Military image encryption plays a vital role in ensuring the secure transmission of sensitive visual information from unauthorized access.This paper proposes a new Tri-independent keying method for encrypting military...Military image encryption plays a vital role in ensuring the secure transmission of sensitive visual information from unauthorized access.This paper proposes a new Tri-independent keying method for encrypting military images.The proposed encryption method is based on multilevel security stages of pixel-level scrambling,bitlevel manipulation,and block-level shuffling operations.For having a vast key space,the input password is hashed by the Secure Hash Algorithm 256-bit(SHA-256)for generating independently deterministic keys used in the multilevel stages.A piecewise pixel-level scrambling function is introduced to perform a dual flipping process controlled with an adaptive key for obscuring the spatial relationships between the adjacent pixels.Adynamicmasking scheme is presented for conducting a bit-level manipulation based on distinct keys that change over image regions,providing completely different encryption results on identical regions.To handle the global correlation between large-scale patterns,a chaotic index-map system is employed for shuffling image regions randomly across the image domain based on a logistic map seeded with a private key.Experimental results on a dataset of military images show the effectiveness of the proposed encryption method in producing excellent quantitative and qualitative results.The proposed method obtains uniform histogram distributions,high entropy values around the ideal(≈8 bits),Number of Pixel Change Rate(NPCR)values above 99.5%,and low Peak Signal-to-Noise Ratio(PSNR)over all encrypted images.This validates the robustness of the proposed method against cryptanalytic attacks,verifying its ability to serve as a practical basis for secure image transmission in defense systems.展开更多
Electroacoustic Tomography(EAT)is an imaging technique that detects ultrasound waves induced by electrical pulses,offering a solution for real-time electroporation monitoring.This study presents EAT system using a dua...Electroacoustic Tomography(EAT)is an imaging technique that detects ultrasound waves induced by electrical pulses,offering a solution for real-time electroporation monitoring.This study presents EAT system using a dual-frequency ultrasound array.The broadband nature of electroacoustic signals requires ultrasound detector to cover both the high-frequency range(around 6MHz)signals generated by small targets and the low-frequency range(around 1MHz)signals generated by large targets.In our EAT system,we use the 6 MHz array to detect high-frequency signals from the electrodes,and the 1 MHz array for the electrical field.To test this,we conducted simulations using COMSOL Multiphysics^(®) and MATLAB's k-Wave toolbox,followed by experiments using a custom-built setup with a dual-frequency transducer and real-time data acquisition.The results demonstrated that the dual-frequency EAT system could accurately and simultaneously monitor the electroporation process,effectively showing both the treatment area and electrode placement with the application of 1 kV electric pulses with 100 ns duration.The axial resolution of the 6MHz array for EAT was 0.45 mm,significantly better than the 2mm resolution achieved with the 1MHz array.These findings validate the potential of dual-frequency EAT as a superior method for real-time electroporation monitoring.展开更多
As a fundamental component in computer vision,edges can be categorized into four types based on discontinuities in reflectance,illumination,surface normal,or depth.While deep CNNs have significantly advanced generic e...As a fundamental component in computer vision,edges can be categorized into four types based on discontinuities in reflectance,illumination,surface normal,or depth.While deep CNNs have significantly advanced generic edge detection,real-time multi-class semantic edge detection under resource constraints remains challenging.To address this,we propose a lightweight framework based on PiDiNet that enables fine-grained semantic edge detection.Our model simultaneously predicts background and four edge categories from full-resolution inputs,balancing accuracy and efficiency.Key contributions include:a multi-channel output structure expanding binary edge prediction to five classes,supported by a deep supervision mechanism;a dynamic class-balancing strategy combining adaptive weighting with physical priors to handle extreme class imbalance;and maintained architectural efficiency enabling real-time inference.Extensive evaluations on BSDS-RIND show our approach achieves accuracy competitive with state-of-the-art methods while operating in real time.展开更多
Driven by advancements in mobile internet technology,images have become a crucial data medium.Ensuring the security of image information during transmission has thus emerged as an urgent challenge.This study proposes ...Driven by advancements in mobile internet technology,images have become a crucial data medium.Ensuring the security of image information during transmission has thus emerged as an urgent challenge.This study proposes a novel image encryption algorithm specifically designed for grayscale image security.This research introduces a new Cantor diagonal matrix permutation method.The proposed permutation method uses row and column index sequences to control the Cantor diagonal matrix,where the row and column index sequences are generated by a spatiotemporal chaotic system named coupled map lattice(CML).The high initial value sensitivity of the CML system makes the permutation method highly sensitive and secure.Additionally,leveraging fractal theory,this study introduces a chaotic fractal matrix and applies this matrix in the diffusion process.This chaotic fractal matrix exhibits selfsimilarity and irregularity.Using the Cantor diagonal matrix and chaotic fractal matrix,this paper introduces a fast image encryption algorithm involving two diffusion steps and one permutation step.Moreover,the algorithm achieves robust security with only a single encryption round,ensuring high operational efficiency.Experimental results show that the proposed algorithm features an expansive key space,robust security,high sensitivity,high efficiency,and superior statistical properties for the ciphered images.Thus,the proposed algorithm not only provides a practical solution for secure image transmission but also bridges fractal theory with image encryption techniques,thereby opening new research avenues in chaotic cryptography and advancing the development of information security technology.展开更多
As cyberattacks become increasingly sophisticated and intelligent,demand for machine-learning-based anomaly detection systems is growing.However,conventional systems generally assume a trusted server environment,where...As cyberattacks become increasingly sophisticated and intelligent,demand for machine-learning-based anomaly detection systems is growing.However,conventional systems generally assume a trusted server environment,where traffic data is collected and analyzed in plaintext.This assumption introduces inherent privacy risks,as privacy-sensitive information may be exposed if the server is compromised or misused.To address this limitation,privacy-preserving anomaly detection approaches have been actively studied,enabling anomaly detection to be performed directly on encrypted traffic without revealing privacy-sensitive data.While these approaches offer strong confidentiality guarantees,they suffer from significant drawbacks,including substantial computational overhead,high latency,and degraded detection accuracy.To overcome these limitations,we propose a privacy-aware anomaly detection(PAAD)model that adaptively applies homomorphic encryption based on the privacy sensitivity of incoming traffic.Instead of encrypting all data indiscriminately,PAAD dynamically determines whether traffic should be processed in plaintext or ciphertext and performs homomorphic inference only for privacy-sensitive data.This selective encryption strategy effectively balances privacy protection and system efficiency.Extensive experiments conducted under diverse network environments demonstrate that the proposed PAAD model significantly outperforms conventional anomaly detection models.In particular,PAAD improves detection accuracy by up to 73%,reduces latency by up to 8.6 times,and achieves negligible information leakage,highlighting its practicality for real-world privacy-sensitive network monitoring scenarios.展开更多
