Abstract

Neuroimaging methods like resting state fMRI and diffusion weighted tractography capture the large scale(macroscopic level) functional and structural connectivity between distant regions of the cortex. Using this data from healthy subjects, earlier studies have shown that structure and function are strongly correlated. In this thesis I have investigated the effects of structural perturbations on function and more importantly identified potential underlying neuronal mechanisms that are effected by such structural per-turbations. First we looked at how structure-function correlation of default mode network changes with age to gain insight into processes that might be responsible for cognitive and motor decline with ageing. Next using a bio-physically realistic computational model we investigated how focal lesions across cor-tex effect spontaneous neuronal dynamics. Computational modeling of the spontaneous dynamics over the whole brain provides critical insight into the spatio-temporal organization of brain dynamics at mul-tiple resolutions and their alteration to changes in brain structure (e.g. in diseased states, aging, across individuals). Recent experimental evidence further suggests that the adverse effect of lesions are visible on spontaneous dynamics characterized by changes in resting state functional connectivity and its graph theoretical properties (e.g. modularity). These changes originate from altered neuronal dynamics in individual brain areas that are otherwise poised towards a homeostatic equilibrium to maintain a stable excitatory and inhibitory activity. In this work, we employ a homeostatic inhibitory mechanism, balanc-ing excitation and inhibition in the local brain areas of the entire cortex under neurological impairments like lesions to understand global functional recovery (across brain networks and individuals). Previ-ous computational and empirical studies have demonstrated that the resting state functional connectivity varies primarily due to the location and specific topological characteristics of the lesion. We have shown that local homeostatic balance provides a functional recovery by re-establishing excitation-inhibition balance in all areas that are effected by lesion. We systematically compare the extent of recovery in the primary hub areas (e.g. default mode network (DMN), medial temporal lobe, medial prefrontal cortex) as well as other sensory areas like primary motor area, supplementary motor area, frontoparietal and temporo-parietal networks. Our findings suggest that stability and richness similar to the normal brain dynamics at rest is achievable by re-establishment of local excitation-inhibition balance.

Abstract

Water security, simplistically defined as the capability of a society to ensure that its demands for water are met, is an important aspect which governs not only the well being of individuals in the present, but also acts as a deciding factor for the future prospects and expansion plans of any urban sprawl. The city of Hyderabad is rapidly expanding, resulting in a drastic increase in the population of the metropolis. It is estimated that with this rate of urban growth, the current municipal water distribution system would not be able to fulfil the demands efficiently of the city. Water problem has became the key obstacle for the sustainable development of any city and same goes with Hyderabad. Though the city has a large amount of water bodies within its perimeter , most of them are not being utilized for water distribution. Instead, water is being pumped in the city from rivers far-off. However, the initial and operational cost of such long-distance water projects in terms of resources and energy consumption is several times higher than that of local distribution system. Thus, though this model is fulfilling the current demands of the city, it is not sustainable and cannot be scaled up while maintaining profitability. The solution to this problem needs to rely upon local water bodies in the city. Because of its undulating geography and rocky soil, Hyderabad has a large number of lakes within its vicinity. Several of these are natural, and an equally significant number of reservoirs are man-made. These reservoirs were built with the primary intent of harnessing surface run-off water for local use. However, with rapid urbanization, they succumbed to pollution and encroachment, because of which they are not being utilized currently for supplying water to the city. If these water bodies are included in the citys main water supply, it may prove to be an efficient solution for the cities water issues. The approach described above is more of a proposal, verification of which needs comparative analysis of the citys overall water consumption and the volume of water that can be drawn from the lakes. This poses a major obstacle in verification of the approach described. Calculation of water volume for any irregular shaped water body is a complicated and laborious task. As the circle of study expands from a small locality to a city, state or a country, existing methods to calculate the water volume tend to become highly expensive and thus non scalable. Therefore, a need arises to devise a simpler, yet highly efficient method which can be generically applied to any geographical area for analysing water volume of lakes. The objective of this thesis is to devise a system that relies on local water bodies for supplying water to the city, and in the process propose efficient ways to calculate water volume of any irregularly-shaped water body. In this study, a novel system has been proposed which would utilize local water bodiesfor water distribution in the city. It also presents an efficient approach using open source geospatial technologies (GDAL/OGR library) to determine the volume of water in irregularly shaped lakes with embankment. The approach uses spatial vector data for boundaries, vector data for embankments, and depth data to calculate the amount of water present in any given lake. Algorithms for water volume calculation were written as python modules, leveraging the capabilities offered by the programming language and open source GIS library - the OGR/GDAL library. Calculations for water volume of lakes gave results with considerable accuracy. The error percentage in water volume was found to be mainly dependent on the size of the lake under consideration. It was noticed that for medium-sized lakes with capacity between 50 Mcft. and 70 Mcft, the error percentage was 6.5% and for larger lakes with capacity between 2800 Mcft. to 3500 Mcft, the error percentage was 7.51%. The error percentage was also found to be dependent on the granularity of the grid which was used to model the surface of lake. When applied to all the lakes in Hyderabad, the algorithms gave a gross 9 TMC of water which could be used for supplying to the city. Population data for the city was also gathered from official sources and using the per-capita water consumption of the city, total water demands of the city were estimated. When the water consumption was compared against the volume available for supply, it was found that 67% of the population can be supplied water from local water bodies. This number, though fairly decent, can be improved if the lakes are deepened. By manipulating the embankment depths of lakes used for calculation, this scenario was simulated and it was observed that with increased embankment depths, the water storage capacity of lakes increased to an extent that they could easily meet the demands of 80% of the citys population. This study targets only quantitative analysis

