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2016


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System and method to magnetically actuate a capsule endoscopic robot for diagnosis and treatment

Sitti, M., Yim, S.

May 2016, US Patent 9,445,711 (patent)

Abstract
Present invention describes a swallowable device with a soft, compliant exterior, whose shape can be changed through the use of magnetic fields, and which can be locomoted in a rolling motion through magnetic control from the exterior of the patient. The present invention could be used for a variety of medical applications inside the GI tract including but not limited to drug delivery, biopsy, heat cauterization, pH sensing, biochemical sensing, micro-surgery, and active imaging.

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link (url) [BibTex]


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Remotely addressable magnetic composite micro-actuators

Sitti, M., Diller, E., Miyashita, S.

Febuary 2016, US Patent App. 15/018,008 (patent)

Abstract
The present invention describes methods to fabricate actuators that can be remotely controlled in an addressable manner, and methods to provide remote control such micro-actuators. The actuators are composites of two permanent magnet materials, one of which is has high coercivity, and the other of which switches magnetization direction by applied fields. By switching the second material's magnetization direction, the two magnets either work together or cancel each other, resulting in distinct “on” and “off” behavior of the devices. The device can be switched “on” or “off” remotely using a field pulse of short duration.

pi

[BibTex]

[BibTex]


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Remotely addressable magnetic composite micro-actuators

Sitti, M., Diller, E., Miyashita, S.

Febuary 2016, US Patent 9,281,112 (patent)

Abstract
The present invention describes methods to fabricate actuators that can be remotely controlled in an addressable manner, and methods to provide remote control such micro-actuators. The actuators are composites of two permanent magnet materials, one of which is has high coercivity, and the other of which switches magnetization direction by applied fields. By switching the second material's magnetization direction, the two magnets either work together or cancel each other, resulting in distinct “on” and “off” behavior of the devices. The device can be switched “on” or “off” remotely using a field pulse of short duration.

pi

link (url) [BibTex]

link (url) [BibTex]


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Method for encapsulating a nanostructure, coated nanostructure and use of a coated nanostructure

Jeong, H. H., Lee, T. C., Fischer, P.

Google Patents, 2016, WO Patent App. PCT/EP2016/056,377 (patent)

Abstract
The present invention relates to a method for encapsulating a nanostructure, the method comprising the steps of: -providing a substrate; -forming a plug composed of plug material at said substrate; -forming a nanostructure (on or) at said plug; -forming a shell composed of at least one shell material on external surfaces of the nanostructure, with the at least one shell material covering said nanostructure and at least some of the plug material,whereby the shell and the plug encapsulate the nanostructure. The invention further relates to a coated nanostructure and to the use of a coated nanostructure.

pf

link (url) [BibTex]

2013


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Human Pose Calculation from Optical Flow Data

Black, M., Loper, M., Romero, J., Zuffi, S.

European Patent Application EP 2843621 , August 2013 (patent)

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Google Patents [BibTex]

2013


Google Patents [BibTex]


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System and method for generating bilinear spatiotemporal basis models

Matthews, I. A. I. S. T. S. K. S. Y.

US Patent Application 13/425,369, March 2013 (patent)

Abstract
Techniques are disclosed for generating a bilinear spatiotemporal basis model. A method includes the steps of predefining a trajectory basis for the bilinear spatiotemporal basis model, receiving three-dimensional spatiotemporal data for a training sequence, estimating a shape basis for the bilinear spatiotemporal basis model using the three-dimensional spatiotemporal data, and computing coefficients for the bilinear spatiotemporal basis model using the trajectory basis and the shape basis.

ps

Google Patents [BibTex]

2004


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Pattern detection methods and systems and face detection methods and systems

Blake, A., Romdhani, S., Schölkopf, B., Torr, P. H. S.

United States Patent, No 6804391, October 2004 (patent)

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[BibTex]

2004


[BibTex]


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Kernel Methods in Computational Biology

Schölkopf, B., Tsuda, K., Vert, J.

pages: 410, Computational Molecular Biology, MIT Press, Cambridge, MA, USA, August 2004 (book)

Abstract
Modern machine learning techniques are proving to be extremely valuable for the analysis of data in computational biology problems. One branch of machine learning, kernel methods, lends itself particularly well to the difficult aspects of biological data, which include high dimensionality (as in microarray measurements), representation as discrete and structured data (as in DNA or amino acid sequences), and the need to combine heterogeneous sources of information. This book provides a detailed overview of current research in kernel methods and their applications to computational biology. Following three introductory chapters—an introduction to molecular and computational biology, a short review of kernel methods that focuses on intuitive concepts rather than technical details, and a detailed survey of recent applications of kernel methods in computational biology—the book is divided into three sections that reflect three general trends in current research. The first part presents different ideas for the design of kernel functions specifically adapted to various biological data; the second part covers different approaches to learning from heterogeneous data; and the third part offers examples of successful applications of support vector machine methods.

ei

Web [BibTex]

Web [BibTex]

2002


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Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond

Schölkopf, B., Smola, A.

pages: 644, Adaptive Computation and Machine Learning, MIT Press, Cambridge, MA, USA, December 2002, Parts of this book, including an introduction to kernel methods, can be downloaded here. (book)

Abstract
In the 1990s, a new type of learning algorithm was developed, based on results from statistical learning theory: the Support Vector Machine (SVM). This gave rise to a new class of theoretically elegant learning machines that use a central concept of SVMs-kernels—for a number of learning tasks. Kernel machines provide a modular framework that can be adapted to different tasks and domains by the choice of the kernel function and the base algorithm. They are replacing neural networks in a variety of fields, including engineering, information retrieval, and bioinformatics. Learning with Kernels provides an introduction to SVMs and related kernel methods. Although the book begins with the basics, it also includes the latest research. It provides all of the concepts necessary to enable a reader equipped with some basic mathematical knowledge to enter the world of machine learning using theoretically well-founded yet easy-to-use kernel algorithms and to understand and apply the powerful algorithms that have been developed over the last few years.

ei

Web [BibTex]

2002


Web [BibTex]


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test jon
(book)

[BibTex]