Time–frequency-based non-harmonic analysis to reduce line-noise impact for LIGO observation system

Jia, Dongbao; Yanagisawa, Kenta; Hasegawa, M.; Tagoshi, Hideyuki; Narikawa, T.; Uchikata, N.; Takahashi, Hideya

doi:10.1016/j.ascom.2018.10.003

Cited by 16 publications

(9 citation statements)

References 31 publications

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“…Once the signal entering the cell body surpasses the sustaining threshold, the neuron is burned, and the signal is transmitted to other neurons through axons [7]. e amplitude of low frequency fluctuation (ALFF) is a good measurement index of brain activation signal, which is calculated to obtain the power spectrum of brain voxel signal time series using time-frequency (TF) analysis method [8][9][10][11]. It is considered able to directly reflect the spontaneous synchronous changes of neural activity in a resting state [12,13] and to some extent reflect the interaction and neural network connection among the relevant brain regions, which has been verified in the research of visual stimulus differences caused by eye opening and closing.…”

Section: Introductionmentioning

confidence: 99%

“…Moreover, some researches proposed improving the existing FSST by using higher-order amplitude and phase approximation to calculate more accurate instantaneous frequency estimates of the modal components of the signal. It is achieving a perfect concentration and reconstruction in a wider range of AM-FM modes, and most of the real signals are composed of strongly modulated AM-FM modes, such as chirps involved in radar [31], speech processing [32], or gravitational waves [9,11,33]. As a consequence, this technique provides a highly concentrated TF representation for a wide variety of AM-FM multicomponent signals and enables the reconstruction of their modes with high accuracy; we hypothesized that FSST can be used in the analysis of time series of brain science and to improve the analytical accuracy.…”

Section: Introductionmentioning

confidence: 99%

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Application and Evolution for Neural Network and Signal Processing in Large‐Scale Systems

Jia

et al. 2021

Complexity

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Low frequency oscillation is an important attribute of human brain activity, and the amplitude of low frequency fluctuation (ALFF) is an effective method to reflect the characteristics of low frequency oscillation, which has been widely used in the treatment of brain diseases and other fields. However, due to the low accuracy of the current analysis methods for low frequency signal extraction of ALFF, we propose the Fourier-based synchrosqueezing transform (FSST), which is often used in the field of signal processing to extract the ALFF of the low frequency power spectrum of the whole-time dimension. The low frequency characteristics of the extracted signal are compared with those of FSST and fast Fourier transform (FFT) through the resting-state data. It is clear that the signal extracted by FSST has more low frequency characteristics, which is significantly different from FFT.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Application and Evolution for Neural Network and Signal Processing in Large‐Scale Systems

Jia

et al. 2021

Complexity

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“…Many real-world signals like ECG and gravitational wave signals can be closely approximated by periodic signals [8,9]. Discrete-time periodic sequences also occur naturally in proteins and DNA [10,11].…”

Section: Introductionmentioning

confidence: 99%

Periodic Signal Denoising: An Analysis-Synthesis Framework Based on Ramanujan Filter Banks and Dictionaries

Kulkarni

Vaidyanathan

2021

ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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Ramanujan filter banks (RFB) have in the past been used to identify periodicities in data. These are analysis filter banks with no synthesis counterpart for perfect reconstruction of the original signal, so they have not been useful for denoising periodic signals. This paper proposes to use a hybrid analysissynthesis framework for denoising discrete-time periodic signals. The synthesis occurs via a pruned dictionary designed based on the output energies of the RFB analysis filters. A unique property of the framework is that the denoised output signal is guaranteed to be periodic unlike any of the other methods. For a large range of input noise levels, the proposed approach achieves a stable and high SNR gain outperforming many traditional denoising techniques.

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“…In addition, the method of multi-point exploration, which is used as a model to classify according to natural phenomena and laws, is documented [24]. For example, the gravitational search algorithm is a basic learning algorithm to simulate physical phenomena [25][26][27][28][29], biogeography-based optimization (BBO) [30] is generally used for simulating ecological concepts since the accuracy and stability are the most outstanding among the models using representative metaheuristics [31], and some basic learning algorithms can simulate the moving sample population of organisms such as particle swarm optimization (PSO) [32,33] and ant colony optimization. Moreover, as a variant of PSO, the competitive swarm optimizer (CSO) [34,35] is a simplified metaheuristics set that is suitable for both multi-point and local exploration.…”

Section: Introductionmentioning

confidence: 99%

Validation of Large-Scale Classification Problem in Dendritic Neuron Model Using Particle Antagonism Mechanism

Jia

Fujishita²,

et al. 2020

Electronics

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With the characteristics of simple structure and low cost, the dendritic neuron model (DNM) is used as a neuron model to solve complex problems such as nonlinear problems for achieving high-precision models. Although the DNM obtains higher accuracy and effectiveness than the middle layer of the multilayer perceptron in small-scale classification problems, there are no examples that apply it to large-scale classification problems. To achieve better performance for solving practical problems, an approximate Newton-type method-neural network with random weights for the comparison; and three learning algorithms including back-propagation (BP), biogeography-based optimization (BBO), and a competitive swarm optimizer (CSO) are used in the DNM in this experiment. Moreover, three classification problems are solved by using the above learning algorithms to verify their precision and effectiveness in large-scale classification problems. As a consequence, in the case of execution time, DNM + BP is the optimum; DNM + CSO is the best in terms of both accuracy stability and execution time; and considering the stability of comprehensive performance and the convergence rate, DNM + BBO is a wise choice.

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Time–frequency-based non-harmonic analysis to reduce line-noise impact for LIGO observation system

Cited by 16 publications

References 31 publications

Application and Evolution for Neural Network and Signal Processing in Large‐Scale Systems

Application and Evolution for Neural Network and Signal Processing in Large‐Scale Systems

Periodic Signal Denoising: An Analysis-Synthesis Framework Based on Ramanujan Filter Banks and Dictionaries

Validation of Large-Scale Classification Problem in Dendritic Neuron Model Using Particle Antagonism Mechanism

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