Abstract

Urbanization is rapidly increasing in almost every city in India. Huge infrastructure developmental plans have been laid by government and private organizations. There are quite a few codes and buildings by laws are available for construction of these buildings but many of them lack in addressing the parameters for seismic design. Some issues related to seismic behaviour are still not resolved. Researchers all over the world are continuously working towards development of latest techniques for improving earthquake safety of buildings. Among several aspects, the fundamental natural period ‘T’ of the building is one of the important parameters in the earthquake resistant design of buildings. The fundamental period will decide the base shear to be resisted by the structure. Apart from knowing the seismic demand the natural period also gives insight about the damage status of the structure. Present version of Indian seismic code gives empirical formulae for natural period. These expressions of the fundamental period is originally developed based on Californian earthquakes and adopted by many of the seismic codes around the world; including current Indian seismic code IS 1893: 2002. This research study aims to check the viability of the current provision of Indian seismic code for the approximate formulae given for RC buildings with infill walls. For this purpose, ambient vibration study has been carried out in Indian cities viz. Hyderabad, Chandigarh, Mumbai and Nanded, by placing vibration sensor on topmost accessible floor. All together there are 90 RC MRF buildings and 14 RC SW buildings were tested out of which 74 RC MRF and 14 RC SW buildings were selected for further analysis. With the regression analysis performed on the new database, a conservative empirical expression is proposed for finding natural period T of RC MRF buildings and RC SW buildings.The measured periods have been compared with the code provisions. Study found that current empirical equation holds good only for low-rise RC MRF buildings, it often overestimates the natural period for medium-rise buildings with more than thirty metre heights. In case of RC SW buildings it underestimates the natural period and there by overestimates the seismic demand of a building. Hence there is an urgent need for revision of the empirical expression.

Abstract

Image based reconstruction of urban environments is a challenging problem that deals with optimization of large number of variables, and has several sources of errors like the presence of dynamic objects. Since most large scale approaches make the assumption of observing static scenes, dynamic objects are relegated to the noise modelling section of such systems. This is an approach of convenience since the RANSAC based framework used to compute most multiview geometric quantities for static scenes nat- urally confine dynamic objects to the class of outlier measurements. However, reconstructing dynamic objects along with the static environment helps us get a complete picture of an urban environment. Such understanding can then be used for important robotic tasks like path planning for autonomous navigation, obstacle tracking and avoidance, and other areas. While the literature has been fairly dense in the areas of scene understanding and semantic labeling there have been few works that make use of motion cues to embellish semantic performance and vice versa. we address the problem of semantic motion segmentation, and show how semantic and motion priors augments performance. We propose an algorithm that jointly infers the semantic class and motion labels of an object. Integrating semantic, geometric and optical flow based constraints into a dense CRF model we infer both the object class as well as motion class, for each pixel. We found improvement in performance using a fully connected CRF as compared to a standard clique-based CRFs. For inference, we use a Mean Field approximation based algorithm. We also propose a system for robust SLAM that works in both static and dynamic environments. To overcome the challenge of dynamic objects in the scene, we propose a new model to incorporate semantic constraints into the reconstruction algorithm. While some of these constraints are based on multi-layered dense CRFs trained over appearance as well as motion cues, other proposed constraints can be expressed as additional terms in the bundle adjustment optimization process that does iterative refinement of 3D structure and camera / object motion trajectories. Our method outperforms recently proposed motion detection algorithms and also improves the semantic labeling compared to the state-of-the-art Automatic Labeling Environment algorithm on the challenging KITTI dataset especially for object classes such as pedestrians and cars that are critical to an outdoor robotic navigation scenario. We show results for accuracy of motion segmentation and reconstruction of the trajectory and shape of moving objects relative to ground truth. We are able to show average relative error reduction by 41% for moving object trajectory reconstruction relative to state-of-the-art methods like TriTrack[23], as well as on standard bundle adjustment algorithms with motion segmentation.

