Defects-rich heterointerfaces integrated with adjustable crystalline phases and atom vacancies,as well as veiled dielectric-responsive character,are instrumental in electromagnetic dissipation.Conventional methods,how...Defects-rich heterointerfaces integrated with adjustable crystalline phases and atom vacancies,as well as veiled dielectric-responsive character,are instrumental in electromagnetic dissipation.Conventional methods,however,constrain their delicate constructions.Herein,an innovative alternative is proposed:carrageenan-assistant cations-regulated(CACR)strategy,which induces a series of sulfides nanoparticles rooted in situ on the surface of carbon matrix.This unique configuration originates from strategic vacancy formation energy of sulfides and strong sulfides-carbon support interaction,benefiting the delicate construction of defects-rich heterostructures in M_(x)S_(y)/carbon composites(M-CAs).Impressively,these generated sulfur vacancies are firstly found to strengthen electron accumulation/consumption ability at heterointerfaces and,simultaneously,induct local asymmetry of electronic structure to evoke large dipole moment,ultimately leading to polarization coupling,i.e.,defect-type interfacial polarization.Such“Janus effect”(Janus effect means versatility,as in the Greek two-headed Janus)of interfacial sulfur vacancies is intuitively confirmed by both theoretical and experimental investigations for the first time.Consequently,the sulfur vacancies-rich heterostructured Co/Ni-CAs displays broad absorption bandwidth of 6.76 GHz at only 1.8 mm,compared to sulfur vacancies-free CAs without any dielectric response.Harnessing defects-rich heterostructures,this one-pot CACR strategy may steer the design and development of advanced nanomaterials,boosting functionality across diverse application domains beyond electromagnetic response.展开更多
Accurate prediction of the remaining useful life(RUL)is crucial for the design and management of lithium-ion batteries.Although various machine learning models offer promising predictions,one critical but often overlo...Accurate prediction of the remaining useful life(RUL)is crucial for the design and management of lithium-ion batteries.Although various machine learning models offer promising predictions,one critical but often overlooked challenge is their demand for considerable run-to-failure data for training.Collection of such training data leads to prohibitive testing efforts as the run-to-failure tests can last for years.Here,we propose a semi-supervised representation learning method to enhance prediction accuracy by learning from data without RUL labels.Our approach builds on a sophisticated deep neural network that comprises an encoder and three decoder heads to extract time-dependent representation features from short-term battery operating data regardless of the existence of RUL labels.The approach is validated using three datasets collected from 34 batteries operating under various conditions,encompassing over 19,900 charge and discharge cycles.Our method achieves a root mean squared error(RMSE)within 25 cycles,even when only 1/50 of the training dataset is labelled,representing a reduction of 48%compared to the conventional approach.We also demonstrate the method's robustness with varying numbers of labelled data and different weights assigned to the three decoder heads.The projection of extracted features in low space reveals that our method effectively learns degradation features from unlabelled data.Our approach highlights the promise of utilising semi-supervised learning to reduce the data demand for reliability monitoring of energy devices.展开更多
A non-orthogonal multiple access(NOMA) power allocation scheme on the basis of the sparrow search algorithm(SSA) is proposed in this work. Specifically, the logarithmic utility function is utilized to address the pote...A non-orthogonal multiple access(NOMA) power allocation scheme on the basis of the sparrow search algorithm(SSA) is proposed in this work. Specifically, the logarithmic utility function is utilized to address the potential fairness issue that may arise from the maximum sum-rate based objective function and the optical power constraints are set considering the non-negativity of the transmit signal, the requirement of the human eyes safety and all users' quality of service(Qo S). Then, the SSA is utilized to solve this optimization problem. Moreover, to demonstrate the superiority of the proposed strategy, it is compared with the fixed power allocation(FPA) and the gain ratio power allocation(GRPA) schemes. Results show that regardless of the number of users considered, the sum-rate achieved by SSA consistently outperforms that of FPA and GRPA schemes. Specifically, compared to FPA and GRPA schemes, the sum-rate obtained by SSA is increased by 40.45% and 53.44% when the number of users is 7, respectively. The proposed SSA also has better performance in terms of user fairness. This work will benefit the design and development of the NOMA-visible light communication(VLC) systems.展开更多
In this paper,we study the ρ-meson electromagnetic form factors(EMFFs)within the framework of the light-front quark model.The physical form factors G_(C,M,Q)(Q^(2))of the ρ-meson,as well as the charged square radius...In this paper,we study the ρ-meson electromagnetic form factors(EMFFs)within the framework of the light-front quark model.The physical form factors G_(C,M,Q)(Q^(2))of the ρ-meson,as well as the charged square radius<r^(2)>,the magnetic moment μ,and the quadrupole moment Q,are calculated,which describe the behaviors of EMFFs at zero momentum transfer.Using the type-Ⅱ replacement,we find that the zero-mode does contribute zero to the matrix element S_(00)^(+).It is found that the“M→M_(0)”replacement improves the angular condition remarkably,which permits different prescriptions of ρ-meson EMFFs to give the consistent results.The residual tiny violation of angular condition needs other explanations beyond the zero-mode contributions.Our results indicate that the relativistic effects or interaction internal structure are weaken in the zero-binding limit.This work is also applicable to other spin-1 particles.展开更多
Using the existing positioning technology can easily obtain high-precision positioning information,which can save resources and reduce complexity when used in the communication field.In this paper,we propose a locatio...Using the existing positioning technology can easily obtain high-precision positioning information,which can save resources and reduce complexity when used in the communication field.In this paper,we propose a location-based user scheduling and beamforming scheme for the downlink of a massive multi-user input-output system.Specifically,we combine an analog outer beamformer with a digital inner beamformer.An outer beamformer can be selected from a codebook formed by antenna steering vectors,and then a reduced-complexity inner beamformer based on iterative orthogonal matrices and right triangular matrices(QR)decomposition is applied to cancel interuser interference.Then,we propose a low-complexity user selection algorithm using location information in this paper.We first derive the geometric angle between channel matrices,which represent the correlation between users.Furthermore,we derive the asymptotic signal to interference-plus-noise ratio(SINR)of the system in the context of two-stage beamforming using random matrix theory(RMT),taking into account inter-channel correlations and energies.Simulation results show that the algorithm can achieve higher system and speed while reducing computational complexity.展开更多