With the rapid development of intelligent electronic and military equipment,multifunctional flexible materials that integrat electromagnetic interference(EMI)shielding,temperature sensing,and information encryption ar...With the rapid development of intelligent electronic and military equipment,multifunctional flexible materials that integrat electromagnetic interference(EMI)shielding,temperature sensing,and information encryption are urgently required.This study presents a bio-inspired hierarchical composite foam fabricated using supercritical nitrogen foaming technology.This material exhibits a honeycomb structure,with pore cell sizes controllable within a range of 30–92μm by regulating the filler.The carbon fiber felt(CFf)provides efficient reflection of electromagnetic waves,while the chloroprene rubber/carbon fiber/carbon black foam facilitates both wave absorption and temperature monitoring through its optimized conductive network.This synergistic mechanism results in an EMI shielding effectiveness(SE)of 60.06 d B with excellent temperature sensing performance(The temperature coefficient of resistance(TCR)is-2.642%/℃)in the 24–70℃ range.Notably,the material has a thermal conductivity of up to 0.159 W/(m·K),and the bio-inspired layered design enables information encryption,demonstrating the material's potential for secure communication applications.The foam also has tensile properties of up to 5.13 MPa and a tear strength of 33.02 N/mm.This biomimetic design overcomes the traditional limitations of flexible materials and provides a transformative solution for next-generation applications such as flexible electronics,aerospace systems and military equipment,which urgently need integrated electromagnetic protection,thermal management and information security.展开更多
The advent of 5G technology has significantly enhanced the transmission of images over networks,expanding data accessibility and exposure across various applications in digital technology and social media.Consequently...The advent of 5G technology has significantly enhanced the transmission of images over networks,expanding data accessibility and exposure across various applications in digital technology and social media.Consequently,the protection of sensitive data has become increasingly critical.Regardless of the complexity of the encryption algorithm used,a robust and highly secure encryption key is essential,with randomness and key space being crucial factors.This paper proposes a new Robust Deoxyribonucleic Acid(RDNA)nucleotide-based encryption method.The RDNA encryption method leverages the unique properties of DNA nucleotides,including their inherent randomness and extensive key space,to generate a highly secure encryption key.By employing transposition and substitution operations,the RDNA method ensures significant diffusion and confusion in the encrypted images.Additionally,it utilises a pseudorandom generation technique based on the random sequence of nucleotides in the DNA secret key.The performance of the RDNA encryption method is evaluated through various statistical and visual tests,and compared against established encryption methods such as 3DES,AES,and a DNA-based method.Experimental results demonstrate that the RDNA encryption method outperforms its rivals in the literature,and achieves superior performance in terms of information entropy,avalanche effect,encryption execution time,and correlation reduction,while maintaining competitive values for NMAE,PSNR,NPCR,and UACI.The high degree of randomness and sensitivity to key changes inherent in the RDNA method offers enhanced security,making it highly resistant to brute force and differential attacks.展开更多
In the era of the Internet of Things,distributed computing alleviates the problem of insufficient terminal computing power by integrating idle resources of heterogeneous devices.However,the imbalance between task exec...In the era of the Internet of Things,distributed computing alleviates the problem of insufficient terminal computing power by integrating idle resources of heterogeneous devices.However,the imbalance between task execution delay and node energy consumption,and the scheduling and adaptation challenges brought about by device heterogeneity,urgently need to be addressed.To tackle this problem,this paper constructs a multi-objective real-time task scheduling model that considers task real-time performance,execution delay,system energy consumption,and node interests.The model aims to minimize the delay upper bound and total energy consumption while maximizing system satisfaction.A real-time task scheduling algorithm based on bilateral matching game is proposed.By designing a bidirectional preference mechanism between tasks and computing nodes,combined with a multi-round stable matching strategy,accurate matching between tasks and nodes is achieved.Simulation results show that compared with the baseline scheme,the proposed algorithm significantly reduces the total execution cost,effectively balances the task execution delay and the energy consumption of compute nodes,and takes into account the interests of each network compute node.展开更多
Elliptic curve(EC)based cryptosystems gained more attention due to enhanced security than the existing public key cryptosystems.A substitution box(S-box)plays a vital role in securing modern symmetric key cryptosystem...Elliptic curve(EC)based cryptosystems gained more attention due to enhanced security than the existing public key cryptosystems.A substitution box(S-box)plays a vital role in securing modern symmetric key cryptosystems.However,the recently developed EC based algorithms usually trade off between computational efficiency and security,necessitating the design of a new algorithm with the desired cryptographic strength.To address these shortcomings,this paper proposes a new scheme based onMordell elliptic curve(MEC)over the complex field for generating distinct,dynamic,and highly uncorrelated S-boxes.Furthermore,we count the exact number of the obtained S-boxes,and demonstrate that the permuted version of the presented S-box is statistically optimal.The nonsingularity of the presented algorithm and the injectivity of the resultant output are explored.Rigorous theoretical analysis and experimental results demonstrate that the proposedmethod is highly effective in generating a large number of dynamic S-boxes with adequate cryptographic properties,surpassing current state-of-the-art S-box generation algorithms in terms of security.Apart fromthis,the generated S-box is benchmarked using side-channel attacks,and its performance is compared with highly nonlinear S-boxes,demonstrating comparable results.In addition,we present an application of our proposed S-box generator by incorporating it into an image encryption technique.The encrypted and decrypted images are tested by employing extensive standard security metrics,including the Number of Pixel Change Rate,the Unified Average Changing Intensity,information entropy,correlation coefficient,and histogram analysis.Moreover,the analysis is extended beyond conventional metrics to validate the new method using advanced tests,such as the NIST statistical test suite,robustness analysis,and noise and cropping attacks.Experimental outcomes show that the presented algorithm strengthens the existing encryption scheme against various well-known cryptographic attacks.展开更多
During geothermal resource exploitation,the potential deterioration of mechanical properties in high-temperature granite subjected to cooling poses a significant safety concern.To address this,the present study invest...During geothermal resource exploitation,the potential deterioration of mechanical properties in high-temperature granite subjected to cooling poses a significant safety concern.To address this,the present study investigates the coupled thermo-mechanical behavior of granite during heating and cooling through a combination of laboratory tests and finite difference method analysis.Initial investigations involve X-ray diffraction,thermal expansion test,thermogravimetric analysis,and uniaxial compression test.Results show the significant variations of granite properties under different thermal conditions,attributed to temperature gradients,water evaporation,and mineral phase transitions.Subsequently,a model considering temperature-dependent parameters and real-time cooling rates was employed to simulate linear heating and nonlinear cooling processes.Simulation results indicate that the thermal cracking predominantly occurs during the heating stage,with tensile failure as the primary mode.Additionally,a faster real-time cooling rate at higher temperatures intensifies the thermal cracking behavior in granite.This study effectively elucidates the thermomechanical coupling behavior of granite during heating and cooling processes,providing insights into the mechanisms of mechanical property changes with rising or decreasing temperatures.展开更多
