Accelerated multiple-pass moving average: A novel algorithm for baseline estimation in CE and its application to baseline correction on real-time bases

2007 ◽  
Vol 28 (8) ◽  
pp. 1181-1188 ◽  
Author(s):  
Alejandro Solis ◽  
Mathew Rex ◽  
Andres D. Campiglia ◽  
Pedro Sojo
Keyword(s):  
Real Time ◽  
Moving Average ◽  
Baseline Correction ◽  
Multiple Pass ◽  
Baseline Estimation ◽  
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.

scholarly journals A RED modified weighted moving average for soft real-time application

2014 ◽  
Vol 24 (3) ◽  
pp. 697-707 ◽  
Author(s):  
Joanna Domanśka ◽  
Adam Domański ◽  
Dariusz R. Augustyn ◽  
Jerzy Klamka
Keyword(s):  
Real Time ◽  
Optimization Problem ◽  
Optimality Criterion ◽  
Task Force ◽  
Moving Average ◽  
Timing Constraints ◽  
Real Time Traffic ◽  
Real Time Application ◽  
Weighted Moving Average

Abstract The popularity of TCP/IP has resulted in an increase in usage of best-effort networks for real-time communication. Much effort has been spent to ensure quality of service for soft real-time traffic over IP networks. The Internet Engineering Task Force has proposed some architecture components, such as Active Queue Management (AQM). The paper investigates the influence of the weighted moving average on packet waiting time reduction for an AQM mechanism: the RED algorithm. The proposed method for computing the average queue length is based on a difference equation (a recursive equation). Depending on a particular optimality criterion, proper parameters of the modified weighted moving average function can be chosen. This change will allow reducing the number of violations of timing constraints and better use of this mechanism for soft real-time transmissions. The optimization problem is solved through simulations performed in OMNeT++ and later verified experimentally on a Linux implementation

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.

Real-Time Harmonic Component Decomposition for LV Grids Using Heterodyne Method With Adaptive Moving Average Filters

2021 ◽  
Author(s):  
Tawichai Premgamone ◽  
Jan Kortenbruck ◽  
Egon Ortjohann ◽  
Andreas Schmelter ◽  
Daniel Holtschulte ◽  
...  
Keyword(s):  
Real Time ◽  
Moving Average ◽  
Harmonic Component ◽  
Heterodyne Method ◽  
Component Decomposition ◽  
Time Harmonic

scholarly journals Indian COVID-19 dynamics: Prediction using autoregressive integrated moving average modelling

2021 ◽  
Vol 52 (1) ◽  
pp. 6-14
Author(s):  
Amit Tak ◽  
Sunita Dia ◽  
Mahendra Dia ◽  
Todd Wehner
Keyword(s):  
Root Mean Square Error ◽  
Real Time ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Moving Average ◽  
Information Criteria ◽  
Evidence Based ◽  
Mean Square ◽  
Autoregressive Integrated Moving Average ◽  
Akaike Information Criteria

Background: The forecasting of Coronavirus Disease-19 (COVID-19) dynamics is a centrepiece in evidence-based disease management. Numerous approaches that use mathematical modelling have been used to predict the outcome of the pandemic, including data-driven models, empirical and hybrid models. This study was aimed at prediction of COVID-19 evolution in India using a model based on autoregressive integrated moving average (ARIMA). Material and Methods: Real-time Indian data of cumulative cases and deaths of COVID-19 was retrieved from the Johns Hopkins dashboard. The dataset from 11 March 2020 to 25 June 2020 (n = 107 time points) was used to fit the autoregressive integrated moving average model. The model with minimum Akaike Information Criteria was used for forecasting. The predicted root mean square error (PredRMSE) and base root mean square error (BaseRMSE) were used to validate the model. Results: The ARIMA (1,3,2) and ARIMA (3,3,1) model fit best for cumulative cases and deaths, respectively, with minimum Akaike Information Criteria. The prediction of cumulative cases and deaths for next 10 days from 26 June 2020 to 5 July 2020 showed a trend toward continuous increment. The PredRMSE and BaseRMSE of ARIMA (1,3,2) model were 21,137 and 166,330, respectively. Similarly, PredRMSE and BaseRMSE of ARIMA (3,3,1) model were 668.7 and 5,431, respectively. Conclusion: It is proposed that data on COVID-19 be collected continuously, and that forecasting continue in real time. The COVID-19 forecast assist government in resource optimisation and evidence-based decision making for a subsequent state of affairs.

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.

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