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2006


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Network-based de-noising improves prediction from microarray data

Kato, T., Murata, Y., Miura, K., Asai, K., Horton, P., Tsuda, K., Fujibuchi, W.

BMC Bioinformatics, 7(Suppl. 1):S4-S4, March 2006 (article)

Abstract
Prediction of human cell response to anti-cancer drugs (compounds) from microarray data is a challenging problem, due to the noise properties of microarrays as well as the high variance of living cell responses to drugs. Hence there is a strong need for more practical and robust methods than standard methods for real-value prediction. We devised an extended version of the off-subspace noise-reduction (de-noising) method to incorporate heterogeneous network data such as sequence similarity or protein-protein interactions into a single framework. Using that method, we first de-noise the gene expression data for training and test data and also the drug-response data for training data. Then we predict the unknown responses of each drug from the de-noised input data. For ascertaining whether de-noising improves prediction or not, we carry out 12-fold cross-validation for assessment of the prediction performance. We use the Pearson‘s correlation coefficient between the true and predicted respon se values as the prediction performance. De-noising improves the prediction performance for 65% of drugs. Furthermore, we found that this noise reduction method is robust and effective even when a large amount of artificial noise is added to the input data. We found that our extended off-subspace noise-reduction method combining heterogeneous biological data is successful and quite useful to improve prediction of human cell cancer drug responses from microarray data.

ei

PDF PDF DOI [BibTex]

2006


PDF PDF DOI [BibTex]


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Gaussian Process Models for Robust Regression, Classification, and Reinforcement Learning

Kuss, M.

Biologische Kybernetik, Technische Universität Darmstadt, Darmstadt, Germany, March 2006, passed with distinction, published online (phdthesis)

ei

PDF [BibTex]

PDF [BibTex]


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Classification of Natural Scenes: Critical Features Revisited

Drewes, J., Wichmann, F., Gegenfurtner, K.

9, pages: 92, 9th T{\"u}bingen Perception Conference (TWK), March 2006 (poster)

Abstract
Human observers are capable of detecting animals within novel natural scenes with remarkable speed and accuracy. Despite the seeming complexity of such decisions it has been hypothesized that a simple global image feature, the relative abundance of high spatial frequencies at certain orientations, could underly such fast image classification [1]. We successfully used linear discriminant analysis to classify a set of 11.000 images into “animal” and “non-animal” images based on their individual amplitude spectra only [2]. We proceeded to sort the images based on the performance of our classifier, retaining only the best and worst classified 400 images ("best animals", "best distractors" and "worst animals", "worst distractors"). We used a Go/No-go paradigm to evaluate human performance on this subset of our images. Both reaction time and proportion of correctly classified images showed a significant effect of classification difficulty. Images more easily classified by our algorithm were also classified faster and better by humans, as predicted by the Torralba & Oliva hypothesis. We then equated the amplitude spectra of the 400 images, which, by design, reduced algorithmic performance to chance whereas human performance was only slightly reduced [3]. Most importantly, the same images as before were still classified better and faster, suggesting that even in the original condition features other than specifics of the amplitude spectrum made particular images easy to classify, clearly at odds with the Torralba & Oliva hypothesis.

ei

Web [BibTex]

Web [BibTex]


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Machine Learning Methods For Estimating Operator Equations

Steinke, F., Schölkopf, B.

In Proceedings of the 14th IFAC Symposium on System Identification (SYSID 2006), pages: 6, (Editors: B Ninness and H Hjalmarsson), Elsevier, Oxford, United Kingdom, 14th IFAC Symposium on System Identification (SYSID), March 2006 (inproceedings)

Abstract
We consider the problem of fitting a linear operator induced equation to point sampled data. In order to do so we systematically exploit the duality between minimizing a regularization functional derived from an operator and kernel regression methods. Standard machine learning model selection algorithms can then be interpreted as a search of the equation best fitting given data points. For many kernels this operator induced equation is a linear differential equation. Thus, we link a continuous-time system identification task with common machine learning methods. The presented link opens up a wide variety of methods to be applied to this system identification problem. In a series of experiments we demonstrate an example algorithm working on non-uniformly spaced data, giving special focus to the problem of identifying one system from multiple data recordings.

ei

PDF Web [BibTex]

PDF Web [BibTex]


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Factorial Coding of Natural Images: How Effective are Linear Models in Removing Higher-Order Dependencies?

