Performance Estimate of Range Profile Feature Extraction for the Case of Defined Viewing Aspect by Means of Fisher Score

To solve the problem of translation compensation on ballistic target under complex scattering model in midcourse, a novel rule based on cycle subtracting of micro-Doppler curve is proposed. Firstly, the period is estimated by the period feature extraction algorithm which is based on the correlation of range profile envelopes. Then, extract the Doppler curve of the strongest scattering point through the Viterbi algorithm. Lastly, do the cycle subtracting and single cycle integral to the curve, the precious translation parameters can be got by the least squares estimating. This method is not only suitable for the rotating model, but also valuable for the precession model and the sliding model. The effectiveness of the proposed rule is verified by simulation results.

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A Hybrid Wi-Fi Fingerprint-Based Localization Scheme Achieved by Combining Fisher Score and Stacked Sparse Autoencoder Algorithms

Mobile Information Systems ◽

10.1155/2020/5710450 ◽

2020 ◽

Vol 2020 ◽

pp. 1-14

Author(s):

Zhongyuan Wang ◽

Zijian Wang ◽

Li Fan ◽

Zhihao Yu

Keyword(s):

Feature Extraction ◽

Indoor Localization ◽

Support Vector ◽

Fisher Score ◽

Feature Extraction Method ◽

Localization Scheme ◽

Localization Model ◽

Localization Errors ◽

Sparse Autoencoder ◽

Stacked Sparse Autoencoder

Along with the advancement of wireless technology, indoor localization technology based on Wi-Fi has received considerable attention from academia and industry. The fingerprint-based method is the mainstream approach for Wi-Fi indoor localization and can be easily implemented without additional hardware. However, signal fluctuations constitute a critical issue pertaining to the extraction of robust features to achieve the required localization performance. This study presents a fingerprint feature extraction method commonly referred to as the Fisher score–stacked sparse autoencoder (Fisher–SSAE) method. Some features with low Fisher scores were eliminated, and the representative features were then extracted by the SSAE. Furthermore, this study establishes a hybrid localization model constructed with the use of the global model and the submodel to avoid significant coordinate localization errors attributed to subregional localization errors. Combined with three accessible fingerprint-based positioning methods, namely, the support vector regression, random forest regression, and the multiplayer perceptron classification, the experimental results demonstrate that the proposed methods improve the localization accuracy and response time compared to other feature extraction methods and the single localization model. Compared with some state-of-the-art methods, the proposed methods have better localization performances when large number of features are used.

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Classification of Pain Event Related Potential for Evaluation of Pain Perception Induced by Electrical Stimulation

Sensors ◽

10.3390/s20051491 ◽

2020 ◽

Vol 20 (5) ◽

pp. 1491

Author(s):

Kornkanok Tripanpitak ◽

Waranrach Viriyavit ◽

Shao Ying Huang ◽

Wenwei Yu

Keyword(s):

Feature Extraction ◽

Electrical Stimulation ◽

Pain Assessment ◽

Correlation Dimension ◽

Pain Perception ◽

Event Related Potential ◽

Fisher Score ◽

Variance Functions ◽

Nonlinear Feature

Variability in individual pain sensitivity is a major problem in pain assessment. There have been studies reported using pain-event related potential (pain-ERP) for evaluating pain perception. However, none of them has achieved high accuracy in estimating multiple pain perception levels. A major reason lies in the lack of investigation of feature extraction. The goal of this study is to assess four different pain perception levels through classification of pain-ERP, elicited by transcutaneous electrical stimulation on healthy subjects. Nonlinear methods: Higuchi’s fractal dimension, Grassberger-Procaccia correlation dimension, with auto-correlation, and moving variance functions were introduced into the feature extraction. Fisher score was used to select the most discriminative channels and features. As a result, the correlation dimension with a moving variance without channel selection achieved the best accuracies of 100% for both the two-level and the three-level classification but degraded to 75% for the four-level classification. The best combined feature group is the variance-based one, which achieved accuracy of 87.5% and 100% for the four-level and three-level classification, respectively. Moreover, the features extracted from less than 20 trials could not achieve sensible accuracy, which makes it difficult for an instantaneous pain perception levels evaluation. These results show strong evidence on the possibility of objective pain assessment using nonlinear feature-based classification of pain-ERP.

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Radar high‐resolution range profile feature extraction method based on multiple kernel projection subspace fusion

IET Radar Sonar & Navigation ◽

10.1049/iet-rsn.2017.0391 ◽

2018 ◽

Vol 12 (4) ◽

pp. 417-425 ◽

Cited By ~ 1

Author(s):

Long Li ◽

Zheng Liu ◽

Tao Li

Keyword(s):

Feature Extraction ◽

High Resolution ◽

Extraction Method ◽

Feature Extraction Method ◽

Multiple Kernel ◽

Range Profile ◽

High Resolution Range Profile

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Power Quality Disturbance Recognition Using VMD-Based Feature Extraction and Heuristic Feature Selection

Applied Sciences ◽

10.3390/app9224901 ◽

2019 ◽

Vol 9 (22) ◽

pp. 4901 ◽

Cited By ~ 3

Author(s):

Lei Fu ◽

Tiantian Zhu ◽

Guobing Pan ◽

Sihan Chen ◽

Qi Zhong ◽

...

