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2007


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Metal hydride materials for solid hydrogen storage: a review

Sakintuna, B., Lamari-Darkrim, F., Hirscher, M.

{International Journal of Hydrogen Energy}, 32, pages: 1121-1140, 2007 (article)

mms

[BibTex]

2007


[BibTex]


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Thermal reversal of exchange spring composite media in magnetic fields

Goll, D., Macke, S., Bertram, H. N.

{Applied Physics Letters}, 90, 2007 (article)

mms

[BibTex]

[BibTex]


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Magnetism of Co-doped ZnO thin films

Gacic, M., Jakob, G., Herbort, C., Adrian, H., Tietze, T., Brück, S., Goering, E.

{Physical Review B}, 75, 2007 (article)

mms

DOI [BibTex]

DOI [BibTex]


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iCub - The Design and Realization of an Open Humanoid Platform for Cognitive and Neuroscience Research

Tsagarakis, N., Metta, G., Sandini, G., Vernon, D., Beira, R., Becchi, F., Righetti, L., Santos-Victor, J., Ijspeert, A., Carrozza, M., Caldwell, D.

Advanced Robotics, 21(10):1151-1175, 2007 (article)

Abstract
The development of robotic cognition and the advancement of understanding of human cognition form two of the current greatest challenges in robotics and neuroscience, respectively. The RobotCub project aims to develop an embodied robotic child (iCub) with the physical (height 90 cm and mass less than 23 kg) and ultimately cognitive abilities of a 2.5-year-old human child. The iCub will be a freely available open system which can be used by scientists in all cognate disciplines from developmental psychology to epigenetic robotics to enhance understanding of cognitive systems through the study of cognitive development. The iCub will be open both in software, but more importantly in all aspects of the hardware and mechanical design. In this paper the design of the mechanisms and structures forming the basic 'body' of the iCub are described. The papers considers kinematic structures dynamic design criteria, actuator specification and selection, and detailed mechanical and electronic design. The paper concludes with tests of the performance of sample joints, and comparison of these results with the design requirements and simulation projects.

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link (url) DOI [BibTex]

link (url) DOI [BibTex]


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Subfeature patterning of organic and inorganic materials using robotic assembly

Tafazzoli, A., Cheng, C., Pawashe, C., Sabo, E. K., Trofin, L., Sitti, M., LeDuc, P. R.

Journal of materials research, 22(06):1601-1608, Cambridge University Press, 2007 (article)

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[BibTex]

[BibTex]


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Effect of backing layer thickness on adhesion of single-level elastomer fiber arrays

Kim, S., Sitti, M., Hui, C., Long, R., Jagota, A.

Applied Physics Letters, 91(16):161905, AIP, 2007 (article)

pi

[BibTex]

[BibTex]


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Herstellung und Charakterisierung dünner Niob-Schichten auf verschiedenen Substraten

Mayer, M. W. R.

Universität Stuttgart, Stuttgart, 2007 (mastersthesis)

mms

[BibTex]

[BibTex]


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Formation of hard magnetic L10-FePt/FePd monolayers from elemental multilayers

Goo, N. H.

Universität Stuttgart, Stuttgart, 2007 (phdthesis)

mms

link (url) [BibTex]

link (url) [BibTex]


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Universal temperature scaling of flux line pinning in high-temperature superconducting thin films

Albrecht, J., Djupmyr, M., Brück, S.

{Journal of Physics: Condensed Matter}, 19, 2007 (article)

mms

[BibTex]

[BibTex]


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Dependence of the critical temperature of YBCO thin films on spin-polarized quasiparticle injection

Habermeier, H.-U., Soltan, S., Albrecht, J.

{Physica C}, 460-462, pages: 32-35, 2007 (article)

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DOI [BibTex]

DOI [BibTex]


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Interaction of ferromagnetic LCMO layers through a superconducting YBCO spacer

Ravikumar, G., Yashwant, G., Singh, M. R., Gupta, S. K., Bhattacharya, S., Soltan, S., Albrecht, J., Habermeier, H.-U.

{Physica C}, 460-462, pages: 1375-1376, 2007 (article)

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DOI [BibTex]

DOI [BibTex]


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Vortex dynamics in Permalloy disks with artificial defects: suppression of the gyrotropic mode

Kuepper, K., Bischoff, L., Akhmadaliev, C., Fassbinder, J., Stoll, H., Chou, K., Puzic, A., Fauth, K., Dolgos, D., Schütz, G., Van Waeyenberge, B., Tyliszczak, T., Neudecker, I., Woltersdorf, G., Back, C.

