bluuebunny/crossref_metadata_2025_split huggingface dataset:
Smart Health Monitoring Bodyarea Network Using Raspberry Pi
Authors: Mohil Varmaa K , Prendra K , Ranjith K V , Robinsingh T , Nandalal V , Sathish Kumar N 
Published: July 2021

Abstract
In today’s world more death occurs due to not immediate treatment for bed ridden patients. It also requires more man power. This document presents the model, installation and looks of a hardware and software platform for health monitoring system for patients. The main purpose of this system is to set up a system that can watch over bedridden patient’s health. This project depends on both hardware and software. It is developed using sp02, temperature, heartbeat connected to a raspberry pi micro controller, which get the sensor values and process it in machine learning process to produce a dosage prediction. It also intimates the doctor by triggering an automatic email. In addition, one can know the patients records and prediction of dosage from anywhere using the mobile application linked with it.
Disease Detection In Cotton Leaf Spot Using Image Processing
Authors: Karthika J. , Mathan kumar K. , Santhose M. , Sharan T. , Sri hariharan S 
Published: July 2021

Abstract
Agriculture isn’t solely a method of food for ever-growing populations, however over that. The infected plants cause ill-health in humans and additionally decrease gradually the economy of the country. Therefore, distinguishing plant diseases at an early stage becomes necessary method to avoid losses in an agricultural production system. Largely the symptoms of the malady are visible on leaves. Manually observance of each plant during an immense in acre is hard. Hence, image process techniques area unit accustomed to observe and determine the diseases in the plant that may be a higher various to observe diseases quickly and correctly.
A solution of dialectical systems theory to P/NP problem
Authors: Shi Wei-Jiang 
Published: July 2021

Abstract
The key to solve the NP problem is how to judge the existence of an algorithm for a conversion from NP to P? For this purpose, dialectical systems theory is introduced for a transformations which processes “self-denial” from its structure and function conversely and interactively of some kind of NP, a dualistic contradiction indeed. Mathematics is subject to the “identity” rule and difficult to tackle self-denial transformations. Furthermore, mathematics, being of incompleteness, isn’t helpful to solve the NP problem. Dialectics is an evolutionary theory, a dynamic process, demanding connection all aspects of things, so a systematic approach is adopted. Regarding the transformation from NP to P as an emergence of self-organization evolution, four requirements for dissipative structure can be proposed for the judging possibility of emergence of evolution: opening, non-equilibrium, non-linearity, fluctuation. Taken the “Travelling Salesman Problem” as a typical example of NPC, it be concluded that none of the four requirements could be met. The reason is that the condition given by TSP is one-dimensional data only, which is the essence of Monism that couldn’t boost evolution and emergence quickly and efficiently.
Few-shot Object Detection with Camouflage Animals
Authors: Lei Yi , Lei Lei 
Published: July 2021

Abstract
Object detection is a hot issue in the field of computer vision, which is widely used in intelligent video surveillance, medical image analysis, and practice in the fields of military strategy. Previous object detection is based on a lot of training data, and for some special target, it is difficult to find enough annotation data to training model, so few-shot object detection was born. In this paper, a data set of animals with protective colors is constructed(CAD), which contains ten categories of different objects with high quality annotations. We combine the existing attention RPN and Feature Reweighting module to train our net, the experimental results prove that the Camouflage animals in nature can be well identified, and our results obtain 1.9% AP50, 1.2% AP75 improvement compared with before work.
Enhancement algorithm for color images based on improved FC-MSPCNN
Authors: Kang Yuan , Lian Jing , Guo Chenxi , Zheng Li , Zhang Mingxuan 
Published: July 2021

Abstract
Image enhancement has been a hot research topic in image processing in recent years, and Pulse-Coupled Neural Network (PCNN) based image processing has become one of the important channels for image processing. Recently, we have applied this network to image enhancement based on Fire-controlled Pulse-coupled NeuralNetwork (FC-MSPCNN) without changing its model structure by combining the adaptive parameter setting method to redefine the link coefficients β. On the one hand, and reprocessing the mapping matrix of the extracted three channels on the other hand. The experimental results of the improved FC-MSPCNN are proved to be more effective and robust compared with the traditional classical algorithm, both in terms of competent analysis and objective comparison.
Research on crop disease recognition based on Multi-Branch ResNet-18
Authors: Wang Chaofan , Ni Peng , Cao Maoyong 
Published: July 2021

