi receiver like a cell phone can be located even indoors. Any application that would depend on indoor positioning may need an exact location. This work on indoor localization in 3D using spherical co-ordinates would have an edge to all the future needs. A combination of Pico, Femto, Wi-Fi, otherwise termed as hybrid localization techniques are used in conjunction with leveling and sectoring. Leveling and sectoring are discussed using base station and Wi-Fi access points and the received signal strength (RSS) nger prints are used to aid in precise localization. Indoor localization applied to physical analytics is also discussed. This thesis work also focuses in an improvement over our above discussed works in terms of achieving more accuracy and reduction of delay in the Wireless Sensor Networks (WSNs). This is achieved by using the Received Channel Power Indi- cator (RCPI) in contrast to RSS. We assume that RCPI shall be used by all chip vendor for all the Wi-Fi devices coming into the market due to its precise way of measuring the Received signal power. This work also focuses on Wireless Sensor Networks (WSN) with respect to the need for optimizing the parallel distributed computational architecture.It also discussed how it can be acheived using our proposed model. This gains impor- tance as identication of an event in WSNs should be done as fast as possible by minimizing the delay. Optimizing the grid based architecture for time complexity, transmission delay and fault tolerance in computing the fusion functions, our work also focuses on localizing an event in an outdoor WSN and respond to the event based on the need as soon as possible. Hence our overall work on 3D localization and wireless sensor networks helps us localize events in both indoor as well as outdoor with reduced time-complexity as well as delay parameters.