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High-precision piezoresistive pressure sensors play a significant role in aerospace, automotive, and other fields. Nonlinear error is the key factor that restricts the improvement of the sensor precision. A mathematical model for evaluating the sensor's nonlinear error is established, based on which a piezoresistor sensitivity matching method is proposed to suppress the nonlinear error. By adjusting the piezoresistors' structure and position on the sensing membrane, four piezoresistors with equal sensitivity are obtained, and theoretical quasi-zero nonlinear error is achieved. To verify the design, sensor prototypes are fabricated utilizing the MEMS technology. After sensor packaging, a cylindrical absolute pressure sensor featuring a 4 mm diameter with a range from 0 to 100 kPa is acquired. The experimental results demonstrate the excellent performance of the proposed sensor, which indicates a nonlinear error as low as ±0.004%FS. Besides, the proposed sensor has a sensitivity of 1.6810 mV/kPa, a hysteresis of 0.025%, a repeatability of 0.015%, a zero drift of 0.03%FS, and a 3 dB frequency from 0 to 121.82 kHz. Moreover, the prototype is tested in the Mach 4 wind tunnel, and the measurement error between the proposed sensor and the true pressure is ±0.98%. This paper provides key sensing technology for high-precision surface pressure analysis of aircraft.
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http://dx.doi.org/10.1038/s41378-025-00959-7 | DOI Listing |
IEEE Trans Image Process
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3D imaging based on phase-shifting structured light is widely used in industrial measurement due to its non-contact nature. However, it typically requires a large number of additional images (multi-frequency heterodyne (M-FH) method) or introduces intensity features that compromise accuracy (space domain modulation phase-shifting (SDM-PS) method) for phase unwrapping, and it remains sensitive to motion. To overcome these issues, this article proposes a nonlinear phase coding-based stereo phase unwrapping (NPC-SPU) method that requires no additional patterns while maintaining measurement accuracy.
View Article and Find Full Text PDFIEEE Trans Cybern
September 2025
This article presents an approach to ensure the robust forward invariance of safe sets for sampled-data input nonlinear dynamical systems with model uncertainties. We first design a continuous-time composite controller structure for the uncertain system by integrating an uncertainty compensation term and a state feedback term. The uncertainty compensation term is generated by a nonlinear observer, while the feedback term is subject to linear constraints on a high order control barrier function (HOCBF) which effectively mitigates the adverse effects of the uncertainty observation error on the safety constraints.
View Article and Find Full Text PDFChaos
September 2025
Graduate School of Information Science and Technology, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku 113-8656, Tokyo, Japan.
The output-side behaviors of typical digital computing systems, such as simulated neural networks, are generally unaffected by the act of observation; however, this is not the case for the burgeoning field of physical reservoir computers (PRCs). Observer dynamics can limit or modify the natural state information of a PRC in many ways, and among the most common is the conversion from analog to digital data needed for calculations. Here, to aid in the development of novel PRCs, we investigate the effects of bounded, quantized observations on systems' natural computational abilities.
View Article and Find Full Text PDFIEEE Trans Med Imaging
September 2025
Photon-counting computed tomography (PCCT) based on photon-counting detectors (PCDs) represents a cutting-edge CT technology, offering higher spatial resolution, reduced radiation dose, and advanced material decomposition capabilities. Accurately modeling complex and nonlinear PCDs under limited calibration data becomes one of the challenges hindering the widespread accessibility of PCCT. This paper introduces a physics-ASIC architecture-driven deep learning detector model for PCDs.
View Article and Find Full Text PDFACS Sustain Resour Manag
August 2025
Aragón Institute for Engineering Research (I3A), Thermal Engineering and Energy Systems Group, University of Zaragoza, Agustín de Betancourt Building, C/María de Luna 3, Zaragoza 50018, Spain.
Global and local sensitivity analyses are essential for identifying key parameters in life cycle assessment models. However, due to limited information on parameter uncertainty, they are often overlooked. This paper's objective is to address this gap by proposing a methodological framework for defining input sensitivity, for midpoint and end point indicators, and a quantitative approach for determining input uncertainties.
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