Traditional agricultural irrigation systems waste significant amounts of water and energy due to inefficient scheduling and the absence of real-time monitoring capabilities.This research developed a comprehensive IoT-...Traditional agricultural irrigation systems waste significant amounts of water and energy due to inefficient scheduling and the absence of real-time monitoring capabilities.This research developed a comprehensive IoT-based smart irrigation control systemto optimize water and energy management in agricultural greenhouses while enhancing crop productivity.The system employs a sophisticated four-layer Internet ofThings(IoT)architecture based on an ESP32 microcontroller,integrated with multiple environmental sensors,including soil moisture,temperature,humidity,and light intensity sensors,for comprehensive environmental monitoring.The system utilizes the Message Queuing Telemetry Transport(MQTT)communication protocol for reliable data transmission and incorporates a Random Forest machine learning algorithm for automated irrigation decision-making processes.The Random Forest model achieved exceptional performance with 99.3%overall accuracy,demonstrating high model reliability.Six operational modules were developed and implemented with three distinct control methods:manual operation,condition-based automatic control,and AI-driven intelligent control systems.A comprehensive one-month comparative analysis demonstrated remarkable improvements across multiple performance metrics:a 50%reduction in both water consumption(from 140 to 70 L/day)and energy usage(from 7.00 to 3.50 kWh/day),a substantial 130%increase in water use efficiency,and a significant 50%decrease in CO_(2) emissions.Furthermore,detailed factor importance analysis revealed soil moisture as the primary decision factor(38.6%),followed by temporal factors(20.3%)and light intensity(18.4%).The system demonstrates exceptional potential for annual energy conservation of 1277.5 kWh and CO_(2) emission reduction of 638.75 kg,contributing substantially to sustainable development goals and advancing smart agriculture technologies.展开更多
Due to the continuous rising demand of handheld devices like iPods, mobile, tablets;specific applications like biomedical applications like pacemakers, hearing aid machines and space applications which require stable ...Due to the continuous rising demand of handheld devices like iPods, mobile, tablets;specific applications like biomedical applications like pacemakers, hearing aid machines and space applications which require stable digital systems with low power consumptions are required. As a main part in digital system the SRAM (Static Random Access Memory) should have low power consumption and stability. As we are continuously moving towards scaling for the last two decades the effect of this is process variations which have severe effect on stability, performance. Reducing the supply voltage to sub-threshold region, which helps in reducing the power consumption to an extent but side by side it raises the issue of the stability of the memory. Static Noise Margin of SRAM cell enforces great challenges to the sub threshold SRAM design. In this paper we have analyzed the cell stability of 9T SRAM Cell at various processes. The cell stability is checked at deep submicron (DSM) technology. In this paper we have analyzed the effect of temperature and supply voltage (Vdd) on the stability parameters of SRAM which is Static Noise Margin (SNM), Write Margin (WM) and Read Current. The effect has been observed at various process corners at 45 nm technology. The temperature has a significant effect on stability along with the Vdd. The Cell has been working efficiently at all process corners and has 50% more SNM from conventional 6T SRAM and 30% more WM from conventional 6T SRAM cell.展开更多
Cyberbullying is a remarkable issue in the Arabic-speaking world,affecting children,organizations,and businesses.Various efforts have been made to combat this problem through proposed models using machine learning(ML)...Cyberbullying is a remarkable issue in the Arabic-speaking world,affecting children,organizations,and businesses.Various efforts have been made to combat this problem through proposed models using machine learning(ML)and deep learning(DL)approaches utilizing natural language processing(NLP)methods and by proposing relevant datasets.However,most of these endeavors focused predominantly on the English language,leaving a substantial gap in addressing Arabic cyberbullying.Given the complexities of the Arabic language,transfer learning techniques and transformers present a promising approach to enhance the detection and classification of abusive content by leveraging large and pretrained models that use a large dataset.Therefore,this study proposes a hybrid model using transformers trained on extensive Arabic datasets.It then fine-tunes the hybrid model on a newly curated Arabic cyberbullying dataset collected from social media platforms,in particular Twitter.Additionally,the following two hybrid transformer models are introduced:the first combines CAmelid Morphologically-aware pretrained Bidirectional Encoder Representations from Transformers(CAMeLBERT)with Arabic Generative Pre-trained Transformer 2(AraGPT2)and the second combines Arabic BERT(AraBERT)with Cross-lingual Language Model-RoBERTa(XLM-R).Two strategies,namely,feature fusion and ensemble voting,are employed to improve the model performance accuracy.Experimental results,measured through precision,recall,F1-score,accuracy,and AreaUnder the Curve-Receiver Operating Characteristic(AUC-ROC),demonstrate that the combined CAMeLBERT and AraGPT2 models using feature fusion outperformed traditional DL models,such as Long Short-Term Memory(LSTM)and Bidirectional Long Short-Term Memory(BiLSTM),as well as other independent Arabic-based transformer models.展开更多
A recommender system is a tool designed to suggest relevant items to users based on their preferences and behaviors.Collaborative filtering,a popular technique within recommender systems,predicts user interests by ana...A recommender system is a tool designed to suggest relevant items to users based on their preferences and behaviors.Collaborative filtering,a popular technique within recommender systems,predicts user interests by analyzing patterns in interactions and similarities between users,leveraging past behavior data to make personalized recommendations.Despite its popularity,collaborative filtering faces notable challenges,and one of them is the issue of grey-sheep users who have unusual tastes in the system.Surprisingly,existing research has not extensively explored outlier detection techniques to address the grey-sheep problem.To fill this research gap,this study conducts a comprehensive comparison of 12 outlier detectionmethods(such as LOF,ABOD,HBOS,etc.)and introduces innovative user representations aimed at improving the identification of outliers within recommender systems.More specifically,we proposed and examined three types of user representations:1)the distribution statistics of user-user similarities,where similarities were calculated based on users’rating vectors;2)the distribution statistics of user-user similarities,but with similarities derived from users represented by latent factors;and 3)latent-factor vector representations.Our experiments on the Movie Lens and Yahoo!Movie datasets demonstrate that user representations based on latent-factor vectors consistently facilitate the identification of more grey-sheep users when applying outlier detection methods.展开更多