Abstract

Gold nanostructures especially gold nanoparticles and nanoclusters (NCs) have emerged as very prominent metal nanosystems because of their several unique properties. We present here our studies of a physical and a chemical property that are modified enormously by such gold nanostructures, in addition to the synthesis and characterizations of the gold NCs and nanoparticles. The physical and the chemical properties were „surface-enhanced Raman scattering (SERS)’ and the catalytic activities respectively.Currently it is of paramount importance to develop new synthesis strategies that will give us opportunity to observe a system evolving from its few-atom, ultra-small, clusters to nanoparticle size. We have been successful to develop a new one-pot aqueous synthesis protocol where gold nanoclusters starting with size < 1 nm evolved with time to almost ~7.5 nm size nanoparticles. The temporal evolution of nanosystem was studied by various methods like UVVis spectroscopy, electrochemical, MALDI-mass spectrometry, TEM, etc. These systems can be used to study the particle size effects on the catalytic reactions.Raman scattering provides a wealth of information about the chemical structure and dynamics of molecules and materials. However, the limitations of low Raman cross sections of the scatterers can be overcome by the surface-enhanced Raman scattering (SERS) on the roughened metal electrodes and/or colloidal aggregations. Researchers had attempted a variety of substrates for the signal enhancement. Since the magnitude of Raman enhancement depends on a number of factors such as the size, shape, orientation, extent of aggregation, etc. of the nanostructures, it is, therefore, practically very difficult to produce nanostructures that give reproducible and stable SERS. Nanoclusters that have precise compositions, well-defined structures and coordination can be viable SERS substrates. Here we explored the enhanced Raman scattering properties of different sized gold nanoclusters. The gold clusters were found to produce significant static chemical enhancements in Raman signals for small molecules. Various factors such as molecule–cluster interaction energies, charge transfer from the molecule to the metal cluster and total change in the polarizability of the system were calculated and their roles and contributions in the Raman enhancement mechanism were explored. Ligand to cluster charge transfer, HOMO-LUMO gaps of the complexes and the orientation of ligand molecules with respect to gold clusters played significant roles in the chemical enhancement mechanism.We investigated the nanoparticle surface and morphology effects on SERS. We demonstrated that the nature of the surface structure and morphology of the gold nanoparticles strongly influence the Raman signal of a Raman probe. Drastic drop in Raman signal due to the disappearance of sharp edges and tips of the particles directly proved that such sharp structures can enhance the Raman signal to a large extent which is popularly known as “lightening rod effect”. In addition to the individual size and geometry effects, assembled nanosystems can produce extremely large Raman signal enhancements. This effect has been employed for the detection of low concentration analytes placed among assembled nanoparticles. We used sulfur containing amino acid cysteine for linear and controlled assembly of gold nanoparticles of different shapes. Raman enhancement factor was maximal for multi-spiked gold nanoparticles followed by gold nanorod and nanospheres. Finally, we studied the enhanced chemical effects in terms of catalytic activities of gold nanoclusters and nanoparticles. We compared the catalytic effects of the gold nanosystem sizes/concentrations on the reduction reaction of o–nitro aniline to 1,2–benzenediamine. Rate constant value decreased as the size of gold nanocluster increased from <1 nm to ~7.5 nm. Smaller gold nanosystems acted as better catalysts for the reduction reaction compared to the larger ones. This demonstrates that free-standing gold nanoclusters can act as better catalysts than the nanoparticles.