The Discrete Walsh Hadamard Transform(DWHT)has emerged as an efficient alternative to the Discrete Fourier Transform(DFT)for Orthogonal Frequency Division Multiplexing(OFDM)implementations,particularly in handling cha...The Discrete Walsh Hadamard Transform(DWHT)has emerged as an efficient alternative to the Discrete Fourier Transform(DFT)for Orthogonal Frequency Division Multiplexing(OFDM)implementations,particularly in handling channel impairments.In this article,we proposed an efficient Joint Low Complexity Regularized Zero Forcing-Wavelet Domain Equalizer(JLCRLZF-WDE)to replace the traditional Frequency Domain Equalizer(FDE)in DWHT-OFDM systems.Unlike FDE,which requires additional DFT and Inverse DFT(IDFT)computations,the proposed JLCRLZF-WDE directly operates in the Walsh domain,effectively mitigating the computational overhead.The derivation of the proposed JLCRLZF-WDE equations take the effect of the channel,Co-Carrier Frequency Offset(Co-CFO),as well as the noise into account.During the derivation of the system model equations,we assume a MultipleInput-Multiple-Output(MIMO)-OFDM communication system through a Rayleigh fading channel.The Bit Error Rate(BER)performance and computational complexity of the proposed and the conventional algorithms are compared,indicating the significance of the proposed algorithm.Simulation results confirm the superiority of the proposed equalizer,demonstrating a 23.68%±28.4%reduction in computational complexity compared to Minimum Mean Square Error(LMMSE)-FDE based on DFT,while maintaining comparable BER performance at various MIMO configuration.Furthermore,at a BER of 10^(-4),the JLCRLZF-WDE achieves performance parity with conventional Walsh domain LMMSE equalizers,whereas other equalizers require an additional Signal-to-Noise Ratio(SNR)of 3.06 d B to achieve the same performance.展开更多
This paper proposes an extension of the Modified-Plant ADRC(MP-ADRC)strategy to broaden its application to minimum phase dynamical systems.The main features of the MP-ADRC method are the inclusion of a constant gain i...This paper proposes an extension of the Modified-Plant ADRC(MP-ADRC)strategy to broaden its application to minimum phase dynamical systems.The main features of the MP-ADRC method are the inclusion of a constant gain in series with the plant output error and a linear filter in parallel with the overall error system.These structural changes do not influence the input/output dynamics of the original plant,but are intentionally introduced to modify the dynamics to be estimated by the extended state observer(ESO)and,thus,promote an increase in the robustness of the method.Some advantages can also be attributed to the proposed methodology,such as(i)the design procedures of both the controller and the ESO only require knowledge of the sign(±)of the plant input channel coefficient(or control gain);(ii)the plant control input is generated directly by a single ESO state variable.Despite the advantages and the characteristics of MP-ADRC mentioned earlier,closed-loop stability cannot be guaranteed when it is applied to dynamical systems that have finite zeros.To overcome this difficulty,this work introduces an extension in the MP-ADRC method.It basically consists of rewriting the minimum phase plant dynamics according to its relative order,and then follows with the design of the ESO by conveniently increasing the number of ESO state variables.The simulation results are also presented to illustrate the application of the proposed method.展开更多
With the future substantial increase in coverage and network heterogeneity,emerging networks will encounter unprecedented security threats.Covert communication is considered a potential enhanced security and privacy s...With the future substantial increase in coverage and network heterogeneity,emerging networks will encounter unprecedented security threats.Covert communication is considered a potential enhanced security and privacy solution for safeguarding future wireless networks,as it can enable monitors to detect the transmitter's transmission behavior with a low probability,thereby ensuring the secure transmission of private information.Due to its favorable security,it is foreseeable that covert communication will be widely used in various wireless communication settings such as medical,financial,and military scenarios.However,existing covert communication methods still present many challenges toward practical applications.In particular,it is difficult to guarantee the effectiveness of covert schemes based on the randomness of eavesdropping environments,and it is challenging for legitimate users to detect weak covert signals.Considering that emerging artificial-intelligence-aided transmission technologies can open up entirely new opportunities to address the above challenges,we provide a comprehensive review of recent advances and potential research directions in the field of intelligent covert communications in this work.First,the basic concepts and performance metrics of covert communications are introduced.Then,existing effective covert communication techniques in the time,frequency,spatial,power,and modulation domains are reviewed.Finally,this paper discusses potential implementations and challenges for intelligent covert communications in future networks.展开更多
Heart failure prediction is crucial as cardiovascular diseases become the leading cause of death worldwide,exacerbated by the COVID-19 pandemic.Age,cholesterol,and blood pressure datasets are becoming inadequate becau...Heart failure prediction is crucial as cardiovascular diseases become the leading cause of death worldwide,exacerbated by the COVID-19 pandemic.Age,cholesterol,and blood pressure datasets are becoming inadequate because they cannot capture the complexity of emerging health indicators.These high-dimensional and heterogeneous datasets make traditional machine learning methods difficult,and Skewness and other new biomarkers and psychosocial factors bias the model’s heart health prediction across diverse patient profiles.Modern medical datasets’complexity and high dimensionality challenge traditional predictionmodels like SupportVectorMachines and Decision Trees.Quantum approaches include QSVM,QkNN,QDT,and others.These Constraints drove research.The“QHF-CS:Quantum-Enhanced Heart Failure Prediction using Quantum CNN with Optimized Feature Qubit Selection with Cuckoo Search in Skewed Clinical Data”system was developed in this research.This novel system leverages a Quantum Convolutional Neural Network(QCNN)-based quantum circuit,enhanced by meta-heuristic algorithms—Cuckoo SearchOptimization(CSO),Artificial BeeColony(ABC),and Particle SwarmOptimization(PSO)—for feature qubit selection.Among these,CSO demonstrated superior performance by consistently identifying the most optimal and least skewed feature subsets,which were then encoded into quantum states for circuit construction.By integrating advanced quantum circuit feature maps like ZZFeatureMap,RealAmplitudes,and EfficientSU2,the QHF-CS model efficiently processes complex,high-dimensional data,capturing intricate patterns that classical models overlook.The QHF-CS model improves precision,recall,F1-score,and accuracy to 0.94,0.95,0.94,and 0.94.Quantum computing could revolutionize heart failure diagnostics by improving model accuracy and computational efficiency,enabling complex healthcare diagnostic breakthroughs.展开更多
Although the image dehazing problem has received considerable attention over recent years,the existing models often prioritise performance at the expense of complexity,making them unsuitable for real-world application...Although the image dehazing problem has received considerable attention over recent years,the existing models often prioritise performance at the expense of complexity,making them unsuitable for real-world applications,which require algorithms to be deployed on resource constrained-devices.To address this challenge,we propose WaveLiteDehaze-Network(WLD-Net),an end-to-end dehazing model that delivers performance comparable to complex models while operating in real time and using significantly fewer parameters.This approach capitalises on the insight that haze predominantly affects low-frequency infor-mation.By exclusively processing the image in the frequency domain using discrete wavelet transform(DWT),we segregate the image into high and low frequencies and process them separately.This allows us to preserve high-frequency details and recover low-frequency components affected by haze,distinguishing our method from existing approaches that use spatial domain processing as the backbone,with DWT serving as an auxiliary component.DWT is applied at multiple levels for better in-formation retention while also accelerating computation by downsampling feature maps.Subsequently,a learning-based fusion mechanism reintegrates the processed frequencies to reconstruct the dehazed image.Experiments show that WLD-Net out-performs other low-parameter models on real-world hazy images and rivals much larger models,achieving the highest PSNR and SSIM scores on the O-Haze dataset.Qualitatively,the proposed method demonstrates its effectiveness in handling a diverse range of haze types,delivering visually pleasing results and robust performance,while also generalising well across different scenarios.With only 0.385 million parameters(more than 100 times smaller than comparable dehazing methods),WLD-Net processes 1024×1024 images in just 0.045 s,highlighting its applicability across various real-world scenarios.The code is available at https://github.com/AliMurtaza29/WLD-Net.展开更多
