The underwater path planning problem deals with finding an optimal or sub-optimal route between an origin point and a termination point in marine environments.The underwater environment is still considered as a great ...The underwater path planning problem deals with finding an optimal or sub-optimal route between an origin point and a termination point in marine environments.The underwater environment is still considered as a great challenge for the path planning of autonomous underwater vehicles(AUVs)because of its hostile and dynamic nature.The major constraints for path planning are limited data transmission capability,power and sensing technology available for underwater operations.The sea environment is subjected to a large set of challenging factors classified as atmospheric,coastal and gravitational.Based on whether the impact of these factors can be approximated or not,the underwater environment can be characterized as predictable and unpredictable respectively.The classical path planning algorithms based on artificial intelligence assume that environmental conditions are known apriori to the path planner.But the current path planning algorithms involve continual interaction with the environment considering the environment as dynamic and its effect cannot be predicted.Path planning is necessary for many applications involving AUVs.These are based upon planning safety routes with minimum energy cost and computation overheads.This review is intended to summarize various path planning strategies for AUVs on the basis of characterization of underwater environments as predictable and unpredictable.The algorithms employed in path planning of single AUV and multiple AUVs are reviewed in the light of predictable and unpredictable environments.展开更多
Because of everyone＇s involvement in social networks, social networks are full of massive multimedia data, and events are got released and disseminated through social networks in the form of multi-modal and multi-att...Because of everyone＇s involvement in social networks, social networks are full of massive multimedia data, and events are got released and disseminated through social networks in the form of multi-modal and multi-attribute heterogeneous data. There have been numerous researches on social network search. Considering the spatio-temporal feature of messages and social relationships among users, we summarized an overall social network search framework from the perspective of semantics based on existing researches. For social network search, the acquisition and representation of spatio-temporal data is the basis, the semantic analysis and modeling of social network cross-media big data is an important component, deep semantic learning of social networks is the key research field, and the indexing and ranking mechanism is the indispensable part. This paper reviews the current studies in these fields, and then main challenges of social network search are given. Finally, we give an outlook to the prospect and further work of social network search.展开更多
Based on density functional theory (DFT) of the first-principle for the cathode materials of lithium ion battery, the electronic structures of Li(Fe1-xMex)PO4 (Me = Ag/Mn, x = 0―0.40) are calculated by plane wave pse...Based on density functional theory (DFT) of the first-principle for the cathode materials of lithium ion battery, the electronic structures of Li(Fe1-xMex)PO4 (Me = Ag/Mn, x = 0―0.40) are calculated by plane wave pseudo-potential method using Cambridge serial total energy package (CASTEP) program. The calculated results show that the Fermi level of mixed atoms Fe1-xAgx moves into its conduction bands (CBs) due to the Ag doping. The Li(Fe1-xAgx)PO4 system displays the periodic direct semiconductor characteristic with the increase of Ag-doped concentration. However, for Fe1-xMnx mixed atoms, the Fermi level is pined at the bottom of conduction bands (CBs), which is ascribed to the interaction be-tween Mn(3d) electrons and Fe(4s) electrons. The intensity of the partial density of states (PDOS) near the bottom of CBs becomes stronger with the increase of Mn-doped concentration. The Fermi energy of the Li(Fe1-xMnx)PO4 reaches maximum at x = 0.25, which is consistent with the experimental value of x = 0.20. The whole conduction property of Mn-doped LiFePO4 is superior to that of Ag-doped LiFePO4 cathode material, but the structural stability is reverse.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
The development of power system informatization,the massive access of distributed power supply and electric vehicles have increased the complexity of power consumption in the distribution network,which puts forward hi...The development of power system informatization,the massive access of distributed power supply and electric vehicles have increased the complexity of power consumption in the distribution network,which puts forward higher requirements for the accuracy and stability of load forecasting.In this paper,an integrated network architecture which consists of the self-organized mapping,chaotic time series,intelligent optimization algorithm and long short-term memory(LSTM)is proposed to extend the load forecasting length,decrease artificial debugging,and improve the prediction precision for the short-term power load forecasting.Compared with LSTM prediction,the algorithm in this paper improves the prediction accuracy by 61.87%in terms of root mean square error(RMSE),and reduces the prediction error by 50%in the 40-fold forecast window under some circumstances.展开更多
Internet of Things (IoT) has attracted extensive interest from both academia and industries, and is recognized as an ultimate infrastructure to connect everything at anytime and anywhere. The implementation of IoT gen...Internet of Things (IoT) has attracted extensive interest from both academia and industries, and is recognized as an ultimate infrastructure to connect everything at anytime and anywhere. The implementation of IoT generally faces the challenges from energy constraint and implementation cost. In this paper, we will introduce a new green communication paradigm, the ambient backscatter (AmBC), that could utilize the environmental wireless signals for both powering a tiny-cost device and backscattering the information symbols. Specifically, we will present the basic principles of AmBC, analyze its features and advantages, suggest its open problems, and predict its potential applications for our future IoT.展开更多
This paper is devoted to studying the growth problem, the zeros and fixed points distribution of the solutions of linear differential equations f″＋e^-zf′＋Q（z）f=F（z）,whereQ（z）≡h（z）e^cz and c∈R.