Abstract
Traditional image processing has many problems in crop disease identification, such as complicated manual design and low efficiency. This article studies the application of deep learning algorithms in the identification of crop diseases. In this article, attention mechanism and feature fusion are introduced to optimize ResNet-18, and for the problem that the network has only a single output, based on the optimized ResNet-18, a Multi-Branch ResNet-18(MB-ResNet-18) and a joint loss function are proposed to achieve the simultaneous classification of crop-type level, disease-type level, and disease-degree level. The experimental results show that, compared with ResNet-18, the network structure proposed in this article basically maintains the classification accuracy of crop-type level and disease-type level, and the classification accuracy on crop disease-degree level has increased by 2.11%.
Research on the Application of Computer P2P Network Technology in Local Area Network
Authors: Li Shao 
Published: July 2021

Abstract
This article analyzes the basic content, application advantages, and infrastructure of computer P2P network technology. The author studies the specific application of computer P2P network technology in application structure, download mode, node function design, overlay network protocol, network data distribution, multiple copy redundancy processing, node failure detection, and copy repair processing. The author studied the countermeasures against hacker attacks, virus transmission problems, and transmission speed problems in the process of technology application. The purpose of this article is to give full play to the application effects of computer P2P network technology and improve the security of the local area network operation process.
Research on Circular Promotions of Graphic Advertising Design and Computer
Authors: Daifang Zhou 
Published: July 2021

Abstract
Graphic advertising design is developed on the basis of advertising and design, mainly aiming at strengthening sales. Traditional advertising design is mainly published in newspaper bulletin boards and presented to people in a plane form. With the rapid development of social economy and the continuous progress of science and technology, the graphic advertising design is constantly innovating and perfecting, which is embodied in the use of special ways to realize digital image processing. The special methods mentioned here include computer technology, digital technology and so on. It can be seen that under the background of information age, the computer system will have an impact on the effect of graphic advertising design in different degrees. Based on this, this paper will make an in-depth analysis of the development process of graphic advertising design, study the specific application of computer technology in graphic advertising design, and explore the circular promotion relationship between them, hoping to provide reference for professionals.
Few-Shot Scene Classification with attention mechanism in Remote Sensing
Authors: Li Xuanye , Li Hongguang , Yu Ruonan , Wang Fei 
Published: July 2021

Abstract
Remote sensing scene classification is a hot research topic in computer vision and it is of great significance to the semantic understanding of remote sensing images. At present, remote sensing scene classification methods based on deep learning occupy a dominant position in this field. However, it suffers from the lack of samples and poor model generalization ability in actual application scenarios. Therefore, this paper proposes a few-shot remote scene classification method based on attention mechanism, and designs a structure of dual-branches similarity measurement. This method is based on the meta-learning training strategy to divide the dataset into tasks. At the meantime, the input images are divided into blocks to preserve the feature distribution in the remote sensing image. Then the lightweight attention module is introduced into the feature extraction network to reduce the risk of overfitting and ensure the acquisition of discriminative features. Finally we add a dual-branches similarity measurement module on the basis of Earth Mover’s Distance to improve the discriminative ability of the classifier. The results show that compared with the classic small-sample learning method, the few-shot remote scene classification method proposed in this paper can significantly improve the classification performance.
Analysis of the Spatiotemporal Variation in Habitat Quality Based on the InVEST Model- A Case Study of Shangri-La City, Northwest Yunnan, China
Authors: Sun Xingqi , Yu Yuanhe , Wang Jinliang , Liu Weidong 
Published: July 2021

Abstract
Habitat quality is an important spatial dynamic factor for evaluating the effectiveness of biodiversity conservation. This study aimed to understand the characteristic variation of habitat quality in Shangri-La City, which can provide a basis for decision-making by relevant departments to protect the biodiversity of the area. The spatiotemporal variation of habitat quality of Shangri-La City in 1989-2015 was estimated based on the Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) model. The results showed that: (1) The overall habitat quality presented a decreasing trend from 1989 to 2015, with Jiantang, Naxi, Xiaozhongdian, the northern part of Hutiaoxia and the southern part of Sanba the most significant. (2) The highest value of the average habitat quality of Shangri-La City was located in the middle-elevation regions, while the lowest value was located in the low-elevation areas, and the average habitat quality of the two regions showed a decreasing trend from 1989 to 2015. (3) The average habitat quality of the steep and extremely steep slopes in Shangri-La was higher than that of the slight slopes. This study was useful for biodiversity conservation policy-making for ecological fragile region in China.
The Limited-FCMSPCNN algorithm for image defogging
Authors: Guo Chenxi , Lian Jing , Kang Yuan , Zheng Li , Zhang Mingxuan 
Published: July 2021