The rapid expansion of Internet of Things(IoT)networks has introduced challenges in network management,primarily in maintaining energy efficiency and robust connectivity across an increasing array of devices.This pape...The rapid expansion of Internet of Things(IoT)networks has introduced challenges in network management,primarily in maintaining energy efficiency and robust connectivity across an increasing array of devices.This paper introduces the Adaptive Blended Marine Predators Algorithm(AB-MPA),a novel optimization technique designed to enhance Quality of Service(QoS)in IoT systems by dynamically optimizing network configurations for improved energy efficiency and stability.Our results represent significant improvements in network performance metrics such as energy consumption,throughput,and operational stability,indicating that AB-MPA effectively addresses the pressing needs ofmodern IoT environments.Nodes are initiated with 100 J of stored energy,and energy is consumed at 0.01 J per square meter in each node to emphasize energy-efficient networks.The algorithm also provides sufficient network lifetime extension to a resourceful 7000 cycles for up to 200 nodes with a maximum Packet Delivery Ratio(PDR)of 99% and a robust network throughput of up to 1800 kbps in more compact node configurations.This study proposes a viable solution to a critical problem and opens avenues for further research into scalable network management for diverse applications.展开更多
Correction to:Nano-Micro Lett.(2025)17:24 https://doi.org/10.1007/s40820-024-01515-0 Following publication of the original article[1],the authors reported the author list needed to be updated because the last three au...Correction to:Nano-Micro Lett.(2025)17:24 https://doi.org/10.1007/s40820-024-01515-0 Following publication of the original article[1],the authors reported the author list needed to be updated because the last three author names were duplicated.The correct author list has been provided in this Correction.The original article[1]has been corrected.展开更多
Despite the dazzling theoretical capacity,the devasting electrochemical activity of Li_(2)MnO_(3)(LMO)caused by the difficult oxidation of Mn4+impedes its practical application as the lithium-ion battery(LIB)cathode.T...Despite the dazzling theoretical capacity,the devasting electrochemical activity of Li_(2)MnO_(3)(LMO)caused by the difficult oxidation of Mn4+impedes its practical application as the lithium-ion battery(LIB)cathode.The efficacious activation of the Li_(2)MnO_(3) by importing electrochemically active Mn3+ions or morphological engineering is instrumental to its lithium storage activity and structural integrity upon cycling.Herein,we propose a conceptual strategy with metal-organic frameworks(MOFs)as self-sacrificial templates to prepare oxygen-deficient Li_(2)MnO_(3)(O_v-LMO)for exalted lithium storage performance.Attributed to optimized morphological features,LMO materials derived from Mn-BDC(H_(2)BDC=1,4-dicarboxybenzene)delivered superior cycling/rate performances compared with their counterparts derived from Mn-BTC(H_(3)BTC=1,3,5-benzenetricarboxylicacid)and Mn-PTC(H_(4)PTC=pyromellitic acid).Both experimental and theoretical studies elucidate the efficacious activation of primitive LMO materials toward advanced lithium storage by importing oxygen deficiencies.Impressively,O_v-LMO derived from Mn-BDC(O_v-BDC-LMO)delivered intriguing reversible capacities(179.2 mA h g^(-1)at 20 mA g^(-1)after 200 cycles and 100.1 mA h g^(-1)at 80 mA g^(-1)after 300 cycles),which can be attributed to the small particle size that shortens pathways for Li+/electron transport,the enhanced redox activity induced by abundant oxygen vacancies,and the optimized electronic configuration that contributes to the faster lithium diffusivity.This work provides insights into the rational design of LMO by morphological and atomic modulation to direct its activation and practical application as an advanced LIB cathode.展开更多
Traditional orthogonal frequency division multiplexing(OFDM)based visible light communication(VLC)system is susceptible to high peak-to-average power ratio(PAPR),thus leading to low power efficiency.To address this is...Traditional orthogonal frequency division multiplexing(OFDM)based visible light communication(VLC)system is susceptible to high peak-to-average power ratio(PAPR),thus leading to low power efficiency.To address this issue,a sparse code multiple access(SCMA)codebook design method has been proposed to lower the PAPR of the clipping based OFDM-VLC system.展开更多
The safe and reliable operation of lithium-ion batteries necessitates the accurate prediction of remaining useful life(RUL).However,this task is challenging due to the diverse ageing mechanisms,various operating condi...The safe and reliable operation of lithium-ion batteries necessitates the accurate prediction of remaining useful life(RUL).However,this task is challenging due to the diverse ageing mechanisms,various operating conditions,and limited measured signals.Although data-driven methods are perceived as a promising solution,they ignore intrinsic battery physics,leading to compromised accuracy,low efficiency,and low interpretability.In response,this study integrates domain knowledge into deep learning to enhance the RUL prediction performance.We demonstrate accurate RUL prediction using only a single charging curve.First,a generalisable physics-based model is developed to extract ageing-correlated parameters that can describe and explain battery degradation from battery charging data.The parameters inform a deep neural network(DNN)to predict RUL with high accuracy and efficiency.The trained model is validated under 3 types of batteries working under 7 conditions,considering fully charged and partially charged cases.Using data from one cycle only,the proposed method achieves a root mean squared error(RMSE)of 11.42 cycles and a mean absolute relative error(MARE)of 3.19%on average,which are over45%and 44%lower compared to the two state-of-the-art data-driven methods,respectively.Besides its accuracy,the proposed method also outperforms existing methods in terms of efficiency,input burden,and robustness.The inherent relationship between the model parameters and the battery degradation mechanism is further revealed,substantiating the intrinsic superiority of the proposed method.展开更多
With the explosive growth of highdefinition video streaming data,a substantial increase in network traffic has ensued.The emergency of mobile edge caching(MEC)can not only alleviate the burden on core network,but also...With the explosive growth of highdefinition video streaming data,a substantial increase in network traffic has ensued.The emergency of mobile edge caching(MEC)can not only alleviate the burden on core network,but also significantly improve user experience.Integrating with the MEC and satellite networks,the network is empowered popular content ubiquitously and seamlessly.Addressing the research gap between multilayer satellite networks and MEC,we study the caching placement problem in this paper.Initially,we introduce a three-layer distributed network caching management architecture designed for efficient and flexible handling of large-scale networks.Considering the constraint on satellite capacity and content propagation delay,the cache placement problem is then formulated and transformed into a markov decision process(MDP),where the content coded caching mechanism is utilized to promote the efficiency of content delivery.Furthermore,a new generic metric,content delivery cost,is proposed to elaborate the performance of caching decision in large-scale networks.Then,we introduce a graph convolutional network(GCN)-based multi-agent advantage actor-critic(A2C)algorithm to optimize the caching decision.Finally,extensive simulations are conducted to evaluate the proposed algorithm in terms of content delivery cost and transferability.展开更多