Bethge, M.

9, pages: 90, 9th T{\"u}bingen Perception Conference (TWK), March 2006 (poster)

Abstract
The performance of unsupervised learning models for natural images is evaluated quantitatively by means of information theory. We estimate the gain in statistical independence (the multi-information reduction) achieved with independent component analysis (ICA), principal component analysis (PCA), zero-phase whitening, and predictive coding. Predictive coding is translated into the transform coding framework, where it can be characterized by the constraint of a triangular filter matrix. A randomly sampled whitening basis and the Haar wavelet are included into the comparison as well. The comparison of all these methods is carried out for different patch sizes, ranging from 2x2 to 16x16 pixels. In spite of large differences in the shape of the basis functions, we find only small differences in the multi-information between all decorrelation transforms (5% or less) for all patch sizes. Among the second-order methods, PCA is optimal for small patch sizes and predictive coding performs best for large patch sizes. The extra gain achieved with ICA is always less than 2%. In conclusion, the `edge filters‘ found with ICA lead only to a surprisingly small improvement in terms of its actual objective.

ei

Web [BibTex]

Web [BibTex]


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Implicit Volterra and Wiener Series for Higher-Order Image Analysis

Franz, M., Schölkopf, B.

In Advances in Data Analysis: Proceedings of the 30th Annual Conference of The Gesellschaft für Klassifikation, 30, pages: 1, March 2006 (inproceedings)

Abstract
The computation of classical higher-order statistics such as higher-order moments or spectra is difficult for images due to the huge number of terms to be estimated and interpreted. We propose an alternative approach in which multiplicative pixel interactions are described by a series of Wiener functionals. Since the functionals are estimated implicitly via polynomial kernels, the combinatorial explosion associated with the classical higher-order statistics is avoided. In addition, the kernel framework allows for estimating infinite series expansions and for the regularized estimation of the Wiener series. First results show that image structures such as lines or corners can be predicted correctly, and that pixel interactions up to the order of five play an important role in natural images.

ei

PDF [BibTex]

PDF [BibTex]


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Model-based Design Analysis and Yield Optimization

Pfingsten, T., Herrmann, D., Rasmussen, C.

IEEE Transactions on Semiconductor Manufacturing, 19(4):475-486, February 2006 (article)

Abstract
Fluctuations are inherent to any fabrication process. Integrated circuits and micro-electro-mechanical systems are particularly affected by these variations, and due to high quality requirements the effect on the devices’ performance has to be understood quantitatively. In recent years it has become possible to model the performance of such complex systems on the basis of design specifications, and model-based Sensitivity Analysis has made its way into industrial engineering. We show how an efficient Bayesian approach, using a Gaussian process prior, can replace the commonly used brute-force Monte Carlo scheme, making it possible to apply the analysis to computationally costly models. We introduce a number of global, statistically justified sensitivity measures for design analysis and optimization. Two models of integrated systems serve us as case studies to introduce the analysis and to assess its convergence properties. We show that the Bayesian Monte Carlo scheme can save costly simulation runs and can ensure a reliable accuracy of the analysis.

ei

PDF Web DOI [BibTex]

PDF Web DOI [BibTex]


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Weighting of experimental evidence in macromolecular structure determination

Habeck, M., Rieping, W., Nilges, M.

Proceedings of the National Academy of Sciences of the United States of America, 103(6):1756-1761, February 2006 (article)

Abstract
The determination of macromolecular structures requires weighting of experimental evidence relative to prior physical information. Although it can critically affect the quality of the calculated structures, experimental data are routinely weighted on an empirical basis. At present, cross-validation is the most rigorous method to determine the best weight. We describe a general method to adaptively weight experimental data in the course of structure calculation. It is further shown that the necessity to define weights for the data can be completely alleviated. We demonstrate the method on a structure calculation from NMR data and find that the resulting structures are optimal in terms of accuracy and structural quality. Our method is devoid of the bias imposed by an empirical choice of the weight and has some advantages over estimating the weight by cross-validation.

ei

Web DOI [BibTex]

Web DOI [BibTex]


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Cross-Validation Optimization for Structured Hessian Kernel Methods

Seeger, M., Chapelle, O.

