Fast and accurate plant disease detection is critical to increasing agricultural productivity in a sustainable way.Traditionally,human experts have been relied upon to diagnose anomalies in plants caused by diseases,p...Fast and accurate plant disease detection is critical to increasing agricultural productivity in a sustainable way.Traditionally,human experts have been relied upon to diagnose anomalies in plants caused by diseases,pests,nutritional deficiencies or extreme weather.However,this is expensive,time consuming and in some cases impractical.To counter these challenges,research into the use of image processing techniques for plant disease recognition has become a hot research topic.In this paper,we provide a comprehensive review of recent studies carried out in the area of crop pest and disease recognition using image processing and machine learning techniques.We hope that this work will be a valuable resource for researchers in this area of crop pest and disease recognition using image processing techniques.In particular,we concentrate on the use of RGB images owing to the low cost and high availability of digital RGB cameras.We report that recent efforts have focused on the use of deep learning instead of training shallow classifiers using handcrafted features.Researchers have reported high recognition accuracies on particular datasets but in many cases,the performance of those systems deteriorated significantly when tested on different datasets or in field conditions.Nevertheless,progress made so far has been encouraging.Experimental results showing the leaf disease recognition performance of ten CNN architectures in terms of recognition accuracy,recall,precision,specificity,F1-score,training duration and storage requirements are also presented.Subsequently,recommendations are made on the most suitable architectures to deploy in conventional as well as mobile/embedded computing environments.We also discuss some of the unresolved challenges that need to be addressed in order to develop practical automatic plant disease recognition systems for use in field conditions.展开更多
With the proliferation of smart grid research, the Advanced Metering Infrastructure （AMI） has become the first ubiquitous and fixed computing platform. However, due to the unique characteristics of AMI, such as comp...With the proliferation of smart grid research, the Advanced Metering Infrastructure （AMI） has become the first ubiquitous and fixed computing platform. However, due to the unique characteristics of AMI, such as complex network structure, resource-constrained smart meter, and privacy-sensitive data, it is an especially challenging issue to make AMI secure. Energy theft is one of the most important concerns related to the smart grid implementation. It is estimated that utility companies lose more than S25 billion every year due to energy theft around the world. To address this challenge, in this paper, we discuss the background of AMI and identify major security requirements that AMI should meet. Specifically, an attack tree based threat model is first presented to illustrate the energy-theft behaviors in AMI. Then, we summarize the current AMI energy-theft detection schemes into three categories, i.e., classification-based, state estimation-based, and game theory-based ones, and make extensive comparisons and discussions on them. In order to provide a deep understanding of security vulnerabilities and solutions in AMI and shed light on future research directions, we also explore some open challenges and potential solutions for energy-theft detection.展开更多
In this paper,a grid interface current control strategy is presented for a DC microgrid,which aims to reduce the disturbance from PV generation and the load variation to the main grid without a grid interface converte...In this paper,a grid interface current control strategy is presented for a DC microgrid,which aims to reduce the disturbance from PV generation and the load variation to the main grid without a grid interface converter.The grid interface current is directly controlled by a battery DC-DC converter within the DC microgrid.Based on a comprehensive analysis of the battery DC-DC converter and interface current control,the control system has been mathematically modelled.This enabled two transfer functions to be derived that reflect the dynamic response of the inductor current to the duty cycle variation(inner loop),and the dynamic response of the grid interface current to the inductor current variation(outer loop).Experimental study has been done to assess the effectiveness of the proposed control strategy.The experimental results indicate that the proposed control strategy has a good performance to control the grid interface current without an interface converter,regardless the variations of both PV and the load conditions.展开更多
To overcome the shortcomings of 1 D and 2 D Otsu’s thresholding techniques, the 3 D Otsu method has been developed.Among all Otsu’s methods, 3 D Otsu technique provides the best threshold values for the multi-level ...To overcome the shortcomings of 1 D and 2 D Otsu’s thresholding techniques, the 3 D Otsu method has been developed.Among all Otsu’s methods, 3 D Otsu technique provides the best threshold values for the multi-level thresholding processes. In this paper, to improve the quality of segmented images, a simple and effective multilevel thresholding method is introduced. The proposed approach focuses on preserving edge detail by computing the 3 D Otsu along the fusion phenomena. The advantages of the presented scheme include higher quality outcomes, better preservation of tiny details and boundaries and reduced execution time with rising threshold levels. The fusion approach depends upon the differences between pixel intensity values within a small local space of an image;it aims to improve localized information after the thresholding process. The fusion of images based on local contrast can improve image segmentation performance by minimizing the loss of local contrast, loss of details and gray-level distributions. Results show that the proposed method yields more promising segmentation results when compared to conventional1 D Otsu, 2 D Otsu and 3 D Otsu methods, as evident from the objective and subjective evaluations.展开更多
Real-time pedestrian detection is an important task for unmanned driving systems and video surveillance.The existing pedestrian detection methods often work at low speed and also fail to detect smaller and densely dis...Real-time pedestrian detection is an important task for unmanned driving systems and video surveillance.The existing pedestrian detection methods often work at low speed and also fail to detect smaller and densely distributed pedestrians by losing some of their detection accuracy in such cases.Therefore,the proposed algorithm YOLOv2(“YOU ONLY LOOK ONCE Version 2”)-based pedestrian detection(referred to as YOLOv2PD)would be more suitable for detecting smaller and densely distributed pedestrians in real-time complex road scenes.The proposed YOLOv2PD algorithm adopts a Multi-layer Feature Fusion(MLFF)strategy,which helps to improve the model’s feature extraction ability.In addition,one repeated convolution layer is removed from the final layer,which in turn reduces the computational complexity without losing any detection accuracy.The proposed algorithm applies the K-means clustering method on the Pascal Voc-2007+2012 pedestrian dataset before training to find the optimal anchor boxes.Both the proposed network structure and the loss function are improved to make the model more accurate and faster while detecting smaller pedestrians.Experimental results show that,at 544×544 image resolution,the proposed model achieves 80.7%average precision(AP),which is 2.1%higher than the YOLOv2 Model on the Pascal Voc-2007+2012 pedestrian dataset.Besides,based on the experimental results,the proposed model YOLOv2PD achieves a good trade-off balance between detection accuracy and real-time speed when evaluated on INRIA and Caltech test pedestrian datasets and achieves state-of-the-art detection results.展开更多