In this paper a stable formation control law that simultaneously ensures collision avoidance has been proposed.It is assumed that the communication graph is undirected and connected.The proposed formation control law ...In this paper a stable formation control law that simultaneously ensures collision avoidance has been proposed.It is assumed that the communication graph is undirected and connected.The proposed formation control law is a combination of the consensus term and the collision avoidance term(CAT).The first order consensus term is derived for the proposed model,while ensuring the Lyapunov stability.The consensus term creates and maintains the desired formation shape,while the CAT avoids the collision.During the collision avoidance,the potential function based CAT makes the agents repel from each other.This unrestricted repelling magnitude cannot ensure the graph connectivity at the time of collision avoidance.Hence we have proposed a formation control law,which ensures this connectivity even during the collision avoidance.This is achieved by the proposed novel adaptive potential function.The potential function adapts itself,with the online tuning of the critical variable associated with it.The tuning has been done based on the lower bound of the critical variable,which is derived from the proposed connectivity property.The efficacy of the proposed scheme has been validated using simulations done based on formations of six and thirty-two agents respectively.展开更多
The density based notion for clustering approach is used widely due to its easy implementation and ability to detect arbitrary shaped clusters in the presence of noisy data points without requiring prior knowledge of ...The density based notion for clustering approach is used widely due to its easy implementation and ability to detect arbitrary shaped clusters in the presence of noisy data points without requiring prior knowledge of the number of clusters to be identified. Density-based spatial clustering of applications with noise （DBSCAN） is the first algorithm proposed in the literature that uses density based notion for cluster detection. Since most of the real data set, today contains feature space of adjacent nested clusters, clearly DBSCAN is not suitable to detect variable adjacent density clusters due to the use of global density parameter neighborhood radius Y,.ad and minimum number of points in neighborhood Np~,. So the efficiency of DBSCAN depends on these initial parameter settings, for DBSCAN to work properly, the neighborhood radius must be less than the distance between two clusters otherwise algorithm merges two clusters and detects them as a single cluster. Through this paper： 1） We have proposed improved version of DBSCAN algorithm to detect clusters of varying density adjacent clusters by using the concept of neighborhood difference and using the notion of density based approach without introducing much additional computational complexity to original DBSCAN algorithm. 2） We validated our experimental results using one of our authors recently proposed space density indexing （SDI） internal cluster measure to demonstrate the quality of proposed clustering method. Also our experimental results suggested that proposed method is effective in detecting variable density adjacent nested clusters.展开更多
The leakage of medical audio data in telemedicine seriously violates the privacy of patients.In order to avoid the leakage of patient information in telemedicine,a two-stage reversible robust audio watermarking algori...The leakage of medical audio data in telemedicine seriously violates the privacy of patients.In order to avoid the leakage of patient information in telemedicine,a two-stage reversible robust audio watermarking algorithm is proposed to protect medical audio data.The scheme decomposes the medical audio into two independent embedding domains,embeds the robust watermark and the reversible watermark into the two domains respectively.In order to ensure the audio quality,the Hurst exponent is used to find a suitable position for watermark embedding.Due to the independence of the two embedding domains,the embedding of the second-stage reversible watermark will not affect the first-stage watermark,so the robustness of the first-stage watermark can be well maintained.In the second stage,the correlation between the sampling points in the medical audio is used to modify the hidden bits of the histogram to reduce the modification of the medical audio and reduce the distortion caused by reversible embedding.Simulation experiments show that this scheme has strong robustness against signal processing operations such as MP3 compression of 48 db,additive white Gaussian noise(AWGN)of 20 db,low-pass filtering,resampling,re-quantization and other attacks,and has good imperceptibility.展开更多
In a telemedicine diagnosis system,the emergence of 3D imaging enables doctors to make clearer judgments,and its accuracy also directly affects doctors’diagnosis of the disease.In order to ensure the safe transmissio...In a telemedicine diagnosis system,the emergence of 3D imaging enables doctors to make clearer judgments,and its accuracy also directly affects doctors’diagnosis of the disease.In order to ensure the safe transmission and storage of medical data,a 3D medical watermarking algorithm based on wavelet transform is proposed in this paper.The proposed algorithm employs the principal component analysis(PCA)transform to reduce the data dimension,which can minimize the error between the extracted components and the original data in the mean square sense.Especially,this algorithm helps to create a bacterial foraging model based on particle swarm optimization(BF-PSO),by which the optimal wavelet coefficient is found for embedding and is used as the absolute feature of watermark embedding,thereby achieving the optimal balance between embedding capacity and imperceptibility.A series of experimental results from MATLAB software based on the standard MRI brain volume dataset demonstrate that the proposed algorithm has strong robustness and make the 3D model have small deformation after embedding the watermark.展开更多
Construction of road infrastructure is fundamental to city operation and development, as well as an important pathway and focus in physical urban-rural integration. The long-term implementation of a system of ring roa...Construction of road infrastructure is fundamental to city operation and development, as well as an important pathway and focus in physical urban-rural integration. The long-term implementation of a system of ring roads plus radiating roads in Beijing has strongly impacted urban infrastructure construction and space-time accessibility. Particularly, recent rapid growth of private car ownership in Beijing has imposed greater loads on its road system, seriously hampering urban commuting efficiency and negatively impacting quality of life. To address such challenges and enhance the rapid development of transport infrastructure, Beijing has accelerated rail transit construction since 2008 in an effort to improve commuting capacity. This paper aims to measure time accessibility and its spatial characteristics in urban areas of Beijing by applying a comprehensive method that combines vector and raster attribute data generated from road network and subway transport infrastructure. By using a dual index of accessibility and road density, the study further reveals the features of and differences in spatial accessibility and the construction of road systems in urban areas of the northern and southern parts of Beijing. The findings of this study can provide a scientific basis for future urban planning and road system construction both in general and with respect to Beijing, given its aspirations to become a world city.展开更多
