A Chirplet Transform-based Mode Retrieval Method for Multicomponent Signals with Crossover Instantaneous Frequencies

We focus on the decomposition problem for nonstationary multicomponent signals involving Big Data. We propose the kernel sparse learning (KSL), developed for the T-F reassignment algorithm by the path penalty function, to decompose the instantaneous frequencies (IFs) ridges of the overlapped multicomponent from a time-frequency representation (TFR). The main objective of KSL is to minimize the error of the prediction process while minimizing the amount of training samples used and thus to cut the costs interrelated with the training sample collection. The IFs first extraction is decided using the framework of the intrinsic mode polynomial chirp transform (IMPCT), which obtains a brief local orthogonal TFR of signals. Then, the IFs curves of the multicomponent signal can be easily reconstructed by the T-F reassignment. After the IFs are extracted, component decomposition is performed through KSL. Finally, the performance of the method is compared when applied to several simulated micro-Doppler signals, which shows its effectiveness in various applications.

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Spiral Resonators Array in inductive Wireless Power Transfer Applications: an Equivalent Lumped Circuit Retrieval Method

2020 XXXIIIrd General Assembly and Scientific Symposium of the International Union of Radio Science ◽

10.23919/ursigass49373.2020.9232334 ◽

2020 ◽

Author(s):

Danilo Brizi ◽

Nunzia Fontana ◽

Sami Barmada ◽

Agostino Monorchio

Keyword(s):

Wireless Power Transfer ◽

Power Transfer ◽

Wireless Power ◽

Retrieval Method

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A Review on Correlation Maximized Similarity Measurement in Cross Media Retrieval Method

International Journal of Computer Sciences and Engineering ◽

10.26438/ijcse/v6i3.214218 ◽

2018 ◽

Vol 6 (3) ◽

pp. 214-218

Author(s):

Monelli Ayyavaraiah ◽

Keyword(s):

Similarity Measurement ◽

Retrieval Method ◽

Cross Media

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Experimental Design as a Retrieval Method for Assessing the Phenomenological Characteristics of Episodic Memories, Episodic Future Thoughts, and Episodic Counterfactual Thoughts

10.31234/osf.io/h56a9 ◽

2020 ◽

Author(s):

Jared Branch

Keyword(s):

Experimental Design ◽

Effect Sizes ◽

Retrieval Method ◽

Future Studies ◽

Within Subjects ◽

Demand Effects ◽

Episodic Memories ◽

Theoretical Considerations

Studies assessing the phenomenological characteristics of episodic memories, episodic future thoughts, and episodic counterfactual thoughts normally utilize a within-subjects design. As such, there are concerns that the observed similarities in phenomenological characteristics are the result of demand effects or other related matters, rather than theoretical considerations. In this study, a within-subjects experimental design was directly compared with a between-subjects experimental design. In both conditions, participants responded to existing questionnaires used to assess phenomenological characteristics of episodic memories, episodic future thoughts, and episodic counterfactual thoughts. The within-subjects design resulted more often in significant findings and larger effect sizes compared to the between-subjects design. The implications for experimental design in future studies is discussed.

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