Sea-Land Segmentation of Remote Sensing Images Based on SDW-UNet

Image segmentation of sea-land remote sensing images is of great importance for downstream applications including shoreline extraction,the monitoring of near-shore marine environment,and near-shore target recognition.To mitigate large number of parameters and improve the segmentation accuracy,we propose a new Squeeze-Depth-Wise UNet(SDW-UNet)deep learning model for sea-land remote sensing image segmentation.The proposed SDW-UNet model leverages the squeeze-excitation and depth-wise separable convolution to construct new convolution modules,which enhance the model capacity in combining multiple channels and reduces the model parameters.We further explore the effect of position-encoded information in NLP(Natural Language Processing)domain on sea-land segmentation task.We have conducted extensive experiments to compare the proposed network with the mainstream segmentation network in terms of accuracy,the number of parameters and the time cost for prediction.The test results on remote sensing data sets of Guam,Okinawa,Taiwan China,San Diego,and Diego Garcia demonstrate the effectiveness of SDW-UNet in recognizing different types of sea-land areas with a smaller number of parameters,reduces prediction time cost and improves performance over other mainstream segmentation models.We also show that the position encoding can further improve the accuracy of model segmentation.

Identification of Weather Phenomena Based on Lightweight Convolutional Neural Networks

Weather phenomenon recognition plays an important role in the field of meteorology.Nowadays,weather radars and weathers sensor have been widely used for weather recognition.However,given the high cost in deploying and maintaining the devices,it is difficult to apply them to intensive weather phenomenon recognition.Moreover,advanced machine learning models such as Convolutional Neural Networks(CNNs)have shown a lot of promise in meteorology,but these models also require intensive computation and large memory,which make it difficult to use them in reality.In practice,lightweight models are often used to solve such problems.However,lightweight models often result in significant performance losses.To this end,after taking a deep dive into a large number of lightweight models and summarizing their shortcomings,we propose a novel lightweight CNNs model which is constructed based on new building blocks.The experimental results show that the model proposed in this paper has comparable performance with the mainstream non-lightweight model while also saving 25 times of memory consumption.Such memory reduction is even better than that of existing lightweight models.

Fast CU Partition for VVC Using Texture Complexity Classification Convolutional Neural Network

Versatile video coding(H.266/VVC),which was newly released by the Joint Video Exploration Team(JVET),introduces quad-tree plus multitype tree(QTMT)partition structure on the basis of quad-tree(QT)partition structure in High Efficiency Video Coding(H.265/HEVC).More complicated coding unit(CU)partitioning processes in H.266/VVC significantly improve video compression efficiency,but greatly increase the computational complexity compared.The ultra-high encoding complexity has obstructed its real-time applications.In order to solve this problem,a CU partition algorithm using convolutional neural network(CNN)is proposed in this paper to speed up the H.266/VVC CU partition process.Firstly,64×64 CU is divided into smooth texture CU,mildly complex texture CU and complex texture CU according to the CU texture characteristics.Second,CU texture complexity classification convolutional neural network(CUTCC-CNN)is proposed to classify CUs.Finally,according to the classification results,the encoder is guided to skip different RDO search process.And optimal CU partition results will be determined.Experimental results show that the proposed method reduces the average coding time by 32.2%with only 0.55%BD-BR loss compared with VTM 10.2.

Advances in Doubling Methods of Haploids by in vivo Induction in Maize

Haploid breeding can shorten the breeding process and improve the breeding efficiency. Currently, in vivo haploid induction technology has been com- monly used in maize （ Zea mays L. ）. This paper briefly introduced in vivo haploid induction technologies, summarized doubling methods of maize haploids and described the significance and application of maize haploids, which provided the basis for further development of haploid breeding in maize.

