Lipreading with Auditory Low-frequency Information Contextual Constraints

Low-frequency information can reflect the basic trend of a formation, enhance the accuracy of velocity analysis and improve the imaging accuracy of deep structures in seismic exploration. However, the low-frequency information obtained by the conventional seismic acquisition method is seriously polluted by noise, which will be further lost in processing. Compressed sensing (CS) theory is used to exploit the sparsity of the reflection coefficient in the frequency domain to expand the low-frequency components reasonably, thus improving the data quality. However, the conventional CS method is greatly affected by noise, and the effective expansion of low-frequency information can only be realized in the case of a high signal-to-noise ratio (SNR). In this paper, well information is introduced into the objective function to constrain the inversion process of the estimated reflection coefficient, and then, the low-frequency component of the original data is expanded by extracting the low-frequency information of the reflection coefficient. It has been proved by model tests and actual data processing results that the objective function of estimating the reflection coefficient constrained by well logging data based on CS theory can improve the anti-noise interference ability of the inversion process and expand the low-frequency information well in the case of a low SNR.

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Application of full waveform inversion algorithms to seismic data lacking low-frequency information from a simple starting model

Exploration Geophysics ◽

10.1071/eg17007 ◽

2018 ◽

Vol 49 (4) ◽

pp. 434-449 ◽

Cited By ~ 3

Author(s):

Hyunggu Jun ◽

Jungkyun Shin ◽

Changsoo Shin

Keyword(s):

Seismic Data ◽

Waveform Inversion ◽

Low Frequency ◽

Full Waveform Inversion ◽

Frequency Information ◽

Full Waveform ◽

Starting Model

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Research on Improvement Algorithm of Image Edge Detection

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.539.141 ◽

2014 ◽

Vol 539 ◽

pp. 141-145

Author(s):

Shui Li Zhang

Keyword(s):

Cognitive Psychology ◽

Edge Detection ◽

High Frequency ◽

Detection Method ◽

Low Frequency ◽

Frequency Information ◽

Image Edge Detection ◽

Improvement Algorithm ◽

Image Edge ◽

Edge Detection Method

This paper presents new theorems Stevens edge detection method based on cognitive psychology on. Firstly, based on the number of the image is decomposed into high-frequency and low-frequency information, and the high-frequency information extracted by subtracting the maximum number of images to the image after the filter, then the amount of high frequency information into psychological cognitive psychology based on Stevenss theorem. The algorithm suppression refined edge after the non-minimum, applications Pillar K-means algorithm to extract image edge. Experimental results show that: the brightness of the image is converted to the amount of psychological edge can better unify under different brightness values.

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The enhanced deep Plug-and-play super-resolution algorithm with residual channel attention networks

Journal of Intelligent & Fuzzy Systems ◽

10.3233/jifs-202696 ◽

2021 ◽

pp. 1-10

Author(s):

Hongguang Pan ◽

Fan Wen ◽

Xiangdong Huang ◽

Xinyu Lei ◽

Xiaoling Yang

Keyword(s):

Signal To Noise Ratio ◽

Low Frequency ◽

Super Resolution ◽

Structural Similarity ◽

Plug And Play ◽

Attention Networks ◽

Frequency Information ◽

Blind Deblurring ◽

Adaptive Adjustment ◽

Deep Feature Extraction

In the field of super-resolution image reconstruction, as a learning-based method, deep plug-and-play super-resolution (DPSR) algorithm can be used to find the blur kernel by using the existing blind deblurring methods. However, DPSR is not flexible enough in processing images with high- and low-frequency information. Considering a channel attention mechanism can distinguish low-frequency information and features in low-resolution images, in this paper, we firstly introduce this mechanism and design a new residual channel attention networks (RCAN); then the RCAN is adopted to replace deep feature extraction part in DPSR to achieve the adaptive adjustment of channel characteristics. Through four test experiments based on Set5, Set14, Urban100 and BSD100 datasets, we find that, under different blur kernels and different scale factors, the average peak signal to noise ratio (PSNR) and structural similarity (SSIM) values of our proposed method increase by 0.31dB and 0.55%, respectively; under different noise levels, the average PSNR and SSIM values increase by 0.26dB and 0.51%, respectively.

