Bright room temperature single photon source at telecom range in cubic silicon carbide

Single photon emitters (SPEs) play an important role in a number of quantum information tasks such as quantum key distributions. In these protocols, telecom wavelength photons are desired due to their low transmission loss in optical fibers. In this paper, we present a study of bright single-photon emitters in cubic silicon carbide (3C-SiC) emitting in the telecom range. We find that these emitters are photostable and bright at room temperature with a count rate of ~ MHz. Together with the fact that SiC is a growth and fabrication-friendly material, our result may pave the way for its future application in quantum communication technology applications.Comment: Accepted by Nature Communication

High-efficiency generation of nanoscale single silicon vacancy defect array in silicon carbide

Color centers in silicon carbide have increasingly attracted attention in recent years owing to their excellent properties such as single photon emission, good photostability, and long spin coherence time even at room temperature. As compared to diamond which is widely used for holding Nitrogen-vacancy centers, SiC has the advantage in terms of large-scale, high-quality and low cost growth, as well as advanced fabrication technique in optoelectronics, leading to the prospects for large scale quantum engineering. In this paper, we report experimental demonstration of the generation of nanoscale $V_{Si}$ single defect array through ion implantation without the need of annealing. $V_{Si}$ defects are generated in pre-determined locations with resolution of tens of nanometers. This can help in integrating $V_{Si}$ defects with the photonic structures which, in turn, can improve the emission and collection efficiency of $V_{Si}$ defects when it is used in spin photonic quantum network. On the other hand, the defects are shallow and they are generated $\sim 40nm$ below the surface which can serve as critical resources in quantum sensing application

Fault diagnosis for rotating machinery based on multi-differential empirical mode decomposition

The fault diagnosis of rotating machinery has crucial significance for the safety of modern industry, and the fault feature extraction is the key link of the diagnosis process. As an effective time-frequency method, Empirical Mode Decomposition (EMD) has been widely used in signal processing and feature extraction. However, the mode mixing phenomenon may lead to confusion in the identification of multi frequency signals and restricts the applications of EMD. In this paper, a novel method based on Multi-Differential Empirical Mode Decomposition (MDEMD) was proposed to extract the energy distribution characteristics of fault signals. Firstly, multi-order differential signals were deduced and decomposed by EMD. Then, their energy distribution characteristics were extracted and utilized to construct the feature matrix. Finally, taking the feature matrix as input, the classifiers were applied to diagnosis the existence and severity of rotating machinery faults. Simulative and practical experiments were implemented respectively, and the results demonstrated that the proposed method, i.e. MDEMD, is able to eliminate the mode mixing effectively, and the feature matrix extracted by MDEMD has high separability and universality, furthermore, the fault diagnosis based on MDEMD can be accomplished more effectively and efficiently with satisfactory accuracy

Evaluation and optimization of a novel cascade refrigeration system driven by waste heat

Direct discharge of waste heat from internal combustion engines (ICEs) is unfavorable for the efficient and clean fuel utilization. Here, a novel combined absorption-compression cascade refrigeration cycle is proposed to efficiently capture low-grade waste heat and supply cooling capacity for food freezing in vessels or refrigerated trucks. The intention of this work lies in: i) Comprehensively evaluating the performances of the proposed system; ii) Gaining the optimal operating conditions of the system. Aimed that, analysis models of energy, exergy, economy, and environment are set up to evaluate the sweeping performances. Further, multi-objective optimization is introduced to obtain the optimal operating parameters including evaporation and condensation temperature of the low-temperature stage, generation temperature and condensation temperature of the high-temperature stage, and cascade temperature differences. By applying multi-objective optimization, the coefficient of performance and exergy efficiency of the system are elevated from 1.283 to 1.547, and 0.222 to 0.246, respectively, the discharge amount of carbon dioxide are reduced from 71.40 to 59.57 tons year−1, and annual total cost are decreased from 16,028 to 15,055 $ year−1 compared to initial operating conditions

ASSIST: Interactive Scene Nodes for Scalable and Realistic Indoor Simulation

We present ASSIST, an object-wise neural radiance field as a panoptic representation for compositional and realistic simulation. Central to our approach is a novel scene node data structure that stores the information of each object in a unified fashion, allowing online interaction in both intra- and cross-scene settings. By incorporating a differentiable neural network along with the associated bounding box and semantic features, the proposed structure guarantees user-friendly interaction on independent objects to scale up novel view simulation. Objects in the scene can be queried, added, duplicated, deleted, transformed, or swapped simply through mouse/keyboard controls or language instructions. Experiments demonstrate the efficacy of the proposed method, where scaled realistic simulation can be achieved through interactive editing and compositional rendering, with color images, depth images, and panoptic segmentation masks generated in a 3D consistent manner