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Browsing by Subject "nonlinearity"

Now showing 1 - 2 of 2
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    Microwave Interaction with Magnons for Nonlinear Devices
    (2021-09) Venugopal, Aneesh
    Perturbations of the magnetic order, known as spin-waves or magnons, within a ferri- or ferromagnet can exhibit nonlinear properties. The nonlinearity of the magnons can be exploited for information processing applications and for understanding fundamental aspects of nonlinear processes. When using insulators such as Yittrium iron garnet (YIG), various functionalities such as signal processing can be realized in the absence of Ohmic losses. Moreover, the small wavelengths of spin waves can also help with the miniaturization of devices. Such advantages have made magnons attractive for a wide variety of applications ranging from communications to logic circuits. Although magnons have been studied in the past, precise understanding and the details of various nonlinear processes are still largely lacking. Device design is often based on trial-and-error approaches with regard to magnonic properties. Efficient and robust design, however, requires a deterministic understanding of material behavior. Moreover, given the long experimental cycles involved in device design, the ability to predict properties accurately is crucial. In this thesis, I will discuss the development of a high-speed CUDA-GPU (graphics processing unit)-based parallel platform to study magnons that are created by microwave excitation of magnetic materials. The goal is twofold: to enable a better understanding of nonlinear properties and to improve device design capabilities. Device characteristics of magnet-based frequency-selective limiters (FSLs) used for microwave signal processing are studied using simulations involving rigorous calculations of dipolar-, exchange-, and thermal-magnetic fields. These studies offer beneficial insights into the role of physical processes like higher-order scattering on the device behavior. A key requirement in many applications is the dynamic control of the threshold field -the minimum microwave field needed to turn on the nonlinear behavior in a magnetic sample. The ability to dynamically vary the threshold field using an additional microwave is explained analytically and demonstrated using simulations. The importance of magnon-phase in the nonlinear processes is also explicitly demonstrated. Despite the crucial role of magnon-phase in nonlinear physics, few studies focus on the impacts of magnonic phase-noise. I have developed an analytical theory to understand the impacts of magnon phase noise. The conclusions of the theory are verified using micromagnetic simulations.Magnetic recording comprises highly nonlinear processes that, unlike perturbative effects, involve the reversal of magnetization. Using micromagnetic simulations, I designed a high-density magnetic recording scheme employing state-of-the-art heat-assisted and bit-patterned techniques. Even after considering noise factors such as jitter and track misregistration, the design provides an extremely high density of 16 Terabits per square centimeters (Tbpsi).
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    Unsupervised Learning of Latent Structure from Linear and Nonlinear Measurements
    (2019-06) Yang, Bo
    The past few decades have seen a rapid expansion of our digital world. While early dwellers of the Internet exchanged simple text messages via email, modern citizens of the digital world conduct a much richer set of activities online: entertainment, banking, booking for restaurants and hotels, just to name a few. In our digitally enriched lives, we not only enjoy great convenience and efficiency, but also leave behind massive amounts of data that offer ample opportunities for improving these digital services, and creating new ones. Meanwhile, technical advancements have facilitated the emergence of new sensors and networks, that can measure, exchange and log data about real world events. These technologies have been applied to many different scenarios, including environmental monitoring, advanced manufacturing, healthcare, and scientific research in physics, chemistry, bio-technology and social science, to name a few. Leveraging the abundant data, learning-based and data-driven methods have become a dominating paradigm across different areas, with data analytics driving many of the recent developments. However, the massive amount of data also bring considerable challenges for analytics. Among them, the collected data are often high-dimensional, with the true knowledge and signal of interest hidden underneath. It is of great importance to reduce data dimension, and transform the data into the right space. In some cases, the data are generated from certain generative models that are identifiable, making it possible to reduce the data back to the original space. In addition, we are often interested in performing some analysis on the data after dimensionality reduction (DR), and it would be helpful to be mindful about these subsequent analysis steps when performing DR, as latent structures can serve as a valuable prior. Based on this reasoning, we develop two methods, one for the linear generative model case, and the other one for the nonlinear case. In a related setting, we study parameter estimation under unknown nonlinear distortion. In this case, the unknown nonlinearity in measurements poses a severe challenge. In practice, various mechanisms can introduce nonlinearity in the measured data. To combat this challenge, we put forth a nonlinear mixture model, which is well-grounded in real world applications. We show that this model is in fact identifiable up to some trivial indeterminancy. We develop an efficient algorithm to recover latent parameters of this model, and confirm the effectiveness of our theory and algorithm via numerical experiments.