Novel algorithm implementations in DARC: the Durham AO real-time controller

2016 ◽  
Author(s):  
Alastair Basden ◽  
Urban Bitenc ◽  
David Jenkins
Keyword(s):  
Real Time ◽  
Novel Algorithm

scholarly journals Enhancing spatial perception through sound: mapping human movements into MIDI

2021 ◽  
Author(s):  
Bernardo Breve ◽  
Stefano Cirillo ◽  
Mariano Cuofano ◽  
Domenico Desiato
Keyword(s):  
Real Time ◽  
User Study ◽  
Spatial Perception ◽  
Human Movements ◽  
Different Types ◽  
Real Time Tracking ◽  
People’S Perceptions ◽  
Novel Algorithm

AbstractGestural expressiveness plays a fundamental role in the interaction with people, environments, animals, things, and so on. Thus, several emerging application domains would exploit the interpretation of movements to support their critical designing processes. To this end, new forms to express the people’s perceptions could help their interpretation, like in the case of music. In this paper, we investigate the user’s perception associated with the interpretation of sounds by highlighting how sounds can be exploited for helping users in adapting to a specific environment. We present a novel algorithm for mapping human movements into MIDI music. The algorithm has been implemented in a system that integrates a module for real-time tracking of movements through a sample based synthesizer using different types of filters to modulate frequencies. The system has been evaluated through a user study, in which several users have participated in a room experience, yielding significant results about their perceptions with respect to the environment they were immersed.

REAL-TIME DISPERSIVE REFRACTION WITH ADAPTIVE SPECTRAL MAPPING

2013 ◽  
Vol 22 (06) ◽  
pp. 1360019
Author(s):  
DAMON BLANCHETTE ◽  
EMMANUEL AGU
Keyword(s):  
Real Time ◽  
White Light ◽  
Spectral Mapping ◽  
Mapping Algorithm ◽  
Physically Based ◽  
Refractive Medium ◽  
Interactive Frame ◽  
Screen Space ◽  
Special Case ◽  
Novel Algorithm

Spectral rendering, or the synthesis of images by taking into account the constituent wavelengths of white light, enables the rendering of iridescent colors caused by phenomena such as dispersion, diffraction, interference and scattering. Caustics, the focusing and defocusing of light through a refractive medium, can be interpreted as a special case of dispersion where all the wavelengths travel along the same paths. In this paper we extend Adaptive Caustic Mapping (ACM), a previously proposed caustics mapping algorithm, to handle physically-based dispersion. Because ACM can display caustics in real-time, it is amenable to extension to handle the more general case of dispersion. We also present a novel algorithm for filling in the gaps that occur due to discrete sampling of the spectrum. Our proposed method runs in screen-space, and is fast enough to display plausible dispersion phenomena at real-time and interactive frame rates.

scholarly journals Novel Algorithm for Radon Real-Time Measurements with a Pixelated Detector

Sensors ◽  
2022 ◽  
Vol 22 (2) ◽  
pp. 516
Author(s):  
Alessandro Rizzo ◽  
Francesco Cardellini ◽  
Claudio Poggi ◽  
Enrico Borra ◽  
Luca Ciciani ◽  
...  
Keyword(s):  
Real Time ◽  
Building Materials ◽  
Silicon Detector ◽  
Active Area ◽  
Matrix Structure ◽  
Potential Applications ◽  
Deposited Energy ◽  
Simple Measurement ◽  
Compact Design ◽  
Novel Algorithm

