We propose and demonstrate a Michelson interferometer modulator with integrated Bragg reflectors on a silicon-rich nitride–thin-film lithium niobate hybrid platform. High-reflectivity Bragg reflectors are placed at the ends of both arms, which double the electro-optic (E-O) interaction length and reduce the velocity mismatch between the microwave and optical wave. The presented Michelson interferometer modulator achieves a measured half-wave voltage length product as low as 1.06 V cm and high-speed modulation up to 70 Gbps. A 3-dB E-O bandwidth beyond 40 GHz is also achieved, which is, to the best of our knowledge, the highest modulation bandwidth of Michelson interferometer modulators.

Background Ali CMB polarization telescope (AliCPT) project is a well-timed and well-planned ground-based CMB project in Ali (Nagri) area of Tibet, China. It has been approved at the end of 2016. Aims To give an introduction to the detection technology of AliCPT. Method The whole receiver of AliCPT is introduced and discussed, including the optics, the cryostat, the preliminary design of focal plane TES bolometers, multiplexing SQUID readout, and so on. Conclusions The raw sensitivity of r will reach below 0.001 by 10-year observation as AliCPT project being carried out and upgraded.

Burn scar extraction using remote sensing data is an efficient way to precisely evaluate burn area and measure vegetation recovery. Traditional burn scar extraction methodologies have no well effect on burn scar image with blurred and irregular edges. To address these issues, this paper proposes an automatic method to extract burn scar based on Level Set Method (LSM). This method utilizes the advantages of the different features in remote sensing images, as well as considers the practical needs of extracting the burn scar rapidly and automatically. This approach integrates Change Vector Analysis (CVA), Normalized Difference Vegetation Index (NDVI) and the Normalized Burn Ratio (NBR) to obtain difference image and modifies conventional Level Set Method Chan-Vese (C-V) model with a new initial curve which results from a binary image applying K-means method on fitting errors of two near-infrared band images. Landsat 5 TM and Landsat 8 OLI data sets are used to validate the proposed method. Comparison with conventional C-V model, OSTU algorithm, Fuzzy C-mean (FCM) algorithm are made to show that the proposed approach can extract the outline curve of fire burn scar effectively and exactly. The method has higher extraction accuracy and less algorithm complexity than that of the conventional C-V model.