Abstract

The present work deals with a detailed analysis of the structural and energetic features of base pairing interactions in RNA macromolecules. Crystal structures of RNA molecules provide examples of a fascinating variety of base pairing interactions, which play an important role in maintaining their three dimensional structures and in facilitating their functions. An accurate atomic level description of interactions in these well dened structural building blocks of RNA, and an understanding of their roles in higher order structures involving recurring motifs and tertiary interactions, is essential for understanding structurefunction relationship in these macromolecules. Some of these higher order structures and motifs are base triplets, quartets etc. in general, and dinucleotide platforms, kink-turn motifs etc. in particular. In the present work, a detailed analysis of base pairing geometries and interactions in RNA molecules is carried out using combined quantum chemical and structural (database) analysis methods. The role of base-base hydrogen bonding interactions in the formation of structural and functional motifs as mentioned above, is assessed. An eort has been made to project the conclusions drawn from gas phase quantum chemical computations on RNA base pairs in a wide biochemical context of their occurence in macromolecular RNA structures. In addition, two theoretical investigations into specic structural and functional contexts in nucleic acid systems are carried out- one involving the analysis of geometric and energetic features of ligand binding in a specic type of noncoding RNA- the purine riboswitches, and the other involving the interaction of gold clusters with a novel type of synthetically modied DNA- the size expanded DNA.

Abstract

The problem of document clustering is to arrange a set of documents into groups such that documents within a group are related to each other. Document clustering has been studied extensively because of the huge increase in the amount of digital text content online and also due to its wide applicability in areas such as web mining, information retrieval, etc. Typical document clustering algorithms have issues like handling high dimensionality, scalability, building an efficient hierarchy, good clustering accuracy, etc.

Abstract

The acoustic contrast perceived among the basic units (phonemes) of human speech is the result of dierent configurations of the articulatory system excited by dierent configurations of the excitation source. While one can perceive the combined eect of source and system in the speech, it is dicult to analyze and detect acoustic features corresponding to both the system as well as the source from the standard spectrum-based methods of analysis. Glottal stop and ejective sounds form an important class of phonemes in most of the Semitic languages such as Amharic, which dier from other phonemes mainly due to their source of excitation. Therefore the study of acoustic characteristics of speech including glottal stop and ejective sounds based on spectrum-based methods becomes dicult, as these methods reflect mostly the resonance characteristics of the vocal tract system,. The features in a spec- trographic display bring out the distinction between voiced and voiceless excitations. But it is dicult to derive details of the features of the source of excitation from spectrum analysis alone. This thesis proposes non-spectral methods of analysis to derive features of the source of excitation from the speech signal, and demonstrate their significance in the analysis of glottal stop and glottalized (ejective) sounds in Amharic. The features of excitation source such as the instant of significant excitation, strength of excitation, instantaneous pitch period and jitter, are derived using a technique based on applying zero frequency resonator to speech signal. The normalized residual of LP analysis is used to detect the instants of oral release. It is seen that these non-spectral methods of analysis can be used to extract some source related features more precisely than the spectral-based methods. Voice onset time (VOT), closure duration, and phonation quality and amplitude rising time (ART) of the post-target vowels are some of the production-based attributes of ejective sounds, which are derived using the excitation source features. Ejective sounds in Amharic are found to have longer closure duration and shorter VOT than their voiceless pulmonic counterparts at the same place of articulation. The first few glottal cycles of the vowels following the ejective sounds are found to have more creaky phonation than that of vowels following pulmonic voiceless sounds. Also, the amplitude of these vowels grows more slowly than that of vowels following voiceless pulmonic sounds. It is observed that ejective sounds in Amharic have shorter duration and higher strength of frication than their voiceless pulmonic conjugates. The acoustic realization of glottal stop sounds in Amharic is found to vary from a brief closure period followed and/or preceded by a few cycles of creaky phonation to a few glottal cycles of creaky phonation without any closure duration. Since creaky regions have irregular glottal cycles, creakiness is used as a primary measure in the analysis of glottal stop phonemes. The creakiness of the speech signal is measured using the cross-correlation coecient computed between successive pitch cycles. Once the acoustic characteristics of the glottal stop and glottalized (ejective) sounds are understood, the learned acoustic features are used to spot these sounds in continuous speech.

Abstract

Blind modulation classification is the process of identification of the modulation format of the transmitted signal using only the samples of the received signal. In this thesis, we address two topics related to blind modulation classification. The first topic deals with blind symbol rate estimation. A reliable estimate of the symbol rate irrespective of the modulation format of the transmitted signal is essential for blind modulation classification. We present a method based on the cyclic autocorrelation function for blind estimation of the symbol rate of a linear digitally modulated signal propagated through multipath in the presence of timing and carrier frequency offsets. The performance of the proposed symbol rate estimation algorithm is evaluated under the above conditions through simulations. The second topic of the thesis deals with the problem of modulation classification. We propose and evaluate a feature-based hierarchical modulation classification method, which is developed to discriminate between the various modulation formats in the presence of Rayleigh fading multipath and, timing and carrier phase offsets. In our feature-based modulation classification method, we first estimate the overall impulse response (inclusive of both the pulse shape and multipath channel) using only second-order statistics and then use the estimated channel to compute the various features of interest. We use both cumulants and moments as features to discriminate between the various modulation formats. We use a hierarchical structure in which lower-order features are used in the earlier stages and higher-order features in the later stages. Performance of the proposed classification method is evaluated under Rayleigh fading flat as well as multipath environment in the presence of timing and carrier phase offsets using simulations.