Abstract
In order to address the problem of defogging images of foggy scenes, this paper proposes a parameter-tunable image defogging algorithm based on the FC-MSPCNN model. Firstly, we simplify the traditional PCNN model and also propose the Limited-FCMSPCNN (LFC-MSPCNN) model. Secondly, we give the five key parameters in the model. There are five key parameters in the model: α, β, V, G and a modulation parameter q, and how they are set. Thirdly, we verified that the algorithm in this paper has good defogging effect on foggy sky images by comparing with the Dark Channel Prior algorithm, the Retinex algorithm and the FC-MSPCNN algorithm, showing that the LFC-MSPCNN method proposed in this paper has robustness.
Design of an Intelligent Alarm System Based on Multi-sensor Data Fusion
Authors: Wen Chunming , Li Kechang , Liao Yikui , Xiao Zhanpeng 
Published: July 2021

Abstract
The fire alarm system plays a very important role in life, but the system has problems such as false alarms and false alarms. Therefore, this paper proposes the application of fire detection based on GA-BP neural network. Firstly, the algorithm takes temperature, smoke concentration and CO concentration as the input of BP neural network, and the output is whether there is fire or not. Secondly, it combines the characteristics of genetic algorithm with strong global search ability and strong robustness. The algorithm has achieved 100% correct classification on the test set through simulation experiments. At the same time, the absolute error of the sample prediction is only 0.006, which proves that it has strong robustness, reliability and generalization ability. Finally, the model was transplanted to STM32 to prove its feasibility. This method provides a new method for intelligent identification of fire signals for early warning of fires and accurate identification of non-fire signals.
Research Progress in Remote Sensing Image Texture Analysis
Authors: Sun Changqing , Wen Zhenwei 
Published: July 2021

Abstract
Current remote sensing image texture analysis and research is based texture feature. Specialist, who home and abroad, has been continuously explore an optimal extraction process and texture analyze method or through a variety of methods to realize extraction of remote sensing texture information. There are three directions, which including the research of optimal scale, the fusion of method, the proposal and improvement of new algorithm, could comprehensive summary about recent research progress in remote sensing image texture analysis by sorting and analyzing information extraction of remote sensing texture features, the progress of texture analysis methods and the application of analysis methods in recent years. At the same time, a prediction was made for future development of remote sensing image texture analysis. Five development directions are summarized: fusion method, scale selection, continuation and improvement of traditional methods based on symbiotic gray matrix and wavelet analysis, research and exploration of object-oriented and deep learning methods, and research of new algorithms to improve the information extraction effect.
Automatic end-to-end veneer grading system based on machine vision
Authors: Ma Fengying , Zhang Jiyin , Ji Peng 
Published: July 2021

Abstract
In order to realize automatic grading of veneers of different quality during the processing of veneer veneers, an end-to-end veneer automatic grading system based on YOLOV3 was designed. Using YOLOV3 network as the defect detection model, self-made veneer defect data set for model training. Firstly, the system obtains the veneer surface image; then the trained model detects the obtained veneer surface image, identifies scars and cavities in the surface defects, and marks the scars and cavities with rectangular boxes respectively; finally, the quality level of the veneer is determined according to the characteristic information of the detected scars and holes, and the level signal is transmitted to the PLC system to drive the grading actuator for grading. Experiments show that the end-to-end veneer automatic grading system based on machine vision can effectively carry out the automatic veneer grading, which is helpful to realize the intelligent and automated production of veneer processing and improve work efficiency.
Research on Integrated Navigation Algorithm Based on Radial Basis Function Neural Network
Authors: Liu Hang , Li Kaicheng , Fu Qiang , Yuan Lei 
Published: July 2021

Abstract
The Qinghai-Tibet Railway is located on the Qinghai-Tibet Plateau in China. Due to its harsh climatic conditions and geographic environment, the line currently uses the Global Position System (GPS) to achieve train positioning. However, the navigation satellites are easily blocked by obstructions such as tunnels, resulting in a decrease in positioning accuracy. The conventional solution is to use multiple sensors such as an inertial navigation system (INS) for data fusion. But in the INS/GPS integrated navigation system, when the GPS signal is lost, the navigation accuracy will still decrease. In order to solve this problem, this paper proposes a radial basis function (RBF) neural network-assisted integrated navigation filtering algorithm. When the two systems are working normally, the measured raw information is used to train the RBF neural network. When the GPS fails, the measured and calculated values of the inertial navigation system are input to the trained network model to obtain the predicted error value, which is corrected to the inertial navigation system to obtain the final navigation information. The MATLAB simulation results show that when the short-term GPS information is missing, the integrated navigation system assisted by the RBF neural network has better estimation accuracy.
Ecological Vulnerability of Lake Basin by Integrating Human Activity Indicators based on RS and GIS: A Case of Fuxian Lake in Yunnan Province, China
Authors: Zhou Jing , Tang Jinli , Yang Yanjun , Yang Hailan , Li Lan , Wu Li 
Published: July 2021

