Identifying Materials of Photographic Images and Photorealistic Computer Generated Graphics Based on Deep CNNs

Currently,some photorealistic computer graphics are very similar to photographic images.Photorealistic computer generated graphics can be forged as photographic images,causing serious security problems.The aim of this work is to use a deep neural network to detect photographic images(PI)versus computer generated graphics(CG).In existing approaches,image feature classification is computationally intensive and fails to achieve realtime analysis.This paper presents an effective approach to automatically identify PI and CG based on deep convolutional neural networks(DCNNs).Compared with some existing methods,the proposed method achieves real-time forensic tasks by deepening the network structure.Experimental results show that this approach can effectively identify PI and CG with average detection accuracy of 98%.

Survey on digital twins for Internet of Vehicles:Fundamentals,challenges,and opportunities

As autonomous vehicles and the other supporting infrastructures(e.g.,smart cities and intelligent transportation systems)become more commonplace,the Internet of Vehicles(IoV)is getting increasingly prevalent.There have been attempts to utilize Digital Twins(DTs)to facilitate the design,evaluation,and deployment of IoV-based systems,for example by supporting high-fidelity modeling,real-time monitoring,and advanced predictive capabilities.However,the literature review undertaken in this paper suggests that integrating DTs into IoV-based system design and deployment remains an understudied topic.In addition,this paper explains how DTs can benefit IoV system designers and implementers,as well as describes several challenges and opportunities for future researchers.

Assessing the Applicability of Multi-Source Precipitation Products over the Chinese Mainland and Its Seven Regions

Satellite-based and reanalysis precipitation products provide valuable information for various applications.However,their performance varies widely across regions due to different data sources and production processes.This paper evaluated the daily performance of four precipitation products(MSWEP,ERA5,PERSIANN,and TRMM)for seven regions of the Chinese mainland,using observations from 2462 ground stations across the country as a benchmark.We used four statistical and four classification indicators to describe their spatial and temporal accuracy,and capability to detect precipitation events while analyzing their applicability.The results show that according to the precipitation char-acteristics and accuracy of different types of precipitation products over the Chinese mainland,MSWEP was the most suitable product over the Chinese mainland,having the lowest root mean square error and mean absolute error,along with the highest coefficient of determination.It was followed by TRMM and ERA5,whereas PERSIANN lagged behind in terms of performance.In terms of different regions,MSWEP still performed well,especially in North China and East China.The accuracy of the four precipitation products was relatively low in the summer months,and they all overestimated in the northwest region.In other months,MSWEP and TRMM were better than PERSIANN and ERA5.The four precipitation products had good detection performance over the Chinese mainland,with probability of detection above 0.5.However,with the increase of precipitation threshold,the detection capability of the four products decreased,and MSWEP and ERA5 had good detection capability for moderate rain.TRMM’s detection capability for heavy rain and rainstorms was better than that of the other three products,and PERSIANN’s detection capability for moderate rain,heavy rain and rainstorms was relatively poor,with a large deviation.

Research on Data Extraction and Analysis of Software Defect in IoT Communication Software

Software defect feature selection has problems of feature space dimensionality reduction and large search space.This research proposes a defect prediction feature selection framework based on improved shuffled frog leaping algorithm(ISFLA).Using the two-level structure of the framework and the improved hybrid leapfrog algorithm's own advantages,the feature values are sorted,and some features with high correlation are selected to avoid other heuristic algorithms in the defect prediction that are easy to produce local The case where the convergence rate of the optimal or parameter optimization process is relatively slow.The framework improves generalization of predictions of unknown data samples and enhances the ability to search for features related to learning tasks.At the same time,this framework further reduces the dimension of the feature space.After the contrast simulation experiment with other common defect prediction methods,we used the actual test data set to verify the framework for multiple iterations on Internet of Things(IoT)system platform.The experimental results show that the software defect prediction feature selection framework based on ISFLA is very effective in defect prediction of IoT communication software.This framework can save the testing time of IoT communication software,effectively improve the performance of software defect prediction,and ensure the software quality.

