"Recently, there has been some good interest in the field of Dynamic Materials, also referred to as Spatio-Temporal Composites. These materials have been theoretically attributed to show ability to switch their electromagnetic properties in time, as contrast to the spatial variations shown by regular materials of non-dynamic nature, existing naturally. Though there is no exhibition of dynamic material in nature yet, there are suggestions for its synthesis. This paper follows the idea of using standard lossless transmission line model approximating a material substance. Such a material though not truly homogeneous, could be made to vary its properties in time. The aim of this work is to test this idea for its functional efficiency in comparison to analytical results obtained from earlier works on the subject. We make use of Spice simulation for this. An important aspect of this work is to facilitate the dynamic operations in a static environment. Almost all the simulators available today like Spice, ADS, etc intrinsically provide no ability for parameter variations in time. Nonetheless, we make use of certain popular tricks to implement circuits imitating the dynamic circuit components we need. Such implementations are separately tested to demonstrate their success in providing us with the dynamic environment we desire. Finally, within the limitations of the computing capabilities, we could successfully show an agreement between the results obtained and the existing theory. "

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Doctor of Philosophy (PhD) / Data mining is the process of extracting knowledge from large amounts of data. It covers a variety of techniques aimed at discovering diverse types of patterns on the basis of the requirements of the domain. These techniques include association rules mining, classification, cluster analysis and outlier detection. The availability of applications that produce massive amounts of spatial, spatio-temporal (ST) and time series data (TSD) is the rationale for developing specialized techniques to excavate such data. In spatial data mining, the spatial co-location rule problem is different from the association rule problem, since there is no natural notion of transactions in spatial datasets that are embedded in continuous geographic space. Therefore, we have proposed an efficient algorithm (GridClique) to mine interesting spatial co-location patterns (maximal cliques). These patterns are used as the raw transactions for an association rule mining technique to discover complex co-location rules. Our proposal includes certain types of complex relationships – especially negative relationships – in the patterns. The relationships can be obtained from only the maximal clique patterns, which have never been used until now. Our approach is applied on a well-known astronomy dataset obtained from the Sloan Digital Sky Survey (SDSS). ST data is continuously collected and made accessible in the public domain. We present an approach to mine and query large ST data with the aim of finding interesting patterns and understanding the underlying process of data generation. An important class of queries is based on the flock pattern. A flock is a large subset of objects moving along paths close to each other for a predefined time. One approach to processing a “flock query” is to map ST data into high-dimensional space and to reduce the query to a sequence of standard range queries that can be answered using a spatial indexing structure; however, the performance of spatial indexing structures rapidly deteriorates in high-dimensional space. This thesis sets out a preprocessing strategy that uses a random projection to reduce the dimensionality of the transformed space. We use probabilistic arguments to prove the accuracy of the projection and to present experimental results that show the possibility of managing the curse of dimensionality in a ST setting by combining random projections with traditional data structures. In time series data mining, we devised a new space-efficient algorithm (SparseDTW) to compute the dynamic time warping (DTW) distance between two time series, which always yields the optimal result. This is in contrast to other approaches which typically sacrifice optimality to attain space efficiency. The main idea behind our approach is to dynamically exploit the existence of similarity and/or correlation between the time series: the more the similarity between the time series, the less space required to compute the DTW between them. Other techniques for speeding up DTW, impose a priori constraints and do not exploit similarity characteristics that may be present in the data. Our experiments demonstrate that SparseDTW outperforms these approaches. We discover an interesting pattern by applying SparseDTW algorithm: “pairs trading” in a large stock-market dataset, of the index daily prices from the Australian stock exchange (ASX) from 1980 to 2002.

Moving objects may stimulate many retinal photoreceptors within the integration time of the receptors without motion blur being experienced. Anderson and vanEssen (1987) suggested that the neuronal representation of retinal images is shifted on its way to the cortex, in an opposite direction to the motion. Thus, the cortical representation of objects would be stationary. I have measured thresholds for two vernier stimuli, moving simultaneously into opposite directions over identical positions. Motion blur for these stimuli is not stronger than with a single moving stimulus, and thresholds can be below a photoreceptor diameter. This result cannot be easily reconciled with the hypothesis of Tshifter circuitsU.