Abstract

This thesis is driven by the idea of advancing computing to address critical challenges in the domain of education. In particular, focuses on facilitating the design of educational technologies for scale and variety, where scale is the number of systems to be developed and variety stems from a diversified range of instructional designs [consisting of varied goals, processes, content, teacher styles, learner styles and so on]. More specifically, we address this challenge in the context of adult literacy in India [287 million learners spread across 22 Indian languages and instructional designs developed by 32 state resource centers spread across India]. How to address these challenges and design educational technologies that adhere to instructional design principles, provide flexibility and are less effort intensive? This thesis presents an approach for design of educational technologies for scale and variety in education. The crux of this approach is the application of software engineering ideas like patterns, ontologies and software product lines without losing focus on instructional design. This approach consists of (i) modeling different aspects of instructional design like goals, process and content using patterns and mapping them to commonly accepted approaches like Bloom’s taxonomy and Merrill’s first principles of instruction (ii) representing these patterns using an ontology based framework that systematically captures different aspects of instructional design (iii) based on the modeling of domain through patterns and ontologies, this thesis presents a pattern oriented software product lines approach for modeling a family of instructional designs and for further semi-automatically generating eLearning Systems based on these instructional designs. The core concern of quality of instruction is mitigated by deriving patterns and ontologies from well-established and field tested methodologies and instructional material under the aegis of National Literacy Mission Authority of Government of India. Finally, we demonstrate how software tools built on top of our approach [http://rice.iiit.ac.in] can be used to semi-automatically generate eLearning Systems for flexible instructional designs and multiple Indian languages. We see that the proposed approach for scale and variety based on patterns, ontologies and software product lines could be applicable beyond adult literacy to the broad spectrum of education, and in particular to skill and school education.

Abstract

This thesis is driven by the idea of advancing computing to address critical challenges in the domain of education. In particular, focuses on facilitating the design of educational technologies for scale and variety, where scale is the number of systems to be developed and variety stems from a diversified range of instructional designs consisting of varied goals,processes, content, teacher styles, learner styles and so on]. More specifically, we address this challenge in the context of adult literacy in India [287 million learners spread across 22 Indian languages and instructional designs developed by 32 state resource centers spread across India]. How to address these challenges and design educational technologies that adhere to instructional design principles, provide flexibility and are less effort intensive? This thesis presents an approach for design of educational technologies for scale and variety in education. The crux of this approach is the application of software engineering ideas like patterns , ontologies and software product lines without losing focus on instructional design. This approach consists of (i) modeling different aspects of instructional design like goals, process and content using patterns and mapping them to commonly accepted approaches like Bloom’s taxonomy and Merrill’s first principles of instruction (ii) representing these patterns using an ontology based framework that systematically captures different aspects of instructional design (iii) based on the modeling of domain through patterns and ontologies, this thesis presents a pattern oriented software product lines approach for modeling a family of instructional designs and for further semi-automatically generating eLearning Systems based on these instructional designs. The core concern of quality of instruction is mitigated by deriving patterns and ontologies from well-established and field tested methodologies and instructional material under the aegis of National Literacy Mission Authority of Government of India. Finally, we demonstrate how software tools built on top of our approach[ http://rice.iiit.ac.in] can be used to semi-automatically generate e Learning Systems for flexible instructional designs and multiple Indian languages. We see that the proposed approach for scale and variety based on patterns, ontologies and software product lines could be applicable beyond adult literacy to the broad spectrum of education, and in particular to skill and school education.