This paper deals with the problem of robust stability for continuous-time singular systems with state delay and parameter uncertainty. The uncertain singular systems with delay considered in this paper are assumed to ...This paper deals with the problem of robust stability for continuous-time singular systems with state delay and parameter uncertainty. The uncertain singular systems with delay considered in this paper are assumed to be regular and impulse free.By decomposing the systems into slow and fast subsystems,a robust delay-dependent asymptotic stability criteria based on linear matrix inequality is proposed, which is derived by using Lyapunov-Krasovskii functionals, neither model transformation nor bounding for cross terms is required in the derivation of our delay-dependent result. The robust delay-dependent stability criterion proposed in this paper is a sufficient condition. Finally, numerical examples and Matlab simulation are provided to illustrate the effectiveness of the proposed method.展开更多
An ultrasensitive metamaterial sensor based on double-slot vertical split ring resonators(DVSRRs) is designed and numerically calculated in the terahertz frequency. This DVSRR design produces a fundament LC resonance ...An ultrasensitive metamaterial sensor based on double-slot vertical split ring resonators(DVSRRs) is designed and numerically calculated in the terahertz frequency. This DVSRR design produces a fundament LC resonance with a quality factor of about 20 when the incidence magnetic field component normal to the DVSRR array. The resonant characteristics and sensing performance of the DVSRR array design are systematically analyzed employing a contrast method among three similar vertical split ring resonator(SRRs) structures. The research results show that the elimination of bianisotropy, induced by the structural symmetry of the DVSRR design, helps to achieve LC resonance of a high quality factor. Lifting the SRRs up from the substrate sharply reduces the dielectric loss introduced by the substrate. All these factors jointly result in superior sensitivity of the DVSRR to the attributes of analytes. The maximum refractive index sensitivity is 788 GHz/RIU or 1.04 × 10~5 nm∕RIU.Also, the DVSRR sensor maintains its superior sensing performance for fabrication tolerance ranging from -4% to 4% and wide range incidence angles up to 50° under both TE and TM illuminations.展开更多
Big data has the ability to open up innovative and ground-breaking prospects for the electrical grid,which also supports to obtain a variety of technological,social,and financial benefits.There is an unprecedented amo...Big data has the ability to open up innovative and ground-breaking prospects for the electrical grid,which also supports to obtain a variety of technological,social,and financial benefits.There is an unprecedented amount of heterogeneous big data as a consequence of the growth of power grid technologies,along with data processing and advanced tools.The main obstacles in turning the heterogeneous large dataset into useful results are computational burden and information security.The original contribution of this paper is to develop a new big data framework for detecting various intrusions from the smart grid systems with the use of AI mechanisms.Here,an AdaBelief Exponential Feature Selection(AEFS)technique is used to efficiently handle the input huge datasets from the smart grid for boosting security.Then,a Kernel based Extreme Neural Network(KENN)technique is used to anticipate security vulnerabilities more effectively.The Polar Bear Optimization(PBO)algorithm is used to efficiently determine the parameters for the estimate of radial basis function.Moreover,several types of smart grid network datasets are employed during analysis in order to examine the outcomes and efficiency of the proposed AdaBelief Exponential Feature Selection-Kernel based Extreme Neural Network(AEFS-KENN)big data security framework.The results reveal that the accuracy of proposed AEFS-KENN is increased up to 99.5%with precision and AUC of 99%for all smart grid big datasets used in this study.展开更多
Brain-Computer interfacing(BCI)has currently added a new dimension in assistive robotics.Existing braincomputer interfaces designed for position control applications suffer from two fundamental limitations.First,most ...Brain-Computer interfacing(BCI)has currently added a new dimension in assistive robotics.Existing braincomputer interfaces designed for position control applications suffer from two fundamental limitations.First,most of the existing schemes employ open-loop control,and thus are unable to track positional errors,resulting in failures in taking necessary online corrective actions.There are examples of a few works dealing with closed-loop electroencephalography(EEG)-based position control.These existing closed-loop brain-induced position control schemes employ a fixed order link selection rule,which often creates a bottleneck preventing time-efficient control.Second,the existing brain-induced position controllers are designed to generate a position response like a traditional firstorder system,resulting in a large steady-state error.This paper overcomes the above two limitations by keeping provisions for steady-state visual evoked potential(SSVEP)induced linkselection in an arbitrary order as required for efficient control and generating a second-order response of the position-control system with gradually diminishing overshoots/undershoots to reduce steady-state errors.Other than the above,the third innovation is to utilize motor imagery and P300 signals to design the hybrid brain-computer interfacing system for the said application with gradually diminishing error-margin using speed reversal at the zero-crossings of positional errors.Experiments undertaken reveal that the steady-state error is reduced to 0.2%.The paper also provides a thorough analysis of the stability of the closed-loop system performance using the Root Locus technique.展开更多
Due to its high mobility and flexible deployment,unmanned aerial vehicle(UAV)is drawing unprecedented interest in both military and civil applications to enable agile and ubiquitous connectivity.Mainly operating in an...Due to its high mobility and flexible deployment,unmanned aerial vehicle(UAV)is drawing unprecedented interest in both military and civil applications to enable agile and ubiquitous connectivity.Mainly operating in an open environment,UAV communications benefit from dominant line-of-sight links;however,this on the other hand renders the communications more vulnerable to malicious attacks.Recently,physical layer security(PLS)has been introduced to UAV systems as an important complement to the conventional cryptography-based approaches.In this paper,a comprehensive survey on the current achievements of UAV-PLS is conducted.We first introduce the basic concepts including typical static/-mobile UAV deployment scenarios,the unique air-toground channel and aerial nodes distribution models,as well as various roles that a UAV may act when PLS is concerned.Then,we start by reviewing the secrecy performance analysis and enhancing techniques for statically deployed UAV systems,and extend the discussion to the more general scenario where the UAVs’mobility is further exploited.For both cases,respectively,we summarize the commonly adopted methodologies,then describe important works in the litera ture in detail.Finally,potential research directions and challenges are discussed to provide an outlook for future works in the area of UAV-PLS.展开更多
