An improved building detection approach using L-band POLSAR two-dimensional time-frequency decomposition over oriented built-up areas

2018 ◽  
Vol 56 (1) ◽  
pp. 1-21
Author(s):  
Lei Deng ◽  
Ya-nan Yan ◽  
Ying He ◽  
Zhi-hui Mao ◽  
Jie Yu
Keyword(s):  
Building Detection ◽  
Two Dimensional ◽  
Time Frequency ◽  
Frequency Decomposition ◽  
Detection Approach ◽  
L Band

Myoelectric signal classification based on S transform and two-directional two-dimensional principal component analysis

2017 ◽  
Vol 40 (7) ◽  
pp. 2387-2395 ◽  
Author(s):  
Yi Ji ◽  
Hong-Bo Xie
Keyword(s):  
Principal Component Analysis ◽  
Feature Extraction ◽  
Computational Cost ◽  
Principal Component ◽  
Component Analysis ◽  
Two Dimensional ◽  
Time Frequency ◽  
Frequency Decomposition ◽  
S Transform ◽  
Efficiency And Effectiveness

Time-frequency representiation has been intensively employed for the analysis of biomedical signals. In order to extract discriminative information, time-frequency matrix is often transformed into a 1D vector followed by principal component analysis (PCA). This study contributes a two-directional two-dimensional principal component analysis (2D2PCA)-based technique for time-frequency feature extraction. The S transform, integrating the strengths of short time Fourier transform and wavelet transform, is applied to perform the time-frequency decomposition. Then, 2D2PCA is directly conducted on the time-frequency matrix rather than 1D vectors for feature extraction. The proposed method can significantly reduce the computational cost while capture the directions of maximal time-frequency matrix variance. The efficiency and effectiveness of the proposed method is demonstrated by classifying eight hand motions using 4-channel myoelectric signals recorded in health subjects and amputees.

A Fault Detection Approach Based on Sound Signal Analysis for Equipment Monitoring

2020 ◽  
pp. 129-139
Author(s):  
Weihai Sun ◽  
Lemei Han
Keyword(s):  
Fault Detection ◽  
Signal Analysis ◽  
Energy Analysis ◽  
Detection Method ◽  
Sound Signal ◽  
Practical Significance ◽  
Time Frequency ◽  
Detection Approach ◽  
Machine Fault ◽  
Learning Data

Machine fault detection has great practical significance. Compared with the detection method that requires external sensors, the detection of machine fault by sound signal does not need to destroy its structure. The current popular audio-based fault detection often needs a lot of learning data and complex learning process, and needs the support of known fault database. The fault detection method based on audio proposed in this paper only needs to ensure that the machine works normally in the first second. Through the correlation coefficient calculation, energy analysis, EMD and other methods to carry out time-frequency analysis of the subsequent collected sound signals, we can detect whether the machine has fault.

scholarly journals ‘Awake delta’ and theta-rhythmic hippocampal network modes during intermittent locomotor behaviors in the rat

2019 ◽  
Author(s):  
Nathan W. Schultheiss ◽  
Maximillian Schlecht ◽  
Maanasa Jayachandran ◽  
Deborah R. Brooks ◽  
Jennifer L. McGlothan ◽  
...  
Keyword(s):  
Clustering Algorithms ◽  
Field Potential ◽  
Small Sample ◽  
Network Activity ◽  
Time Frequency ◽  
Theta Power ◽  
Frequency Decomposition ◽  
Speed Modulation ◽  
Delta Band ◽  
Locomotor Behaviors

AbstractDelta-frequency network activity is commonly associated with sleep or behavioral disengagement accompanied by a dearth of cortical spiking, but delta in awake behaving animals is not well understood. We show that hippocampal (HC) synchronization in the delta frequency band (1-4 Hz) is related to animals’ locomotor behavior using a detailed analysis of simultaneous head- and body-tracking data. In contrast to running-speed modulation of the theta rhythm (6-10 Hz, a critical mechanism in navigation models), we observed that strong delta synchronization occurred when animals were stationary or moving slowly and while theta and fast gamma (55-120 Hz) were weak. We next combined time-frequency decomposition of the local field potential with hierarchical clustering algorithms to categorize momentary estimations of the power spectral density (PSD) into putative modes of HC activity. Delta and theta power measures from these modes were notably orthogonal, and theta and delta coherences between HC recording sites were monotonically related to theta-delta ratios across modes. Next, we focused on bouts of precisely-defined running and stationary behavior. Extraction of delta and theta power density estimates for each instance of these bout types confirmed the orthogonality between frequency bands seen across modes. We found that delta-band and theta-band coherence within HC, and in a small sample, between HC and medial prefrontal cortex (mPFC), mirrored delta and theta components of the PSD. Delta-band synchronization often developed rapidly when animals paused briefly between runs, as well as appearing throughout longer stationary bouts. Taken together, our findings suggest that delta-dominated network modes (and corresponding mPFC-HC couplings) represent functionally-distinct circuit dynamics that are temporally and behaviorally interspersed amongst theta-dominated modes during navigation. As such these modes of mPFC-HC circuit dynamics could play a fundamental role in coordinating encoding and retrieval mechanisms or decision-making processes at a timescale that segments event sequences within behavioral episodes.

Time-frequency decomposition-based weighted ensemble learning for motor imagery EEG classification

2021 ◽  
Author(s):  
Liangsheng Zheng ◽  
Yue Ma ◽  
Mengyao Li ◽  
Yang Xiao ◽  
Wei Feng ◽  
...  
Keyword(s):  
Ensemble Learning ◽  
Motor Imagery ◽  
Eeg Classification ◽  
Time Frequency ◽  
Frequency Decomposition
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