Energy limitation of traditional Wireless Sensor Networks(WSNs)greatly confines the network lifetime due to generating and processing massive sensing data with a limited battery.The energy harvesting WSN is a novel ne...Energy limitation of traditional Wireless Sensor Networks(WSNs)greatly confines the network lifetime due to generating and processing massive sensing data with a limited battery.The energy harvesting WSN is a novel network architecture to address the limitation of traditional WSN.However,existing coverage and deployment schemes neglect the environmental correlation of sensor nodes and external energy with respect to physical space.Comprehensively considering the spatial correlation of the environment and the uneven distribution of energy in energy harvesting WSN,we investigate how to deploy a collection of sensor nodes to save the deployment cost while ensuring the target perpetual coverage.The Confident Information Coverage(CIC)model is adopted to formulate the CIC Minimum Deployment Cost Target Perpetual Coverage(CICMTP)problem to minimize the deployed sensor nodes.As the CICMTP is NP-hard,we devise two approximation algorithms named Local Greedy Threshold Algorithm based on CIC(LGTA-CIC)and Overall Greedy Search Algorithm based on CIC(OGSA-CIC).The LGTA-CIC has a low time complexity and the OGSA-CIC has a better approximation rate.Extensive simulation results demonstrate that the OGSA-CIC is able to achieve lower deployment cost and the performance of the proposed algorithms outperforms GRNP,TPNP and EENP algorithms.展开更多
We present a quantitative measurement of the horizontal component of the microwave magnetic field of a coplanar waveguide using a quantum diamond probe in fiber format.The measurement results are compared in detail wi...We present a quantitative measurement of the horizontal component of the microwave magnetic field of a coplanar waveguide using a quantum diamond probe in fiber format.The measurement results are compared in detail with simulation,showing a good consistence.Further simulation shows fiber diamond probe brings negligible disturbance to the field under measurement compared to bulk diamond.This method will find important applications ranging from electromagnetic compatibility test and failure analysis of high frequency and high complexity integrated circuits.展开更多
Vehicular Ad-hoc Networks(VANETs)are mobile ad-hoc networks that use vehicles as nodes to create a wireless network.Whereas VANETs offer many advantages over traditional transportation networks,ensuring security in VA...Vehicular Ad-hoc Networks(VANETs)are mobile ad-hoc networks that use vehicles as nodes to create a wireless network.Whereas VANETs offer many advantages over traditional transportation networks,ensuring security in VANETs remains a significant challenge due to the potential for malicious attacks.This study addresses the critical issue of security in VANETs by introducing an intelligent Intrusion Detection System(IDS)that merges Machine Learning(ML)–based attack detection with Explainable AI(XAI)explanations.This study ML pipeline involves utilizing correlation-based feature selection followed by a Random Forest(RF)classifier that achieves a classification accuracy of 100%for the binary classification task of identifying normal and malicious traffic.An innovative aspect of this study is the incorporation of XAI methodologies,specifically the Local Interpretable Model-agnostic Explanations(LIME)and SHapley Additive exPlanations(SHAP).In addition,this research also considered key features identified by mutual information-based feature selection for the task at hand.The major findings from this study reveal that the XAI-based intrusion detection methods offer distinct insights into feature importance.Key features identified by mutual information,LIME,and SHAP predominantly relate to Transmission Control Protocol(TCP),Hypertext Transfer Protocol(HTTP),Domain Name System(DNS),and Message Queuing Telemetry Transport(MQTT)protocols,highlighting their significance in distinguishing normal and malicious network activity.This XAI approach equips cybersecurity experts with a robust means of identifying and understanding VANET malicious activities,forming a foundation for more effective security countermeasures.展开更多
In this paper,a new compact ultrawideband(UWB)circularly polarized(CP)antenna array for vehicular communications is proposed.The antenna array consists of a 2×2 sequentially rotated T-shaped cross dipole,four par...In this paper,a new compact ultrawideband(UWB)circularly polarized(CP)antenna array for vehicular communications is proposed.The antenna array consists of a 2×2 sequentially rotated T-shaped cross dipole,four parasitic elements,and a feeding network.By loading the T-shaped cross dipoles with parasitic rectangular elements with cut corners,the bandwidth can be expanded.On this basis,the radiation pattern can be improved by the topology with sequential rotation of four T-shaped cross-dipole antennas,and the axial ratio(AR)bandwidth of the antenna also can be further enhanced.In addition,due to the special topology that the vertical arms of all Tshaped cross dipoles are all oriented toward the center of the antenna array,the gain of proposed antenna is improved while the size of the antenna is almost the same as the traditional cross dipole.Simulated and measured results show that the proposed antenna has good CP characteristics,an impedance bandwidth for S11<-10 d B of about 106.1%(3.26:1,1.57-5.12 GHz)and the 3-d B AR bandwidth of about 104.1%(3.17:1,1.57-4.98 GHz),a wide 3-d B gain bandwidth of 73.3%as well as the peak gain of 8.6 d Bic at 3.5 GHz.The overall size of antenna is 0.56λ×0.56λ×0.12λ(λrefers to the wavelength of the lowest operating frequency in free space).The good performance of this compact UWB CP antenna array is promising for applications in vehicular communications.展开更多
Knowledge distillation,as a pivotal technique in the field of model compression,has been widely applied across various domains.However,the problem of student model performance being limited due to inherent biases in t...Knowledge distillation,as a pivotal technique in the field of model compression,has been widely applied across various domains.However,the problem of student model performance being limited due to inherent biases in the teacher model during the distillation process still persists.To address the inherent biases in knowledge distillation,we propose a de-biased knowledge distillation framework tailored for binary classification tasks.For the pre-trained teacher model,biases in the soft labels are mitigated through knowledge infusion and label de-biasing techniques.Based on this,a de-biased distillation loss is introduced,allowing the de-biased labels to replace the soft labels as the fitting target for the student model.This approach enables the student model to learn from the corrected model information,achieving high-performance deployment on lightweight student models.Experiments conducted on multiple real-world datasets demonstrate that deep learning models compressed under the de-biased knowledge distillation framework significantly outperform traditional response-based and feature-based knowledge distillation models across various evaluation metrics,highlighting the effectiveness and superiority of the de-biased knowledge distillation framework in model compression.展开更多
