A 392-pW 42.7-dB Gm-C wavelet filter for low-frequency feature extraction used for wearable sensor

2021 ◽  
Author(s):  
Yuzhen Zhang ◽  
Wenshan Zhao
Keyword(s):  
Feature Extraction ◽  
Low Frequency ◽  
Wavelet Filter ◽  
Wearable Sensor ◽  
Frequency Feature

scholarly journals A Novel Linear Spectrum Frequency Feature Extraction Technique for Warship Radio Noise Based on Complete Ensemble Empirical Mode Decomposition with Adaptive Noise, Duffing Chaotic Oscillator, and Weighted-Permutation Entropy

Entropy ◽  
2019 ◽  
Vol 21 (5) ◽  
pp. 507 ◽  
Author(s):  
Yuxing Li ◽  
Long Wang ◽  
Xueping Li ◽  
Xiaohui Yang
Keyword(s):  
Feature Extraction ◽  
Empirical Mode Decomposition ◽  
Low Frequency ◽  
Extraction Technique ◽  
Permutation Entropy ◽  
Radio Noise ◽  
Mode Decomposition ◽  
Linear Spectrum ◽  
Frequency Feature ◽  
Feature Extraction Technique

Warships play an important role in the modern sea battlefield. Research on the line spectrum features of warship radio noise signals is helpful to realize the classification and recognition of different types of warships, and provides critical information for sea battlefield. In this paper, we proposed a novel linear spectrum frequency feature extraction technique for warship radio noise based on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), duffing chaotic oscillator (DCO), and weighted-permutation entropy (W-PE). The proposed linear spectrum frequency feature extraction technique, named CEEMDAN-DCO-W-PE has the following advantages in comparison with other linear spectrum frequency feature extraction techniques; (i) as an adaptive data-driven algorithm, CEEMDAN has more accurate and more reliable decomposition performance than empirical mode decomposition (EMD) and ensemble EMD (EEMD), and there is no need for presetting parameters, such as decomposition level and basis function; (ii) DCO can detect the linear spectrum of narrow band periodical warship signals by way of utilizing its properties of sensitivity for weak periodical signals and the immunity for noise; and (iii) W-PE is used in underwater acoustic signal feature extraction for the first time, and compared with traditional permutation entropy (PE), W-PE increases amplitude information to some extent. Firstly, warship radio noise signals are decomposed into some intrinsic mode functions (IMFs) from high frequency to low frequency by CEEMDAN. Then, DCO is used to detect linear spectrum of low-frequency IMFs. Finally, we can determine the linear spectrum frequency of low-frequency IMFs using W-PE. The experimental results show that the proposed technique can accurately extract the line spectrum frequency of the simulation signals, and has a higher classification and recognition rate than the traditional techniques for real warship radio noise signals.

scholarly journals Research on comment target extracting in Chinese online shopping platform

2018 ◽  
Vol 2 (3) ◽  
pp. 247-258
Author(s):  
Zhishuo Liu ◽  
Qianhui Shen ◽  
Jingmiao Ma ◽  
Ziqi Dong
Keyword(s):  
Association Rule ◽  
Online Shopping ◽  
Low Frequency ◽  
Online Reviews ◽  
Content Type ◽  
Pos Tagging ◽  
Part Of Speech ◽  
Frequency Feature ◽  
Practical Implications ◽  
Data Reading

Purpose This paper aims to extract the comment targets in Chinese online shopping platform. Design/methodology/approach The authors first collect the comment texts, word segmentation, part-of-speech (POS) tagging and extracted feature words twice. Then they cluster the evaluation sentence and find the association rules between the evaluation words and the evaluation object. At the same time, they establish the association rule table. Finally, the authors can mine the evaluation object of comment sentence according to the evaluation word and the association rule table. At last, they obtain comment data from Taobao and demonstrate that the method proposed in this paper is effective by experiment. Findings The extracting comment target method the authors proposed in this paper is effective. Research limitations/implications First, the study object of extracting implicit features is review clauses, and not considering the context information, which may affect the accuracy of the feature excavation to a certain degree. Second, when extracting feature words, the low-frequency feature words are not considered, but some low-frequency feature words also contain effective information. Practical implications Because of the mass online reviews data, reading every comment one by one is impossible. Therefore, it is important that research on handling product comments and present useful or interest comments for clients. Originality/value The extracting comment target method the authors proposed in this paper is effective.

Low frequency Raman spectra of the liquid and plastic crystal phases of O2, N2, CO, and of Ar crystals with these molecules as guests

1982 ◽  
Vol 60 (9) ◽  
pp. 1358-1364 ◽  
Author(s):  
N. H. Rich ◽  
M. J. Clouter ◽  
H. Kiefte ◽  
S. F. Ahmad
Keyword(s):  
Single Crystals ◽  
Raman Spectra ◽  
Phonon Mode ◽  
Low Frequency ◽  
Impurity Molecule ◽  
Spectral Features ◽  
Background Impurity ◽  
Crystal Phases ◽  
Distinct Features ◽  
Frequency Feature

Low frequency Raman spectra of single crystals of orientationally disordered phases of oxygen, nitrogen, and carbon monoxide, and spectra of those substances as liquids show two linear segments in semi-log plots. Slopes of the higher frequency segments are nearly equal for all cases; slopes of the lower frequency segments are particular to the substance and are nearly the same in both liquid and crystal for O2 and CO. Spectra of single crystals of argon doped with O2, N2, or CO show two distinct features superimposed on a sloping background. Impurity molecule reorientation apparently accounts satisfactorily for all spectral features, but translation–rotation coupling may allow a contribution to the higher frequency feature arising from a local phonon mode in argon.