In recent years,with the development of machine learning and deep learning,it is possible to identify and even control crop diseases by using electronic devices instead of manual observation.In this paper,an image rec...In recent years,with the development of machine learning and deep learning,it is possible to identify and even control crop diseases by using electronic devices instead of manual observation.In this paper,an image recognition method of citrus diseases based on deep learning is proposed.We built a citrus image dataset including six common citrus diseases.The deep learning network is used to train and learn these images,which can effectively identify and classify crop diseases.In the experiment,we use MobileNetV2 model as the primary network and compare it with other network models in the aspect of speed,model size,accuracy.Results show that our method reduces the prediction time consumption and model size while keeping a good classification accuracy.Finally,we discuss the significance of using MobileNetV2 to identify and classify agricultural diseases in mobile terminal,and put forward relevant suggestions.展开更多
The main purpose of this paper is to present and apply a genetic and simulated annealing combined algorithm to solve an optimization problem of Radio Frequency Identification(RFID)network planning in an emergency depa...The main purpose of this paper is to present and apply a genetic and simulated annealing combined algorithm to solve an optimization problem of Radio Frequency Identification(RFID)network planning in an emergency department of a hospital.Accordingly,though genetic algorithm(GA)and simulated annealing(SA)have advantages and disadvantages,but they are also complementary.Hence,the combined algorithm not only takes advantages of the two methods,but also avoids their disadvantages.The simulation results in an emergency department of a hospital present that the proposed method provides minimum total cost and maximum RFID network coverage in a simultaneous way with the efficient use of multi-antenna RFID readers.Besides,the results of comparison of two scenarios of the model with the results of other existing models in the relevant literature show that the proposed model has better outcomes.展开更多
This paper examines the effect of the observation time on source identification of a discrete-time susceptible-infectedrecovered diffusion process in a network with snapshot of partial nodes.We formulate the source id...This paper examines the effect of the observation time on source identification of a discrete-time susceptible-infectedrecovered diffusion process in a network with snapshot of partial nodes.We formulate the source identification problem as a maximum likelihood(ML)estimator and develop a statistical inference method based on Monte Carlo simulation(MCS)to estimate the source location and the initial time of diffusion.Experimental results in synthetic networks and real-world networks demonstrate evident impact of the observation time as well as the fraction of the observers on the concerned problem.展开更多
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 rem...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.展开更多
The key to preventing the COVID-19 is to diagnose patients quickly and accurately.Studies have shown that using Convolutional Neural Networks(CNN)to analyze chest Computed Tomography(CT)images is helpful for timely CO...The key to preventing the COVID-19 is to diagnose patients quickly and accurately.Studies have shown that using Convolutional Neural Networks(CNN)to analyze chest Computed Tomography(CT)images is helpful for timely COVID-19 diagnosis.However,personal privacy issues,public chest CT data sets are relatively few,which has limited CNN’s application to COVID-19 diagnosis.Also,many CNNs have complex structures and massive parameters.Even if equipped with the dedicated Graphics Processing Unit(GPU)for acceleration,it still takes a long time,which is not conductive to widespread application.To solve above problems,this paper proposes a lightweight CNN classification model based on transfer learning.Use the lightweight CNN MobileNetV2 as the backbone of the model to solve the shortage of hardware resources and computing power.In order to alleviate the problem of model overfitting caused by insufficient data set,transfer learning is used to train the model.The study first exploits the weight parameters trained on the ImageNet database to initialize the MobileNetV2 network,and then retrain the model based on the CT image data set provided by Kaggle.Experimental results on a computer equipped only with the Central Processing Unit(CPU)show that it consumes only 1.06 s on average to diagnose a chest CT image.Compared to other lightweight models,the proposed model has a higher classification accuracy and reliability while having a lightweight architecture and few parameters,which can be easily applied to computers without GPU acceleration.Code:github.com/ZhouJie-520/paper-codes.展开更多
We consider the mixed arrangement which is composed of the central hyperplane arrangement and a sphere. We discuss the lattice of its intersection set and the relationship between the Mobius function of the mixed arra...We consider the mixed arrangement which is composed of the central hyperplane arrangement and a sphere. We discuss the lattice of its intersection set and the relationship between the Mobius function of the mixed arrangement and the original hyperplane arangement. The Mobius function of the mixed arrangement is equal to the positive or the negative Mobius function of original hyperplane arrangement. Moreover, we give an equality of the chambers and the characteristic polynomial for the mixed arrangement.展开更多
Real-time dense reconstruction of indoor scenes is of great research value for the application and development of service robots,augmented reality,cultural relics conservation and other fields.ORB-SLAM2 method is one ...Real-time dense reconstruction of indoor scenes is of great research value for the application and development of service robots,augmented reality,cultural relics conservation and other fields.ORB-SLAM2 method is one of the excellent open source algorithms in visual SLAM system,which is often used in indoor scene reconstruction.However,it is time-consuming and can only build sparse scene map by using ORB features to solve camera pose.In view of the shortcomings of ORB-SLAM2 method,this article proposes an improved ORB-SLAM2 solution,which uses a direct method based on light intensity to solve the camera pose.It can greatly reduce the amount of computation,the speed is significantly improved by about 5 times compared with the ORB feature method.A parallel thread of map reconstruction is added with surfel model,and depth map and RGB map are fused to build the dense map.A Realsense D415 sensor is used as RGB-D cameras to obtain the three-dimensional(3D)point clouds of an indoor environments.After calibration and alignment processing,the sensor is applied in the reconstruction experiment of indoor scene with the improved ORB-SLAM2 method.Results show that the improved ORB-SLAM2 algorithm cause a great improvement in processing speed and reconstructing density of scenes.展开更多