With the development of wireless mobile communication technology,the demand for wireless communication rate and frequency increases year by year.Existing wireless mobile communication frequency tends to be saturated,w...With the development of wireless mobile communication technology,the demand for wireless communication rate and frequency increases year by year.Existing wireless mobile communication frequency tends to be saturated,which demands for new solutions.Terahertz(THz)communication has great potential for the future mobile communications(Beyond 5G),and is also an important technique for the high data rate transmission in spatial information network.THz communication has great application prospects in military-civilian integration and coordinated development.In China,important breakthroughs have been achieved for the key techniques of THz high data rate communications,which is practically keeping up with the most advanced technological level in the world.Therefore,further intensifying efforts on the development of THz communication have the strategic importance for China in leading the development of future wireless communication techniques and the standardization process of Beyond 5G.This paper analyzes the performance of the MIMO channel in the Terahertz(THz)band and a discrete mathematical method is used to propose a novel channel model.Then,a channel capacity model is proposed by the combination of path loss and molecular absorption in the THz band based on the CSI at the receiver.Simulation results show that the integration of MIMO in the THz band gives better data rate and channel capacity as compared with a single channel.展开更多
Label-free surface-enhanced Raman scattering(SERS)technique with ultra-sensitivity becomes more and more desirable in biomedical analysis,which is yet hindered by inefficient follow-up data analysis.Here we report an ...Label-free surface-enhanced Raman scattering(SERS)technique with ultra-sensitivity becomes more and more desirable in biomedical analysis,which is yet hindered by inefficient follow-up data analysis.Here we report an integrative method based on SERS and Artificial Intelligence for Cancer Screening(SERS-AICS)for liquid biopsy such as serum via silver nanowires,combining molecular vibrational signals processing with large-scale data mining algorithm.According to 382 healthy controls and 1582 patients from two independent cohorts,SERS-AICS not only distinguishes pan-cancer patients from health controls with 95.81% overall accuracy and 95.87% sensitivity at 95.40% specificity,but also screens out those samples at early cancer stage.The supereminent efficiency potentiates SERS-AICS a promising tool for detecting cancer with broader types at earlier stage,accompanying with the establishment of a data platform for further deep analysis.展开更多
A brain tumor is a lethal neurological disease that affects the average performance of the brain and can be fatal.In India,around 15 million cases are diagnosed yearly.To mitigate the seriousness of the tumor it is es...A brain tumor is a lethal neurological disease that affects the average performance of the brain and can be fatal.In India,around 15 million cases are diagnosed yearly.To mitigate the seriousness of the tumor it is essential to diagnose at the beginning.Notwithstanding,the manual evaluation process utilizing Magnetic Resonance Imaging(MRI)causes a few worries,remarkably inefficient and inaccurate brain tumor diagnoses.Similarly,the examination process of brain tumors is intricate as they display high unbalance in nature like shape,size,appearance,and location.Therefore,a precise and expeditious prognosis of brain tumors is essential for implementing the of an implicit treatment.Several computer models adapted to diagnose the tumor,but the accuracy of the model needs to be tested.Considering all the above mentioned things,this work aims to identify the best classification system by considering the prediction accuracy out of Alex-Net,ResNet 50,and Inception V3.Data augmentation is performed on the database and fed into the three convolutions neural network(CNN)models.A comparison line is drawn between the three models based on accuracy and performance.An accuracy of 96.2%is obtained for AlexNet with augmentation and performed better than ResNet 50 and Inception V3 for the 120th epoch.With the suggested model with higher accuracy,it is highly reliable if brain tumors are diagnosed with available datasets.展开更多
New fractional operators, the COVID-19 model has been studied in this paper. By using different numericaltechniques and the time fractional parameters, the mechanical characteristics of the fractional order model arei...New fractional operators, the COVID-19 model has been studied in this paper. By using different numericaltechniques and the time fractional parameters, the mechanical characteristics of the fractional order model areidentified. The uniqueness and existence have been established. Themodel’sUlam-Hyers stability analysis has beenfound. In order to justify the theoretical results, numerical simulations are carried out for the presented methodin the range of fractional order to show the implications of fractional and fractal orders.We applied very effectivenumerical techniques to obtain the solutions of themodel and simulations. Also, we present conditions of existencefor a solution to the proposed epidemicmodel and to calculate the reproduction number in certain state conditionsof the analyzed dynamic system. COVID-19 fractional order model for the case of Wuhan, China, is offered foranalysis with simulations in order to determine the possible efficacy of Coronavirus disease transmission in theCommunity. For this reason, we employed the COVID-19 fractal fractional derivative model in the example ofWuhan, China, with the given beginning conditions. In conclusion, again the mathematical models with fractionaloperators can facilitate the improvement of decision-making for measures to be taken in the management of anepidemic situation.展开更多
Assessing the behaviour and concentration of waste pollutants deposited between two parallel plates is essential for effective environmental management.Determining the effectiveness of treatment methods in reducing po...Assessing the behaviour and concentration of waste pollutants deposited between two parallel plates is essential for effective environmental management.Determining the effectiveness of treatment methods in reducing pollution scales is made easier by analysing waste discharge concentrations.The waste discharge concentration analysis is useful for assessing how effectively wastewater treatment techniques reduce pollution levels.This study aims to explore the Casson micropolar fluid flow through two parallel plates with the influence of pollutant concentration and thermophoretic particle deposition.To explore the mass and heat transport features,thermophoretic particle deposition and thermal radiation are considered.The governing equations are transformed into ordinary differential equations with the help of suitable similarity transformations.The Runge-Kutta-Fehlberg’s fourthfifth order technique and shooting procedure are used to solve the reduced set of equations and boundary conditions.The integration of a neural network model based on the Levenberg-Marquardt algorithm serves to improve the accuracy of predictions and optimize the analysis of parameters.Graphical outcomes are displayed to analyze the characteristics of the relevant dimensionless parameters in the current problem.Results reveal that concentration upsurges as the micropolar parameter increases.The concentration reduces with an upsurge in the thermophoretic parameter.An upsurge in the external pollutant source variation and the local pollutant external source parameters enhances mass transport.The surface drag force declines for improved values of porosity and micropolar parameters.展开更多