The rapid development of brain-like neural networks and secure data transmission technologies has placed greater demands on highly complex neural network systems and highly secure encryption methods.To this end,the pa...The rapid development of brain-like neural networks and secure data transmission technologies has placed greater demands on highly complex neural network systems and highly secure encryption methods.To this end,the paper proposes a novel high-dimensional memristor synapse-coupled hyperchaotic neural network by using the designed memristor as the synapse to connect an inertial neuron(IN)and a Hopfield neural network(HNN).By using numerical tools including bifurcation plots,phase plots,and basins of attraction,it is found that the dynamics of this system are closely related to the memristor coupling strength,self-connection synaptic weights,and inter-connection synaptic weights,and it can exhibit excellent hyperchaotic behaviors and coexisting multi-stable patterns.Through PSIM circuit simulations,the complex dynamics of the coupled IN-HNN system are verified.Furthermore,a DNA-encoded encryption algorithm is given,which utilizes generated hyperchaotic sequences to achieve encoding,operation,and decoding of DNA.The results show that this algorithm possesses strong robustness against statistical attacks,differential attacks,and noise interference,and can effectively resist known/selected plaintext attacks.This work will provide new ideas for the modeling of large-scale brainlike neural networks and high-security image encryption.展开更多
The intelligent environmental sensing systems are quickly transforming the sparse and retrospective monitoring to dense and decision-oriented environmental intelligence.This review brings together the manner in which ...The intelligent environmental sensing systems are quickly transforming the sparse and retrospective monitoring to dense and decision-oriented environmental intelligence.This review brings together the manner in which integration of Internet of Things(IoT)sensing,edge computing,and real-time analytics facilitates timely detection,interpretation,and prediction of the environmental conditions across the applications,such as urban air quality,watershed and coastal surveillance,industrial safety,agriculture,and disaster response.We define end-to-end architectural patterns to organize devices,edge nodes,and cloud services to satisfy latency,reliability,bandwidth,and governance constraints with emphasis on event-time processing,adaptive offloading,and hierarchical aggregation.Then we look at sensing and infrastructure foundations,emphasizing the effects of sensor modality and power autonomy,connectivity,and the practices of calibration on the practicable analytics and eventual plausibility.It is on this basis that we examine real-time analytics pipelines and Artificial Intelligence(AI)techniques to preprocess,sensor combine,anomaly detect,and short-horizon forecast,with a focus on edge-deployable models,quantification of uncertainties,and query resistance to drift and domain shift.Lastly,we address the realities of deployment that condition operational success,such as lifecycle engineering,provenance-aware data management,security and privacy risks,ethical governance,and evaluation methodologies,which place end-to-end latency and field generalization as a priority.This review offers cohesion to algorithmic capabilities and systems engineering and governance to define an overall framework,show open areas of research directions,and provide practical recommendations on how to design trustworthy,scalable,and sustainable environmental monitoring systems.展开更多
The exploration of solvent-driven reversible structural transformation in clusters is crucial for advanced stimulus-responsive optical applications and understanding of structure-property relationships.Herein,we repor...The exploration of solvent-driven reversible structural transformation in clusters is crucial for advanced stimulus-responsive optical applications and understanding of structure-property relationships.Herein,we report a solvent-driven reversible trans-formation between two copper(I)clusters:[Cu(totp)(CH_(3)CN)_(3)][Cu_(2)I_(3)(totp)(DPPPy)]·CH_(3)CN 1 and Cu_(4)I_(4)(DPPPy)_(2)·0.5CH_(2)Cl_(2)2(totp=tri-o-tolylphosphine,DPPPy=2-[diphenylphosphino]pyridine).X-ray radioluminescence and encryption applications were studied based on structure-dependent photophysical properties difference.The noncovalent interaction-mediated space charge transition between isolated ion units of 1 enables more efficient thermally activated delayed fluorescence by reverse intersystem crossing,accounting for structure-dependent luminescence.Notably,compared to 2,1 exhibits a higher scintillation light yield of 14832 photons MeV^(-1),exceeding that of the commercial scintillator Bi_(4)Ge_(3)O_(12)(8000 photons MeV^(-1)),and a low X-ray detection limit of 22.49 nGy s^(-1),far below the typical diagnostic dose(5.5μGy s^(-1)).Furthermore,scintillating film fabricated by 1 achieves X-ray imaging with a high spatial resolution of 16 lp/mm.The reversible structural interconversion enables solvent-responsive luminescent switches,and thus,the dynamic encryption system capable of multistage decryption was developed.This work not only offers new insight into solvent-regulated clusters transformations but also provides a promising strategy for developing high-performance copper(I)clusters-based scintillators and stimulus-responsive optical devices.展开更多
To investigate the energy relief effect of real-time drilling in preventing rockburst in high-stress rock,a series of high-stress real-time drilling uniaxial compression tests were conducted on red sandstone specimens...To investigate the energy relief effect of real-time drilling in preventing rockburst in high-stress rock,a series of high-stress real-time drilling uniaxial compression tests were conducted on red sandstone specimens using the SG4500 drilling rig.Results showed that the mechanical behavior(i.e.peak strength and rockburst intensity)of the rock was weakened under high-stress real-time drilling and exhibited a downward trend as the drilling diameter increased.The real-time drilling energy dissipation index(ERD)was proposed to characterize the energy relief during high-stress real-time drilling.The ERD exhibited a linear increase with the real-time drilling diameter.Furthermore,the elastic strain energy of post-drilling rock showed a linear relationship with the square of stress across different stress levels,which also applied to the peak elastic strain energy and the square of peak stress.This findingreveals the intrinsic link between the weakening effect of peak elastic strain energy and peak strength due to high-stress real-time drilling,confirmingthe consistency between energy relief and pressure relief effects.By establishing relationships among rockburst proneness,peak elastic strain energy,and peak strength,it was demonstrated that high-stress real-time drilling reduces rockburst proneness through energy dissipation.Specifically,both peak elastic strain energy and rockburst proneness decreased with larger drill bit diameters,consistent with reductions in peak strength,rockburst intensity,and fractal dimensions of high-stress real-time drilled rock.These results validate the energy relief mechanism of real-time drilling in mitigating rockburst risks.展开更多
An innovative real-time monitoring method for surrounding rock damage based on microseismic time-lapse double-difference tomography is proposed for delayed dynamic damage identification and insufficient detection of a...An innovative real-time monitoring method for surrounding rock damage based on microseismic time-lapse double-difference tomography is proposed for delayed dynamic damage identification and insufficient detection of adverse geological conditions in deep-buried tunnel construction.The installation techniques for microseismic sensors were optimized by mounting sensors at bolt ends which significantly improves signal-to-noise ratio(SNR)and anti-interference capability compared to conventional borehole placement.Subsequently,a 3D wave velocity evolution model that incorporates construction-induced disturbances was established,enabling the first visualization of spatiotemporal variations in surrounding rock wave velocity.It finds significant wave velocity reduction near the tunnel face,with roof and floor damage zones extending 40–50 m;wave velocities approaching undisturbed levels at 15 m ahead of the working face and on the laterally undisturbed side;pronounced spatial asymmetry in wave velocity distribution—values on the left side exceed those on the right,with a clear stress concentration or transition zone located 10–15 m;and systematically lower velocities behind the face than in front,indicating asymmetric rock damage development.These results provide essential theoretical support and practical guidance for optimizing dynamic construction strategies,enabling real-time adjustment of support parameters,and establishing safety early warning systems in deep-buried tunnel engineering.展开更多
