Abstract

Abstract Morphological analysis is a fundamental task in most Natural Language Processing (NLP) applications, especially for morphologically rich languages such as Hindi. Morphological analysis for Hindi involves predicting lemma, POS, gender, number, person, case-marker, TAM and vibhakti. In general, morphological analyzers predicts all possible analyses for a given word. For most of the NLP tasks, instead of having multiple multiple analyses for a word, we need to disambiguate those multiple analyses and arrive at one analysis which best fits in the given sentential context. The prime motivation for carrying out the research in this thesis comes from the initial set of Hindi parsing experiments carried out by us. The existing morphological analyzers for Hindi do not predict context based morphological analysis. Because of lack of automatic context based morphological information, parsing accuracy could not be improved. In this thesis, we try to predict a single analysis for a word in a given context. This thesis deals with predicting context based morph information for the attributes viz. lemma, gender, number, person, case-marker, TAM and vibhakti. The existing analyzers also perform poorly for Out-Of-Vocabulary (OOV) words. We also aim to address this issue and make an exhaustive evaluation of our predictions for OOV words. For lemma prediction, we adopt a machine translation approach. For gender, number, person and case-marker prediction we perceive it as a classification problem. TAM and vibhakti are better predicted by rule based approach. For lemma, gender, number, person and case prediction, we achieved an overall accuracy and OOV accuracy of 84.25% and 63.06% respectively. To present our case that the predicted morphological information helps in NLP applications, we carry out parsing experiments without and with the predicted morph information and report Labeled Attachment Score of 87.75% and 89.41% for both the experiments respectively. Building machine translation models are time complex. Hence, in the later part of the thesis we conceive lemma prediction also as a classification problem. We also experiment with other features for gender, number, person and case prediction. We report overall and OOV accuracies of 85.87% and 65.96% respectively. We have seen an 1-2% improvement from our earlier set of experiment results. We extend our approach to other Indian languages viz. Urdu and Telugu for predicting context based morphological information.

Abstract

Strong ground motion plays a vital role in engineering for design of important structures such as dams and nuclear power plants. The site of construction seldom contains past strong motion records, which is a major constraint in earthquake resistant design. Simulated strong motion records at such sites serve the purpose of safe design criteria. Researchers have been producing ground motions using analytical models, for the earthquakes that have occurred in the past. In the area of ground motion analysis, numerical studies on actual eld conditions are quite few. Proposed research is to generate ground motions in near fault regions for both strike-slip and dip-slip faults. For this purpose, these faults are modeled to simualte the ground motion records using numerical approach. Semi-empirical approach is used to validate the characteristics of ground motions of both the earthquakes. It is found that the PGA values follow attenuation with respect to epicentral distance. It is observed that the simulated ground motions are in good agreement with the observed ground motions along FN and FP directions. A ground motion record of PGA 0.62 g is estimated at maximum horizontal slip (8.8 m) of Denali fault during 2002 Denali earthquake. Further the analysis has been carried out to derive and propose geophysical parameters such as ground motion prediction equation (GMPE), stress drop, kappa parameter and frequency dependent quality parameter for Alaskan region. These parameters are important and play vital role in generating synthetic ground motions. The variation in magnitude of these parameters is significantly changed the peak ground acceleration and spectral acceleration. For this purpose, a study has been carried out on effect of geophysical parameters on ground motion. It is observed that there is no effect of quality factor on spectral acceleration in high period range, but slight effect is seen in low period range. Significant increase in spectral acceleration and PGA values is observed with increase in stress drop. As shear wave velocity increases, the rupture velocity increases as well and consequently the fault will be fractured more rapidly. Generally, the rupture velocity is assumed as 80-90% of shear wave velocity, which is also important parameter in accessing the ground motion. A simplied approach has been proposed to estimate Vr=Vs through numerical approach. It is observed that the ratios of rupture and shear wave velocities at 20 s, 25 s, 30 s, 35 s and 50 s are 0.75, 0.63, 0.84, 0.76 and 0.53, respectively. The maximum rupture velocity is equal to 0.76 times the shear wave velocity, whereas, v the assumed value was 0.8 times the shear wave velocity. Further, a uniform hazard spectrum (UHS) has been developed for Alaska region. The seismicity parameters a and b are generated for Alaska region and those are 5.76 and 0.776, respectively. A 10 % probability in 50 years corresponding to a 475 year return period for uniform hazard spectrum has been assumed. A maximum of 0.51 g spectral acceleration has been observed at the bed rock level.

