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Neuchatel, Switzerland

The Swiss Center for Electronics and Microtechnology is a Swiss Research and Development company with expertise in Micro Nano Technologies, Microelectronics, Systems Engineering und Communication Technologies. The headquarters is in Neuchâtel. CSEM also has centers in the cities Muttenz, Zürich, Alpnach and Landquart in Switzerland Wikipedia.

Kruger A.,University College Dublin | Neels A.,Swiss Center for Electronics and Microtechnology | Albrecht M.,University College Dublin
Chemical Communications | Year: 2010

Abnormal C4-bonding of N-heterocyclic carbenes effectively modulates the electron density at rhodium and allows for the selective cleavage of an unactivated C(sp3)-H bond, whereas no such intramolecular C-H bond breaking is observed when the carbene binds normally through the C2 carbon. © 2010 The Royal Society of Chemistry. Source

Ranieri J.,Ecole Polytechnique Federale de Lausanne | Chebira A.,Swiss Center for Electronics and Microtechnology | Vetterli M.,Ecole Polytechnique Federale de Lausanne
IEEE Transactions on Signal Processing | Year: 2014

A classic problem is the estimation of a set of parameters from measurements collected by only a few sensors. The number of sensors is often limited by physical or economical constraints and their placement is of fundamental importance to obtain accurate estimates. Unfortunately, the selection of the optimal sensor locations is intrinsically combinatorial and the available approximation algorithms are not guaranteed to generate good solutions in all cases of interest. We propose FrameSense, a greedy algorithm for the selection of optimal sensor locations. The core cost function of the algorithm is the frame potential, a scalar property of matrices that measures the orthogonality of its rows. Notably, FrameSense is the first algorithm that is near-optimal in terms of mean square error, meaning that its solution is always guaranteed to be close to the optimal one. Moreover, we show with an extensive set of numerical experiments that FrameSense achieves state-of-the-art performance while having the lowest computational cost, when compared to other greedy methods. © 2014 IEEE. Source

Gallinet B.,Swiss Center for Electronics and Microtechnology | Gallinet B.,Ecole Polytechnique Federale de Lausanne | Martin O.J.F.,Ecole Polytechnique Federale de Lausanne
ACS Nano | Year: 2013

Plasmonic modes with long radiative lifetimes, subradiant modes, combine strong confinement of the electromagnetic energy at the nanoscale with a steep spectral dispersion, which makes them promising for biochemical sensors or immunoassays. Subradiant modes have three decay channels: Ohmic losses, their extrinsic coupling to radiation, and possibly their intrinsic dipole moment. In this work, the performance of subradiant modes for refractive index sensing is studied with a general analytical and numerical approach. We introduce a model for the impact that has different decay channels of subradiant modes on the spectral resolution and contrast. It is shown analytically and verified numerically that there exists an optimal value of the mode coupling for which the spectral dispersion of the resonance line shape is maximal. The intrinsic width of subradiant modes determines the value of the dispersion maximum and depends on the penetration of the electric field in the metallic nanostructure. A figure of merit, given by the ratio of the sensitivity to the intrinsic width, which are both intrinsic properties of subradiant modes, is introduced. This figure of merit can be directly calculated from the line shape in the far-field optical spectrum and accounts for the fact that both the spectral resolution and contrast determine the limit of detection. An expression for the intrinsic width of a plasmonic mode is derived and calculated from the line shape parameters and using perturbation theory. The method of analysis introduced in this work is illustrated for dolmen and heptamer nanostructures. Fano-resonant systems have the potential to act as very efficient refractive index sensing platforms compared to Lorentz-resonant systems, due to control of their radiative losses. This study paves the way toward sensitive nanoscale biochemical sensors and immunoassays with a low limit of detection and, in general, any nano-optical device where Ohmic losses limit the performance. © 2013 American Chemical Society. Source

Hakanson M.,Swiss Center for Electronics and Microtechnology | Cukierman E.,Fox Chase Cancer Center | Charnley M.,Swinburne University of Technology
Advanced Drug Delivery Reviews | Year: 2014

Cancer is one of the most common causes of death worldwide. Consequently, important resources are directed towards bettering treatments and outcomes. Cancer is difficult to treat due to its heterogeneity, plasticity and frequent drug resistance. New treatment strategies should strive for personalized approaches. These should target neoplastic and/or activated microenvironmental heterogeneity and plasticity without triggering resistance and spare host cells. In this review, the putative use of increasingly physiologically relevant microfabricated cell-culturing systems intended for drug development is discussed. There are two main reasons for the use of miniaturized systems. First, scaling down model size allows for high control of microenvironmental cues enabling more predictive outcomes. Second, miniaturization reduces reagent consumption, thus facilitating combinatorial approaches with little effort and enables the application of scarce materials, such as patient-derived samples. This review aims to give an overview of the state-of-the-art of such systems while predicting their application in cancer drug development. © 2013 Elsevier B.V. Source

Ali K.,Ecole Polytechnique Federale de Lausanne | Fleuret F.,Idiap Research Institute | Hasler D.,Swiss Center for Electronics and Microtechnology | Fua P.,Ecole Polytechnique Federale de Lausanne
IEEE Transactions on Pattern Analysis and Machine Intelligence | Year: 2012

We propose a new learning strategy for object detection. The proposed scheme forgoes the need to train a collection of detectors dedicated to homogeneous families of poses, and instead learns a single classifier that has the inherent ability to deform based on the signal of interest. We train a detector with a standard AdaBoost procedure by using combinations of pose-indexed features and pose estimators. This allows the learning process to select and combine various estimates of the pose with features able to compensate for variations in pose without the need to label data for training or explore the pose space in testing. We validate our framework on three types of data: hand video sequences, aerial images of cars, and face images. We compare our method to a standard boosting framework, with access to the same ground truth, and show a reduction in the false alarm rate of up to an order of magnitude. Where possible, we compare our method to the state of the art, which requires pose annotations of the training data, and demonstrate comparable performance. © 2012 IEEE. Source

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