A nanoheterojunction composite photocatalyst Bi2O3/TiO2 working under visible-light （λ≥ 420 nm） was prepared by combining two semiconductors Bi2O3 and TiO2 varying the Bi2O3/TiO2 molar ratio. Maleic acid was emplo...A nanoheterojunction composite photocatalyst Bi2O3/TiO2 working under visible-light （λ≥ 420 nm） was prepared by combining two semiconductors Bi2O3 and TiO2 varying the Bi2O3/TiO2 molar ratio. Maleic acid was employed as an organic binder to unite Bi2O3 and TiO2 nanoparticles. The SEM, TEM, XRD and diffuse reflectance spectra were utilized to characterize the prepared Bi2O3/TiO2 nanoheterojunction. The nanocomposite exhibited unusual high photocatalytic activity in decomposing 2-propanol in gas phase and phenol in aqueous phase and, evolution of CO2 under visible light irradiation while the end members exhibited low photocatalytic activity. The composite was optimized to 5 mol% Bi2O3/TiO2. The remarkable high photocatalytic efficiency originates from the unique relative energy band position of Bi2O3 and TiO2 as well as the absorption of visible light by Bi2O3.展开更多
Due to the rise of 5G,IoT,AI,and high-performance computing applications,datacenter trafc has grown at a compound annual growth rate of nearly 30%.Furthermore,nearly three-fourths of the datacenter trafc resides withi...Due to the rise of 5G,IoT,AI,and high-performance computing applications,datacenter trafc has grown at a compound annual growth rate of nearly 30%.Furthermore,nearly three-fourths of the datacenter trafc resides within datacenters.The conventional pluggable optics increases at a much slower rate than that of datacenter trafc.The gap between application requirements and the capability of conventional pluggable optics keeps increasing,a trend that is unsustainable.Copackaged optics(CPO)is a disruptive approach to increasing the interconnecting bandwidth density and energy efciency by dramatically shortening the electrical link length through advanced packaging and co-optimization of electronics and photonics.CPO is widely regarded as a promising solution for future datacenter interconnections,and silicon platform is the most promising platform for large-scale integration.Leading international companies(e.g.,Intel,Broadcom and IBM)have heavily investigated in CPO technology,an inter-disciplinary research feld that involves photonic devices,integrated circuits design,packaging,photonic device modeling,electronic-photonic co-simulation,applications,and standardization.This review aims to provide the readers a comprehensive overview of the state-of-the-art progress of CPO in silicon platform,identify the key challenges,and point out the potential solutions,hoping to encourage collaboration between diferent research felds to accelerate the development of CPO technology.展开更多
This article analyzes the transmission line characteristics of plasmonic hybrid metal insulator metal(HMIM) wave-guide, circular ring resonator(CRR) based dual-band band-pass filters with two transmission poles in bot...This article analyzes the transmission line characteristics of plasmonic hybrid metal insulator metal(HMIM) wave-guide, circular ring resonator(CRR) based dual-band band-pass filters with two transmission poles in both pass-bands in the optical regime using coupled line feed. The transmission line characteristics of an HMIM waveguide, such as characteristic impedance(Z_(PV)), effective refractive index(n_(eff)) and propagation length(L_(spp)), have been obtained by using full wave simulation.展开更多
Micro-light-emitting diodes(μLEDs)have gained significant interest as an activation source for gas sensors owing to their advantages,including room temperature operation and low power consumption.However,despite thes...Micro-light-emitting diodes(μLEDs)have gained significant interest as an activation source for gas sensors owing to their advantages,including room temperature operation and low power consumption.However,despite these benefits,challenges still exist such as a limited range of detectable gases and slow response.In this study,we present a blueμLED-integrated light-activated gas sensor array based on SnO_(2)nanoparticles(NPs)that exhibit excellent sensitivity,tunable selectivity,and rapid detection with micro-watt level power consumption.The optimal power forμLED is observed at the highest gas response,supported by finite-difference time-domain simulation.Additionally,we first report the visible light-activated selective detection of reducing gases using noble metal-decorated SnO_(2)NPs.The noble metals induce catalytic interaction with reducing gases,clearly distinguishing NH3,H2,and C2H5OH.Real-time gas monitoring based on a fully hardwareimplemented light-activated sensing array was demonstrated,opening up new avenues for advancements in light-activated electronic nose technologies.展开更多
Organic and inorganic phase change materials(PCMs) are considered potential materials for thermal energy storage(TES) with different phase change characteristics. In this study, a novel organic-inorganic composite pha...Organic and inorganic phase change materials(PCMs) are considered potential materials for thermal energy storage(TES) with different phase change characteristics. In this study, a novel organic-inorganic composite phase change material(PCM) called disodium hydrogen phosphate dodecahydrate-lauric-palmitic acid(D-LA-PACM) was prepared. Expanded graphite(EG) was selected as the support material, and the novel organic-inorganic form-stable PCM called D-LA-PAPCM/EG was prepared using the vacuum adsorption method. Differential scanning calorimetry, Fourier transform infrared spectroscopy, X-ray diffraction, leakage testing, melting and solidification cycle testing, thermal conductivity testing, scanning electron microscopy observation of the micromorphology, and other characterization methods were used to study the microstructure and morphology, thermal physical parameters, thermal conductivity, stability of the PCMs, and the comprehensive material properties of D-LA-PAPCM under the composite action of EG. Results indicated that the melting and freezing temperatures and latent heats of D-LA-PAPCM/EG were measured to be 31.6℃ and 34.3℃ and 142.9 and 142.8 J/g, respectively. Although some of the lauric-palmitic acid(LA-PA) and disodium hydrogen phosphate dodecahydrate(DHPD) separated in the multiple porous structures of EG after 1000 cycles, they could still absorb and release latent heats independently, with D-LA-PAPCM/EG still exhibiting good thermal stability. The thermal conductivity of D-LA-PAPCM/EG was 1.361 W/(m·K). Therefore, the material and thermal properties of the prepared D-LA-PAPCM/EG indicate that it could be well used as a feasible material for energy-saving phase change floor units in indoor TES systems.展开更多
