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Epidemiology and genomics of a slow outbreak of methicillin-resistant Staphyloccus aureus (MRSA) in a neonatal intensive care unit: Successful chronic decolonization of MRSA-positive healthcare personnel
- Kathleen A. Quan, Mohamad R. A. Sater, Cherry Uy, Robin Clifton-Koeppel, Linda L. Dickey, William Wilson, Pat Patton, Wayne Chang, Pamela Samuelson, Georgia K. Lagoudas, Teri Allen, Lenny Merchant, Rick Gannotta, Cassiana E. Bittencourt, J. C. Soto, Kaye D. Evans, Paul C. Blainey, John Murray, Dawn Shelton, Helen S. Lee, Matthew Zahn, Julia Wolfe, Keith Madey, Jennifer Yim, Shruti K. Gohil, Yonatan H. Grad, Susan S. Huang
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- Journal:
- Infection Control & Hospital Epidemiology / Volume 44 / Issue 4 / April 2023
- Published online by Cambridge University Press:
- 16 June 2022, pp. 589-596
- Print publication:
- April 2023
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Objective:
To describe the genomic analysis and epidemiologic response related to a slow and prolonged methicillin-resistant Staphylococcus aureus (MRSA) outbreak.
Design:Prospective observational study.
Setting:Neonatal intensive care unit (NICU).
Methods:We conducted an epidemiologic investigation of a NICU MRSA outbreak involving serial baby and staff screening to identify opportunities for decolonization. Whole-genome sequencing was performed on MRSA isolates.
Results:A NICU with excellent hand hygiene compliance and longstanding minimal healthcare-associated infections experienced an MRSA outbreak involving 15 babies and 6 healthcare personnel (HCP). In total, 12 cases occurred slowly over a 1-year period (mean, 30.7 days apart) followed by 3 additional cases 7 months later. Multiple progressive infection prevention interventions were implemented, including contact precautions and cohorting of MRSA-positive babies, hand hygiene observers, enhanced environmental cleaning, screening of babies and staff, and decolonization of carriers. Only decolonization of HCP found to be persistent carriers of MRSA was successful in stopping transmission and ending the outbreak. Genomic analyses identified bidirectional transmission between babies and HCP during the outbreak.
Conclusions:In comparison to fast outbreaks, outbreaks that are “slow and sustained” may be more common to units with strong existing infection prevention practices such that a series of breaches have to align to result in a case. We identified a slow outbreak that persisted among staff and babies and was only stopped by identifying and decolonizing persistent MRSA carriage among staff. A repeated decolonization regimen was successful in allowing previously persistent carriers to safely continue work duties.
Setting a Research Agenda in Prevention of Healthcare-Associated Infections (HAIs) and Multidrug-Resistant Organisms (MDROs) Outside of Acute Care Settings
- Part of
- Charlesnika T. Evans, Robin L. Jump, Sarah L. Krein, Suzanne F. Bradley, Christopher J. Crnich, Kalpana Gupta, Eli N. Perencevich, Mark W. Vander Weg, Daniel J. Morgan
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- Journal:
- Infection Control & Hospital Epidemiology / Volume 39 / Issue 2 / February 2018
- Published online by Cambridge University Press:
- 08 February 2018, pp. 210-213
- Print publication:
- February 2018
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Herbicidal Effects of Vinegar and a Clove Oil Product on Redroot Pigweed (Amaranthus retroflexus) and Velvetleaf (Abutilon theophrasti)
- Glenn J. Evans, Robin R. Bellinder, Martin C. Goffinet
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- Journal:
- Weed Technology / Volume 23 / Issue 2 / June 2009
- Published online by Cambridge University Press:
- 20 January 2017, pp. 292-299
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Weed management can be difficult and expensive in organic agricultural systems. Because of the potentially high cost of the natural product herbicides vinegar and clove oil, their efficacy with regard to weed species growth stages needs to be determined. A further objective was to identify anatomical and morphological features of redroot pigweed and velvetleaf that influence the effectiveness of vinegar and clove oil. Research was conducted on greenhouse-grown cotyledon, two-leaf, and four-leaf redroot pigweed and velvetleaf. Dose–response treatments for vinegar included 150-, 200-, 250-, and 300-grain vinegar at 318 L/ha and at 636 L/ha. Clove oil treatments included 1.7, 3.4, 5.1, and 6.8% (v/v) dilutions of a clove oil product in water (318 L/ha), and a 1.7% (v/v) dilution in 200-grain vinegar (318 L/ha). An untreated control was included. Separate plantings of velvetleaf and pigweed were treated with vinegar or clove oil and were used to study anatomical and morphological differences between the two species. Redroot pigweed was easier to control with both products than velvetleaf. Whereas 200-grain vinegar applied at 636 L/ha provided 100% control (6 d after treatment [DAT]) and mortality (9 DAT) of two-leaf redroot pigweed, this same treatment on two-leaf velvetleaf provided only 73% control and 18% mortality. The obtuse leaf blade angle in velvetleaf moved product away from the shoot tip, whereas in pigweed, the acute leaf blade angle, deep central leaf vein, and groove on the upper side of the leaf petiole facilitated product movement toward the stem axis and shoot tip. For both species, and at all application timings, 150-grain vinegar at 636 L/ha provided control equal to that of 300-grain vinegar at 318 L/ha. As growth stage advanced, control and biomass reduction decreased and survival increased. Application timing will be critical to maximizing weed control with vinegar and clove oil.