Abstract

Today, general-purpose GPUs (GPGPUs) have become very ubiquitous and available in something as simple as a personal laptop. These GPUs can be utilized to a great extent for their huge parallelism such as NVIDIA GT520 having 48 cores and GTX580 having 512 cores that we use for our implementations. On such devices Single Instruction Multiple Data(SIMD) operations can be performed using NVIDIA CUDA programming model. GPUs for general purpose computing has brought forth several success stories with respect to time taken, cost, power, and other metrics. However, accelerator based computing has significantly relegated the role of CPUs in computation. As CPUs evolve and also oer matching computational resources, it is important to also include CPUs in the computation. We call this the hybrid computing model. Indeed, most computer systems of the present age oer a degree of heterogeneity and therefore such a model is quite natural. With such a hybrid model in mind, in this thesis, we try to explore ways to harvest the benefits of hybrid computing. In this thesis we explore two such ways namely Dynamic Load Balancing where we split the work among GPU and CPU dynamically and the other technique is graph pruning to recursively remove 1-degree nodes before applying an algorithm on a much smaller resultant graph and later post-processing the results to the complete graph. The first two chapters propose hybrid solution to two of major application in GPU computing such as Lattice Boltzmann Method(LBM), a class of computational uid dynamic problem and Ray Casting, an image processing primitive.Later we propose a light-weight, low overhead, and completely dynamic framework that addresses the load balancing problem of heterogeneous algorithms.Our framework will be applicable for workloads which have a few simple characteristics such as having a collection of largely independent tasks that are easily describable. To show the efficacy of our framework, we consider two different heterogeneous computing platforms, and two of the above mentioned workloads: LBM and ray casting. For each of these workloads, we demonstrate that using our framework, we can identify the proportion of work to be allotted to each device up to 8% on average with reference to the base hybrid solution. Further, solutions using our framework require no more than 5% additional time on average compared to best possible load assignment obtained via empirical search. In the later part of the thesis we introduce graph pruning as a technique that aims to reduce the size of the graph. Certain elements of the graph can be pruned depending on the nature of the computation. Once a solution is obtained on the pruned graph, the solution is extended to the entire graph. We apply the above technique on a very fundamental graph algorithm: Single Pair Shortest Path that we later generalize to solve All Pairs Shortest Paths (APSP). To validate our technique, we implement our algorithms on a heterogeneous platform consisting of a multicore CPU and a GPU. On this platform, we achieve an average of 35% improvement compared to state-of-the-art solutions. Such an improvement has the potential to speed up other applications reliant on these algorithms.

Abstract

In recent years, the amount of information generated and stored is inconceivable. A plethora repsesentations are available to store such kind of data, with graphs being one of the most efficient ap- proaches, useful for studying and analysing various attributes of data. Considering amount of data to process, parallel computing is the most suitable option but many state of the art algorithms are not ef- ficient enough to take maximum advantage of available architectures. Hence researchers are focusing on development and enhancement of parallel graph algorithms. Parallel graph algorithms continue to attract a lot of attention in research given their applications to several fields of sciences and engineering. However, efficient design and implementations of graph algorithms on modern many-core accelerators has to contend with a host of challenges including not being able to reach full memory system through-put and irregularity. Of late, focusing on real-world graphs, researchers are addressing these challenges by using decomposition and preprocessing techniques guided by the structural properties of such graphs. In this direction, we present a new algorithm for obtaining an ear decomposition of a graph. Our implementation of the proposed algorithm on an NVidia Tesla K40c and Intel Xeon E5-2650 improves the state-of-the-art by a factor of 2.3x and 2.02x on average respectively on a collection of real-world and synthetic graphs. The improved performance of our algorithm is due to our proposed characterization that identifies edges of the graph as redundant for the purposes of an ear decomposition. We then study an application of the ear decomposition of a graph in computing the betweenness-centrality values of nodes in the graph. We use an ear decomposition of the input graph to systematically remove nodes of degree two. The actual computation of betweenness-centrality is done on the remaining nodes and the results are extended to nodes removed in the previous step. We show that this approach improves the state-of-the-art for computing betweenness-centrality on an NVidia K40c GPU by a factor of 1.9x on average over a collection of real-world graphs.