文摘Fast and accurate plant disease detection is critical to increasing agricultural productivity in a sustainable way.Traditionally,human experts have been relied upon to diagnose anomalies in plants caused by diseases,pests,nutritional deficiencies or extreme weather.However,this is expensive,time consuming and in some cases impractical.To counter these challenges,research into the use of image processing techniques for plant disease recognition has become a hot research topic.In this paper,we provide a comprehensive review of recent studies carried out in the area of crop pest and disease recognition using image processing and machine learning techniques.We hope that this work will be a valuable resource for researchers in this area of crop pest and disease recognition using image processing techniques.In particular,we concentrate on the use of RGB images owing to the low cost and high availability of digital RGB cameras.We report that recent efforts have focused on the use of deep learning instead of training shallow classifiers using handcrafted features.Researchers have reported high recognition accuracies on particular datasets but in many cases,the performance of those systems deteriorated significantly when tested on different datasets or in field conditions.Nevertheless,progress made so far has been encouraging.Experimental results showing the leaf disease recognition performance of ten CNN architectures in terms of recognition accuracy,recall,precision,specificity,F1-score,training duration and storage requirements are also presented.Subsequently,recommendations are made on the most suitable architectures to deploy in conventional as well as mobile/embedded computing environments.We also discuss some of the unresolved challenges that need to be addressed in order to develop practical automatic plant disease recognition systems for use in field conditions.
基金supported by China Scholarship Councilthe National Natural Science Foundation of China (Nos. 61170261 and 61202369)NSERC,Canada
文摘With the proliferation of smart grid research, the Advanced Metering Infrastructure （AMI） has become the first ubiquitous and fixed computing platform. However, due to the unique characteristics of AMI, such as complex network structure, resource-constrained smart meter, and privacy-sensitive data, it is an especially challenging issue to make AMI secure. Energy theft is one of the most important concerns related to the smart grid implementation. It is estimated that utility companies lose more than S25 billion every year due to energy theft around the world. To address this challenge, in this paper, we discuss the background of AMI and identify major security requirements that AMI should meet. Specifically, an attack tree based threat model is first presented to illustrate the energy-theft behaviors in AMI. Then, we summarize the current AMI energy-theft detection schemes into three categories, i.e., classification-based, state estimation-based, and game theory-based ones, and make extensive comparisons and discussions on them. In order to provide a deep understanding of security vulnerabilities and solutions in AMI and shed light on future research directions, we also explore some open challenges and potential solutions for energy-theft detection.
基金funding from the U.K.EPSRC UKRI Innovation Fellowship scheme(EP/S001662/2)the European Union’s Horizon 2020 research and innovation programme under grant agreement No.734796.
文摘In this paper,a grid interface current control strategy is presented for a DC microgrid,which aims to reduce the disturbance from PV generation and the load variation to the main grid without a grid interface converter.The grid interface current is directly controlled by a battery DC-DC converter within the DC microgrid.Based on a comprehensive analysis of the battery DC-DC converter and interface current control,the control system has been mathematically modelled.This enabled two transfer functions to be derived that reflect the dynamic response of the inductor current to the duty cycle variation(inner loop),and the dynamic response of the grid interface current to the inductor current variation(outer loop).Experimental study has been done to assess the effectiveness of the proposed control strategy.The experimental results indicate that the proposed control strategy has a good performance to control the grid interface current without an interface converter,regardless the variations of both PV and the load conditions.
文摘To overcome the shortcomings of 1 D and 2 D Otsu’s thresholding techniques, the 3 D Otsu method has been developed.Among all Otsu’s methods, 3 D Otsu technique provides the best threshold values for the multi-level thresholding processes. In this paper, to improve the quality of segmented images, a simple and effective multilevel thresholding method is introduced. The proposed approach focuses on preserving edge detail by computing the 3 D Otsu along the fusion phenomena. The advantages of the presented scheme include higher quality outcomes, better preservation of tiny details and boundaries and reduced execution time with rising threshold levels. The fusion approach depends upon the differences between pixel intensity values within a small local space of an image;it aims to improve localized information after the thresholding process. The fusion of images based on local contrast can improve image segmentation performance by minimizing the loss of local contrast, loss of details and gray-level distributions. Results show that the proposed method yields more promising segmentation results when compared to conventional1 D Otsu, 2 D Otsu and 3 D Otsu methods, as evident from the objective and subjective evaluations.
基金The authors are grateful to the Deanship of Scientific Research,King Saud University,Riyadh,Saudi Arabia,for funding this work through the Vice Deanship of Scientific Research Chairs:Research Chair of Pervasive and Mobile Computing.
文摘Real-time pedestrian detection is an important task for unmanned driving systems and video surveillance.The existing pedestrian detection methods often work at low speed and also fail to detect smaller and densely distributed pedestrians by losing some of their detection accuracy in such cases.Therefore,the proposed algorithm YOLOv2(“YOU ONLY LOOK ONCE Version 2”)-based pedestrian detection(referred to as YOLOv2PD)would be more suitable for detecting smaller and densely distributed pedestrians in real-time complex road scenes.The proposed YOLOv2PD algorithm adopts a Multi-layer Feature Fusion(MLFF)strategy,which helps to improve the model’s feature extraction ability.In addition,one repeated convolution layer is removed from the final layer,which in turn reduces the computational complexity without losing any detection accuracy.The proposed algorithm applies the K-means clustering method on the Pascal Voc-2007+2012 pedestrian dataset before training to find the optimal anchor boxes.Both the proposed network structure and the loss function are improved to make the model more accurate and faster while detecting smaller pedestrians.Experimental results show that,at 544×544 image resolution,the proposed model achieves 80.7%average precision(AP),which is 2.1%higher than the YOLOv2 Model on the Pascal Voc-2007+2012 pedestrian dataset.Besides,based on the experimental results,the proposed model YOLOv2PD achieves a good trade-off balance between detection accuracy and real-time speed when evaluated on INRIA and Caltech test pedestrian datasets and achieves state-of-the-art detection results.