Nowadays, radon gas exposure is considered one of the main health concerns for the population because, by carrying about half the total dose due to environmental radioactivity, it is the second cause of lung cancer after smoking. Due to a relatively long half-life of 3.82 days, the chemical inertia and since its parent Ra-226 is largely diffuse on the earthrgb]0,0,1’s crust and especially in the building materials, radon can diffuse and potentially saturate human habitats, with a concentration that can suddenly change during the 24 h day depending on temperature, pressure, and relative humidity. For such reasons, `real-time’ measurements performed by an active detector, possibly of small dimensions and a handy configuration, can play an important role in evaluating the risk and taking the appropriate countermeasures to mitigate it. In this work, a novel algorithm for pattern recognition was developed to exploit the potentialities of silicon active detectors with a pixel matrix structure to measure radon through the α emission, in a simple measurement configuration, where the device is placed directly in air with no holder, no collection filter or electrostatic field to drift the radon progenies towards the detector active area. This particular measurement configuration (dubbed as bare) requires an α/β-discrimination method that is not based on spectroscopic analysis: as the gas surrounds the detector the α particles are emitted at different distances from it, so they lose variable energy amount in air depending on the traveled path-length which implies a variable deposited energy in the active area. The pixels matrix structure allows overcoming this issue because the interaction of α, β and γ particles generate in the active area of the detector clusters (group of pixels where a signal is read) of different shape and energy dispersion. The novel algorithm that exploits such a phenomenon was developed using a pixelated silicon detector of the TimePix family with a compact design. An α(Am-241) and a β(Sr-90) source were used to calibrate the algorithm and to evaluate its performances in terms of β rejection capability and α recognition efficiency. Successively, the detector was exposed to different radon concentrations at the ENEA-INMRI radon facility in `bare’ configuration, in order to check the linearity of the device response over a radon concentration range. The results for this technique are presented and discussed, highlighting the potential applications especially the possibility to exploit small and handy detectors to perform radon active measurements in the simplest configuration.

scholarly journals Particle Swarm Based Approach of a Real-Time Discrete Neural Identifier for Linear Induction Motors

2013 ◽  
Vol 2013 ◽  
pp. 1-9 ◽  
Author(s):  
Alma Y. Alanis ◽  
E. Rangel ◽  
J. Rivera ◽  
N. Arana-Daniel ◽  
C. Lopez-Franco
Keyword(s):  
Neural Network ◽  
Real Time ◽  
Discrete Time ◽  
Induction Motors ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Linear Induction ◽  
Linear Induction Motor ◽  
High Order Neural Network ◽  
Novel Algorithm

This paper focusses on a discrete-time neural identifier applied to a linear induction motor (LIM) model, whose model is assumed to be unknown. This neural identifier is robust in presence of external and internal uncertainties. The proposed scheme is based on a discrete-time recurrent high-order neural network (RHONN) trained with a novel algorithm based on extended Kalman filter (EKF) and particle swarm optimization (PSO), using an online series-parallel con…figuration. Real-time results are included in order to illustrate the applicability of the proposed scheme.

Tornado Formation and Intensity Prediction Using Polarimetric Radar Estimates of Updraft Area

2021 ◽  
Author(s):  
Michael M. French ◽  
Darrel M. Kingfield
Keyword(s):  
Real Time ◽  
Risk Estimation ◽  
Radar Reflectivity ◽  
Polarimetric Radar ◽  
Intensity Prediction ◽  
Doppler Data ◽  
Azimuthal Shear ◽  
Size Sorting ◽  
Future Work ◽  
Novel Algorithm

AbstractA sample of 198 supercells are investigated to determine if a radar proxy for the area of the storm midlevel updraft may be a skillful predictor of imminent tornado formation and/or peak tornado intensity. A novel algorithm, a modified version of the Thunderstorm Risk Estimation from Nowcasting Development via Size Sorting (TRENDSS) algorithm is used to estimate the area of the enhanced differential radar reflectivity factor (ZDR) column in Weather Surveillance Radar – 1988 Doppler data; the ZDR column area is used as a proxy for the area of the midlevel updraft. The areas of ZDR columns are compared for 154 tornadic supercells and 44 non-tornadic supercells, including 30+ supercells with tornadoes rated EF1, EF2, and EF3; nine supercells with EF4+ tornadoes also are analyzed. It is found that (i) at the time of their peak 0-1 km azimuthal shear, non-tornadic supercells have consistently small (< 20 km2) ZDR column areas while tornadic cases exhibit much greater variability in areas, and (ii) at the time of tornadogenesis, EF3+ tornadic cases have larger ZDR column areas than tornadic cases rated EF1/2. In addition, all nine violent tornadoes sampled have ZDR column areas > 30 km2 at the time of tornadogenesis. However, only weak positive correlation is found between ZDR column area and both radar-estimated peak tornado intensity and maximum tornado path width. Planned future work focused on mechanisms linking updraft size and tornado formation and intensity is summarized and the use of the modified TRENDSS algorithm, which is immune to ZDR bias and thus ideal for real-time operational use, is emphasized.

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