A simple model of thermoelastic dissipation is proposed for general, free standing microelectromechanical ͑MEMS͒ and nanoelectromechanical ͑NEMS͒ oscillators. The theory defines a flexural modal participation factor, the fraction of potential energy stored in flexure, and approximates the internal friction by assuming the energy loss to occur solely via classical thermoelastic dissipation of this component of the motion. The theory is compared to the measured internal friction of a high Q mode of a single-crystal silicon double paddle oscillator. The loss at high temperature ͑above 150 K͒ is found to be in good agreement with the theoretical prediction. The importance of this dissipation mechanism as a function of scale is briefly discussed. We find that the relative importance of this mechanism scales with the size of the structure, and that for nanoscale structures it is less important than intrinsic phonon-phonon scattering. © 2002 American Institute of Physics. ͓DOI: 10.1063/1.1449534͔Obtaining high Q performance in Si based oscillators in microelectromechanical systems ͑MEMS͒ and nanoelectromechanical systems ͑NEMS͒ is desirable for applications that include charge detectors, 1 radio-frequency filters, 2 magnetic resonance force microscopes, 3 and torque magnetometers. 4 Achieving this goal has proven to be difficult however, and to date, the achieved Qs in MEMS/NEMS oscillators have been orders of magnitude smaller than expected from considerations of fundamental loss mechanisms.5 Explanations of the observed behavior include attachment losses, 6,7 bulk defects, 4 losses associated with the electrical contacts, 8 and surface effects. 9 Unfortunately, untangling the various potential mechanisms is not a straightforward exercise because of the difficulty of controlling and measuring the properties of microscale and nanoscale devices.The MEMS oscillators are typically free standing, planar samples vibrating predominantly either in flexure or torsion. In the case of a simple beam in flexure, thermoelastic ͑TE͒ dissipation is known to be a significant loss mechanism near room temperature. 10 The loss mechanisms in other oscillators involving torsional vibration, or in general possessing a nontrivial mode shape, are however still unknown. We have developed a simple model which predicts the internal friction arising from TE dissipation in these types of oscillators. We have found the internal friction can be quite significant even for nominally torsional vibration modes which one might conclude have no TE loss. Analysis of the experimental results for a macroscopic single-crystal silicon oscillator yields good agreement with the prediction.TE dissipation arises from thermal currents generated by compression/decompression in elastic media. Typically, the TE dissipation arising from longitudinal waves in solids is very small 11 owing to the large distance between the compressed and rarefied regions. In contrast, the compression associated with flexural vibration can give rise to TE dissipation orders of magnitude l...

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GaSe layers with thicknesses ranging from a monolayer to 100 nm are successfully mechanically exfoliated for use in gas sensing. In combination with density functional theory calculations, general guidelines to determine the number of layers using Raman spectra are presented. With decreasing layer numbers, quantum confinement induces a red-shift for out-of-plane modes and a blue-shift for in-plane modes. The relative Raman shifts of the out-of-plane vibrational modes A A ( (1 1 ) ′ ′ mode of monolayer GaSe (≈−1.99 × 10 −2 cm −1 K −1 ) being almost double that of 100 layer GaSe (≈−1.22 × 10 −2 cm −1 K −1 ). Finally, the exfoliated GaSe is used for gas sensing and shows high sensitivity, displaying a minimum detection limit of 4 ppm for NH 3 at room temperature, confirming the potential of mechanically exfoliated GaSe in high-sensitivity gas sensors.

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Visual tracking is one of the most fundamental topics in computer vision. Numerous tracking approaches based on discriminative correlation filters or Siamese convolutional networks have attained remarkable performance over the past decade. However, it is still commonly recognized as an open research problem to develop robust and effective trackers which can achieve satisfying performance with high computational and memory storage efficiency in real-world scenarios. In this paper, we investigate the impacts of three main aspects of visual tracking, i.e., the backbone network, the attentional mechanism, and the detection component, and propose a Siamese Attentional Keypoint Network, dubbed SATIN, for efficient tracking and accurate localization. Firstly, a new Siamese lightweight hourglass network is specially designed for visual tracking. It takes advantage of the benefits of the repeated bottom-up and top-down inference to capture more global and local contextual information at multiple scales. Secondly, a novel cross-attentional module is utilized to leverage both channel-wise and spatial intermediate attentional information, which can enhance both discriminative and localization capabilities of feature maps. Thirdly, a keypoints detection approach is invented to trace any target object by detecting the top-left corner point, the centroid point, and the bottom-right corner point of its bounding box. Therefore, our SATIN tracker not only has a strong capability to learn more effective object representations, but also is computational and memory storage efficiency, either during the training or testing stages. To the best of our knowledge, we are the first to propose this approach. Without bells and whistles, experimental results demonstrate that our approach achieves state-of-the-art performance on several recent benchmark datasets, at a speed far exceeding 27 frames per second. with real-time speed are widely pursued in numerous applications, including video surveillance, autonomous driving, pose estimation, and human-computer interfaces. Although considerable progress has been made by many tracking approaches in the last decade, visual tracking is still a challenging problem due to multiple negative scenarios such as occlusions, fast motions, scale variations, and background clutters [1,2,3,4].Most recent tracking approaches are developed based on two successful frameworks. The first one is the discriminative correlation filter (DCF). Thanks to the circular shifting of training samples and fast learning correlation filters in the Fourier frequency domain, DCF demonstrates superior computational efficiency and fairly good tracking accuracy and has attracted increasing attention since MOSSE [5]first exploited it. Later, in order to overcome different kinds of challenges and achieve competitive tracking performance, the advancement in DCF-based tracking is focused on the use of kernel functions [6], motion information [7,8], multi-dimensional features [9,10], multi-scale estimation [11,12], boundary effects allevi...