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The non-magnetic and non-Fermi-liquid CaRuO 3 is the iso-structural analog of the ferromagnetic (FM) and Fermi-liquid SrRuO 3 . We show that an FM order in the orthorhombic CaRuO 3 can be established by the means of tensile epitaxial strain. The structural and magnetic property correlations in the CaRuO 3 films formed on SrTiO 3 (100) substrate establish a scaling relation between the FM moment and the tensile strain. The strain dependent crossover from non-magnetic to FM CaRuO 3 was observed to be associated with switching of non-Fermi liquid to Fermi-liquid behavior. The intrinsic nature of this strain-induced FM order manifests in the Hall resistivity too; the anomalous Hall component realizes in FM tensile-strained CaRuO 3 films on SrTiO 3 (100) whereas the non-magnetic compressive-strained films on LaAlO 3 (100) exhibit only the ordinary Hall effect. These observations of an elusive FM order are consistent with the theoretical predictions of scaling of the tensile epitaxial strain and the magnetic order in tensile CaRuO 3 . We further establish that the tensile strain is more efficient than the chemical route to induce FM order in CaRuO 3 .

The exchange-bias effects in the mosaic epitaxial bilayers of the itinerant ferromagnet (FM) SrRuO 3 and the antiferromagnetic (AFM) charge-ordered La 0.3 Sr 0.7 FeO 3 were investigated. An uncharacteristic low-field positive exchange bias, a cooling-field driven reversal of positive to negative exchange-bias and a layer thickness optimised unusual vertical magnetization shift were all novel facets of exchange bias realized for the first time in magnetic oxides. The successive magnetic training induces a transition from positive to negative exchange bias regime with changes in domain configurations. These observations are well corroborated by the hysteretic loop asymmetries which display the modifications in the AFM spin correlations. These exotic features emphasize the key role of i) mosaic disorder induced subtle interplay of competing AFM-superexchange and FM double exchange at the exchange biased interface and, ii) training induced irrecoverable alterations in the AFM spin structure.

Mobile computing is one of the largest untapped reservoirs in today’s pervasive computing world as it has the potential to enable a variety of in-situ, real-time applications. However, the domain of mobile computing suffers from the constraints of limited resources such as device battery, CPU, and memory while at the same time users’ expectations in terms of response times, accuracy, and data rates are increasing at a fast pace. As a result, achieving high energy efficiency while maintaining a high quality of service is a crucial challenge. Many of the mobile applications that are pervasive in our lives–such as localization, object/activity recognition, and mobile gaming to name a few–are expected to perform seamlessly with near-instantaneous responses, but are also affected by the same constraints. Current solutions based on offloading computationally-intensive applications from resource-constrained mobile devices to powerful remote computing platforms (such as the Cloud) or nearby mobile devices, suffer from uncertainty in wireless network connectivity or availability of devices in proximity, respectively.To overcome the limitation of current works, the paradigm of approximate computing emerges as a solution to enable resource-intensive mobile applications in resource-constrained environment. Approximate computing reduces the amount of computation that an application is expected to perform, as a result of which the execution time reduces, which in turn reduces the energy consumption of the application. The gain achieved via reduction in energy consumption, however, comes with a potential loss in the accuracy of the results (within acceptable limits). By leveraging approximate computing, we achieve dynamically a tradeoff between accuracy (or optimality of the results produced by an application) and utilization of the available resources (such as battery, CPU cycles, memory, and I/O data rate).The goal of this thesis is to design new techniques so as to enable real-time computation intensive mobile applications in resource-limited and uncertain environments. In order to achieve this goal, we leverage the paradigm of approximate computing and propose the following three solutions. First, approximation at the application level is introduced by joint optimization of algorithm and parameter space of different tasks in the application and a light-weight algorithm is developed that selects the approximated tasks that should be executed to meet the application deadline under uncertainties encountered at run-time. Second, temporal correlation between the continuous stream of frames obtained from the camera sensors is exploited to learn the application parameters that give acceptable accuracy in each frame of the video with significant savings in time and energy. The problem of selecting the algorithm and input parameters for a video is cast as a Markov Decision Process. Third, to reduce the energy consumption of data-intensive applications in distributed camera networks a novel protocol is proposed to identify the camera nodes in the network with correlated multimedia data. Low-computational-complexity metrics are used to quantify the correlation across cameras nodes by using only local knowledge of the network available to the camera nodes. Furthermore, the effectiveness of the proposed approaches is validated through extensive simulations on publicly available datasets and data collected by building multiple end-to-end computationally-intensive applications from the computer vision domain. The proposed innovations in this research will provide novel solutions to the issue of limited resource availability in mobile devices and will foster the development of mobile research community.

