Cognitive radio networks (CRNs) have been recognized as a promising technology that allows secondary users (SUs) extensively explore spectrum resource usage efficiency, while not introducing interference to licensed users. Due to the unregulated wireless network environment, CRNs are susceptible to various malicious entities. Thus, it is critical to detect anomalies in the first place. However,...

Cognitive radio networks (CRNs) is a promising technology that allows secondary users (SUs) extensively explore spectrum resource usage efficiency, while not introducing interference to licensed users. Due to the unregulated wireless network environment, CRNs are susceptible to various malicious entities. Thus, it is critical to detect anomalies in the first place. However, from the perspective...

The surface electromyography (sEMG) signal is a low amplitude signal that emanates from contracting muscles. It can be used directly to measure muscle activity (once noise has been removed) or it can be smoothed for some other application, e.g., orthoses or prostheses control. Here, an automatic heuristic procedure is presented which applies singular spectrum analysis (SSA) and cluster analysis...

1. The technique used by the author (Grigorov, 2006) relies on singular-spectrum analysis (SSA). While the author has claimed to have used a nonlinear time series approach, SSA by very definition is a linear decomposition technique. The lag-correlation matrix constructed as a part of the SSA technique is a measure of linear correlation between the samples in the time series, hence representativ...

Artifacts removal and rhythms extraction from electroencephalography (EEG) signals are important for portable and wearable EEG recording devices. Incorporating a novel grouping rule, we proposed an adaptive singular spectrum analysis (SSA) method for artifacts removal and rhythms extraction. Based on the EEG signal amplitude, the grouping rule determines adaptively the first one or two SSA reco...

Singular spectrum analysis (SSA) is a popular filtering and forecasting method that used in wide range of fields such as time series signal processing. A commonly approach to identify the meaningful components grouping step SSA utilization visual information eigentriples. Another supplementary employing an algorithm performs clustering based on dissimilarity matrix defined by weighted correlati...

The analysis of chaotic time series requires proper reconstruction of the state space from the available data in order to successfully estimate invariant properties of the embedded attractor. Using the correlation dimension, we discuss the applicability of the two most common methods of reconstruction, the method of delays (MOD) and the Singular Spectrum Approach (SSA). Contrary to previous dis...

The market demand for electric vehicles (EVs) has increased in recent years. Suitable models are necessary to understand and forecast EV sales. This study presents a singular spectrum analysis (SSA) as a univariate time-series model and vector autoregressive model (VAR) as a multivariate model. Empirical results suggest that SSA satisfactorily indicates the evolving trend and provides reasonabl...

In this article, we propose a combination of an noise-reduction algorithm based on Singular Spectrum Analysis (SSA) and a standard feedforward neural prediction model. Basically, the proposed algorithm consists of two different steps: data preprocessing based on the SSA filtering method and step-by-step training procedure in which we use a simple feedforward multilayer neural network with backp...

While the importance of continuous monitoring of electrocardiographic (ECG) or photoplethysmographic (PPG) signals to detect cardiac anomalies is generally accepted in preventative medicine, there remain numerous challenges to its widespread adoption. Most notably, difficulties arise regarding crucial characteristics such as real-time capability, computational complexity, the amount of required...