An Evaluation of Two Novel Cultivation Tools
- Glenn J. Evans, Robin R. Bellinder, Russell R. Hahn
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- Journal:
- Weed Technology / Volume 26 / Issue 2 / June 2012
- Published online by Cambridge University Press:
- 20 January 2017, pp. 316-325
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Cultivation is a critical component of organic weed management and has relevance in conventional farming. Limitations with current cultivation tools include high costs, limited efficacy, and marginal applicability across a range of crops, soil types, soil moisture conditions, and weed growth stages. The objectives of this research were to compare the weed control potential of two novel tools, a block cultivator and a stirrup cultivator, with that of a conventional S-tine cultivator, and to evaluate crop response when each tool was used in pepper and broccoli. Block and stirrup cultivators were mounted on a toolbar with an S-tine sweep. In 2008, the tripart cultivator was tested in 20 independently replicated noncrop field events. Weed survival and reemergence data were collected from the cultivated area of each of the three tools. Environmental data were also collected. A multivariable model was created to assess the importance of cultivator design and environmental and operational variables on postcultivation weed survival. Additional trials in 2009 evaluated the yield response of pepper and broccoli to interrow cultivations with each tool. Cultivator design significantly influenced postcultivation weed survival (P < 0.0001). When weed survival was viewed collectively across all 20 cultivations, both novel cultivators significantly increased control. Relative to the S-tine sweep, the stirrup cultivator reduced weed survival by about one-third and the block cultivator reduced weed survival by greater than two-thirds. Of the 11 individually assessed environmental and operational parameters, 7 had significant implications for weed control with the sweep; 5 impacted control with the stirrup cultivator, and only 1 (surface weed cover at the time of cultivation) influenced control with the block cultivator. Crop response to each cultivator was identical. The block cultivator, because of its increased effectiveness and operational flexibility, has the potential to improve interrow mechanical weed management.
Cultivation Tool Design: Design and Construction of Two Novel Cultivation Tools
- Glenn J. Evans, Robin R. Bellinder, Russell R. Hahn
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- Weed Technology / Volume 26 / Issue 2 / June 2012
- Published online by Cambridge University Press:
- 20 January 2017, pp. 382-388
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Cultivation tools have a long history of use. The integration of cultivation within current organic and conventional weed management programs is conditional on the availability of functional, practical cultivation tools. However, there are performance and operational limitations with current cultivation tools. Serviceable improvement in weed control is the impetus behind creation of new tool designs. The primary objective of this research was to design and construct two cultivators that might address the limitations of current cultivation tools. A secondary objective was to identify historical influences on the technology, availability, and capability of cultivation tools. Two new tractor-mounted cultivators were designed and constructed as loose extractions of antique handheld tools. The first tool, a block cultivator, has a flat surface in the front of the tool that rests against the soil and limits the entrance of a rear-mounted blade. The second tool resembles a stirrup hoe, where a horizontal steel blade with a beveled front edge slices through the upper layer of the soil. Block and stirrup cultivator units were mounted on a toolbar with a traditional S-tine sweep, so that the novel cultivators could be compared directly with a common standard. Relative to the S-tine sweep, the stirrup cultivator reduced weed survival by about one-third and the block cultivator reduced weed survival by greater than two-thirds. Of the three tools, block cultivator performance was least influenced by environmental and operational variances.