Abstract

In future, it is possible to have small sized autonomous vehicles traveling along with larger vehicles on the same roads. Autonomous vehicles are no longer in the realm of science fiction, thanks to the artificial intelligence and integration of numerous auto motive technologies that have matured over many years. Autonomous vehicles could save lives, chase criminals, reduce accidents, and help the environment by reducing fuel demands and pollution. One such example can be police motorcycles. Such autonomous vehicles has to both keep track of the other vehicles and also drive his/her vehicle safely and fast. In this paper, we present AUTOSTEPS, a multi-agent simulation environment where autonomous small vehicles perceive and learn about the environment for fast navigation to reach their goal. The goal for a agent is defined as the destination he wants to reach. The key idea is the notion of spatial temporal spaces into which the small vehicles continue to move till they reach their goal. These spaces are present on the road between vehicles and may change with time and position of vehicles. We built a simulator and ran experiments to show that our strategies for AUTOSTEPS ensures fast and safer navigation for small autonomous vehicles.

Abstract

Abstract Machine Translation (MT) is a task in Natural Language Processing (NLP), where the automatic systems are used to translate the text from one language to another while preserving the meaning of source language. In this work, we provide our efforts in developing a rule-based translation system on the Analyze-Transfer-Generate paradigm which employs morphological and syntactic analysis of source language. We utilized shallow parser for Hindi language along with dependency parse labels for syntactic analysis of Hindi language, developed modules for transfer of Hindi to English and generation of English language. Due to wide difference in word order of the two languages (Hindi following SOV and English SVO word order), a lot of re-ordering rules need to be crafted to capture the irregularity of the language pair. As a result of drawbacks of the aforementioned approach, we shifted to statistical methods for developing a system. A wide variety of machine translation approaches have been developed in past years. As each model has its pros and cons, we propose an approach where we try to capture the advantages of each system, thereby developing a better MT system. We then incorporate semantic information in phrase-based machine translation using monolingual corpus where the system learns semantically meaningful representations. Recent studies in machine translation support the fact that multi-model systems perform better than the individual models. In this thesis, we describe a Hindi to English statistical machine translation system and improve over the baselines using multiple translation models. We work on MOSES which is a free statistical machine translation framework, which allows automatically training translation model using parallel corpus. MOSES provides support for multiple algorithms for training a model. We propose an approach for computing the quality score for each translation by using automatic evaluation metric as our quality score. The computed quality score is used for guided selection among the translations from multiple models, thereby providing a better system. We have used support vector regression to train a model using syntactic,textual and linguistic features extracted from the source and target translation with evaluation metric as our regression output. Quality Estimation of Machine Translation is a task where the system tries to predict the quality of output on the basis of features extracted from source and target languages. The system dynamically(run time) computes a quality score corresponding to each translation, which is the measurement for the correctness of the output. Different from MT evaluation, quality estimation systems do not rely on reference translations and are generally addressed using machine learning techniques. The approach offers a great advantage to the readers of target language as well as for professional translators.

