scholarly journals Cascade of Linear Predictors for Deconvolution of Non-Stationary Channels in Sparse and Antisparse Scenarios

2021 ◽  
Vol 36 (1) ◽  
pp. 90-99
Author(s):  
Renan Brotto ◽  
Kenji Nose-Filho ◽  
Romis Attux ◽  
João Romano
Keyword(s):  
Linear Predictors

scholarly journals Do Learning Approaches Set the Stage for Emotional Well-Being in College Students?

2020 ◽  
Vol 12 (17) ◽  
pp. 6984
Author(s):  
Jesús de la Fuente ◽  
Francisco Javier Peralta-Sánchez ◽  
José Manuel Martínez-Vicente ◽  
Flavia H. Santos ◽  
Salvatore Fadda ◽  
...  
Keyword(s):  
Approaches To Learning ◽  
Well Being ◽  
Personality Factors ◽  
Learning Approaches ◽  
Achievement Emotions ◽  
Approach To Learning ◽  
Ex Post ◽  
Ex Post Facto ◽  
Deep Approach To Learning ◽  
Linear Predictors

The research aim of this paper was two-fold: to generate evidence that personality factors are linear predictors of the variable approaches to learning (a relevant cognitive-motivational variable of Educational Psychology); and to show that each type of learning approach differentially predicts positive or negative achievement emotions, in three learning situations: class time, study time, and testing. A total of 658 university students voluntarily completed validated questionnaires referring to these three variables. Using an ex post facto design, we conducted correlational analyses, regression analyses, and multiple structural predictions. The results showed that Conscientiousness is associated with and predicts a Deep Approach to learning, while also predicting positive achievement emotions. By contrast, Neuroticism is associated with and significantly predicts a Surface Approach to learning, as well as negative achievement emotions. There are important psychoeducational implications in the university context, both for prevention and for self-improvement, and for programs that offer psychoeducational guidance.

FPGA Implementation of Adaptive Non-linear Predictors for Video Compression

2003 ◽  
pp. 1016-1019
Author(s):  
Rafael Gadea-Girones ◽  
Agustín Ramirez-Agundis ◽  
Joaquín Cerdá-Boluda ◽  
Ricardo Colom-Palero
Keyword(s):  
Video Compression ◽  
Fpga Implementation ◽  
Non Linear ◽  
Linear Predictors

Linear Predictors

2014 ◽  
pp. 89-100
Author(s):  
Shai Shalev-Shwartz ◽  
Shai Ben-David
Keyword(s):  
Linear Predictors

Nonlinear forecasting of non-uniform chaotic attractors in an enzyme reaction

1994 ◽  
Vol 348 (1688) ◽  
pp. 421-430
Keyword(s):  
Time Series ◽  
Enzyme Reaction ◽  
Experimental Conditions ◽  
Biological Time ◽  
Alternative Means ◽  
Nonlinear Forecasting ◽  
Linear Predictors ◽  
Short Term Forecasting ◽  
Low Dimensional ◽  
Frequent Sampling

Nonlinear forecasting was used to predict the time evolution of fluctuating concentrations of dissolved oxygen in the peroxidase-oxidase reaction. This reaction entails the oxidation of NADH with molecular oxygen as the electron acceptor. Depending upon the experimental conditions, either regular or highly irregular oscillations obtain. Previous work suggests that the latter fluctuations are almost certainly chaotic. In either case, the dynamics contain multiple timescales, which fact results in an uneven distribution of points in the phase space. Such ‘nonuniformity,’ as it is called, is a rock on which conventional methods for analysing chaotic time series often founder. The results of the present study are as follows. 1. Short-term forecasting with local linear predictors yields results that are consistent with a hypothesis of low-dimensional chaos. 2. Most of the evidence for nonlinear determinism disappears upon the addition of small amounts of observational error. 3. It is essentially impossible to make predictions over time intervals longer than the average period of oscillation for time series subject to continuous and frequent sampling. 4. Far more effective forecasting is possible for points on Poincare sections. 5. An alternative means for improving forecasting efficacy using the continuous data is to include a second variable (NADH concentration) in the analysis. Since non-uniformity is common in biological time series, we conclude that the application of nonlinear forecasting to univariate time series requires care both in implementation and interpretation.

Feature extraction of linear predictors at spectral bands of interest

2009 ◽  
Author(s):  
S.S. Leondopulos ◽  
W.A. Chaovalitwongse ◽  
E. Micheli-Tzanakou ◽  
S. Wong ◽  
Y.W. Brenda
Keyword(s):  
Feature Extraction ◽  
Spectral Bands ◽  
Linear Predictors
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