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This paper considers the problem of blindly calibrating large sensor networks to account for unknown gain and offset in each sensor. Under the assumption that the true signals measured by the sensors lie in a known lower dimensional sub-space, previous work has shown that blind calibration is possible. In practical scenarios, perfect signal subspace… (More)

Detection and analysis of epileptic seizures is of clinical and research interest. We propose a novel seizure detection and analysis scheme based on the phase-slope index (PSI) of directed influence applied to multichannel electrocorticogram data. The PSI metric identifies increases in the spatio-temporal interactions between channels that clearly… (More)

—Subspace clustering has typically been approached as an unsupervised machine learning problem. However in several applications where the union of subspaces model is useful, it is also reasonable to assume you have access to a small number of labels. In this paper we investigate the benefit labeled data brings to the subspace clustering problem. We focus on… (More)

In multiple-input multiple-output (MIMO) radar setting, it is often desirable to design correlated waveforms such that power is transmitted only to a given set of locations, a process known as beampattern design. To design desired beam-pattern, current research uses iterative algorithms, first to synthesize the waveform covariance matrix, R, then to design… (More)

Many clustering problems in computer vision and other contexts are also classification problems, where each cluster shares a meaningful label. Subspace clustering algorithms in particular are often applied to problems that fit this description, for example with face images or handwritten digits. While it is straightforward to request human input on these… (More)

— Adaptive sampling theory has shown that, with proper assumptions on the signal class, algorithms exist to reconstruct a signal in R d with an optimal number of samples. We generalize this problem to when the cost of sampling is not only the number of samples but also the distance traveled between samples. This is motivated by our work studying regions of… (More)

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