Nicholas Roseveare

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This paper is concerned with determining the number of correlated signals between two data sets when the number of samples from these data sets is extremely small. In such a scenario, a principal component analysis (PCA) preprocessing step is commonly performed before applying canonical correlation analysis (CCA). We present a reduced-rank version of the(More)
We analyze the data throughput maximization problem over fading channels for a energy harvesting wireless sensor network. The effective use of energy harvesting wireless sensor networks requires an apt understanding of the underlying environmental processes. Energy as a constrained resource must be carefully utilized so as to enable good performance through(More)
This paper is concerned with determining the number of correlated signals between two data sets using canonical correlation analysis (CCA) when a principal component analysis (PCA) preprocessing step is performed for initial rank reduction. In signal processing applications, it is commonplace in scenarios with large dimensions, insufficient samples, or(More)
We consider a wireless sensor network engaged in the task of distributed tracking. Here, multiple remote sensor nodes estimate a physical process (viz., a moving object) and transmit quantized estimates to a fusion center for processing. At the fusion node a BLUE (Best Linear Unbiased Estimation) approach is used to combine the sensor estimates and create a(More)
In this paper, we attempt to uncover the fundamental limitations of implementing decentralized optimization in an energy harvesting sensor network by quantifying the impact of stochastic energy availability on convergence. Specifically, the discrete energy quanta being harvested by a network of wireless sensors are modelled via a marked Poisson process. The(More)
We consider the challenging problem of distributed tracking using wireless sensor networks (WSN). In our scenario, multiple spatially distributed sensor nodes estimate a physical process (viz. a moving object) and transmit quantized state estimates to a central fusion node for processing. The fusion node utilizes a BLUE (Best Linear Unbiased Estimation)(More)
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