Abstract

Preventing traffic congestion by forecasting near time traffic flows is an interesting and important problem as it leads to effective use of transport resources. Many times congestion is caused due to social events like concerts, conferences, etc. happening in the city. Social network provides relevant and useful information about social events and about sentiments of humans towards such events. Thus, with the help of social network, we can estimate which humans might attend a particular event (in near time). This can help to estimate which route is expected to see more traffic. Using these estimates, transport authorities can take appropriate measures to meet increase in ridership and possibly prevent congestion. This opens up a wide area of research and experimentation which poses need to have a framework for traffic management that can capture parameters of real-life behavior and provide an easy way to iterate upon and evaluate new ideas. In our work, we design a framework which provides modules to simulate: • a city with points of interests (house, office, school, shops etc). • human population and their daily activities. • temporally and geographically distributed social events with varied size of target audience. • a social network for humans to share their likes/dislikes with their friends and in turn influence their interest about simulated events. • a transport management system for humans transportation needs. It continuously monitors and plans train schedule to meet the needs of ridership. Our framework has a plug-and-play modular design; it supports easy integration and replacement of components. This makes it favorable for experimentation. We also emphasize on relevance of simulation parameters and design of modules with respect to how closely it mimics real-life. We also define metrics used to evaluate effectiveness of strategies in controlling congestion. To show the utility of the framework, we built a multi-agent simulation system and conducted exper- imental studies of the strategies developed. We simulated city of Singapore and its metro rail as means of public transportation. Singapore’s demographic data is easily available, geographical data available in form of OSM ([28]) is up-to-date, it has a robust public transportation system that is heavily used and its population is technologically advanced, hence social networking sites are massively used. As such, Singapore city is a fitting example to study and explain our work. We build our framework on top of DMASF ([52]), which is python-based and SQL-backed multi- agent simulation platform. Humans and transport management actors are modeled as agents. We present experimental studies to demonstrate utility and scalability of our framework, involving 100,000 humans. These experiments involve simulating social events. We found that our framework captures effect of social events which is seen as increased use of public transportation (trains). We also implemented few experimental strategies to estimate traffic flows in case of social events and strategies to control congestion. We found that for up-to medium scale events (i.e. for events involving up-to 1000 humans), our strategy performed well and prevented trains to be congested. But for higher number of attendees (approx 10,000), our strategy did not scale well. We also implemented a strategy in which human alters its trip based on state of ridership in trains and selects route which takes shortest total travel time to reach destination. We noted that there is slight improvement in waiting time and average total trip time for humans with this strategy. Results of these experiments revealed that traffic congestion can be curbed using social network data based near time forecasting. In this thesis, our focus is on design of simulation and component interfaces used in framework. We do not emphasize on any particular solution for congestion avoidance strategy. To show the utility of our system, we focus on trains and walking as means of transport. This work can be further extended to consider full multi-modal transport system having trains, buses and uber like taxis.

Abstract

In a task oriented domain, recognizing the intention of a speaker is important so that the conversation can proceed in the correct direction. This is possible only if there is a way to label the utterance with its proper intent. One such labeling technique is Dialog Act(DA) tagging. The main goal of this thesis is to build a Dialog Act tagger for the Telugu corpus. This work focuses on discussing various n-gram DA tagging techniques so as to tag the Telugu data. The n-gram DA tagging methods proposed earlier for English will not work for free word order languages like Telugu as English language follows strict subject–verb–object(SVO) syntax. This thesis explains in detail about the two DA tagging methods for tagging free word order languages preferentially Telugu. In this thesis, we propose a method to perform DA tag- ging for the Telugu corpus using advanced machine learning techniques combined with karaka dependency relation modifiers. The use of karaka dependencies for free word order languages like Telugu helps in ex- tracting the modifier-modified relationships between words or word clusters for an utterance. The modifier-modified relationships remain fixed even though the word order in an utterance changes. These extracted modifier-modified relationships appear similar to n-grams. Later, statistical machine learning methods are applied to predict DA for an utterance in a dialog. The first method uses n-gram karakas with back-off as n-gram language modeling technique at n-gram level and Memory Based Learning at utterance level. In the second method, we use syntactic features such as anaphora resolution, conjunct identification and using modifier modified relationship rules we automatically extract n-gram karakas. Then we apply language modeling (LM) with Hidden Markov Model (HMM) method for DA tagging. The proposed methods are compared with several baseline tagging algorithms. The results show that the proposed methods perform better DA tagging for free word order languages like Telugu.