Abstract

Histopathology, the study of tissues, has a major contribution in determining prognosis of diseases at the tissue level. Quantitative analysis of histopathological images is used to determine the future course of treatment. However, the analysis is a very laborious process given the complexity of structures in tissues. Pathologists require a lot of expertise and skill to perform the analysis. Hence, development of Computer Aided Diagnostic (CAD) algorithms which rely on automated image analysis, is of great interest. CAD development in histopathology is a relatively new field of research. Some of the major hurdles in this field are non - availability of public datasets, difficulty in acquiring data and ground - truth marking. This thesis aims at developing CAD tools for assisting pathologists. In our work, we have identified two problems in this field and provided solution for the same. The first is identification of stromal regions in tissue and the second is detecting mitotic candidates. Both these are ne cessary for assessing and staging cancer. For stroma identification we propose a novel block - based approach of suppressing foreground texture to learn discriminative textural features in a block. The results of the bloc k - based stage are refined to obtain t he stroma segments. We have teste d our method on 3 datasets (two public and one locally acquired) varying in size and type (breast and prostate tissue) and have obtained around 80% accuracy and 90% sensitivity. Both the qualitative as well as the quantitat ive results show an improvement over the existing methods. The existing algorithms for mitosis detection employ a traditional approach of classification post feature - extraction. A major road - block in this problem is the presence of visual clutter ( false positives , similar in appearance to mitotic candidates ). We use biological cues to design rejection stages to get rid of the false positives for mitosis detection followed by classification. The pipeline design comprising of rejection stages adapt to variability in the magnification levels of datasets. This was confirmed by determining percentage of rejection after each stage on a locally acquired dataset at different magnification settings. We further improve the performance by incorporating an eff icient classifier regenerative random forests . The algorithm was validated on MITOS dataset against the ground truth and a recall of ,0.81,precision of 0.84 and f - measure of 0.83 was obtained.

Abstract

Abstract Speech communication is a major medium of communication among human beings. All human beings have flexibility in changing parameters of speech like loudness, duration, pitch and intonation within their voice limits. Voice imitation is a fine art in which the professional imitator develops his ability to mimic other speakers. Professional imitators have the ability to convince the listeners that they are listening to someone else. It is the flexibility of speech production mechanism that allows imitators to perform voice imitation. In this work, analysis and synthesis of mimicry speech has been carried out. Previous studies on voice imitation have focussed on features at segmental and suprasegmental levels. The present analysis of voice imitation is carried out at suprasegmental, segmental and subsegmental levels. The supraseg- mental features studied in this work are the instantaneous fundamental frequency (f0) contour and du- ration. The segmental feature used is linear prediction cepstral coefficients (LPCCs). The strength of excitation at the instants of significant excitation and a loudness measure reflecting the sharpness of the impulse-like excitation around epochs are the subsegmental features. The study focusses on how close the imitation is to the target speech and how much deviation happens from his natural speech. The ob- servations are correlated with perceptual studies. The suprasegmental and subsegmental features show a tendency to get closer to the target features. The segmental features which represent the vocal tract shape and size are difficult to change for the imitator. The importance of source and system parameters is studied by synthesis experiments. The natural utterance of the professional imitator is transformed into imitated utterance by variations in excitation source and system parameters. Subjective studies on the synthesised speech shows that suprasegmental features play a significant role in imitation. This analysis is extended into an application where the natural and imitated speech are distinguished using neural network models. The models are built using both excitation source and system features. The models built using excitation source features have better performance than the models built using system features.

Abstract

Inductors are one of the major constraints in the design of Radio Frequency Integrated Circuits (RFICs). This is because they consume large area and the quality factor is very low. Low quality factor is attributed to the parasitic losses at the higher frequencies. Substrate eddy current losses is one among the contributing parasitic losses. Research in this area is towards fabrication of 3D-MEMS inductors and synthesis of new magnetic materials. 3D MEMS inductors are difficult to implement in monolithic RFICs due to packaging and stability issues. Patterned electrical ground or amorphous magnetic ground planes beneath the inductor are employed to reduce the eddy current losses. To improve the inductance density and quality factor of on-chip spiral inductors in RFICs employment of magnetic materials is one of the popular approaches. In cases where bulk magnetic material is used around inductors, eddy current losses become significant at higher frequencies restricting their usage to lower frequencies. Patterning the magnetic material has shown to reduce these drawbacks. Permalloy, which is known for its soft magnetic properties and compatibility with CMOS process, has been used widely for enhancement of inductance of spiral inductors but the frequency is restricted to <1GHz. When patterned as magnetic bars and square dots, improvement in the performance of inductors has been observed at frequencies >1 GHz and in such cases Ferromagnetic Resonance(FMR) of the patterned material has set the limitations to employment at further higher frequencies. For magnetic materials, formation of magnetic domains and their dynamics may play a major role in defining their properties at higher frequencies. In this thesis, effect of size and shape of Permalloy patterns in the formation of magnetic domains, their dynamics, and the influence of these domain controlled patterns on properties of spiral inductor are investigated. It is shown experimentally that the shape and size induced domain patterns in Permalloy play a varied role in the improvement of inductance. Micromagnetic simulations with Object Oriented Micro-Magnetic Framework (OOMMF) are used to determine the domain patterns. Secondly, 60 nm thick domain-patterned-Permalloy have given more improvement in inductance and quality factor compared to 96 nm thick patterns. OOMMF simulations show the same trend that 60 nm patterns which contain cross tie walls perform better than 96 nm patterns which contain asymmetric Bloch wall. Permalloy domain-controlled patterns have also been incorporated as magnetic shield and an improvement in the performance of the inductors is observed. All the experimental observations confirm the necessity of incorporation of proper magnetic patterns whose shape and size could be controlled to form appropriate domain patterns for high frequency applications like inductors in RFICs.