{Appplied Physics Letters}, 90, 2007 (article)

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[BibTex]

[BibTex]


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Vacancy-interstitial annihilation in titanomagnetite by thermal annealing

Walz, F., Brabers, V. A. M., Brabers, J. H. V. J., Kronmüller, H.

{Physica Status Solidi (A)}, 204(10):3514-3525, 2007 (article)

mms

DOI [BibTex]

DOI [BibTex]


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Theory of X-ray absorption spectroscopy in solids: mixing of the core states by the aspherical effective potential

Kostoglou, C., Komelj, M., Fähnle, M.

{Physical Review B}, 75, 2007 (article)

mms

[BibTex]

[BibTex]


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Zinc oxide microcapsules obtained via a bio-inspired approach

Lipowsky, P., Hirscher, M., Hoffmann, R. C., Bill, J., Aldinger, F.

{Nanotechnology}, 18, 2007 (article)

mms

[BibTex]

[BibTex]


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Grain boundary phase observed in Al-5 at.\textpercent Zn alloy by using HREM

Straumal, B. B., Mazilkin, A. A., Kogtenkova, O. A., Protasova, S. G., Baretzky, B.

{Philosophical Magazine Letters}, 87(6):423-430, 2007 (article)

mms

[BibTex]

[BibTex]


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Transport current improvements of in situ MgB2 tapes by the addition of carbon nanotubes, silicon carbide or graphite

Kovac, P., Husek, I., Skakalova, V., Meyer, J., Dobrocka, E., Hirscher, M., Roth, S.

{Superconductor Science and Technology}, 20, pages: 105-111, 2007 (article)

mms

DOI [BibTex]

DOI [BibTex]


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Adhesion and anisotropic friction enhancements of angled heterogeneous micro-fiber arrays with spherical and spatula tips

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

Journal of Adhesion Science and Technology, 21(12-13):1281-1296, Taylor & Francis Group, 2007 (article)

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Project Page [BibTex]

Project Page [BibTex]


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Surface-tension-driven biologically inspired water strider robots: Theory and experiments

Song, Y. S., Sitti, M.

IEEE Transactions on robotics, 23(3):578-589, IEEE, 2007 (article)

pi

[BibTex]

[BibTex]


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Zur ab-initio Elektronentheorie stark nichtkollinearer Spinsysteme

Köberle, I.

Universität Stuttgart, Stuttgart, 2007 (mastersthesis)

mms

[BibTex]

[BibTex]


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Theorie der Kernspektroskopie mit zirkular polarisierter Gammastrahlung

Engelhart, W.

Universität Stuttgart, Stuttgart, 2007 (mastersthesis)

mms

[BibTex]

[BibTex]


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Untersuchung der Adsorption von Wasserstoff in porösen Materialien

Hönes, K.

Universität Stuttgart, Stuttgart, 2007 (mastersthesis)

mms

[BibTex]

[BibTex]


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Untersuchung der mechanischen Eigenschaften dünner Chromschichten

Jüllig, P.

Universität Stuttgart, Stuttgart, 2007 (mastersthesis)

mms

[BibTex]

[BibTex]


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Absorption spectroscopy and XMCD at the Verwey transition of Fe3O4

Goering, E., Lafkioti, M., Gold, S., Schütz, G.

{Journal of Magnetism and Magnetic Materials}, 310, pages: 249-251, 2007 (article)

mms

[BibTex]

[BibTex]


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Overcoming the Dipolar Disorder in Dense CoFe Nanoparticle Ensembles: Superferromagnetism

Bedanta, S., Eimüller, T., Kleemann, W., Rhensius, J., Stromberg, F., Amaladass, E., Cardoso, S., Freitas, P. P.

{Physical Review Letters}, 98, 2007 (article)

mms

DOI [BibTex]

DOI [BibTex]


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Ultrafast nanomagnetic toggle switching of vortex cores

Hertel, R., Gliga, S., Fähnle, M., Schneider, C. M.

{Physical Review Letters}, 98, 2007 (article)

mms

[BibTex]

[BibTex]


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Element-specific spin and orbital momentum dynamics of Fe/Gd multilayers

Bartelt, A. F., Comin, A., Feng, J., Nasiatka, J. R., Eimüller, T., Ludescher, B., Schütz, G., Padmore, H. A., Young, A. T., Scholl, A.