Contributors
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- By Mitchell Aboulafia, Frederick Adams, Marilyn McCord Adams, Robert M. Adams, Laird Addis, James W. Allard, David Allison, William P. Alston, Karl Ameriks, C. Anthony Anderson, David Leech Anderson, Lanier Anderson, Roger Ariew, David Armstrong, Denis G. Arnold, E. J. Ashworth, Margaret Atherton, Robin Attfield, Bruce Aune, Edward Wilson Averill, Jody Azzouni, Kent Bach, Andrew Bailey, Lynne Rudder Baker, Thomas R. Baldwin, Jon Barwise, George Bealer, William Bechtel, Lawrence C. Becker, Mark A. Bedau, Ernst Behler, José A. Benardete, Ermanno Bencivenga, Jan Berg, Michael Bergmann, Robert L. Bernasconi, Sven Bernecker, Bernard Berofsky, Rod Bertolet, Charles J. Beyer, Christian Beyer, Joseph Bien, Joseph Bien, Peg Birmingham, Ivan Boh, James Bohman, Daniel Bonevac, Laurence BonJour, William J. Bouwsma, Raymond D. Bradley, Myles Brand, Richard B. Brandt, Michael E. Bratman, Stephen E. Braude, Daniel Breazeale, Angela Breitenbach, Jason Bridges, David O. Brink, Gordon G. Brittan, Justin Broackes, Dan W. Brock, Aaron Bronfman, Jeffrey E. Brower, Bartosz Brozek, Anthony Brueckner, Jeffrey Bub, Lara Buchak, Otavio Bueno, Ann E. Bumpus, Robert W. Burch, John Burgess, Arthur W. Burks, Panayot Butchvarov, Robert E. Butts, Marina Bykova, Patrick Byrne, David Carr, Noël Carroll, Edward S. Casey, Victor Caston, Victor Caston, Albert Casullo, Robert L. Causey, Alan K. L. Chan, Ruth Chang, Deen K. Chatterjee, Andrew Chignell, Roderick M. Chisholm, Kelly J. Clark, E. J. Coffman, Robin Collins, Brian P. Copenhaver, John Corcoran, John Cottingham, Roger Crisp, Frederick J. Crosson, Antonio S. Cua, Phillip D. Cummins, Martin Curd, Adam Cureton, Andrew Cutrofello, Stephen Darwall, Paul Sheldon Davies, Wayne A. Davis, Timothy Joseph Day, Claudio de Almeida, Mario De Caro, Mario De Caro, John Deigh, C. F. Delaney, Daniel C. Dennett, Michael R. DePaul, Michael Detlefsen, Daniel Trent Devereux, Philip E. Devine, John M. Dillon, Martin C. Dillon, Robert DiSalle, Mary Domski, Alan Donagan, Paul Draper, Fred Dretske, Mircea Dumitru, Wilhelm Dupré, Gerald Dworkin, John Earman, Ellery Eells, Catherine Z. Elgin, Berent Enç, Ronald P. Endicott, Edward Erwin, John Etchemendy, C. Stephen Evans, Susan L. Feagin, Solomon Feferman, Richard Feldman, Arthur Fine, Maurice A. Finocchiaro, William FitzPatrick, Richard E. Flathman, Gvozden Flego, Richard Foley, Graeme Forbes, Rainer Forst, Malcolm R. Forster, Daniel Fouke, Patrick Francken, Samuel Freeman, Elizabeth Fricker, Miranda Fricker, Michael Friedman, Michael Fuerstein, Richard A. Fumerton, Alan Gabbey, Pieranna Garavaso, Daniel Garber, Jorge L. A. Garcia, Robert K. Garcia, Don Garrett, Philip Gasper, Gerald Gaus, Berys Gaut, Bernard Gert, Roger F. Gibson, Cody Gilmore, Carl Ginet, Alan H. Goldman, Alvin I. Goldman, Alfonso Gömez-Lobo, Lenn E. Goodman, Robert M. Gordon, Stefan Gosepath, Jorge J. E. Gracia, Daniel W. Graham, George A. Graham, Peter J. Graham, Richard E. Grandy, I. Grattan-Guinness, John Greco, Philip T. Grier, Nicholas Griffin, Nicholas Griffin, David A. Griffiths, Paul J. Griffiths, Stephen R. Grimm, Charles L. Griswold, Charles B. Guignon, Pete A. Y. Gunter, Dimitri Gutas, Gary Gutting, Paul Guyer, Kwame Gyekye, Oscar A. Haac, Raul Hakli, Raul Hakli, Michael Hallett, Edward