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Alex Small
Researcher at California State Polytechnic University, Pomona
Publications - 52
Citations - 2552
Alex Small is an academic researcher from California State Polytechnic University, Pomona. The author has contributed to research in topics: Image processing & Percolation threshold. The author has an hindex of 13, co-authored 50 publications receiving 2414 citations. Previous affiliations of Alex Small include National Institutes of Health & University of California, Santa Barbara.
Papers
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Journal ArticleDOI
Quantum information processing using quantum dot spins and cavity QED
Atac Imamoglu,David D. Awschalom,Guido Burkard,David P. DiVincenzo,Daniel Loss,Mark S. Sherwin,Alex Small +6 more
TL;DR: In this paper, a scheme that realizes controlled interactions between two distant quantum dot spins is proposed, where the effective long-range interaction is mediated by the vacuum field of a high finesse microcavity.
Journal ArticleDOI
Fluorophore localization algorithms for super-resolution microscopy
Alex Small,Shane Stahlheber +1 more
TL;DR: This Review surveys the fundamental issues for single-fluorophore fitting routines, localization algorithms based on principles other than fitting, three-dimensional imaging, dipole imaging and techniques for estimating fluorophore positions from images of multiple activated fluorophores.
Posted Content
Quantum information processing using quantum dot spins and cavity-QED
Atac Imamoglu,David D. Awschalom,Guido Burkard,David P. DiVincenzo,Daniel Loss,Mark S. Sherwin,Alex Small +6 more
TL;DR: In this paper, a scheme that realizes controlled interactions between two distant quantum dot spins is proposed, where the effective long-range interaction is mediated by the vacuum field of a high finesse microcavity.
Journal ArticleDOI
Theoretical Limits on Errors and Acquisition Rates in Localizing Switchable Fluorophores
TL;DR: A formalism for defining error rates is introduced, a general relationship between error rates, image acquisition rates, and the performance characteristics of the image processing algorithms are derived, and it is shown that there is a minimum acquisition time irrespective of algorithm performance.
Journal ArticleDOI
Superresolution Localization Methods
TL;DR: This review examines several different families of fluorophore localization algorithms, comparing their complexity, performance, and range of applicability (e.g., whether they require particular types of experimental information, are optimized for specific situations, or are more general).