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2018


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Nanoscale robotic agents in biological fluids and tissues

Palagi, S., Walker, D. Q. T., Fischer, P.

In The Encyclopedia of Medical Robotics, 2, pages: 19-42, 2, (Editors: Desai, J. P. and Ferreira, A.), World Scientific, October 2018 (inbook)

Abstract
Nanorobots are untethered structures of sub-micron size that can be controlled in a non-trivial way. Such nanoscale robotic agents are envisioned to revolutionize medicine by enabling minimally invasive diagnostic and therapeutic procedures. To be useful, nanorobots must be operated in complex biological fluids and tissues, which are often difficult to penetrate. In this chapter, we first discuss potential medical applications of motile nanorobots. We briefly present the challenges related to swimming at such small scales and we survey the rheological properties of some biological fluids and tissues. We then review recent experimental results in the development of nanorobots and in particular their design, fabrication, actuation, and propulsion in complex biological fluids and tissues. Recent work shows that their nanoscale dimension is a clear asset for operation in biological tissues, since many biological tissues consist of networks of macromolecules that prevent the passage of larger micron-scale structures, but contain dynamic pores through which nanorobots can move.

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link (url) DOI [BibTex]

2018


link (url) DOI [BibTex]


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Haptics and Haptic Interfaces

Kuchenbecker, K. J.

In Encyclopedia of Robotics, (Editors: Marcelo H. Ang and Oussama Khatib and Bruno Siciliano), Springer, May 2018 (incollection)

Abstract
Haptics is an interdisciplinary field that seeks to both understand and engineer touch-based interaction. Although a wide range of systems and applications are being investigated, haptics researchers often concentrate on perception and manipulation through the human hand. A haptic interface is a mechatronic system that modulates the physical interaction between a human and his or her tangible surroundings. Haptic interfaces typically involve mechanical, electrical, and computational layers that work together to sense user motions or forces, quickly process these inputs with other information, and physically respond by actuating elements of the user’s surroundings, thereby enabling him or her to act on and feel a remote and/or virtual environment.

hi

link (url) DOI [BibTex]

link (url) DOI [BibTex]


Thumb xl us20180021892a1 20180125 d00000
Method and device for reversibly attaching a phase changing metal to an object

Zhou Ye, G. Z. L. M. S.

US Patent Application US 2018/0021892 A1, January 2018 (patent)

Abstract
A method for reversibly attaching a phase changing metal to an object, the method comprising the steps of: providing a substrate having at least one surface at which the phase changing metal is attached, heating the phase changing metal above a phase changing temperature at which the phase changing metal changes its phase from solid to liquid, bringing the phase changing metal, when the phase changing metal is in the liquid phase or before the phase changing metal is brought into the liquid phase, into contact with the object, permitting the phase changing metal to cool below the phase changing temperature, whereby the phase changing metal becomes solid and the object and the phase changing metal become attached to each other, reheating the phase changing metal above the phase changing temperature to liquefy the phase changing metal, and removing the substrate from the object, with the phase changing metal separating from the object and remaining with the substrate.

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US Patent Application Database US Patent Application (PDF) [BibTex]


Thumb xl us20180012693a1 20180111 d00000
Method of fabricating a shape-changeable magentic member, method of producing a shape changeable magnetic member and shape changeable magnetic member

Guo Zhan Lum, Z. Y. M. S.

US Patent Application US 2018/0012693 A1, January 2018 (patent)

Abstract
The present invention relates to a method of fabricating a shape-changeable magnetic member comprising a plurality of segments with each segment being able to be magnetized with a desired magnitude and orientation of magnetization, to a method of producing a shape changeable magnetic member composed of a plurality of segments and to a shape changeable magnetic member.

pi

US Patent Application Database US Patent Application (PDF) [BibTex]


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Methods in Psychophysics

Wichmann, F. A., Jäkel, F.

In Stevens’ Handbook of Experimental Psychology and Cognitive Neuroscience, 5 (Methodology), 7, 4th, John Wiley & Sons, Inc., 2018 (inbook)

ei

[BibTex]

[BibTex]


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Transfer Learning for BCIs

Jayaram, V., Fiebig, K., Peters, J., Grosse-Wentrup, M.

In Brain–Computer Interfaces Handbook, pages: 425-442, 22, (Editors: Chang S. Nam, Anton Nijholt and Fabien Lotte), CRC Press, 2018 (incollection)

ei

Project Page [BibTex]

Project Page [BibTex]

1999


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Nonparametric regression for learning nonlinear transformations

Schaal, S.

In Prerational Intelligence in Strategies, High-Level Processes and Collective Behavior, 2, pages: 595-621, (Editors: Ritter, H.;Cruse, H.;Dean, J.), Kluwer Academic Publishers, 1999, clmc (inbook)

Abstract
Information processing in animals and artificial movement systems consists of a series of transformations that map sensory signals to intermediate representations, and finally to motor commands. Given the physical and neuroanatomical differences between individuals and the need for plasticity during development, it is highly likely that such transformations are learned rather than pre-programmed by evolution. Such self-organizing processes, capable of discovering nonlinear dependencies between different groups of signals, are one essential part of prerational intelligence. While neural network algorithms seem to be the natural choice when searching for solutions for learning transformations, this paper will take a more careful look at which types of neural networks are actually suited for the requirements of an autonomous learning system. The approach that we will pursue is guided by recent developments in learning theory that have linked neural network learning to well established statistical theories. In particular, this new statistical understanding has given rise to the development of neural network systems that are directly based on statistical methods. One family of such methods stems from nonparametric regression. This paper will compare nonparametric learning with the more widely used parametric counterparts in a non technical fashion, and investigate how these two families differ in their properties and their applicabilities. We will argue that nonparametric neural networks offer a set of characteristics that make them a very promising candidate for on-line learning in autonomous system.

am

link (url) [BibTex]

1999


link (url) [BibTex]