文摘This paper deals with the problem of robust stability for continuous-time singular systems with state delay and parameter uncertainty. The uncertain singular systems with delay considered in this paper are assumed to be regular and impulse free.By decomposing the systems into slow and fast subsystems,a robust delay-dependent asymptotic stability criteria based on linear matrix inequality is proposed, which is derived by using Lyapunov-Krasovskii functionals, neither model transformation nor bounding for cross terms is required in the derivation of our delay-dependent result. The robust delay-dependent stability criterion proposed in this paper is a sufficient condition. Finally, numerical examples and Matlab simulation are provided to illustrate the effectiveness of the proposed method.
基金National Natural Science Foundation of China(NSFC)(61327006,61620106014)
文摘An ultrasensitive metamaterial sensor based on double-slot vertical split ring resonators(DVSRRs) is designed and numerically calculated in the terahertz frequency. This DVSRR design produces a fundament LC resonance with a quality factor of about 20 when the incidence magnetic field component normal to the DVSRR array. The resonant characteristics and sensing performance of the DVSRR array design are systematically analyzed employing a contrast method among three similar vertical split ring resonator(SRRs) structures. The research results show that the elimination of bianisotropy, induced by the structural symmetry of the DVSRR design, helps to achieve LC resonance of a high quality factor. Lifting the SRRs up from the substrate sharply reduces the dielectric loss introduced by the substrate. All these factors jointly result in superior sensitivity of the DVSRR to the attributes of analytes. The maximum refractive index sensitivity is 788 GHz/RIU or 1.04 × 10~5 nm∕RIU.Also, the DVSRR sensor maintains its superior sensing performance for fabrication tolerance ranging from -4% to 4% and wide range incidence angles up to 50° under both TE and TM illuminations.
文摘Big data has the ability to open up innovative and ground-breaking prospects for the electrical grid,which also supports to obtain a variety of technological,social,and financial benefits.There is an unprecedented amount of heterogeneous big data as a consequence of the growth of power grid technologies,along with data processing and advanced tools.The main obstacles in turning the heterogeneous large dataset into useful results are computational burden and information security.The original contribution of this paper is to develop a new big data framework for detecting various intrusions from the smart grid systems with the use of AI mechanisms.Here,an AdaBelief Exponential Feature Selection(AEFS)technique is used to efficiently handle the input huge datasets from the smart grid for boosting security.Then,a Kernel based Extreme Neural Network(KENN)technique is used to anticipate security vulnerabilities more effectively.The Polar Bear Optimization(PBO)algorithm is used to efficiently determine the parameters for the estimate of radial basis function.Moreover,several types of smart grid network datasets are employed during analysis in order to examine the outcomes and efficiency of the proposed AdaBelief Exponential Feature Selection-Kernel based Extreme Neural Network(AEFS-KENN)big data security framework.The results reveal that the accuracy of proposed AEFS-KENN is increased up to 99.5%with precision and AUC of 99%for all smart grid big datasets used in this study.
文摘Brain-Computer interfacing(BCI)has currently added a new dimension in assistive robotics.Existing braincomputer interfaces designed for position control applications suffer from two fundamental limitations.First,most of the existing schemes employ open-loop control,and thus are unable to track positional errors,resulting in failures in taking necessary online corrective actions.There are examples of a few works dealing with closed-loop electroencephalography(EEG)-based position control.These existing closed-loop brain-induced position control schemes employ a fixed order link selection rule,which often creates a bottleneck preventing time-efficient control.Second,the existing brain-induced position controllers are designed to generate a position response like a traditional firstorder system,resulting in a large steady-state error.This paper overcomes the above two limitations by keeping provisions for steady-state visual evoked potential(SSVEP)induced linkselection in an arbitrary order as required for efficient control and generating a second-order response of the position-control system with gradually diminishing overshoots/undershoots to reduce steady-state errors.Other than the above,the third innovation is to utilize motor imagery and P300 signals to design the hybrid brain-computer interfacing system for the said application with gradually diminishing error-margin using speed reversal at the zero-crossings of positional errors.Experiments undertaken reveal that the steady-state error is reduced to 0.2%.The paper also provides a thorough analysis of the stability of the closed-loop system performance using the Root Locus technique.
基金supported in part by the National Key Research and Development Program of China under Grant 2020YFA0711301in part by the National Natural Science Foundation of China under Grant 61922049,61941104,61921004,62171240,61771264,62001254,61801248,61971467+2 种基金the Key Research and Development Program of Shandong Province under Grant 2020CXGC010108the Key Research and Development Program of Jiangsu Province of China under Grant BE2021013-1the Science and Technology Program of Nantong under Grants JC2021121,JC2021017。
文摘Due to its high mobility and flexible deployment,unmanned aerial vehicle(UAV)is drawing unprecedented interest in both military and civil applications to enable agile and ubiquitous connectivity.Mainly operating in an open environment,UAV communications benefit from dominant line-of-sight links;however,this on the other hand renders the communications more vulnerable to malicious attacks.Recently,physical layer security(PLS)has been introduced to UAV systems as an important complement to the conventional cryptography-based approaches.In this paper,a comprehensive survey on the current achievements of UAV-PLS is conducted.We first introduce the basic concepts including typical static/-mobile UAV deployment scenarios,the unique air-toground channel and aerial nodes distribution models,as well as various roles that a UAV may act when PLS is concerned.Then,we start by reviewing the secrecy performance analysis and enhancing techniques for statically deployed UAV systems,and extend the discussion to the more general scenario where the UAVs’mobility is further exploited.For both cases,respectively,we summarize the commonly adopted methodologies,then describe important works in the litera ture in detail.Finally,potential research directions and challenges are discussed to provide an outlook for future works in the area of UAV-PLS.