C. Halper, Jean Hampton, R. James Hankinson, K. R. Hanley, Russell Hardin, Robert M. Harnish, William Harper, David Harrah, Kevin Hart, Ali Hasan, William Hasker, John Haugeland, Roger Hausheer, William Heald, Peter Heath, Richard Heck, John F. Heil, Vincent F. Hendricks, Stephen Hetherington, Francis Heylighen, Kathleen Marie Higgins, Risto Hilpinen, Harold T. Hodes, Joshua Hoffman, Alan Holland, Robert L. Holmes, Richard Holton, Brad W. Hooker, Terence E. Horgan, Tamara Horowitz, Paul Horwich, Vittorio Hösle, Paul Hoβfeld, Daniel Howard-Snyder, Frances Howard-Snyder, Anne Hudson, Deal W. Hudson, Carl A. Huffman, David L. Hull, Patricia Huntington, Thomas Hurka, Paul Hurley, Rosalind Hursthouse, Guillermo Hurtado, Ronald E. Hustwit, Sarah Hutton, Jonathan Jenkins Ichikawa, Harry A. Ide, David Ingram, Philip J. Ivanhoe, Alfred L. Ivry, Frank Jackson, Dale Jacquette, Joseph Jedwab, Richard Jeffrey, David Alan Johnson, Edward Johnson, Mark D. Jordan, Richard Joyce, Hwa Yol Jung, Robert Hillary Kane, Tomis Kapitan, Jacquelyn Ann K. Kegley, James A. Keller, Ralph Kennedy, Sergei Khoruzhii, Jaegwon Kim, Yersu Kim, Nathan L. King, Patricia Kitcher, Peter D. Klein, E. D. Klemke, Virginia Klenk, George L. Kline, Christian Klotz, Simo Knuuttila, Joseph J. Kockelmans, Konstantin Kolenda, Sebastian Tomasz Kołodziejczyk, Isaac Kramnick, Richard Kraut, Fred Kroon, Manfred Kuehn, Steven T. Kuhn, Henry E. Kyburg, John Lachs, Jennifer Lackey, Stephen E. Lahey, Andrea Lavazza, Thomas H. Leahey, Joo Heung Lee, Keith Lehrer, Dorothy Leland, Noah M. Lemos, Ernest LePore, Sarah-Jane Leslie, Isaac Levi, Andrew Levine, Alan E. Lewis, Daniel E. Little, Shu-hsien Liu, Shu-hsien Liu, Alan K. L. Chan, Brian Loar, Lawrence B. Lombard, John Longeway, Dominic McIver Lopes, Michael J. Loux, E. J. Lowe, Steven Luper, Eugene C. Luschei, William G. Lycan, David Lyons, David Macarthur, Danielle Macbeth, Scott MacDonald, Jacob L. Mackey, Louis H. Mackey, Penelope Mackie, Edward H. Madden, Penelope Maddy, G. B. Madison, Bernd Magnus, Pekka Mäkelä, Rudolf A. Makkreel, David Manley, William E. Mann (W.E.M.), Vladimir Marchenkov, Peter Markie, Jean-Pierre Marquis, Ausonio Marras, Mike W. Martin, A. P. Martinich, William L. McBride, David McCabe, Storrs McCall, Hugh J. McCann, Robert N. McCauley, John J. McDermott, Sarah McGrath, Ralph McInerny, Daniel J. McKaughan, Thomas McKay, Michael McKinsey, Brian P. McLaughlin, Ernan McMullin, Anthonie Meijers, Jack W. Meiland, William Jason Melanson, Alfred R. Mele, Joseph R. Mendola, Christopher Menzel, Michael J. Meyer, Christian B. Miller, David W. Miller, Peter Millican, Robert N. Minor, Phillip Mitsis, James A. Montmarquet, Michael S. Moore, Tim Moore, Benjamin Morison, Donald R. Morrison, Stephen J. Morse, Paul K. Moser, Alexander P. D. Mourelatos, Ian Mueller, James Bernard Murphy, Mark C. Murphy, Steven Nadler, Jan Narveson, Alan Nelson, Jerome Neu, Samuel Newlands, Kai Nielsen, Ilkka Niiniluoto, Carlos G. Noreña, Calvin G. Normore, David Fate Norton, Nikolaj Nottelmann, Donald Nute, David S. Oderberg, Steve Odin, Michael O’Rourke, Willard G. Oxtoby, Heinz Paetzold, George S. Pappas, Anthony J. Parel, Lydia Patton, R. P. Peerenboom, Francis Jeffry Pelletier, Adriaan T. Peperzak, Derk Pereboom, Jaroslav Peregrin, Glen Pettigrove, Philip Pettit, Edmund L. Pincoffs, Andrew Pinsent, Robert