Online examinations have become a dominant assessment mode,increasing concerns over academic integrity.To address the critical challenge of detecting cheating behaviours,this study proposes a hybrid deep learning appr...Online examinations have become a dominant assessment mode,increasing concerns over academic integrity.To address the critical challenge of detecting cheating behaviours,this study proposes a hybrid deep learning approach that combines visual detection and temporal behaviour classification.The methodology utilises object detection models—You Only Look Once(YOLOv12),Faster Region-based Convolutional Neural Network(RCNN),and Single Shot Detector(SSD)MobileNet—integrated with classification models such as Convolutional Neural Networks(CNN),Bidirectional Gated Recurrent Unit(Bi-GRU),and CNN-LSTM(Long Short-Term Memory).Two distinct datasets were used:the Online Exam Proctoring(EOP)dataset from Michigan State University and the School of Computer Science,Duy Tan Unievrsity(SCS-DTU)dataset collected in a controlled classroom setting.A diverse set of cheating behaviours,including book usage,unauthorised interaction,internet access,and mobile phone use,was categorised.Comprehensive experiments evaluated the models based on accuracy,precision,recall,training time,inference speed,and memory usage.We evaluate nine detector-classifier pairings under a unified budget and score them via a calibrated harmonic mean of detection and classification accuracies,enabling deployment-oriented selection under latency and memory constraints.Macro-Precision/Recall/F1 and Receiver Operating Characteristic-Area Under the Curve(ROC-AUC)are reported for the top configurations,revealing consistent advantages of object-centric pipelines for fine-grained cheating cues.The highest overall score is achieved by YOLOv12+CNN(97.15%accuracy),while SSD-MobileNet+CNN provides the best speed-efficiency trade-off for edge devices.This research provides valuable insights into selecting and deploying appropriate deep learning models for maintaining exam integrity under varying resource constraints.展开更多
The highly dynamic nature,strong uncertainty,and coupled multiple safety constraints inherent in carrier aircraft recovery operations pose severe challenges for real-time decision-making.Addressing bolter scenarios,th...The highly dynamic nature,strong uncertainty,and coupled multiple safety constraints inherent in carrier aircraft recovery operations pose severe challenges for real-time decision-making.Addressing bolter scenarios,this study proposes an intelligent decision-making framework based on a deep long short-term memory Q-network.This framework transforms the real-time sequencing for bolter recovery problem into a partially observable Markov decision process.It employs a stacked long shortterm memory network to accurately capture the long-range temporal dependencies of bolter event chains and fuel consumption.Furthermore,it integrates a prioritized experience replay training mechanism to construct a safe and adaptive scheduling system capable of millisecond-level real-time decision-making.Experimental demonstrates that,within large-scale mass recovery scenarios,the framework achieves zero safety violations in static environments and maintains a fuel safety violation rate below 10%in dynamic scenarios,with single-step decision times at the millisecond level.The model exhibits strong generalization capability,effectively responding to unforeseen emergent situations—such as multiple bolters and fuel emergencies—without requiring retraining.This provides robust support for efficient carrier-based aircraft recovery operations.展开更多
This paper presents an image encryption scheme for underwater optical wireless communication(UOWC)systems based on dynamically generated hyperchaotic S-boxes,aiming to enhance both data security and transmission perfo...This paper presents an image encryption scheme for underwater optical wireless communication(UOWC)systems based on dynamically generated hyperchaotic S-boxes,aiming to enhance both data security and transmission performance in underwater environments.The proposed encryption approach provides strong confusion and diffusion properties and is evaluated over five Jerlov water types with different optical attenuation characteristics.Security analysis demonstrates that the encrypted images achieve information entropy values close to the ideal value of 8(7.9925–7.9993),with very low correlation coefficients in horizontal,vertical,and diagonal directions,as well as the system achieves high values in key metrics such as the Unified Average Changing Intensity(UACI)and Number of Pixel Change Rate(NPCR),ranging from 33.42 to 33.47 and from 99.58%to 99.62%,respectively,both near their theoretical optima.In addition to improving confidentiality,the hyperchaotic encryption process decorrelates pixel intensities and redistributes image spectral content,which enhances robustness against underwater absorption and scattering effects.As a result,improved transmission performance is observed;for example,in Jerlov type I(JI)water,the effective transmission distance is extended from16mfor plain images to 24mfor encrypted images,while the Peak Signal to Noise Ratio(PSNR)at 24 m increases from 9.25 to 20.13 dB after decryption and enhancement.These results confirmthat the proposed scheme provides a dual benefit of secure and reliable image transmission in UOWC systems.展开更多
Dear Editor,This letter studies the problem of stealthy attacks targeting stochastic event-based estimation,alongside proposing measures for their mitigation.A general attack framework is introduced,and the correspond...Dear Editor,This letter studies the problem of stealthy attacks targeting stochastic event-based estimation,alongside proposing measures for their mitigation.A general attack framework is introduced,and the corresponding stealthiness condition is analyzed.To enhance system security,we advocate for a single-dimensional encryption method,showing that securing a singular data element is sufficient to shield the system from the perils of stealthy attacks.展开更多
With the widespread adoption of encrypted Domain Name System(DNS)technologies such as DNS over Hyper Text Transfer Protocol Secure(HTTPS),traditional port and protocol-based traffic analysis methods have become ineffe...With the widespread adoption of encrypted Domain Name System(DNS)technologies such as DNS over Hyper Text Transfer Protocol Secure(HTTPS),traditional port and protocol-based traffic analysis methods have become ineffective.Although encrypted DNS enhances user privacy protection,it also provides concealed communication channels for malicious software,compelling detection technologies to shift towards statistical featurebased and machine learning approaches.However,these methods still face challenges in real-time performance and privacy protection.This paper proposes a real-time identification technology for encrypted DNS traffic with privacy protection.Firstly,a hierarchical architecture of cloud-edge-end collaboration is designed,incorporating task offloading strategies to balance privacy protection and identification efficiency.Secondly,a privacy-preserving federated learning mechanismbased on Federated Robust Aggregation(FedRA)is proposed,utilizingMedoid aggregation and differential privacy techniques to ensure data privacy and enhance identification accuracy.Finally,an edge offloading strategy based on a dynamic priority scheduling algorithm(DPSA)is designed to alleviate terminal burden and reduce latency.Simulation results demonstrate that the proposed technology significantly improves the accuracy and realtime performance of encrypted DNS traffic identification while protecting privacy,making it suitable for various network environments.展开更多
文摘Military image encryption plays a vital role in ensuring the secure transmission of sensitive visual information from unauthorized access.This paper proposes a new Tri-independent keying method for encrypting military images.The proposed encryption method is based on multilevel security stages of pixel-level scrambling,bitlevel manipulation,and block-level shuffling operations.For having a vast key space,the input password is hashed by the Secure Hash Algorithm 256-bit(SHA-256)for generating independently deterministic keys used in the multilevel stages.A piecewise pixel-level scrambling function is introduced to perform a dual flipping process controlled with an adaptive key for obscuring the spatial relationships between the adjacent pixels.Adynamicmasking scheme is presented for conducting a bit-level manipulation based on distinct keys that change over image regions,providing completely different encryption results on identical regions.To handle the global correlation between large-scale patterns,a chaotic index-map system is employed for shuffling image regions randomly across the image domain based on a logistic map seeded with a private key.Experimental results on a dataset of military images show the effectiveness of the proposed encryption method in producing excellent quantitative and qualitative results.The proposed method obtains uniform histogram distributions,high entropy values around the ideal(≈8 bits),Number of Pixel Change Rate(NPCR)values above 99.5%,and low Peak Signal-to-Noise Ratio(PSNR)over all encrypted images.This validates the robustness of the proposed method against cryptanalytic attacks,verifying its ability to serve as a practical basis for secure image transmission in defense systems.