Max-Planck Institute for Biological Cybernetics, Tübingen, Germany, February 2006 (techreport)

Abstract
We address the problem of learning hyperparameters in kernel methods for which the Hessian of the objective is structured. We propose an approximation to the cross-validation log likelihood whose gradient can be computed analytically, solving the hyperparameter learning problem efficiently through nonlinear optimization. Crucially, our learning method is based entirely on matrix-vector multiplication primitives with the kernel matrices and their derivatives, allowing straightforward specialization to new kernels or to large datasets. When applied to the problem of multi-way classification, our method scales linearly in the number of classes and gives rise to state-of-the-art results on a remote imaging task.

ei

PDF Web [BibTex]

PDF Web [BibTex]


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Classification of Faces in Man and Machine

Graf, A., Wichmann, F., Bülthoff, H., Schölkopf, B.

Neural Computation, 18(1):143-165, January 2006 (article)

ei

PDF Web [BibTex]

PDF Web [BibTex]


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Gaussian Processes for Machine Learning

Rasmussen, CE., Williams, CKI.

pages: 248, Adaptive Computation and Machine Learning, MIT Press, Cambridge, MA, USA, January 2006 (book)

Abstract
Gaussian processes (GPs) provide a principled, practical, probabilistic approach to learning in kernel machines. GPs have received increased attention in the machine-learning community over the past decade, and this book provides a long-needed systematic and unified treatment of theoretical and practical aspects of GPs in machine learning. The treatment is comprehensive and self-contained, targeted at researchers and students in machine learning and applied statistics. The book deals with the supervised-learning problem for both regression and classification, and includes detailed algorithms. A wide variety of covariance (kernel) functions are presented and their properties discussed. Model selection is discussed both from a Bayesian and a classical perspective. Many connections to other well-known techniques from machine learning and statistics are discussed, including support-vector machines, neural networks, splines, regularization networks, relevance vector machines and others. Theoretical issues including learning curves and the PAC-Bayesian framework are treated, and several approximation methods for learning with large datasets are discussed. The book contains illustrative examples and exercises, and code and datasets are available on the Web. Appendixes provide mathematical background and a discussion of Gaussian Markov processes.

ei

Web [BibTex]

Web [BibTex]


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Causal Inference by Choosing Graphs with Most Plausible Markov Kernels

Sun, X., Janzing, D., Schölkopf, B.

In Proceedings of the 9th International Symposium on Artificial Intelligence and Mathematics, pages: 1-11, ISAIM, January 2006 (inproceedings)

Abstract
We propose a new inference rule for estimating causal structure that underlies the observed statistical dependencies among n random variables. Our method is based on comparing the conditional distributions of variables given their direct causes (the so-called Markov kernels") for all hypothetical causal directions and choosing the most plausible one. We consider those Markov kernels most plausible, which maximize the (conditional) entropies constrained by their observed first moment (expectation) and second moments (variance and covariance with its direct causes) based on their given domain. In this paper, we discuss our inference rule for causal relationships between two variables in detail, apply it to a real-world temperature data set with known causality and show that our method provides a correct result for the example.

ei

PDF Web [BibTex]

PDF Web [BibTex]


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Classification of natural scenes: critical features revisited

Drewes, J., Wichmann, F., Gegenfurtner, K.

Experimentelle Psychologie: Beitr{\"a}ge zur 48. Tagung experimentell arbeitender Psychologen, 48, pages: 251, 2006 (poster)

ei

[BibTex]

[BibTex]


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Machine Learning Challenges: evaluating predictive uncertainty, visual object classification and recognising textual entailment

Quinonero Candela, J., Dagan, I., Magnini, B., Lauria, F.

