Real-time Version Research Articles

Comparison of measured electron energy spectra for six matched, radiotherapy accelerators

This study compares energy spectra of the multiple electron beams of individual radiotherapy machines, as well as the sets of spectra across multiple matched machines. Also, energy spectrum metrics are compared with central‐axis percent depth‐dose (PDD) metrics.MethodsA lightweight, permanent magnet spectrometer was used to measure energy spectra for seven electron beams (7–20 MeV) on six matched Elekta Infinity accelerators with the MLCi2 treatment head. PDD measurements in the distal falloff region provided R 50 and R 80–20 metrics in Plastic Water®, which correlated with energy spectrum metrics, peak mean energy (PME) and full‐width at half maximum (FWHM).ResultsVisual inspection of energy spectra and their metrics showed whether beams on single machines were properly tuned, i.e., FWHM is expected to increase and peak height decrease monotonically with increased PME. Also, PME spacings are expected to be approximately equal for 7–13 MeV beams (0.5‐cm R90 spacing) and for 13–16 MeV beams (1.0‐cm R90 spacing). Most machines failed these expectations, presumably due to tolerances for initial beam matching (0.05 cm in R 90; 0.10 cm in R 80–20) and ongoing quality assurance (0.2 cm in R 50). Also, comparison of energy spectra or metrics for a single beam energy (six machines) showed outlying spectra. These variations in energy spectra provided ample data spread for correlating PME and FWHM with PDD metrics. Least‐squares fits showed that R 50 and R 80–20 varied linearly and supralinearly with PME, respectively; however, both suggested a secondary dependence on FWHM. Hence, PME and FWHM could serve as surrogates for R 50 and R 80–20 for beam tuning by the accelerator engineer, possibly being more sensitive (e.g., 0.1 cm in R 80–20 corresponded to 2.0 MeV in FWHM).ConclusionsResults of this study suggest a lightweight, permanent magnet spectrometer could be a useful beam‐tuning instrument for the accelerator engineer to (a) match electron beams prior to beam commissioning, (b) tune electron beams for the duration of their clinical use, and (c) provide estimates of PDD metrics following machine maintenance. However, a real‐time version of the spectrometer is needed to be practical.

Individual strategies in adaptation to altered auditory feedback

Speakers monitor themselves while talking. When they hear a real-time altered version of their speech, they will change their articulation so that when they hear their altered speech, it matches their acoustic target [Houde and Jordan (1998, Science 20;279(5354):1213–1216)]. The experiment presented here used the novel addition of ultrasound imaging to reveal how speakers (n = 30) change their articulations in response to two different formant perturbations: raising of F1 in “head” and F2 in “hood.” Principal components analysis was used to identify speakers' individual strategies during adaptation. Some speakers use a single strategy for an entire adaptation block, while others change strategy. Speakers are also known to change production in a formant that was not altered [Katseff et al. (2010, JASA 127(3), 1955)]. The ultrasound analysis shows that at least for some speakers, change in two formants is linked to independent and uncorrelated articulatory components and possibly serves a perceptual purpose in compensation, rather than being an unintended result of the compensatory response. Preliminary results (n = 4) of adaptation to raising F3 in “heard” will also be presented. Modeling with the Maeda and Manzara synthesizers [Bakst and Johnson (2016, JASA 140(4), 3223)] correctly predicted speakers' articulatory strategies.

