An adaptive array for interference rejection

Adaptive array based on feedback system for rejection of interfering signal

Depth Estimation via Affinity Learned with Convolutional Spatial Propagation Network

Depth estimation from a single image is a fundamental problem in computer vision. In this paper, we propose a simple yet effective convolutional spatial propagation network (CSPN) to learn the affinity matrix for depth prediction. Specifically, we adopt an efficient linear propagation model, where the propagation is performed with a manner of recurrent convolutional operation, and the affinity among neighboring pixels is learned through a deep convolutional neural network (CNN). We apply the designed CSPN to two depth estimation tasks given a single image: (1) To refine the depth output from state-of-the-art (SOTA) existing methods; and (2) to convert sparse depth samples to a dense depth map by embedding the depth samples within the propagation procedure. The second task is inspired by the availability of LIDARs that provides sparse but accurate depth measurements. We experimented the proposed CSPN over two popular benchmarks for depth estimation, i.e. NYU v2 and KITTI, where we show that our proposed approach improves in not only quality (e.g., 30% more reduction in depth error), but also speed (e.g., 2 to 5 times faster) than prior SOTA methods.Comment: 14 pages, 8 figures, ECCV 201

Parallelization of chip-based fluorescence immuno-assays with quantum-dot labelled beads

This paper presents an optical concept for the read-out of a parallel, bead-based fluorescence immunoassay conducted on a lab-on-a-disk platform. The reusable part of the modular setup comprises a detection unit featuring a single LED as light source, two emission-filters, and a color CCD-camera as standard components together with a spinning drive as actuation unit. The miniaturized lab-on-a-disk is devised as a disposable. In the read-out process of the parallel assay, beads are first identified by the color of incorporated quantum dots (QDs). Next, the reaction-specific fluorescence signal is quantified with FluoSpheres-labeled detection anti-bodies. To enable a fast and automated read-out, suitable algorithms have been implemented in this work. Based on this concept, we successfully demonstrated a Hepatitis-A assay on our disk-based lab-on-a-chip

Analytic expressions for static electric fields in an infinite plane condenser with one or three homogeneous layers

Expressions for the electrostatic field of a point charge in an infinite plane condenser comprising one or three homogeneous isolating parallel dielectric layers are presented. These solutions are essential for detector physics simulations of Parallel Plate Chambers (PPCs) and Resistive Plate Chambers (RPCs). In addition, expressions for the weighting field of a strip electrode are presented which allow calculation of induced signals and crosstalk in these detectors. A detailed discussion of the derivation of these solutions can be found in \cite{schnizer}

Static electric fields in an infinite plane condensor with one or three homogeneous layers

Various expressions are derived for the Green's functions for a point charge in an infinite plane condensor comprising one or three homogeneous isolating parallel dielectric layers. In view of numerical evaluations needed for calculating space charge effects in detectors (e.g. RPC's) the merits of these (series and integral) representations are discussed. It turns out that in most cases the integral representations are more favourable after their convergence has been improved. This is done by subtracting simple terms having the same asymptotic behaviour as certain too slowly converging terms and adding closed expressions resulting from the integration of the simple terms. The method is demonstrated in some detail. In addition analytic expressions for the weighting field of a strip electrode are derived which allow calculation of induced signals and crosstalk

On-Line AdaTron Learning of Unlearnable Rules

We study the on-line AdaTron learning of linearly non-separable rules by a simple perceptron. Training examples are provided by a perceptron with a non-monotonic transfer function which reduces to the usual monotonic relation in a certain limit. We find that, although the on-line AdaTron learning is a powerful algorithm for the learnable rule, it does not give the best possible generalization error for unlearnable problems. Optimization of the learning rate is shown to greatly improve the performance of the AdaTron algorithm, leading to the best possible generalization error for a wide range of the parameter which controls the shape of the transfer function.)Comment: RevTeX 17 pages, 8 figures, to appear in Phys.Rev.

Rate effects in high-resolution drift chambers

The impact of high counting rates on the spatial resolution of cylindrical drift tubes is investigated in detail and the results are compared with simulations. Electronics effects and space-charge effects are quantitatively analysed. A spatial resolution of $\sigma < 80\,\mu\mathrm{m}$ can be achieved even at rates as high as 1500\,Hz/cm wire length (300\,kHz per wire)

Front-end electronics for drift tubes in a high-rate environment

A front-end electronics readout for drift tubes in a high-rate environment is presented. This system allows us to encode several pieces of information (leading edge time, trailing edge time, signal charge and piled-up hits from multiple tracks) into a single readout channel that is presented to the TDC. The advantage of active baseline restoration compared to bipolar signal shaping is discussed