Proceedings of the First Pascal Machine Learning Challenges Workshop on Machine Learning Challenges, Evaluating Predictive Uncertainty, Visual Object Classification and Recognizing Textual Entailment (MLCW 2005), pages: 462, Lecture Notes in Computer Science, Springer, Heidelberg, Germany, First Pascal Machine Learning Challenges Workshop (MLCW), 2006 (proceedings)

Abstract
This book constitutes the thoroughly refereed post-proceedings of the First PASCAL (pattern analysis, statistical modelling and computational learning) Machine Learning Challenges Workshop, MLCW 2005, held in Southampton, UK in April 2005. The 25 revised full papers presented were carefully selected during two rounds of reviewing and improvement from about 50 submissions. The papers reflect the concepts of three challenges dealt with in the workshop: finding an assessment base on the uncertainty of predictions using classical statistics, Bayesian inference, and statistical learning theory; the second challenge was to recognize objects from a number of visual object classes in realistic scenes; the third challenge of recognizing textual entailment addresses semantic analysis of language to form a generic framework for applied semantic inference in text understanding.

ei

Web DOI [BibTex]

Web DOI [BibTex]


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Texture and haptic cues in slant discrimination: combination is sensitive to reliability but not statistically optimal

Rosas, P., Wagemans, J., Ernst, M., Wichmann, F.

Beitr{\"a}ge zur 48. Tagung experimentell arbeitender Psychologen (TeaP 2006), 48, pages: 80, 2006 (poster)

ei

[BibTex]

[BibTex]


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Combining a Filter Method with SVMs

Lal, T., Chapelle, O., Schölkopf, B.

In Feature Extraction: Foundations and Applications, Studies in Fuzziness and Soft Computing, Vol. 207, pages: 439-446, Studies in Fuzziness and Soft Computing ; 207, (Editors: I Guyon and M Nikravesh and S Gunn and LA Zadeh), Springer, Berlin, Germany, 2006 (inbook)

Abstract
Our goal for the competition (feature selection competition NIPS 2003) was to evaluate the usefulness of simple machine learning techniques. We decided to use the correlation criteria as a feature selection method and Support Vector Machines for the classification part. Here we explain how we chose the regularization parameter C of the SVM, how we determined the kernel parameter and how we estimated the number of features used for each data set. All analyzes were carried out on the training sets of the competition data. We choose the data set Arcene as an example to explain the approach step by step. In our view the point of this competition was the construction of a well performing classifier rather than the systematic analysis of a specific approach. This is why our search for the best classifier was only guided by the described methods and that we deviated from the road map at several occasions. All calculations were done with the software Spider [2004].

ei

PDF DOI [BibTex]

PDF DOI [BibTex]


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Embedded methods

Lal, T., Chapelle, O., Weston, J., Elisseeff, A.

In Feature Extraction: Foundations and Applications, pages: 137-165, Studies in Fuzziness and Soft Computing ; 207, (Editors: Guyon, I. , S. Gunn, M. Nikravesh, L. A. Zadeh), Springer, Berlin, Germany, 2006 (inbook)

Abstract
Embedded methods are a relatively new approach to feature selection. Unlike filter methods, which do not incorporate learning, and wrapper approaches, which can be used with arbitrary classifiers, in embedded methods the features selection part can not be separated from the learning part. Existing embedded methods are reviewed based on a unifying mathematical framework.

ei

PDF Web [BibTex]

PDF Web [BibTex]


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Ähnlichkeitsmasse in Modellen zur Kategorienbildung

Jäkel, F., Wichmann, F.

Experimentelle Psychologie: Beitr{\"a}ge zur 48. Tagung experimentell arbeitender Psychologen, 48, pages: 223, 2006 (poster)

ei

[BibTex]

[BibTex]


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The pedestal effect is caused by off-frequency looking, not nonlinear transduction or contrast gain-control

Wichmann, F., Henning, B.

Experimentelle Psychologie: Beitr{\"a}ge zur 48. Tagung experimentell arbeitender Psychologen, 48, pages: 205, 2006 (poster)

ei

[BibTex]

[BibTex]


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Learning operational space control

Peters, J., Schaal, S.