A Robust Watermarking Scheme Based on ROI and IWT for Remote Consultation of COVID-19

In the current dire situation of the corona virus COVID-19,remote consultations were proposed to avoid cross-infection and regional differences in medical resources.However,the safety of digital medical imaging in remote consultations has also attracted more and more attention from the medical industry.To ensure the integrity and security of medical images,this paper proposes a robust watermarking algorithm to authenticate and recover from the distorted medical images based on regions of interest(ROI)and integer wavelet transform(IWT).First,the medical image is divided into two different parts,regions of interest and non-interest regions.Then the integrity of ROI is verified using the hash algorithm,and the recovery data of the ROI region is calculated at the same time.Also,binary images with the basic information of patients are processed by logistic chaotic map encryption,and then the synthetic watermark is embedded in the medical carrier image using IWT transform.The performance of the proposed algorithm is tested by the simulation experiments based on the MATLAB program in CT images of the lungs.Experimental results show that the algorithm can precisely locate the distorted areas of an image and recover the original ROI on the basis of verifying image reliability.The maximum peak signal to noise ratio(PSNR)value of 51.24 has been achieved,which proves that the watermark is invisible and has strong robustness against noise,compression,and filtering attacks.

Intelligent Fusion of Infrared and Visible Image Data Based on Convolutional Sparse Representation and Improved Pulse-Coupled Neural Network

Multi-source information can be obtained through the fusion of infrared images and visible light images,which have the characteristics of complementary information.However,the existing acquisition methods of fusion images have disadvantages such as blurred edges,low contrast,and loss of details.Based on convolution sparse representation and improved pulse-coupled neural network this paper proposes an image fusion algorithm that decompose the source images into high-frequency and low-frequency subbands by non-subsampled Shearlet Transform(NSST).Furthermore,the low-frequency subbands were fused by convolutional sparse representation(CSR),and the high-frequency subbands were fused by an improved pulse coupled neural network(IPCNN)algorithm,which can effectively solve the problem of difficulty in setting parameters of the traditional PCNN algorithm,improving the performance of sparse representation with details injection.The result reveals that the proposed method in this paper has more advantages than the existing mainstream fusion algorithms in terms of visual effects and objective indicators.

A Smart Wearable Ring Device for Sensing Hand Tremor of Parkinson’s Patients

Parkinson’s disease(PD)is a very common neurodegenerative disease that occurs mostly in the elderly.There are many main clinical manifestations of PD,such as tremor,bradykinesia,muscle rigidity,etc.Based on the current research on PD,the accurate and convenient detection of early symptoms is the key to detect PD.With the development of microelectronic and sensor technology,it is much easier to measure the barely noticeable tremor in just one hand for the early detection of Parkinson’s disease.In this paper,we present a smart wearable device for detecting hand tremor,in which MPU6050(MIDI Processing Unit)consisting of a 3-axis gyroscope and a 3-axis accelerometer is used to collect acceleration and angular velocity of fingers.By analyzing the time of specific finger movements,we successfully recognized the tremor signals with high accuracy.Meanwhile,with Bluetooth 4.0(Bluetooth Low Energy,BLE)and networking terminal ability,tremor data can be transferred to a monitoring device in real time with extremely lowenergy consumption.The experimental results have shown that the proposed device(smart ring)is convenient for long-term tremor detection which is vital for early detection and treatment for Parkinson’s disease.

Soft Tissue Deformation Model Based on Marquardt Algorithm and Enrichment Function

In order to solve the problem of high computing cost and low simulation accuracy caused by discontinuity of incision in traditional meshless model,this paper proposes a soft tissue deformation model based on the Marquardt algorithm and enrichment function.The model is based on the element-free Galerkin method,in which Kelvin viscoelastic model and adjustment function are integrated.Marquardt algorithm is applied to fit the relation between force and displacement caused by surface deformation,and the enrichment function is applied to deal with the discontinuity in the meshless method.To verify the validity of the model,the Sensable Phantom Omni force tactile interactive device is used to simulate the deformations of stomach and heart.Experimental results show that the proposed model improves the real-time performance and accuracy of soft tissue deformation simulation,which provides a new perspective for the application of the meshless method in virtual surgery.

