Time–frequency analysis

About: Time–frequency analysis is a research topic. Over the lifetime, 5407 publications have been published within this topic receiving 104346 citations.

Interference Mitigation in GNSS Receivers by a Time-Frequency Approach

Abstract: Undesired interfering signals are considered to be one of the main threats to the correct behavior of new Global Navigation Satellite System (GNSS) receivers. There is a huge variety of interference that can occur in the real world, such as narrow band, wide band, impulsive, stationary, or nonstationary CWs. Processing techniques based on time-frequency (TF) distributions allow one to detect and mitigate many different types of these undesired signals. A TF mitigation technique is proposed to enable GNSS receivers to reduce the interference affecting the incoming signal. The proposed algorithm uses a synthesis technique based on the orthogonal-like Gabor expansion, in order to obtain an estimate of the interference, that is then subtracted from the input signal. In the presence of a wide class of deterministic interfering signals, the proposed mitigation strategy is effective in terms of acquisition and tracking performance and outperforms other algorithms described in the literature.

Time‒Frequency and Time‒Scale Methods: Adaptive Decompositions, Uncertainty Principles, and Sampling

Abstract: Preface Wavelets: Basic properties, parametrizations and sampling Derivatives and multiwavelets Sampling in Fourier and wavelet analysis Bases for time--frequency analysis Fourier uncertainty principles Function spaces and operator theory Uncertainty principles in mathematical physics Appendix References Index

Adaptive Tunable Q Wavelet Transform-Based Emotion Identification

Abstract: Emotion is a neuronic transient that drives a person to a certain action. Emotion recognition from electroencephalogram (EEG) signals plays a vital role in the development of a brain–computer interface (BCI). Extracting the important information from raw EEG signals is difficult due to its nonstationary nature. Fixing a factual predefined basis function for efficient decomposition using a tunable $Q$ wavelet transform is an arduous task. In this article, an adaptive tunable $Q$ wavelet transform is proposed for the automatic selection of tuning parameters. Optimum tuning parameters are obtained using gray wolf optimization (GWO). Tuning parameters obtained by GWO are used to decompose the EEG signals into subbands (SBs). The set of time-domain features elicited from the SBs are used as an input to multiclass least-squares support vector machine. Classification accuracy of four basic emotions, namely, happy, fear, sad, and relax, is tested and compared with existing methods. An accuracy of 95.70% is achieved with a radial basis function kernel that is about 5% more than the existing methods using the same data set. This article proposes the development of a nonparameterized decomposition method for efficient decomposition of EEG signals. This method can be used with machine learning algorithms to take a step forward in the development of BCI systems.