Development of a low damping MEMS resonator

MEMS based low damping inertial resonators are the key element in the development of precision vibratory gyroscopes. High quality factor (Q factor) is a crucial parameter for the development of high precision inertial resonators. Q factor indicates how efficient a resonator is at retaining its energy during oscillations. Q factor can be limited by different types of energy losses, such as anchor damping, squeeze-film damping, and thermoelastic damping (TED). Understanding the energy loss-mechanism can show a path for designing high Q resonator. This thesis explores the effects of different design parameters on Q factor of 3D inertial resonators. TED loss mechanisms in a 3D non-inverted wineglass (hemispherical) shell resonator and a disk resonator were investigated. Both the disk and shell share the same vibration modes, and they are widely used as a vibratory resonator shape. Investigation with loss-mechanism shows that robust mechanical materials such as fused silica can offer ultra-low damping during oscillation. TED loss resulting from the effects of geometric parameters (such as thickness, height, and radius), mass imbalance, thickness non-uniformity, and edge defects were investigated. Glassblowing was used to fabricate hemispherical 3D shell resonators and conventional silicon based dry etching was used to fabricate micro disk resonators. The results presented in this thesis can facilitate selecting efficient geometric and material properties for achieving a higher Q-factor in 3D inertial resonators. Enhancing the Q-factor in MEMS based 3D resonators can further enable the development of high precision resonators and gyroscopes

Chirality driven topological electronic structure of DNA-like materials

Topological aspects of the geometry of DNA and similar chiral molecules have received a lot of attention, while the topology of their electronic structure is less explored. Previous experiments have revealed that DNA can efficiently filter spin-polarized electrons between metal contacts, a process called chiral-induced spin-selectivity (CISS). However, the underlying correlation between chiral structure and electronic spin remains elusive. In this work, we reveal an orbital texture in the band structure, a topological characteristic induced by the chirality. We find that this orbital texture enables the chiral molecule to polarize the quantum orbital. This orbital polarization effect (OPE) induces spin polarization assisted by the spin-orbit interaction from a metal contact and leads to magnetorestistance and chiral separation. The orbital angular momentum of photoelectrons also plays an essential role in related photoemission experiments. Beyond CISS, we predict that OPE can induce spin-selective phenomena even in achiral but inversion-breaking materials.Comment: 24 pages, 4 figures, and Supplementary Material

Low-complexity Resource Allocation for User Paired RSMA in Future 6G Wireless Networks

Rate-splitting multiple access (RSMA) uplink requires optimization of decoding order and power allocation, while decoding order is a discrete variable, and it is very complex to find the optimal decoding order if the number of users is large enough. This letter proposes a low-complexity user pairing-based resource allocation algorithm with the objective of minimizing the maximum latency, which significantly reduces the computational complexity and also achieves similar performance to unpaired uplink RSMA. A closed-form expression for power and bandwidth allocation is first derived, and then a bisection method is used to determine the optimal resource allocation. Finally, the proposed algorithm is compared with unpaired RSMA, paired NOMA and unpaired NOMA. The results demonstrate the effectiveness of the proposed algorithm

Joint Perceptual Learning for Enhancement and Object Detection in Underwater Scenarios

Underwater degraded images greatly challenge existing algorithms to detect objects of interest. Recently, researchers attempt to adopt attention mechanisms or composite connections for improving the feature representation of detectors. However, this solution does \textit{not} eliminate the impact of degradation on image content such as color and texture, achieving minimal improvements. Another feasible solution for underwater object detection is to develop sophisticated deep architectures in order to enhance image quality or features. Nevertheless, the visually appealing output of these enhancement modules do \textit{not} necessarily generate high accuracy for deep detectors. More recently, some multi-task learning methods jointly learn underwater detection and image enhancement, accessing promising improvements. Typically, these methods invoke huge architecture and expensive computations, rendering inefficient inference. Definitely, underwater object detection and image enhancement are two interrelated tasks. Leveraging information coming from the two tasks can benefit each task. Based on these factual opinions, we propose a bilevel optimization formulation for jointly learning underwater object detection and image enhancement, and then unroll to a dual perception network (DPNet) for the two tasks. DPNet with one shared module and two task subnets learns from the two different tasks, seeking a shared representation. The shared representation provides more structural details for image enhancement and rich content information for object detection. Finally, we derive a cooperative training strategy to optimize parameters for DPNet. Extensive experiments on real-world and synthetic underwater datasets demonstrate that our method outputs visually favoring images and higher detection accuracy