Keyword(s):

Computer Simulation ◽

Feature Extraction ◽

Feature Selection ◽

Power Quality ◽

Negative Impact ◽

Feature Selection Method ◽

Permutation Entropy ◽

Experimental Investigations ◽

Support Vector ◽

Fisher Score

Power quality disturbances (PQDs) have a large negative impact on electric power systems with the increasing use of sensitive electrical loads. This paper presents a novel hybrid algorithm for PQD detection and classification. The proposed method is constructed while using the following main steps: computer simulation of PQD signals, signal decomposition, feature extraction, heuristic selection of feature selection, and classification. First, different types of PQD signals are generated by computer simulation. Second, variational mode decomposition (VMD) is used to decompose the signals into several instinct mode functions (IMFs). Third, the statistical features are calculated in the time series for each IMF. Next, a two-stage feature selection method is imported to eliminate the redundant features by utilizing permutation entropy and the Fisher score algorithm. Finally, the selected feature vectors are fed into a multiclass support vector machine (SVM) model to classify the PQDs. Several experimental investigations are performed to verify the performance and effectiveness of the proposed method in a noisy environment. Moreover, the results demonstrate that the start and end points of the PQD can be efficiently detected.

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Motor Imagery EEG Decoding Based on New Spatial-Frequency Feature and Hybrid Feature Selection Method

Mathematical Problems in Engineering ◽

10.1155/2022/2856818 ◽

2022 ◽

Vol 2022 ◽

pp. 1-12

Author(s):

Yuan Tang ◽

Zining Zhao ◽

Shaorong Zhang ◽

Zhi Li ◽

Yun Mo ◽

...

Keyword(s):

Feature Extraction ◽

Feature Selection ◽

Spatial Frequency ◽

Motor Imagery ◽

Feature Selection Method ◽

Selection Method ◽

Support Vector ◽

Fisher Score ◽

Feature Extraction And Selection ◽

Frequency Feature

Feature extraction and selection are important parts of motor imagery electroencephalogram (EEG) decoding and have always been the focus and difficulty of brain-computer interface (BCI) system research. In order to improve the accuracy of EEG decoding and reduce model training time, new feature extraction and selection methods are proposed in this paper. First, a new spatial-frequency feature extraction method is proposed. The original EEG signal is preprocessed, and then the common spatial pattern (CSP) is used for spatial filtering and dimensionality reduction. Finally, the filter bank method is used to decompose the spatially filtered signals into multiple frequency subbands, and the logarithmic band power feature of each frequency subband is extracted. Second, to select the subject-specific spatial-frequency features, a hybrid feature selection method based on the Fisher score and support vector machine (SVM) is proposed. The Fisher score of each feature is calculated, then a series of threshold parameters are set to generate different feature subsets, and finally, SVM and cross-validation are used to select the optimal feature subset. The effectiveness of the proposed method is validated using two sets of publicly available BCI competition data and a set of self-collected data. The total average accuracy of the three data sets achieved by the proposed method is 82.39%, which is 2.99% higher than the CSP method. The experimental results show that the proposed method has a better classification effect than the existing methods, and at the same time, feature extraction and feature selection time also have greater advantages.

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An Improved Quantitative Image Analyser

Proceedings, annual meeting, Electron Microscopy Society of America ◽

10.1017/s0424820100078663 ◽

1977 ◽

Vol 35 ◽

pp. 226-227

Author(s):

J.P. Fallon ◽

P.J. Gregory ◽

C.J. Taylor

Keyword(s):

Feature Extraction ◽

Quantitative Analysis ◽

Frequency Content ◽

General Sense ◽

Physical Measurements ◽

Quantitative Image ◽

Image Analyser ◽

Automatic Methods ◽

Quantitative Results

Quantitative image analysis systems have been used for several years in research and quality control applications in various fields including metallurgy and medicine. The technique has been applied as an extension of subjective microscopy to problems requiring quantitative results and which are amenable to automatic methods of interpretation.Feature extraction. In the most general sense, a feature can be defined as a portion of the image which differs in some consistent way from the background. A feature may be characterized by the density difference between itself and the background, by an edge gradient, or by the spatial frequency content (texture) within its boundaries. The task of feature extraction includes recognition of features and encoding of the associated information for quantitative analysis.Quantitative Analysis. Quantitative analysis is the determination of one or more physical measurements of each feature. These measurements may be straightforward ones such as area, length, or perimeter, or more complex stereological measurements such as convex perimeter or Feret's diameter.

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