GitHub repository recommendation is a research hotspot in the field of open-source software. The current problemswith the repository recommendation systemare the insufficient utilization of open-source community infor...GitHub repository recommendation is a research hotspot in the field of open-source software. The current problemswith the repository recommendation systemare the insufficient utilization of open-source community informationand the fact that the scoring metrics used to calculate the matching degree between developers and repositoriesare developed manually and rely too much on human experience, leading to poor recommendation results. Toaddress these problems, we design a questionnaire to investigate which repository information developers focus onand propose a graph convolutional network-based repository recommendation system (GCNRec). First, to solveinsufficient information utilization in open-source communities, we construct a Developer-Repository networkusing four types of behavioral data that best reflect developers’ programming preferences and extract features ofdevelopers and repositories from the repository content that developers focus on. Then, we design a repositoryrecommendation model based on a multi-layer graph convolutional network to avoid the manual formulation ofscoringmetrics. Thismodel takes the Developer-Repository network, developer features and repository features asinputs, and recommends the top-k repositories that developers are most likely to be interested in by learning theirpreferences. We have verified the proposed GCNRec on the dataset, and by comparing it with other open-sourcerepository recommendation methods, GCNRec achieves higher precision and hit rate.展开更多
Telemedicine plays an important role in Corona Virus Disease 2019(COVID-19).The virtual surgery simulation system,as a key component in telemedicine,requires to compute in real-time.Therefore,this paper proposes a rea...Telemedicine plays an important role in Corona Virus Disease 2019(COVID-19).The virtual surgery simulation system,as a key component in telemedicine,requires to compute in real-time.Therefore,this paper proposes a realtime cutting model based on finite element and order reduction method,which improves the computational speed and ensure the real-time performance.The proposed model uses the finite element model to construct a deformation model of the virtual lung.Meanwhile,a model order reduction method combining proper orthogonal decomposition and Galerkin projection is employed to reduce the amount of deformation computation.In addition,the cutting path is formed according to the collision intersection position of the surgical instrument and the lesion area of the virtual lung.Then,the Bezier curve is adopted to draw the incision outline after the virtual lung has been cut.Finally,the simulation system is set up on the PHANTOM OMNI force haptic feedback device to realize the cutting simulation of the virtual lung.Experimental results show that the proposed model can enhance the real-time performance of telemedicine,reduce the complexity of the cutting simulation and make the incision smoother and more natural.展开更多
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 dise...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.展开更多
Entity recognition and extraction are the foundations of knowledge graph construction.Entity data in the field of software engineering come from different platforms and communities,and have different formats.This pape...Entity recognition and extraction are the foundations of knowledge graph construction.Entity data in the field of software engineering come from different platforms and communities,and have different formats.This paper divides multi-source software knowledge entities into unstructured data,semi-structured data and code data.For these different types of data,Bi-directional Long Short-Term Memory(Bi-LSTM)with Conditional Random Field(CRF),template matching,and abstract syntax tree are used and integrated into a multi-source software knowledge entity extraction integration model(MEIM)to extract software entities.The model can be updated continuously based on user’s feedbacks to improve the accuracy.To deal with the shortage of entity annotation datasets,keyword extraction methods based on Term Frequency–Inverse Document Frequency(TF-IDF),TextRank,and K-Means are applied to annotate tasks.The proposed MEIM model is applied to the Spring Boot framework,which demonstrates good adaptability.The extracted entities are used to construct a knowledge graph,which is applied to association retrieval and association visualization.展开更多
文摘Traditional agricultural irrigation systems waste significant amounts of water and energy due to inefficient scheduling and the absence of real-time monitoring capabilities.This research developed a comprehensive IoT-based smart irrigation control systemto optimize water and energy management in agricultural greenhouses while enhancing crop productivity.The system employs a sophisticated four-layer Internet ofThings(IoT)architecture based on an ESP32 microcontroller,integrated with multiple environmental sensors,including soil moisture,temperature,humidity,and light intensity sensors,for comprehensive environmental monitoring.The system utilizes the Message Queuing Telemetry Transport(MQTT)communication protocol for reliable data transmission and incorporates a Random Forest machine learning algorithm for automated irrigation decision-making processes.The Random Forest model achieved exceptional performance with 99.3%overall accuracy,demonstrating high model reliability.Six operational modules were developed and implemented with three distinct control methods:manual operation,condition-based automatic control,and AI-driven intelligent control systems.A comprehensive one-month comparative analysis demonstrated remarkable improvements across multiple performance metrics:a 50%reduction in both water consumption(from 140 to 70 L/day)and energy usage(from 7.00 to 3.50 kWh/day),a substantial 130%increase in water use efficiency,and a significant 50%decrease in CO_(2) emissions.Furthermore,detailed factor importance analysis revealed soil moisture as the primary decision factor(38.6%),followed by temporal factors(20.3%)and light intensity(18.4%).The system demonstrates exceptional potential for annual energy conservation of 1277.5 kWh and CO_(2) emission reduction of 638.75 kg,contributing substantially to sustainable development goals and advancing smart agriculture technologies.