Learning Heavily-Degraded Prior for Underwater Object Detection

Underwater object detection suffers from low detection performance because the distance and wavelength dependent imaging process yield evident image quality degradations such as haze-like effects, low visibility, and color distortions. Therefore, we commit to resolving the issue of underwater object detection with compounded environmental degradations. Typical approaches attempt to develop sophisticated deep architecture to generate high-quality images or features. However, these methods are only work for limited ranges because imaging factors are either unstable, too sensitive, or compounded. Unlike these approaches catering for high-quality images or features, this paper seeks transferable prior knowledge from detector-friendly images. The prior guides detectors removing degradations that interfere with detection. It is based on statistical observations that, the heavily degraded regions of detector-friendly (DFUI) and underwater images have evident feature distribution gaps while the lightly degraded regions of them overlap each other. Therefore, we propose a residual feature transference module (RFTM) to learn a mapping between deep representations of the heavily degraded patches of DFUI- and underwater- images, and make the mapping as a heavily degraded prior (HDP) for underwater detection. Since the statistical properties are independent to image content, HDP can be learned without the supervision of semantic labels and plugged into popular CNNbased feature extraction networks to improve their performance on underwater object detection. Without bells and whistles, evaluations on URPC2020 and UODD show that our methods outperform CNN-based detectors by a large margin. Our method with higher speeds and less parameters still performs better than transformer-based detectors. Our code and DFUI dataset can be found in https://github.com/xiaoDetection/Learning-Heavily-Degraed-Prior

Effect of Surcharge on the Stability of Rock Slope under Complex Conditions

In this paper, a general analytical expression for the factor of safety of the rock slope against plane failure is proposed, incorporating most of the practically occurring under complex conditions such as depth of tension crack, depth of water in tension crack, seismic loads and surcharge. Several special cases of this expression are established, which can be found similarly to those reported in the literature. A detailed parametric analysis is presented to study the effect of surcharge on the stability of the rock slope for practical ranges of main parameters such as depth of tension crack, depth of water in tension crack, the horizontal seismic coefficient and the vertical seismic coefficient. The parametric analysis has shown that the factor of safety of the rock slope decreases with increase in surcharge for the range of those parameters in this paper. It is also shown that the horizontal seismic coefficient is the most important factor which effects on the factor of safety in the above four influence factors. The general analytical expression proposed in this paper and the results of the parametric analysis can be used to carry out a quantitative assessment of the stability of the rock slopes by engineers and researchers

Spatio-Temporal Adaptive Embedding Makes Vanilla Transformer SOTA for Traffic Forecasting

With the rapid development of the Intelligent Transportation System (ITS), accurate traffic forecasting has emerged as a critical challenge. The key bottleneck lies in capturing the intricate spatio-temporal traffic patterns. In recent years, numerous neural networks with complicated architectures have been proposed to address this issue. However, the advancements in network architectures have encountered diminishing performance gains. In this study, we present a novel component called spatio-temporal adaptive embedding that can yield outstanding results with vanilla transformers. Our proposed Spatio-Temporal Adaptive Embedding transformer (STAEformer) achieves state-of-the-art performance on five real-world traffic forecasting datasets. Further experiments demonstrate that spatio-temporal adaptive embedding plays a crucial role in traffic forecasting by effectively capturing intrinsic spatio-temporal relations and chronological information in traffic time series.Comment: Accepted as CIKM2023 Short Pape

Unusual Spin Polarization in the Chirality- Induced Spin Selectivity

Chirality-induced spin selectivity (CISS) refers to the fact that electrons get spin polarized after passing through chiral molecules in a nanoscale transport device or in photoemission experiments. In CISS, chiral molecules are commonly believed to be a spin filter through which one favored spin transmits and the opposite spin gets reflected; that is, transmitted and reflected electrons exhibit opposite spin polarization. In this work, we point out that such a spin filter scenario contradicts the principle that equilibrium spin current must vanish. Instead, we find that both transmitted and reflected electrons present the same type of spin polarization, which is actually ubiquitous for a two-terminal device. More accurately, chiral molecules play the role of a spin polarizer rather than a spin filter. The direction of spin polarization is determined by the molecule chirality and the electron incident direction. And the magnitude of spin polarization relies on local spin???orbit coupling in the device. Our work brings a deeper understanding on CISS and interprets recent experiments, for example, the CISS-driven anomalous Hall effect