Development of a Hydrological Telemetry System in Bago River

Hydrological monitoring is one of the key aspects in early warning systems that are vital to flood disaster management in flood-prone areas such as Bago River Basin in Myanmar. Thousands of people are affected due to the perennial flooding. Owing to the increasing pressure of rapid urbanization in the region and future climate change impacts, an early warning system in the basin is urgently required for disaster risk mitigation. This paper introduces the co-establishment of the telemetry system by a group of stakeholders. The co-establishment of the system through intensive consultations, proactive roles in responsibility sharing, and capacity building efforts, is essential in developing a base platform for flood forecasting and an early warning system in the basin. Herein, we identify the key challenges that have been central to the participatory approach in co-establishing the system. We also highlight opportunities as a result of the ongoing process and future impact on the disaster management system in the basin. We also highlight the potential for scientific contributions in understanding the local weather and hydrological characteristics through the establishment of the high-temporal resolution observation network. Using the observation at Zaung Tu Weir, Global Satellite Mapping of Precipitation (GSMaP) and Global Precipitation Measurement (GPM) satellite estimates were assessed. Near real-time and standard versions of both satellite estimates show potential utility over the basin. Hourly aggregation shows slightly higher than 40% probability of detection (POD), on average, for both satellite estimates regardless of the production type. Thus, the hourly aggregation requires correction before usage. The results show useful skills at 60% POD for standard GSMaP (GSMAP-ST), 55% POD for near real-time GSMaP (GSMAP-NR), and 46% POD for GPM, at 3-hourly aggregations. Six-hourly aggregations show maximum benefit for providing useful skill and good correspondence to gauge the observation with GSMAP-ST showing the best true skill score (TSS) at 0.54 and an equitable threat score (ETS) at 0.37. While, both final run GPM and GSMAP-NR show lower POD, TSS, and ETS scores. Considering the latency of near real-time satellite estimates, the GSMAP-NR shows the best potential with a 4-hour latency period for monitoring and forecasting purposes in the basin. The result of the GSMAP-NR does not vary significantly from the GSMAP-ST and all GPM estimates. However, it requires some correction before its usage in any applications, for modeling and forecasting purposes.

An Iterative Method for Detecting and Localizing Islands Within Sparse Matrixes Using DSSim-RT

Nowadays, the simulation tools used to perform power system analysis are evolving into the many-core computation era; some of these techniques propose to tear the power system network into several subnetworks (islands) for its parallel processing. The island's detection is an issue considered by the power flow analysis due to management activities such as feeder reconfiguration, fault detection, and isolation, among others, that generate topological changes. These methods include graph theory, checking the circuit breaker status and on-site measurements, decision trees, frequency deviation, and pattern recognition, among other techniques, which are focused to detect topology changes that may generate islands and affect the state estimation of the system. In contrast, other techniques such as the approximate minimum degree perform a reorganization of the $Y_{{\text{BUS}}}$ matrix that describes the network components and their connectivity, looking to factorizing the $Y_{{\text{Bus}}}$ matrix to solve the power flow problem. These techniques are very functional and efficient; however, some of them require detailed information of the network, and are addressed to cover meshed and radial network configurations separately. This paper presents an iterative algorithm that uses the compressed coordinate branch-to-node matrix for detecting, classifying, and grouping islands within sparse matrixes, describing mesh or radial networks. This algorithm is a valuable tool to simplify islands location and can be implemented using any programing language due to its simplicity. This method is used in Distribution System Simulator - Real Time Version (DSSim-RT), which is a simulator based in OpenDSS, for tearing the power system network to allow the multithread power flow analysis of distribution systems in real time.

Anomaly Detection and Classification for Streaming Data using PDEs

Nondominated sorting, also called Pareto Depth Analysis (PDA), is widely used in multi-objective optimization and has recently found important applications in multi-criteria anomaly detection. Recently, a partial differential equation (PDE) continuum limit was discovered for nondominated sorting leading to a very fast approximate sorting algorithm called PDE-based ranking. We propose in this paper a fast real-time streaming version of the PDA algorithm for anomaly detection that exploits the computational advantages of PDE continuum limits. Furthermore, we derive new PDE continuum limits for sorting points within their nondominated layers and show how the new PDEs can be used to classify anomalies based on which criterion was more significantly violated. We also prove statistical convergence rates for PDE-based ranking, and present the results of numerical experiments with both synthetic and real data.