B. Pippin, Alvin Plantinga, Louis P. Pojman, Richard H. Popkin, John F. Post, Carl J. Posy, William J. Prior, Richard Purtill, Michael Quante, Philip L. Quinn, Philip L. Quinn, Elizabeth S. Radcliffe, Diana Raffman, Gerard Raulet, Stephen L. Read, Andrews Reath, Andrew Reisner, Nicholas Rescher, Henry S. Richardson, Robert C. Richardson, Thomas Ricketts, Wayne D. Riggs, Mark Roberts, Robert C. Roberts, Luke Robinson, Alexander Rosenberg, Gary Rosenkranz, Bernice Glatzer Rosenthal, Adina L. Roskies, William L. Rowe, T. M. Rudavsky, Michael Ruse, Bruce Russell, Lilly-Marlene Russow, Dan Ryder, R. M. Sainsbury, Joseph Salerno, Nathan Salmon, Wesley C. Salmon, Constantine Sandis, David H. Sanford, Marco Santambrogio, David Sapire, Ruth A. Saunders, Geoffrey Sayre-McCord, Charles Sayward, James P. Scanlan, Richard Schacht, Tamar Schapiro, Frederick F. Schmitt, Jerome B. Schneewind, Calvin O. Schrag, Alan D. Schrift, George F. Schumm, Jean-Loup Seban, David N. Sedley, Kenneth Seeskin, Krister Segerberg, Charlene Haddock Seigfried, Dennis M. Senchuk, James F. Sennett, William Lad Sessions, Stewart Shapiro, Tommie Shelby, Donald W. Sherburne, Christopher Shields, Roger A. Shiner, Sydney Shoemaker, Robert K. Shope, Kwong-loi Shun, Wilfried Sieg, A. John Simmons, Robert L. Simon, Marcus G. Singer, Georgette Sinkler, Walter Sinnott-Armstrong, Matti T. Sintonen, Lawrence Sklar, Brian Skyrms, Robert C. Sleigh, Michael Anthony Slote, Hans Sluga, Barry Smith, Michael Smith, Robin Smith, Robert Sokolowski, Robert C. Solomon, Marta Soniewicka, Philip Soper, Ernest Sosa, Nicholas Southwood, Paul Vincent Spade, T. L. S. Sprigge, Eric O. Springsted, George J. Stack, Rebecca Stangl, Jason Stanley, Florian Steinberger, Sören Stenlund, Christopher Stephens, James P. Sterba, Josef Stern, Matthias Steup, M. A. Stewart, Leopold Stubenberg, Edith Dudley Sulla, Frederick Suppe, Jere Paul Surber, David George Sussman, Sigrún Svavarsdóttir, Zeno G. Swijtink, Richard Swinburne, Charles C. Taliaferro, Robert B. Talisse, John Tasioulas, Paul Teller, Larry S. Temkin, Mark Textor, H. S. Thayer, Peter Thielke, Alan Thomas, Amie L. Thomasson, Katherine Thomson-Jones, Joshua C. Thurow, Vzalerie Tiberius, Terrence N. Tice, Paul Tidman, Mark C. Timmons, William Tolhurst, James E. Tomberlin, Rosemarie Tong, Lawrence Torcello, Kelly Trogdon, J. D. Trout, Robert E. Tully, Raimo Tuomela, John Turri, Martin M. Tweedale, Thomas Uebel, Jennifer Uleman, James Van Cleve, Harry van der Linden, Peter van Inwagen, Bryan W. Van Norden, René van Woudenberg, Donald Phillip Verene, Samantha Vice, Thomas Vinci, Donald Wayne Viney, Barbara Von Eckardt, Peter B. M. Vranas, Steven J. Wagner, William J. Wainwright, Paul E. Walker, Robert E. Wall, Craig Walton, Douglas Walton, Eric Watkins, Richard A. Watson, Michael V. Wedin, Rudolph H. Weingartner, Paul Weirich, Paul J. Weithman, Carl Wellman, Howard Wettstein, Samuel C. Wheeler, Stephen A. White, Jennifer Whiting, Edward R. Wierenga, Michael Williams, Fred Wilson, W. Kent Wilson, Kenneth P. Winkler, John F. Wippel, Jan Woleński, Allan B. Wolter, Nicholas P. Wolterstorff, Rega Wood, W. Jay Wood, Paul Woodruff, Alison Wylie, Gideon Yaffe, Takashi Yagisawa, Yutaka Yamamoto, Keith E. Yandell, Xiaomei Yang, Dean Zimmerman, Günter Zoller, Catherine Zuckert, Michael Zuckert, Jack A. Zupko (J.A.Z.)
