Wavelet-based filtration procedure for denoising the predicted CO2 waveforms in smart home within the Internet of Things

The operating cost minimization of smart homes can be achieved with the optimization of the management of the building's technical functions by determination of the current occupancy status of the individual monitored spaces of a smart home. To respect the privacy of the smart home residents, indirect methods (without using cameras and microphones) are possible for occupancy recognition of space in smart homes. This article describes a newly proposed indirect method to increase the accuracy of the occupancy recognition of monitored spaces of smart homes. The proposed procedure uses the prediction of the course of CO2 concentration from operationally measured quantities (temperature indoor and relative humidity indoor) using artificial neural networks with a multilayer perceptron algorithm. The mathematical wavelet transformation method is used for additive noise canceling from the predicted course of the CO2 concentration signal with an objective increase accuracy of the prediction. The calculated accuracy of CO2 concentration waveform prediction in the additive noise-canceling application was higher than 98% in selected experiments.Web of Science203art. no. 62

Assessing Time Series Correlation Significance: A Parametric Approach with Application to Physiological Signals

Correlation coefficients play a pivotal role in quantifying linear relationships between random variables. Yet, their application to time series data is very challenging due to temporal dependencies. This paper introduces a novel approach to estimate the statistical significance of correlation coefficients in time series data, addressing the limitations of traditional methods based on the concept of effective degrees of freedom (or effective sample size, ESS). These effective degrees of freedom represent the independent sample size that would yield comparable test statistics under the assumption of no temporal correlation. We propose to assume a parametric Gaussian form for the autocorrelation function. We show that this assumption, motivated by a Laplace approximation, enables a simple estimator of the ESS that depends only on the temporal derivatives of the time series. Through numerical experiments, we show that the proposed approach yields accurate statistics while significantly reducing computational overhead. In addition, we evaluate the adequacy of our approach on real physiological signals, for assessing the connectivity measures in electrophysiology and detecting correlated arm movements in motion capture data. Our methodology provides a simple tool for researchers working with time series data, enabling robust hypothesis testing in the presence of temporal dependencies.Comment: 14 pages, 8 figure

Towards Real-World BCI: CCSPNet, A Compact Subject-Independent Motor Imagery Framework

A conventional subject-dependent (SD) brain-computer interface (BCI) requires a complete data-gathering, training, and calibration phase for each user before it can be used. In recent years, a number of subject-independent (SI) BCIs have been developed. However, there are many problems preventing them from being used in real-world BCI applications. A weaker performance compared to the subject-dependent (SD) approach, and a relatively large model requiring high computational power are the most important ones. Therefore, a potential real-world BCI would greatly benefit from a compact low-power subject-independent BCI framework, ready to be used immediately after the user puts it on. To move towards this goal, we propose a novel subject-independent BCI framework named CCSPNet (Convolutional Common Spatial Pattern Network) trained on the motor imagery (MI) paradigm of a large-scale electroencephalography (EEG) signals database consisting of 21600 trials for 54 subjects performing two-class hand-movement MI tasks. The proposed framework applies a wavelet kernel convolutional neural network (WKCNN) and a temporal convolutional neural network (TCNN) in order to represent and extract the diverse spectral features of EEG signals. The outputs of the convolutional layers go through a common spatial pattern (CSP) algorithm for spatial feature extraction. The number of CSP features is reduced by a dense neural network, and the final class label is determined by a linear discriminative analysis (LDA) classifier. The CCSPNet framework evaluation results show that it is possible to have a low-power compact BCI that achieves both SD and SI performance comparable to complex and computationally expensive.Comment: 15 pages, 6 figures, 6 tables, 1 algorith

GA for feature selection of EEG heterogeneous data

The electroencephalographic (EEG) signals provide highly informative data on brain activities and functions. However, their heterogeneity and high dimensionality may represent an obstacle for their interpretation. The introduction of a priori knowledge seems the best option to mitigate high dimensionality problems, but could lose some information and patterns present in the data, while data heterogeneity remains an open issue that often makes generalization difficult. In this study, we propose a genetic algorithm (GA) for feature selection that can be used with a supervised or unsupervised approach. Our proposal considers three different fitness functions without relying on expert knowledge. Starting from two publicly available datasets on cognitive workload and motor movement/imagery, the EEG signals are processed, normalized and their features computed in the time, frequency and time-frequency domains. The feature vector selection is performed by applying our GA proposal and compared with two benchmarking techniques. The results show that different combinations of our proposal achieve better results in respect to the benchmark in terms of overall performance and feature reduction. Moreover, the proposed GA, based on a novel fitness function here presented, outperforms the benchmark when the two different datasets considered are merged together, showing the effectiveness of our proposal on heterogeneous data.Comment: submitted to Expert Systems with Application