Disorder-Induced Low-Frequency Raman Band Observed in Deposited MoS2 Films

1994 ◽  
Vol 48 (6) ◽  
pp. 733-736 ◽  
Author(s):  
N. T. McDevitt ◽  
J. S. Zabinski ◽  
M. S. Donley ◽  
J. E. Bultman
Keyword(s):  
Crystal Structure ◽  
Thin Films ◽  
Raman Band ◽  
Low Frequency ◽  
Pulsed Laser ◽  
Laser Deposition ◽  
First Order ◽  
Order Spectrum ◽  
History Of ◽  
Frequency Feature

Crystalline disorder in thin films plays an important role in determining their properties. Disorder in the crystal structure of MoS2 films prepared by magnetron sputtering and pulsed laser deposition was evaluated with the use of Raman spectroscopy. The peak positions and bandwidths of the first-order Raman bands, in the region 100 to 500 cm−1, were used as a measure of crystalline order. In addition, a low-frequency feature was observed at 223 cm−1 that is not part of the normal first-order spectrum of a fully crystalline specimen. Data presented here demonstrate that this band is characteristic of crystalline disorder, and its intensity depends on the annealing history of the film. This behavior seems to be analogous to the disorder found in graphite thin films.

scholarly journals Underwater Target Recognition Based on Multi-Decision LOFAR Spectrum Enhancement: A Deep-Learning Approach

2021 ◽  
Vol 13 (10) ◽  
pp. 265
Author(s):  
Jie Chen ◽  
Bing Han ◽  
Xufeng Ma ◽  
Jian Zhang
Keyword(s):  
Feature Extraction ◽  
Deep Learning ◽  
Recognition Accuracy ◽  
Target Recognition ◽  
Signal To Noise Ratio ◽  
Recognition Performance ◽  
Low Frequency ◽  
Learning Approach ◽  
Decision Algorithm ◽  
Underwater Target

Underwater target recognition is an important supporting technology for the development of marine resources, which is mainly limited by the purity of feature extraction and the universality of recognition schemes. The low-frequency analysis and recording (LOFAR) spectrum is one of the key features of the underwater target, which can be used for feature extraction. However, the complex underwater environment noise and the extremely low signal-to-noise ratio of the target signal lead to breakpoints in the LOFAR spectrum, which seriously hinders the underwater target recognition. To overcome this issue and to further improve the recognition performance, we adopted a deep-learning approach for underwater target recognition, and a novel LOFAR spectrum enhancement (LSE)-based underwater target-recognition scheme was proposed, which consists of preprocessing, offline training, and online testing. In preprocessing, we specifically design a LOFAR spectrum enhancement based on multi-step decision algorithm to recover the breakpoints in LOFAR spectrum. In offline training, the enhanced LOFAR spectrum is adopted as the input of convolutional neural network (CNN) and a LOFAR-based CNN (LOFAR-CNN) for online recognition is developed. Taking advantage of the powerful capability of CNN in feature extraction, the recognition accuracy can be further improved by the proposed LOFAR-CNN. Finally, extensive simulation results demonstrate that the LOFAR-CNN network can achieve a recognition accuracy of 95.22%, which outperforms the state-of-the-art methods.

Research on Adaptive Face Gender Recognition Based on Compressive Sensing

2014 ◽  
Vol 989-994 ◽  
pp. 4187-4190 ◽  
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.

A Gait Feature Extraction Algorithm Based on Fusion of Spatial and Frequency Feature

2013 ◽  
Vol 291-294 ◽  
pp. 2492-2495
Author(s):  
Xiao Ke Zhu ◽  
Xiao Pan Chen ◽  
Fan Zhang
Keyword(s):  
Feature Extraction ◽  
Moving Objects ◽  
Gait Recognition ◽  
Morphological Processing ◽  
Moving Object ◽  
Classification Rate ◽  
Feature Extraction Algorithm ◽  
Extraction Algorithm ◽  
Gait Feature ◽  
Frequency Feature

In order to enhance the accuracy of gait recognition, a new gait feature extraction algorithm is proposed. Firstly, the gait images are preprocessed to extract moving objects, including background modeling, moving object extracting and morphological processing. Secondly, an equidistant slicing curve model based on system of polar coordinate is designed to slice the moving object, and the slicing vector is used to describe the spatial feature; Thirdly, the slicing vector is converted into frequency signal by Fourier transform to extract the frequency feature. Finally, the above two features are fused and used for the classification. The experimental results show that proposed algorithm provides higher correct classification rate than the algorithms using single feature, and meets the requirements of the real-time.

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