Abstract

Spatial modulation (SM) is a recently developed multiple-input-multiple-output (MIMO) technique that uses multiple transmit antennas in a innovative fashion. In SM, a group of information bits is mapped into two constellations: a signal constellation based on modulation scheme, and a spatial con- stellation to encode the index of a transmit antenna. At any time instant, only one transmit antenna is active, whereas other transmit antennas radiate zero power. This completely avoids inter-channel in- terference at the receiver and relaxes the stringent requirement of synchronization among the transmit antennas. Moreover, unlike conventional MIMO system, SM does not require multiple RF chains at the transmitter. SM can outperform other state-of-the-art MIMO schemes in terms of computational complexity, if many transmit antennas are available. Unfortunately, this makes SM useful only in the downlink of the cellular network, where a base station can be equipped with a large number of antennas. On the other hand, use of multiple antennas in mobile terminals has practical limitations. Besides constraints on complexity and cost, decreasing terminal size restricts the application of SM in the uplink of cellular network. To overcome these problems, distributed spatial modulation (DSM) offers a promising solu- tion. In DSM, multiple relays form a virtual antenna array and assist a source to transmit its information to a destination by applying SM in a distributed manner. The source broadcasts its signal, which is inde- pendently demodulated by all the relays. Each of the relays then divides the received data in two parts: the first part is used to decide which one of the relays will be active, and the other part decides what data it will transmit to the destination. An analytical expression for symbol error probability is derived for DSM in independent and identically distributed (i.i.d.) Rayleigh fading channels. The analytical results are later compared with Monte Carlo simulations. Next, DSM implementation is extended to a cognitive network scenario where the source, relays, and destination are all equipped with cognitive radios. A dynamic frequency allocation scheme is proposed to improve the performance of DSM in this scenario. The frequency allocation is modeled through a bipartite graph with end-to-end symbol error rate (SER) as a weight function. The optimal frequency allocation problem is formulated as minimum weight perfect matching problem and is solved using the Hungarian method. Finally, numerical results are provided to illustrate the efficacy of the proposed scheme.

Abstract

Reconstruction of images from projections lays the foundations for computed tomography (CT). Tomographic image reconstruction, due to its numerous real world applications, from medical scanners in radiology and nuclear medicine to industrial scanning and seismic equipment, is an extensively studied problem. The study of reconstructing function from its projections/line integrals, is around a century old. The classical tomographic reconstruction problem was originally solved 1917 by J. Radon, proposing and inversion method now known as filtered backprojection (FBP). It was later shown that infinitely many projections are required to reconstruct an image perfectly. It is understood that incomplete data would leads to artifacts in the reconstructed images. In addition to the artifact problem, arising due to limited data availability, the reconstructed images are known to be corrupted by noise. We study these two problems of noisy and incomplete data in the follwoing two setups. Nuclear imaging modalities like Positron emission tomography (PET) are characterized by a low SNR value due to the underlying signal generation mechanism. Given the significant role images play in current-day diagnostics, obtaining noise-free PET images is of great interest. With its higher packing density and larger and symmetrical neighbourhood, the hexagonal lattice offers a natural robustness to degradation in signal. Based on this observation, we propose an alternate solution to denoising, namely by changing the sampling lattice. We use filtered back projection for reconstruction, followed by a sparse dictionary based denoising and compare noise-free reconstruction on the Square and Hexagonal lattices. Experiments with PET phantoms (NEMA, Hoffman) and the Shepp-Logan phantom show that the improvement in denoising, post reconstruction, is not only at the qualitative but also quantitative level. The improvement in PSNR in the hexagonal lattice is on an average between 2 to 10 dB. These results establish the potential of the hexagonal lattice for reconstruction from noisy data, in general. In the limited data scenario we consider the Circular arc Radon Transform (CAR). Circular arc Radon transforms associate to a function, its integrals along arcs of circles. The transforms involve the integrals of a function $f$ on the plane along a family of circular arcs. These transforms arise naturally in the study of several medical imaging modalities including thermoacoustic and photoacoustic tomography, ultrasound, intravascular, radar and sonar imaging. The inversion of such transforms is of natural interest. Unlike the full circle counterpart -- the circular Radon transform -- which has attracted significant attention in recent years, the circular arc Radon transforms are scarcely studied objects. We present an efficient algorithm that gives a numerical inversion of such transforms for the cases in which the support of the function lies entirely inside or outside the acquisition circle. The numerical algorithm is non-iterative and is very efficient as the entire scheme, once processed, can be stored and used repeatedly for reconstruction of images.

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.