Multi-station basis for Polar Cap (PC) indices: ensuring credibility and operational reliability

The Polar Cap (PC) indices, PCN (North) and PCS (South) are based on polar geomagnetic observations from Qaanaaq (Thule) and Vostok, respectively, processed to measure the transpolar plasma convection that may seriously affect space weather conditions. To establish reliable space weather forecasts based on PC indices, and also to ensure credibility of their use for scientific analyses of solar wind-magnetosphere interactions, additional sources of data for the PC indices are investigated. In the search for alternative index sources, objective quality criteria are established here to be used for the selection among potential candidates. These criteria are applied to existing PC index series to establish a quality scale. In the Canadian region, the data from Resolute Bay magnetometer are shown to provide alternative PCN indices of adequate quality. In Antarctica, the data from Concordia Dome-C observatory are shown to provide basis for alternative PCS indices. In examples to document the usefulness of these alternative index sources it is shown that PCN indices in a real-time version based on magnetometer data from Resolute Bay could have given 6 h of early warning, of which the last 2 h were “red alert”, up to the onset of the strong substorm event on 13 March 1989 that caused power outage in Quebec. The alternative PCS indices based on data from Dome-C have helped to disclose that presently available Vostok-based PCS index values are corrupted throughout most of 2011.

Intercarrier Interference-Aware Pilot-Aided Channel Estimation in OFDM Systems

Linear pilot-aided channel estimation in orthogonal-frequency division multiplexing systems is carried out optimally via 2D Wiener filters. The standard approach employed in conventional receivers is based on cascaded $2\times 1\text{D}$ Wiener filtering. In doing so, suboptimal performance is tolerated for the sake of lower complexity. By means of the 2D+1D filtering scheme proposed in this paper, the performance of cascaded filtering is enhanced while maintaining the low complexity of the $2\times 1\text{D}$ approach. A theoretical analysis of different cascaded Wiener filtering approaches is provided, and the approaches are compared. Furthermore, the effect of Doppler-induced intercarrier interference (ICI) is taken into account for the calculation of the mean-squared error (MSE) of the different channel estimation schemes. Additionally, practical real-time adaptive versions of these schemes are demonstrated. Results from full link-level simulations with respect to the digital video broadcasting standard for satellite services to handheld devices are presented. The proposed 2D+1D filtering scheme shows enhanced MSE and bit error rate performance than both conventional $2\times 1\text{D}$ scheme and more advanced ICI cancellation schemes, since it provides more accurate channel estimates in high Doppler spread conditions.

Correcting satellite-based precipitation products through SMOS soil moisture data assimilation in two land-surface models of different complexity: API and SURFEX

Global rainfall information is useful for many applications. However, real-time versions of satellite-based rainfall products are known to contain errors. Recent studies have demonstrated how the information about rainfall intrinsically contained in soil moisture data can be utilised for improving rainfall estimates. That is, soil moisture dynamics are impacted for several days by the accumulated amount of rainfall following within a particular event. In this context, soil moisture data from the Soil Moisture Ocean Salinity (SMOS) satellite is used in this study to correct rainfall accumulation estimates provided by satellite-based real-time precipitation products such as CMORPH, TRMM-3B42RT or PERSIANN. An algorithm based on the SMOS measurements data assimilation is tested in two land-surface models of different complexity: a simple hydrological model (Antecedent Precipitation Index (API)) and a more sophisticated state-of-the-art land-surface model (SURFEX (Surface Externalisée)). We show how the assimilation technique, based on a particle filter method, generally leads to a significant improvement in rainfall estimates, with slightly better results for the simpler (and less computationally demanding) API model. This methodology has been evaluated for six years at ten sites around the world with different land use and climatological features. The results also show the limitations of the methodology in regions highly affected by mountainous terrain, forest or intense radio-frequency interference (RFI), which can notably affect the quality of the retrievals. The satisfactory results shown here invite the future operational application of the methodology in near-real time on a global scale.

FINANCIAL BUBBLE IMPLOSION AND REVERSE REGRESSION

Expansion and collapse are two key features of a financial asset bubble. Bubble expansion may be modeled using a mildly explosive process. Bubble implosion may take several different forms depending on the nature of the collapse and therefore requires some flexibility in modeling. This paper first strengthens the theoretical foundation of the real time bubble monitoring strategy proposed in Phillips, Shi and Yu (2015a,b, PSY) by developing analytics and studying the performance characteristics of the testing algorithm under alternative forms of bubble implosion which capture various return paths to market normalcy. Second, we propose a new reverse sample use of the PSY procedure for detecting crises and estimating the date of market recovery. Consistency of the dating estimators is established and the limit theory addresses new complications arising from the alternative forms of bubble implosion and the endogeneity effects present in the reverse regression. A real-time version of the strategy is provided that is suited for practical implementation. Simulations explore the finite sample performance of the strategy for dating market recovery. The use of the PSY strategy for bubble monitoring and the new procedure for crisis detection are illustrated with an application to the Nasdaq stock market.