Largely driven by advances in communication and information technology, such as the increasing availability and accuracy of GPS technology and the miniaturization of wireless communication devices, Location–Based Services (LBS) are continuously gaining popularity. Innovative LBSes integrate knowledge about the users into the service. Such knowledge can be derived by analyzing the location data of users. Such data contain two unique dimensions, space and time, which need to be analyzed. The objectives of this thesis are three–fold. First, to extend popular data mining methods to the spatio–temporal domain. Second, to demonstrate the usefulness of the extended methods and the derived knowledge in two promising LBS examples. Finally, to eliminate privacy concerns in connection with spatio–temporal data mining by devising systems for privacy–preserving location data collection and mining.   To this extent, Chapter 2 presents a general methodology, pivoting, to extend a popular data mining method, namely rule mining, to the spatio–temporal domain. By considering the characteristics of a number of real–world data sources, Chapter 2 also derives a taxonomy of spatio–temporal data, and demonstrates the usefulness of the rules that the extended spatio–temporal rule mining method can discover. In Chapter 4 the proposed spatio–temporal extension is applied to find long, sharable patterns in trajectories of moving objects. Empirical evaluations show that the extended method and its variants, using high–level SQL implementations, are effective tools for analyzing trajectories of moving objects. Real–world trajectory data about a large population of objects moving over extended periods within a limited geographical space is difficult to obtain. To aid the development in spatio–temporal data management and data mining, Chapter 3 develops a Spatio–Temporal ACTivity Simulator (ST–ACTS). ST–ACTS uses a number of real–world geo–statistical data sources and intuitive principles to effectively generate realistic spatio–temporal activities of mobile users.   Chapter 5 proposes an LBS in the transportation domain, namely cab–sharing. To deliver an effective service, a unique spatio–temporal grouping algorithm is presented and implemented as a sequence of SQL statements. Chapter 6 identifies ascalability bottleneck in the grouping algorithm. To eliminate the bottleneck, the chapter expresses the grouping algorithm as a continuous stream query in a data stream management system, and then devises simple but effective spatio–temporal partitioning methods for streams to parallelize the computation. Experimental results show that parallelization through adaptive partitioning methods leads to speed–ups of orders of magnitude without significantly effecting the quality of the grouping. Spatio–temporal stream partitioning is expected to be an effective method to scale computation–intensive spatial queries and spatial analysis methods for streams.   Location–Based Advertising (LBA), the delivery of relevant commercial information to mobile consumers, is considered to be one of the most promising business opportunities amongst LBSes. To this extent, Chapter 7 describes an LBA framework and an LBA database that can be used for the management of mobile ads. Using a simulated but realistic mobile consumer population and a set of mobile ads, the LBA database is used to estimate the capacity of the mobile advertising channel. The estimates show that the channel capacity is extremely large, which is evidence for a strong business case, but it also necessitates adequate user controls.   When data about users is collected and analyzed, privacy naturally becomes a concern. To eliminate the concerns, Chapter 8 first presents a grid–based framework in which location data is anonymized through spatio–temporal generalization, and then proposes a system for collecting and mining anonymous location data. Experimental results show that the privacy–preserving data mining component discovers patterns that, while probabilistic, are accurate enough to be useful for many LBSes.   To eliminate any uncertainty in the mining results, Chapter 9 proposes a system for collecting exact trajectories of moving objects in a privacy–preserving manner. In the proposed system there are no trusted components and anonymization is performed by the clients in a P2P network via data cloaking and data swapping. Realistic simulations show that under reasonable conditions and privacy/anonymity settings the proposed system is effective. / QC 20120215

The role of information and communication technologies in the development of modern societies has continuously increased over the past several decades. In particular, recent unprecedented growth in use of the Internet in many developing countries has been accompanied by greater information access and use. Along with this increased use, there have been significant advances in the development of technologies that can support the management and decision-making functions of decentralized government. However, the amount of data available to administrators and planners is increasing at a faster rate than their ability to use these resources effectively. A key issue in this context is the storage and retrieval of spatial and temporal data. With static data, a planner or analyst is limited to studying cross-sectional snapshots and has little capability to understand trends or assess the impacts of policies. Education, which is a vital part of the human experience and one of the most important aspects of development, is a spatio-temporal process that demands the capacities to store and analyze spatial distributions and temporal sequences simultaneously. Local planners must not only be able to identify problem areas, but also know if a problem is recent or on-going. They must also be able to identify factors which are causing problems for remediation and, most importantly, to assess the impact of remedial interventions. Internet-based tools that allow for fast and easy on-line exploration of spatio-temporal data will better equip planners for doing all of the above. This thesis presents a spatio-temporal on-line data model using the concept or paradigm of space-time. The thesis demonstrates how such a model can be of use in the development of customized software that addresses the evaluation of early childhood education quality in Peru.