基金Hallym University Research Fund,2019(HRF-201905-013).
文摘With the development of wireless mobile communication technology,the demand for wireless communication rate and frequency increases year by year.Existing wireless mobile communication frequency tends to be saturated,which demands for new solutions.Terahertz(THz)communication has great potential for the future mobile communications(Beyond 5G),and is also an important technique for the high data rate transmission in spatial information network.THz communication has great application prospects in military-civilian integration and coordinated development.In China,important breakthroughs have been achieved for the key techniques of THz high data rate communications,which is practically keeping up with the most advanced technological level in the world.Therefore,further intensifying efforts on the development of THz communication have the strategic importance for China in leading the development of future wireless communication techniques and the standardization process of Beyond 5G.This paper analyzes the performance of the MIMO channel in the Terahertz(THz)band and a discrete mathematical method is used to propose a novel channel model.Then,a channel capacity model is proposed by the combination of path loss and molecular absorption in the THz band based on the CSI at the receiver.Simulation results show that the integration of MIMO in the THz band gives better data rate and channel capacity as compared with a single channel.
基金supported by the National Natural Science Foundation of China(12025503,12102086)Science Fund for Creative Research Groups of the Natural Science Foundation of Hubei Province(No.2022CFA005)+3 种基金Experimental Technology project of Wuhan University(WHU-2021-SYJS-06)Sichuan Science and Technology Program(2021YJ0182)supported by the Fundamental Research Funds for the Central Universities(No.2042021kf0227,2042022kf1181)medical Sci-Tech innovation platform of Zhongnan Hospital(PTXM2021001).
文摘Label-free surface-enhanced Raman scattering(SERS)technique with ultra-sensitivity becomes more and more desirable in biomedical analysis,which is yet hindered by inefficient follow-up data analysis.Here we report an integrative method based on SERS and Artificial Intelligence for Cancer Screening(SERS-AICS)for liquid biopsy such as serum via silver nanowires,combining molecular vibrational signals processing with large-scale data mining algorithm.According to 382 healthy controls and 1582 patients from two independent cohorts,SERS-AICS not only distinguishes pan-cancer patients from health controls with 95.81% overall accuracy and 95.87% sensitivity at 95.40% specificity,but also screens out those samples at early cancer stage.The supereminent efficiency potentiates SERS-AICS a promising tool for detecting cancer with broader types at earlier stage,accompanying with the establishment of a data platform for further deep analysis.
基金Ahmed Alhussen would like to thank the Deanship of Scientific Research at Majmaah University for supporting this work under Project No.R-2022-####.
文摘A brain tumor is a lethal neurological disease that affects the average performance of the brain and can be fatal.In India,around 15 million cases are diagnosed yearly.To mitigate the seriousness of the tumor it is essential to diagnose at the beginning.Notwithstanding,the manual evaluation process utilizing Magnetic Resonance Imaging(MRI)causes a few worries,remarkably inefficient and inaccurate brain tumor diagnoses.Similarly,the examination process of brain tumors is intricate as they display high unbalance in nature like shape,size,appearance,and location.Therefore,a precise and expeditious prognosis of brain tumors is essential for implementing the of an implicit treatment.Several computer models adapted to diagnose the tumor,but the accuracy of the model needs to be tested.Considering all the above mentioned things,this work aims to identify the best classification system by considering the prediction accuracy out of Alex-Net,ResNet 50,and Inception V3.Data augmentation is performed on the database and fed into the three convolutions neural network(CNN)models.A comparison line is drawn between the three models based on accuracy and performance.An accuracy of 96.2%is obtained for AlexNet with augmentation and performed better than ResNet 50 and Inception V3 for the 120th epoch.With the suggested model with higher accuracy,it is highly reliable if brain tumors are diagnosed with available datasets.
基金Lucian Blaga University of Sibiu&Hasso Plattner Foundation Research Grants LBUS-IRG-2020-06.
文摘New fractional operators, the COVID-19 model has been studied in this paper. By using different numericaltechniques and the time fractional parameters, the mechanical characteristics of the fractional order model areidentified. The uniqueness and existence have been established. Themodel’sUlam-Hyers stability analysis has beenfound. In order to justify the theoretical results, numerical simulations are carried out for the presented methodin the range of fractional order to show the implications of fractional and fractal orders.We applied very effectivenumerical techniques to obtain the solutions of themodel and simulations. Also, we present conditions of existencefor a solution to the proposed epidemicmodel and to calculate the reproduction number in certain state conditionsof the analyzed dynamic system. COVID-19 fractional order model for the case of Wuhan, China, is offered foranalysis with simulations in order to determine the possible efficacy of Coronavirus disease transmission in theCommunity. For this reason, we employed the COVID-19 fractal fractional derivative model in the example ofWuhan, China, with the given beginning conditions. In conclusion, again the mathematical models with fractionaloperators can facilitate the improvement of decision-making for measures to be taken in the management of anepidemic situation.
文摘Assessing the behaviour and concentration of waste pollutants deposited between two parallel plates is essential for effective environmental management.Determining the effectiveness of treatment methods in reducing pollution scales is made easier by analysing waste discharge concentrations.The waste discharge concentration analysis is useful for assessing how effectively wastewater treatment techniques reduce pollution levels.This study aims to explore the Casson micropolar fluid flow through two parallel plates with the influence of pollutant concentration and thermophoretic particle deposition.To explore the mass and heat transport features,thermophoretic particle deposition and thermal radiation are considered.The governing equations are transformed into ordinary differential equations with the help of suitable similarity transformations.The Runge-Kutta-Fehlberg’s fourthfifth order technique and shooting procedure are used to solve the reduced set of equations and boundary conditions.The integration of a neural network model based on the Levenberg-Marquardt algorithm serves to improve the accuracy of predictions and optimize the analysis of parameters.Graphical outcomes are displayed to analyze the characteristics of the relevant dimensionless parameters in the current problem.Results reveal that concentration upsurges as the micropolar parameter increases.The concentration reduces with an upsurge in the thermophoretic parameter.An upsurge in the external pollutant source variation and the local pollutant external source parameters enhances mass transport.The surface drag force declines for improved values of porosity and micropolar parameters.
