Micro-Electro-Mechanical-Systems (MEMS) and Fluid Flows

The micromachining technology that emerged in the late 1980s can provide micron-sized sensors and actuators. These micro transducers are able to be integrated with signal conditioning and processing circuitry to form micro-electro-mechanical-systems (MEMS) that can perform real-time distributed control. This capability opens up a new territory for flow control research. On the other hand, surface effects dominate the fluid flowing through these miniature mechanical devices because of the large surface-to-volume ratio in micron-scale configurations. We need to reexamine the surface forces in the momentum equation. Owing to their smallness, gas flows experience large Knudsen numbers, and therefore boundary conditions need to be modified. Besides being an enabling technology, MEMS also provide many challenges for fundamental flow-science research

Acoustic spin pumping: Direct generation of spin currents from sound waves in Pt/Y3Fe5O12 hybrid structures

Using a Pt/Y3Fe5O12 (YIG) hybrid structure attached to a piezoelectric actuator, we demonstrate the generation of spin currents from sound waves. This "acoustic spin pumping" (ASP) is caused by the sound wave generated by the piezoelectric actuator, which then modulates the distribution function of magnons in the YIG layer and results in a pure-spin-current injection into the Pt layer across the Pt/YIG interface. In the Pt layer, this injected spin current is converted into an electric voltage due to the inverse spin-Hall effect (ISHE). The ISHE voltage induced by the ASP is detected by measuring voltage in the Pt layer at the piezoelectric resonance frequency of the actuator coupled with the Pt/YIG system. The frequency-dependent measurements enable us to separate the ASP-induced signals from extrinsic heating effects. Our model calculation based on the linear response theory provides us with a qualitative and quantitative understanding of the ASP in the Pt/YIG system.Comment: 8 pages, 6 figure

Thermal spin pumping and magnon-phonon-mediated spin-Seebeck effect

The spin-Seebeck effect (SSE) in ferromagnetic metals and insulators has been investigated systematically by means of the inverse spin-Hall effect (ISHE) in paramagnetic metals. The SSE generates a spin voltage as a result of a temperature gradient in a ferromagnet, which injects a spin current into an attached paramagnetic metal. In the paramagnet, this spin current is converted into an electric field due to the ISHE, enabling the electric detection of the SSE. The observation of the SSE is performed in longitudinal and transverse configurations consisting of a ferromagnet/paramagnet hybrid structure, where thermally generated spin currents flowing parallel and perpendicular to the temperature gradient are detected, respectively. Our results explain the SSE in terms of a two-step process: (1) the temperature gradient creates a non-equilibrium state in the ferromagnet governed by both magnon and phonon propagations and (2) the non-equilibrium between magnons in the ferromagnet and electrons in the paramagnet at the contact interface leads to "thermal spin pumping" and the ISHE signal. The non-equilibrium state of metallic magnets (e.g. Ni81Fe19) under a temperature gradient is governed mainly by the phonons in the sample and the substrate, while in insulating magnets (e.g. Y3Fe5O12) both magnon and phonon propagations appear to be important. The phonon-mediated non-equilibrium that drives the thermal spin pumping is confirmed also by temperature-dependent measurements, giving rise to a giant enhancement of the SSE signals at low temperatures.Comment: 13 pages, 13 figure

Speech dereverberation for enhancement and recognition using dynamic features constrained deep neural networks and feature adaptation

This paper investigates deep neural networks (DNN) based on nonlinear feature mapping and statistical linear feature adaptation approaches for reducing reverberation in speech signals. In the nonlinear feature mapping approach, DNN is trained from parallel clean/distorted speech corpus to map reverberant and noisy speech coefficients (such as log magnitude spectrum) to the underlying clean speech coefficients. The constraint imposed by dynamic features (i.e., the time derivatives of the speech coefficients) are used to enhance the smoothness of predicted coefficient trajectories in two ways. One is to obtain the enhanced speech coefficients with a least square estimation from the coefficients and dynamic features predicted by DNN. The other is to incorporate the constraint of dynamic features directly into the DNN training process using a sequential cost function. In the linear feature adaptation approach, a sparse linear transform, called cross transform, is used to transform multiple frames of speech coefficients to a new feature space. The transform is estimated to maximize the likelihood of the transformed coefficients given a model of clean speech coefficients. Unlike the DNN approach, no parallel corpus is used and no assumption on distortion types is made. The two approaches are evaluated on the REVERB Challenge 2014 tasks. Both speech enhancement and automatic speech recognition (ASR) results show that the DNN-based mappings significantly reduce the reverberation in speech and improve both speech quality and ASR performance. For the speech enhancement task, the proposed dynamic feature constraint help to improve cepstral distance, frequency-weighted segmental signal-to-noise ratio (SNR), and log likelihood ratio metrics while moderately degrades the speech-to-reverberation modulation energy ratio. In addition, the cross transform feature adaptation improves the ASR performance significantly for clean-condition trained acoustic models.Published versio