Herein,g-C_(3)N_(4)quantum-dot-modified TiO_(2)nanofibers were fabricated and used as an efficient photocatalyst for the investigation of the influence of Cu^(2+)and the interaction mechanism between Cu^(2+)and surfac...Herein,g-C_(3)N_(4)quantum-dot-modified TiO_(2)nanofibers were fabricated and used as an efficient photocatalyst for the investigation of the influence of Cu^(2+)and the interaction mechanism between Cu^(2+)and surface defects in tetracycline degradation.Results showed that the effect of Cu^(2+)switched from promoting to inhibiting the tetracycline degradation as the amount of Cu^(2+)accumulated on the catalyst surface increased.The introduction of surface defects can prevent the inhibiting effect of Cu^(2+),resulting in the more complete degradation of tetracycline in contrast to the non-defective sample.Theoretical calculations further revealed that the defects can be used to tune the conduction band of the composite,inducing the reduction reaction of Cu^(2+)and inhibiting the accumulation of Cu on the surface of catalysts.Moreover,the Cu introduced to the catalyst surface provided new active sites,thereby promoting photocatalytic degradation.These findings provide new insights into the design of advanced fiber materials for water purification in complex environments.展开更多
Infection of leukemia in humans causes many complications in its later stages.It impairs bone marrow’s ability to produce blood.Morphological diagnosis of human blood cells is a well-known and well-proven technique f...Infection of leukemia in humans causes many complications in its later stages.It impairs bone marrow’s ability to produce blood.Morphological diagnosis of human blood cells is a well-known and well-proven technique for diagnosis in this case.The binary classification is employed to distinguish between normal and leukemiainfected cells.In addition,various subtypes of leukemia require different treatments.These sub-classes must also be detected to obtain an accurate diagnosis of the type of leukemia.This entails using multi-class classification to determine the leukemia subtype.This is usually done using a microscopic examination of these blood cells.Due to the requirement of a trained pathologist,the decision process is critical,which leads to the development of an automated software framework for diagnosis.Researchers utilized state-of-the-art machine learning approaches,such as Support Vector Machine(SVM),Random Forest(RF),Na飗e Bayes,K-Nearest Neighbor(KNN),and others,to provide limited accuracies of classification.More advanced deep-learning methods are also utilized.Due to constrained dataset sizes,these approaches result in over-fitting,reducing their outstanding performances.This study introduces a deep learning-machine learning combined approach for leukemia diagnosis.It uses deep transfer learning frameworks to extract and classify features using state-of-the-artmachine learning classifiers.The transfer learning frameworks such as VGGNet,Xception,InceptionResV2,Densenet,and ResNet are employed as feature extractors.The extracted features are given to RF and XGBoost classifiers for the binary and multi-class classification of leukemia cells.For the experimentation,a very popular ALL-IDB dataset is used,approaching a maximum accuracy of 100%.A private real images dataset with three subclasses of leukemia images,including Acute Myloid Leukemia(AML),Chronic Lymphocytic Leukemia(CLL),and Chronic Myloid Leukemia(CML),is also employed to generalize the system.This dataset achieves an impressive multi-class cl展开更多
Wireless technology is transforming the future of transportation through the development of the Internet of Vehicles(IoV).However,intricate security challenges are intertwinedwith technological progress:Vehicular ad h...Wireless technology is transforming the future of transportation through the development of the Internet of Vehicles(IoV).However,intricate security challenges are intertwinedwith technological progress:Vehicular ad hoc Networks(VANETs),a core component of IoV,face security issues,particularly the Black Hole Attack(BHA).This malicious attack disrupts the seamless flow of data and threatens the network’s overall reliability;also,BHA strategically disrupts communication pathways by dropping data packets from legitimate nodes altogether.Recognizing the importance of this challenge,we have introduced a new solution called ad hoc On-Demand Distance Vector-Reputation-based mechanism Local Outlier Factor(AODV-RL).The significance of AODVRL lies in its unique approach:it verifies and confirms the trustworthiness of network components,providing robust protection against BHA.An additional safety layer is established by implementing the Local Outlier Factor(LOF),which detects and addresses abnormal network behaviors.Rigorous testing of our solution has revealed its remarkable ability to enhance communication in VANETs.Specifically,Our experimental results achieve message delivery ratios of up to 94.25%andminimal packet loss ratios of just 0.297%.Based on our experimental results,the proposedmechanismsignificantly improves VANET communication reliability and security.These results promise a more secure and dependable future for IoV,capable of transforming transportation safety and efficiency.展开更多
Tight focusing properties of partially coherent radially polarized vortex beams are studied based on vectorial Debye theory.We focus on the focal properties including the intensity and the partially coherent and polar...Tight focusing properties of partially coherent radially polarized vortex beams are studied based on vectorial Debye theory.We focus on the focal properties including the intensity and the partially coherent and polarized properties of such partially coherent vortex beams through a high numerical aperture objective. It is found that the source coherence length and the maximal numerical aperture angle have direct influence on the focal intensity,as well as coherence and polarization properties.This research is important in optical micromanipulation and beam shaping.展开更多