Artificial intelligence(AI)technology has become integral in the realm of medicine and healthcare,particularly in human activity recognition(HAR)applications such as fitness and rehabilitation tracking.This study intr...Artificial intelligence(AI)technology has become integral in the realm of medicine and healthcare,particularly in human activity recognition(HAR)applications such as fitness and rehabilitation tracking.This study introduces a robust coupling analysis framework that integrates four AI-enabled models,combining both machine learning(ML)and deep learning(DL)approaches to evaluate their effectiveness in HAR.The analytical dataset comprises 561 features sourced from the UCI-HAR database,forming the foundation for training the models.Additionally,the MHEALTH database is employed to replicate the modeling process for comparative purposes,while inclusion of the WISDM database,renowned for its challenging features,supports the framework’s resilience and adaptability.The ML-based models employ the methodologies including adaptive neuro-fuzzy inference system(ANFIS),support vector machine(SVM),and random forest(RF),for data training.In contrast,a DL-based model utilizes one-dimensional convolution neural network(1dCNN)to automate feature extraction.Furthermore,the recursive feature elimination(RFE)algorithm,which drives an ML-based estimator to eliminate low-participation features,helps identify the optimal features for enhancing model performance.The best accuracies of the ANFIS,SVM,RF,and 1dCNN models with meticulous featuring process achieve around 90%,96%,91%,and 93%,respectively.Comparative analysis using the MHEALTH dataset showcases the 1dCNN model’s remarkable perfect accuracy(100%),while the RF,SVM,and ANFIS models equipped with selected features achieve accuracies of 99.8%,99.7%,and 96.5%,respectively.Finally,when applied to the WISDM dataset,the DL-based and ML-based models attain accuracies of 91.4%and 87.3%,respectively,aligning with prior research findings.In conclusion,the proposed framework yields HAR models with commendable performance metrics,exhibiting its suitability for integration into the healthcare services system through AI-driven applications.展开更多
Database watermarking technologies provide an effective solution to data security problems by embedding the watermark in the database to prove copyright or trace the source of data leakage.However,when the watermarked...Database watermarking technologies provide an effective solution to data security problems by embedding the watermark in the database to prove copyright or trace the source of data leakage.However,when the watermarked database is used for data mining model building,such as decision trees,it may cause a different mining result in comparison with the result from the original database caused by the distortion of watermark embedding.Traditional watermarking algorithms mainly consider the statistical distortion of data,such as the mean square error,but very few consider the effect of the watermark on database mining.Therefore,in this paper,a consistency preserving database watermarking algorithm is proposed for decision trees.First,label classification statistics and label state transfer methods are proposed to adjust the watermarked data so that the model structure of the watermarked decision tree is the same as that of the original decision tree.Then,the splitting values of the decision tree are adjusted according to the defined constraint equations.Finally,the adjusted database can obtain a decision tree consistent with the original decision tree.The experimental results demonstrated that the proposed algorithm does not corrupt the watermarks,and makes the watermarked decision tree consistent with the original decision tree with a small distortion.展开更多
Normally,in the downlink Network-Coded Multiple Access(NCMA)system,the same power is allocated to different users.However,equal power allocation is unsuitable for some scenarios,such as when user devices have differen...Normally,in the downlink Network-Coded Multiple Access(NCMA)system,the same power is allocated to different users.However,equal power allocation is unsuitable for some scenarios,such as when user devices have different Quality of Service(QoS)requirements.Hence,we study the power allocation in the downlink NCMA system in this paper,and propose a downlink Network-Coded Multiple Access with Diverse Power(NCMA-DP),wherein different amounts of power are allocated to different users.In terms of the Bit Error Rate(BER)of the multi-user decoder,and the number of packets required to correctly decode the message,the performance of the user with more allocated power is greatly improved compared to the Conventional NCMA(NCMA-C).Meanwhile,the performance of the user with less allocated power is still much better than NCMA-C.Furthermore,the overall throughput of NCMA-DP is greatly improved compared to that of NCMA-C.The simulation results demonstrate the remarkable performance of the proposed NCMA-DP.展开更多
This research focuses on addressing the challenges associated with image detection in low-light environments,particularly by applying artificial intelligence techniques to machine vision and object recognition systems...This research focuses on addressing the challenges associated with image detection in low-light environments,particularly by applying artificial intelligence techniques to machine vision and object recognition systems.The primary goal is to tackle issues related to recognizing objects with low brightness levels.In this study,the Intel RealSense Lidar Camera L515 is used to simultaneously capture color information and 16-bit depth information images.The detection scenarios are categorized into normal brightness and low brightness situations.When the system determines a normal brightness environment,normal brightness images are recognized using deep learning methods.In low-brightness situations,three methods are proposed for recognition.The first method is the SegmentationwithDepth image(SD)methodwhich involves segmenting the depth image,creating amask from the segmented depth image,mapping the obtained mask onto the true color(RGB)image to obtain a backgroundreduced RGB image,and recognizing the segmented image.The second method is theHDVmethod(hue,depth,value)which combines RGB images converted to HSV images(hue,saturation,value)with depth images D to form HDV images for recognition.The third method is the HSD(hue,saturation,depth)method which similarly combines RGB images converted to HSV images with depth images D to form HSD images for recognition.In experimental results,in normal brightness environments,the average recognition rate obtained using image recognition methods is 91%.For low-brightness environments,using the SD method with original images for training and segmented images for recognition achieves an average recognition rate of over 82%.TheHDVmethod achieves an average recognition rate of over 70%,while the HSD method achieves an average recognition rate of over 84%.The HSD method allows for a quick and convenient low-light object recognition system.This research outcome can be applied to nighttime surveillance systems or nighttime road safety systems.展开更多
基金financially supported by the National Natural Science Foundation of China(Grants nos.62201411,62371378,22205168,52302150 and 62304171)the China Postdoctoral Science Foundation(2022M722500)+1 种基金the Fundamental Research Funds for the Central Universities(Grants nos.ZYTS2308 and 20103237929)Startup Foundation of Xidian University(10251220001).