基金supported by the National Institute of Health(R37CA240806,U01CA288351,and R50CA283816)support from UCI Chao Family Comprehensive Cancer Center(P30CA062203).
文摘Electroacoustic Tomography(EAT)is an imaging technique that detects ultrasound waves induced by electrical pulses,offering a solution for real-time electroporation monitoring.This study presents EAT system using a dual-frequency ultrasound array.The broadband nature of electroacoustic signals requires ultrasound detector to cover both the high-frequency range(around 6MHz)signals generated by small targets and the low-frequency range(around 1MHz)signals generated by large targets.In our EAT system,we use the 6 MHz array to detect high-frequency signals from the electrodes,and the 1 MHz array for the electrical field.To test this,we conducted simulations using COMSOL Multiphysics^(®) and MATLAB's k-Wave toolbox,followed by experiments using a custom-built setup with a dual-frequency transducer and real-time data acquisition.The results demonstrated that the dual-frequency EAT system could accurately and simultaneously monitor the electroporation process,effectively showing both the treatment area and electrode placement with the application of 1 kV electric pulses with 100 ns duration.The axial resolution of the 6MHz array for EAT was 0.45 mm,significantly better than the 2mm resolution achieved with the 1MHz array.These findings validate the potential of dual-frequency EAT as a superior method for real-time electroporation monitoring.
基金supported by the National Natural Science Foundation of China 62402171.
文摘As a fundamental component in computer vision,edges can be categorized into four types based on discontinuities in reflectance,illumination,surface normal,or depth.While deep CNNs have significantly advanced generic edge detection,real-time multi-class semantic edge detection under resource constraints remains challenging.To address this,we propose a lightweight framework based on PiDiNet that enables fine-grained semantic edge detection.Our model simultaneously predicts background and four edge categories from full-resolution inputs,balancing accuracy and efficiency.Key contributions include:a multi-channel output structure expanding binary edge prediction to five classes,supported by a deep supervision mechanism;a dynamic class-balancing strategy combining adaptive weighting with physical priors to handle extreme class imbalance;and maintained architectural efficiency enabling real-time inference.Extensive evaluations on BSDS-RIND show our approach achieves accuracy competitive with state-of-the-art methods while operating in real time.
基金supported by the National Natural Science Foundation of China(62376106)The Science and Technology Development Plan of Jilin Province(20250102212JC).
文摘Driven by advancements in mobile internet technology,images have become a crucial data medium.Ensuring the security of image information during transmission has thus emerged as an urgent challenge.This study proposes a novel image encryption algorithm specifically designed for grayscale image security.This research introduces a new Cantor diagonal matrix permutation method.The proposed permutation method uses row and column index sequences to control the Cantor diagonal matrix,where the row and column index sequences are generated by a spatiotemporal chaotic system named coupled map lattice(CML).The high initial value sensitivity of the CML system makes the permutation method highly sensitive and secure.Additionally,leveraging fractal theory,this study introduces a chaotic fractal matrix and applies this matrix in the diffusion process.This chaotic fractal matrix exhibits selfsimilarity and irregularity.Using the Cantor diagonal matrix and chaotic fractal matrix,this paper introduces a fast image encryption algorithm involving two diffusion steps and one permutation step.Moreover,the algorithm achieves robust security with only a single encryption round,ensuring high operational efficiency.Experimental results show that the proposed algorithm features an expansive key space,robust security,high sensitivity,high efficiency,and superior statistical properties for the ciphered images.Thus,the proposed algorithm not only provides a practical solution for secure image transmission but also bridges fractal theory with image encryption techniques,thereby opening new research avenues in chaotic cryptography and advancing the development of information security technology.
基金supported by the Ministry of Trade,Industry and Energy(MOTIE)under Training Industrial Security Specialist for High-Tech Industry[grant number RS-2024-00415520]supervised by the Korea Institute for Advancement of Technology(KIAT)Ministry of Science and ICT(MSIT)under the ICAN(ICT Challenge and Advanced Network of HRD)program[grant number IITP-2022-RS-2022-00156310]+1 种基金National Research Foundation of Korea(NRF)grant[RS-2025-00518150]the Information Security Core Technology Development program[grant number RS-2024-00437252]supervised by the Institute of Information&Communication Technology Planning&Evaluation(IITP).
文摘As cyberattacks become increasingly sophisticated and intelligent,demand for machine-learning-based anomaly detection systems is growing.However,conventional systems generally assume a trusted server environment,where traffic data is collected and analyzed in plaintext.This assumption introduces inherent privacy risks,as privacy-sensitive information may be exposed if the server is compromised or misused.To address this limitation,privacy-preserving anomaly detection approaches have been actively studied,enabling anomaly detection to be performed directly on encrypted traffic without revealing privacy-sensitive data.While these approaches offer strong confidentiality guarantees,they suffer from significant drawbacks,including substantial computational overhead,high latency,and degraded detection accuracy.To overcome these limitations,we propose a privacy-aware anomaly detection(PAAD)model that adaptively applies homomorphic encryption based on the privacy sensitivity of incoming traffic.Instead of encrypting all data indiscriminately,PAAD dynamically determines whether traffic should be processed in plaintext or ciphertext and performs homomorphic inference only for privacy-sensitive data.This selective encryption strategy effectively balances privacy protection and system efficiency.Extensive experiments conducted under diverse network environments demonstrate that the proposed PAAD model significantly outperforms conventional anomaly detection models.In particular,PAAD improves detection accuracy by up to 73%,reduces latency by up to 8.6 times,and achieves negligible information leakage,highlighting its practicality for real-world privacy-sensitive network monitoring scenarios.