Based on the rapid experimental developments of circuit QED,we propose a feasible scheme to simulate the spin-boson model with superconducting circuits,which can be used to detect quantum Kosterlitz-Thouless(KT) phase...Based on the rapid experimental developments of circuit QED,we propose a feasible scheme to simulate the spin-boson model with superconducting circuits,which can be used to detect quantum Kosterlitz-Thouless(KT) phase transition.We design the spinboson model by using a superconducting phase qubit coupled to a semi-infinite transmission line,which is regarded as a bosonic reservoir with a continuum spectrum.By tuning the bias current or the coupling capacitance,the quantum KT transition can be directly detected through tomography measurement on the states of the phase qubit.We also estimate the experimental parameters using the numerical renormalization group method.展开更多
Whispering gallery mode(WGM)microcavities provide increasing opportunities for precision measurement due to their ultrahigh sensitivity,compact size,and fast response.However,the conventional WGM sensors rely on monit...Whispering gallery mode(WGM)microcavities provide increasing opportunities for precision measurement due to their ultrahigh sensitivity,compact size,and fast response.However,the conventional WGM sensors rely on monitoring the changes of a single mode,and the abundant sensing information in WGM transmission spectra has not been fully utilized.Here,empowered by machine learning(ML),we propose and demonstrate an ergodic spectra sensing method in an optofluidic microcavity for high-precision pressure measurement.The developed ML method realizes the analysis of the full features of optical spectra.The prediction accuracy of 99.97%is obtained with the average error as low as 0.32 kPa in the pressure range of 100 kPa via the training and testing stages.We further achieve the real-time readout of arbitrary unknown pressure within the range of measurement,and a prediction accuracy of 99.51%is obtained.Moreover,we demonstrate that the ergodic spectra sensing accuracy is∼11.5%higher than that of simply extracting resonating modes’wavelength.With the high sensitivity and prediction accuracy,this work opens up a new avenue for integrated intelligent optical sensing.展开更多
Rice plant counting is crucial for many applications in rice production,such as yield estimation,growth diagnosis,disaster loss assessment,etc.Currently,rice counting still heavily relies on tedious and time-consuming...Rice plant counting is crucial for many applications in rice production,such as yield estimation,growth diagnosis,disaster loss assessment,etc.Currently,rice counting still heavily relies on tedious and time-consuming manual operation.To alleviate the workload of rice counting,we employed an UAV(unmanned aerial vehicle)to collect the RGB images of the paddy field.Then,we proposed a new rice plant counting,locating,and sizing method(RiceNet),which consists of one feature extractor frontend and 3 feature decoder modules,namely,density map estimator,plant location detector,and plant size estimator.In RiceNet,rice plant attention mechanism and positive–negative loss are designed to improve the ability to distinguish plants from background and the quality of the estimated density maps.To verify the validity of our method,we propose a new UAV-based rice counting dataset,which contains 355 images and 257,793 manual labeled points.Experiment results show that the mean absolute error and root mean square error of the proposed RiceNet are 8.6 and 11.2,respectively.Moreover,we validated the performance of our method with two other popular crop datasets.On these three datasets,our method significantly outperforms state-of-the-art methods.Results suggest that RiceNet can accurately and efficiently estimate the number of rice plants and replace the traditional manual method.展开更多
Aluminum-metal batteries show great potential as next-generation energy storage due to their abundant resources and intrinsic safety.However,the crucial limitations of metallic Al anodes,such as dendrite and corrosion...Aluminum-metal batteries show great potential as next-generation energy storage due to their abundant resources and intrinsic safety.However,the crucial limitations of metallic Al anodes,such as dendrite and corrosion problems in conventional aluminum-metal batteries,remain challenging and elusive.Here,we report a novel electrodeposition strategy to prepare an optimized 3D Al anode on carbon cloth with an uniform deposition morphology,low local current density,and mitigatory volume change.The symmetrical cells with the 3D Al anode show superior stable cycling(>450 h)and low-voltage hysteresis(~170 mV)at 0.5 mA cm^(−2).High reversibility(~99.7%)is achieved for the Al plating/stripping.The graphite||Al‐4/CC full batteries show a long lifespan of 800 cycles with 54 mAh g^(−1) capacity at a high current density of 1000 mA g^(−1),benefiting from the high capacitive-controlled distribution.This study proposes a novel strategy to design 3D Al anodes for metallic-Al-based batteries by eliminating the problems of planar Al anodes and realizing the potential applications of aluminum-graphite batteries.展开更多
文摘The underwater path planning problem deals with finding an optimal or sub-optimal route between an origin point and a termination point in marine environments.The underwater environment is still considered as a great challenge for the path planning of autonomous underwater vehicles(AUVs)because of its hostile and dynamic nature.The major constraints for path planning are limited data transmission capability,power and sensing technology available for underwater operations.The sea environment is subjected to a large set of challenging factors classified as atmospheric,coastal and gravitational.Based on whether the impact of these factors can be approximated or not,the underwater environment can be characterized as predictable and unpredictable respectively.The classical path planning algorithms based on artificial intelligence assume that environmental conditions are known apriori to the path planner.But the current path planning algorithms involve continual interaction with the environment considering the environment as dynamic and its effect cannot be predicted.Path planning is necessary for many applications involving AUVs.These are based upon planning safety routes with minimum energy cost and computation overheads.This review is intended to summarize various path planning strategies for AUVs on the basis of characterization of underwater environments as predictable and unpredictable.The algorithms employed in path planning of single AUV and multiple AUVs are reviewed in the light of predictable and unpredictable environments.