An Effective CU Depth Decision Method for HEVC Using Machine Learning

This paper presents an effective machine learning-based depth selection algorithm for CTU(Coding Tree Unit)in HEVC(High Efficiency Video Coding).Existing machine learning methods are limited in their ability in handling the initial depth decision of CU(Coding Unit)and selecting the proper set of input features for the depth selection model.In this paper,we first propose a new classification approach for the initial division depth prediction.In particular,we study the correlation of the texture complexity,QPs(quantization parameters)and the depth decision of the CUs to forecast the original partition depth of the current CUs.Secondly,we further aim to determine the input features of the classifier by analysing the correlation between depth decision of the CUs,picture distortion and the bit-rate.Using the found relationships,we also study a decision method for the end partition depth of the current CUs using bit-rate and picture distortion as input.Finally,we formulate the depth division of the CUs as a binary classification problem and use the nearest neighbor classifier to conduct classification.Our proposed method can significantly improve the efficiency of interframe coding by circumventing the traversing cost of the division depth.It shows that the mentioned method can reduce the time spent by 34.56%compared to HM-16.9 while keeping the partition depth of the CUs correct.

Nonlinear Correction of Pressure Sensor Based on Depth Neural Network

With the global climate change,the high-altitude detection is more and more important in the climate prediction,and the input-output characteristic curve of the air pressure sensor is offset due to the interference of the tested object and the environment under test,and the nonlinear error is generated.Aiming at the difficulty of nonlinear correction of pressure sensor and the low accuracy of correction results,depth neural network model was established based on wavelet function,and Levenberg-Marquardt algorithm is used to update network parameters to realize the nonlinear correction of pressure sensor.The experimental results show that compared with the traditional neural network model,the improved depth neural network not only accelerates the convergence rate,but also improves the correction accuracy,meets the error requirements of upper-air detection,and has a good generalization ability,which can be extended to the nonlinear correction of similar sensors.

Remote Sensing Plateau Forest Segmentation with Boundary Preserving Double Loss Function Collaborative Learning

Plateau forest plays an important role in the high-altitude ecosystem,and contributes to the global carbon cycle.Plateau forest monitoring request in-suit data from field investigation.With recent development of the remote sensing technic,large-scale satellite data become available for surface monitoring.Due to the various information contained in the remote sensing data,obtain accurate plateau forest segmentation from the remote sensing imagery still remain challenges.Recent developed deep learning(DL)models such as deep convolutional neural network(CNN)has been widely used in image processing tasks,and shows possibility for remote sensing segmentation.However,due to the unique characteristics and growing environment of the plateau forest,generate feature with high robustness needs to design structures with high robustness.Aiming at the problem that the existing deep learning segmentation methods are difficult to generate the accurate boundary of the plateau forest within the satellite imagery,we propose a method of using boundary feature maps for collaborative learning.There are three improvements in this article.First,design a multi input model for plateau forest segmentation,including the boundary feature map as an additional input label to increase the amount of information at the input.Second,we apply a strong boundary search algorithm to obtain boundary value,and propose a boundary value loss function.Third,improve the Unet segmentation network and combine dense block to improve the feature reuse ability and reduces the image information loss of the model during training.We then demonstrate the utility of our method by detecting plateau forest regions from ZY-3 satellite regarding to Sanjiangyuan nature reserve.The experimental results show that the proposed method can utilize multiple feature information comprehensively which is beneficial to extracting information from boundary,and the detection accuracy is generally higher than several state-of-art algorithms.As a result of this investigation,the study will contribute in several ways to our understanding of DL for region detection and will provide a basis for further researches.

