A5000165736- Advanced MEMS and NEMS Technologies
- Mechanical and Optical Resonators
- Quantum-Dot Cellular Automata
- Photonic and Optical Devices
- Low-power high-performance VLSI design
- Air Quality Monitoring and Forecasting
- Remote Sensing in Agriculture
- Advancements in Semiconductor Devices and Circuit Design
- Acoustic Wave Resonator Technologies
- Advanced Fiber Optic Sensors
- Smart Agriculture and AI
- Microfluidic and Capillary Electrophoresis Applications
- Analytical Chemistry and Sensors
- Geophysics and Sensor Technology
- Experimental Learning in Engineering
- Nanowire Synthesis and Applications
- Smart Grid Energy Management
- Advanced Battery Technologies Research
- Advanced Surface Polishing Techniques
- Advanced Measurement and Metrology Techniques
- Electrowetting and Microfluidic Technologies
- Analog and Mixed-Signal Circuit Design
- Space Exploration and Technology
- Force Microscopy Techniques and Applications
- Fluid Dynamics and Heat Transfer
Zhejiang University
2021-2024
University of Bridgeport
2013-2024
Ministry of Agriculture and Rural Affairs
2021
Beijing Institute of Technology
2021
Nanjing Agricultural University
2019
University of Cincinnati
2004-2006
Shanghai Institute of Microsystem and Information Technology
1997-1998
State Key Laboratory of Transducer Technology
1998
Chinese Academy of Sciences
1998
MEMS (Microelectromechanical Systems) refers to the technology integrating electrical and mechanical components with feature size of 1~1000 microns. comb accelerometers have been successfully applied for air-bag deployment systems in automobiles. In this paper, design optimization a polysilicon surface-micromachined accelerometer is discussed. The device uses folded-beam structure enhance sensitivity. movable mass connected two anchors through folded-beams. There are fingers extruding from...
Timely and accurate cropland information at large spatial scales can improve crop management support the government in decision making. Mapping extent distribution of crops on a scale is challenging work due to variability. A multi-task spatiotemporal deep learning model, named LSTM-MTL, was developed this study for large-scale rice mapping by utilizing time-series Sentinel-1 SAR data. The model showed reasonable classification accuracy major production areas U.S. (OA = 98.3%, F1 score...
Accurate crop yield estimation is important for global food security. Data-driven deep learning approaches have shown great potential agricultural system monitoring, but are limited by their out-of-sample prediction failure and low interpretability. How to embed knowledge into models address the above challenges remains an open question. In this study, we developed a model named PSNet following concept of hierarchical levels estimate county-level yield. The mainly consists PotentialNet...
Accurately modeling the impacts of climate stress on crop growth and yield is crucial for ensuring food security. Data-driven models are increasingly utilized estimation because they can learn effective features from vast amounts remote sensing meteorological data. However, extreme conditions have few labels available these to interaction in responses. The response crops often exhibits varied delays which captured observations. In this study, we explicitly encode time lag effect quantified...
A dual-mode built-in self-test (BIST) scheme which partitions the fixed (instead of movable) capacitance plates a capacitive microelectromechanical system (MEMS) device is proposed. The BIST technique divides plate(s) at each side movable microstructure into three portions: one for electrostatic activation and other two equal portions sensing. Due to such partitioning method, can be applied surface- bulk-micromachined MEMS devices technologies. Further, sensitivity symmetry dual modes based...