How valid and accurate are measurements of golf impact parameters obtained using commercially available radar and stereoscopic optical launch monitors?

© 2017 The application of measurement technology in golf is increasing. In particular, measures of golf performance are valuable to coaches, golfers, club-fitters and equipment manufacturers. Commercially available launch monitors, such as the TrackMan Pro IIIe and Foresight GC2 + HMT, offer bespoke instantaneous methods to measure such parameters. Uncertainty in the outputs, however, is not well established nor independently verified. This study aimed to determine the degree of agreement between parameters from two launch monitors with measurements taken using a benchmark system. A total of 240 shots were collected with a driver, 7-iron and utility wedge. Shots were simultaneously tracked by each system and outputs compared using Limits of Agreement analysis. In addition, two reference grades were defined based on different levels of agreement; research and coaching grade. Agreement between the launch monitors and the benchmark system was noticeably stronger for ball parameters with greater variability in clubhead parameters. Furthermore, for both launch monitors, the strength of agreement for several parameters varied between clubs. The majority of ball parameters from both launch monitors fell within the research reference grade, but caution is needed for the use of clubhead parameters within a research environment. For coaches and clubfitters, the results suggest the launch monitor parameters are largely of sufficient quality

Assessment of Dual-frequency Signal Quality Monitor to Support CAT II/III GBAS

International audienceThis paper assesses the performance of the signal quality monitors for the Ground-Based Augmentation System (GBAS) which supports Category (CAT) II and III precision approach. Three types of monitors are used for signal deformation faults: Honeywell signal quality monitor (SQM), ENAC Code-Carrier Divergence (CCD) monitor and a proposed Divergence-Free (DF)-Innovation monitor. The existing Honeywell SQM and ENAC CCD monitors have some response time due to the smoothing filter used for their metrics. Consequently, the performance of those monitors is limited right after the fault onset. To improve the monitor performance in this time period, the DF-Innovation monitor has been proposed. The performance of the monitors has been assessed by comparing the minimum value of the probabilities of missed detection of three monitors and the required probability of missed detection according to the differential range error, which is defined in the standard document. As a result, for GBAS Approach Service Types (GAST) F, the probability of missed detection of the monitor was compliant with respect to the requirements for all fault cases and receiver configurations, for GPS L1/L5 and Galileo E1/E5. In addition, we observed that the proposed DF-Innovation monitor is effective in reducing time delay, which is the required time at airborne from filter initialization time to the time when the airborne user incorporates the measurement and correction for navigation. To be more specific, the use of the proposed monitor can reduce the time delay by 80% compared to the case without using the proposed monitor, and moreover, it can even reduce the value of time delay below 50 second, which is the recommended value currently

Implementing and Evaluating a Wireless Body Sensor System for Automated Physiological Data Acquisition at Home

Advances in embedded devices and wireless sensor networks have resulted in new and inexpensive health care solutions. This paper describes the implementation and the evaluation of a wireless body sensor system that monitors human physiological data at home. Specifically, a waist-mounted triaxial accelerometer unit is used to record human movements. Sampled data are transmitted using an IEEE 802.15.4 wireless transceiver to a data logger unit. The wearable sensor unit is light, small, and consumes low energy, which allows for inexpensive and unobtrusive monitoring during normal daily activities at home. The acceleration measurement tests show that it is possible to classify different human motion through the acceleration reading. The 802.15.4 wireless signal quality is also tested in typical home scenarios. Measurement results show that even with interference from nearby IEEE 802.11 signals and microwave ovens, the data delivery performance is satisfactory and can be improved by selecting an appropriate channel. Moreover, we found that the wireless signal can be attenuated by housing materials, home appliances, and even plants. Therefore, the deployment of wireless body sensor systems at home needs to take all these factors into consideration.Comment: 15 page

Lyot-based Low Order Wavefront Sensor: Implementation on the Subaru Coronagraphic Extreme Adaptive Optics System and its Laboratory Performance