In Robotics: Science and Systems II (RSS 2006), pages: 255-262, (Editors: Gaurav S. Sukhatme and Stefan Schaal and Wolfram Burgard and Dieter Fox), Cambridge, MA: MIT Press, RSS , 2006, clmc (inproceedings)

Abstract
While operational space control is of essential importance for robotics and well-understood from an analytical point of view, it can be prohibitively hard to achieve accurate control in face of modeling errors, which are inevitable in complex robots, e.g., humanoid robots. In such cases, learning control methods can offer an interesting alternative to analytical control algorithms. However, the resulting learning problem is ill-defined as it requires to learn an inverse mapping of a usually redundant system, which is well known to suffer from the property of non-covexity of the solution space, i.e., the learning system could generate motor commands that try to steer the robot into physically impossible configurations. A first important insight for this paper is that, nevertheless, a physically correct solution to the inverse problem does exits when learning of the inverse map is performed in a suitable piecewise linear way. The second crucial component for our work is based on a recent insight that many operational space controllers can be understood in terms of a constraint optimal control problem. The cost function associated with this optimal control problem allows us to formulate a learning algorithm that automatically synthesizes a globally consistent desired resolution of redundancy while learning the operational space controller. From the view of machine learning, the learning problem corresponds to a reinforcement learning problem that maximizes an immediate reward and that employs an expectation-maximization policy search algorithm. Evaluations on a three degrees of freedom robot arm illustrate the feasability of our suggested approach.

am ei

link (url) [BibTex]

link (url) [BibTex]


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Reinforcement Learning for Parameterized Motor Primitives

Peters, J., Schaal, S.

In Proceedings of the 2006 International Joint Conference on Neural Networks, pages: 73-80, IJCNN, 2006, clmc (inproceedings)

Abstract
One of the major challenges in both action generation for robotics and in the understanding of human motor control is to learn the "building blocks of movement generation", called motor primitives. Motor primitives, as used in this paper, are parameterized control policies such as splines or nonlinear differential equations with desired attractor properties. While a lot of progress has been made in teaching parameterized motor primitives using supervised or imitation learning, the self-improvement by interaction of the system with the environment remains a challenging problem. In this paper, we evaluate different reinforcement learning approaches for improving the performance of parameterized motor primitives. For pursuing this goal, we highlight the difficulties with current reinforcement learning methods, and outline both established and novel algorithms for the gradient-based improvement of parameterized policies. We compare these algorithms in the context of motor primitive learning, and show that our most modern algorithm, the Episodic Natural Actor-Critic outperforms previous algorithms by at least an order of magnitude. We demonstrate the efficiency of this reinforcement learning method in the application of learning to hit a baseball with an anthropomorphic robot arm.

am ei

link (url) DOI [BibTex]

link (url) DOI [BibTex]


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An ultrasonic standing-wave-actuated nano-positioning walking robot: piezoelectric-metal composite beam modeling

Son, K. J., Kartik, V., Wickert, J. A., Sitti, M.

Journal of vibration and control, 12(12):1293-1309, Sage Publications, 2006 (article)

pi

[BibTex]

[BibTex]


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IEEE TRANSACTIONS ON ROBOTICS

VOLZ, RICHARD A, TARN, TJ, MACIEJEWSKI, ANTHONY A, LEE, SUKHAN, BICCHI, ANTONIO, DE LUCA, ALESSANDRO, LUH, PETER B, TAYLOR, RUSSELL H, BEKEY, GEORGE A, ARAI, HIROHIKO, others

2006 (article)

pi

[BibTex]

[BibTex]


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Design methodology for biomimetic propulsion of miniature swimming robots

Behkam, B., Sitti, M.

Trans.-ASME Journal of Dynamic Systems Measurement and Control, 128(1):36, ASME, 2006 (article)

pi

Project Page [BibTex]

Project Page [BibTex]


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Augmented reality user interface for an atomic force microscope-based nanorobotic system

Vogl, W., Ma, B. K., Sitti, M.

IEEE transactions on nanotechnology, 5(4):397-406, IEEE, 2006 (article)

pi

[BibTex]

[BibTex]


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Friction enhancement via micro-patterned wet elastomer adhesives on small intestinal surfaces

Kwon, J., Cheung, E., Park, S., Sitti, M.

