MAP: Medial Axis Based Geometric Routing in Sensor Networks

One of the challenging tasks in the deployment of dense wireless networks (like sensor networks) is in devising a routing scheme for node to node communication. Important consideration includes scalability, routing complexity, the length of the communication paths and the load sharing of the routes. In this paper, we show that a compact and expressive abstraction of network connectivity by the medial axis enables efficient and localized routing. We propose MAP, a Medial Axis based naming and routing Protocol that does not require locations, makes routing decisions locally, and achieves good load balancing. In its preprocessing phase, MAP constructs the medial axis of the sensor field, defined as the set of nodes with at least two closest boundary nodes. The medial axis of the network captures both the complex geometry and non-trivial topology of the sensor field. It can be represented compactly by a graph whose size is comparable with the complexity of the geometric features (e.g., the number of holes). Each node is then given a name related to its position with respect to the medial axis. The routing scheme is derived through local decisions based on the names of the source and destination nodes and guarantees delivery with reasonable and natural routes. We show by both theoretical analysis and simulations that our medial axis based geometric routing scheme is scalable, produces short routes, achieves excellent load balancing, and is very robust to variations in the network model

Quench in high temperature superconductor magnets

High field superconducting magnets using high temperature superconductors are being developed for high energy physics, nuclear magnetic resonance and energy storage applications. Although the conductor technology has progressed to the point where such large magnets can be readily envisioned, quench protection remains a key challenge. It is well-established that quench propagation in HTS magnets is very slow and this brings new challenges that must be addressed. In this paper, these challenges are discussed and potential solutions, driven by new technologies such as optical fiber based sensors and thermally conducting electrical insulators, are reviewed.Comment: 9 pages, Contribution to WAMSDO 2013: Workshop on Accelerator Magnet, Superconductor, Design and Optimization; 15 - 16 Jan 2013, CERN, Geneva, Switzerlan

Graph Signal Processing: Overview, Challenges and Applications

Research in Graph Signal Processing (GSP) aims to develop tools for processing data defined on irregular graph domains. In this paper we first provide an overview of core ideas in GSP and their connection to conventional digital signal processing. We then summarize recent developments in developing basic GSP tools, including methods for sampling, filtering or graph learning. Next, we review progress in several application areas using GSP, including processing and analysis of sensor network data, biological data, and applications to image processing and machine learning. We finish by providing a brief historical perspective to highlight how concepts recently developed in GSP build on top of prior research in other areas.Comment: To appear, Proceedings of the IEE

Surface-wave-enabled darkfield aperture for background suppression during weak signal detection

Sensitive optical signal detection can often be confounded by the presence of a significant background, and, as such, predetection background suppression is substantively important for weak signal detection. In this paper, we present a novel optical structure design, termed surface-wave-enabled darkfield aperture (SWEDA), which can be directly incorporated onto optical sensors to accomplish predetection background suppression. This SWEDA structure consists of a central hole and a set of groove pattern that channels incident light to the central hole via surface plasmon wave and surface-scattered wave coupling. We show that the surface wave component can mutually cancel the direct transmission component, resulting in near-zero net transmission under uniform normal incidence illumination. Here, we report the implementation of two SWEDA structures. The first structure, circular-groove-based SWEDA, is able to provide polarization-independent suppression of uniform illumination with a suppression factor of 1230. The second structure, linear-groove-based SWEDA, is able to provide a suppression factor of 5080 for transverse-magnetic wave and can serve as a highly compact (5.5 micrometer length) polarization sensor (the measured transmission ratio of two orthogonal polarizations is 6100). Because the exact destructive interference balance is highly delicate and can be easily disrupted by the nonuniformity of the localized light field or light field deviation from normal incidence, the SWEDA can therefore be used to suppress a bright background and allow for sensitive darkfield sensing and imaging (observed image contrast enhancement of 27 dB for the first SWEDA)

Characterization of Thin p-on-p Radiation Detectors with Active Edges

Active edge p-on-p silicon pixel detectors with thickness of 100 $\mu$m were fabricated on 150 mm Float zone silicon wafers at VTT. By combining measured results and TCAD simulations, a detailed study of electric field distributions and charge collection performances as a function of applied voltage in a p-on-p detector was carried out. A comparison with the results of a more conventional active edge p-on-n pixel sensor is presented. The results from 3D spatial mapping show that at pixel-to-edge distances less than 100 $\mu$m the sensitive volume is extended to the physical edge of the detector when the applied voltage is above full depletion. The results from a spectroscopic measurement demonstrate a good functionality of the edge pixels. The interpixel isolation above full depletion and the breakdown voltage were found to be equal to the p-on-n sensor while lower charge collection was observed in the p-on-p pixel sensor below 80 V. Simulations indicated this to be partly a result of a more favourable weighting field in the p-on-n sensor and partly of lower hole lifetimes in the p-bulk.Comment: 23 pages, 16 figures, 1 tabl

TactileGCN: A Graph Convolutional Network for Predicting Grasp Stability with Tactile Sensors

Tactile sensors provide useful contact data during the interaction with an object which can be used to accurately learn to determine the stability of a grasp. Most of the works in the literature represented tactile readings as plain feature vectors or matrix-like tactile images, using them to train machine learning models. In this work, we explore an alternative way of exploiting tactile information to predict grasp stability by leveraging graph-like representations of tactile data, which preserve the actual spatial arrangement of the sensor's taxels and their locality. In experimentation, we trained a Graph Neural Network to binary classify grasps as stable or slippery ones. To train such network and prove its predictive capabilities for the problem at hand, we captured a novel dataset of approximately 5000 three-fingered grasps across 41 objects for training and 1000 grasps with 10 unknown objects for testing. Our experiments prove that this novel approach can be effectively used to predict grasp stability