{Applied Physics Letters}, 90, 2007 (article)

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DOI [BibTex]

DOI [BibTex]


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Slow relaxation of spin reorientation following ultrafast optical excitation

Eimüller, T., Scholl, A., Ludescher, B., Schütz, G., Thiele, J.

{Applied Physics Letters}, 91, 2007 (article)

mms

[BibTex]

[BibTex]


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One-pot synthesis of core-shell FeRh nanoparticles

Ciuculescu, D., Amiens, C., Respaud, M., Falqui, A., Lecante, P., Benfield, R. E., Jiang, L., Fauth, K., Chaudret, B.

{Chemistry of Materials}, 19(19):4624-4626, 2007 (article)

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[BibTex]

[BibTex]


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Spin-polarized quasiparticles injection effects in the normal state of YBCO thin films

Soltan, S., Albrecht, J., Habermeier, H.-U.

{Physica C}, 460-462, pages: 1088-1089, 2007 (article)

mms

DOI [BibTex]

DOI [BibTex]


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Direct observation of the vortex core magnetization and its dynamics

Chou, K. W., Puzic, A., Stoll, H., Dolgos, D., Schütz, G., Van Waeyenberge, B., Vansteenkiste, A., Tyliszczak, T., Woltersdorf, G., Back, C. H.

{Applied Physics Letters}, 90, 2007 (article)

mms

[BibTex]

[BibTex]


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Superparamagnetism in small Fe clusters on Cu(111)

Ballentine, G., He\ssler, M., Kinza, M., Fauth, K.

{The European Physical Journal D}, 45, pages: 535-537, 2007 (article)

mms

DOI [BibTex]

DOI [BibTex]

2005


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Kernel Methods for Measuring Independence

Gretton, A., Herbrich, R., Smola, A., Bousquet, O., Schölkopf, B.

Journal of Machine Learning Research, 6, pages: 2075-2129, December 2005 (article)

Abstract
We introduce two new functionals, the constrained covariance and the kernel mutual information, to measure the degree of independence of random variables. These quantities are both based on the covariance between functions of the random variables in reproducing kernel Hilbert spaces (RKHSs). We prove that when the RKHSs are universal, both functionals are zero if and only if the random variables are pairwise independent. We also show that the kernel mutual information is an upper bound near independence on the Parzen window estimate of the mutual information. Analogous results apply for two correlation-based dependence functionals introduced earlier: we show the kernel canonical correlation and the kernel generalised variance to be independence measures for universal kernels, and prove the latter to be an upper bound on the mutual information near independence. The performance of the kernel dependence functionals in measuring independence is verified in the context of independent component analysis.

ei

PDF PostScript PDF [BibTex]

2005


PDF PostScript PDF [BibTex]


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A Unifying View of Sparse Approximate Gaussian Process Regression

Quinonero Candela, J., Rasmussen, C.

Journal of Machine Learning Research, 6, pages: 1935-1959, December 2005 (article)

Abstract
We provide a new unifying view, including all existing proper probabilistic sparse approximations for Gaussian process regression. Our approach relies on expressing the effective prior which the methods are using. This allows new insights to be gained, and highlights the relationship between existing methods. It also allows for a clear theoretically justified ranking of the closeness of the known approximations to the corresponding full GPs. Finally we point directly to designs of new better sparse approximations, combining the best of the existing strategies, within attractive computational constraints.

ei

PDF [BibTex]

PDF [BibTex]


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Representing cyclic human motion using functional analysis

Ormoneit, D., Black, M. J., Hastie, T., Kjellström, H.

Image and Vision Computing, 23(14):1264-1276, December 2005 (article)

Abstract
We present a robust automatic method for modeling cyclic 3D human motion such as walking using motion-capture data. The pose of the body is represented by a time-series of joint angles which are automatically segmented into a sequence of motion cycles. The mean and the principal components of these cycles are computed using a new algorithm that enforces smooth transitions between the cycles by operating in the Fourier domain. Key to this method is its ability to automatically deal with noise and missing data. A learned walking model is then exploited for Bayesian tracking of 3D human motion.

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pdf pdf from publisher DOI [BibTex]

pdf pdf from publisher DOI [BibTex]


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Extension to Kernel Dependency Estimation with Applications to Robotics

BakIr, G.