Abstract

In the recent years there has been a continuous demand for increase in the power performance of the wireless electronic systems. This has been due to the tremendous increase in the data rate of the signal acquisition systems, thus resulting in huge power consumption at the RF transceiver and the A/D converter. Various data compression techniques have been used for quite some time to reduce power consumption. The recent theory of compressed sensing (CS) enables more efficient acquisition of signals at a rate much lower than the Nyquist rate. Further extension in the theory of CS has resulted in distributed compressed sensing (DCS), through which multiple signals can be acquired and reconstructed simultaneously. This brings about an increased compression efficiency, or in other words, the signal ensemble can be reconstructed with a fewer number of measurements when compared to a single channel CS. So, the need for low power signal compression circuits along with an efficient architecture for multichannel signal acquisition are the issues addressed here. In this work, a few low power hardware realizations of these data compression systems in analog domain before analog to digital conversion have been realized. Initially, a capacitor charge redistribution based multiply and accumulate (MAC) circuit capable of operating at very low power is introduced to perform evaluation of a linear equation. The application of this technique is the core contribution to the circuit level aspect of this work. Firstly, this circuit has been successfully utilized to realize a 2-D discrete cosine transform (DCT) completely on hardware in analog domain. The complete 8-point DCT architecture has been implemented in a 1.8 V, 0.18 μm digital CMOS technology. The performance of the implementation shows an average power consumption of 2.16 mW, a PSNR of 23.4 dB and the system occupies an active area of 0.0667 mm 2 . A completely digital implementation of the same system has been performed to compare the power consumption. It is observed that the proposed system utilizes only 1/5 th of the power of digital implementation. Secondly, a wireless capsule endoscopic system has been built on an embedded platform to evaluate the complexity of data compression of a multicamera scenario. Following that, an application specific integrated circuit (ASIC) implementation of a low power multichannel signal acquisition system based on the principle of distributed compressed sensing (DCS), thereby saving power and hardware complexity has been performed. The sensing operation has been realized in the charge domain using the mentioned switched capacitor technique based vector matrix multiplication (VMM) and thus consuming very low power. The technique has been tried upon two different signal ensembles, a) images sensed using CMOS imagers and b) real time neural or EEG signals. The respective systems have been implemented in a 1.8 V, 0.18 μm CMOS technology and is capable of sensing upto 64 EEG signal channels and 16 images simultaneously. The system performance has been tested with a set of EEG signals from practical neural recordings and the results show an average PSNR of 29.17 dB while sensing 64 channels at a compression ratio of 1/4, whereas the performance measures of the DCS imager system show a compression ratio of 1/4 with a power consumption of 36.8 mW per channel while sensing 16 channels simultaneously. The compressed analog signals in all of the above mentioned systems are digitized with an 8 bit ADC. The binary weighted capacitor (BWC) array based 8 bit SAR ADC has also been designed in the same technology targeting the specifications of these systems. The ADC is measured to operate with 7.97 effective number of bits (ENOB) and SINAD of 49 dB while consuming a power of 2.486 mW including the band gap reference (BGR) and associated buffer amplifiers. Finally, an ASIC consisting of a single channel CS based EEG acquisition system has been designed and fabricated which is capable of compressing sampled points in batches of 64 each while occupying an active silicon area of 1.25 x 0.85 mm 2 . In summary, a low power circuit to perform VMM in analog domain has been proposed and a few compression systems and architectures based on compressed sensing have been designed and implemented followed by a system on chip (SoC) realization.

Abstract

Indian languages are MoR-FWO and hence differ from English in structure and morphology. There are many distinguished characteristics possessed by Indian languages. While working with these languages we have to keep in mind, these characteristics and plan strategies accord- ingly. We worked on improving Dependency Parsing for Indian Languages, more specifically for Hindi, an Indo-Aryan Language. In the conventional Dependency Parsing methods, the focus has been on developing robust data driven dependency parsing techniques. This initiated efforts in creating hand annotated large treebanks, consisting of hand annotated features. These treebanks serve as input for the training of data-driven parsers. The annotations in Indian Languages’ treebanks are generally multi-layered and furnish information on part of speech category of word forms, their morpho- logical features, related word groups and the syntactic relations. For improvements, richer and richer features are being added. This process of manual annotation is expensive, as it requires a lot of human efforts. It is a tedious task to create treebanks for all the languages. Even if we make the treebanks available, in the real time scenario we require many tools to extract features automatically. Building such tools is also a complex task. We are in an era with almost unlimited access to raw data. Nevertheless, we often struggle to make sense of most of it. Much of this data is unlabeled and thus useless in many of the traditional supervised ma- chine learning scenarios, that require explicit labeled/hand-annotated examples. In this work, we present our efforts towards exploring cost effective approaches for building and improving parsers for resource-poor languages. For this purpose we try to use unsupervised techniques to extract features from the largely available mono-lingual raw corpus. Using cross-lingual treebank transfer, we exploit the available treebanks for other languages and using some techniques like MT and try to generate a treebank for the target language. We can use this treebank for training of parser. We first try this approach for Hindi. An important constraint for using this approach is that the annotation of treebank needs to be similar cross linguistically. For this, we use UD framework. Universal Dependencies is an initiative to create cross-linguistically consistent treebank annotation for many languages, with the goal of vii facilitating multilingual parser development, cross-lingual learning, and parsing research from a language typology perspective. In the previous studies, we have seen that CPG framework is better suited for Indian Languages. So we try to compare other techniques with the cross-lingual parsing in UD framework. In the concluding work, we try to make use of Vector Space Modeling on a large monolingual raw data, a recent technique being used widely across different tasks. Use of large monolingual corpus helps reducing the problem of data sparsity. We try to explore this technique to achieve three goals. Using word-embeddings extracted from vector space modeling as features, we first try to improve the state-of-the art accuracy for Hindi. Here we use word-embeddings as additional features other than the conventional features. The second goal is to help building parsers for less resourced languages. This is done by replacing the costly linguistic features with word-embeddings. This requires minimal human annotation. The third goal we achieve, is improving parser’s performance in general domain data. We show results where parser is trained on News domain and input sentences are from four different domains Box-Office, Cricket, Gadget and Recipe.

