Adaptive compressed photon counting 3D imaging based on wavelet trees and depth map sparse representation.

We demonstrate a photon counting 3D imaging system with short-pulsed structured illumination and a single-pixel photon counting detector. The proposed multiresolution photon counting 3D imaging technique acquires a high-resolution 3D image from a coarse image and details at successfully finer resolution sampled along the wavelet trees and their depth map sparse representations. Both the required measurements and the reconstruction time can be significant reduced, which makes the proposed technique suitable for scenes with high spatial resolution. The experimental results indicate that both the reflectivity and depth map of a scene at resolutions up to 512×512 pixels can be acquired and retrieved with practical times as low as 17.5 seconds. In addition, we demonstrate that this technique has ability to image in presence of partially-transmissive occluders, and to directly acquire novelty images to find changes in a scene.

Colored adaptive compressed imaging with a single photodiode.

Computational ghost imaging is commonly used to reconstruct grayscale images. Currently, however, there is little research aimed at reconstructing color images. In this paper, we theoretically and experimentally demonstrate a colored adaptive compressed imaging method. Benefiting from imaging in YUV color space, the proposed method adequately exploits the sparsity of the U, V components in the wavelet domain, the interdependence between luminance and chrominance, and human visual characteristics. The simulation and experimental results show that our method greatly reduces the measurements required and offers better image quality compared to recovering the red (R), green (G), and blue (B) components separately in RGB color space. As the application of a single photodiode increases, our method shows great potential in many fields.

Adaptive compressed sampling based on extended wavelet trees.

The theory of compressed sensing (CS) indicates that a signal that is sparse or compressible can be recovered from a relatively small number of nonadaptive linear measurements that is far below the Nyquist-Shannon limit. However, CS suffers from a huge stored and computational overhead when dealing with images of high resolution, taking tens of minutes or longer. In this work, we extend the concept of wavelet trees by adding the sibling relationship and propose an imaging strategy named adaptive compressed sampling based on extended wavelet trees (EWT-ACS). Exploiting both parent-children relationship and sibling relationship in extended wavelet trees, EWT-ACS predicts the locations of significant coefficients adaptively and samples the significant coefficients using a binary digital micromirror device directly. The simulation and experimental results reveal that the proposed strategy breaks through the limitation in CS, and the reconstruction time is reduced significantly. Due to its single-pixel detection mechanism, EWT-ACS shows great potential in many imaging applications.

Exploring the Role of an Electrolyte Additive in Suppressing Surface Reconstruction of a Ni-Rich NMC Cathode at Ultrahigh Voltage via Enhanced In Situ and Operando Characterization Methods.

Vinylene carbonate (VC) is a widely used electrolyte additive in lithium-ion batteries for enhanced solid electrolyte interphase formation on the anode side. However, the cathode electrolyte interphase (CEI) formation with VC has received a lot less attention. This study presents a comprehensive investigation employing advanced in situ/operando-based Raman and X-ray absorption spectroscopy (XAS) to explore the effect of electrolyte composition on the CEI formation and suppression of surface reconstruction of LixNiyMnzCo1-y-zO2 (NMC) cathodes. A novel chemical pathway via VC polymerization is proposed based on experimental results. In situ Raman spectra revealed a new peak at 995 cm-1, indicating the presence of C-O semi-carbonates resulting from the radical polymerization of VC. Operando Raman analysis unveiled the formation of NiO at 490 cm-1 in the baseline system under ultrahigh voltage (up to 5.2 V). However, this peak was conspicuously absent in the VC electrolyte, signifying the effectiveness of VC in suppressing surface reconstruction. Further investigation was carried out utilizing in situ XAS compared X-ray absorption near edge structure spectra from cells of 3 and 20 cycles in both electrolytes at different operating voltages. The observed shift at the Ni K-edge confirmed a more substantial reduction of Ni in the baseline electrolyte compared to that in the VC electrolyte, thus indicating less CEI protection in the former. A sophisticated extended X-ray absorption fine structure analysis quantitatively confirmed the effective suppression of rock-salt formation with the VC electrolyte during the charging process, consistent with the operando Raman results. The in situ XAS results thus provided additional support for the key findings of this study, establishing the crucial role of VC polymerization in enhancing CEI stability and mitigating surface reconstruction on NMC cathodes. This work clarifies the relationship between the enhanced CEI layer and NMC degradation and inspires rational electrolyte design for long-cycling NMC cathodes.