Abstract

This work investigates some interesting questions arising out of mechanistical studies of purine riboswitches. As in any other riboswitch, the purine riboswitch has an aptamer domain linked to the expression platform of mRNAs. Discriminative ligand binding by the aptamer domain is known to regulate the mRNA expression. The aptamer domain of the purine (adenine, guanine and their variants) riboswitches con- sists of three helices P1, P2 and P3 which form a ‘Y’-shaped three-way junction (3WJ). The junction loops are J1-2, J2-3 and J3-1 which connect the corresponding helices. Loops L2 and L3 cap the helical stems P2 and P3, respectively. The helices P1 and P3 are collinear - forming a coaxial stack. Each of the stems P2 and P3 usually consist of 7 base-pairs. Helix P2 is adjacent to P3 and the two are held in parallel arrangement by interactions between the terminal loops L2 and L3. Complementary base pairs between nucleotides of the two loops have been found to form a kissing loop interaction. Earlier work carried out in our group on the study of the add adenine (A) riboswitch ap- tamer. It has provided insightful answers to the question how the recognition of the adenine by the ligand free APO form of the riboswitch is communicated to the downstream region via a conformational change in the downstream expression platform which is responsible for gene regulation [1], [2]. These ideas, as understood from computational studies as well as from biochemical and biophysical experiments conducted in vivo or in vitro have been discussed in the first chapter. Crucial to this mechanism were (i) involvement of kissing loop interactions in facilitating the stabilization of the binding pocket, and (ii) ligand binding mediated stabi- lization of a helix which is involved in the switching event. In the second chapter we have reviewed single-molecule experiments carried out with sim- ple hairpin, hairpins involving kissing loops and aptamer domain of the adenine riboswitch, both in the presence as well as the absence of the ligand. It has been shown that a detailed understanding of the mechanism of helix stabilization due to tertiary interactions may be ob- tained using computational models of these motifs. The third chapter explores the standard worm-like chain (WLC) model for semi-flexible poly- mers. Extensive simulations of the WLC models have been carried out to observe the variation of the end-to-end distances of the polymer at different stiffness of the polymer using a bead- spring and a bending potential. The fourth chapter applies the WLC model to the problem of understanding the behaviour of RNA hairpins by suitably assigning the bond stiffness, base-pair strength, base-pair breaking threshold and strand stiffness under external pulling force in an attempt to simulate some of the observations in Chapter 2. In addition to this, extensive work was done in implementing a querieable database which relates the occurrence frequencies of different base pairs in RNA with their crystal and ground state optimized geometries (calculated at different levels of QM theory) and corresponding intrinsic interaction energies, RNABP COGEST, which was published. Since it is work that is distinct from the rest of the study, the work is reported as an Appendix titled ‘RNA Base Pair Count Geometry and Stability (RNABP COGEST) - Database Development and Imple- mentation of Useful Features’.