Biologische Kybernetik, Technische Universität Berlin, Berlin, November 2005 (phdthesis)

Abstract
Kernel Dependency Estimation(KDE) is a novel technique which was designed to learn mappings between sets without making assumptions on the type of the involved input and output data. It learns the mapping in two stages. In a first step, it tries to estimate coordinates of a feature space representation of elements of the set by solving a high dimensional multivariate regression problem in feature space. Following this, it tries to reconstruct the original representation given the estimated coordinates. This thesis introduces various algorithmic extensions to both stages in KDE. One of the contributions of this thesis is to propose a novel linear regression algorithm that explores low-dimensional subspaces during learning. Furthermore various existing strategies for reconstructing patterns from feature maps involved in KDE are discussed and novel pre-image techniques are introduced. In particular, pre-image techniques for data-types that are of discrete nature such as graphs and strings are investigated. KDE is then explored in the context of robot pose imitation where the input is a an image with a human operator and the output is the robot articulated variables. Thus, using KDE, robot pose imitation is formulated as a regression problem.

ei

PDF PDF [BibTex]

PDF PDF [BibTex]


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Geometrical aspects of statistical learning theory

Hein, M.

Biologische Kybernetik, Darmstadt, Darmstadt, November 2005 (phdthesis)

ei

PDF [BibTex]

PDF [BibTex]


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Maximal Margin Classification for Metric Spaces

Hein, M., Bousquet, O., Schölkopf, B.

Journal of Computer and System Sciences, 71(3):333-359, October 2005 (article)

Abstract
In order to apply the maximum margin method in arbitrary metric spaces, we suggest to embed the metric space into a Banach or Hilbert space and to perform linear classification in this space. We propose several embeddings and recall that an isometric embedding in a Banach space is always possible while an isometric embedding in a Hilbert space is only possible for certain metric spaces. As a result, we obtain a general maximum margin classification algorithm for arbitrary metric spaces (whose solution is approximated by an algorithm of Graepel. Interestingly enough, the embedding approach, when applied to a metric which can be embedded into a Hilbert space, yields the SVM algorithm, which emphasizes the fact that its solution depends on the metric and not on the kernel. Furthermore we give upper bounds of the capacity of the function classes corresponding to both embeddings in terms of Rademacher averages. Finally we compare the capacities of these function classes directly.

ei

PDF PDF DOI [BibTex]

PDF PDF DOI [BibTex]


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Selective integration of multiple biological data for supervised network inference

Kato, T., Tsuda, K., Asai, K.

Bioinformatics, 21(10):2488 , October 2005 (article)

ei

PDF [BibTex]

PDF [BibTex]


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Assessing Approximate Inference for Binary Gaussian Process Classification

Kuss, M., Rasmussen, C.

Journal of Machine Learning Research, 6, pages: 1679 , October 2005 (article)

Abstract
Gaussian process priors can be used to define flexible, probabilistic classification models. Unfortunately exact Bayesian inference is analytically intractable and various approximation techniques have been proposed. In this work we review and compare Laplace‘s method and Expectation Propagation for approximate Bayesian inference in the binary Gaussian process classification model. We present a comprehensive comparison of the approximations, their predictive performance and marginal likelihood estimates to results obtained by MCMC sampling. We explain theoretically and corroborate empirically the advantages of Expectation Propagation compared to Laplace‘s method.

ei

PDF PDF [BibTex]

PDF PDF [BibTex]


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Implicit Surfaces For Modelling Human Heads

Steinke, F.

Biologische Kybernetik, Eberhard-Karls-Universität, Tübingen, September 2005 (diplomathesis)

ei

[BibTex]

[BibTex]


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Clustering on the Unit Hypersphere using von Mises-Fisher Distributions

Banerjee, A., Dhillon, I., Ghosh, J., Sra, S.

