Dual-Mass MEMS Gyroscope Structure, Design, and Electrostatic Compensation

Dual-mass MEMS gyroscope is one of the most popular inertial sensors. In this chapter, the structure design and electrostatic compensation technology for dual-mass MEMS gyroscope is introduced. Firstly, a classical dual-mass MEMS gyroscope structure is proposed, how it works as a tuning fork (drive anti-phase mode), and the structure dynamical model together with the monitoring system are presented. Secondly, the imperfect elements during the structure manufacture process are analyzed, and the quadrature error coupling stiffness model for dual-mass structure is proposed. After that, the quadrature error correction system based on coupling stiffness electrostatic compensation method is designed and evaluated. Thirdly, the dual-mass structure sensing mode modal is proposed, and the force rebalancing combs stimulation method is utilized to achieve sensing mode transform function precisely. The bandwidth of sensing open loop is calculated and experimentally proved as 0.54 times with the resonant frequency difference between sensing and drive modes. Then, proportional-integral-phase-leading controller is presented in sensing close loop to expand the bandwidth, and the experiment shows that the bandwidth is improved from 13 to 104 Hz. Finally, the results are concluded and summarized

Mathematical Model of Helical Gear Topography Measurements and Tooth Flank Errors Separation

During large-size gear topological modification by form grinding, the helical gear tooth surface geometrical shape will be complex and it is difficult for the traditional scanning measurement to characterize the whole tooth surface. Therefore, in order to characterize the actual tooth surfaces, an on-machine topography measurement approach is proposed for topological modification helical gears on the five-axis CNC gear form grinding machine that can measure the modified gear tooth deviations on the machine immediately after grinding. Combined with gear form grinding kinematics principles, the mathematical model of topography measurements is established based on the polar coordinate method. The mathematical models include calculating trajectory of the centre of measuring probe, defining gear flanks by grid of points, and solving coordinate values of topology measurement. Finally, a numerical example of on-machine topography measurement is presented. By establishing the topography diagram and the contour map of tooth error, the tooth surface modification amount and the tooth flank errors are separated, respectively. Research results can serve as foundation for topological modification and tooth surface errors closed-loop feedback correction

Pulsed ultrasound-modulated optical tomography using spectral hole-burning

We present a novel optical quantum sensor using spectral hole-burning for detecting signals in ultrasound-modulated optical tomography. In this technique, we utilize the capability of sub-MHz spectral filtering afforded by a spectral hole burning crystal to select the desired spectral component from the ultrasound-modulated diffuse light. This technique is capable of providing a large etendue, processing a large number of speckles in parallel, tolerating speckle decorrelation, and imaging in real-time. Experimental results are presented

Expression mapping of GREM1 and functional contribution of its-secreting-cells in the brain using transgenic mouse models

Gremlin 1 (Grem1) is a secreted protein that antagonizes bone morphogenetic proteins (BMPs). While abnormal Grem1 expression has been reported to cause behavioral defects in postpartum mice, the spatial and cellular distribution of GREM1 in the brain and the influence of the Grem1-secreating cells on brain function and behavior remain unclear. To address this, we designed a genetic cassette incorporating a 3 × Flag-TeV-HA-T2A-tdTomato sequence, resulting in the creation of a novel Grem1Tag mouse model, expressing an epitope tag (3 × Flag-TeV-HA-T2A) followed by a fluorescent reporter (tdTomato) under the control of the endogenous Grem1 promoter. This design facilitated precise tracking of the cell origin and distribution of GREM1 in the brain using tdTomato and Flag (or HA) markers, respectively. We confirmed that the Grem1Tag mouse exhibited normal motor, cognitive, and social behaviors at postnatal 60 days (P60), compared with C57BL/6 J controls. Through immunofluorescence staining, we comprehensively mapped the distribution of Grem1-secreting cells across the central nervous system. Pervasive Grem1 expression was observed in the cerebral cortex (Cx), medulla, pons, and cerebellum, with the highest levels in the Cx region. Notably, within the Cx, GREM1 was predominantly secreted by excitatory neurons, particularly those expressing calcium/calmodulin-dependent protein kinase II alpha (Camk2a), while inhibitory neurons (parvalbumin-positive, PV+) and glial cells (oligodendrocytes, astrocytes, and microglia) showed little or no Grem1 expression. To delineate the functional significance of Grem1-secreting cells, a selective ablation at P42 using a diphtheria toxin A (DTA) system resulted in increased anxiety-like behavior and impaired memory in mice. Altogether, our study harnessing the Grem1Tag mouse model reveals the spatial and cellular localization of GREM1 in the mouse brain, shedding light on the involvement of Grem1-secreting cell in modulating brain function and behavior. Our Grem1Tag mouse serves as a valuable tool for further exploring the precise role of Grem1 in brain development and disease

