Role of low Gd concentrations in the maximum pulse duration for thermally induced switching.

Thermally induced magnetization switching (TIMS) relying solely on a single laser without any applied magnetic field is a key research direction of current spintronics. Most studies on TIMS so far have focused on GdFeCo with Gd concentration above 20%. In this work, we observe the TIMS at low Gd concentration excited by picosecond laser through atomic spin simulations. The results show that the maximum pulse duration for switching can be increased by an appropriate pulse fluence at the intrinsic damping in low Gd concentrations. At the appropriate pulse fluence, TIMS with pulse duration longer than one picosecond is possible for Gd concentration of only 12%. Our simulation results provide new insights for the exploration of the physical mechanism of ultrafast TIMS.

Detection of Performance of Hybrid Rice Pot-Tray Sowing Utilizing Machine Vision and Machine Learning Approach.

Monitoring the performance of hybrid rice seeding is very important for the seedling production line to adjust the sowing amount of the seeding device. The objective of this paper was to develop a system for the real-time online monitoring of the performance of hybrid rice seeding based on embedded machine vision and machine learning technology. The embedded detection system captured images of pot trays that passed under the illuminant cabinet installed in the seedling production line. This paper proposed an algorithm for fixed threshold segmentation by analyzing the images with the exploratory analysis method. With the algorithm, the grid image and seed image were extracted from the pot tray image. The paper also proposed a method for obtaining pixel coordinates of gridlines from the grid image. Binary images of seeds were divided into small pieces, according to the pixel coordinates of gridlines. Each piece corresponded to a cell on the pot tray. By scanning the contours in each piece of the image to check whether there were seeds in the cell, the number of empty cells was counted and then used to calculate the missing rate of hybrid rice seeding. The seed number sowed in pot trays was monitored while using the machine learning approach. The experimental results demonstrated that it would consume 4.863 s for the device to process an image, which allowed for the detection of the missing rate and seed number in real-time at the rate of 500 trays per hour (7.2 s per tray). The average accuracy of the detection of missing rates of a seedling production line was 94.67%. The average accuracy of the detection of seed numbers was 95.68%.

Anderson localization in metallic nanoparticle arrays.

Anderson localization has been observed in various types of waves, such as matter waves, optical waves and acoustic waves. Here we reveal that the effect of Anderson localization can be also induced in metallic nonlinear nanoparticle arrays excited by a random electrically driving field. We find that the dipole-induced nonlinearity results in ballistic expansion of dipole intensity during evolution; while the randomness of the external driving field can suppress such an expansion. Increasing the strength of randomness above the threshold value, a localized pattern of dipole intensity can be generated in the metallic nanoparticle arrays. By means of statistics, the mean intensity distribution of the dipoles reveals the formation of Anderson localization. We further show that the generated Anderson localization is highly confined, with its size down to the scale of incident wavelength. The reported results might facilitate the manipulations of electromagnetic fields in the scale of wavelength.

Machine Learning Approaches for Rice Seedling Growth Stages Detection.

Recognizing rice seedling growth stages to timely do field operations, such as temperature control, fertilizer, irrigation, cultivation, and disease control, is of great significance of crop management, provision of standard and well-nourished seedlings for mechanical transplanting, and increase of yield. Conventionally, rice seedling growth stage is performed manually by means of visual inspection, which is not only labor-intensive and time-consuming, but also subjective and inefficient on a large-scale field. The application of machine learning algorithms on UAV images offers a high-throughput and non-invasive alternative to manual observations and its applications in agriculture and high-throughput phenotyping are increasing. This paper presented automatic approaches to detect rice seedling of three critical stages, BBCH11, BBCH12, and BBCH13. Both traditional machine learning algorithms and deep learning algorithms were investigated the discriminative ability of the three growth stages. UAV images were captured vertically downward at 3-m height from the field. A dataset consisted of images of three growth stages of rice seedlings for three cultivars, five nursing seedling densities, and different sowing dates. In the traditional machine learning algorithm, histograms of oriented gradients (HOGs) were selected as texture features and combined with the support vector machine (SVM) classifier to recognize and classify three growth stages. The best HOG-SVM model obtained the performance with 84.9, 85.9, 84.9, and 85.4% in accuracy, average precision, average recall, and F1 score, respectively. In the deep learning algorithm, the Efficientnet family and other state-of-art CNN models (VGG16, Resnet50, and Densenet121) were adopted and investigated the performance of three growth stage classifications. EfficientnetB4 achieved the best performance among other CNN models, with 99.47, 99.53, 99.39, and 99.46% in accuracy, average precision, average recall, and F1 score, respectively. Thus, the proposed method could be effective and efficient tool to detect rice seedling growth stages.