Gyro in the Air

3D orientation tracking is an essential ingredient for many Internet-of-Things applications. Yet existing orientation tracking systems commonly require motion sensors that are only available on battery-powered devices. In this paper, we propose Tagyro, which attaches an array of passive RFID tags as orientation sensors on everyday objects. Tagyro uses a closed-form model to transform the run-time phase offsets between tags into orientation angle. To enable orientation tracking in 3D space, we found the key challenge lies in the imperfect radiation pattern of practical tags, caused by the antenna polarity, non-isotropic emission and electromagnetic coupling, which substantially distort phase measurement. We address these challenges by designing a set of phase sampling and recovery algorithms, which together enable reliable orientation sensing with 3 degrees of freedom. We have implemented a real-time version of Tagyro on a commodity RFID system. Our experiments show that Tagyro can track the 3D orientation of passive objects with a small error of 4°, at a processing rate of 37.7 samples per second.

A new method for QRS detection in ECG signals using QRS-preserving filtering techniques.

Detection of QRS complexes in ECG signals is required for various purposes such as determination of heart rate, feature extraction and classification. The problem of automatic QRS detection in ECG signals is complicated by the presence of noise spectrally overlapping with the QRS frequency range. As a solution to this problem, we propose the use of least-squares-optimisation-based smoothing techniques that suppress the noise peaks in the ECG while preserving the QRS complexes. We also propose a novel nonlinear transformation technique that is applied after the smoothing operations, which equalises the QRS amplitudes without boosting the supressed noise peaks. After these preprocessing operations, the R-peaks can finally be detected with high accuracy. The proposed technique has a low computational load and, therefore, it can be used for real-time QRS detection in a wearable device such as a Holter monitor or for fast offline QRS detection. The offline and real-time versions of the proposed technique have been evaluated on the standard MIT-BIH database. The offline implementation is found to perform better than state-of-the-art techniques based on wavelet transforms, empirical mode decomposition, etc. and the real-time implementation also shows improved performance over existing real-time QRS detection techniques.

Hand-Based Gesture Recognition for Vehicular Applications Using IR-UWB Radar.

Modern cars continue to offer more and more functionalities due to which they need a growing number of commands. As the driver tries to monitor the road and the graphic user interface simultaneously, his/her overall efficiency is reduced. In order to reduce the visual attention necessary for monitoring, a gesture-based user interface is very important. In this paper, gesture recognition for a vehicle through impulse radio ultra-wideband (IR-UWB) radar is discussed. The gestures can be used to control different electronic devices inside a vehicle. The gestures are based on human hand and finger motion. We have implemented a real-time version using only one radar sensor. Studies on gesture recognition using IR-UWB radar have rarely been carried out, and some studies are merely simple methods using the magnitude of the reflected signal or those whose performance deteriorates largely due to changes in distance or direction. In this study, we propose a new hand-based gesture recognition algorithm that works robustly against changes in distance or direction while responding only to defined gestures by ignoring meaningless motions. We used three independent features, i.e., variance of the probability density function (pdf) of the magnitude histogram, time of arrival (TOA) variation and the frequency of the reflected signal, to classify the gestures. A data fitting method is included to differentiate between gesture signals and unintended hand or body motions. We have used the clustering technique for the classification of the gestures. Moreover, the distance information is used as an additional input parameter to the clustering algorithm, such that the recognition technique will not be vulnerable to distance change. The hand-based gesture recognition proposed in this paper would be a key technology of future automobile user interfaces.