BEVPlace: Learning LiDAR-based Place Recognition using Bird's Eye View Images

Place recognition is a key module for long-term SLAM systems. Current LiDAR-based place recognition methods usually use representations of point clouds such as unordered points or range images. These methods achieve high recall rates of retrieval, but their performance may degrade in the case of view variation or scene changes. In this work, we explore the potential of a different representation in place recognition, i.e. bird's eye view (BEV) images. We observe that the structural contents of BEV images are less influenced by rotations and translations of point clouds. We validate that, without any delicate design, a simple VGGNet trained on BEV images achieves comparable performance with the state-of-the-art place recognition methods in scenes of slight viewpoint changes. For more robust place recognition, we design a rotation-invariant network called BEVPlace. We use group convolution to extract rotation-equivariant local features from the images and NetVLAD for global feature aggregation. In addition, we observe that the distance between BEV features is correlated with the geometry distance of point clouds. Based on the observation, we develop a method to estimate the position of the query cloud, extending the usage of place recognition. The experiments conducted on large-scale public datasets show that our method 1) achieves state-of-the-art performance in terms of recall rates, 2) is robust to view changes, 3) shows strong generalization ability, and 4) can estimate the positions of query point clouds. Source codes are publicly available at https://github.com/zjuluolun/BEVPlace.Comment: Accepted by ICCV 202

Closed-Loop Feedback Flank Errors Correction of Topographic Modification of Helical Gears Based on Form Grinding

To increase quality, reduce heavy-duty gear noise, and avoid edge contact in manufacturing helical gears, a closed-loop feedback correction method in topographic modification tooth flank is proposed based on the gear form grinding. Equations of grinding wheel profile and grinding wheel additional radial motion are derived according to tooth segmented profile modification and longitudinal modification. Combined with gear form grinding kinematics principles, the equations of motion for each axis of five-axis computer numerical control forming grinding machine are established. Such topographical modification is achieved in gear form grinding with on-machine measurement. Based on a sensitivity analysis of polynomial coefficients of axis motion and the topographic flank errors by on-machine measuring, the corrections are determined through an optimization process that targets minimization of the tooth flank errors. A numerical example of gear grinding, including on-machine measurement and closed-loop feedback correction completing process, is presented. The validity of this flank correction method is demonstrated for tooth flank errors that are reduced. The approach is useful to precision manufacturing of spiral bevel and hypoid gears, too

A Game-Theoretic Response Strategy for Coordinator Attack in Wireless Sensor Networks

The coordinator is a specific node that controls the whole network and has a significant impact on the performance in cooperative multihop ZigBee wireless sensor networks (ZWSNs). However, the malicious node attacks coordinator nodes in an effort to waste the resources and disrupt the operation of the network. Attacking leads to a failure of one round of communication between the source nodes and destination nodes. Coordinator selection is a technique that can considerably defend against attack and reduce the data delivery delay, and increase network performance of cooperative communications. In this paper, we propose an adaptive coordinator selection algorithm using game and fuzzy logic aiming at both minimizing the average number of hops and maximizing network lifetime. The proposed game model consists of two interrelated formulations: a stochastic game for dynamic defense and a best response policy using evolutionary game formulation for coordinator selection. The stable equilibrium best policy to response defense is obtained from this game model. It is shown that the proposed scheme can improve reliability and save energy during the network lifetime with respect to security