The transmitted waveforms in digital communications are rarely stationary, so they do not have a power spectral density (PSD) in the classical sense. To teach their PSD one needs a definition that is both general and useful. The traditional approach is to define the PSD via the average autocovariance function. Here I shall describe an alternative approach and offer some comparisons.

The importance of mucosal wave detection and estimation has been stressed in literature regarding the automatic classification and recognition of larynx pathologies from voice records. Using a new estimation method of the mucosal wave correlate and simulation results from a 2-mass model of the vocal folds the present paper shows that the main fingerprints found in the power spectral density of ...

Spectral analysis is an important area in signal processing with wide range of application. A low complexity algorithm to compute the power spectral density (PSD) using the Welch method is presented in this paper. The Welch algorithm computes spectral power at the cost of high computational complexity. In order to reduce the complexity and hardware utilization pipelining FFT approach is used. I...

Geometric-mean filters compose a family of filters indexed by a parameter varying between 0 and 1. They have been used to provide frequency-based filtering that mitigates the noise suppression of the optimal-linear Wiener filter in the blurred-signal-plus-noise model. For = 0 and = 1, the geometric-mean filter gives the inverse filter and the Wiener filter for the model, respectively. The geome...

Ablating surfaces with a pulsed laser system in milling processes often leads to surface changes depending on the milling depth. Especially if a constant surface roughness and evenness is essential to the process, structural degradation may advance until the process fails. The process investigated is the generation of precise thrust by laser ablation. Here, it is essential to predict or rather ...

Welch’s method is a power spectral density estimation method that is widely used in spectral analysis for discrete signals. Magnetoencephalography (MEG) is a neuroimaging technique which records magnetic fields inside human brains as discrete signals. Measurements are done by hundreds of channels of magnetometers mapping to different regions of a human brain. To help find out what is going on i...

This work studies the frequency behavior of t h e Lomb method to estimate the power spectral density (PSD) of heart rate (HR) unevenly sampled (UNS) signals. When the UNS can be modeled as uniform plus a random deviation (approximation well satisfied in HR signals), this spectra results in a periodic repa ti t ion of the original continuous t ime spectrum at the mean Nyquist frequency, with a l...

In this paper we consider the hermitian extension of the cross-ΨB-energy operator that we will denote by ΨH . In addition, cross energy terms are formalized through multivariate signals representation. We investigate the connection between the interaction energy function of ΨH and the cross-power spectral density (CPSD) of two complex valued signals. In particular, this link permits to use this...

Artificial neural networks were trained to classify segments of 12 channel EEG data into one of five classes corresponding to five cognitive tasks performed by one subject. Three-layer feedforward neural networks were trained using a validation set to control over-fitting. Independent Component Analysis (ICA) was used to segregate obvious artifactual EEG components from other sources, and a fre...

The vibrations produced by trains include two parts which are deterministic and random vibrations. Due to variation of dynamic loads and patterns of load-time, the random vibration of moving train is one of the most important issues in the field of railway engineering. One of the important sources in producing the train vibrations is rail roughness and irregularities. In this paper, responses o...