Purpose Attentiveness recognition benefits the detection of the mental state and concentration when humans perform specific tasks. Hilbert-Huang transform (HHT) is useful for the analysis of nonlinear or nonstationary bio-signals including brainwaves. In this work, a method is proposed for the characterization of attentiveness levels by using electroencephalogram (EEG) signals and HHT analysis. Methods Single-channel EEG signals from the frontal area were acquired from participants at different levels of attentiveness and were decomposed into a set of intrinsic mode functions (IMF) by empirical mode decomposition (EMD). Hilbert transform analysis was applied to each IMF to obtain the marginal frequency spectrum. Then the band powers and spectral entropies (SEs) were selected as the attributes of a support vector machine (SVM) for a two-class classification task. Results Compared with the predictive models of approximate entropy (ApEn) and fast Fourier transform (FFT), the results show that the band powers extracted from IMF2 to IMF5 of and waves and their SE can best discriminate between attentive and relaxed states with the average classification accuracy of 84.80%. Conclusion In conclusion, this integrated signal processing method is capable of attentiveness recognition that can offer efficient differentiation and may be used in a clinical setting for the detection of attention deficit.Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creat iveco mmons .org/licen ses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
A vibration structure with two-degrees-of-freedom is proposed to increase the usable bandwidth of a micromachined electromagnetic energy harvester. Compared with the structure of a pure cantilever harvester, the proposed structure is formed by integrating a spiral diaphragm into a U-shaped cantilever diaphragm. By performing finite element analysis, the resonance frequencies of the two diaphragms are designed with a slight shift, both lower than 300 Hz. In addition, to achieve output bandwidth broadening, electroplated copper coils on the spiral and the U-shaped cantilever are coupled and the connection sequences of the coupled coils are arranged such that single-or duo-mode tuning of the energy harvester can be realized. The harvester delivers powers of 22.1 and 21.5 nW at two resonance frequencies of 211 and 274 Hz, respectively, in the duo-mode operation. The proposed spiral-cantilever coupled energy harvester has lower resonance frequencies and broader bandwidth than a pure cantilever-type harvester of equal area, and can therefore harvest more energy from the environment.
In this paper, acoustic tweezers which use beam forming performed by a Fresnel zone plate are proposed. The performance has been demonstrated by finite element analysis, including the acoustic intensity, acoustic pressure, acoustic potential energy, gradient force, and particle distribution. The acoustic tweezers use an ultrasound beam produced by a lead zirconate titanate (PZT) transducer operating at 2.4 MHz and 100 Vpeak-to-peak in a water medium. The design of the Fresnel lens (zone plate) is based on air reflection, acoustic impedance matching, and the Fresnel half-wave band (FHWB) theory. This acoustic Fresnel lens can produce gradient force and acoustic potential wells that allow the capture and manipulation of single particles or clusters of particles. Simulation results strongly indicate a good trapping ability, for particles under 150 µm in diameter, in the minimum energy location. This can be useful for cell or microorganism manipulation.
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