Abstract

Increasing complexity and diminishing size of wireless devices often lead to quick depletion of en- ergy stored in batteries with limited capacity. In certain applications, sensors are often installed at loca- tions that are hazardous or inaccessible, which makes the battery replacement or recharging impossible. This could often lead to interruptions in the operation of the network. In such scenarios, transferring energy to these devices wirelessly plays a significant role in prolonging the life of the sensor networks. Since most of these devices perform wireless communication, it is of practical interest to consider the transfer of energy to the device using the same electromagnetic wave that is used for communication. This technique, termed ‘simultaneous wireless information and energy transmission (SWIET)’, holds great promise in many applications. SWIET enables joint transfer of data and energy to the receiver, which performs both information decoding and energy harvesting simultaneously from the same re- ceived electromagnetic wave. This technique will be central to various emerging technologies and has gathered considerable attention recently. Many current and future technologies like wearable devices, sensors used in hazardous areas, 5G and beyond, etc., are expected to use SWIET technique. In this work, we develop transceiver algorithms for the application of SWIET in two types of com- munication networks. In the first scenario, we consider SWIET in a wireless network employing multi- carrier transmission technique. The transmitter is designed to support both information and energy transfer simultaneously. The network employs orthogonal frequency division multiplexing (OFDM) for signal transmission. Receivers are capable of decoding the information as well as harvesting energy from the same received signal. In this context, we address the problem of the optimal transceiver design for maximizing the weighted sum-rate under a constraint on harvested energy and for maximizing the total harvested energy under constraint on minimum rate. In the second scenario, we consider SWIET in a cooperative network employing amplify-and-forward (AF) relays. The source node, relays, and the destination node, are all capable of handling both data and energy transmission simultaneously. In this context, apart from the optimal transmit and receive processing, we also consider the problem of optimal selection of a relay out of L given relays to maximize the rate and the harvested energy. We investigate the problems for both time switching (TS) and power splitting (PS) schemes. In TS, the receiver harvests all the energy from the received signal for a fraction of the block duration; while for the remaining duration, the received signal is utilized for information decoding. On the other hand, a fraction of the received signal power is used for harvesting energy and the remaining power is used for decoding information simultaneously, in case of PS. We have developed optimal and reduced complexity sub-optimal algorithms to solve the problems described above and have analyzed the performance in various scenarios. It is assumed that the channel state information (CSI) is perfectly known for all the scenarios. The benefits of proposed designs under different operating conditions and parameter values are illustrated via numerical simulations.

Abstract

The thesis is based upon two important concepts of foundation of quantum mechanics, locality and complementarity. Violation of locality and presence of complementarity are key features of “quantumness” of a system distinguishing it from all classical systems that conform to the idea of local realism. The first part of the thesis is devoted to the investigations of issues related to non locality and complementarity of quantum correlations in multipartite scenarios, in particular, the Svetlichny model. Our investigations are based upon device independent approach valid irrespective of any theoretical setting. We show that there exists a complementary relationship in terms of the genuine non-locality as a principle between the system and subsystems. We investigate Svetlichny games in a multiparty binary input and output scenario with a threshold value of the winning probability as a signature of genuine multiparty non locality. We analytically show that in the Svetlichny games setup, there exists complementary relationship between Svetlichny correlations of n party versus Svetlichny correlations of k ≤ n party which arise due to no-signaling principle. In other words, the violation of locality for multiparty systems can not be arbitrary, it comes at the price of non violation of locality in the reduced subsystem. The latter is one of the major findings of the thesis. In addition to Svetlichny games, we consider many non local games like Mermin games, Bancal et. al’s IP, Guess Your Neighbors Input game and present the details of our numerical investigations in case of No-signaling and quantum correlations. We demonstrate that the numerical findings support of our main result obtained analytically. In the second part of the thesis, we have considered hypothetical yet theoretically possible situation, by considering the existing models of closed time like curves. It had been noted in the literature earlier that these models of closed time like curves give enormous power to achieve things which are computationally and physically impossible with the existing structure of quantum mechanics in the causality respecting region. Specifically, we show that it is possible to create an arbitrary superposition of unknown states in case CTCs are invoked in the quantum framework which is otherwise impossible. This is an important finding which should be added to the list of no-go results which become permissible by the allowance of closed time like curves in quantum framework.

Abstract

Reasoning about objects in images and videos using 3D representations is re-emerging as a popular paradigm in computer vision. Specifically, in the context of scene understanding for roads, 3D vehicle detection and tracking from monocular videos still needs a lot of attention to enable practical applications. Current approaches leverage two kinds of information to deal with the vehicle detection and tracking problem: (1) 3D representations (eg. wireframe models or voxel based or CAD models) for diverse vehicle skeletal structures learnt from data, and (2) classifiers trained to detect vehicles or vehicle parts in single images built on top of a basic feature extraction step. In this paper, we propose to extend current approaches in two ways. First, we extend detection to a multiple view setting. We show that leveraging information given by feature or part detectors in multiple images can lead to more accurate detection results than single image detection. Secondly, we show that given multiple images of a vehicle, we can also leverage 3D information from the scene generated using a unique structure from motion algorithm. This helps us localize the vehicle in 3D, and constrain the parameters of optimization for fitting the 3D model to image data. We show results on the KITTI dataset, and demonstrate superior results compared with recent state-of-the-art methods, with upto 14.64 % improvement in localization error.