文摘Defects-rich heterointerfaces integrated with adjustable crystalline phases and atom vacancies,as well as veiled dielectric-responsive character,are instrumental in electromagnetic dissipation.Conventional methods,however,constrain their delicate constructions.Herein,an innovative alternative is proposed:carrageenan-assistant cations-regulated(CACR)strategy,which induces a series of sulfides nanoparticles rooted in situ on the surface of carbon matrix.This unique configuration originates from strategic vacancy formation energy of sulfides and strong sulfides-carbon support interaction,benefiting the delicate construction of defects-rich heterostructures in M_(x)S_(y)/carbon composites(M-CAs).Impressively,these generated sulfur vacancies are firstly found to strengthen electron accumulation/consumption ability at heterointerfaces and,simultaneously,induct local asymmetry of electronic structure to evoke large dipole moment,ultimately leading to polarization coupling,i.e.,defect-type interfacial polarization.Such“Janus effect”(Janus effect means versatility,as in the Greek two-headed Janus)of interfacial sulfur vacancies is intuitively confirmed by both theoretical and experimental investigations for the first time.Consequently,the sulfur vacancies-rich heterostructured Co/Ni-CAs displays broad absorption bandwidth of 6.76 GHz at only 1.8 mm,compared to sulfur vacancies-free CAs without any dielectric response.Harnessing defects-rich heterostructures,this one-pot CACR strategy may steer the design and development of advanced nanomaterials,boosting functionality across diverse application domains beyond electromagnetic response.
基金supported by the National Natural Science Foundation of China(No.52207229)the Key Research and Development Program of Ningxia Hui Autonomous Region of China(No.2024BEE02003)+1 种基金the financial support from the AEGiS Research Grant 2024,University of Wollongong(No.R6254)the financial support from the China Scholarship Council(No.202207550010).
文摘Accurate prediction of the remaining useful life(RUL)is crucial for the design and management of lithium-ion batteries.Although various machine learning models offer promising predictions,one critical but often overlooked challenge is their demand for considerable run-to-failure data for training.Collection of such training data leads to prohibitive testing efforts as the run-to-failure tests can last for years.Here,we propose a semi-supervised representation learning method to enhance prediction accuracy by learning from data without RUL labels.Our approach builds on a sophisticated deep neural network that comprises an encoder and three decoder heads to extract time-dependent representation features from short-term battery operating data regardless of the existence of RUL labels.The approach is validated using three datasets collected from 34 batteries operating under various conditions,encompassing over 19,900 charge and discharge cycles.Our method achieves a root mean squared error(RMSE)within 25 cycles,even when only 1/50 of the training dataset is labelled,representing a reduction of 48%compared to the conventional approach.We also demonstrate the method's robustness with varying numbers of labelled data and different weights assigned to the three decoder heads.The projection of extracted features in low space reveals that our method effectively learns degradation features from unlabelled data.Our approach highlights the promise of utilising semi-supervised learning to reduce the data demand for reliability monitoring of energy devices.
基金supported by the Cooperative Research Project between China Coal Energy Research Institute Co.,Ltd. and Xidian University (No.N-KY-HX-1101-202302-00725)the Key Research and Development Program of Shaanxi Province (No.2017ZDCXL-GY-06-02)。
文摘A non-orthogonal multiple access(NOMA) power allocation scheme on the basis of the sparrow search algorithm(SSA) is proposed in this work. Specifically, the logarithmic utility function is utilized to address the potential fairness issue that may arise from the maximum sum-rate based objective function and the optical power constraints are set considering the non-negativity of the transmit signal, the requirement of the human eyes safety and all users' quality of service(Qo S). Then, the SSA is utilized to solve this optimization problem. Moreover, to demonstrate the superiority of the proposed strategy, it is compared with the fixed power allocation(FPA) and the gain ratio power allocation(GRPA) schemes. Results show that regardless of the number of users considered, the sum-rate achieved by SSA consistently outperforms that of FPA and GRPA schemes. Specifically, compared to FPA and GRPA schemes, the sum-rate obtained by SSA is increased by 40.45% and 53.44% when the number of users is 7, respectively. The proposed SSA also has better performance in terms of user fairness. This work will benefit the design and development of the NOMA-visible light communication(VLC) systems.
基金supported by the National Natural Science Foundation of China(Grant Nos.11875122,12175025,and 12147102)Tongling University Talent Program(Grant No.R23100)。
文摘In this paper,we study the ρ-meson electromagnetic form factors(EMFFs)within the framework of the light-front quark model.The physical form factors G_(C,M,Q)(Q^(2))of the ρ-meson,as well as the charged square radius<r^(2)>,the magnetic moment μ,and the quadrupole moment Q,are calculated,which describe the behaviors of EMFFs at zero momentum transfer.Using the type-Ⅱ replacement,we find that the zero-mode does contribute zero to the matrix element S_(00)^(+).It is found that the“M→M_(0)”replacement improves the angular condition remarkably,which permits different prescriptions of ρ-meson EMFFs to give the consistent results.The residual tiny violation of angular condition needs other explanations beyond the zero-mode contributions.Our results indicate that the relativistic effects or interaction internal structure are weaken in the zero-binding limit.This work is also applicable to other spin-1 particles.
基金supported by the National Natural Science Foundation of China(61901341).
文摘Using the existing positioning technology can easily obtain high-precision positioning information,which can save resources and reduce complexity when used in the communication field.In this paper,we propose a location-based user scheduling and beamforming scheme for the downlink of a massive multi-user input-output system.Specifically,we combine an analog outer beamformer with a digital inner beamformer.An outer beamformer can be selected from a codebook formed by antenna steering vectors,and then a reduced-complexity inner beamformer based on iterative orthogonal matrices and right triangular matrices(QR)decomposition is applied to cancel interuser interference.Then,we propose a low-complexity user selection algorithm using location information in this paper.We first derive the geometric angle between channel matrices,which represent the correlation between users.Furthermore,we derive the asymptotic signal to interference-plus-noise ratio(SINR)of the system in the context of two-stage beamforming using random matrix theory(RMT),taking into account inter-channel correlations and energies.Simulation results show that the algorithm can achieve higher system and speed while reducing computational complexity.