An Application Review of Artificial Intelligence in Prevention and Cure of COVID-19 Pandemic

Coronaviruses are a well-known family of viruses that can infect humans or animals.Recently,the new coronavirus(COVID-19)has spread worldwide.All countries in the world are working hard to control the coronavirus disease.However,many countries are faced with a lack of medical equipment and an insufficient number of medical personnel because of the limitations of the medical system,which leads to the mass spread of diseases.As a powerful tool,artificial intelligence(AI)has been successfully applied to solve various complex problems ranging from big data analysis to computer vision.In the process of epidemic control,many algorithms are proposed to solve problems in various fields of medical treatment,which is able to reduce the workload of the medical system.Due to excellent learning ability,AI has played an important role in drug development,epidemic forecast,and clinical diagnosis.This research provides a comprehensive overview of relevant research on AI during the outbreak and helps to develop new and more powerful methods to deal with the current pandemic.

Impact Force Magnitude and Location Recognition of Composite Materials

In order to identify the location and magnitude of the impact force accurately,determine the damage range of the structure and accelerate the health monitoring of key components of the composite,this paper studies the location and magnitude of the impact force of composite plates by an inverse method.Firstly,a PZT sensor mounted on the material plate is used to collect the response signal generated by the impact force,which is from several impact locations,and establish transfer functions between the impact location and the PZT sensor.Secondly,this paper applies several forces to any location on the material plate,and collects the corresponding response signals,and reconstructs the impact force of several locations in turn.Finally,according to the reconstruction result of each location,the correct impact location is identified.Then,an improved regularization method is used to optimize the reconstructed impact force and accurate the magnitude of the impact force.The comparison experiments prove that the recognition error of this method is smaller.

Analysis of the Efficiency-Energy with Regression and Classification in Household Using K-NN

This paper aims to study energy consumption in a house. Home energy managementsystem (HEMS) has become very important, because energy consumption of aresidential sector accounts for a significant amount of total energy consumption.However, a conventional HEMS has some architectural limitations among dimensionalvariables reusability and interoperability. Furthermore, the cost of implementation inHEMS is very expensive, which leads to the disturbance of the spread of a HEMS.Therefore, this study proposes an Internet of Things (IoT) based HEMS with lightweightphotovoltaic (PV) system over dynamic home area networks (DHANs), which enablesthe construction of a HEMS to be scalable reusable and interoperable. The study suggestsa technique for decreasing cost of energy that HEMS is using and various perspectives insystem. The method that proposed is K-NN (K-Nearest Neighbor) which helps us toanalyze the classification and regression datasets. This paper has the result from the datarelevant in October 2018 from some buildings of Nanjing University of InformationScience and Technology.

An Optimization Scheme for Task Offloading and Resource Allocation in Vehicle Edge Networks

The vehicle edge network(VEN)has become a new research hotspot in the Internet of Things(IOT).However,many new delays are generated during the vehicle offloading the task to the edge server,which will greatly reduce the quality of service(QOS)provided by the vehicle edge network.To solve this problem,this paper proposes an evolutionary algorithm-based(EA)task offloading and resource allocation scheme.First,the delay of offloading task to the edge server is generally defined,then the mathematical model of problem is given.Finally,the objective function is optimized by evolutionary algorithm,and the optimal solution is obtained by iteration and averaging.To verify the performance of this method,contrast experiments are conducted.The experimental results show that our purposed method reduces delay and improves QOS,which is superior to other schemes.

A Review of the Current Task Offloading Algorithms,Strategies and Approach in Edge Computing Systems

Task offloading is an important concept for edge computing and the Internet of Things(IoT)because computationintensive tasksmust beoffloaded tomore resource-powerful remote devices.Taskoffloading has several advantages,including increased battery life,lower latency,and better application performance.A task offloading method determines whether sections of the full application should be run locally or offloaded for execution remotely.The offloading choice problem is influenced by several factors,including application properties,network conditions,hardware features,and mobility,influencing the offloading system’s operational environment.This study provides a thorough examination of current task offloading and resource allocation in edge computing,covering offloading strategies,algorithms,and factors that influence offloading.Full offloading and partial offloading strategies are the two types of offloading strategies.The algorithms for task offloading and resource allocation are then categorized into two parts:machine learning algorithms and non-machine learning algorithms.We examine and elaborate on algorithms like Supervised Learning,Unsupervised Learning,and Reinforcement Learning(RL)under machine learning.Under the non-machine learning algorithm,we elaborate on algorithms like non(convex)optimization,Lyapunov optimization,Game theory,Heuristic Algorithm,Dynamic Voltage Scaling,Gibbs Sampling,and Generalized Benders Decomposition(GBD).Finally,we highlight and discuss some research challenges and issues in edge computing.