文摘Because of everyone＇s involvement in social networks, social networks are full of massive multimedia data, and events are got released and disseminated through social networks in the form of multi-modal and multi-attribute heterogeneous data. There have been numerous researches on social network search. Considering the spatio-temporal feature of messages and social relationships among users, we summarized an overall social network search framework from the perspective of semantics based on existing researches. For social network search, the acquisition and representation of spatio-temporal data is the basis, the semantic analysis and modeling of social network cross-media big data is an important component, deep semantic learning of social networks is the key research field, and the indexing and ranking mechanism is the indispensable part. This paper reviews the current studies in these fields, and then main challenges of social network search are given. Finally, we give an outlook to the prospect and further work of social network search.
基金the National Natural Science Foundation of China (Grant No. 50771046)the Natural Science Foundation of Guangdong Province (Grant No. 05200534)the Key Projects of Guangdong Province and Guangzhou City (Grant Nos. 2006A10704003 and 2006Z3-D2031)
文摘Based on density functional theory (DFT) of the first-principle for the cathode materials of lithium ion battery, the electronic structures of Li(Fe1-xMex)PO4 (Me = Ag/Mn, x = 0―0.40) are calculated by plane wave pseudo-potential method using Cambridge serial total energy package (CASTEP) program. The calculated results show that the Fermi level of mixed atoms Fe1-xAgx moves into its conduction bands (CBs) due to the Ag doping. The Li(Fe1-xAgx)PO4 system displays the periodic direct semiconductor characteristic with the increase of Ag-doped concentration. However, for Fe1-xMnx mixed atoms, the Fermi level is pined at the bottom of conduction bands (CBs), which is ascribed to the interaction be-tween Mn(3d) electrons and Fe(4s) electrons. The intensity of the partial density of states (PDOS) near the bottom of CBs becomes stronger with the increase of Mn-doped concentration. The Fermi energy of the Li(Fe1-xMnx)PO4 reaches maximum at x = 0.25, which is consistent with the experimental value of x = 0.20. The whole conduction property of Mn-doped LiFePO4 is superior to that of Ag-doped LiFePO4 cathode material, but the structural stability is reverse.
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.
基金supported by the National Natural Science Foundation of China(Grant No.61673107)the National Ten Thousand Talent Program for Young Top-notch Talents(Grant No.W2070082)+1 种基金the General Joint Fund of the Equipment Advance Research Program of Ministry of Education(Grant No.6141A020223)the Jiangsu Provincial Key Laboratory of Networked Collective Intelligence(Grant No.BM2017002)。
文摘The development of power system informatization,the massive access of distributed power supply and electric vehicles have increased the complexity of power consumption in the distribution network,which puts forward higher requirements for the accuracy and stability of load forecasting.In this paper,an integrated network architecture which consists of the self-organized mapping,chaotic time series,intelligent optimization algorithm and long short-term memory(LSTM)is proposed to extend the load forecasting length,decrease artificial debugging,and improve the prediction precision for the short-term power load forecasting.Compared with LSTM prediction,the algorithm in this paper improves the prediction accuracy by 61.87%in terms of root mean square error(RMSE),and reduces the prediction error by 50%in the 40-fold forecast window under some circumstances.
基金supported in part by National Key R&D Program of China under Grant 2016YFE0200900part by Scientific Research Program of Beijing Municipal Commission of Education under Grant KM201910853003part by Major projects of Beijing Municipal Science and Technology Commission under Grant Z181100003218010
文摘Internet of Things (IoT) has attracted extensive interest from both academia and industries, and is recognized as an ultimate infrastructure to connect everything at anytime and anywhere. The implementation of IoT generally faces the challenges from energy constraint and implementation cost. In this paper, we will introduce a new green communication paradigm, the ambient backscatter (AmBC), that could utilize the environmental wireless signals for both powering a tiny-cost device and backscattering the information symbols. Specifically, we will present the basic principles of AmBC, analyze its features and advantages, suggest its open problems, and predict its potential applications for our future IoT.
基金Tian Yuan Fund for Mathematics (Grant No.10426007)Shanghai Postdoctoral Scientific Program
文摘This paper is devoted to studying the growth problem, the zeros and fixed points distribution of the solutions of linear differential equations f″＋e^-zf′＋Q（z）f=F（z）,whereQ（z）≡h（z）e^cz and c∈R.
基金supported by National Natural Science Foundation of China(Grant No.61871209,No.62272182 and No.61901210)Shenzhen Science and Technology Program under Grant JCYJ20220530161004009+2 种基金Natural Science Foundation of Hubei Province(Grant No.2022CF011)Wuhan Business University Doctoral Fundamental Research Funds(Grant No.2021KB005)in part by Artificial Intelligence and Intelligent Transportation Joint Technical Center of HUST and Hubei Chutian Intelligent Transportation Co.,LTD under project Intelligent Tunnel Integrated Monitoring and Management System.