文摘The Discrete Walsh Hadamard Transform(DWHT)has emerged as an efficient alternative to the Discrete Fourier Transform(DFT)for Orthogonal Frequency Division Multiplexing(OFDM)implementations,particularly in handling channel impairments.In this article,we proposed an efficient Joint Low Complexity Regularized Zero Forcing-Wavelet Domain Equalizer(JLCRLZF-WDE)to replace the traditional Frequency Domain Equalizer(FDE)in DWHT-OFDM systems.Unlike FDE,which requires additional DFT and Inverse DFT(IDFT)computations,the proposed JLCRLZF-WDE directly operates in the Walsh domain,effectively mitigating the computational overhead.The derivation of the proposed JLCRLZF-WDE equations take the effect of the channel,Co-Carrier Frequency Offset(Co-CFO),as well as the noise into account.During the derivation of the system model equations,we assume a MultipleInput-Multiple-Output(MIMO)-OFDM communication system through a Rayleigh fading channel.The Bit Error Rate(BER)performance and computational complexity of the proposed and the conventional algorithms are compared,indicating the significance of the proposed algorithm.Simulation results confirm the superiority of the proposed equalizer,demonstrating a 23.68%±28.4%reduction in computational complexity compared to Minimum Mean Square Error(LMMSE)-FDE based on DFT,while maintaining comparable BER performance at various MIMO configuration.Furthermore,at a BER of 10^(-4),the JLCRLZF-WDE achieves performance parity with conventional Walsh domain LMMSE equalizers,whereas other equalizers require an additional Signal-to-Noise Ratio(SNR)of 3.06 d B to achieve the same performance.
基金supported in part by the Brazilian research agencies CNPq and CAPESby the Fundação Carlos Chagas Filho de AmparoàPesquisa do Estado do Rio de Janeiro,FAPERJ-Brasil(Project E-26/210.425/2024).
文摘This paper proposes an extension of the Modified-Plant ADRC(MP-ADRC)strategy to broaden its application to minimum phase dynamical systems.The main features of the MP-ADRC method are the inclusion of a constant gain in series with the plant output error and a linear filter in parallel with the overall error system.These structural changes do not influence the input/output dynamics of the original plant,but are intentionally introduced to modify the dynamics to be estimated by the extended state observer(ESO)and,thus,promote an increase in the robustness of the method.Some advantages can also be attributed to the proposed methodology,such as(i)the design procedures of both the controller and the ESO only require knowledge of the sign(±)of the plant input channel coefficient(or control gain);(ii)the plant control input is generated directly by a single ESO state variable.Despite the advantages and the characteristics of MP-ADRC mentioned earlier,closed-loop stability cannot be guaranteed when it is applied to dynamical systems that have finite zeros.To overcome this difficulty,this work introduces an extension in the MP-ADRC method.It basically consists of rewriting the minimum phase plant dynamics according to its relative order,and then follows with the design of the ESO by conveniently increasing the number of ESO state variables.The simulation results are also presented to illustrate the application of the proposed method.
基金supported by the National Natural Science Foundation of China(62425103)the National Key Research and Development Program of China(2022YFC3301300)。
文摘With the future substantial increase in coverage and network heterogeneity,emerging networks will encounter unprecedented security threats.Covert communication is considered a potential enhanced security and privacy solution for safeguarding future wireless networks,as it can enable monitors to detect the transmitter's transmission behavior with a low probability,thereby ensuring the secure transmission of private information.Due to its favorable security,it is foreseeable that covert communication will be widely used in various wireless communication settings such as medical,financial,and military scenarios.However,existing covert communication methods still present many challenges toward practical applications.In particular,it is difficult to guarantee the effectiveness of covert schemes based on the randomness of eavesdropping environments,and it is challenging for legitimate users to detect weak covert signals.Considering that emerging artificial-intelligence-aided transmission technologies can open up entirely new opportunities to address the above challenges,we provide a comprehensive review of recent advances and potential research directions in the field of intelligent covert communications in this work.First,the basic concepts and performance metrics of covert communications are introduced.Then,existing effective covert communication techniques in the time,frequency,spatial,power,and modulation domains are reviewed.Finally,this paper discusses potential implementations and challenges for intelligent covert communications in future networks.
文摘Heart failure prediction is crucial as cardiovascular diseases become the leading cause of death worldwide,exacerbated by the COVID-19 pandemic.Age,cholesterol,and blood pressure datasets are becoming inadequate because they cannot capture the complexity of emerging health indicators.These high-dimensional and heterogeneous datasets make traditional machine learning methods difficult,and Skewness and other new biomarkers and psychosocial factors bias the model’s heart health prediction across diverse patient profiles.Modern medical datasets’complexity and high dimensionality challenge traditional predictionmodels like SupportVectorMachines and Decision Trees.Quantum approaches include QSVM,QkNN,QDT,and others.These Constraints drove research.The“QHF-CS:Quantum-Enhanced Heart Failure Prediction using Quantum CNN with Optimized Feature Qubit Selection with Cuckoo Search in Skewed Clinical Data”system was developed in this research.This novel system leverages a Quantum Convolutional Neural Network(QCNN)-based quantum circuit,enhanced by meta-heuristic algorithms—Cuckoo SearchOptimization(CSO),Artificial BeeColony(ABC),and Particle SwarmOptimization(PSO)—for feature qubit selection.Among these,CSO demonstrated superior performance by consistently identifying the most optimal and least skewed feature subsets,which were then encoded into quantum states for circuit construction.By integrating advanced quantum circuit feature maps like ZZFeatureMap,RealAmplitudes,and EfficientSU2,the QHF-CS model efficiently processes complex,high-dimensional data,capturing intricate patterns that classical models overlook.The QHF-CS model improves precision,recall,F1-score,and accuracy to 0.94,0.95,0.94,and 0.94.Quantum computing could revolutionize heart failure diagnostics by improving model accuracy and computational efficiency,enabling complex healthcare diagnostic breakthroughs.
基金Japan International Cooperation Agency(JICA)via Malaysia-Japan Linkage Research Grant 2024.