Performance of CMORPH, TMPA, and PERSIANN rainfall datasets over plain, mountainous, and glacial regions of Pakistan

The present study aims at the assessment of six satellite rainfall estimates (SREs) in Pakistan. For each assessed products, both real-time (RT) and post adjusted (Adj) versions are considered to highlight their potential benefits in the rainfall estimation at annual, monthly, and daily temporal scales. Three geomorphological climatic zones, i.e., plain, mountainous, and glacial are taken under considerations for the determination of relative potentials of these SREs over Pakistan at global and regional scales. All SREs, in general, have well captured the annual north-south rainfall decreasing patterns and rainfall amounts over the typical arid regions of the country. Regarding the zonal approach, the performance of all SREs has remained good over mountainous region comparative to arid regions. This poor performance in accurate rainfall estimation of all the six SREs over arid regions has made their use questionable in these regions. Over glacier region, all SREs have highly overestimated the rainfall. One possible cause of this overestimation may be due to the low surface temperature and radiation absorption over snow and ice cover, resulting in their misidentification with rainy clouds as daily false alarm ratio has increased from mountainous to glacial regions. Among RT products, CMORPH-RT is the most biased product. The Bias was almost removed on CMORPH-Adj thanks to the gauge adjustment. On a general way, all Adj versions outperformed their respective RT versions at all considered temporal scales and have confirmed the positive effects of gauge adjustment. CMORPH-Adj and TMPA-Adj have shown the best agreement with in situ data in terms of Bias, RMSE, and CC over the entire study area.

An Online Solution of LiDAR Scan Matching Aided Inertial Navigation System for Indoor Mobile Mapping

Multisensors (LiDAR/IMU/CAMERA) integrated Simultaneous Location and Mapping (SLAM) technology for navigation and mobile mapping in a GNSS-denied environment, such as indoor areas, dense forests, or urban canyons, becomes a promising solution. An online (real-time) version of such system can extremely extend its applications, especially for indoor mobile mapping. However, the real-time response issue of multisensors is a big challenge for an online SLAM system, due to the different sampling frequencies and processing time of different algorithms. In this paper, an online Extended Kalman Filter (EKF) integrated algorithm of LiDAR scan matching and IMU mechanization for Unmanned Ground Vehicle (UGV) indoor navigation system is introduced. Since LiDAR scan matching is considerably more time consuming than the IMU mechanism, the real-time synchronous issue is solved via a one-step-error-state-transition method in EKF. Stationary and dynamic field tests had been performed using a UGV platform along typical corridor of office building. Compared to the traditional sequential postprocessed EKF algorithm, the proposed method can significantly mitigate the time delay of navigation outputs under the premise of guaranteeing the positioning accuracy, which can be used as an online navigation solution for indoor mobile mapping.

Homing vector automata

We introduce homing vector automata, which are finite automata augmented by a vector that is multiplied at each step by a matrix determined by the current transition, and have to return the vector to its original setting in order to accept the input. The computational power and properties of deterministic, nondeterministic, blind, non-blind, real-time and one-way versions of these machines are examined and compared to various related types of automata. A generalized version of the Stern−Brocot encoding method, suitable for representing strings on arbitrary alphabets, is also developed.

Remote sensing-based characterization of rainfall during atmospheric rivers over the central United States

Atmospheric rivers (ARs) play a central role in the hydrology and hydroclimatology of the central United States. More than 25% of the annual rainfall is associated with ARs over much of this region, with many large flood events tied to their occurrence. Despite the relevance of these storms for flood hydrology and water budget, the characteristics of rainfall associated with ARs over the central United has not been investigated thus far. This study fills this major scientific gap by describing the rainfall during ARs over the central United States using five remote sensing-based precipitation products over a 12-year study period. The products we consider are: Stage IV, Tropical Rainfall Measuring Mission – Multi-satellite Precipitation Analysis (TMPA, both real-time and research version); Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN); the CPC MORPHing Technique (CMORPH). As part of the study, we evaluate these products against a rain gauge-based dataset using both graphical- and metrics-based diagnostics. Based on our analyses, Stage IV is found to better reproduce the reference data. Hence, we use it for the characterization of rainfall in ARs.Most of the AR-rainfall is located in a narrow region within ∼150km on both sides of the AR major axis. In this region, rainfall has a pronounced positive relationship with the magnitude of the water vapor transport. Moreover, we have also identified a consistent increase in rainfall intensity with duration (or persistence) of AR conditions. However, there is not a strong indication of diurnal variability in AR rainfall. These results can be directly used in developing flood protection strategies during ARs. Further, weather prediction agencies can benefit from the results of this study to achieve higher skill of resolving precipitation processes in their models.