文摘Due to the continuous rising demand of handheld devices like iPods, mobile, tablets;specific applications like biomedical applications like pacemakers, hearing aid machines and space applications which require stable digital systems with low power consumptions are required. As a main part in digital system the SRAM (Static Random Access Memory) should have low power consumption and stability. As we are continuously moving towards scaling for the last two decades the effect of this is process variations which have severe effect on stability, performance. Reducing the supply voltage to sub-threshold region, which helps in reducing the power consumption to an extent but side by side it raises the issue of the stability of the memory. Static Noise Margin of SRAM cell enforces great challenges to the sub threshold SRAM design. In this paper we have analyzed the cell stability of 9T SRAM Cell at various processes. The cell stability is checked at deep submicron (DSM) technology. In this paper we have analyzed the effect of temperature and supply voltage (Vdd) on the stability parameters of SRAM which is Static Noise Margin (SNM), Write Margin (WM) and Read Current. The effect has been observed at various process corners at 45 nm technology. The temperature has a significant effect on stability along with the Vdd. The Cell has been working efficiently at all process corners and has 50% more SNM from conventional 6T SRAM and 30% more WM from conventional 6T SRAM cell.
基金funded by the Deanship of Scientific Research at Northern Border University,Arar,Saudi Arabia,through the project number“NBU-FFR-2025-1197-01”.
文摘Cyberbullying is a remarkable issue in the Arabic-speaking world,affecting children,organizations,and businesses.Various efforts have been made to combat this problem through proposed models using machine learning(ML)and deep learning(DL)approaches utilizing natural language processing(NLP)methods and by proposing relevant datasets.However,most of these endeavors focused predominantly on the English language,leaving a substantial gap in addressing Arabic cyberbullying.Given the complexities of the Arabic language,transfer learning techniques and transformers present a promising approach to enhance the detection and classification of abusive content by leveraging large and pretrained models that use a large dataset.Therefore,this study proposes a hybrid model using transformers trained on extensive Arabic datasets.It then fine-tunes the hybrid model on a newly curated Arabic cyberbullying dataset collected from social media platforms,in particular Twitter.Additionally,the following two hybrid transformer models are introduced:the first combines CAmelid Morphologically-aware pretrained Bidirectional Encoder Representations from Transformers(CAMeLBERT)with Arabic Generative Pre-trained Transformer 2(AraGPT2)and the second combines Arabic BERT(AraBERT)with Cross-lingual Language Model-RoBERTa(XLM-R).Two strategies,namely,feature fusion and ensemble voting,are employed to improve the model performance accuracy.Experimental results,measured through precision,recall,F1-score,accuracy,and AreaUnder the Curve-Receiver Operating Characteristic(AUC-ROC),demonstrate that the combined CAMeLBERT and AraGPT2 models using feature fusion outperformed traditional DL models,such as Long Short-Term Memory(LSTM)and Bidirectional Long Short-Term Memory(BiLSTM),as well as other independent Arabic-based transformer models.
文摘A recommender system is a tool designed to suggest relevant items to users based on their preferences and behaviors.Collaborative filtering,a popular technique within recommender systems,predicts user interests by analyzing patterns in interactions and similarities between users,leveraging past behavior data to make personalized recommendations.Despite its popularity,collaborative filtering faces notable challenges,and one of them is the issue of grey-sheep users who have unusual tastes in the system.Surprisingly,existing research has not extensively explored outlier detection techniques to address the grey-sheep problem.To fill this research gap,this study conducts a comprehensive comparison of 12 outlier detectionmethods(such as LOF,ABOD,HBOS,etc.)and introduces innovative user representations aimed at improving the identification of outliers within recommender systems.More specifically,we proposed and examined three types of user representations:1)the distribution statistics of user-user similarities,where similarities were calculated based on users’rating vectors;2)the distribution statistics of user-user similarities,but with similarities derived from users represented by latent factors;and 3)latent-factor vector representations.Our experiments on the Movie Lens and Yahoo!Movie datasets demonstrate that user representations based on latent-factor vectors consistently facilitate the identification of more grey-sheep users when applying outlier detection methods.
基金supported and funded by the CC&BT Division of the Department of Electronics & Information Technology,Govt,of India(23011/22/2013-R&DIN CC&BT)
文摘In this paper a stable formation control law that simultaneously ensures collision avoidance has been proposed.It is assumed that the communication graph is undirected and connected.The proposed formation control law is a combination of the consensus term and the collision avoidance term(CAT).The first order consensus term is derived for the proposed model,while ensuring the Lyapunov stability.The consensus term creates and maintains the desired formation shape,while the CAT avoids the collision.During the collision avoidance,the potential function based CAT makes the agents repel from each other.This unrestricted repelling magnitude cannot ensure the graph connectivity at the time of collision avoidance.Hence we have proposed a formation control law,which ensures this connectivity even during the collision avoidance.This is achieved by the proposed novel adaptive potential function.The potential function adapts itself,with the online tuning of the critical variable associated with it.The tuning has been done based on the lower bound of the critical variable,which is derived from the proposed connectivity property.The efficacy of the proposed scheme has been validated using simulations done based on formations of six and thirty-two agents respectively.