Biomedical Materials, 1(4):216, IOP Publishing, 2006 (article)

pi

[BibTex]

[BibTex]


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Miniature endoscopic capsule robot using biomimetic micro-patterned adhesives

Karagozler, M. E., Cheung, E., Kwon, J., Sitti, M.

In Biomedical Robotics and Biomechatronics, 2006. BioRob 2006. The First IEEE/RAS-EMBS International Conference on, pages: 105-111, 2006 (inproceedings)

pi

[BibTex]

[BibTex]


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Compliant and low-cost humidity nanosensors using nanoporous polymer membranes

Yang, B., Aksak, B., Lin, Q., Sitti, M.

Sensors and Actuators B: Chemical, 114(1):254-262, Elsevier, 2006 (article)

pi

[BibTex]

[BibTex]


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Task-based and stable telenanomanipulation in a nanoscale virtual environment

Kim, S., Sitti, M.

IEEE Transactions on automation science and engineering, 3(3):240-247, IEEE, 2006 (article)

pi

[BibTex]

[BibTex]


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Drawing suspended polymer micro-/nanofibers using glass micropipettes

Nain, A. S., Wong, J. C., Amon, C., Sitti, M.

Applied Physics Letters, 89(18):183105, AIP, 2006 (article)

pi

[BibTex]

[BibTex]


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Toward micro wall-climbing robots using biomimetic fibrillar adhesives

Greuter, M., Shah, G., Caprari, G., Tâche, F., Siegwart, R., Sitti, M.

In Proceedings of the 3rd International Symposium on Autonomous Minirobots for Research and Edutainment (AMiRE 2005), pages: 39-46, 2006 (inproceedings)

pi

[BibTex]

[BibTex]


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Geckobot: A gecko inspired climbing robot using elastomer adhesives

Unver, O., Uneri, A., Aydemir, A., Sitti, M.

In Robotics and Automation, 2006. ICRA 2006. Proceedings 2006 IEEE International Conference on, pages: 2329-2335, 2006 (inproceedings)

pi

[BibTex]

[BibTex]


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Towards hybrid swimming microrobots: bacteria assisted propulsion of polystyrene beads

Behkam, B., Sitti, M.

In Engineering in Medicine and Biology Society, 2006. EMBS’06. 28th Annual International Conference of the IEEE, pages: 2421-2424, 2006 (inproceedings)

pi

Project Page [BibTex]

Project Page [BibTex]


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Biologically inspired polymer microfibers with spatulate tips as repeatable fibrillar adhesives

Kim, S., Sitti, M.

Applied Physics Letters, 89(26):261911-261911, AIP, 2006 (article)

pi

Project Page [BibTex]


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Soft microcontact printing with force control using microrobotic assembly based templates

Tafazzoli, A., Sitti, M.

In Advanced Motion Control, 2006. 9th IEEE International Workshop on, pages: 500-505, 2006 (inproceedings)

pi

[BibTex]

[BibTex]


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Modeling of the supporting legs for designing biomimetic water strider robots

Song, Y. S., Suhr, S. H., Sitti, M.

In Robotics and Automation, 2006. ICRA 2006. Proceedings 2006 IEEE International Conference on, pages: 2303-2310, 2006 (inproceedings)

pi

[BibTex]

[BibTex]


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Two-dimensional vision-based autonomous microparticle manipulation using a nanoprobe

Pawashe, C., Sitti, M.

Journal of Micromechatronics, 3(3):285-306, Brill, 2006 (article)

pi

[BibTex]

[BibTex]


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A novel water running robot inspired by basilisk lizards

Floyd, S., Keegan, T., Palmisano, J., Sitti, M.

In Intelligent Robots and Systems, 2006 IEEE/RSJ International Conference on, pages: 5430-5436, 2006 (inproceedings)

pi

[BibTex]

[BibTex]


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A biomimetic climbing robot based on the gecko

Menon, C., Sitti, M.

Journal of Bionic Engineering, 3(3):115-125, 2006 (article)

pi

[BibTex]

[BibTex]


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Force-controlled microcontact printing using microassembled particle templates

Tafazzoli, A., Pawashe, C., Sitti, M.

