Mass measurement under medium vacuum in optically levitated nanoparticles based on Maxwell speed distribution law.

As one of the directions of optical levitation technology, the mass measurement of micro-nano particles has always been a research hotspot in extremely weak mechanical measurements. When nanoscale particles are trapped in an optical trap, parameters such as density, diameter, and shape are unknown. Here we propose what we believe to be a new method to measure mass by fitting particle motion information to the Maxwell speed distribution law, with an accuracy better than 7% at 10 mbar. This method has the characteristics of requiring no external driving force, no precise natural frequency, no prior information such as density, and non-destructive testing within the medium vacuum range. With the increasing iterations, the uncertainty of mass measurement is reduced, and the accuracy of mass measurement of levitated particles is verified under multiple air pressures. It provides what we believe is a new method for the future non-destructive testing of nanoscale particles, and provides an apparently new way for the sensing measurement and metrology application fields of levitation dynamics systems.

Compact and high-reliability fiber-optic open-loop gyroscope enabled by an in-fiber polarizer.

The performance of an open-loop fiber-optic gyroscope is strongly dependent on the optical characteristics of its polarizer. Here we report the implementation of an in-house fabricated 45° tilted-fiber-grating-based polarizer, for the first time on an ultra-fine diameter polarization-maintaining fiber platform in an open-loop fiber-optic gyroscope. This special in-line polarizer is proven to have the merits of high extinction ratio, broad spectrum, bendability, stretchability, temperature insensitivity, and high reliability, all of which make it a perfect match for practical fiber optic gyros that need to be packaged compactly without affecting performance. Our prototype fiber optic gyroscope has a compact volume of only ϕ35 × 20 mm2, achieving a bias instability of less than 0.1 °/h, full temperature bias stability of less than 1 °/h, and scale factor linearity of better than 200 ppm. This compact and high-performance fiber gyro enabled by TFG polarizer may promise great potential in the field of automation and control.

Lightweight cotton diseases real-time detection model for resource-constrained devices in natural environments.

Cotton, a vital textile raw material, is intricately linked to people's livelihoods. Throughout the cotton cultivation process, various diseases threaten cotton crops, significantly impacting both cotton quality and yield. Deep learning has emerged as a crucial tool for detecting these diseases. However, deep learning models with high accuracy often come with redundant parameters, making them challenging to deploy on resource-constrained devices. Existing detection models struggle to strike the right balance between accuracy and speed, limiting their utility in this context. This study introduces the CDDLite-YOLO model, an innovation based on the YOLOv8 model, designed for detecting cotton diseases in natural field conditions. The C2f-Faster module replaces the Bottleneck structure in the C2f module within the backbone network, using partial convolution. The neck network adopts Slim-neck structure by replacing the C2f module with the GSConv and VoVGSCSP modules, based on GSConv. In the head, we introduce the MPDIoU loss function, addressing limitations in existing loss functions. Additionally, we designed the PCDetect detection head, integrating the PCD module and replacing some CBS modules with PCDetect. Our experimental results demonstrate the effectiveness of the CDDLite-YOLO model, achieving a remarkable mean average precision (mAP) of 90.6%. With a mere 1.8M parameters, 3.6G FLOPS, and a rapid detection speed of 222.22 FPS, it outperforms other models, showcasing its superiority. It successfully strikes a harmonious balance between detection speed, accuracy, and model size, positioning it as a promising candidate for deployment on an embedded GPU chip without sacrificing performance. Our model serves as a pivotal technical advancement, facilitating timely cotton disease detection and providing valuable insights for the design of detection models for agricultural inspection robots and other resource-constrained agricultural devices.

Yoctonewton force detection based on optically levitated oscillator.

Optically levitated oscillators in high vacuum have excellent environmental isolation and low mass compared with conventional solid-state sensors, which makes them suitable for ultrasensitive force detection. The force resolution usually scales with the measurement bandwidth, which represents the ultimate detection capability of the system under ideal conditions if sufficient time is provided for measurement. However, considering the stability of a real system, a method based on the Allan variance is more reliable to evaluate the actual force detection performance. In this study, a levitated optomechanical system with a force detection sensitivity of 6.33 ± 1.62 zN/Hz1/2 was demonstrated. And for the first time, the Allan variance was introduced to evaluate the system stability due to the force sensitivity fluctuations. The force detection resolution of 166.40 ± 55.48 yN was reached at the optimal measurement time of 2751 s. The system demonstrated in this work has the best force detection performance in both sensitivity and resolution that have been reported so far for optically levitated particles. The reported high-sensitivity force detection system is an excellent candidate for the exploration of new physics such as fifth force searching, high-frequency gravitational waves detection, dark matter research and so on.