Splitting and placement of data-intensive applications with machine learning for power system in cloud computing

Aiming to meet the growing demand for observation and analysis in power systems that based on Internet of Things(IoT),machine learning technology has been adopted to deal with the data-intensive power electronics applications in IoT.By feeding previous power electronic data into the learning model,accurate information is drawn,and the quality of IoT-based power services is improved.Generally,the data-intensive electronic applications with machine learning are split into numerous data/control constrained tasks by workflow technology.The efficient execution of this data-intensive Power Workflow(PW)needs massive computing resources,which are available in the cloud infrastructure.Nevertheless,the execution efficiency of PW decreases due to inappropriate sub-task and data placement.In addition,the power consumption explodes due to massive data acquisition.To address these challenges,a PW placement method named PWP is devised.Specifically,the Non-dominated Sorting Differential Evolution(NSDE)is used to generate placement strategies.The simulation experiments show that PWP achieves the best trade-off among data acquisition time,power consumption,load distribution and privacy preservation,confirming that PWP is effective for the placement problem.

A Combined Approach of Principal Component Analysis and Support Vector Machine for Early Development Phase Modeling of Ohrid Trout(Salmo Letnica)

Ohrid trout(Salamo letnica)is an endemic species of fish found in Lake Ohrid in the Former Yugoslav Republic of Macedonia(FYROM).The growth of Ohrid trout was examined in a controlled environment for a certain period,thereafter released into the lake to grow their natural population.The external features of the fish were measured regularly during the cultivation period in the laboratory to monitor their growth.The data mining methods-based computational model can be used for fast,accurate,reliable,automatic,and improved growth monitoring procedures and classification of Ohrid trout.With this motivation,a combined approach of principal component analysis(PCA)and support vectormachine(SVM)has been implemented for the visual discrimination and quantitative classification of Ohrid trout of the experimental and natural breeding and their growth stages.The PCA results in better discrimination of breeding categories of Ohrid trout at different development phases while the maximum classification accuracy of 98.33% was achieved using the combination of PCA and SVM.The classification performance of the combination of PCA and SVM has been compared to combinations of PCA and other classification methods(multilayer perceptron,naive Bayes,randomcommittee,decision stump,random forest,and random tree).Besides,the classification accuracy of multilayer perceptron using the original features has been studied.

Deletion and Recovery Scheme of Electronic Health Records Based onMedical Certificate Blockchain

The trusted sharing of Electronic Health Records(EHRs)can realize the efficient use of medical data resources.Generally speaking,EHRs are widely used in blockchain-based medical data platforms.EHRs are valuable private assets of patients,and the ownership belongs to patients.While recent research has shown that patients can freely and effectively delete the EHRs stored in hospitals,it does not address the challenge of record sharing when patients revisit doctors.In order to solve this problem,this paper proposes a deletion and recovery scheme of EHRs based on Medical Certificate Blockchain.This paper uses cross-chain technology to connect the Medical Certificate Blockchain and the Hospital Blockchain to real-ize the recovery of deleted EHRs.At the same time,this paper uses the Medical Certificate Blockchain and the InterPlanetary File System(IPFS)to store Personal Health Records,which are generated by patients visiting different medical institutions.In addition,this paper also combines digital watermarking technology to ensure the authenticity of the restored electronic medical records.Under the combined effect of blockchain technology and digital watermarking,our proposal will not be affected by any other rights throughout the process.System analysis and security analysis illustrate the completeness and feasibility of the scheme.