Journal of Machine Learning Research, 6, pages: 1345-1382, September 2005 (article)

Abstract
Several large scale data mining applications, such as text categorization and gene expression analysis, involve high-dimensional data that is also inherently directional in nature. Often such data is L2 normalized so that it lies on the surface of a unit hypersphere. Popular models such as (mixtures of) multi-variate Gaussians are inadequate for characterizing such data. This paper proposes a generative mixture-model approach to clustering directional data based on the von Mises-Fisher (vMF) distribution, which arises naturally for data distributed on the unit hypersphere. In particular, we derive and analyze two variants of the Expectation Maximization (EM) framework for estimating the mean and concentration parameters of this mixture. Numerical estimation of the concentration parameters is non-trivial in high dimensions since it involves functional inversion of ratios of Bessel functions. We also formulate two clustering algorithms corresponding to the variants of EM that we derive. Our approach provides a theoretical basis for the use of cosine similarity that has been widely employed by the information retrieval community, and obtains the spherical kmeans algorithm (kmeans with cosine similarity) as a special case of both variants. Empirical results on clustering of high-dimensional text and gene-expression data based on a mixture of vMF distributions show that the ability to estimate the concentration parameter for each vMF component, which is not present in existing approaches, yields superior results, especially for difficult clustering tasks in high-dimensional spaces.

ei

PDF [BibTex]

PDF [BibTex]


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Support Vector Machines for 3D Shape Processing

Steinke, F., Schölkopf, B., Blanz, V.

Computer Graphics Forum, 24(3, EUROGRAPHICS 2005):285-294, September 2005 (article)

Abstract
We propose statistical learning methods for approximating implicit surfaces and computing dense 3D deformation fields. Our approach is based on Support Vector (SV) Machines, which are state of the art in machine learning. It is straightforward to implement and computationally competitive; its parameters can be automatically set using standard machine learning methods. The surface approximation is based on a modified Support Vector regression. We present applications to 3D head reconstruction, including automatic removal of outliers and hole filling. In a second step, we build on our SV representation to compute dense 3D deformation fields between two objects. The fields are computed using a generalized SVMachine enforcing correspondence between the previously learned implicit SV object representations, as well as correspondences between feature points if such points are available. We apply the method to the morphing of 3D heads and other objects.

ei

PDF [BibTex]

PDF [BibTex]


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Correlation of EEG spectral entropy with regional cerebral blood flow during sevoflurane and propofol anaesthesia

Maksimow, A., Kaisti, K., Aalto, S., Mäenpää, M., Jääskeläinen, S., Hinkka, S., Martens, SMM., Särkelä, M., Viertiö-Oja, H., Scheinin, H.

Anaesthesia, 60(9):862-869, September 2005 (article)

Abstract
ENTROPY index monitoring, based on spectral entropy of the electroencephalogram, is a promising new method to measure the depth of anaesthesia. We examined the association between spectral entropy and regional cerebral blood flow in healthy subjects anaesthetised with 2%, 3% and 4% end-expiratory concentrations of sevoflurane and 7.6, 12.5 and 19.0 microg.ml(-1) plasma drug concentrations of propofol. Spectral entropy from the frequency band 0.8-32 Hz was calculated and cerebral blood flow assessed using positron emission tomography and [(15)O]-labelled water at baseline and at each anaesthesia level. Both drugs induced significant reductions in spectral entropy and cortical and global cerebral blood flow. Midfrontal-central spectral entropy was associated with individual frontal and whole brain blood flow values across all conditions, suggesting that this novel measure of anaesthetic depth can depict global changes in neuronal activity induced by the drugs. The cortical areas of the most significant associations were remarkably similar for both drugs.

ei

DOI [BibTex]

DOI [BibTex]


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Fast Protein Classification with Multiple Networks

Tsuda, K., Shin, H., Schölkopf, B.

Bioinformatics, 21(Suppl. 2):59-65, September 2005 (article)

Abstract
Support vector machines (SVM) have been successfully used to classify proteins into functional categories. Recently, to integrate multiple data sources, a semidefinite programming (SDP) based SVM method was introduced Lanckriet et al (2004). In SDP/SVM, multiple kernel matrices corresponding to each of data sources are combined with weights obtained by solving an SDP. However, when trying to apply SDP/SVM to large problems, the computational cost can become prohibitive, since both converting the data to a kernel matrix for the SVM and solving the SDP are time and memory demanding. Another application-specific drawback arises when some of the data sources are protein networks. A common method of converting the network to a kernel matrix is the diffusion kernel method, which has time complexity of O(n^3), and produces a dense matrix of size n x n. We propose an efficient method of protein classification using multiple protein networks. Available protein networks, such as a physical interaction network or a metabolic network, can be directly incorporated. Vectorial data can also be incorporated after conversion into a network by means of neighbor point connection. Similarly to the SDP/SVM method, the combination weights are obtained by convex optimization. Due to the sparsity of network edges, the computation time is nearly linear in the number of edges of the combined network. Additionally, the combination weights provide information useful for discarding noisy or irrelevant networks. Experiments on function prediction of 3588 yeast proteins show promising results: the computation time is enormously reduced, while the accuracy is still comparable to the SDP/SVM method.

ei

PDF Web DOI [BibTex]

PDF Web DOI [BibTex]


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Analyzing microarray data using quantitative association rules

Georgii, E., Richter, L., Rückert, U., Kramer, S.