文摘Although the image dehazing problem has received considerable attention over recent years,the existing models often prioritise performance at the expense of complexity,making them unsuitable for real-world applications,which require algorithms to be deployed on resource constrained-devices.To address this challenge,we propose WaveLiteDehaze-Network(WLD-Net),an end-to-end dehazing model that delivers performance comparable to complex models while operating in real time and using significantly fewer parameters.This approach capitalises on the insight that haze predominantly affects low-frequency infor-mation.By exclusively processing the image in the frequency domain using discrete wavelet transform(DWT),we segregate the image into high and low frequencies and process them separately.This allows us to preserve high-frequency details and recover low-frequency components affected by haze,distinguishing our method from existing approaches that use spatial domain processing as the backbone,with DWT serving as an auxiliary component.DWT is applied at multiple levels for better in-formation retention while also accelerating computation by downsampling feature maps.Subsequently,a learning-based fusion mechanism reintegrates the processed frequencies to reconstruct the dehazed image.Experiments show that WLD-Net out-performs other low-parameter models on real-world hazy images and rivals much larger models,achieving the highest PSNR and SSIM scores on the O-Haze dataset.Qualitatively,the proposed method demonstrates its effectiveness in handling a diverse range of haze types,delivering visually pleasing results and robust performance,while also generalising well across different scenarios.With only 0.385 million parameters(more than 100 times smaller than comparable dehazing methods),WLD-Net processes 1024×1024 images in just 0.045 s,highlighting its applicability across various real-world scenarios.The code is available at https://github.com/AliMurtaza29/WLD-Net.
文摘The rapid expansion of Internet of Things(IoT)networks has introduced challenges in network management,primarily in maintaining energy efficiency and robust connectivity across an increasing array of devices.This paper introduces the Adaptive Blended Marine Predators Algorithm(AB-MPA),a novel optimization technique designed to enhance Quality of Service(QoS)in IoT systems by dynamically optimizing network configurations for improved energy efficiency and stability.Our results represent significant improvements in network performance metrics such as energy consumption,throughput,and operational stability,indicating that AB-MPA effectively addresses the pressing needs ofmodern IoT environments.Nodes are initiated with 100 J of stored energy,and energy is consumed at 0.01 J per square meter in each node to emphasize energy-efficient networks.The algorithm also provides sufficient network lifetime extension to a resourceful 7000 cycles for up to 200 nodes with a maximum Packet Delivery Ratio(PDR)of 99% and a robust network throughput of up to 1800 kbps in more compact node configurations.This study proposes a viable solution to a critical problem and opens avenues for further research into scalable network management for diverse applications.
文摘Correction to:Nano-Micro Lett.(2025)17:24 https://doi.org/10.1007/s40820-024-01515-0 Following publication of the original article[1],the authors reported the author list needed to be updated because the last three author names were duplicated.The correct author list has been provided in this Correction.The original article[1]has been corrected.
基金the financial support from the Special Funds for the Cultivation of Guangdong College Students’Scientific and Technological Innovation(“Climbing Program”Special Funds,pdjh2023b0145)the Research and Development Plan Project in Key Fields of Guangdong Province(2020B0101030005)+1 种基金the Applied special project of Guangdong Provincial Science and Technology Plan(2017B090917002)the Basic and Applied Basic Research Fund of Guangdong Province(2019B1515120027)。
文摘Despite the dazzling theoretical capacity,the devasting electrochemical activity of Li_(2)MnO_(3)(LMO)caused by the difficult oxidation of Mn4+impedes its practical application as the lithium-ion battery(LIB)cathode.The efficacious activation of the Li_(2)MnO_(3) by importing electrochemically active Mn3+ions or morphological engineering is instrumental to its lithium storage activity and structural integrity upon cycling.Herein,we propose a conceptual strategy with metal-organic frameworks(MOFs)as self-sacrificial templates to prepare oxygen-deficient Li_(2)MnO_(3)(O_v-LMO)for exalted lithium storage performance.Attributed to optimized morphological features,LMO materials derived from Mn-BDC(H_(2)BDC=1,4-dicarboxybenzene)delivered superior cycling/rate performances compared with their counterparts derived from Mn-BTC(H_(3)BTC=1,3,5-benzenetricarboxylicacid)and Mn-PTC(H_(4)PTC=pyromellitic acid).Both experimental and theoretical studies elucidate the efficacious activation of primitive LMO materials toward advanced lithium storage by importing oxygen deficiencies.Impressively,O_v-LMO derived from Mn-BDC(O_v-BDC-LMO)delivered intriguing reversible capacities(179.2 mA h g^(-1)at 20 mA g^(-1)after 200 cycles and 100.1 mA h g^(-1)at 80 mA g^(-1)after 300 cycles),which can be attributed to the small particle size that shortens pathways for Li+/electron transport,the enhanced redox activity induced by abundant oxygen vacancies,and the optimized electronic configuration that contributes to the faster lithium diffusivity.This work provides insights into the rational design of LMO by morphological and atomic modulation to direct its activation and practical application as an advanced LIB cathode.
基金supported by the National Natural Science Foundation of China(No.62071365)the Key Research and Development Program of Shaanxi Province(No.2017ZDCXL-GY-06-02)。
文摘Traditional orthogonal frequency division multiplexing(OFDM)based visible light communication(VLC)system is susceptible to high peak-to-average power ratio(PAPR),thus leading to low power efficiency.To address this issue,a sparse code multiple access(SCMA)codebook design method has been proposed to lower the PAPR of the clipping based OFDM-VLC system.
基金the financial support from the National Natural Science Foundation of China(52207229)the financial support from the China Scholarship Council(202207550010)。
文摘The safe and reliable operation of lithium-ion batteries necessitates the accurate prediction of remaining useful life(RUL).However,this task is challenging due to the diverse ageing mechanisms,various operating conditions,and limited measured signals.Although data-driven methods are perceived as a promising solution,they ignore intrinsic battery physics,leading to compromised accuracy,low efficiency,and low interpretability.In response,this study integrates domain knowledge into deep learning to enhance the RUL prediction performance.We demonstrate accurate RUL prediction using only a single charging curve.First,a generalisable physics-based model is developed to extract ageing-correlated parameters that can describe and explain battery degradation from battery charging data.The parameters inform a deep neural network(DNN)to predict RUL with high accuracy and efficiency.The trained model is validated under 3 types of batteries working under 7 conditions,considering fully charged and partially charged cases.Using data from one cycle only,the proposed method achieves a root mean squared error(RMSE)of 11.42 cycles and a mean absolute relative error(MARE)of 3.19%on average,which are over45%and 44%lower compared to the two state-of-the-art data-driven methods,respectively.Besides its accuracy,the proposed method also outperforms existing methods in terms of efficiency,input burden,and robustness.The inherent relationship between the model parameters and the battery degradation mechanism is further revealed,substantiating the intrinsic superiority of the proposed method.