The role of information and communication technologies in the development of modern societies has continuously increased over the past several decades. In particular, recent unprecedented growth in use of the Internet in many developing countries has been accompanied by greater information access and use. Along with this increased use, there have been significant advances in the development of technologies that can support the management and decision-making functions of decentralized government. However, the amount of data available to administrators and planners is increasing at a faster rate than their ability to use these resources effectively. A key issue in this context is the storage and retrieval of spatial and temporal data. With static data, a planner or analyst is limited to studying cross-sectional snapshots and has little capability to understand trends or assess the impacts of policies. Education, which is a vital part of the human experience and one of the most important aspects of development, is a spatio-temporal process that demands the capacities to store and analyze spatial distributions and temporal sequences simultaneously. Local planners must not only be able to identify problem areas, but also know if a problem is recent or on-going. They must also be able to identify factors which are causing problems for remediation and, most importantly, to assess the impact of remedial interventions. Internet-based tools that allow for fast and easy on-line exploration of spatio-temporal data will better equip planners for doing all of the above. This thesis presents a spatio-temporal on-line data model using the concept or paradigm of space-time. The thesis demonstrates how such a model can be of use in the development of customized software that addresses the evaluation of early childhood education quality in Peru.

We propose a dynamical systems approach to the study of weak turbulence(spatiotemporal chaos) based on the periodic orbit theory, emphasizing the role of recurrent patterns and coherent structures. After a brief review of the periodic orbit theory and its application to low-dimensional dynamics, we discuss its possible extension to study dynamics of spatially extended systems. The discussion is three-fold. First, we introduce a novel variational scheme for finding periodic orbits in high-dimensional systems. Second, we prove rigorously the existence of periodic structures (modulated amplitude waves) near the first instability of the complex Ginzburg-Landau equation, and check their role in pattern formation. Third, we present the extensive numerical exploration of the Kuramoto-Sivashinsky system in the chaotic regime: structure of the equilibrium solutions, our search for the shortest periodic orbits, description of the chaotic invariant set in terms of intrinsic coordinates and return maps on the Poincare section.

The main contributions of this thesis to the field of ultrashort pulse measurement are a new set of experimental tools to measure the spatio-temporal fields of femtosecond pulses, and a new simplified formalism to describe such fields in the presence of distortions. More specifically, we developed an experimental technique based on scanning-wavelength digital holography and frequency-resolved optical gating that allows the complete measurement of the electric field E(x,y,t) of trains of identical femtosecond pulses. A related method, wavelength-multiplexed digital holography, is also introduced. It achieves a single-shot measurement of the three-dimensional field E(x,y,t) but at a reduced resolution using a simple experimental apparatus. Both methods can be used to measure various spatio-temporal distortions that often plague femtosecond laser systems, in particular amplified ones. Finally, to unambiguously and intuitively quantify such distortions, we introduce normalized correlation coefficients so that a common language can be used to describe the severity of these effects.

Various works have demonstrated the usage of action as a critical component in allowing autonomous agents to learn about objects in the environment. The importance of memory becomes evident when these agents try to learn about complex objects. This necessity primarily stems from the fact that simpler agents behave reactively to stimuli in their attempt to learn about the nature of the object. However, complex objects have the property of giving rise to temporally varying sensory data as the agent interacts with the object. Therefore, reactive behavior becomes a hindrance in learning these complex objects, thus, prompting the need for memory. A straightforward approach to memory, visual memory, is where sensory data is directly represented. Another mechanism is skill-based memory or habit formation. In the latter mechanism the sequence of actions performed for a task is retained. The main hypothesis of this thesis is that since action seems to play an important role in simple perceptual understanding it may also serve as a good memory representation. In order to test this hypothesis a series of comparative tests were carried out to determine the merits of each of these representations. It turns out that skill memory performs significantly better at recognition tasks than visual memory. Furthermore, it was demonstrated in a related experiment that action forms a good intermediate representation of the sensory data. This provides support to theories that propose that various sensory modalities can ideally be represented in terms of action. This thesis successfully extends action to the role of understanding of complex objects.