In Robotics and Automation, 2006. ICRA 2006. Proceedings 2006 IEEE International Conference on, pages: 263-268, 2006 (inproceedings)

pi

[BibTex]

[BibTex]


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Proximal probes based nanorobotic drawing of polymer micro/nanofibers

Nain, A. S., Amon, C., Sitti, M.

IEEE transactions on nanotechnology, 5(5):499-510, IEEE, 2006 (article)

pi

[BibTex]

[BibTex]


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Waalbot: An agile small-scale wall climbing robot utilizing pressure sensitive adhesives

Murphy, M. P., Tso, W., Tanzini, M., Sitti, M.

In Intelligent Robots and Systems, 2006 IEEE/RSJ International Conference on, pages: 3411-3416, 2006 (inproceedings)

pi

[BibTex]

[BibTex]

2004


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Attentional Modulation of Auditory Event-Related Potentials in a Brain-Computer Interface

Hill, J., Lal, T., Bierig, K., Birbaumer, N., Schölkopf, B.

In BioCAS04, (S3/5/INV- S3/17-20):4, IEEE Computer Society, Los Alamitos, CA, USA, 2004 IEEE International Workshop on Biomedical Circuits and Systems, December 2004 (inproceedings)

Abstract
Motivated by the particular problems involved in communicating with "locked-in" paralysed patients, we aim to develop a brain-computer interface that uses auditory stimuli. We describe a paradigm that allows a user to make a binary decision by focusing attention on one of two concurrent auditory stimulus sequences. Using Support Vector Machine classification and Recursive Channel Elimination on the independent components of averaged event-related potentials, we show that an untrained user‘s EEG data can be classified with an encouragingly high level of accuracy. This suggests that it is possible for users to modulate EEG signals in a single trial by the conscious direction of attention, well enough to be useful in BCI.

ei

PDF Web DOI [BibTex]

2004


PDF Web DOI [BibTex]


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On the representation, learning and transfer of spatio-temporal movement characteristics

Ilg, W., Bakir, GH., Mezger, J., Giese, M.

International Journal of Humanoid Robotics, 1(4):613-636, December 2004 (article)

ei

[BibTex]

[BibTex]


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Insect-inspired estimation of egomotion

Franz, MO., Chahl, JS., Krapp, HG.

Neural Computation, 16(11):2245-2260, November 2004 (article)

Abstract
Tangential neurons in the fly brain are sensitive to the typical optic flow patterns generated during egomotion. In this study, we examine whether a simplified linear model based on the organization principles in tangential neurons can be used to estimate egomotion from the optic flow. We present a theory for the construction of an estimator consisting of a linear combination of optic flow vectors that incorporates prior knowledge both about the distance distribution of the environment, and about the noise and egomotion statistics of the sensor. The estimator is tested on a gantry carrying an omnidirectional vision sensor. The experiments show that the proposed approach leads to accurate and robust estimates of rotation rates, whereas translation estimates are of reasonable quality, albeit less reliable.

ei

PDF PostScript Web DOI [BibTex]

PDF PostScript Web DOI [BibTex]


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Efficient face detection by a cascaded support-vector machine expansion

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

Proceedings of The Royal Society of London A, 460(2501):3283-3297, A, November 2004 (article)

Abstract
We describe a fast system for the detection and localization of human faces in images using a nonlinear ‘support-vector machine‘. We approximate the decision surface in terms of a reduced set of expansion vectors and propose a cascaded evaluation which has the property that the full support-vector expansion is only evaluated on the face-like parts of the image, while the largest part of typical images is classified using a single expansion vector (a simpler and more efficient classifier). As a result, only three reduced-set vectors are used, on average, to classify an image patch. Hence, the cascaded evaluation, presented in this paper, offers a thirtyfold speed-up over an evaluation using the full set of reduced-set vectors, which is itself already thirty times faster than classification using all the support vectors.

ei

PDF DOI [BibTex]

PDF DOI [BibTex]


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Joint Kernel Maps

Weston, J., Schölkopf, B., Bousquet, O., Mann, .., Noble, W.

(131), Max-Planck-Institute for Biological Cybernetics, Tübingen, November 2004 (techreport)

ei

PDF [BibTex]

PDF [BibTex]