- Edited by Robert Audi, University of Notre Dame, Indiana
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- The Cambridge Dictionary of Philosophy
- Published online:
- 05 August 2015
- Print publication:
- 27 April 2015, pp ix-xxx
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Notes on Contributors
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- By John Dennis Anderson, William Blazek, Linda Costanzo Cahir, Sharon Kehl Califano, Donna Campbell, Helena Chance, Melanie Dawson, Linda De Roche, Anne-Marie Evans, Susan Goodman, Jennifer Haytock, Adam Jabbur, Katherine Joslin, Pamela Knights, Heidi M. Kunz, Jessica Schubert McCarthy, Bonnie Shannon McMullen, Cecilia Macheski, Maureen E. Montgomery, Elsa Nettels, Julie Olin-Ammentorp, Emily J. Orlando, Robin Peel, Melissa M. Pennell, Laura Rattray, Judith P. Saunders, Sharon Shaloo, Gail D. Sinclair, Carol J. Singley, Margaret Toth, Gary Totten, Linda Wagner-Martin
- Edited by Laura Rattray, University of Hull
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- Edith Wharton in Context
- Published online:
- 05 November 2012
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- 08 October 2012, pp ix-xvi
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Contributors
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- By Nozomi Akanuma, Gonzalo Alarcón, R. Arunachalam, Sarah H. Bernard, Frank M. C. Besag, Istvan Bodi, Stephen Brown, Franz Brunnhuber, Antonella Cerquiglini, J. Helen Cross, R. Shane Delamont, Archana Desurkar, Lee Drummond, Rona Eade, Robert D. C. Elwes, Bidi Evans, Peter Fenwick, Colin D. Ferrie, Paul L. Furlong, Laura H. Goldstein, Sally Gomersall, Sushma Goyal, Jane Hanna, Yvonne Hart, Dominic C. Heaney, Graham E. Holder, Mrinalini Honavar, Elaine Hughes, Jozef M. Jarosz, John G. R. Jefferys, Jane Juler, Mathias Koepp, Michalis Koutroumanidis, Maureen Lahiff, Louis Lemieux, David McCormick, Brian Meldrum, John D. C. Mellers, Nicholas Moran, John Moriarty, Robin G. Morris, Nandini Mullatti, Lina Nashef, Jennifer Nightingale, T. J. von Oertzen, Corina O'Neill, Philip N. Patsalos, Stella Pearson, Charles E. Polkey, Ronit Pressler, Edward H. Reynolds, Mark P. Richardson, Leone Ridsdale, Robert Robinson, Greg Rogers, Euan M. Ross, Richard P. Selway, Stefano Seri, Simeran Sharma, Graeme J. Sills, Andrew Simmons, Shiri Spector, Mark Stevenson, Jade N. Thai, Brian Toone, Antonio Valentín, Nuria T. Villagra, Matthew Walker, William Whitehouse
- Edited by Gonzalo Alarcón, King's College London, Antonio Valentín, King's College London
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- Introduction to Epilepsy
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- 05 July 2012
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- 26 April 2012, pp xii-xv
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8 - Object tracking with time-delayed, out-of-sequence measurements
- Subhash Challa, University of Melbourne, Mark R. Morelande, University of Melbourne, Darko Mušicki, Robin J. Evans, University of Melbourne
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- Fundamentals of Object Tracking
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- 07 September 2011
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- 28 July 2011, pp 289-311
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Contents
- Subhash Challa, University of Melbourne, Mark R. Morelande, University of Melbourne, Darko Mušicki, Robin J. Evans, University of Melbourne
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- Fundamentals of Object Tracking
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- 07 September 2011
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- 28 July 2011, pp v-viii
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Appendix A - Mathematical and statistical preliminaries
- Subhash Challa, University of Melbourne, Mark R. Morelande, University of Melbourne, Darko Mušicki, Robin J. Evans, University of Melbourne
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- Fundamentals of Object Tracking
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- 07 September 2011
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- 28 July 2011, pp 344-353
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Summary
Probability laws and distributions
Sample space and events
The sample space
The sample space is the set of all possible values, or outcomes, of a realization that is not known, be it in the past, present or future. In the Bayesian probabilistic framework, every unknown quantity is treated as a random quantity.
Examples:
When tracking an object in 3D space, the position of the target at some point in time in the future is not known. The 3D space is the sample space.
The exact position of that object in the past may not be known. In many tracking situations, the exact position is never observed, only estimated. In that case, although in the past, the exact position of the target is a random quantity and the 3D space is the sample space.
Measurements in object tracking are the results of observations by sensing devices. They are subject to random fluctuations. Measurement errors are attached to the measurements, making them random quantities. The focus is on the errors and they are treated as random values. Their sample space is problem dependent, but often is the value space of the measurements.
The sample space is the mathematical set of all values that can be taken by an unknown quantity of interest. One of the simplest examples would be the tossing of a coin. The sample space is {H, T}, where H is the outcome of a head in the tossing and T is tail.