文摘Energy limitation of traditional Wireless Sensor Networks(WSNs)greatly confines the network lifetime due to generating and processing massive sensing data with a limited battery.The energy harvesting WSN is a novel network architecture to address the limitation of traditional WSN.However,existing coverage and deployment schemes neglect the environmental correlation of sensor nodes and external energy with respect to physical space.Comprehensively considering the spatial correlation of the environment and the uneven distribution of energy in energy harvesting WSN,we investigate how to deploy a collection of sensor nodes to save the deployment cost while ensuring the target perpetual coverage.The Confident Information Coverage(CIC)model is adopted to formulate the CIC Minimum Deployment Cost Target Perpetual Coverage(CICMTP)problem to minimize the deployed sensor nodes.As the CICMTP is NP-hard,we devise two approximation algorithms named Local Greedy Threshold Algorithm based on CIC(LGTA-CIC)and Overall Greedy Search Algorithm based on CIC(OGSA-CIC).The LGTA-CIC has a low time complexity and the OGSA-CIC has a better approximation rate.Extensive simulation results demonstrate that the OGSA-CIC is able to achieve lower deployment cost and the performance of the proposed algorithms outperforms GRNP,TPNP and EENP algorithms.
基金Project supported by the National Key Research and Development Program of China (Grant No.2021YFB2012600)。
文摘We present a quantitative measurement of the horizontal component of the microwave magnetic field of a coplanar waveguide using a quantum diamond probe in fiber format.The measurement results are compared in detail with simulation,showing a good consistence.Further simulation shows fiber diamond probe brings negligible disturbance to the field under measurement compared to bulk diamond.This method will find important applications ranging from electromagnetic compatibility test and failure analysis of high frequency and high complexity integrated circuits.
基金supported in part by the National Natural Science Foundation of China(NSFC)under Grant Number 62127802.
文摘Vehicular Ad-hoc Networks(VANETs)are mobile ad-hoc networks that use vehicles as nodes to create a wireless network.Whereas VANETs offer many advantages over traditional transportation networks,ensuring security in VANETs remains a significant challenge due to the potential for malicious attacks.This study addresses the critical issue of security in VANETs by introducing an intelligent Intrusion Detection System(IDS)that merges Machine Learning(ML)–based attack detection with Explainable AI(XAI)explanations.This study ML pipeline involves utilizing correlation-based feature selection followed by a Random Forest(RF)classifier that achieves a classification accuracy of 100%for the binary classification task of identifying normal and malicious traffic.An innovative aspect of this study is the incorporation of XAI methodologies,specifically the Local Interpretable Model-agnostic Explanations(LIME)and SHapley Additive exPlanations(SHAP).In addition,this research also considered key features identified by mutual information-based feature selection for the task at hand.The major findings from this study reveal that the XAI-based intrusion detection methods offer distinct insights into feature importance.Key features identified by mutual information,LIME,and SHAP predominantly relate to Transmission Control Protocol(TCP),Hypertext Transfer Protocol(HTTP),Domain Name System(DNS),and Message Queuing Telemetry Transport(MQTT)protocols,highlighting their significance in distinguishing normal and malicious network activity.This XAI approach equips cybersecurity experts with a robust means of identifying and understanding VANET malicious activities,forming a foundation for more effective security countermeasures.
基金supported in part by the National Natural Science Foundation of China(NSFC)under Grant No.62071306in part by Shenzhen Science and Technology Program under Grants JCYJ202001091-13601723,JSGG20210802154203011 and JSGG-20210420091805014。
文摘In this paper,a new compact ultrawideband(UWB)circularly polarized(CP)antenna array for vehicular communications is proposed.The antenna array consists of a 2×2 sequentially rotated T-shaped cross dipole,four parasitic elements,and a feeding network.By loading the T-shaped cross dipoles with parasitic rectangular elements with cut corners,the bandwidth can be expanded.On this basis,the radiation pattern can be improved by the topology with sequential rotation of four T-shaped cross-dipole antennas,and the axial ratio(AR)bandwidth of the antenna also can be further enhanced.In addition,due to the special topology that the vertical arms of all Tshaped cross dipoles are all oriented toward the center of the antenna array,the gain of proposed antenna is improved while the size of the antenna is almost the same as the traditional cross dipole.Simulated and measured results show that the proposed antenna has good CP characteristics,an impedance bandwidth for S11<-10 d B of about 106.1%(3.26:1,1.57-5.12 GHz)and the 3-d B AR bandwidth of about 104.1%(3.17:1,1.57-4.98 GHz),a wide 3-d B gain bandwidth of 73.3%as well as the peak gain of 8.6 d Bic at 3.5 GHz.The overall size of antenna is 0.56λ×0.56λ×0.12λ(λrefers to the wavelength of the lowest operating frequency in free space).The good performance of this compact UWB CP antenna array is promising for applications in vehicular communications.
基金supported by the National Natural Science Foundation of China under Grant No.62172056Young Elite Scientists Sponsorship Program by CAST under Grant No.2022QNRC001.
文摘Knowledge distillation,as a pivotal technique in the field of model compression,has been widely applied across various domains.However,the problem of student model performance being limited due to inherent biases in the teacher model during the distillation process still persists.To address the inherent biases in knowledge distillation,we propose a de-biased knowledge distillation framework tailored for binary classification tasks.For the pre-trained teacher model,biases in the soft labels are mitigated through knowledge infusion and label de-biasing techniques.Based on this,a de-biased distillation loss is introduced,allowing the de-biased labels to replace the soft labels as the fitting target for the student model.This approach enables the student model to learn from the corrected model information,achieving high-performance deployment on lightweight student models.Experiments conducted on multiple real-world datasets demonstrate that deep learning models compressed under the de-biased knowledge distillation framework significantly outperform traditional response-based and feature-based knowledge distillation models across various evaluation metrics,highlighting the effectiveness and superiority of the de-biased knowledge distillation framework in model compression.