Wind missing data arrangement using wavelet based techniques for getting maximum likelihood

Long time series of wind data can have data gaps that may lead to errors in the subsequent analyses of the time series. This study proposes using the wavelet transform as a system to verify that a data completion technique is correct and that the data series behaves correctly, enabling the user to infer the expected results. Wind speed data from three weather stations located in southern Europe were used to test the proposed method. The series consist of data measured every 10 minutes for 11 years. Various techniques are used to complete the data of one of the series; the wavelet transform is used as the control method, and its scalogram is used to visualize it. If the representation in the scalogram has zero magnitude, it shows the absence of data, so that if the data are properly filled in, then they have similar magnitudes to the rest of the series. The proposed method has shown that in case of data series inconsistencies, the wavelet transform can identify the lack of accuracy of the natural periodicity of these data. This result can be visually checked using the WT’s scalogram. Additionally, the scallograms provide valuable information on the variables studied, e.g. periods of higher wind speed. In summary, the wavelet transform has proven to be an excellent analysis tool that reveals the seasonal pattern of wind speed in periodograms at various scales

TFN: An Interpretable Neural Network with Time-Frequency Transform Embedded for Intelligent Fault Diagnosis

Convolutional Neural Networks (CNNs) are widely used in fault diagnosis of mechanical systems due to their powerful feature extraction and classification capabilities. However, the CNN is a typical black-box model, and the mechanism of CNN's decision-making are not clear, which limits its application in high-reliability-required fault diagnosis scenarios. To tackle this issue, we propose a novel interpretable neural network termed as Time-Frequency Network (TFN), where the physically meaningful time-frequency transform (TFT) method is embedded into the traditional convolutional layer as an adaptive preprocessing layer. This preprocessing layer named as time-frequency convolutional (TFconv) layer, is constrained by a well-designed kernel function to extract fault-related time-frequency information. It not only improves the diagnostic performance but also reveals the logical foundation of the CNN prediction in the frequency domain. Different TFT methods correspond to different kernel functions of the TFconv layer. In this study, four typical TFT methods are considered to formulate the TFNs and their effectiveness and interpretability are proved through three mechanical fault diagnosis experiments. Experimental results also show that the proposed TFconv layer can be easily generalized to other CNNs with different depths. The code of TFN is available on https://github.com/ChenQian0618/TFN.Comment: 20 pages, 15 figures, 5 table

Estimation of the co-movements between biofuel production and food prices: A wavelet-based analysis

Recently, the significance of biofuel production on food prices has become an important topic of discussion within the framework of sustainable development. Based on the relevant discussions, this work aims at observing the influence of biofuel production on food prices in the US for the monthly period 1981–2018 by considering all possible structural changes between the co-movements of the variables. In the analyses, oil prices and population variables are also employed as control variables. To this end, we use continuous wavelet model estimations for the whole sample period and sub-sample periods at different frequencies. All computations have considered the potential changes in co-movements of the variables at different sub-sample periods corresponding to high and low frequencies of observed time series data. Estimation results show that there exist significant relationships between biofuel production and food prices in the short-term and long-term cycles. The outcomes of the research hence may provide some insights into the design of sustainable energy and food policies in the United States. © 2020 Elsevier Lt

Unraveling the influence of trial-based motivational changes on performance monitoring stages in a flanker task

Performance monitoring (PM) is a vital component of adaptive behavior and known to be influenced by motivation. We examined effects of potential gain (PG) and loss avoidance (LA) on neural correlates of PM at different processing stages, using a task with trial-based changes in these motivational contexts. Findings suggest more attention is allocated to the PG context, with higher amplitudes for respective correlates of stimulus and feedback processing. The PG context favored rapid responses, while the LA context emphasized accurate responses. Lower response thresholds in the PG context after correct responses derived from a drift–diffusion model also indicate a more approach-oriented response style in the PG context. This cognitive shift is mirrored in neural correlates: negative feedback in the PG context elicited a higher feedback-related negativity (FRN) and higher theta power, whereas positive feedback in the LA context elicited higher P3a and P3b amplitudes, as well as higher theta power. There was no effect of motivational context on response-locked brain activity. Given the similar frequency of negative feedback in both contexts, the elevated FRN and theta power in PG trials cannot be attributed to variations in reward prediction error. The observed variations in the FRN indicate that the effect of outcome valence is modulated by motivational salience

A better way to define and describe Morlet wavelets for time-frequency analysis

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