The digital elevation model (DEM) serves as a vital data source for surface 3D modeling. Due to the limitations in sampling conditions and cost constraints, we usually obtain unevenly distributed and relatively sparse sampling points. To reconstruct a complete DEM of the sampling area, we need to utilize spatial interpolation algorithms. However, traditional spatial interpolation methods typically have lower model complexity and often involve a large number of iterative calculations to approximate the points to be interpolated. This often results in significant interpolation errors and low real-time performance. We propose a multi-scale conditional generative adversarial network (multi-scale cGAN) with adaptive joint loss weights. In addition, during the model training process, we design a joint loss function that incorporates generator adversarial loss, content loss, and perceptual loss, with the ability to adaptively adjust the weight coefficients of each component, thereby optimizing model training and further improving its generalization and generation ability. The experimental results demonstrate that compared with the traditional spatial interpolation algorithm and other typical deep learning–based models, the interpolation error on typical land DEM data (including slopes, valleys, and ridges) is smaller, and the interpolated image has the highest clarity and similarity compared with the original image. Overall, our approach demonstrates high robustness and low error when dealing with DEM spatial interpolation tasks in complex terrain environments while also possessing the potential for expansion into various terrain environment DEM reconstruction applications.
Modular multiplication is a basic operation in public key cryptosystems, like RSA and elliptic curve cryptography (ECC).There are many algorithms to speed up its calculation.Among them, Montgomery algorithm is the most efficient method for avoiding expensive divisions.Recently, due to the increasing use of diverse embedded systems, variable precision modular multiplications with scalable architectures gain more and more attentions.In this paper, we propose a new word-based implementation of Montgomery modular multiplication.A predict policy is incorporated with a scalable architecture to reduce area cost and time latency.Compared with other scalable designs, our area-time product is the best among all, with little memory overhead.
Idioms are those that are used in particular form, with a particular meaning，most widely used language.Idioms have a long history both in China and west.The different culture has bred the ethnic characteristics idioms，thus the use of idioms is deeply effected by the geographical environment, national characteristics, historical origin, religious beliefs,Can deeply reflect the contrast between Chinese and western idioms which contain the Chinese and western cultural differences.The study of the cultural differences between English and Chinese idioms can effectively avoid cultural differences so as to better use two kinds of language, and doing intercultural communication.
With the development of computers in recent years, human body recognition technology has been vigorously developed and is widely used in motion analysis, video surveillance and other fields. This study is based on deep learning to improve human pose estimation. Firstly, Involution's feature extraction network was proposed for lightweight human pose estimation, and this feature extraction network was combined with existing human pose estimation models to recognize human pose. Label and classify each joint point of the human body separately, add weights to each different part, extract feature between joint points at different times, and then input the extracted feature into long short-term memory neural networks for recognition. The experimental results show that the improved human pose estimation model reduces the parameter and computational complexity by about 40% compared to the original model, while also slightly improving accuracy. Comparing the performance of models under various algorithms with the proposed model in this study, the accuracy under the Eigen method is 81.3%, the accuracy under the STOP method is 82.5%, the accuracy under the DMM&HOG method is 85.3%, the accuracy under the Actionlet method is 87.6%, and the accuracy under the JAS&HOG2 method is 83.5%. The accuracy of the InNet LSTM method is 90.6%. The results indicate that the proposed model has good performance and can recognize different martial arts movements.
Remote sensing image analysis plays a vital role in achieving intelligent agricultural monitoring. However, the acquisition of high-resolution agricultural remote sensing data can be resource-intensive, resulting in an imbalance between training samples and artificial intelligence model parameters. In order to achieve accurate agricultural land recognition of limited-resolution remote sensing images, this article proposes a joint network of super-resolution and active learning (AL). The network introduces a pretrained image super-resolution model and optimizes this for remote sensing classification tasks. It effectively detects detailed features and completes the reconstruction. Based on the reconstructed data, an AL algorithm is proposed with a DBSS. It balances the contributions between interclass and boundary samples. Furthermore, we propose a semisupervisory assistance strategy based on consistency, it fully utilizes the predictive power of deep learning models aiming to reduce labeling costs. This framework is proved effective by experiments on an agricultural remote sensing image dataset, it reduces the cost of agricultural data annotation and improves the efficiency of model learning for low-resolution agricultural remote sensing.
Object detection and target localization are important technologies in image processing and computer vision, which have a wide range of applications in agricultural systems. By fusing the YOLOv5 algorithm and monocular vision-based method, a target localization algorithm is proposed to accurately identify and locate the various objects in agricultural scenes. The GPS information of the target object can be obtained by means of processing the attitude angle information of the unmanned aerial vehicle (UAV) at the time when it takes pictures. The superiority of the proposed method is demonstrated through the test on the agricultural scenario dataset taken by UAV, with the experimental results showing the algorithm’s satisfactory speed and accuracy.
Sea surface temperature (SST) is an important parameter to measure the energy and heat balance of sea surface. The change of sea surface temperature has an important impact on the marine ecosystem, marine climate and marine environment. Therefore, sea surface temperature prediction has become an significant research direction in the field of ocean. This article proposes a DBULSTM-Adaboost model based on ensemble learning. The model is composed of Deep Bidirectional and Unidirectional Long Short Term Memory (DBULSTM) and Adaboost strong learner. DBULSTM can capture the forward and backward dependence of time series, and the DBULSTM model is integrated with Adaboost strong learner to reduce the variance and bias of prediction and realize the short and medium term prediction of SST at a single point scale. Experimental results show that the model can improve the accuracy and stability of SST prediction. Experiments on the East China Sea and South China Sea with different prediction lengths show that the model is almost superior to other classical models in different sea areas and at different prediction levels. Compared with full-connected LSTM (FC-LSTM) model, the root-mean-square error is reduced by about 0.1.
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