This thesis presents an improved method for reconstructing the spectra of astronomical objects from two-dimensional (2D) charge-coupled device (CCD) images. We address two issues, namely, estimation of calibration matrix for a CCD, and reconstruction of spectra of astronomical objects, which is referred to as extraction. In the rst part of the thesis we estimate the elements of system calibration matrix by modeling the two-dimensional point-spread functions (PSF) in calibration images. Our PSF model is valid for arbitrarily complicated 2D point-spread functions, as compared to the state-of-art extraction methods which are valid only for class of separable PSFs. We present various models for PSFs and give a quantitative comparison between the models. In the second part of the thesis the system calibration matrix is used to extract the spectra of a particular type of calibration images called arc-images. We also address the issue of resolution and covariance in the extracted spectra, and present a method that establishes optimal properties in both these regards. We also compare quantitatively the performance of our extraction technique with the state-of-art extraction technique. The work presented in this thesis can be deployed for estimation of spectra of faint galaxies in presence of strong night-sky foregrounds.

Most applications in autonomous navigation using mounted cameras rely on the construction and processing of geometric 3D point clouds, which is an expensive process. However, there is another simpler way to make a space navigable quickly: to use semantic information (e.g., traffic signs) to guide the agent. However, detecting and acting on semantic information involves Computer Vision~(CV) algorithms such as object detection, which themselves are demanding for agents such as aerial drones with limited onboard resources. To solve this problem, we introduce a novel Markov Decision Process~(MDP) framework to reduce the workload of these CV approaches. We apply our proposed framework to both feature-based and neural-network-based object-detection tasks, using open-loop and closed-loop simulations as well as hardware-in-the-loop emulations. These holistic tests show significant benefits in energy consumption and speed with only a limited loss in accuracy compared to models based on static features and neural networks.

One of the traditional mechanisms used in distributed systems for maintaining the consistency of replicated data is voting. A problem involved in voting mechanisms is the size of the Quorums needed on each access to the data. In this paper, we present a novel and efficient distributed algorithm for managing replicated data. We impose a logical wheel structure on the set of copies of an object. The protocol ensures minimum read quorum size of one, by reading one copy of an object while guaranteeing fault-tolerance of write operations.Wheel structure has a wider application area as it can be imposed in a network with any number of nodes.

A hybrid mobile/fixed device cloud that harnesses sensing, computing, communication, and storage capabilities of mobile and fixed devices in the field as well as those of computing and storage servers in remote datacenters is envisioned. Mobile device clouds can be harnessed to enable innovative pervasive applications that rely on real-time, in-situ processing of sensor data collected in the field. To support concurrent mobile applications on the device cloud, a robust and secure distributed computing framework, called Maestro, is proposed. The key components of Maestro are (i) a task scheduling mechanism that employs controlled task replication in addition to task reallocation for robustness and (ii) Dedup for task deduplication among concurrent pervasive workflows. An architecture-based solution that relies on task categorization and authorized access to the categories of tasks is proposed for different levels of protection. Experimental evaluation through prototype testbed of Android- and Linux-based mobile devices as well as simulations is performed to demonstrate Maestro's capabilities.

Exchange bias stems from the interaction between different magnetic phases and therefore it generally occurs in magnetic multilayers. Here we present a large exchange bias in a single SrRuO3 layer induced by helium ion irradiation. When the fluence increases, the induced defects not only suppress the magnetization and the Curie temperature, but also drive a metal-insulator transition at a low temperature. In particular, a large exchange bias field up to around 0.36 T can be created by the irradiation. This large exchange bias is related to the coexistence of different magnetic and structural phases that are introduced by embedded defects. Our work demonstrates that spintronic properties in complex oxides can be created and enhanced by applying ion irradiation.

Mobile Edge Computing (MEC) is an emerging paradigm that provides computing, storage, and networking resources within the edge of the mobile Radio Access Network (RAN). MEC servers are deployed on generic computing platform within the RAN and allow for delay-sensitive and context-aware applications to be executed in close proximity to the end users. This approach alleviates the backhaul and core network and is crucial for enabling low-latency, high-bandwidth, and agile mobile services. This article envisages a real-time, context-aware collaboration framework that lies at the edge of the RAN, constituted of MEC servers and mobile devices, and that amalgamates the heterogeneous resources at the edge. Specifically, we introduce and study three strong use cases ranging from mobile-edge orchestration, collaborative caching and processing and multi-layer interference cancellation. We demonstrate the promising benefits of these approaches in facilitating the evolution to 5G networks. Finally, we discuss the key technical challenges and open-research issues that need to be addressed in order to make an efficient integration of MEC into 5G ecosystem.