Induced signals in resistive plate chambers

We derive theorems for induced signals on electrodes embedded in a medium with a position and frequency dependent permittivity \vep(\vx,s) and conductivity \sigma(\vx,s) that are connected with arbitrary discrete elements. The problem is treated using the quasi-static approximation of Maxwell's equations for weakly conducting media \cite{melcher}\cite{quasi}. The induced signals can be derived by time dependent weighting fields and potentials and the result is the same as the one given in \cite{gatti}. We also show how these time dependent weighting fields can be derived from electrostatic solutions. Finally we will apply the results to Resistive Plate Chambers (RPCs) where we discuss the effects of the resistive plates and thin resistive layers on the signals induced on plane electrodes and strips

Signal Propagation, Termination, Crosstalk and Losses in Resistive Plate Chambers

We discuss the signal propagation, strip termination and crosstalk in Resistive Plate Chambers (RPCs) by analyzing the explicit time domain solution of a two dimensional multi-conductor transmission line. It is shown that all the effects can be calculated by elementary matrix manipulations

The Mutual Interpretation of Active and Passive Microwave Sensor Outputs

Mutual interpretation of active and passive microwave sensor output

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

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}

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

A Frequency-Controlled Magnetic Vortex Memory

Using the ultra low damping NiMnSb half-Heusler alloy patterned into vortex-state magnetic nano-dots, we demonstrate a new concept of non-volatile memory controlled by the frequency. A perpendicular bias magnetic field is used to split the frequency of the vortex core gyrotropic rotation into two distinct frequencies, depending on the sign of the vortex core polarity $p=\pm1$ inside the dot. A magnetic resonance force microscope and microwave pulses applied at one of these two resonant frequencies allow for local and deterministic addressing of binary information (core polarity)

Learning and generation of long-range correlated sequences

We study the capability to learn and to generate long-range, power-law correlated sequences by a fully connected asymmetric network. The focus is set on the ability of neural networks to extract statistical features from a sequence. We demonstrate that the average power-law behavior is learnable, namely, the sequence generated by the trained network obeys the same statistical behavior. The interplay between a correlated weight matrix and the sequence generated by such a network is explored. A weight matrix with a power-law correlation function along the vertical direction, gives rise to a sequence with a similar statistical behavior.Comment: 5 pages, 3 figures, accepted for publication in Physical Review