基金support of this research by the National Natural Science Foundation of China(Grant nos.51909165,42177438)China Postdoctoral Science Foundation(2020TQ0109,2020M682753).Science and Technology Program of Guangzhou(2019050001)+1 种基金National Key Research and Development Program of China(2019YFE0198000)F.Chen acknowledges the Pearl River Talent Program(2019QN01L951).
文摘Herein,g-C_(3)N_(4)quantum-dot-modified TiO_(2)nanofibers were fabricated and used as an efficient photocatalyst for the investigation of the influence of Cu^(2+)and the interaction mechanism between Cu^(2+)and surface defects in tetracycline degradation.Results showed that the effect of Cu^(2+)switched from promoting to inhibiting the tetracycline degradation as the amount of Cu^(2+)accumulated on the catalyst surface increased.The introduction of surface defects can prevent the inhibiting effect of Cu^(2+),resulting in the more complete degradation of tetracycline in contrast to the non-defective sample.Theoretical calculations further revealed that the defects can be used to tune the conduction band of the composite,inducing the reduction reaction of Cu^(2+)and inhibiting the accumulation of Cu on the surface of catalysts.Moreover,the Cu introduced to the catalyst surface provided new active sites,thereby promoting photocatalytic degradation.These findings provide new insights into the design of advanced fiber materials for water purification in complex environments.
基金supported by the Centre for Advanced Modelling and Geospatial Information Systems (CAMGIS),the University of Technology Sydney,the Ministry of Education of the Republic of Korea,and the National Research Foundation of Korea (NRF-2023R1A2C1007742)in part by the Researchers Supporting Project Number RSP-2023/14,King Saud University。
文摘Infection of leukemia in humans causes many complications in its later stages.It impairs bone marrow’s ability to produce blood.Morphological diagnosis of human blood cells is a well-known and well-proven technique for diagnosis in this case.The binary classification is employed to distinguish between normal and leukemiainfected cells.In addition,various subtypes of leukemia require different treatments.These sub-classes must also be detected to obtain an accurate diagnosis of the type of leukemia.This entails using multi-class classification to determine the leukemia subtype.This is usually done using a microscopic examination of these blood cells.Due to the requirement of a trained pathologist,the decision process is critical,which leads to the development of an automated software framework for diagnosis.Researchers utilized state-of-the-art machine learning approaches,such as Support Vector Machine(SVM),Random Forest(RF),Na飗e Bayes,K-Nearest Neighbor(KNN),and others,to provide limited accuracies of classification.More advanced deep-learning methods are also utilized.Due to constrained dataset sizes,these approaches result in over-fitting,reducing their outstanding performances.This study introduces a deep learning-machine learning combined approach for leukemia diagnosis.It uses deep transfer learning frameworks to extract and classify features using state-of-the-artmachine learning classifiers.The transfer learning frameworks such as VGGNet,Xception,InceptionResV2,Densenet,and ResNet are employed as feature extractors.The extracted features are given to RF and XGBoost classifiers for the binary and multi-class classification of leukemia cells.For the experimentation,a very popular ALL-IDB dataset is used,approaching a maximum accuracy of 100%.A private real images dataset with three subclasses of leukemia images,including Acute Myloid Leukemia(AML),Chronic Lymphocytic Leukemia(CLL),and Chronic Myloid Leukemia(CML),is also employed to generalize the system.This dataset achieves an impressive multi-class cl
文摘Wireless technology is transforming the future of transportation through the development of the Internet of Vehicles(IoV).However,intricate security challenges are intertwinedwith technological progress:Vehicular ad hoc Networks(VANETs),a core component of IoV,face security issues,particularly the Black Hole Attack(BHA).This malicious attack disrupts the seamless flow of data and threatens the network’s overall reliability;also,BHA strategically disrupts communication pathways by dropping data packets from legitimate nodes altogether.Recognizing the importance of this challenge,we have introduced a new solution called ad hoc On-Demand Distance Vector-Reputation-based mechanism Local Outlier Factor(AODV-RL).The significance of AODVRL lies in its unique approach:it verifies and confirms the trustworthiness of network components,providing robust protection against BHA.An additional safety layer is established by implementing the Local Outlier Factor(LOF),which detects and addresses abnormal network behaviors.Rigorous testing of our solution has revealed its remarkable ability to enhance communication in VANETs.Specifically,Our experimental results achieve message delivery ratios of up to 94.25%andminimal packet loss ratios of just 0.297%.Based on our experimental results,the proposedmechanismsignificantly improves VANET communication reliability and security.These results promise a more secure and dependable future for IoV,capable of transforming transportation safety and efficiency.
基金supported by the Natural Science Foundation of China(No.60877068)the Plan Project of Science and Technology of Guangzhou City(No.2007J1- C0011)the Technology Project of Guangdong Province(No.2007B010200041).
文摘Tight focusing properties of partially coherent radially polarized vortex beams are studied based on vectorial Debye theory.We focus on the focal properties including the intensity and the partially coherent and polarized properties of such partially coherent vortex beams through a high numerical aperture objective. It is found that the source coherence length and the maximal numerical aperture angle have direct influence on the focal intensity,as well as coherence and polarization properties.This research is important in optical micromanipulation and beam shaping.