Snow Cover Mapping for Mountainous Areas by Fusion of MODIS L1B and Geographic Data Based on Stacked Denoising Auto-Encoders

Snow cover plays an important role in meteorological and hydrological researches.However,the accuracies of currently available snow cover products are significantly lower in mountainous areas than in plains,due to the serious snow/cloud confusion problem caused by high altitude and complex topography.Aiming at this problem,an improved snow cover mapping approach for mountainous areas was proposed and applied in Qinghai-Tibetan Plateau.In this work,a deep learning framework named Stacked Denoising Auto-Encoders(SDAE)was employed to fuse the MODIS multispectral images and various geographic datasets,which are then classified into three categories:Snow,cloud and snow-free land.Moreover,two independent SDAE models were trained for snow mapping in snow and snow-free seasons respectively in response to the seasonal variations of meteorological conditions.The proposed approach was verified using in-situ snow depth records,and compared to the most widely used snow products MOD10A1 and MYD10A1.The comparison results show that our method got the best performance:Overall accuracy of 98.95%and F-measure of 73.84%.The results indicated that our method can effectively improve the snow recognition accuracy,and it can be further extended to other multi-source remote sensing image classification issues.

An Example of a Supporting Combination by Using GA to Evolve More Advanced and Deeper CNN Architecture

It has become an annual tradition for Convolutional Neural Networks(CNNs)to continuously improve their performance in image classification and other applications.These advancements are often attributed to the adoption of more intricate network architectures,such as modules and skip connections,as well as the practice of stacking additional layers to create increasingly complex networks.However,the quest to identify the most optimizedmodel is a daunting task,given that stateof the artConvolutionalNeuralNetwork(CNN)models aremanually engineered.In this research paper,we leveraged a conventional Genetic Algorithm(GA)to craft optimized Convolutional Neural Network(CNN)architectures and pinpoint the ideal set of hyper parameters for image classification tasks using the MNIST dataset.Our experimentation with the MNIST dataset yielded remarkable results.Compared to earlier semi-automatic and automated approaches,our proposed GA demonstrated its efficiency by swiftly identifying the perfect CNN design,accomplishing this feat in just 6 GPU days while achieving an outstanding accuracy of 95.50%.

The Technical Design and Implementation of Cross-Platform Industrial Product Order System

According to some data in the Industrial Purchasing Trends report released by China in 2017,we can see that e-commerce purchasing channels have ranked first among all industrial products purchasing channels in China compared with European and American countries.In addition,in the whole industrial product purchasing market,we can also see that both manufacturers and suppliers are making active e-commerce transformation,and some other Internet giants are also actively entering the industrial product e-commerce industry.But at present,the revenue of all kinds of subjects is still a lot of room for improvement compared to the United States industrial giants.Although the domestic e-commerce market of industrial products has a variety of problems,also contains huge opportunities and development space.Today mobile Internet technology is becoming more and more popular.It is particularly important to develop a cross-platform industrial product order system that supports the collaborative work and unified experience of Android,iOS,and Web.This system uses a uni-app framework to develop front-end applications,which can realize an order management system with code running across multiple platforms.The back end is built based on LNMP architecture.Linux is the most popular free operating system.Nginx is a free and efficient web server with good stable performance,rich functions,simple operation and maintenance,fast processing of static files,and minimal system resource consumption.MySQL database is one of the most widely used relational databases in Web application data processing.The server side is written by PHP script under ThinkPHP framework,which is quick,open-source,and cross-platform in system construction.And these four kinds of software are free,open-source software,together,they can become a free,efficient,highly extensible website service system.

Design and Implementation of Enterprise Recruitment Mini Program

Since the 21st century,the Internet has been updated and developed at an alarming speed.At the same time,WeChat applets are constantly improving and introducing new functions.Develop an enterprise recruitment system based on WeChat applets for the majority of job seekers and recruiter users,provide job seekers with easy-to-reach employment opportunities,and provide a convenient and clear screening environment for job seekers.The front-end part of the applet is developed using WeChat developer tools,and the back-end system is developed using MyEclipse.Use Spring Boot+Spring MVC framework,implemented in Java language.Data is managed using MySql database.The function of this company’s recruitment applet is similar to the ordinary traditional native recruitment APP.It achieves basic functions such as job search,job search,collection of jobs,delivery of resumes,viewing of the job search process,recruitment of job information,screening of job resumes,notification of interviews,etc.