Soil nitrogen functional transformation microbial genes response to biochar application in different irrigation paddy field in southern China.

Growing evidence points to the controlled irrigation (CI) and biochar application (BA) having agricultural economic value and ecological benefits, but their synergistic effect and microbial mechanism of nitrogen conversion remain unknown in paddy fields. The effects of different BA (0, 20, 40 t/hm2) on the soil nitrogen functional transformation microbial genes (nifH, AOA-amoA, AOB-amoA) in different irrigation (CI, flooding irrigation) were clarified. After one seasonal growth of paddy, the correlation between the abundance of functional genes OUT and soil nitrogen transformation environment factors during the typical growth period was analyzed. High-throughput sequencing results illustrated that the application of CC (40 t/hm2 biochar) increased the nifH genes bacterial community abundance; the abundance of dominant microorganism increased by 79.68~86.19%. Because biochar can potentially control the rates of N cycling in soil systems by adsorbing ammonia and increasing NH4+ storage, it increased soil NH4+-N and NO3--N content by 60.77% and 26.14%, improving microbial nitrogen fixation. Rare species Nitrosopumilus, Nitrosococcus, and Methylocystis appeared in biochar treatments group, which increased the diversity of microbial in paddy. The combined use of CI and BA affected soil inorganic nitrogen content, temperature (T), pH, Eh, etc., which affected urease, urea hydrolysis, and nitrogen functional transformation microorganism genes. Correlation analysis shows that soil NH4+-N, T, and Eh, respectively, are significant factors for the formation of nifH, AOA-amoA, and AOB-amoA soil bacterial communities, respectively. This study suggests that to maintain the biodiversity of soil and realize the sustainable development of rice cultivation, CI is of great importance in combination with BA.

Controlled release urea improves rice production and reduces environmental pollution: a research based on meta-analysis and machine learning.

To reveal the comprehensive impacts of controlled release urea (CRU) on rice production, nitrogen (N) loss, and greenhouse gas (GHG) emissions, a research based on global meta-analysis and machine learning (ML) was conducted. The results revealed that the CRU application instead of conventional fertilizer can increase rice yield, N use efficiency (NUE), and net benefits by 5.24%, 20.18%, and 9.30%, respectively, under the same amount of N. Furthermore, the emission of N2O and CH4, global warming potential (GWP), the loss of N leaching, and NH3 volatilization were respectively reduced by 25.64%, 18.33%, 21.10%, 14.90%, and 35.88%. The enhancing effects of CRU on rice yield and NUE were greater when the nitrogen application rate was 150 kg N ha-1. Nevertheless, the reducing effects of CRU on GHG emission reduction, nitrogen leaching, and NH3 volatilization was greater at high nitrogen application rate (≥150 kg ha-1). Mitigating effects of CRU on N2O and CH4 emission were significant when soil pH ≥ 6, while CRU posed a measurable effect on reducing nitrogen leaching and NH3 volatilization in paddy fields with soil organic carbon lower than 15 g kg-1 and pH lower than 6. Based on the data collected from meta-analysis, the results of ML demonstrated that it was feasible to use soil data and N application rate to predict N losses in rice fields under CRU. The performance of random forest is better than multilayer perceptron regression in predicting N losses from paddy fields. Thus, it is necessary to promote the application of CRU in paddy fields, especially in coarse soil, in which scenario the environmental pollution would be decreased and the rice yields, NUE, and net benefits would be increased. Meanwhile, machine learning models can be used to predict N losses in CRU paddy fields.