文摘The density based notion for clustering approach is used widely due to its easy implementation and ability to detect arbitrary shaped clusters in the presence of noisy data points without requiring prior knowledge of the number of clusters to be identified. Density-based spatial clustering of applications with noise （DBSCAN） is the first algorithm proposed in the literature that uses density based notion for cluster detection. Since most of the real data set, today contains feature space of adjacent nested clusters, clearly DBSCAN is not suitable to detect variable adjacent density clusters due to the use of global density parameter neighborhood radius Y,.ad and minimum number of points in neighborhood Np~,. So the efficiency of DBSCAN depends on these initial parameter settings, for DBSCAN to work properly, the neighborhood radius must be less than the distance between two clusters otherwise algorithm merges two clusters and detects them as a single cluster. Through this paper： 1） We have proposed improved version of DBSCAN algorithm to detect clusters of varying density adjacent clusters by using the concept of neighborhood difference and using the notion of density based approach without introducing much additional computational complexity to original DBSCAN algorithm. 2） We validated our experimental results using one of our authors recently proposed space density indexing （SDI） internal cluster measure to demonstrate the quality of proposed clustering method. Also our experimental results suggested that proposed method is effective in detecting variable density adjacent nested clusters.
基金This work was supported,in part,by the Natural Science Foundation of Jiangsu Province under Grant Numbers BK20201136,BK20191401in part,by the National Nature Science Foundation of China under Grant Numbers 61502240,61502096,61304205,61773219in part,by the Priority Academic Program Development of Jiangsu Higher Education Institutions(PAPD)fund.Conflicts of Interest:The aut。
文摘The leakage of medical audio data in telemedicine seriously violates the privacy of patients.In order to avoid the leakage of patient information in telemedicine,a two-stage reversible robust audio watermarking algorithm is proposed to protect medical audio data.The scheme decomposes the medical audio into two independent embedding domains,embeds the robust watermark and the reversible watermark into the two domains respectively.In order to ensure the audio quality,the Hurst exponent is used to find a suitable position for watermark embedding.Due to the independence of the two embedding domains,the embedding of the second-stage reversible watermark will not affect the first-stage watermark,so the robustness of the first-stage watermark can be well maintained.In the second stage,the correlation between the sampling points in the medical audio is used to modify the hidden bits of the histogram to reduce the modification of the medical audio and reduce the distortion caused by reversible embedding.Simulation experiments show that this scheme has strong robustness against signal processing operations such as MP3 compression of 48 db,additive white Gaussian noise(AWGN)of 20 db,low-pass filtering,resampling,re-quantization and other attacks,and has good imperceptibility.
基金supported,in part,by the Natural Science Foundation of Jiangsu Province under grant numbers BK20201136,BK20191401in part,by the Priority Academic Program Development of Jiangsu Higher Education Institutions(PAPD)fund.
文摘In a telemedicine diagnosis system,the emergence of 3D imaging enables doctors to make clearer judgments,and its accuracy also directly affects doctors’diagnosis of the disease.In order to ensure the safe transmission and storage of medical data,a 3D medical watermarking algorithm based on wavelet transform is proposed in this paper.The proposed algorithm employs the principal component analysis(PCA)transform to reduce the data dimension,which can minimize the error between the extracted components and the original data in the mean square sense.Especially,this algorithm helps to create a bacterial foraging model based on particle swarm optimization(BF-PSO),by which the optimal wavelet coefficient is found for embedding and is used as the absolute feature of watermark embedding,thereby achieving the optimal balance between embedding capacity and imperceptibility.A series of experimental results from MATLAB software based on the standard MRI brain volume dataset demonstrate that the proposed algorithm has strong robustness and make the 3D model have small deformation after embedding the watermark.
基金National Natural Science Foundation of China,No.41601164,No.41601427Key Program of National Natural Science Foundation of China,No.71433008Cultivate Project of Institute of Geographic Sciences and Natural Resources Research,CAS,No.TSYJS03
文摘Construction of road infrastructure is fundamental to city operation and development, as well as an important pathway and focus in physical urban-rural integration. The long-term implementation of a system of ring roads plus radiating roads in Beijing has strongly impacted urban infrastructure construction and space-time accessibility. Particularly, recent rapid growth of private car ownership in Beijing has imposed greater loads on its road system, seriously hampering urban commuting efficiency and negatively impacting quality of life. To address such challenges and enhance the rapid development of transport infrastructure, Beijing has accelerated rail transit construction since 2008 in an effort to improve commuting capacity. This paper aims to measure time accessibility and its spatial characteristics in urban areas of Beijing by applying a comprehensive method that combines vector and raster attribute data generated from road network and subway transport infrastructure. By using a dual index of accessibility and road density, the study further reveals the features of and differences in spatial accessibility and the construction of road systems in urban areas of the northern and southern parts of Beijing. The findings of this study can provide a scientific basis for future urban planning and road system construction both in general and with respect to Beijing, given its aspirations to become a world city.