4 - Single-object tracking in clutter
- Subhash Challa, University of Melbourne, Mark R. Morelande, University of Melbourne, Darko Mušicki, Robin J. Evans, University of Melbourne
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- Fundamentals of Object Tracking
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- 07 September 2011
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- 28 July 2011, pp 103-132
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Summary
In Chapters 2 and 3, we introduced state estimation and filtering theory and its application to idealistic object tracking problems. The fact that makes practical object tracking problems both challenging and interesting is that the sensor measurements, more often than not, contain detections from false targets. For example, in many radar and sonar applications, measurements (detections) originate not only from objects of interest, but also from thermal noise, terrain reflections, clouds, etc. Such unwanted measurements are usually termed clutter. In vision-based object tracking, where tracking can be used to count moving targets, shadows created by an afternoon sun, light reflections on snow or the movement of leaves on a tree can all generate clutter data in the images.
One of the defining characteristics of clutter or false alarms is that their number changes from one time instant to the next in a random manner and, to make matters worse, target- and clutter-originated measurements share the same measurement space and look alike. Practical tracking problems are considerably difficult since sometimes, even when there are targets in the sensor's field of view, they can go undetected or fail to appear in the set of measurements. In other words, true measurements from the target are present during each measurement scan with only a certain probability of detection. Hence, determining the state of the object using a combination of false alarms and true target returns is at the heart of all practical object tracking problems and is the subject of this chapter.
Index
- Subhash Challa, University of Melbourne, Mark R. Morelande, University of Melbourne, Darko Mušicki, Robin J. Evans, University of Melbourne
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- Fundamentals of Object Tracking
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- 07 September 2011
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- 28 July 2011, pp 370-375
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Fundamentals of Object Tracking
- Subhash Challa, Mark R. Morelande, Darko Mušicki, Robin J. Evans
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- 07 September 2011
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- 28 July 2011
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Kalman filter, particle filter, IMM, PDA, ITS, random sets... The number of useful object-tracking methods is exploding. But how are they related? How do they help track everything from aircraft, missiles and extra-terrestrial objects to people and lymphocyte cells? How can they be adapted to novel applications? Fundamentals of Object Tracking tells you how. Starting with the generic object-tracking problem, it outlines the generic Bayesian solution. It then shows systematically how to formulate the major tracking problems – maneuvering, multiobject, clutter, out-of-sequence sensors – within this Bayesian framework and how to derive the standard tracking solutions. This structured approach makes very complex object-tracking algorithms accessible to the growing number of users working on real-world tracking problems and supports them in designing their own tracking filters under their unique application constraints. The book concludes with a chapter on issues critical to successful implementation of tracking algorithms, such as track initialization and merging.
6 - Multiple-object tracking in clutter: random-set-based approach
- Subhash Challa, University of Melbourne, Mark R. Morelande, University of Melbourne, Darko Mušicki, Robin J. Evans, University of Melbourne
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- Fundamentals of Object Tracking
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- 07 September 2011
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- 28 July 2011, pp 223-264
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Summary
Typically, multiple-object tracking problems are handled by extending the singleobject tracking algorithms where each object is tracked as an isolated entity. The challenge comes when the targets are close by and there is ambiguity about the origin of the measurement, i.e., which measurements are from which track (in general). Using similar techniques of data association, multiple measurements are assigned to multiple objects (in general). However, such an extension of singleobject trackers to multiple-object trackers assumes that one knows the number of objects present in the surveillance space, which is not true.
This problem leads to some of the serious advances and methods of “data association” logic of these trackers. The data association step calculates the origin of the measurements in a probabilistic manner. It hypothesizes the measurement origin and calculates probabilities for each of the hypotheses. For example, a single-object tracking algorithm considers two hypotheses under measurement origin uncertainty – “the measurement is from an object of interest” or “the measurement is from clutter.” Such algorithms ignore the possibility of the measurements originating from other objects. This problem is partially solved by introducing the hypothesis “the measurement is from the ith (out of N) objects.” But setting the number of objects to a specific value is a limitation by itself. Moreover, this approach does not provide any measure for the validity of the number of objects. Multi-object trackers need to estimate the number of objects and their individual states jointly.
Preface
- Subhash Challa, University of Melbourne, Mark R. Morelande, University of Melbourne, Darko Mušicki, Robin J. Evans, University of Melbourne
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- Fundamentals of Object Tracking
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- 07 September 2011
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- 28 July 2011, pp ix-xii
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Summary
Tracking the paths of moving objects is an activity with a long history. People in ancient societies used to track moving prey to hunt and feed their kith and kin, and invented ways to track the motion of stars for navigation purposes and to predict seasonal changes in their environments. Object tracking has been an essential technology for human survival and has significantly contributed to human progress.
