In this paper, the load modulation process of a Doherty power amplifier (DPA) is analyzed to address the issue of why designed DPAs have a very low efficiency in the back-off state in some cases. A general formula of the real load modulation process is also given for analyzing the load modulation of a peak PA matching network. This provides a new perspective for improving the back-off efficiency of a DPA. To improve the power back-off efficiency of a DPA, a dual load-modulated DPA (D-DPA) design method is proposed. The core principle of the proposed design method is to control the load modulation process from the carrier PA to the peaking PA based on the design method of the traditional two-way DPA. The efficiency of the peaking PA in the back-off region is enhanced, thereby improving the efficiency in the entire back-off region of the DPA. Based on the proposed design method, a D-DPA operating at 2 GHz is designed and fabricated. The test results show that the saturated output power and gain are 43.7 dBm and 9.7 dB, respectively, while the efficiency at 6 dB output power back-off is 59.2%. The designed D-DPA eliminates the efficiency pit of the traditional two-way DPA in the output power back-off region.
Achieving high-quality surface profiles under strong ambient light is challenging in fringe projection profilometry (FPP) since ambient light inhibits functional illumination from exhibiting sinusoidal stripes with high quantization levels. Conventionally, large-step phase shifting approaches are presented to enhance the anti-interference capability of FPP, but the image acquisition process in these approaches is highly time-consuming. Inspired by the promising performance of deep learning in optical metrology, we propose a deep learning-enabled anti-ambient light (DLAL) approach that can help FPP extract phase distributions from a single fringe image exposed to unbalanced lighting. In this work, the interference imposed by ambient light on FPP is creatively modeled as ambient light-induced phase error (ALPE). Guided by the ALPE model, we generate the dataset by precisely adjusting the stripe contrast before performing active projection, overcoming the challenge of collecting a large sample of fringe images with various illumination conditions. Driven by the novel dataset, the generated deep learning model can effectively suppress outliers among surface profiles in the presence of strong ambient light, thereby implementing high-quality 3D surface imaging. Experimentally, we verify the effectiveness and adaptability of the proposed DLAL approach in both indoor and outdoor scenarios with strong irradiation.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.