基金financially supported by the Natural Science Foundation of Shandong Province(No.ZR2024QE446)。
文摘With the rapid development of intelligent electronic and military equipment,multifunctional flexible materials that integrat electromagnetic interference(EMI)shielding,temperature sensing,and information encryption are urgently required.This study presents a bio-inspired hierarchical composite foam fabricated using supercritical nitrogen foaming technology.This material exhibits a honeycomb structure,with pore cell sizes controllable within a range of 30–92μm by regulating the filler.The carbon fiber felt(CFf)provides efficient reflection of electromagnetic waves,while the chloroprene rubber/carbon fiber/carbon black foam facilitates both wave absorption and temperature monitoring through its optimized conductive network.This synergistic mechanism results in an EMI shielding effectiveness(SE)of 60.06 d B with excellent temperature sensing performance(The temperature coefficient of resistance(TCR)is-2.642%/℃)in the 24–70℃ range.Notably,the material has a thermal conductivity of up to 0.159 W/(m·K),and the bio-inspired layered design enables information encryption,demonstrating the material's potential for secure communication applications.The foam also has tensile properties of up to 5.13 MPa and a tear strength of 33.02 N/mm.This biomimetic design overcomes the traditional limitations of flexible materials and provides a transformative solution for next-generation applications such as flexible electronics,aerospace systems and military equipment,which urgently need integrated electromagnetic protection,thermal management and information security.
文摘The advent of 5G technology has significantly enhanced the transmission of images over networks,expanding data accessibility and exposure across various applications in digital technology and social media.Consequently,the protection of sensitive data has become increasingly critical.Regardless of the complexity of the encryption algorithm used,a robust and highly secure encryption key is essential,with randomness and key space being crucial factors.This paper proposes a new Robust Deoxyribonucleic Acid(RDNA)nucleotide-based encryption method.The RDNA encryption method leverages the unique properties of DNA nucleotides,including their inherent randomness and extensive key space,to generate a highly secure encryption key.By employing transposition and substitution operations,the RDNA method ensures significant diffusion and confusion in the encrypted images.Additionally,it utilises a pseudorandom generation technique based on the random sequence of nucleotides in the DNA secret key.The performance of the RDNA encryption method is evaluated through various statistical and visual tests,and compared against established encryption methods such as 3DES,AES,and a DNA-based method.Experimental results demonstrate that the RDNA encryption method outperforms its rivals in the literature,and achieves superior performance in terms of information entropy,avalanche effect,encryption execution time,and correlation reduction,while maintaining competitive values for NMAE,PSNR,NPCR,and UACI.The high degree of randomness and sensitivity to key changes inherent in the RDNA method offers enhanced security,making it highly resistant to brute force and differential attacks.
基金Supported by the National Program on Key Basic Research Project(2020YFA0713600)the National Natural Science Foundation of China(62272214)。
文摘In the era of the Internet of Things,distributed computing alleviates the problem of insufficient terminal computing power by integrating idle resources of heterogeneous devices.However,the imbalance between task execution delay and node energy consumption,and the scheduling and adaptation challenges brought about by device heterogeneity,urgently need to be addressed.To tackle this problem,this paper constructs a multi-objective real-time task scheduling model that considers task real-time performance,execution delay,system energy consumption,and node interests.The model aims to minimize the delay upper bound and total energy consumption while maximizing system satisfaction.A real-time task scheduling algorithm based on bilateral matching game is proposed.By designing a bidirectional preference mechanism between tasks and computing nodes,combined with a multi-round stable matching strategy,accurate matching between tasks and nodes is achieved.Simulation results show that compared with the baseline scheme,the proposed algorithm significantly reduces the total execution cost,effectively balances the task execution delay and the energy consumption of compute nodes,and takes into account the interests of each network compute node.
文摘Elliptic curve(EC)based cryptosystems gained more attention due to enhanced security than the existing public key cryptosystems.A substitution box(S-box)plays a vital role in securing modern symmetric key cryptosystems.However,the recently developed EC based algorithms usually trade off between computational efficiency and security,necessitating the design of a new algorithm with the desired cryptographic strength.To address these shortcomings,this paper proposes a new scheme based onMordell elliptic curve(MEC)over the complex field for generating distinct,dynamic,and highly uncorrelated S-boxes.Furthermore,we count the exact number of the obtained S-boxes,and demonstrate that the permuted version of the presented S-box is statistically optimal.The nonsingularity of the presented algorithm and the injectivity of the resultant output are explored.Rigorous theoretical analysis and experimental results demonstrate that the proposedmethod is highly effective in generating a large number of dynamic S-boxes with adequate cryptographic properties,surpassing current state-of-the-art S-box generation algorithms in terms of security.Apart fromthis,the generated S-box is benchmarked using side-channel attacks,and its performance is compared with highly nonlinear S-boxes,demonstrating comparable results.In addition,we present an application of our proposed S-box generator by incorporating it into an image encryption technique.The encrypted and decrypted images are tested by employing extensive standard security metrics,including the Number of Pixel Change Rate,the Unified Average Changing Intensity,information entropy,correlation coefficient,and histogram analysis.Moreover,the analysis is extended beyond conventional metrics to validate the new method using advanced tests,such as the NIST statistical test suite,robustness analysis,and noise and cropping attacks.Experimental outcomes show that the presented algorithm strengthens the existing encryption scheme against various well-known cryptographic attacks.
基金National Natural Science Foundation of China,Grant/Award Number:52104120Hunan Provincial Key Laboratory of Key Technology on Hydropower Development,Grant/Award Number:PKLHD202303。
文摘During geothermal resource exploitation,the potential deterioration of mechanical properties in high-temperature granite subjected to cooling poses a significant safety concern.To address this,the present study investigates the coupled thermo-mechanical behavior of granite during heating and cooling through a combination of laboratory tests and finite difference method analysis.Initial investigations involve X-ray diffraction,thermal expansion test,thermogravimetric analysis,and uniaxial compression test.Results show the significant variations of granite properties under different thermal conditions,attributed to temperature gradients,water evaporation,and mineral phase transitions.Subsequently,a model considering temperature-dependent parameters and real-time cooling rates was employed to simulate linear heating and nonlinear cooling processes.Simulation results indicate that the thermal cracking predominantly occurs during the heating stage,with tensile failure as the primary mode.Additionally,a faster real-time cooling rate at higher temperatures intensifies the thermal cracking behavior in granite.This study effectively elucidates the thermomechanical coupling behavior of granite during heating and cooling processes,providing insights into the mechanisms of mechanical property changes with rising or decreasing temperatures.