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Research on Adaptive Face Gender Recognition Based on Compressive Sensing

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.989-994.4187 ◽

2014 ◽

Vol 989-994 ◽

pp. 4187-4190 ◽

Cited By ~ 1

Author(s):

Lin Zhang

Keyword(s):

Feature Extraction ◽

Compressive Sensing ◽

Recognition Accuracy ◽

Recognition Rate ◽

Low Frequency ◽

Face Image ◽

Gender Recognition ◽

Recognition Method ◽

Frequency Information ◽

Learning Machine

An adaptive gender recognition method is proposed in this paper. At first, do multiwavlet transform to face image and get its low frequency information, then do feature extraction to the low frequency information using compressive sensing (CS), use extreme learning machine (ELM) to achieve gender recognition finally. In the process of feature extraction, we use genetic algorithm (GA) to get the number of measurements of CS in order to gain the highest recognition rate, so the method can adaptive access optimal performance. Experimental results show that compared with PDA and LDA, the new method improved the recognition accuracy substantially.

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Laplace-domain full-waveform inversion of seismic data lacking low-frequency information

Geophysics ◽

10.1190/geo2011-0411.1 ◽

2012 ◽

Vol 77 (5) ◽

pp. R199-R206 ◽

Cited By ~ 31

Author(s):

Wansoo Ha ◽

Changsoo Shin

Keyword(s):

Seismic Data ◽

Field Data ◽

Waveform Inversion ◽

Low Frequency ◽

Single Frequency ◽

Frequency Content ◽

Laplace Domain ◽

Frequency Information ◽

Velocity Models ◽

Gaussian Source

The lack of the low-frequency information in field data prohibits the time- or frequency-domain waveform inversions from recovering large-scale background velocity models. On the other hand, Laplace-domain waveform inversion is less sensitive to the lack of the low frequencies than conventional inversions. In theory, frequency filtering of the seismic signal in the time domain is equivalent to a constant multiplication of the wavefield in the Laplace domain. Because the constant can be retrieved using the source estimation process, the frequency content of the seismic data does not affect the gradient direction of the Laplace-domain waveform inversion. We obtained inversion results of the frequency-filtered field data acquired in the Gulf of Mexico and two synthetic data sets obtained using a first-derivative Gaussian source wavelet and a single-frequency causal sine function. They demonstrated that Laplace-domain inversion yielded consistent results regardless of the frequency content within the seismic data.

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Driving Simulators for Human Vehicle Interaction Design

Balkan Region Conference on Engineering and Business Education ◽

10.1515/cplbu-2015-0034 ◽

2015 ◽

Vol 1 (1) ◽

pp. 297-306

Author(s):

Alexandra Tucă ◽

Valerian Croitorescu ◽

Mircea Oprean ◽

Thomas Brandemeir

Keyword(s):

Real World ◽

Input Signal ◽

Interaction Design ◽

High Frequency ◽

Driver Behavior ◽

Low Frequency ◽

Driving Simulators ◽

Sensory Inputs ◽

Frequency Information ◽

Busy Road

AbstractThe interaction human-vehicle, as well as driver’s behavior are subject long debated in the automotive engineering domain. Driving simulators have an extraordinary important role allowing research that would not be possible to study in real world scenarios.A driver uses his sensory inputs to obtain the required input to base his decision on. The bandwidth of the required input signal should be in accordance to the driver’s task. For simple tasks, like turning on the screen wipers or direction indicator, low frequency information is sufficient. High frequency information is required when cornering on a busy road or when driving in relatively limit situations.The optimal configuration of each sub-system remains a significant cause for debate and still poses a major challenge when considering the ability of simulators to extract realistic driver behavior. If a difference is observed between real and virtual conditions, the factors specifically cause these differences are very difficult to be explained.

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