Bioinformatics, 21(Suppl. 2):123-129, September 2005 (article)

Abstract
Motivation: We tackle the problem of finding regularities in microarray data. Various data mining tools, such as clustering, classification, Bayesian networks and association rules, have been applied so far to gain insight into gene-expression data. Association rule mining techniques used so far work on discretizations of the data and cannot account for cumulative effects. In this paper, we investigate the use of quantitative association rules that can operate directly on numeric data and represent cumulative effects of variables. Technically speaking, this type of quantitative association rules based on half-spaces can find non-axis-parallel regularities. Results: We performed a variety of experiments testing the utility of quantitative association rules for microarray data. First of all, the results should be statistically significant and robust against fluctuations in the data. Next, the approach should be scalable in the number of variables, which is important for such high-dimensional data. Finally, the rules should make sense biologically and be sufficiently different from rules found in regular association rule mining working with discretizations. In all of these dimensions, the proposed approach performed satisfactorily. Therefore, quantitative association rules based on half-spaces should be considered as a tool for the analysis of microarray gene-expression data.

ei

Web DOI [BibTex]

Web DOI [BibTex]


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Iterative Kernel Principal Component Analysis for Image Modeling

Kim, K., Franz, M., Schölkopf, B.

IEEE Transactions on Pattern Analysis and Machine Intelligence, 27(9):1351-1366, September 2005 (article)

Abstract
In recent years, Kernel Principal Component Analysis (KPCA) has been suggested for various image processing tasks requiring an image model such as, e.g., denoising or compression. The original form of KPCA, however, can be only applied to strongly restricted image classes due to the limited number of training examples that can be processed. We therefore propose a new iterative method for performing KPCA, the Kernel Hebbian Algorithm which iteratively estimates the Kernel Principal Components with only linear order memory complexity. In our experiments, we compute models for complex image classes such as faces and natural images which require a large number of training examples. The resulting image models are tested in single-frame super-resolution and denoising applications. The KPCA model is not specifically tailored to these tasks; in fact, the same model can be used in super-resolution with variable input resolution, or denoising with unknown noise characteristics. In spite of this, both super-resolution a nd denoising performance are comparable to existing methods.

ei

Web DOI [BibTex]

Web DOI [BibTex]


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Machine Learning Methods for Brain-Computer Interdaces

Lal, TN.

Biologische Kybernetik, University of Darmstadt, September 2005 (phdthesis)

ei

Web [BibTex]

Web [BibTex]


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Large Margin Methods for Structured and Interdependent Output Variables

Tsochantaridis, I., Joachims, T., Hofmann, T., Altun, Y.

Journal of Machine Learning Research, 6, pages: 1453-1484, September 2005 (article)

Abstract
Learning general functional dependencies between arbitrary input and output spaces is one of the key challenges in computational intelligence. While recent progress in machine learning has mainly focused on designing flexible and powerful input representations, this paper addresses the complementary issue of designing classification algorithms that can deal with more complex outputs, such as trees, sequences, or sets. More generally, we consider problems involving multiple dependent output variables, structured output spaces, and classification problems with class attributes. In order to accomplish this, we propose to appropriately generalize the well-known notion of a separation margin and derive a corresponding maximum-margin formulation. While this leads to a quadratic program with a potentially prohibitive, i.e. exponential, number of constraints, we present a cutting plane algorithm that solves the optimization problem in polynomial time for a large class of problems. The proposed method has important applications in areas such as computational biology, natural language processing, information retrieval/extraction, and optical character recognition. Experiments from various domains involving different types of output spaces emphasize the breadth and generality of our approach.

ei

PDF [BibTex]

PDF [BibTex]


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Contact Location Display for Haptic Perception of Curvature and Object Motion

Provancher, W. R., Cutkosky, M. R., Kuchenbecker, K. J., Niemeyer, G.

International Journal of Robotics Research, 24(9):691-702, sep 2005 (article)

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[BibTex]

[BibTex]