基金Project supported by the Training Plan of Young Backbone Teachers in Universities of Henan Province(Grant No.2023GGJS142)the Key Scientific Research of Colleges and Universities in Henan Province,China(Grant No.25A120009)+1 种基金Changzhou Leading Innovative Talent Introduction and Cultivation Project(Grant No.CQ20240102)Changzhou Applied Basic Research Program(Grant No.CJ20253065)。
文摘The rapid development of brain-like neural networks and secure data transmission technologies has placed greater demands on highly complex neural network systems and highly secure encryption methods.To this end,the paper proposes a novel high-dimensional memristor synapse-coupled hyperchaotic neural network by using the designed memristor as the synapse to connect an inertial neuron(IN)and a Hopfield neural network(HNN).By using numerical tools including bifurcation plots,phase plots,and basins of attraction,it is found that the dynamics of this system are closely related to the memristor coupling strength,self-connection synaptic weights,and inter-connection synaptic weights,and it can exhibit excellent hyperchaotic behaviors and coexisting multi-stable patterns.Through PSIM circuit simulations,the complex dynamics of the coupled IN-HNN system are verified.Furthermore,a DNA-encoded encryption algorithm is given,which utilizes generated hyperchaotic sequences to achieve encoding,operation,and decoding of DNA.The results show that this algorithm possesses strong robustness against statistical attacks,differential attacks,and noise interference,and can effectively resist known/selected plaintext attacks.This work will provide new ideas for the modeling of large-scale brainlike neural networks and high-security image encryption.
基金supported by Jiangxi Polytechnic Institute Key Research Topics in Educational Reform 2025-JGJG-07.
文摘The intelligent environmental sensing systems are quickly transforming the sparse and retrospective monitoring to dense and decision-oriented environmental intelligence.This review brings together the manner in which integration of Internet of Things(IoT)sensing,edge computing,and real-time analytics facilitates timely detection,interpretation,and prediction of the environmental conditions across the applications,such as urban air quality,watershed and coastal surveillance,industrial safety,agriculture,and disaster response.We define end-to-end architectural patterns to organize devices,edge nodes,and cloud services to satisfy latency,reliability,bandwidth,and governance constraints with emphasis on event-time processing,adaptive offloading,and hierarchical aggregation.Then we look at sensing and infrastructure foundations,emphasizing the effects of sensor modality and power autonomy,connectivity,and the practices of calibration on the practicable analytics and eventual plausibility.It is on this basis that we examine real-time analytics pipelines and Artificial Intelligence(AI)techniques to preprocess,sensor combine,anomaly detect,and short-horizon forecast,with a focus on edge-deployable models,quantification of uncertainties,and query resistance to drift and domain shift.Lastly,we address the realities of deployment that condition operational success,such as lifecycle engineering,provenance-aware data management,security and privacy risks,ethical governance,and evaluation methodologies,which place end-to-end latency and field generalization as a priority.This review offers cohesion to algorithmic capabilities and systems engineering and governance to define an overall framework,show open areas of research directions,and provide practical recommendations on how to design trustworthy,scalable,and sustainable environmental monitoring systems.
基金supported by the National Natural Science Foundation of China(21971240 and 22271283)the Natural Science Foundation of Shandong Province(ZR2025QC1361)。
文摘The exploration of solvent-driven reversible structural transformation in clusters is crucial for advanced stimulus-responsive optical applications and understanding of structure-property relationships.Herein,we report a solvent-driven reversible trans-formation between two copper(I)clusters:[Cu(totp)(CH_(3)CN)_(3)][Cu_(2)I_(3)(totp)(DPPPy)]·CH_(3)CN 1 and Cu_(4)I_(4)(DPPPy)_(2)·0.5CH_(2)Cl_(2)2(totp=tri-o-tolylphosphine,DPPPy=2-[diphenylphosphino]pyridine).X-ray radioluminescence and encryption applications were studied based on structure-dependent photophysical properties difference.The noncovalent interaction-mediated space charge transition between isolated ion units of 1 enables more efficient thermally activated delayed fluorescence by reverse intersystem crossing,accounting for structure-dependent luminescence.Notably,compared to 2,1 exhibits a higher scintillation light yield of 14832 photons MeV^(-1),exceeding that of the commercial scintillator Bi_(4)Ge_(3)O_(12)(8000 photons MeV^(-1)),and a low X-ray detection limit of 22.49 nGy s^(-1),far below the typical diagnostic dose(5.5μGy s^(-1)).Furthermore,scintillating film fabricated by 1 achieves X-ray imaging with a high spatial resolution of 16 lp/mm.The reversible structural interconversion enables solvent-responsive luminescent switches,and thus,the dynamic encryption system capable of multistage decryption was developed.This work not only offers new insight into solvent-regulated clusters transformations but also provides a promising strategy for developing high-performance copper(I)clusters-based scintillators and stimulus-responsive optical devices.
基金supported by the National Natural Science Foundation of China(Grant No.42077244)the Postgraduate Research&Practice Innovation Program of Jiangsu Province(Grant No.KYCX24_0434).
文摘To investigate the energy relief effect of real-time drilling in preventing rockburst in high-stress rock,a series of high-stress real-time drilling uniaxial compression tests were conducted on red sandstone specimens using the SG4500 drilling rig.Results showed that the mechanical behavior(i.e.peak strength and rockburst intensity)of the rock was weakened under high-stress real-time drilling and exhibited a downward trend as the drilling diameter increased.The real-time drilling energy dissipation index(ERD)was proposed to characterize the energy relief during high-stress real-time drilling.The ERD exhibited a linear increase with the real-time drilling diameter.Furthermore,the elastic strain energy of post-drilling rock showed a linear relationship with the square of stress across different stress levels,which also applied to the peak elastic strain energy and the square of peak stress.This findingreveals the intrinsic link between the weakening effect of peak elastic strain energy and peak strength due to high-stress real-time drilling,confirmingthe consistency between energy relief and pressure relief effects.By establishing relationships among rockburst proneness,peak elastic strain energy,and peak strength,it was demonstrated that high-stress real-time drilling reduces rockburst proneness through energy dissipation.Specifically,both peak elastic strain energy and rockburst proneness decreased with larger drill bit diameters,consistent with reductions in peak strength,rockburst intensity,and fractal dimensions of high-stress real-time drilled rock.These results validate the energy relief mechanism of real-time drilling in mitigating rockburst risks.
基金support of the National Natural Science Foundation of China(No.52274176)the Guangdong Province Key Areas R&D Program(No.2022B0101070001)+5 种基金Chongqing Elite Innovation and Entrepreneurship Leading talent Project(No.CQYC20220302517)the Chongqing Natural Science Foundation Innovation and Development Joint Fund(No.CSTB2022NSCQ-LZX0079)the National Key Research and Development Program Young Scientists Project(No.2022YFC2905700)the Chongqing Municipal Education Commission“Shuangcheng Economic Circle Construction in Chengdu-Chongqing Area”Science and Technology Innovation Project(No.KJCX2020031)the Fundamental Research Funds for the Central Universities(No.2024CDJGF-009)the Key Project for Technological Innovation and Application Development in Chongqing(No.CSTB2025TIAD-KPX0029).