文摘A nanoheterojunction composite photocatalyst Bi2O3/TiO2 working under visible-light （λ≥ 420 nm） was prepared by combining two semiconductors Bi2O3 and TiO2 varying the Bi2O3/TiO2 molar ratio. Maleic acid was employed as an organic binder to unite Bi2O3 and TiO2 nanoparticles. The SEM, TEM, XRD and diffuse reflectance spectra were utilized to characterize the prepared Bi2O3/TiO2 nanoheterojunction. The nanocomposite exhibited unusual high photocatalytic activity in decomposing 2-propanol in gas phase and phenol in aqueous phase and, evolution of CO2 under visible light irradiation while the end members exhibited low photocatalytic activity. The composite was optimized to 5 mol% Bi2O3/TiO2. The remarkable high photocatalytic efficiency originates from the unique relative energy band position of Bi2O3 and TiO2 as well as the absorption of visible light by Bi2O3.
基金supported by the National Key Research and Development Program of China(No.2019YFB2203004).
文摘Due to the rise of 5G,IoT,AI,and high-performance computing applications,datacenter trafc has grown at a compound annual growth rate of nearly 30%.Furthermore,nearly three-fourths of the datacenter trafc resides within datacenters.The conventional pluggable optics increases at a much slower rate than that of datacenter trafc.The gap between application requirements and the capability of conventional pluggable optics keeps increasing,a trend that is unsustainable.Copackaged optics(CPO)is a disruptive approach to increasing the interconnecting bandwidth density and energy efciency by dramatically shortening the electrical link length through advanced packaging and co-optimization of electronics and photonics.CPO is widely regarded as a promising solution for future datacenter interconnections,and silicon platform is the most promising platform for large-scale integration.Leading international companies(e.g.,Intel,Broadcom and IBM)have heavily investigated in CPO technology,an inter-disciplinary research feld that involves photonic devices,integrated circuits design,packaging,photonic device modeling,electronic-photonic co-simulation,applications,and standardization.This review aims to provide the readers a comprehensive overview of the state-of-the-art progress of CPO in silicon platform,identify the key challenges,and point out the potential solutions,hoping to encourage collaboration between diferent research felds to accelerate the development of CPO technology.
文摘This article analyzes the transmission line characteristics of plasmonic hybrid metal insulator metal(HMIM) wave-guide, circular ring resonator(CRR) based dual-band band-pass filters with two transmission poles in both pass-bands in the optical regime using coupled line feed. The transmission line characteristics of an HMIM waveguide, such as characteristic impedance(Z_(PV)), effective refractive index(n_(eff)) and propagation length(L_(spp)), have been obtained by using full wave simulation.
基金supported by the Nano&Material Technology Development Program through the National Research Foundation of Korea(NRF)funded by Ministry of Science and ICT(RS-2024-00405016)supported by“Cooperative Research Program for Agriculture Science and Technology Development(Project No.PJ01706703)”Rural Development Administration,Republic of Korea.The Inter-University Semiconductor Research Center and Institute of Engineering Research at Seoul National University provided research facilities for this work.
文摘Micro-light-emitting diodes(μLEDs)have gained significant interest as an activation source for gas sensors owing to their advantages,including room temperature operation and low power consumption.However,despite these benefits,challenges still exist such as a limited range of detectable gases and slow response.In this study,we present a blueμLED-integrated light-activated gas sensor array based on SnO_(2)nanoparticles(NPs)that exhibit excellent sensitivity,tunable selectivity,and rapid detection with micro-watt level power consumption.The optimal power forμLED is observed at the highest gas response,supported by finite-difference time-domain simulation.Additionally,we first report the visible light-activated selective detection of reducing gases using noble metal-decorated SnO_(2)NPs.The noble metals induce catalytic interaction with reducing gases,clearly distinguishing NH3,H2,and C2H5OH.Real-time gas monitoring based on a fully hardwareimplemented light-activated sensing array was demonstrated,opening up new avenues for advancements in light-activated electronic nose technologies.
基金The authors gratefully acknowledge the Fundamental Research Funds for the Central Universities of China(FRF-TP-17-057A1 and FRF-TP-18-074A1)the China Postdoctoral Science Foundation(No.2019M650491)the National Natural Science Foundation of China(No.11801029).
文摘Organic and inorganic phase change materials(PCMs) are considered potential materials for thermal energy storage(TES) with different phase change characteristics. In this study, a novel organic-inorganic composite phase change material(PCM) called disodium hydrogen phosphate dodecahydrate-lauric-palmitic acid(D-LA-PACM) was prepared. Expanded graphite(EG) was selected as the support material, and the novel organic-inorganic form-stable PCM called D-LA-PAPCM/EG was prepared using the vacuum adsorption method. Differential scanning calorimetry, Fourier transform infrared spectroscopy, X-ray diffraction, leakage testing, melting and solidification cycle testing, thermal conductivity testing, scanning electron microscopy observation of the micromorphology, and other characterization methods were used to study the microstructure and morphology, thermal physical parameters, thermal conductivity, stability of the PCMs, and the comprehensive material properties of D-LA-PAPCM under the composite action of EG. Results indicated that the melting and freezing temperatures and latent heats of D-LA-PAPCM/EG were measured to be 31.6℃ and 34.3℃ and 142.9 and 142.8 J/g, respectively. Although some of the lauric-palmitic acid(LA-PA) and disodium hydrogen phosphate dodecahydrate(DHPD) separated in the multiple porous structures of EG after 1000 cycles, they could still absorb and release latent heats independently, with D-LA-PAPCM/EG still exhibiting good thermal stability. The thermal conductivity of D-LA-PAPCM/EG was 1.361 W/(m·K). Therefore, the material and thermal properties of the prepared D-LA-PAPCM/EG indicate that it could be well used as a feasible material for energy-saving phase change floor units in indoor TES systems.