基金supported by the National Key Research and Development Program of China under Grant 2020YFB1807700the National Natural Science Foundation of China(NSFC)under Grant(No.62201414,62201432)+2 种基金the Qinchuangyuan Project(OCYRCXM-2022-362)the Fundamental Research Funds for the Central Universities and the Innovation Fund of Xidian University under Grant YJSJ24017the Guangzhou Science and Technology Program under Grant 202201011732。
文摘With the explosive growth of highdefinition video streaming data,a substantial increase in network traffic has ensued.The emergency of mobile edge caching(MEC)can not only alleviate the burden on core network,but also significantly improve user experience.Integrating with the MEC and satellite networks,the network is empowered popular content ubiquitously and seamlessly.Addressing the research gap between multilayer satellite networks and MEC,we study the caching placement problem in this paper.Initially,we introduce a three-layer distributed network caching management architecture designed for efficient and flexible handling of large-scale networks.Considering the constraint on satellite capacity and content propagation delay,the cache placement problem is then formulated and transformed into a markov decision process(MDP),where the content coded caching mechanism is utilized to promote the efficiency of content delivery.Furthermore,a new generic metric,content delivery cost,is proposed to elaborate the performance of caching decision in large-scale networks.Then,we introduce a graph convolutional network(GCN)-based multi-agent advantage actor-critic(A2C)algorithm to optimize the caching decision.Finally,extensive simulations are conducted to evaluate the proposed algorithm in terms of content delivery cost and transferability.
基金funded by the National Science and Technology Council,Taiwan(Grant No.NSTC 112-2121-M-039-001)by China Medical University(Grant No.CMU112-MF-79).
文摘Artificial intelligence(AI)technology has become integral in the realm of medicine and healthcare,particularly in human activity recognition(HAR)applications such as fitness and rehabilitation tracking.This study introduces a robust coupling analysis framework that integrates four AI-enabled models,combining both machine learning(ML)and deep learning(DL)approaches to evaluate their effectiveness in HAR.The analytical dataset comprises 561 features sourced from the UCI-HAR database,forming the foundation for training the models.Additionally,the MHEALTH database is employed to replicate the modeling process for comparative purposes,while inclusion of the WISDM database,renowned for its challenging features,supports the framework’s resilience and adaptability.The ML-based models employ the methodologies including adaptive neuro-fuzzy inference system(ANFIS),support vector machine(SVM),and random forest(RF),for data training.In contrast,a DL-based model utilizes one-dimensional convolution neural network(1dCNN)to automate feature extraction.Furthermore,the recursive feature elimination(RFE)algorithm,which drives an ML-based estimator to eliminate low-participation features,helps identify the optimal features for enhancing model performance.The best accuracies of the ANFIS,SVM,RF,and 1dCNN models with meticulous featuring process achieve around 90%,96%,91%,and 93%,respectively.Comparative analysis using the MHEALTH dataset showcases the 1dCNN model’s remarkable perfect accuracy(100%),while the RF,SVM,and ANFIS models equipped with selected features achieve accuracies of 99.8%,99.7%,and 96.5%,respectively.Finally,when applied to the WISDM dataset,the DL-based and ML-based models attain accuracies of 91.4%and 87.3%,respectively,aligning with prior research findings.In conclusion,the proposed framework yields HAR models with commendable performance metrics,exhibiting its suitability for integration into the healthcare services system through AI-driven applications.
基金supported by the National Key Research and Development Program of China under Grant 2021YFB2700600the National Natural Science Foundation of China under Grant 62132013 and 61902292+1 种基金the Key Research and Development Programs of Shaanxi under Grants 2021ZDLGY06-03the Truth-Seeking Research Scholarship Fund of Xidian University。
文摘Database watermarking technologies provide an effective solution to data security problems by embedding the watermark in the database to prove copyright or trace the source of data leakage.However,when the watermarked database is used for data mining model building,such as decision trees,it may cause a different mining result in comparison with the result from the original database caused by the distortion of watermark embedding.Traditional watermarking algorithms mainly consider the statistical distortion of data,such as the mean square error,but very few consider the effect of the watermark on database mining.Therefore,in this paper,a consistency preserving database watermarking algorithm is proposed for decision trees.First,label classification statistics and label state transfer methods are proposed to adjust the watermarked data so that the model structure of the watermarked decision tree is the same as that of the original decision tree.Then,the splitting values of the decision tree are adjusted according to the defined constraint equations.Finally,the adjusted database can obtain a decision tree consistent with the original decision tree.The experimental results demonstrated that the proposed algorithm does not corrupt the watermarks,and makes the watermarked decision tree consistent with the original decision tree with a small distortion.
文摘Normally,in the downlink Network-Coded Multiple Access(NCMA)system,the same power is allocated to different users.However,equal power allocation is unsuitable for some scenarios,such as when user devices have different Quality of Service(QoS)requirements.Hence,we study the power allocation in the downlink NCMA system in this paper,and propose a downlink Network-Coded Multiple Access with Diverse Power(NCMA-DP),wherein different amounts of power are allocated to different users.In terms of the Bit Error Rate(BER)of the multi-user decoder,and the number of packets required to correctly decode the message,the performance of the user with more allocated power is greatly improved compared to the Conventional NCMA(NCMA-C).Meanwhile,the performance of the user with less allocated power is still much better than NCMA-C.Furthermore,the overall throughput of NCMA-DP is greatly improved compared to that of NCMA-C.The simulation results demonstrate the remarkable performance of the proposed NCMA-DP.
基金the National Science and Technology Council of Taiwan under Grant NSTC 112-2221-E-130-005.
文摘This research focuses on addressing the challenges associated with image detection in low-light environments,particularly by applying artificial intelligence techniques to machine vision and object recognition systems.The primary goal is to tackle issues related to recognizing objects with low brightness levels.In this study,the Intel RealSense Lidar Camera L515 is used to simultaneously capture color information and 16-bit depth information images.The detection scenarios are categorized into normal brightness and low brightness situations.When the system determines a normal brightness environment,normal brightness images are recognized using deep learning methods.In low-brightness situations,three methods are proposed for recognition.The first method is the SegmentationwithDepth image(SD)methodwhich involves segmenting the depth image,creating amask from the segmented depth image,mapping the obtained mask onto the true color(RGB)image to obtain a backgroundreduced RGB image,and recognizing the segmented image.The second method is theHDVmethod(hue,depth,value)which combines RGB images converted to HSV images(hue,saturation,value)with depth images D to form HDV images for recognition.The third method is the HSD(hue,saturation,depth)method which similarly combines RGB images converted to HSV images with depth images D to form HSD images for recognition.In experimental results,in normal brightness environments,the average recognition rate obtained using image recognition methods is 91%.For low-brightness environments,using the SD method with original images for training and segmented images for recognition achieves an average recognition rate of over 82%.TheHDVmethod achieves an average recognition rate of over 70%,while the HSD method achieves an average recognition rate of over 84%.The HSD method allows for a quick and convenient low-light object recognition system.This research outcome can be applied to nighttime surveillance systems or nighttime road safety systems.