文摘An innovative real-time monitoring method for surrounding rock damage based on microseismic time-lapse double-difference tomography is proposed for delayed dynamic damage identification and insufficient detection of adverse geological conditions in deep-buried tunnel construction.The installation techniques for microseismic sensors were optimized by mounting sensors at bolt ends which significantly improves signal-to-noise ratio(SNR)and anti-interference capability compared to conventional borehole placement.Subsequently,a 3D wave velocity evolution model that incorporates construction-induced disturbances was established,enabling the first visualization of spatiotemporal variations in surrounding rock wave velocity.It finds significant wave velocity reduction near the tunnel face,with roof and floor damage zones extending 40–50 m;wave velocities approaching undisturbed levels at 15 m ahead of the working face and on the laterally undisturbed side;pronounced spatial asymmetry in wave velocity distribution—values on the left side exceed those on the right,with a clear stress concentration or transition zone located 10–15 m;and systematically lower velocities behind the face than in front,indicating asymmetric rock damage development.These results provide essential theoretical support and practical guidance for optimizing dynamic construction strategies,enabling real-time adjustment of support parameters,and establishing safety early warning systems in deep-buried tunnel engineering.
文摘Online examinations have become a dominant assessment mode,increasing concerns over academic integrity.To address the critical challenge of detecting cheating behaviours,this study proposes a hybrid deep learning approach that combines visual detection and temporal behaviour classification.The methodology utilises object detection models—You Only Look Once(YOLOv12),Faster Region-based Convolutional Neural Network(RCNN),and Single Shot Detector(SSD)MobileNet—integrated with classification models such as Convolutional Neural Networks(CNN),Bidirectional Gated Recurrent Unit(Bi-GRU),and CNN-LSTM(Long Short-Term Memory).Two distinct datasets were used:the Online Exam Proctoring(EOP)dataset from Michigan State University and the School of Computer Science,Duy Tan Unievrsity(SCS-DTU)dataset collected in a controlled classroom setting.A diverse set of cheating behaviours,including book usage,unauthorised interaction,internet access,and mobile phone use,was categorised.Comprehensive experiments evaluated the models based on accuracy,precision,recall,training time,inference speed,and memory usage.We evaluate nine detector-classifier pairings under a unified budget and score them via a calibrated harmonic mean of detection and classification accuracies,enabling deployment-oriented selection under latency and memory constraints.Macro-Precision/Recall/F1 and Receiver Operating Characteristic-Area Under the Curve(ROC-AUC)are reported for the top configurations,revealing consistent advantages of object-centric pipelines for fine-grained cheating cues.The highest overall score is achieved by YOLOv12+CNN(97.15%accuracy),while SSD-MobileNet+CNN provides the best speed-efficiency trade-off for edge devices.This research provides valuable insights into selecting and deploying appropriate deep learning models for maintaining exam integrity under varying resource constraints.
基金supported by the National Natural Science Foundation of China(Grant No.62403486)。
文摘The highly dynamic nature,strong uncertainty,and coupled multiple safety constraints inherent in carrier aircraft recovery operations pose severe challenges for real-time decision-making.Addressing bolter scenarios,this study proposes an intelligent decision-making framework based on a deep long short-term memory Q-network.This framework transforms the real-time sequencing for bolter recovery problem into a partially observable Markov decision process.It employs a stacked long shortterm memory network to accurately capture the long-range temporal dependencies of bolter event chains and fuel consumption.Furthermore,it integrates a prioritized experience replay training mechanism to construct a safe and adaptive scheduling system capable of millisecond-level real-time decision-making.Experimental demonstrates that,within large-scale mass recovery scenarios,the framework achieves zero safety violations in static environments and maintains a fuel safety violation rate below 10%in dynamic scenarios,with single-step decision times at the millisecond level.The model exhibits strong generalization capability,effectively responding to unforeseen emergent situations—such as multiple bolters and fuel emergencies—without requiring retraining.This provides robust support for efficient carrier-based aircraft recovery operations.
基金funded by Prince Sattam bin Abdulaziz University,grant number PSAU/2025/01/34620.
文摘This paper presents an image encryption scheme for underwater optical wireless communication(UOWC)systems based on dynamically generated hyperchaotic S-boxes,aiming to enhance both data security and transmission performance in underwater environments.The proposed encryption approach provides strong confusion and diffusion properties and is evaluated over five Jerlov water types with different optical attenuation characteristics.Security analysis demonstrates that the encrypted images achieve information entropy values close to the ideal value of 8(7.9925–7.9993),with very low correlation coefficients in horizontal,vertical,and diagonal directions,as well as the system achieves high values in key metrics such as the Unified Average Changing Intensity(UACI)and Number of Pixel Change Rate(NPCR),ranging from 33.42 to 33.47 and from 99.58%to 99.62%,respectively,both near their theoretical optima.In addition to improving confidentiality,the hyperchaotic encryption process decorrelates pixel intensities and redistributes image spectral content,which enhances robustness against underwater absorption and scattering effects.As a result,improved transmission performance is observed;for example,in Jerlov type I(JI)water,the effective transmission distance is extended from16mfor plain images to 24mfor encrypted images,while the Peak Signal to Noise Ratio(PSNR)at 24 m increases from 9.25 to 20.13 dB after decryption and enhancement.These results confirmthat the proposed scheme provides a dual benefit of secure and reliable image transmission in UOWC systems.
基金supported by the National Natural Science Foundation of China(62303353,62273030,62573320)。
文摘Dear Editor,This letter studies the problem of stealthy attacks targeting stochastic event-based estimation,alongside proposing measures for their mitigation.A general attack framework is introduced,and the corresponding stealthiness condition is analyzed.To enhance system security,we advocate for a single-dimensional encryption method,showing that securing a singular data element is sufficient to shield the system from the perils of stealthy attacks.
文摘With the widespread adoption of encrypted Domain Name System(DNS)technologies such as DNS over Hyper Text Transfer Protocol Secure(HTTPS),traditional port and protocol-based traffic analysis methods have become ineffective.Although encrypted DNS enhances user privacy protection,it also provides concealed communication channels for malicious software,compelling detection technologies to shift towards statistical featurebased and machine learning approaches.However,these methods still face challenges in real-time performance and privacy protection.This paper proposes a real-time identification technology for encrypted DNS traffic with privacy protection.Firstly,a hierarchical architecture of cloud-edge-end collaboration is designed,incorporating task offloading strategies to balance privacy protection and identification efficiency.Secondly,a privacy-preserving federated learning mechanismbased on Federated Robust Aggregation(FedRA)is proposed,utilizingMedoid aggregation and differential privacy techniques to ensure data privacy and enhance identification accuracy.Finally,an edge offloading strategy based on a dynamic priority scheduling algorithm(DPSA)is designed to alleviate terminal burden and reduce latency.Simulation results demonstrate that the proposed technology significantly improves the accuracy and realtime performance of encrypted DNS traffic identification while protecting privacy,making it suitable for various network environments.
