A fast variational fusion model based on partial differential equations (PDEs) is presented for pansharpening. The functional framework consists of several energy terms. The gradient energy term is created by calculating the gradient vector field of the panchromatic image and the geometry of the pan is injected into the multi-spectral bands. The radiometric reduction energy term and the channel correlation energy term are defined to decrease the radiometric distortion and preserve the correlation of multi-spectral channels while enforcing the inter-bands coherence. Inspired by the shock-filtering model, an inverse diffusion term for image enhancement is put to PDEs which are deduced by minimizing the energy functional. In comparison with the state-of-the-art fusion approaches based onà trous wavelet and non-sampled contourlet, our model can obtain the fused image with a high spatial or spectral quality by adjusting the weight coefficients of the energy terms. It can also achieve a rather good trade-off between the spatial resolution improvement and the spectral quality preserving. Our model's computational complexity for one time step is only O(N ).
This paper presents a novel highly efficient 5-stage RF rectifier in SMIC 65 nm standard CMOS process. To improve power conversion efficiency (PCE) and reduce the minimum input voltage, a hybrid threshold self-compensation approach is applied in this proposed RF rectifier, which combines the gate-bias threshold compensation with the body-effect compensation. The proposed circuit uses PMOSFET in all the stages except for the first stage to allow individual body-bias, which eliminates the need for triple-well technology. The presented RF rectifier exhibits a simulated maximum PCE of 30% at −16.7 dBm (20.25 µW) and produces 1.74 V across 0.5 MΩ load resistance. In the circumstances of 1 MΩ load resistance, it outputs 1.5 V DC voltage from a remarkably low input power level of −20.4 dBm (9 µW) RF input power with PCE of about 25%.
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.