Abstract

Abstract Natural language can be easily understood by everyone irrespective of their differences in age or region or qualification. Based on various theories proposed in psycholinguistics, Roger Schank came up with the proposition that “the basis of natural language is conceptual and the conceptual base is inter-lingual”. According to Indian grammatical tradition, “all content words in a language are either verbal roots or derived from verbal roots”. Inspired by these two ideas we are proposing a new set of meaning primitives based on overlapping senses of verbs. Using these meaning primitives we are trying to develop a new ontological classification of verbs. We propose that meaning of a verb is composed of seven fundamental senses implicating different attribute sets. In this proposal maximally overlapping sense of verbs is taken as “happening” (Sanskrit bh¯ava), as in the Indian linguistic traditions. We have rendered it as a recursive form with a structure “change of state in context”. Each fundamental sense is a “kind of happening” giving different contiguity of states with different attribute set as context. We derive the meaning of verbs from a universal verb happening such that every verb is uniquely specified across languages. The method has been applied to develop ontologically informed etymon in English and Indian language, Sanskrit. The primitives along with ontological attributes have been used to explicate the meaning of verbs. This form of meaning specification of verbs is used to develop language independent ontological classification of verbs which in turn can be used in Natural Language Processing applications like semantic search and machine translation.

Abstract

The ever increasing demand for communication bandwidth and inefficient usage of the existing spectrum has led to spectrum scarcity. In this light, spectrum should be managed as a scare resource. For radio communication systems, efficient utilization of spectrum is the key requirement. The inefficient usage of the existing spectrum can be improved through opportunistic access to the licensed bands without interfering with the primary users. This introduces the concept of dynamic spectrum access and cognitive radio. Applying these techniques provides better throughput even in congested spectrum exploiting better propagation characteristics. Cognitive radio introduces cognition and adaptation at the physical layer. Cognition should also be included in upper layers of the network to effectively utilize the benefits of cognitive radio and dynamic spectrum allocation. Thus, cognitive network is an intelligent network that can adapt the transmission, reception and other network parameters of either a network or a wireless node to achieve efficient communication without interfering with primary users. Routing and spectrum access in cognitive environment is a challenging task as the channel availability is constrained by the presence of primary users. The problem of routing in cognitive networks targets the creation and maintenance of wireless multi-hop paths among cognitive nodes by deciding both the spectrum to be used and the relay nodes of the path. In the recent times various protocols were designed for cognitive networks. Cross layer protocol design is suitable for cognitive networks as dynamic adaptation of parameters is required. This thesis proposes a cognitive cross-layer routing protocol which jointly does power control and spectrum selection for multiple hops in the routing path. In the proposed protocol, routing is defined as a multi-objective optimization problem to derive the cost function which is used as the routing metric. The work shows that the proposed routing metric is convex and gives Pareto optimal solution. The thesis also proposes a medium access protocol for cognitive networks. The proposed protocol provides solutions for the multi-channel hidden node problem, channel synchronization, channel scheduling and other challenges that arise in the design of cognitive networks. For performance evaluation, the proposed protocols are implemented in NS2 and a comparative analysis is also done against existing protocols from the literature. The impact of cognitive user density, cognitive user speed and primary user activity model is analyzed in the thesis. The thesis also develops a Markov model for the performance analysis of the proposed cognitive MAC protocol. The developed model is generalized and can be used for performance analysis of any IEEE 802.11 power saving mode based cognitive MAC protocol for ad hoc networks. This analysis is used to decide the design parameters of the MAC protocol. Protocols specific to cognitive wireless sensor networks are also proposed in this thesis. Cognitive wireless sensor networks are a specific type of cognitive networks which are used to monitor some entity. Traditional wireless sensor networks work in ISM band which is shared by some other technologies like WiFi and Bluetooth. Sensor nodes with cognitive capability are still limited with battery constrains, and efforts are required to increase the lifetime and other performance measures of the network. This thesis also proposes an network architecture for cognitive wireless sensor networks which consists of hierarchical distributed minimum spanning trees. The work also shows that this multilevel network provides fault tolerance, admits simple routing, and offers easy extensibility with power efficiency. The hidden primary user problem arises in cognitive users with passive listening primary users. For avoiding interference to hidden primary users, disable region should be calculated considering the communication range of both primary and cognitive users. Disable region is a region in which cognitive user communication should be suspended to avoid interference to primary users. Cognitive users must be aware of primary user’s location within the region of interest for calculating disable region. This thesis applies a leveling and sectoring based approach for primary user localization. This will avoid the interference to hidden primary users. The cognitive users within the disabled region are sent to sleep mode until a free channel is detected in that area by spectrum sensing module. This approach also saves energy of cognitive users.