Abstract

Imitating the behaviour and characteristics of fluids with the help of a computer is called fluid simulation. Real world fluids are fickle. They are subtle and gentle at times; at times they are ravenous and tumultuous. Needless to say, complex equations are behind even the tiniest of ripple, so much so that often fluid mechanics has been described as ”the physicists nightmare”. The development of fluid simulations has traditionally been in two concurrent streams, viz., Eulerian and Lagrangian Simulations. Lattice Boltzmann Method developed around the same time but unlike the conventional methods, it is a statistical method based on Kinetic Theory. In this work, we combine physical models of non-Newtonian fluids with Lattice Boltzmann Method. We give the CPU implementation, showing how easy it is to understand and code. We show how the method, in spite of its ease of implementation, doesn’t compromise on physical realism or accuracy. We also give a model for GPU implementation for increased efficiency and interactive frame rates.

Abstract

Natural Language Processing (NLP) is a challenging field in the area of artificial intelligence and computational linguistics. In simplistic terms, it can be defined as processing a natural language in any form either speech or written text. Though extensive research has been done for many of the languages in the world, Indian languages are still lagging behind in the race. Processing of any natural language requires analysis to be done at multiple levels like word-level, phrase-level, sentence-level, semantic-level and higher levels of pragmatic and discourse. In this work we are presenting our efforts of making new advancements at the sentence level, which in linguistics terms, is regarded as Syntactic Parsing. Syntactic parsing involves establishing relations between different words of a sentence to convey the possible meaning. Indian languages are morphologically rich and exhibit free-word order (MoR-FWO). Dependency Parsing, a type of syntactic parsing is better suited to such languages. Our efforts start from delivering a state-of-the-art Hindi Dependency Parsing system through the platform provided in the form of a shared task (Sharma et al., 2012). We employed a data-driven transition-based statistical system (Malt Parser), trained on Hindi Dependency Treebank (Bhatt et al., 2009; Palmer et al., 2009). Error-analysis performed in the task, helped us to target the problems in a more specific manner. In the next phase, to target some of the problems like case ambiguity, data sparsity, lack of case marker, etc., we aided the process of dependency parsing by enriching the training model with semantic information. The information is extracted automatically from a rich lexical resource, Hindi Word- Net (Narayan et al., 2002). Learning from the insights obtained in this advancement process, we moved to another well-established approach, Ensembling. Ensembling works on the principle of exploiting diversity of multiple parsing systems and combining their strengths to improve the parsing performance. We explored two ensembling approaches namely, re-parsing algorithms and word-by-word voting, using six different weighting strategies to combine six algorithmic variants of Malt parser. Improvements had been observed in the second approach. After establishing a systematic comparison between both the techniques of ensembling, we obtained the lead to search for better weighting strategy to improve ensembling. The search ended with Parse Quality Estimation (PQE) score. Adapting the work done in the past, we extended this functionality for our purpose of performing ensembling. The approach, which has failed earlier, has now shown improvements. Further, we also expanded the scope of PQE score for dependency arcs (attachment) to capture confusion made by the oracle in the parsing system. The functionality had also been extended for Joint prediction of both arcs and labels simultaneously. To prove the efficacy of the approach, we implemented several real-world applications of PQE score. Finally, we proposed a robust evaluation framework in terms of Domain Adaptability (DA) and Inter-Language Portability (ILP), to better judge the effectiveness of Hindi Dependency Parser. During this evaluation process, using the property of portability, we also built dependency parsers for two of the Dravidian languages: Tamil and Telugu, which can be integrated easily in real world NLP systems like Machine Translation.