基金the National Natural Science Foundation of China under Grant 61772561,author J.Q,http://www.nsfc.gov.cn/in part by the Key Research and Development Plan of Hunan Province under Grant 2018NK2012,author J.Q,http://kjt.hunan.gov.cn/+5 种基金in part by the Key Research and Development Plan of Hunan Province under Grant 2019SK2022,author Y.T,http://kjt.hunan.gov.cn/in part by the Science Research Projects of Hunan Provincial Education Department under Grant 18A174,author X.X,http://kxjsc.gov.hnedu.cn/in part by the Science Research Projects of Hunan Provincial Education Department under Grant 19B584,author Y.T,http://kxjsc.gov.hnedu.cn/in part by the Degree&Postgraduate Education Reform Project of Hunan Province under Grant 2019JGYB154,author J.Q,http://xwb.gov.hnedu.cn/in part by the Postgraduate Excellent teaching team Project of Hunan Province under Grant[2019]370-133,author J.Q,http://xwb.gov.hnedu.cn/,in part by the Postgraduate Education and Teaching Reform Project of Central South University of Forestry&Technology under Grant 2019JG013,author X.X,http://jwc.csuft.edu.cn/in part by the Natural Science Foundation of Hunan Province(No.2020JJ4140),author Y.T,http://kjt.hunan.gov.cn/in part by the Natural Science Foundation of Hunan Province(No.2020JJ4141),author X.X,http://kjt.hunan.gov.cn/.Conflicts of Interest:The authors declare that they have no conflicts of interest to report regarding the present study.
文摘In recent years,with the development of machine learning and deep learning,it is possible to identify and even control crop diseases by using electronic devices instead of manual observation.In this paper,an image recognition method of citrus diseases based on deep learning is proposed.We built a citrus image dataset including six common citrus diseases.The deep learning network is used to train and learn these images,which can effectively identify and classify crop diseases.In the experiment,we use MobileNetV2 model as the primary network and compare it with other network models in the aspect of speed,model size,accuracy.Results show that our method reduces the prediction time consumption and model size while keeping a good classification accuracy.Finally,we discuss the significance of using MobileNetV2 to identify and classify agricultural diseases in mobile terminal,and put forward relevant suggestions.
文摘The main purpose of this paper is to present and apply a genetic and simulated annealing combined algorithm to solve an optimization problem of Radio Frequency Identification(RFID)network planning in an emergency department of a hospital.Accordingly,though genetic algorithm(GA)and simulated annealing(SA)have advantages and disadvantages,but they are also complementary.Hence,the combined algorithm not only takes advantages of the two methods,but also avoids their disadvantages.The simulation results in an emergency department of a hospital present that the proposed method provides minimum total cost and maximum RFID network coverage in a simultaneous way with the efficient use of multi-antenna RFID readers.Besides,the results of comparison of two scenarios of the model with the results of other existing models in the relevant literature show that the proposed model has better outcomes.
基金the National Natural Science Foundation of China(Grant Nos.61673027 and 62106047)the Beijing Social Science Foundation(Grant No.21GLC042)the Humanity and Social Science Youth foundation of Ministry of Education,China(Grant No.20YJCZH228)。
文摘This paper examines the effect of the observation time on source identification of a discrete-time susceptible-infectedrecovered diffusion process in a network with snapshot of partial nodes.We formulate the source identification problem as a maximum likelihood(ML)estimator and develop a statistical inference method based on Monte Carlo simulation(MCS)to estimate the source location and the initial time of diffusion.Experimental results in synthetic networks and real-world networks demonstrate evident impact of the observation time as well as the fraction of the observers on the concerned problem.
基金This work was supported,in part,by the National Nature Science Foundation of China under grant numbers 61502240,61502096,61304205,61773219in part,by the Natural Science Foundation of Jiangsu Province under grant numbers BK20191401in part,by the Priority Academic Program Development of Jiangsu Higher Education Institutions(PAPD)fund.
文摘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.
基金This work was supported,in part,by the Natural Science Foundation of Jiangsu Province under Grant Numbers BK20201136,BK20191401in part,by the National Nature Science Foundation of China under Grant Numbers 61502240,61502096,61304205,61773219in part,by the Priority Academic Program Development of Jiangsu Higher Education Institutions(PAPD)fund.
文摘The key to preventing the COVID-19 is to diagnose patients quickly and accurately.Studies have shown that using Convolutional Neural Networks(CNN)to analyze chest Computed Tomography(CT)images is helpful for timely COVID-19 diagnosis.However,personal privacy issues,public chest CT data sets are relatively few,which has limited CNN’s application to COVID-19 diagnosis.Also,many CNNs have complex structures and massive parameters.Even if equipped with the dedicated Graphics Processing Unit(GPU)for acceleration,it still takes a long time,which is not conductive to widespread application.To solve above problems,this paper proposes a lightweight CNN classification model based on transfer learning.Use the lightweight CNN MobileNetV2 as the backbone of the model to solve the shortage of hardware resources and computing power.In order to alleviate the problem of model overfitting caused by insufficient data set,transfer learning is used to train the model.The study first exploits the weight parameters trained on the ImageNet database to initialize the MobileNetV2 network,and then retrain the model based on the CT image data set provided by Kaggle.Experimental results on a computer equipped only with the Central Processing Unit(CPU)show that it consumes only 1.06 s on average to diagnose a chest CT image.Compared to other lightweight models,the proposed model has a higher classification accuracy and reliability while having a lightweight architecture and few parameters,which can be easily applied to computers without GPU acceleration.Code:github.com/ZhouJie-520/paper-codes.
基金Supported by the National Natural Science Foundation of China(10471020)
文摘We consider the mixed arrangement which is composed of the central hyperplane arrangement and a sphere. We discuss the lattice of its intersection set and the relationship between the Mobius function of the mixed arrangement and the original hyperplane arangement. The Mobius function of the mixed arrangement is equal to the positive or the negative Mobius function of original hyperplane arrangement. Moreover, we give an equality of the chambers and the characteristic polynomial for the mixed arrangement.
基金This work was supported by Henan Province Science and Technology Project under Grant No.182102210065.
