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2020


SIMULTANEOUS CALIBRATION METHOD FOR MAGNETIC LOCALIZATION AND ACTUATION SYSTEMS
SIMULTANEOUS CALIBRATION METHOD FOR MAGNETIC LOCALIZATION AND ACTUATION SYSTEMS

Sitti, M., Son, D., Dong, X.

June 2020, US Patent App. 16/696,605 (misc)

Abstract
The invention relates to a method of simultaneously calibrating magnetic actuation and sensing systems for a workspace, wherein the actuation system comprises a plurality of magnetic actuators and the sensing system comprises a plurality of magnetic sensors, wherein all the measured data is fed into a calibration model, wherein the calibration model is based on a sensor measurement model and a magnetic actuation model, and wherein a solution of the model parameters is found via a numerical solver order to calibrate both the actuation and sensing systems at the same time.

pi

[BibTex]

2013


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Dry adhesives and methods for making dry adhesives

Sitti, M., Kim, S.

sep 2013, US Patent App. 14/016,651 (misc)

pi

[BibTex]

2013


[BibTex]


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Dry adhesives and methods for making dry adhesives

Sitti, M., Kim, S.

sep 2013, US Patent App. 14/016,683 (misc)

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[BibTex]

[BibTex]


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Dry adhesives and methods for making dry adhesives

Sitti, M., Kim, S.

sep 2013, US Patent 8,524,092 (misc)

pi

[BibTex]

[BibTex]


Learning and Optimization with Submodular Functions
Learning and Optimization with Submodular Functions

Sankaran, B., Ghazvininejad, M., He, X., Kale, D., Cohen, L.

ArXiv, May 2013 (techreport)

Abstract
In many naturally occurring optimization problems one needs to ensure that the definition of the optimization problem lends itself to solutions that are tractable to compute. In cases where exact solutions cannot be computed tractably, it is beneficial to have strong guarantees on the tractable approximate solutions. In order operate under these criterion most optimization problems are cast under the umbrella of convexity or submodularity. In this report we will study design and optimization over a common class of functions called submodular functions. Set functions, and specifically submodular set functions, characterize a wide variety of naturally occurring optimization problems, and the property of submodularity of set functions has deep theoretical consequences with wide ranging applications. Informally, the property of submodularity of set functions concerns the intuitive principle of diminishing returns. This property states that adding an element to a smaller set has more value than adding it to a larger set. Common examples of submodular monotone functions are entropies, concave functions of cardinality, and matroid rank functions; non-monotone examples include graph cuts, network flows, and mutual information. In this paper we will review the formal definition of submodularity; the optimization of submodular functions, both maximization and minimization; and finally discuss some applications in relation to learning and reasoning using submodular functions.

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arxiv link (url) [BibTex]

arxiv link (url) [BibTex]


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Dry adhesives and methods of making dry adhesives

Sitti, M., Murphy, M., Aksak, B.

March 2013, US Patent App. 13/845,702 (misc)

pi

[BibTex]

[BibTex]

2006


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Statistical Learning of LQG controllers

Theodorou, E.

Technical Report-2006-1, Computational Action and Vision Lab University of Minnesota, 2006, clmc (techreport)

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PDF [BibTex]

2006


PDF [BibTex]