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Volume 1, December

Signals, Volume 1, Issue 1 (September 2020) – 5 articles

Cover Story (view full-size image): The R package allows to estimate Dynamic Model Averaging (DMA), Dynamic Model Selection (DMS) and Median Probability Model. The original methods, and additionally, some selected modifications of these methods are implemented. The code is written with respect to minimise the computational burden, which is quite an obstacle for DMA algorithm if numerous variables are used. However, clarity and readability of the code, and possibility for an R-familiar user to make his or her own small modifications in reasonably small time and with low effort are also taken under consideration. This package is designed in a way that is hoped to be especially useful for practitioners and researchers in economics and finance. View this paper.
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Open AccessArticle
Clipping Noise Compensation with Neural Networks in OFDM Systems
Signals 2020, 1(1), 100-109; https://doi.org/10.3390/signals1010005 - 03 Aug 2020
Viewed by 831
Abstract
The application of deep learning (DL) to solve physical layer issues has emerged as a prominent topic. In this paper, the mitigation of clipping effects for orthogonal frequency division multiplexing (OFDM) systems with the help of a Neural Network (NN) is investigated. Unlike [...] Read more.
The application of deep learning (DL) to solve physical layer issues has emerged as a prominent topic. In this paper, the mitigation of clipping effects for orthogonal frequency division multiplexing (OFDM) systems with the help of a Neural Network (NN) is investigated. Unlike conventional clipping recovery algorithms, which involve costly iterative procedures, the DL-based method learns to directly reconstruct the clipped part of the signal while the unclipped part is protected. Furthermore, an interpretation of the learned weight matrices of the neural network is presented. It is observed that parts of the network, in effect, implement transformations very similar to the (Inverse) Discrete Fourier Transform (DFT/IDFT) to provide information in both the time and frequency domains. The simulation results show that the proposed method outperforms existing algorithms for recovering clipped OFDM signals in terms of both mean square error (MSE) and Bit Error Rate (BER). Full article
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Open AccessArticle
Dynamic Model Averaging in Economics and Finance with fDMA: A Package for R
Signals 2020, 1(1), 47-99; https://doi.org/10.3390/signals1010004 - 06 Jul 2020
Viewed by 856
Abstract
The described R package allows to estimate Dynamic Model Averaging (DMA), Dynamic Model Selection (DMS) and Median Probability Model. The original methods, and additionally, some selected modifications of these methods are implemented. For example the user can choose between recursive moment estimation and [...] Read more.
The described R package allows to estimate Dynamic Model Averaging (DMA), Dynamic Model Selection (DMS) and Median Probability Model. The original methods, and additionally, some selected modifications of these methods are implemented. For example the user can choose between recursive moment estimation and exponentially moving average for variance updating in the base DMA. Moreover, inclusion probabilities can be computed in a way using “Google Trends” data. The code is written with respect to minimise the computational burden, which is quite an obstacle for DMA algorithm if numerous variables are used. For example, this package allows for parallel computations and implementation of the Occam’s window approach. However, clarity and readability of the code, and possibility for an R-familiar user to make his or her own small modifications in reasonably small time and with low effort are also taken under consideration. Except that, some alternative (benchmark) forecasts can also be quickly performed within this package. Indeed, this package is designed in a way that is hoped to be especially useful for practitioners and researchers in economics and finance. Full article
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Open AccessArticle
Modeling Volatility Characteristics of Epileptic EEGs using GARCH Models
Signals 2020, 1(1), 26-46; https://doi.org/10.3390/signals1010003 - 02 Jun 2020
Viewed by 631
Abstract
Objective: To determine if there was a difference in the volatility characteristics of seizure and non-seizure onset channels in the intracranial electroencephalogram (EEG) in a patient with temporal lobe epilepsy. Methods: The half-life of volatility for the different EEG channels was determined using [...] Read more.
Objective: To determine if there was a difference in the volatility characteristics of seizure and non-seizure onset channels in the intracranial electroencephalogram (EEG) in a patient with temporal lobe epilepsy. Methods: The half-life of volatility for the different EEG channels was determined using Autoregressive Moving Average–Generalized Autoregressive Conditional Heteroscedasticity (ARMA–GARCH) models; confidence intervals were constructed using the delta method and an asymptotic method for comparing the half-lives. Results: Clinically determined seizure onsets occurred over strip electrodes named RAST (Right Anterior Subtemporal) and RMST (Right Mid Subtemporal), at locations 2, 3 and 4, on the strip electrodes. The half-lives of volatility for two of the three seizure channels, RAST3 and RAST4, were found to be significantly lower the rest of the channels for six one-minute EEG segments prior to seizure onset and nine one-minute EEG segments of an awake state. The half-lives of volatility for RAST3 and RAST4 were not significantly different to the non-seizure channels for ten one-minute segments of sleep and ten one-minute segments of sleep-to-awake states. The estimates for the half-lives were consistent for randomly selected one-minute EEG segments. Conclusions: The use of GARCH models may be a useful tool in determining hidden properties in epileptiform EEGs that may lead to better understanding of the seizure generating process. Full article
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Open AccessArticle
The Effect of Data Transformation on Singular Spectrum Analysis for Forecasting
Signals 2020, 1(1), 4-25; https://doi.org/10.3390/signals1010002 - 07 May 2020
Cited by 2 | Viewed by 793
Abstract
Data transformations are an important tool for improving the accuracy of forecasts from time series models. Historically, the impact of transformations have been evaluated on the forecasting performance of different parametric and nonparametric forecasting models. However, researchers have overlooked the evaluation of this [...] Read more.
Data transformations are an important tool for improving the accuracy of forecasts from time series models. Historically, the impact of transformations have been evaluated on the forecasting performance of different parametric and nonparametric forecasting models. However, researchers have overlooked the evaluation of this factor in relation to the nonparametric forecasting model of Singular Spectrum Analysis (SSA). In this paper, we focus entirely on the impact of data transformations in the form of standardisation and logarithmic transformations on the forecasting performance of SSA when applied to 100 different datasets with different characteristics. Our findings indicate that data transformations have a significant impact on SSA forecasts at particular sampling frequencies. Full article
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Open AccessEditorial
Welcome to SIGNALS: A New Open-Access Scientific Journal on Signal Analysis, Retrieval and Processing
Signals 2020, 1(1), 1-3; https://doi.org/10.3390/signals1010001 - 19 Jun 2018
Cited by 1 | Viewed by 1317
Abstract
The sheer exposure to vast amounts of signals created in our modern society and an ever increasing need to make sense of such data calls for continuing advances in signal processing as an enabling technology for a huge number of applications, ranging from [...] Read more.
The sheer exposure to vast amounts of signals created in our modern society and an ever increasing need to make sense of such data calls for continuing advances in signal processing as an enabling technology for a huge number of applications, ranging from wireless communication and medicine, through to bioengineering, the economy and entertainment.[...] Full article
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