文摘Real-time dense reconstruction of indoor scenes is of great research value for the application and development of service robots,augmented reality,cultural relics conservation and other fields.ORB-SLAM2 method is one of the excellent open source algorithms in visual SLAM system,which is often used in indoor scene reconstruction.However,it is time-consuming and can only build sparse scene map by using ORB features to solve camera pose.In view of the shortcomings of ORB-SLAM2 method,this article proposes an improved ORB-SLAM2 solution,which uses a direct method based on light intensity to solve the camera pose.It can greatly reduce the amount of computation,the speed is significantly improved by about 5 times compared with the ORB feature method.A parallel thread of map reconstruction is added with surfel model,and depth map and RGB map are fused to build the dense map.A Realsense D415 sensor is used as RGB-D cameras to obtain the three-dimensional(3D)point clouds of an indoor environments.After calibration and alignment processing,the sensor is applied in the reconstruction experiment of indoor scene with the improved ORB-SLAM2 method.Results show that the improved ORB-SLAM2 algorithm cause a great improvement in processing speed and reconstructing density of scenes.
基金supported by Special Funds for the Construction of an Innovative Province of Hunan,No.2020GK2028.
文摘GitHub repository recommendation is a research hotspot in the field of open-source software. The current problemswith the repository recommendation systemare the insufficient utilization of open-source community informationand the fact that the scoring metrics used to calculate the matching degree between developers and repositoriesare developed manually and rely too much on human experience, leading to poor recommendation results. Toaddress these problems, we design a questionnaire to investigate which repository information developers focus onand propose a graph convolutional network-based repository recommendation system (GCNRec). First, to solveinsufficient information utilization in open-source communities, we construct a Developer-Repository networkusing four types of behavioral data that best reflect developers’ programming preferences and extract features ofdevelopers and repositories from the repository content that developers focus on. Then, we design a repositoryrecommendation model based on a multi-layer graph convolutional network to avoid the manual formulation ofscoringmetrics. Thismodel takes the Developer-Repository network, developer features and repository features asinputs, and recommends the top-k repositories that developers are most likely to be interested in by learning theirpreferences. We have verified the proposed GCNRec on the dataset, and by comparing it with other open-sourcerepository recommendation methods, GCNRec achieves higher precision and hit rate.
基金supported,in part,by the Natural Science Foundation of Jiangsu Province under Grant Numbers BK20201136,BK20191401in part,by the National Nature Science Foundation of China under Grant Numbers 61502240,61502096,61304205,61773219in part,by the Priority Academic Program Development of Jiangsu Higher Education Institutions(PAPD)fund.
文摘Telemedicine plays an important role in Corona Virus Disease 2019(COVID-19).The virtual surgery simulation system,as a key component in telemedicine,requires to compute in real-time.Therefore,this paper proposes a realtime cutting model based on finite element and order reduction method,which improves the computational speed and ensure the real-time performance.The proposed model uses the finite element model to construct a deformation model of the virtual lung.Meanwhile,a model order reduction method combining proper orthogonal decomposition and Galerkin projection is employed to reduce the amount of deformation computation.In addition,the cutting path is formed according to the collision intersection position of the surgical instrument and the lesion area of the virtual lung.Then,the Bezier curve is adopted to draw the incision outline after the virtual lung has been cut.Finally,the simulation system is set up on the PHANTOM OMNI force haptic feedback device to realize the cutting simulation of the virtual lung.Experimental results show that the proposed model can enhance the real-time performance of telemedicine,reduce the complexity of the cutting simulation and make the incision smoother and more natural.
基金This work is supported in part by the Jiangsu Basic Research Programs-Natural Science Foundation under Grant Numbers BK20181407in part by the National Natural Science Foundation of China under Grant Numbers U1936118,61672294+3 种基金in part by Six peak talent project of Jiangsu Province(R2016L13)Qinglan Project of Jiangsu Province,and“333”project of Jiangsu Province,in part by the National Natural Science Foundation of China under Grant Numbers U1836208,61702276,61772283,61602253,and 61601236in part by National Key R&D Program of China under Grant 2018YFB1003205in part by the Priority Academic Program Development of Jiangsu Higher Education Institutions(PAPD)fund,in part by the Collaborative Innovation Center of Atmospheric Environment and Equipment Technology(CICAEET)fund,China.Zhihua Xia is supported by BK21+program from the Ministry of Education of Korea.
文摘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.
基金Zhifang Liao:Ministry of Science and Technology:Key Research and Development Project(2018YFB003800),Hunan Provincial Key Laboratory of Finance&Economics Big Data Scienceand Technology(Hunan University of Finance and Economics)2017TP1025,HNNSF 2018JJ2535Shengzong Liu:NSF61802120.
文摘Entity recognition and extraction are the foundations of knowledge graph construction.Entity data in the field of software engineering come from different platforms and communities,and have different formats.This paper divides multi-source software knowledge entities into unstructured data,semi-structured data and code data.For these different types of data,Bi-directional Long Short-Term Memory(Bi-LSTM)with Conditional Random Field(CRF),template matching,and abstract syntax tree are used and integrated into a multi-source software knowledge entity extraction integration model(MEIM)to extract software entities.The model can be updated continuously based on user’s feedbacks to improve the accuracy.To deal with the shortage of entity annotation datasets,keyword extraction methods based on Term Frequency–Inverse Document Frequency(TF-IDF),TextRank,and K-Means are applied to annotate tasks.The proposed MEIM model is applied to the Spring Boot framework,which demonstrates good adaptability.The extracted entities are used to construct a knowledge graph,which is applied to association retrieval and association visualization.