High throughput, low inner working angle (IWA) phase masks coronagraphs are essential to directly image and characterize (via spectroscopy) earth-like planets. However, the performance of low-IWA coronagraphs is limited by residual pointing errors and other low-order modes. The extent to which wavefront aberrations upstream of the coronagraph are corrected and calibrated drives coronagraphic performance. Addressing this issue is essential for preventing coronagraphic leaks, thus we have developed a Lyot-based low order wave front sensor (LLOWFS) to control the wavefront aberrations in a coronagraph. The LLOWFS monitors the starlight rejected by the coronagraphic mask using a reflective Lyot stop in the downstream pupil plane. The early implementation of LLOWFS at LESIA, Observatoire de Paris demonstrated an open loop measurement accuracy of 0.01 lambda/D for tip-tilt at 638 nm when used in conjunction with a four quadrant phase mask (FQPM) in the laboratory. To further demonstrate our concept, we have installed the reflective Lyot stops on the Subaru Coronagraphic Extreme AO (SCExAO) system at the Subaru Telescope and modified the system to support small IWA phase mask coronagraphs (< 1 lambda/D) on-sky such as FQPM, eight octant phase mask, vector vortex coronagraph and the phase induced amplitude apodization complex phase mask coronagraph with a goal of obtaining milli arc-second pointing accuracy. Laboratory results have shown the measurement of tip, tilt, focus, oblique and right astigmatism at 1.55 um for the vector vortex coronagraph. Our initial on-sky result demonstrate the closed loop accuracy of < 7 x 10-3 lambda/D at 1.6 um for tip, tilt and focus aberrations with the vector vortex coronagraph.Comment: 9 pages, 9 Figures, Proc. of SPIE Astronomical Telescopes + Instrumentation 201

Computation-Communication Trade-offs and Sensor Selection in Real-time Estimation for Processing Networks

Recent advances in electronics are enabling substantial processing to be performed at each node (robots, sensors) of a networked system. Local processing enables data compression and may mitigate measurement noise, but it is still slower compared to a central computer (it entails a larger computational delay). However, while nodes can process the data in parallel, the centralized computational is sequential in nature. On the other hand, if a node sends raw data to a central computer for processing, it incurs communication delay. This leads to a fundamental communication-computation trade-off, where each node has to decide on the optimal amount of preprocessing in order to maximize the network performance. We consider a network in charge of estimating the state of a dynamical system and provide three contributions. First, we provide a rigorous problem formulation for optimal real-time estimation in processing networks in the presence of delays. Second, we show that, in the case of a homogeneous network (where all sensors have the same computation) that monitors a continuous-time scalar linear system, the optimal amount of local preprocessing maximizing the network estimation performance can be computed analytically. Third, we consider the realistic case of a heterogeneous network monitoring a discrete-time multi-variate linear system and provide algorithms to decide on suitable preprocessing at each node, and to select a sensor subset when computational constraints make using all sensors suboptimal. Numerical simulations show that selecting the sensors is crucial. Moreover, we show that if the nodes apply the preprocessing policy suggested by our algorithms, they can largely improve the network estimation performance.Comment: 15 pages, 16 figures. Accepted journal versio

Remote Performance Monitor (RPM)

Mobile, resource-constrained, battery-powered devices have emerged as key access points to the world\u27s digital infrastructure. To enable our understanding of the performance of these devices, we must be able to efficiently collect accurate profile data from these devices after they are deployed in the field. Moreover, understanding the full-system power and energy behavior of these systems for real programs is vital if users are to accurately characterize, model, and develop effective techniques for extending battery life. Unfortunately, extant approaches to measuring and characterizing power and energy consumption focus on high-end processors, do not consider the complete device, employ inaccurate (program-only) simulation, rely on inaccurate, course-grained battery level data from the device, or employ expensive power measurement tools that are difficult to share across research groups and students. To address these issues, we developed remote performance monitor (RPM). The first component of RPM is an efficient technique for collecting accurate sample-based program profiles. The key to the efficacy of this technique is that we identify when to sample using the repeating patterns in program execution, phases. To enable fine-grained, full-system characterization of embedded computers, we couple and unify phase-aware profiling, hardware performance monitoring, and power and energy measurement within RPM. RPM consists of a tightly coupled set of components which (1) control lab equipment for power measurements and analysis, (2) configure target system characteristics at run-time (such as CPU and memory bus speed), (3) collect target system data using on-board hardware performance monitors (HPMs) and (4) provide a remote access interface. Users of RPM can submit and configure experiments that execute programs on the RPM target device (currently a Stargate sensor platform that is very similar to an HP iPAQ) to collect very accurate power, energy, and CPU performance data with high resolution