Micro-Motion Features Analysis for Air Targets Based on Sparse Time-Frequency Decomposition

2018 ◽  
Author(s):  
Wang Yanqing ◽  
Huo Chaoying ◽  
Yin Hongcheng ◽  
Man Liang
Keyword(s):  
Time Frequency ◽  
Frequency Decomposition ◽  
Motion Features ◽  
Micro Motion

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

Complementary ensemble local means decomposition method and its application to rolling element bearings fault diagnosis

2019 ◽  
Vol 233 (5) ◽  
pp. 868-880 ◽  
Author(s):  
Yao Cheng ◽  
Dong Zou
Keyword(s):  
Decomposition Method ◽  
Signal Reconstruction ◽  
Outer Race ◽  
Time Frequency ◽  
Frequency Decomposition ◽  
Local Means ◽  
Residual Noise ◽  
Rolling Element ◽  
Noisy Signals ◽  
Mode Mixing

Local means decomposition is an adaptive and nonparametric time–frequency decomposition method for nonstationary and nonlinear signals. However, in practice, local means decomposition is susceptible to mode mixing phenomena and produces different scale oscillations in one mode or similar scale oscillations in different modes, rendering the decomposition results difficult to interpret in terms of physical meansing. The noise-assisted ensemble local means decomposition method not only effectively resolved mode mixing but also generated a new problem, which tolerates residual noise in signal reconstruction. Targeting these shortcomings, this article proposes complementary ensemble local means decomposition, a novel noise-assisted time–frequency analysis method. First, an ensemble of white noise is added to the original signal via complementary positive and negative pairs. Second, local means decomposition is applied to decompose the noisy signals into a series of product functions, and the final results are obtained by averaging. The simulation results confirm that complementary ensemble local means decomposition offers an innovative improvement over ensemble local means decomposition in terms of eliminating residual noise. The superiority of the proposed method was further validated on fault signals obtained from faulty railway bearings (rolling element and outer race fault signals).

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