Abstract
The variation of landscape pattern and vulnerability can indicate the ecological environmental state. This study established a model for landscape pattern vulnerability based on human activity intensity by taking five sets of land use data (1995, 2000, 2005, 2010 and 2015). Landscape sensitivity index, landscape adaptability index and human activity intensity of land surface were all used to compute the landscape vulnerability index. In virtue of Fragstats 4.2 and ArcGIS 10.5, landscape index can be obtained and spatial-temporal distribution and evolution of landscape pattern vulnerability were analysed. Results showed that, (1) from 1995 to 2015, the human activity intensity and landscape pattern vulnerability in the Fuxian Lake Basin deteriorated gradually, the area of higher intensity/vulnerability and high intensity/vulnerability dramatically expanded with time. (2) the spatial heterogeneity of landscape pattern vulnerability increased, there are significant differences between the north, south ends and east, west sides of Fuxian Lake. (3) The global spatiality of landscape pattern vulnerability exhibited strong positive correlation with the significant form of spatial agglomeration, and the positive spatial autocorrelation continued to keep on but the tendency of spatial concentration was slightly decreasing over time. The local autocorrelation mainly based on high-high accumulation zone and low-low accumulation zone had stronger spatial autocorrelation among neighbouring space units. Due to the implementation of the protection policy, the landscape pattern vulnerability in the east sides of Fuxian Lake had expressed declining trend, but that in the north need more attention and protection.
Progress of Surveying and Mapping Science and Technology in the Information Age
Authors: Zhang Wen , Sun Changqing 
Published: July 2021

Abstract
With the further improvement of my country’s current information technology level, the development of China’s surveying and mapping has inevitably shifted from digital mapping to computerized information mapping. This article expounds the important role of surveying and mapping science and technology in the construction of the country and society, and from this leads to a better construction of national security and convenient governance system. then information-based surveying and mapping will be the future development prospect of surveying and mapping. From the perspective of informatization surveying and mapping, this article expounds its characteristics, development trends and application status, and explains the basic role of informatization surveying and mapping in my country’s surveying and mapping industry.
Optimization and Simulation of DMC Algorithm with Terminal Weight Based on LQR Controller
Authors: Tian Kai , Xiao Zhongjun , Ji Peng 
Published: July 2021

Abstract
Based on the multivariable dynamic matrix control algorithm, there are many similarities between the dynamic matrix control algorithm and LQR in the form of state equation, state variable and objective function. We can use the objective function of LQR to improve the objective function of DMC and improve the control effect of the system. Using the given mathematical model, the terminal weighting matrix of LQR is applied to DMC algorithm, and the optimized multivariable dynamic matrix control algorithm is simulated. The results show that the algorithm can effectively reduce the response time of the system, make the system sink faster and reach the given value faster.
Research on dynamic ocean surface simulation based on 3D sonar image
Authors: Li Menglei , Jiang Hejun , Rong Yingjiao , Wang Shirui , Sun Ping 
Published: July 2021

Abstract
The motion state of the ocean surface is changeable and has special irregularity. It is impossible to describe these characteristics in detail, which leads to the low accuracy of simulation for the sea surface. A simulation method of realistic dynamic ocean surface based on 3D sonar image algorithm is proposed. The number of adjacent points iteration of two adjacent images is calculated, and all the dynamic shallow surface sonar images are matched and combined. According to the edge coordinate information between the nearest adjacent images, the overlapping part between the two sonar image frames is analyzed. The two frames of sonar images beyond the threshold range of overlap are registered by ICP algorithm, The simulation of the dynamic ocean surface with the statistical model and FFT based wave generation method is carried out. The simulation results show that the research method of 3D sonar image algorithm is more accurate and can achieve more real simulation results.
Comparison of spatial interpolation methods based on ArcGIS
Authors: Liu Zenglu , Yan Tianfeng 
Published: July 2021

Abstract
Spatial interpolation algorithm is based on known data to predict the study area, its accuracy is of great significance to research. In different application scenarios, the interpolation method with higher accuracy should be selected. Based on ArcGIS, this paper conducts experimental analysis on inverse distance weighting method and Kriging method, so as to provide a reference for the selection of interpolation method in different application scenarios.
Design of Diagnostic strategy for Radar System Based on Rollout Algorithm
Authors: Du Xiaoshuai , Hu Bing 
Published: July 2021

Abstract
To solve the problem of how to detect and isolate the fault of radar system with low test cost, a design method of diagnostic strategy for radar system based on rollout algorithm is proposed. This method takes the greedy search method based on information heuristic as the benchmark strategy, and uses the rollout strategy to iterate on the benchmark strategy, so as to overcome the problem of poor optimality of greedy search method. Taking the radar system dependency model as an example, this paper uses rollout algorithm to generate the diagnostic strategy of the radar system, which verifies the effectiveness of the method.