Verification of the skill of numerical weather prediction models in forecasting rainfall from U.S. landfalling tropical cyclones

The goal of this study is the evaluation of the skill of five state-of-the-art numerical weather prediction (NWP) systems [European Centre for Medium-Range Weather Forecasts (ECMWF), UK Met Office (UKMO), National Centers for Environmental Prediction (NCEP), China Meteorological Administration (CMA), and Canadian Meteorological Center (CMC)] in forecasting rainfall from North Atlantic tropical cyclones (TCs). Analyses focus on 15 North Atlantic TCs that made landfall along the U.S. coast over the 2007–2012 period. As reference data we use gridded rainfall provided by the Climate Prediction Center (CPC). We consider forecast lead-times up to five days. To benchmark the skill of these models, we consider rainfall estimates from one radar-based (Stage IV) and four satellite-based [Tropical Rainfall Measuring Mission - Multi-satellite Precipitation Analysis (TMPA, both real-time and research version); Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN); the CPC MORPHing Technique (CMORPH)] rainfall products. Daily and storm total rainfall fields from each of these remote sensing products are compared to the reference data to obtain information about the range of errors we can expect from “observational data.” The skill of the NWP models is quantified: (1) by visual examination of the distribution of the errors in storm total rainfall for the different lead-times, and numerical examination of the first three moments of the error distribution; (2) relative to climatology at the daily scale. Considering these skill metrics, we conclude that the NWP models can provide skillful forecasts of TC rainfall with lead-times up to 48h, without a consistently best or worst NWP model.

Does a Survey Based Capacity Utilization Measure Help Predicting Brazilian Output Gap in Real-Time?

The aim of the paper is to assess the informational content of a survey-based measure of the Brazilian manufacturing capacity utilization rate produced by the Brazilian Institute of Economics from Getulio Vargas Foundation. From a theoretical point of view, the survey-based capacity measure should provide meaningful information with respect to the Brazilian business cycle. In this work, we choose to use the output gap as the most comprehensive and convincing proxy of the cyclical situation (Graff and Sturm in CESifo Econ Stud 58(1):220–251, 2012). More specifically, we will refer to a real-time version of the output gap, using an existing real-time GDP data set produced by the Central Bank of Brazil (Cusinato et al. in Empir Econ, 2012). The information efficiency of the real-time output gap estimates is tested checking whether survey data can help producing real-time estimates that are significantly closer to the latest releases. In this sense, the paper provides a significant contribution to the existing literature, extending analysis already available for OECD countries to the Brazilian economy. Our main result is that survey data can indeed help to produce more efficient estimates of the output gap in real time.

Real-Time Infrared Thermography at ASDEX Upgrade

Infrared (IR) thermography is a widely used tool in fusion research to study the thermal load onto plasma-facing components. In present-day fusion experiments with short-pulse duration, off-line data analysis is still feasible. For devices with long-pulse duration and actively cooled plasma-facing components, IR thermography is a common tool for machine protection. In future fusion devices with long-pulse duration, online data evaluation of the thermography measurement for additional physics studies is required. Real-time–capable IR thermography was developed at ASDEX Upgrade. The feasibility of real-time thermography is discussed in this work. The evaluation process from raw data to evaluated temperature and heat flux is shown. The real-time version of the THEODOR code allows online calculation of the heat flux. Exploiting the possibility of the IR system to change the integration time during acquisition opens up the possibility to have automated thermography. The current status of the thermography system at ASDEX Upgrade and future developments for its improvement are discussed.

Can adding web-based support to UK primary care exercise referral schemes improve patients’ physical activity levels? Intervention development for the e-coachER study.

Can adding web-based support to UK primary care exercise referral schemes improve patients’ physical activity levels? Intervention development for the e-coachER study.