Abstract

The field of data mining has emerged to extract information/knowledge hidden in large databases for better decision making. Several data mining patterns, such as association rules, clustering and classification, are being proposed. Research is going on to investigate efficient approaches to extract the patterns pertaining to rarely occurring objects besides discovering new patterns. Due to its usefulness in decision making process, recently, research efforts are going on to investigate efficient approaches for mining rare cases or patterns. Mainly, research efforts are being made to investigate efficient approaches to extract rare association rules and rare classes. In this thesis, we have made an effort to propose improved approaches for extract- ing rare association rules. Frequent pattern mining is a key step in many association rule mining algorithms. In the basic model of association rules, a pattern is said to be frequent if it satisfies the user-defined minimum support (minsup) threshold value. Since only a single minsup is used in the entire database, the basic model of frequent patterns leads to the problem known as “rare item problem” which is as follows: at high minsup, we miss the frequent patterns containing rare items, and at low minsup, combinatorial explosion can occur, producing too many frequent patterns. To confront the rare item problem, an effort has been made in the literature to find frequent patterns with “multiple minsups framework.” In this framework, each item is given a constraint known as minimum item support (MIS). The notion of minimum support for a pattern is defined as the minimal MIS value among all its items. Efforts are being made to propose “Apriori” and “FP-growth” based approaches to extract patterns under “multiple minsups framework.” This generalized framework enables the user to simultaneously specify high minsup for a pattern containing only frequent items and low minsup for a pattern containing rare items.

Abstract

Data mining is the process of discovering significant and potentially useful knowledge in the form of patterns from the data. As a result, the notion of interestingness is very important for extracting useful knowledge patterns. Numerous interestingness measures have been discussed in the literature to assess the interestingness of a knowledge pattern. In this thesis, we focus on selecting a right interestingness measure for mining association rules, in particular rare association rules. Association rule mining is an important knowledge discovery technique in the field of data mining. It involves finding interesting associations between the sets of objects in a transactional database. A rare association rule is an association rule with items having low support. In many real-world applications, rare association rules can provide useful information to the users. Typically, association rules are extracted with support and con f idence measures. Several other interestingness measures, such as li f t and all-con f idence, have also been used to extract association rules. Each interestingness measure has its own selection bias that justifies the significance of an association rule over others. Thus, there exists no single interestingness measure which is better than others in all application domains. Each interestingness measure has a set of properties. A framework exists in the literature which suggests to select a measure based on the properties of interest to the user. However, it is unclear which properties a user should consider for mining rare association rules. In this thesis, we have analyzed the properties of different interestingness measures and suggest the properties which the user should consider for extracting rare association rules. The experimental results from real-world datasets show that the measures satisfying the prescribed properties can efficiently extract rare association rules. In addition, in this thesis, we have proposed a new model to extract periodic-frequent patterns. Periodic-frequent patterns is a class of user-interest based frequent patterns which uses temporal periodicity as the interestingness criterion. Informally, a pattern is said to be periodicfrequent if it occurs at regular intervals specified by the user throughout the database. We have analyzed the existing approaches to mine periodic-frequent patterns. The basic model of periodic-frequent pattern mining uses “single constraints” and suffers from “rare item problem.” To confront the problem, an alternative model based on the notion of “multiple constraints” exists in the literature. However, this model is computationally expensive to implement. It is because the periodic-frequent patterns discovered with this model do not satisfy downward closure property. Furthermore, it has been observed that this model still generates some uninteresting patterns as periodic-frequent patterns. In this thesis, we propose an alternative model for extracting periodic-frequent patterns to address the issues of the existing approaches. We have performed experiments on both synthetic and real-world datasets. The results from the experiments show that the proposed approaches can efficiently extract periodic-frequent patterns with low support.

Abstract

There is a need for a formal language which not only provides various tools for the programmer to use while developing his programs, but also helps while developing the algorithms. mapcode is one such framework which can fulfill this gap. Since mapcode is mathematical, if mapcode has to fulfill the position of a formal language which can assist a developer during the process of development of his programs, there is a need to formalize mapcode. The mathematical mapcode makes a lot of assumptions and borrows all the resources it requires for the development of algorithm from mathematics but the formal mapcode can not make those assumptions. All the resources which the formal mapcode can use have to be defined precisely and made accessible within mapcode itself. Thus, to make mapcode formal, we need to define resources for mapcode which would be useful for the development of the programs - operations, algebras and data types. Another important aspect of formalization is defining well-formed formulaes (wff’s) which are inherent part of formal languages. We define these formulaes for mapcode along with the underlying semantics for each formulae. Thus, this thesis is about bridging the gap between mathematical mapcode into a formal mapcode, a necessary step before we can compare mapcode with a programming language.

Abstract

The concept of similarity is fundamentally important in almost every scientific field. Clustering, distance-based outlier detection, classification, regression and search are major data mining techniques which compute the similarities between instances and hence the choice of a particular similarity measure can turn out to be a major cause of success or failure of the algorithm. The notion of similarity or distance for categorical data is not as straightforward as for continuous data and hence, is a major challenge. This is due to the fact that different values taken by a categorical attribute are not inherently ordered and hence a notion of direct comparison between two categorical values is not possible.

Abstract

Query optimization is a hard problem with exponential complexity. Modern day optimizers employ complex heuristics and optimizations to reduce the time taken for optimization. The process of query optimization has become difficult to understand due to use of the growing number of heuristics and their increasing complexity. The two major questions raised in this thesis are: (i) By observing the optimal plans of a large number of queries, is it possible to propose an abstraction of the nature of the optimal plans which succinctly summarizes their characteristics? and (ii) Is it possible to build a system that can identify the optimal plan of a query directly using the abstract representation developed in the previous step such that the plan enumeration step can be completely by-passed?

Abstract

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