基金supported by the National Natural Science Foundation of China (Grant Nos. 11004065,11104057 and 11125417)the Natural Science Foundation of Guangdong Province (Grant No.10451063101006312)+1 种基金the State Key Program for Basic Research of China(Grant No. 2011CB922104)the GRF and CRF of the RGC of Hong Kong
文摘Based on the rapid experimental developments of circuit QED,we propose a feasible scheme to simulate the spin-boson model with superconducting circuits,which can be used to detect quantum Kosterlitz-Thouless(KT) phase transition.We design the spinboson model by using a superconducting phase qubit coupled to a semi-infinite transmission line,which is regarded as a bosonic reservoir with a continuum spectrum.By tuning the bias current or the coupling capacitance,the quantum KT transition can be directly detected through tomography measurement on the states of the phase qubit.We also estimate the experimental parameters using the numerical renormalization group method.
基金National Natural Science Foundation of China(11974058,62005231,62131002)A3 Foresight Program of NSFC(62061146002)+3 种基金Beijing Nova Program from Beijing Municipal Science and Technology Commission(Z201100006820125)Beijing Municipal Natural Science Foundation(Z210004)State Key Laboratory of Information Photonics and Optical Communications,BUPT,China(IPOC2021ZT01)BUPT Excellent Ph.D.Students Foundation(CX2022114).
文摘Whispering gallery mode(WGM)microcavities provide increasing opportunities for precision measurement due to their ultrahigh sensitivity,compact size,and fast response.However,the conventional WGM sensors rely on monitoring the changes of a single mode,and the abundant sensing information in WGM transmission spectra has not been fully utilized.Here,empowered by machine learning(ML),we propose and demonstrate an ergodic spectra sensing method in an optofluidic microcavity for high-precision pressure measurement.The developed ML method realizes the analysis of the full features of optical spectra.The prediction accuracy of 99.97%is obtained with the average error as low as 0.32 kPa in the pressure range of 100 kPa via the training and testing stages.We further achieve the real-time readout of arbitrary unknown pressure within the range of measurement,and a prediction accuracy of 99.51%is obtained.Moreover,we demonstrate that the ergodic spectra sensing accuracy is∼11.5%higher than that of simply extracting resonating modes’wavelength.With the high sensitivity and prediction accuracy,this work opens up a new avenue for integrated intelligent optical sensing.
基金supported in part by the National Natural Science Foundation of China(grant nos.61701260,61876211,and 62271266)the Postgraduate Research and Practice Innovation Program of Jiangsu Province(grant no.SJCX21_0255)the Postdoctoral Research Program of Jiangsu Province(grant no.2019K287).
文摘Rice plant counting is crucial for many applications in rice production,such as yield estimation,growth diagnosis,disaster loss assessment,etc.Currently,rice counting still heavily relies on tedious and time-consuming manual operation.To alleviate the workload of rice counting,we employed an UAV(unmanned aerial vehicle)to collect the RGB images of the paddy field.Then,we proposed a new rice plant counting,locating,and sizing method(RiceNet),which consists of one feature extractor frontend and 3 feature decoder modules,namely,density map estimator,plant location detector,and plant size estimator.In RiceNet,rice plant attention mechanism and positive–negative loss are designed to improve the ability to distinguish plants from background and the quality of the estimated density maps.To verify the validity of our method,we propose a new UAV-based rice counting dataset,which contains 355 images and 257,793 manual labeled points.Experiment results show that the mean absolute error and root mean square error of the proposed RiceNet are 8.6 and 11.2,respectively.Moreover,we validated the performance of our method with two other popular crop datasets.On these three datasets,our method significantly outperforms state-of-the-art methods.Results suggest that RiceNet can accurately and efficiently estimate the number of rice plants and replace the traditional manual method.
基金This study was funded by the Science and Technology Development Fund,Macao SAR(File no.0191/2017/A3,0041/2019/A1,0046/2019/AFJ,0021/2019/AIR)the University of Macao(File no.MYRG2017-00216-FST and MYRG2018-00192-IAPME)+2 种基金the UEA funding,Science and Technology Program of Guangzhou(2019050001)the National Key Research and Development Program of China(2019YFE0198000)Fuming Chen acknowledges the Pearl River Talent Program(2019QN01L951).
文摘Aluminum-metal batteries show great potential as next-generation energy storage due to their abundant resources and intrinsic safety.However,the crucial limitations of metallic Al anodes,such as dendrite and corrosion problems in conventional aluminum-metal batteries,remain challenging and elusive.Here,we report a novel electrodeposition strategy to prepare an optimized 3D Al anode on carbon cloth with an uniform deposition morphology,low local current density,and mitigatory volume change.The symmetrical cells with the 3D Al anode show superior stable cycling(>450 h)and low-voltage hysteresis(~170 mV)at 0.5 mA cm^(−2).High reversibility(~99.7%)is achieved for the Al plating/stripping.The graphite||Al‐4/CC full batteries show a long lifespan of 800 cycles with 54 mAh g^(−1) capacity at a high current density of 1000 mA g^(−1),benefiting from the high capacitive-controlled distribution.This study proposes a novel strategy to design 3D Al anodes for metallic-Al-based batteries by eliminating the problems of planar Al anodes and realizing the potential applications of aluminum-graphite batteries.
