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Full-Text Articles in Physical Sciences and Mathematics
High Dimensional Data Set Analysis Using A Large-Scale Manifold Learning Approach, Loc Tran
High Dimensional Data Set Analysis Using A Large-Scale Manifold Learning Approach, Loc Tran
Electrical & Computer Engineering Theses & Dissertations
Because of technological advances, a trend occurs for data sets increasing in size and dimensionality. Processing these large scale data sets is challenging for conventional computers due to computational limitations. A framework for nonlinear dimensionality reduction on large databases is presented that alleviates the issue of large data sets through sampling, graph construction, manifold learning, and embedding. Neighborhood selection is a key step in this framework and a potential area of improvement. The standard approach to neighborhood selection is setting a fixed neighborhood. This could be a fixed number of neighbors or a fixed neighborhood size. Each of these has …
Idpal – A Partially-Adiabatic Energy-Efficient Logic Family: Theory And Applications To Secure Computing, Mihail T. Cutitaru
Idpal – A Partially-Adiabatic Energy-Efficient Logic Family: Theory And Applications To Secure Computing, Mihail T. Cutitaru
Electrical & Computer Engineering Theses & Dissertations
Low-power circuits and issues associated with them have gained a significant amount of attention in recent years due to the boom in portable electronic devices. Historically, low-power operation relied heavily on technology scaling and reduced operating voltage, however this trend has been slowing down recently due to the increased power density on chips. This dissertation introduces a new very-low power partially-adiabatic logic family called Input-Decoupled Partially-Adiabatic Logic (IDPAL) with applications in low-power circuits. Experimental results show that IDPAL reduces energy usage by 79% compared to equivalent CMOS implementations and by 25% when compared to the best adiabatic implementation. Experiments ranging …
Transparent Spectrum Co-Access In Cognitive Radio Networks, Jonathan Daniel Backens
Transparent Spectrum Co-Access In Cognitive Radio Networks, Jonathan Daniel Backens
Electrical & Computer Engineering Theses & Dissertations
The licensed wireless spectrum is currently under-utilized by as much as 85%. Cognitive radio networks have been proposed to employ dynamic spectrum access to share this under-utilized spectrum between licensed primary user transmissions and unlicensed secondary user transmissions. Current secondary user opportunistic spectrum access methods, however, remain limited in their ability to provide enough incentive to convince primary users to share the licensed spectrum, and they rely on primary user absence to guarantee secondary user performance. These challenges are addressed by developing a Dynamic Spectrum Co-Access Architecture (DSCA) that allows secondary user transmissions to co-access transparently and concurrently with primary …