SPECTRAL EVALUATION OF SLEEP EEG IN PATIENTS WITH CHRONIC DISORDERS OF CONSCIOUSNESS BY MULTI-WINDOW DISCRETE FOURIER TRANSFORM

In recent years, EEG spectral analysis has become increasingly popular due to the development of computer technologies. Among the methods of spectral analysis, various variants of the window Fourier transform are most often used, taking into account the non-stationary nature of the EEG signal. In this article, the spectral composition of the sleep EEG of 32 patients with impaired consciousness was studied using a discrete Fourier transform with Windows in the form of elongated spheroidal sequences. The classification of the received gipropischeprom patients with CHF on the dynamics of the spectral composition of the detected correlation characteristic changes in the spectral composition of sleep EEG with the level of consciousness and the etiology of the disease

Correlation Investigation for Uniform Circular Array Based on Measured Massive MIMO

In this paper, the performance of a two-dimensional (2D) Massive MIMO angle parameter system with a uniform circular array (UCA) topology at base station is studied based on the field measurements in indoor line-of-sight (LOS) scenario at a frequency of 4.45[Formula: see text]GHz. Many spatial parameters are extracted, including angle information and correlation characteristic. It is shown that correlation lies between antenna elements and users. It is illustrated in the envelope correlation coefficient which goes on a downward trend while the antenna spacing is increasing in the measurements.

A Signal Detection Method for Harmonic Signal Submerged in Complex Noise Background

This paper presents a method to detect weak harmonic signal embedded in chaotic noise. Using different correlation characteristic of harmonic and chaotic signal ,we can transform the sample signal to a new data sequence which has new harmonic . The new harmonic frequency is m times of the original harmonic and beyond the center bandwidth of noise. Then use wavelet packet decomposition to analysis the energy distribution of harmonic and chaotic signals and extract the component which the harmonic energy concentrated on, In the end, a multiple signal classification (MUSIC) algorithm is employed to estimate harmonic frequencies . The method suit for the complex background noise (strong chaotic noise and gaussian noise).

Sensors Network for Ultrasonic Ranging System

The spectrum matching and correlation characteristic are both important in the multiple-user ultrasonic ranging system. As people know, an ultrasonic ranging system, which has a bell-shaped magnitude spectrum, acts like a band-pass filter. If the spectrum of the excitation signal does not match that of the ultrasonic ranging system, some of energy cannot be transmitted by the ultrasonic system. In other words, it does not make full use of the bandwidth of the ultrasonic ranging system. The good correlation characteristics can eliminate cross-talk among multichannel ultrasonic sensors firing simultaneously. To the authors’ knowledge, not many researchers considered how to make the spectrum of the excitation sequence match to that of the ultrasonic ranging system as well as improve the correlation characteristics. In this paper, the chaotic frequency modulation (CFM) excitation sequences are proposed for multiple-user ultrasonic ranging system. To obtain the excitation sequences which are spectrally matched to the ultrasonic ranging system as well as have the best correlation characteristic, the NSGA-II is applied to optimize the CFM excitation sequences.