In recent times, there has been an explosion in the use of object tracking technology in non-military applications. Object tracking algorithms have become an essential part of our daily lives. For example, GPS-based navigation is a daily tool of humankind. In this application a group of artificial satellites in outer space continuously locate the vehicles people drive and the object tracking algorithms within the GPS perform self-localization and enable us to enjoy a number of locationbased services, such as finding places of interest and route planning. Similarly, tracking of objects is used in a wide variety of contexts, such as airspace surveillance, satellite and space vehicle tracking, submarine and whale tracking and intelligent video surveillance. They are also used in autonomous robot navigation using lasers, stereo cameras and other proximity sensors, radiosonde-enabled balloon tracking for accurate weather predictions, and, more recently, in the study of cell biology to study cell fate under different chemical and environmental influences by tracking many kinds of cells, including lymphocyte and stem cells through multiple generations of birth and death.
7 - Bayesian smoothing algorithms for object tracking
- Subhash Challa, University of Melbourne, Mark R. Morelande, University of Melbourne, Darko Mušicki, Robin J. Evans, University of Melbourne
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- Book:
- Fundamentals of Object Tracking
- Published online:
- 07 September 2011
- Print publication:
- 28 July 2011, pp 265-288
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Summary
Estimation of an object state at a particular time based on measurements collected beyond that time is generally termed as smoothing or retrodiction. Smoothing improves the estimates compared to the ones obtained by filters owning to the use of more observations (or information). This comes at the cost of a certain time delay. However, these improvements are highly effective in applications like “situation awareness” or “threat assessment.” These higher level applications improve operator efficiency if a more accurate picture of the actual field scenario is provided to them, even if it is with a time delay. For these applications, besides object state, parameters representing the overall scenario, like number of targets, their initiation/termination instants and locations, may prove to be very useful ones. A smoothing algorithm can result in a better estimation of the overall situational picture and thus help increase the effectiveness of the critical applications like situation/ threat awareness. This chapter will introduce the Bayesian formulation of smoothing and derive the established smoothing algorithms under different tracking scenarios: non-maneuvering, maneuvering, clutter and in the presence of object existence uncertainty.
Introduction to smoothing
Filters, introduced in previous chapters, produce the “best estimate” of the object state at a particular time based on the measurements collected up to that time. Smoothers, on the other hand, produce an estimate of the state at a time based on measurements collected beyond the time in question (the predictor is another estimator where the estimation at a certain time is carried out based on measurements collected until a point before that time).
3 - Maneuvering object tracking
- Subhash Challa, University of Melbourne, Mark R. Morelande, University of Melbourne, Darko Mušicki, Robin J. Evans, University of Melbourne
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- Book:
- Fundamentals of Object Tracking
- Published online:
- 07 September 2011
- Print publication:
- 28 July 2011, pp 62-102
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Summary
Maneuvering objects are those objects whose dynamical behavior changes over time. An object that suddenly turns or accelerates displays a maneuvering behavior with regard to its tracked position. While the definition of a maneuvering object extends beyond the tracking of position and speed, historically it is in this context that maneuvering object tracking theory developed. This chapter presents a unified derivation of some of the most common maneuvering object tracking algorithms in the Chapman–Kolmogorov–Bayesian framework.
Modeling for maneuvering object tracking
In general, maneuvering object tracking refers to the problem of state estimation where the system model undergoes abrupt changes. The standard Kalman filter with a single motion model is limited in performance for such problems because it does not effectively respond to the changes in the dynamics as the object maneuvers. A large number of approaches to the maneuvering object tracking problem have been developed including process noise adaptation (Singer et al., 1974; Moose, 1975; Gholson and Moose, 1977; Ricker and Williams, 1978; Moose et al., 1979; Farina and Studer, 1985), input estimation (Chan et al., 1979), variable dimension filtering (Bar-Shalom and Birmiwal, 1982) and multiple models (MM) (Ackerson and Fu, 1970; Mori et al., 1986; Blom and Bar-Shalom, 1988; Bar-Shalom and Li, 1993), etc. These apparently diverse approaches may be grouped into two broad categories:
single model with state augmentation;
multiple models with Markovian jumps.
2 - Filtering theory and non-maneuvering object tracking
- Subhash Challa, University of Melbourne, Mark R. Morelande, University of Melbourne, Darko Mušicki, Robin J. Evans, University of Melbourne
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- Book:
- Fundamentals of Object Tracking
- Published online:
- 07 September 2011
- Print publication:
- 28 July 2011, pp 22-61
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Appendix B - Finite set statistics (FISST)
- Subhash Challa, University of Melbourne, Mark R. Morelande, University of Melbourne, Darko Mušicki, Robin J. Evans, University of Melbourne
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- Book:
- Fundamentals of Object Tracking
- Published online:
- 07 September 2011
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- 28 July 2011, pp 354-357
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