scholarly journals Accurate Spectral Estimation Technique Based on Decimated Linear Predictor for Leak Detection in Waterworks

Sensors ◽  
2021 ◽  
Vol 21 (6) ◽  
pp. 2185
Author(s):  
Aimé Lay-Ekuakille ◽  
Vito Telesca ◽  
Paolo Visconti ◽  
Nicola Ivan Giannoccaro
Keyword(s):  
Water Distribution ◽  
Linear Prediction ◽  
Spectral Response ◽  
Spectral Estimation ◽  
Economic Problem ◽  
General Statement ◽  
Linear Predictor ◽  
Expected Time ◽  
Water Transportation ◽  
Rural Environments

Rural pipelines dedicated to water distribution, that is, waterworks, are essential for agriculture, notably plantations and greenhouse cultivation. Water is a primary resource for agriculture, and its optimized management is a key aspect. Saving water dispersion is not only an economic problem but also an environmental one. Spectral estimation of leakage is based on processing signals captured from sensors and/or transducers generally mounted on pipelines. There are different techniques capable of processing signals and displaying the actual position of leaks. Not all algorithms are suitable for all signals. That means, for pipelines located underground, for example, external vibrations affect the spectral response quality; then, depending on external vibrations/noises and flow velocity within pipeline, one should choose a suitable algorithm that fits better with the expected results in terms of leak position on the pipeline and expected time for localizing the leak. This paper presents findings related to the application of a decimated linear prediction (DLP) algorithm for agriculture and rural environments. In a certain manner, the application also detects the hydrodynamics of the water transportation. A general statement on the issue, DLP illustration, a real application and results are also included.

Simultaneous Linear Prediction and Construction of Volume Tables

1973 ◽  
Vol 3 (3) ◽  
pp. 418-423
Author(s):  
J. Bélanger ◽  
R. Cléroux
Keyword(s):  
Tree Species ◽  
Linear Prediction ◽  
Scaling Factors ◽  
Linear Predictor ◽  
Future Studies ◽  
Proper Scaling

In this paper a technique is proposed which finds a unique linear predictor which is used to construct volume tables for different tree species, when it is multiplied by proper scaling factors. This technique uses the fact that one species can give information about some other. This information can be used in future studies where data are not available on all the species considered.

scholarly journals Sodium Alginate Cross-Linkable Planar 1D Photonic Crystals as a Promising Tool for Pb2+ Detection in Water

Chemosensors ◽  
2020 ◽  
Vol 8 (2) ◽  
pp. 37 ◽  
Author(s):  
Andrea Dodero ◽  
Paola Lova ◽  
Silvia Vicini ◽  
Maila Castellano ◽  
Davide Comoretto
Keyword(s):  
Refractive Index ◽  
Photonic Crystals ◽  
Sodium Alginate ◽  
Spectral Response ◽  
High Refractive Index ◽  
Polluted Water ◽  
Ionic Exchange ◽  
Promising Tool ◽  
Rural Environments ◽  
First Time

Due to its high toxicity, Pb2+ pollution is a serious threat for human health and environments. However, in situ real-time detection of Pb2+ pollution is difficult and laboratory instruments are usually required. Then, the possibility to monitor water quality without laboratory instruments could lead to the extensive assessment of polluted water sources, especially in rural environments and developing countries where large lead concentrations are often found in surface water. Consequently, new simple colorimetric sensors are highly interesting in the field. In this work we report for the first time disposable polymer planar 1D photonic crystals made of poly (N-vinylcarbazole) as high refractive index medium and sodium alginate as low refractive index and active medium for the detection of Pb2+ in water. The detection relies on the ionic exchange occurring into the alginate matrix. This process effectively induces a physical cross-linking phenomenon, which inhibits water solubilization of the polymer. In turn, this affects the spectral response of the planar 1D photonic crystals modifying its color.

Linear Prediction of a True Score From a Direct Estimate and Several Derived Estimates

2007 ◽  
Vol 32 (1) ◽  
pp. 6-23 ◽  
Author(s):  
Shelby J. Haberman ◽  
Jiahe Qian
Keyword(s):  
Linear Prediction ◽  
Mean Squared Error ◽  
Weighted Average ◽  
Statistical Prediction ◽  
True Score ◽  
Direct Estimate ◽  
Linear Predictor ◽  
Squared Error ◽  
Best Linear Predictor ◽  
Prediction Problems

Statistical prediction problems often involve both a direct estimate of a true score and covariates of this true score. Given the criterion of mean squared error, this study determines the best linear predictor of the true score given the direct estimate and the covariates. Results yield an extension of Kelley’s formula for estimation of the true score to cases in which covariates are present. The best linear predictor is a weighted average of the direct estimate and of the linear regression of the direct estimate onto the covariates. The weights depend on the reliability of the direct estimate and on the multiple correlation of the true score with the covariates. One application of the best linear predictor is to use essay features provided by computer analysis and an observed holistic score of an essay provided by a human rater to approximate the true score corresponding to the holistic score.

scholarly journals Non-Linear prediction problems for Ornstein-Uhlenbeck process

1983 ◽  
Vol 91 ◽  
pp. 173-184 ◽  
Author(s):  
Sheu-San Lee
Keyword(s):  
Stochastic Processes ◽  
Linear Functional ◽  
Linear Prediction ◽  
Basic Process ◽  
Prediction Theory ◽  
Uhlenbeck Process ◽  
Linear Predictor ◽  
Ornstein Uhlenbeck Process ◽  
Non Linear ◽  
Prediction Problems

We shall discuss in this paper some problems in non-linear prediction theory. An Ornstein-Uhlenbeck process {U(t)} is taken to be a basic process, and we shall deal with stochastic processes X(t) that are transformed by functions f satisfying certain condition. Actually, observed processes are expressed in the form X(t) = f(U(t)). Our main problem is to obtain the best non-linear predictor X̂(t, τ) for X(t + τ), τ > 0, assuming that X(s), s ≤t, are observed. The predictor is therefore a non-linear functional of the values X(s), s ≤ t.

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