Impolite Pedestrian Detection by Using Enhanced YOLOv3-Tiny

In recent years,the problem of“Impolite Pedestrian”in front of the zebra crossing has aroused widespread concern from all walks of life.The traffic sector’s governance measures have become more serious.The traditional way of governance is on-site law enforcement,which requires a lot of manpower and material resources and is low efficiency.An enhanced YOLOv3-tiny model is proposed for pedestrians and vehicle detection in traffic monitoring.By modifying the backbone network structure of YOLOv3-tiny model,introducing deep detachable convolution operation,and designing the basic residual block unit of the network,the feature extraction ability of the backbone network is enhanced.The improved model is trained on the VOC2007+VOC2012 training set,and the trained model is tested for performance on the test data set.The experimental results show that:the mean Average Precision(mAP)increased from 0.672 to 0.732,increasing the measurement accuracy by 9%.The Intersection over Union(IoU)increased from 0.783 to 0.855,increasing the coverage accuracy by 7.2%.The enhanced YOLOv3-tiny model has higher measurement accuracy than the original model.Applying this model to the 1080P traffic video on the NVIDIA RTX 2080,the detection speed is 150 FPS,which can fully achieve real-time detection.Through the analysis of pedestrians and vehicle coordinates,it is judged whether or not illegal acts occur.For illegal vehicles,save three pictures as the basis for law enforcement,which forms an important supplement to off-site law enforcement.

Expression and functions of transient receptor potential channels in liver diseases

Liver diseases constitute a major healthcare burden globally,including acute hepatic injury resulted from acetaminophen overdose,ischemia-reperfusion or hepatotropic viral infection and chronic hepatitis,alcoholic liver disease(ALD),non-alcoholic fatty liver disease(NAFLD)and hepatocellular carcinoma(HCC).Attainable treatment strategies for most liver diseases remain inadequate,highlighting the importance of substantial pathogenesis.The transient receptor potential(TRP)channels represent a versatile signalling mechanism regulating fundamental physiological processes in the liver.It is not surprising that liver diseases become a newly explored field to enrich our knowledge of TRP channels.Here,we discuss recent findings revealing TRP functions across the fundamental pathological course from early hepatocellular injury caused by various insults,to inflammation,subsequent fibrosis and hepatoma.We also explore expression levels of TRPs in liver tissues of ALD,NAFLD and HCC patients from Gene Expression Omnibus(GEO)or The Cancer Genome Atlas(TCGA)database and survival analysis estimated by Kaplan-Meier Plotter.At last,we address the therapeutical potential and challenges by pharmacologically targeting TRPs to treat liver diseases.The aim is to provide a better understanding of the implications of TRP channels in liver diseases,contributing to the discovery of novel therapeutic targets and efficient drugs.

Ni(OH)_(2) nanoparticles encapsulated in conductive nanowire array for high-performance alkaline seawater oxidation

Design and development of high-efficiency and durable oxygen evolution reaction(OER)electrocatalysts is crucial for hydrogen production from seawater splitting.Herein,we report the in situ electrochemical conversion of a nanoarray of Ni(TCNQ)2(TCNQ=tetracyanoquinodimethane)on graphite paper into Ni(OH)_(2) nanoparticles confined in a conductive TCNQ nanoarray(Ni(OH)_(2)-TCNQ/GP)by anode oxidation.The Ni(OH)_(2)-TCNQ/GP exhibits high OER performance and demands overpotentials of 340 and 382 mV to deliver 100 mA·cm^(−2) in alkaline freshwater and alkaline seawater,respectively.Meanwhile,the Ni(OH)_(2)-TCNQ/GP also demonstrates steady long-term electrochemical durability for at least 80 h under alkaline seawater.

Recent progress in Cr-based catalysts for oxidative dehydrogenation of light alkanes by employing CO_(2) as a soft oxidant

CO_(2) can be used as a soft oxidant for oxidative dehydrogenation of light alkanes(CO_(2)-ODH),which is beneficial to realize the reuse of CO_(2) and meet the demand for olefins.The core of this reaction is the catalyst.Cr-based catalysts have attracted much attention for their excellent catalytic performance in CO_(2)-ODH reactions due to their various oxidation states and local electronic structures.In this paper,the synthesis and modification methods of Cr-based catalysts for CO_(2)-ODH are reviewed.The structure-activity relationship and reaction mechanism are also summarized.Moreover,the reasons for the deactivation of Cr-based catalysts are analysed and the main challenges faced by Cr-based catalysts in the CO_(2)-ODH process,as well as the future development trend and prospect,are discussed.