The face is central to primate multicomponent signals

A wealth of experimental and observational evidence suggests that faces have become increasingly important in the communication system of primates over evolutionary time and that both the static and moveable aspects of faces convey considerable information. Therefore, whenever there is a visual component to any multicomponent signal the face is potentially relevant. However, the role of the face is not always considered in primate multicomponent communication research. We review the literature and make a case for greater focus on the face going forward. We propose that the face can be overlooked for two main reasons: first, due to methodological difficulty. Examination of multicomponent signals in primates is difficult, so scientists tend to examine a limited number of signals in combination. Detailed examination of the subtle and dynamic components of facial signals is particularly hard to achieve in studies of primates. Second, due to a common assumption that the face contains “emotional” content. A priori categorisation of facial behavior as “emotional” ignores the potentially communicative and predictive information present in the face that might contribute to signals. In short, we argue that the face is central to multicomponent signals (and also many multimodal signals) and suggest future directions for investigating this phenomenon.

Rule-Based EEG Classifier Utilizing Local Entropy of Time–Frequency Distributions

Electroencephalogram (EEG) signals are known to contain signatures of stimuli that induce brain activities. However, detecting these signatures to classify captured EEG waveforms is one of the most challenging tasks of EEG analysis. This paper proposes a novel time–frequency-based method for EEG analysis and characterization implemented in a computer-aided decision-support system that can be used to assist medical experts in interpreting EEG patterns. The computerized method utilizes EEG spectral non-stationarity, which is clearly revealed in the time–frequency distributions (TFDs) of multicomponent signals. The proposed algorithm, which is based on the modification of the Rényi entropy, called local or short-term Rényi entropy (STRE), was upgraded with a blind component separation procedure and instantaneous frequency (IF) estimation. The method was applied to EEGs of both forward and backward movements of the left and right hands, as well as to EEGs of imagined hand movements, which were captured by a 19-channel EEG recording system. The obtained results show that in a given virtual instrument, the proposed methods efficiently distinguish between real and imagined limb movements by considering their signatures in terms of the dominant EEG component’s IFs at the specified subset of EEG channels (namely, F3, F4, F7, F8, T3, and T4). Furthermore, computing the number of EEG signal components, their extraction, and IF estimation provide important information that shows potential to enhance existing clinical diagnostic techniques for detecting the intensity, location, and type of brain function abnormalities in patients with neurological motor control disorders.

Optimization of multicomponent signals entered to the system using estimation of instantaneous frequency

The frequency signal displays are not efficient for analyzing nonstationary signals because of their inability to represent frequency changes over time. In fact, because most of the signals are real, nonstationary, and time varying, analyzing the signals in the time–frequency domain to estimate the instantaneous frequency of a signal is inevitable. The methods of estimating the instantaneous frequency of the multicomponent signals are divided into three groups, which include the methods using signal phase derivatives that are sensitive to noise, methods that calculate the number of zero points of the signal and consider the signal frequency equal to half the frequency of the zero points and are suitable for signals that can be imagined as stationary, and methods based on time–frequency distributions and distributions such as Wigner for instantaneous frequency calculations and more for instantaneous frequency calculations on nonstationary noise signals that exhibit varied time–frequency distributions. In this article, a new hybrid algorithm is used to evaluate different distribution criteria and comparing their performance in investigating one or more features of time–frequency distributions, such as resolution and energy concentration.

A pde-Based Analysis of the Spectrogram Image for Instantaneous Frequency Estimation

Instantaneous frequency (IF) is a fundamental feature in multicomponent signals analysis and its estimation is required in many practical applications. This goal can be successfully reached for well separated components, while it still is an open problem in case of interfering modes. Most of the methods addressing this issue are parametric, that is, they apply to a specific IF class. Alternative approaches consist of non-parametric time filtering-based procedures, which do not show robustness to destructive interference—the most critical scenario in crossing modes. In this paper, a method for IF curves estimation is proposed. The case of amplitude and frequency modulated two-component signals is addressed by introducing a spectrogram time-frequency evolution law, whose coefficients depend on signal IFs time derivatives, that is, the chirp rates. The problem is then turned into the resolution of a two-dimensional linear system which provides signal chirp rates; IF curves are then obtained by a simple integration. The method is non-parametric and it results quite robust to destructive interference. An estimate of the estimation error, as well as a numerical study concerning method sensitivity and robustness to noise are also provided in the paper.

RANGE AND RADIAL VELOCITY MEASUREMENT AMBIGUITY ELIMINATION WITH TRAINS OF MULTICOMPONENT SIGNALS

When designing pulse-Doppler radar, one of the key points is the choice of the pulse repetition period, which determines the boundaries of unambiguous measurement of range and radial velocity and creates contradictions in the measurement of these values. This contradiction is especially acute in the analysis of signals reflected from the propellers and turbines of aircraft. The main approaches to solving the problem of expanding the boundaries of unambiguous measurement of range and radial velocity is the use of variable pulse repetition period and the creation of signal ensembles to separate them by shape. Generation of an ensemble of sounding signals for a pulsed radar must be carried out taking into account both cross-correlation and auto-correlation properties. An approach to the generation of multicomponent signal trains with the possibility of pulse separation inside the train is proposed. Each of the pulses in the train is formed by adding a number of chirp signals, which differ in the values of amplitude and frequency deviation. As the frequency deviation increases, the amplitude of the component decreases. Reducing the cross-correlation coefficient of multicomponent signals from the formed ensemble can be achieved by increasing the number of components of each signal. The size of the signal ensemble, which can be formed on the basis of multicomponent chirp signals, depends on the requirements for the cross-correlation coefficient and auto-correlation function of the signals. It is shown that in order to expand the limits of coordinate measurement at a fixed wavelength, it is necessary to increase the number of pulses in the train. The results of the research demonstrate the potential possibility of using the proposed multicomponent chirp signal to form train of pulses with its subsequent separation.

A Signal Complexity-Based Approach for AM–FM Signal Modes Counting

Frequency modulated signals appear in many applied disciplines, including geology, communication, biology and acoustics. They are naturally multicomponent, i.e., they consist of multiple waveforms, with specific time-dependent frequency (instantaneous frequency). In most practical applications, the number of modes—which is unknown—is needed for correctly analyzing a signal; for instance for separating each individual component and for estimating its instantaneous frequency. Detecting the number of components is a challenging problem, especially in the case of interfering modes. The Rényi Entropy-based approach has proven to be suitable for signal modes counting, but it is limited to well separated components. This paper addresses this issue by introducing a new notion of signal complexity. Specifically, the spectrogram of a multicomponent signal is seen as a non-stationary process where interference alternates with non-interference. Complexity concerning the transition between consecutive spectrogram sections is evaluated by means of a modified Run Length Encoding. Based on a spectrogram time-frequency evolution law, complexity variations are studied for accurately estimating the number of components. The presented method is suitable for multicomponent signals with non-separable modes, as well as time-varying amplitudes, showing robustness to noise.