Abstract

The World Wide Web has evolved from a set of simple static pages connected by hyper links to a complex platform, to meet the demands of users and businesses. The modern web is characterised by complex operations such as online social networking, content sharing, electronic payments, single sign-on etc. The evolution of web APIs (Application Programming Interfaces) and open data initiatives encouraged developers to build Mashups, web applications that integrate data from multiple servers. Data has become the currency on the web, due to which the web has turned into a lucrative target for attackers a.k.a cyber criminals. Newer web standards such as HTML5 are evolving and newer versions of browsers are emerging to meet the needs of the modern web. However, the security policies governing the web have not evolved at the same pace. Due to this, the number of vulnerabilities and newer web based attacks are increasing rapidly. Browsers, being the entry points to the web, are heavily targeted by attackers. One good reason is that the time and effort required to compromise a website due to a vulnerability in the application layer is much lesser compared to that of other layers. E.g., to steal certain sensitive data from a web server, it is much easier to inject a small snippet of JavaScript into a vulnerable web page loaded in a browser and exfiltrate the data, than to intercept a connection and break a fairly strong crypto system, or to bypass firewalls and break into the network. This work attempts to understand the core security policies of web browsers that govern the security and privacy of web interactions. It closely examines a series of client side web attacks, their existing defenses and deficiencies. It observes the need for novel application-level security frameworks as well as browser security policies in mitigating them. The outcome of the work is two fold: Firstly, it presents a security abstraction layer (as an API library) called “SafeMash”, which helps developers build safe mashups over the current low-level security APIs in HTML5. Secondly and more importantly, it proposes a novel declarative browser security policy called CORP (Cross Origin Request Policy) to mitigate a set of attacks which we refer to as “Web Infiltration attacks”. CORP enables a server to control which site can access which resource on a cross-origin server, and through which browser event. To evaluate the effectiveness of SafeMash and CORP, several experiments were conducted. The usage of SafeMash was empirically demonstrated by first building an interactive mashup using open APIs from ProgrammableWeb (without using state-of-the-art security echanisms) and then rebuilding it using SafeMash without losing functionality. To clearly understand the security model of web browsers and its limitations, a corpus of web attacks was developed. The formulation, effectiveness and ease of deployment of CORP was demonstrated based on the insights derived from examining the corpus of web attacks. The design of CORP was formally verified by building a light weight model in the Alloy model finder. An implementation of CORP was provided as a browser extension for Chrome and it is evaluated against real-world cross origin attacks on open source web applications. Our initial investigation revealed that most of the popular websites already segregate their resources in a way which makes deployment of CORP easier.

Abstract

The field of quantum information and computing has gained a lot of attention in the past couple of decades. It is an inter-disciplinary field comprising of mathematics, physics and computer science. Researchers have been able to exploit the quantum properties like superposition and entanglement to improve the efficiency of classical algorithms and communication and cryptographic protocols. In this thesis, we address a few problems in quantum cryptography and communications. We introduce the basic mathematical structures and postulates of quantum theory in the first chapter. We also describe the basic quantum information tools which are essential of this thesis. The field of quantum cryptography is introduced here as well.In the second chapter, we address a problem in quantum secret sharing schemes. We give a protocol for sequential quantum secret sharing where the dealer does not have access to all the secrets at the beginning of the protocol. We also show that the resource requirement for our protocol is optimal. We analyze our protocol in case of a noisy quantum channel. We use weak measurements to demonstrate that the efficiency of the protocol in such noisy environments can be enhanced. In the third chapter. we address a problem in a new emerging sub field of quantum cryptography, namely, device independent cryptography. We discuss the issues of the usual quantum cryptographic schemes and show how device independent cryptography addresses some of those issues. In particular, we deal with the case when Alice and Bob has been provided with untrusted devices and Eve can control Bob’s detectors using tailored bright pulses. We analyze the security of such attacks in a semi-device independent scenario and give the sufficient conditions for unconditional security. In the final chapter, we introduce an useful information processing task, namely broadcasting of correlations. It essentially deals with the procedures of creating a larger number of less correlated systems from a highly correlated system. Then we move on to broadcasting of general correlations (possibly even stronger than quantum correlations). We introduce the the mathematical framework and define linear transformation rules. We study the correlation properties of the resulting resources in terms of the initial correlations and transformation parameter.