Processing and model design of the gamma oscillation activity based on FitzHugh-Nagumo model and its interaction with slow rhythms in the brain

The brain is processing information 24 hours a day. There are millions of processes proceeding in it accompanied by various spectra of rhythms. This paper tests the hypothesis that the slow delta rhythm excites the gamma rhythm oscillations. Unlike other papers, we determine the slow rhythm spectrum not at the hypothesis stage but during the experiment. We design algorithms of filtering, envelope extraction, and correlation coefficient calculation for signal processing. Moreover, we examine the data on all electroencephalogram channels, which allows us to make a more reasonable conclusion. We confirm that a slow delta rhythm excites a fast gamma rhythm with an amplitude-phase type of interaction and calculate a delay between these two signals equal to about half a second.

Voltage Flicker Parameter Estimation Based On Improved Analytic Energy Operator And Novel K-B Mutual Convolution Window

<a></a>It is difficult to measure voltage flicker parameter accurately and in real time under a complex power grid environment, a voltage flicker envelope extraction algorithm based on multi-point differential improved analytic energy operator (IAEO) is proposed, which simplified formula for extracting flicker envelope and a novel K-B optimal mutual convolution window function is constructed. Then, the correction formula of flicker amplitude and frequency is derived based on the three-spectral line interpolation of the novel K-B optimized mutual convolution window, and the estimation algorithm of voltage flicker parameter is proposed based on the IAEO and the novel K-B mutual convolution window. Finally, a voltage-flicker-parameter-estimation platform based on virtual instrument is developed. The simulation and experimental results show that the proposed algorithm can effectively measure voltage flicker parameter under single frequency modulation, multi-frequency modulation and fundamental frequency fluctuation. In addition, it can effectively overcome harmonics, interharmonics and noise interference. Compared with the existing estimation algorithm, the flicker envelope extraction is simpler, the measurement result is more accurate, and it is easy to implement embedded.<br>

Voltage Flicker Parameter Estimation Based On Improved Analytic Energy Operator And Novel K-B Mutual Convolution Window

<a></a>It is difficult to measure voltage flicker parameter accurately and in real time under a complex power grid environment, a voltage flicker envelope extraction algorithm based on multi-point differential improved analytic energy operator (IAEO) is proposed, which simplified formula for extracting flicker envelope and a novel K-B optimal mutual convolution window function is constructed. Then, the correction formula of flicker amplitude and frequency is derived based on the three-spectral line interpolation of the novel K-B optimized mutual convolution window, and the estimation algorithm of voltage flicker parameter is proposed based on the IAEO and the novel K-B mutual convolution window. Finally, a voltage-flicker-parameter-estimation platform based on virtual instrument is developed. The simulation and experimental results show that the proposed algorithm can effectively measure voltage flicker parameter under single frequency modulation, multi-frequency modulation and fundamental frequency fluctuation. In addition, it can effectively overcome harmonics, interharmonics and noise interference. Compared with the existing estimation algorithm, the flicker envelope extraction is simpler, the measurement result is more accurate, and it is easy to implement embedded.<br>

Research on Spectrum Feature Identification of Indoor Multimodal Communication Signal

In order to solve the problem of large signal acquisition error caused by radio wave multipath effect in indoor environment, firstly, the signal source carried on the motion platform is collected for spectrum signal, and the signal processed by wavelet threshold denoising algorithms extracted and stored for spectrum feature extraction. Then, after data training and identification, the signal source is input into the system in random mode for identification. The experimental results show that the improved fuzzy clustering algorithm (FCA) is 12.7% higher than the spectrum envelope extraction method (SEEM) in the recognition rate of spectrum characteristics of different modes of signal source.

Automated Inorganic Pigment Classification in Plastic Material Using Terahertz Spectroscopy

This paper presents an automatic classification of plastic material’s inorganic pigment using terahertz spectroscopy and convolutional neural networks (CNN). The plastic materials were placed between the THz transmitter and receiver, and the acquired THz signals were classified using a supervised learning approach. A THz frequency band between 0.1–1.2 THz produced a one-dimensional (1D) vector that is almost impossible to classify directly using supervised learning. This paper proposes a novel pre-processing of 1D THz data that transforms 1D data into 2D data, which are processed efficiently using a convolutional neural network. The proposed pre-processing algorithm consists of four steps: peak detection, envelope extraction, and a down-sampling procedure. The last main step introduces the windowing with spectrum dilatation that reorders 1D data into 2D data that can be considered as an image. The spectrum dilation techniques ensure the classifier’s robustness by suppressing measurement bias, reducing the complexity of the THz dataset with negligible loss of accuracy, and speeding up the network classification. The experimental results showed that the proposed approach achieved high accuracy using a CNN classifier, and outperforms 1D classification of THz data using support vector machine, naive Bayes, and other popular classification algorithms.

Online damage detection of earthquake-excited structure based on near real-time envelope extraction

A robust real-time damage detection technique of earthquake-excited structures based on a new demodulation technique for nonlinear and non-stationary vibration signals through the identification of signal envelopes in real time is presented. In the present work, the need for the detection of envelope in a vibration signal in real time is addressed by reformulating the concept of Hermitian interpolation functions to a recursive Hermitian polynomial, which is a key entitlement of the present work. Once, the near real-time demodulation is achieved, the proposed framework proceeds to the newly developed error-adapted framework by addressing the errors accrued between the standard and generalized eigen perturbation formulation in the context of real-time estimation of proper orthogonal modes and linear normal modes. In the adaptive framework, the error is modeled as a feedback, which is constructed to account for the truncation in the order of eigen perturbation. In addition to the improved accuracy due to the envelope extraction, the proposed error-adapted eigen perturbation further improves the detectability over the currently available eigen perturbation–based real-time algorithms. To ensure robustness of the proposed algorithm, a new real-time damage indicator based on the maximum of principal eigenvector of the evolving transformed covariance matrix is proposed. The proposed modules together not only improve the detectability of the damage detection in real-time but also enhance the overall performance in presence of non-stationary excitation, that often mask the damage information in the higher energy zones of the amplitude and frequency-modulated response. Simulations for the proposed framework is performed by considering a 5 degrees-of-freedom linear and base-isolated nonlinear structural system driven by non-stationary stochastic excitations with damage simulated at intermediate floor at a particular time instant. Evidence of the near real-time demodulation and/or envelope removal from the signal and improved damage identification is also provided. An examination of the proposed framework using experimental data further validates the robustness of the proposed scheme.