The Solar Activity Monitor Network – SAMNet

The Solar Activity Magnetic Monitor (SAMM) Network (SAMNet) is a future UK-led international network of ground-based solar telescope stations. SAMNet, at its full capacity, will continuously monitor the Sun’s intensity, magnetic, and Doppler velocity fields at multiple heights in the solar atmosphere (from photosphere to upper chromosphere). Each SAMM sentinel will be equipped with a cluster of identical telescopes each with a different magneto-optical filter (MOFs) to take observations in K I, Na D, and Ca I spectral bands. A subset of SAMM stations will have white-light coronagraphs and emission line coronal spectropolarimeters. The objectives of SAMNet are to provide observational data for space weather research and forecast. The goal is to achieve an operationally sufficient lead time of e.g., flare warning of 2–8 h and provide many sought-after continuous synoptic maps (e.g., LoS magnetic and velocity fields, intensity) of the lower solar atmosphere with a spatial resolution limited only by seeing or diffraction limit, and with a cadence of 10 min. The individual SAMM sentinels will be connected to their master HQ hub where data received from all the slave stations will be automatically processed and flare warning issued up to 26 h in advance

A Specific Combination Scheme for Modulation Identification of Mixed Modulation Signal

In this paper, we propose a specific combination scheme for modulation identification of mixed modulation signal based on decision theory and the tree classifier. In order to reduce the noise interference and improve the accuracy of modulation identification, we adopt the joint modulation signal characteristics with eigenvector made up of the number of subcarrier signal, envelope variance of mean normalization and the statistical value of subcarrier signal instantaneous amplitude distribution in identification of external modulation and internal modulation. Simulation results show that modulation identification ratio is close to 90 % when the SNR is 6 dB. And the proposed scheme outperforms the existing mixed modulation scheme

ACA-Net: An Adaptive Convolution and Anchor Network for Metallic Surface Defect Detection

Metallic surface defect detection is critical to ensure the quality of industrial products. Recently, human-advanced surface defect detection algorithms have been proposed. Most of these algorithms rely on convolutional neural networks (CNN) and an anchoring scheme. However, a convolution unit only samples the input feature maps at fixed shapes and locations. Similarly, a set of anchors are uniformly predefined with fixed scales and shapes, which increases the difficulties of bounding box regression. Therefore, we propose an adaptive convolution and anchor network for metallic surface defect detection, named ACA-Net. Specifically, an adaptive convolution and anchor (ACA) module is proposed, which mainly consists of adaptive convolution and an adaptive anchor. Firstly, an adaptive convolution module (ACM) is designed, which adaptively determines the location and shape of each convolution unit. In addition, a multi-scale feature adaptive fusion (MFAF) is proposed, which is used in ACM to extract and integrate multi-scale features. Then, an adaptive anchor module (AAM) is proposed to yield more suitable anchor boxes by adaptively adjusting shapes. Extensive experiments on NEU-DET dataset and GC10 dataset validate the performance of the proposed approach. ACA-Net achieves 1.8% on NEU-DET dataset higher Average Precision (AP) than GA-RetinaNet. Furthermore, the proposed ACA module is also adopted in GA-Faster R-CNN, improving the AP by 1.2% on NEU-DET dataset

Special Structures of Sodium Layer Observed in the Daytime Over Beijing, China

In this paper the observation of sodium (Na) layer in mesosphere and lower thermosphere (MLT) region over complete diurnal cycles based on broadband Na lidar at Yanqing Station, Beijing, China (40.5°N,116°E ) was reported. Faraday filters with dual-channel design were used in the lidar receiving unit to suppress the strong background light in the daytime, which allow observation of Na layer with an acceptable signal-to-noise ratio (SNR) under sunlit condition. Several special structures of Na layer observed in the daytime was discussed. The simultaneous continuous observation of zonal wind by meteor radar was presented for comparison. These observation results can provide direct and reliable supports for the study of mesopause dynamics and solar effect on Na layer

Special Structures of Sodium Layer Observed in the Daytime Over Beijing, China

In this paper the observation of sodium (Na) layer in mesosphere and lower thermosphere (MLT) region over complete diurnal cycles based on broadband Na lidar at Yanqing Station, Beijing, China (40.5°N,116°E ) was reported. Faraday filters with dual-channel design were used in the lidar receiving unit to suppress the strong background light in the daytime, which allow observation of Na layer with an acceptable signal-to-noise ratio (SNR) under sunlit condition. Several special structures of Na layer observed in the daytime was discussed. The simultaneous continuous observation of zonal wind by meteor radar was presented for comparison. These observation results can provide direct and reliable supports for the study of mesopause dynamics and solar effect on Na layer

ACA-Net: An Adaptive Convolution and Anchor Network for Metallic Surface Defect Detection

Metallic surface defect detection is critical to ensure the quality of industrial products. Recently, human-advanced surface defect detection algorithms have been proposed. Most of these algorithms rely on convolutional neural networks (CNN) and an anchoring scheme. However, a convolution unit only samples the input feature maps at fixed shapes and locations. Similarly, a set of anchors are uniformly predefined with fixed scales and shapes, which increases the difficulties of bounding box regression. Therefore, we propose an adaptive convolution and anchor network for metallic surface defect detection, named ACA-Net. Specifically, an adaptive convolution and anchor (ACA) module is proposed, which mainly consists of adaptive convolution and an adaptive anchor. Firstly, an adaptive convolution module (ACM) is designed, which adaptively determines the location and shape of each convolution unit. In addition, a multi-scale feature adaptive fusion (MFAF) is proposed, which is used in ACM to extract and integrate multi-scale features. Then, an adaptive anchor module (AAM) is proposed to yield more suitable anchor boxes by adaptively adjusting shapes. Extensive experiments on NEU-DET dataset and GC10 dataset validate the performance of the proposed approach. ACA-Net achieves 1.8% on NEU-DET dataset higher Average Precision (AP) than GA-RetinaNet. Furthermore, the proposed ACA module is also adopted in GA-Faster R-CNN, improving the AP by 1.2% on NEU-DET dataset

Lidar Technology Based on Fiber System and its Application

The sodium atom existed in the metal layer of the earth’s atmosphere has a high atomic number density and a large scattering cross section. Sodium layer can act as a good tracer for atmospheric detection in the middle and lower-thermosphere (MLT) region. The sodium fluorescence lidar uses ultrashort pulsed laser to excite sodium atoms, which enabling simultaneous detection of wind and temperature in the middle and upper atmosphere. This paper reports on the development of sodium fluorescence laser radar in recent years, especially the integration of fiber-coupled optical switches and fiber-coupled acousto-optic frequency modulation technologies, which greatly improved the stability and reliability of lidar system and reduced the maintenance of lidar operation, laying a good foundation for the application of lidar observations under harsh environments. This technology has been applied to the sodium wind/temperature lidar in Yangbajing, Tibet and has been running stably for a long time