The application of triboelectric nanogenerators(TENGs)for collecting and converting waste energy into usable electrical energy has been widely reported.However,their practical application in real-time,self-powered com...The application of triboelectric nanogenerators(TENGs)for collecting and converting waste energy into usable electrical energy has been widely reported.However,their practical application in real-time,self-powered communication systems,particularly for robust information transmission,remains underexplored.To achieve stable self-energy supply information transmission,this study presents a lightweight and flexible single-electrode TENG sensor based on a copper(Cu)foil and polytetrafluoroethylene(PTFE)composite.We systematically studied the stability of the device and found that it could maintain an output voltage of approximately 9 V after being stored at room temperature for 1 month.We also evaluated its power generation capacity,which was demonstrated by successfully lighting up to seven LEDs simultaneously.Additionally,we utilized its unique voltage signal to transmit Morse code and successfully sent the messages“SOS”and“HELLO”over a long distance.Furthermore,a 2×2 TENG array was fabricated and tested,confirming excellent channel independence with minimal crosstalk during simultaneous or selective activation.This work demonstrates that the Cu/PTFE TENG sensor is not only a stable energy harvester but also a viable platform for self-powered communication and distributed sensing and holds promise in applications integrating flexible electronics and the Internet of things.展开更多
We propose the scaling rule of Morse oscillator,based on this rule and by virtue of the Her-mann-Feymann theorem,we respectively obtain the distribution of potential and kinetic ener-gy of the Morse Hamiltonian.Also,w...We propose the scaling rule of Morse oscillator,based on this rule and by virtue of the Her-mann-Feymann theorem,we respectively obtain the distribution of potential and kinetic ener-gy of the Morse Hamiltonian.Also,we derive the exact upper limit of physical energy level.Further,we derive some recursive relations for energy matrix elements of the potential and other similar operators in the context of Morse oscillator theory.展开更多
Transformer-based models have significantly advanced binary code similarity detection(BCSD)by leveraging their semantic encoding capabilities for efficient function matching across diverse compilation settings.Althoug...Transformer-based models have significantly advanced binary code similarity detection(BCSD)by leveraging their semantic encoding capabilities for efficient function matching across diverse compilation settings.Although adversarial examples can strategically undermine the accuracy of BCSD models and protect critical code,existing techniques predominantly depend on inserting artificial instructions,which incur high computational costs and offer limited diversity of perturbations.To address these limitations,we propose AIMA,a novel gradient-guided assembly instruction relocation method.Our method decouples the detection model into tokenization,embedding,and encoding layers to enable efficient gradient computation.Since token IDs of instructions are discrete and nondifferentiable,we compute gradients in the continuous embedding space to evaluate the influence of each token.The most critical tokens are identified by calculating the L2 norm of their embedding gradients.We then establish a mapping between instructions and their corresponding tokens to aggregate token-level importance into instructionlevel significance.To maximize adversarial impact,a sliding window algorithm selects the most influential contiguous segments for relocation,ensuring optimal perturbation with minimal length.This approach efficiently locates critical code regions without expensive search operations.The selected segments are relocated outside their original function boundaries via a jump mechanism,which preserves runtime control flow and functionality while introducing“deletion”effects in the static instruction sequence.Extensive experiments show that AIMA reduces similarity scores by up to 35.8%in state-of-the-art BCSD models.When incorporated into training data,it also enhances model robustness,achieving a 5.9%improvement in AUROC.展开更多
Li DAR点云为小尺度地表形态的提取与表达提供了精确的数据源,但其高密度性与不确定性,导致应用Morse理论提取的特征点中含有大量的"伪特征点"。这里首先通过定义特征点指数等一系列概念,模拟特征点周围区域的地表形态,建立...Li DAR点云为小尺度地表形态的提取与表达提供了精确的数据源,但其高密度性与不确定性,导致应用Morse理论提取的特征点中含有大量的"伪特征点"。这里首先通过定义特征点指数等一系列概念,模拟特征点周围区域的地表形态,建立特征点重要性度量指标与计算方法;然后给出了地表重要特征点的提取算法;最后,进行了试验验证与分析。结果表明:提出的算法优于现有的持续值法与自然法则法,可以有效剔除"伪特征点",实现基于Li DAR点云小尺度复杂地形的特征点精确提取与多层次表达。展开更多
基金supported by the State Key Laboratory of ASIC and System,Fudan University(Grant No.2021KF005).
文摘The application of triboelectric nanogenerators(TENGs)for collecting and converting waste energy into usable electrical energy has been widely reported.However,their practical application in real-time,self-powered communication systems,particularly for robust information transmission,remains underexplored.To achieve stable self-energy supply information transmission,this study presents a lightweight and flexible single-electrode TENG sensor based on a copper(Cu)foil and polytetrafluoroethylene(PTFE)composite.We systematically studied the stability of the device and found that it could maintain an output voltage of approximately 9 V after being stored at room temperature for 1 month.We also evaluated its power generation capacity,which was demonstrated by successfully lighting up to seven LEDs simultaneously.Additionally,we utilized its unique voltage signal to transmit Morse code and successfully sent the messages“SOS”and“HELLO”over a long distance.Furthermore,a 2×2 TENG array was fabricated and tested,confirming excellent channel independence with minimal crosstalk during simultaneous or selective activation.This work demonstrates that the Cu/PTFE TENG sensor is not only a stable energy harvester but also a viable platform for self-powered communication and distributed sensing and holds promise in applications integrating flexible electronics and the Internet of things.
基金supported by the National Natural Science Foundation of China(No.10874174)。
文摘We propose the scaling rule of Morse oscillator,based on this rule and by virtue of the Her-mann-Feymann theorem,we respectively obtain the distribution of potential and kinetic ener-gy of the Morse Hamiltonian.Also,we derive the exact upper limit of physical energy level.Further,we derive some recursive relations for energy matrix elements of the potential and other similar operators in the context of Morse oscillator theory.
基金supported by Key Laboratory of Cyberspace Security,Ministry of Education,China。
文摘Transformer-based models have significantly advanced binary code similarity detection(BCSD)by leveraging their semantic encoding capabilities for efficient function matching across diverse compilation settings.Although adversarial examples can strategically undermine the accuracy of BCSD models and protect critical code,existing techniques predominantly depend on inserting artificial instructions,which incur high computational costs and offer limited diversity of perturbations.To address these limitations,we propose AIMA,a novel gradient-guided assembly instruction relocation method.Our method decouples the detection model into tokenization,embedding,and encoding layers to enable efficient gradient computation.Since token IDs of instructions are discrete and nondifferentiable,we compute gradients in the continuous embedding space to evaluate the influence of each token.The most critical tokens are identified by calculating the L2 norm of their embedding gradients.We then establish a mapping between instructions and their corresponding tokens to aggregate token-level importance into instructionlevel significance.To maximize adversarial impact,a sliding window algorithm selects the most influential contiguous segments for relocation,ensuring optimal perturbation with minimal length.This approach efficiently locates critical code regions without expensive search operations.The selected segments are relocated outside their original function boundaries via a jump mechanism,which preserves runtime control flow and functionality while introducing“deletion”effects in the static instruction sequence.Extensive experiments show that AIMA reduces similarity scores by up to 35.8%in state-of-the-art BCSD models.When incorporated into training data,it also enhances model robustness,achieving a 5.9%improvement in AUROC.
