Shadow and variable illumination considerably influence the results of image understanding such as image segmentation, object tracking, and object recognition. The intrinsic image decomposition is to separate the refl...Shadow and variable illumination considerably influence the results of image understanding such as image segmentation, object tracking, and object recognition. The intrinsic image decomposition is to separate the reflectance and the illumination image from an observed image. The intrinsic image decomposition is very useful to remove shadows and then improve the performance of image understanding. In this paper, we present a new shadow removal method based on intrinsic image decomposition on a single color image using the Fisher Linear Discriminant (FLD). Under the assumptions-Lambertian surfaces, approximately Planckian lighting, and narrowband camera sensors, there exist an invariant image, which is 1-dimensional greyscale and independent of illuminant color and intensity. The Fisher Linear Discriminant is applied to create the invariant image. And further the shadows can be removed through the difference between invariant image and original color image. The experimental results on real data show good performance of this algorithm.展开更多
In the context of global efforts to mitigate climate change by pursuing sustainable energy sources, wind energy has emerged as a critical contributor. However, the wind energy industry faces substantial challenges in ...In the context of global efforts to mitigate climate change by pursuing sustainable energy sources, wind energy has emerged as a critical contributor. However, the wind energy industry faces substantial challenges in maintaining and preserving the integrity of wind turbine blades. Timely and accurate detection and classification of blade faults, encompassing issues such as cracks, erosion, and ice buildup, are imperative to uphold wind turbines' ongoing efficiency and safety. This study introduces an inventive approach that amalgamates hyperspectral imaging and 3D Convolutional Neural Networks (CNNs) to augment the precision and efficiency of wind turbine blade fault detection and classification. Hyperspectral imaging is harnessed to capture comprehensive spectral information from blade surfaces, facilitating exact fault identification. The process is streamlined through Incremental Principal Component Analysis (IPCA), reducing data dimensions while maintaining integrity. The 3D CNN model demonstrates remarkable performance, achieving high accuracy in detecting all fault categories in full-band hyperspectral images. The model retains high accuracy even with dimensionality reduction to 20 spectral bands. The reduced processing time of the 20-band image enhances the practicality of real-world applications, thereby reducing downtime and maintenance expenditures. This research represents a significant advancement in wind turbine blade inspection, contributing to the sustainability and dependability of wind energy systems and furthering the cause of a cleaner and more sustainable energy future as part of the broader fight against climate change.展开更多
A Prioritized Medium Access Control （P-MAC） protocol is proposed for wireless routers of mesh networks with quality-of-service provisioning. The simple yet effective design of P-MAC offers strict service differentia...A Prioritized Medium Access Control （P-MAC） protocol is proposed for wireless routers of mesh networks with quality-of-service provisioning. The simple yet effective design of P-MAC offers strict service differentiation for prioritized packets. A Markov model is developed to yield important performance matrices including the packet blocking probability due to queue overflow and the packet reneging probability due to delay bound. It is further proved that the service time of P-MAC approximates exponential distribution, and can be effectively estimated. The analytic models with preemptive and non-preemptive schemes, validated via simulations, show that P-MAC can effectively support traffic differentiation and achieve very low packet dropping （both reneging and blocking） probabilities when the traffic load is below the channel capacity. When the network is overloaded, P-MAC can still maintain extremely stable and high channel throughput. Moreover, it is demonstrated that P-MAC performs superior in multihop networks, further proving the advantages of the proposed protocol.展开更多
Nonlinear spin torque nano-oscillators have received substantial attentions due to their important applications in microwave communication and neuromorphic computing.Here we investigate the dynamical behaviors of dire...Nonlinear spin torque nano-oscillators have received substantial attentions due to their important applications in microwave communication and neuromorphic computing.Here we investigate the dynamical behaviors of directly coupled skyrmion oscillators in a synthetic ferrimagnet.We demonstrate through the micromagnetic simulation and Thiele’s equation that the skyrmion oscillators can present either synchronization or frequency comb,depending on the strength of interactions between the skyrmions.The underlying physics of the transition between the two scenarios are unveiled based on a quantitative analysis of the effective potentials,which also successfully interprets the dependence of the transition on parameters.By further demonstrating the tunability of the nonlinear dynamics by the driving current of the oscillators,our work reveals the great potentials of ferrimagnetic-skyrmion-based interacting oscillators for nonlinear applications.展开更多
When electrons are confined in a two-dimensional （2D） system, typical quantum-mechanical phenonl- ena such as Landau quantization can be detected. Graphene systems, including the single atomic layer and few-layer st...When electrons are confined in a two-dimensional （2D） system, typical quantum-mechanical phenonl- ena such as Landau quantization can be detected. Graphene systems, including the single atomic layer and few-layer stacked crystals, are ideal 2D materials for studying a variety of quantum-mechanical problems. In this article, we review the experimental progress in the unusual Landau quantized behav- iors of Dirac fernlions in monolayer and multilayer graphene by using scanning tunneling microscopy （STM） and scanning tulmeling spectroscopy （STS）. Through STS measurement of the strong mag- netic fields, distinct Landau-level spectra and rich level-splitting phenomena are observed in different graphene layers. These unique properties provide an effective method for identifying the number of layers, as well as the stacking orders, and investigating the fllndamentally physical phenomena of graphene. Moreover, in the presence of a strain and charged defects, the Landau quantization of graphene can be significantly modified, leading to unusual spectroscopic and electronic properties.展开更多
The timing and Hamming weight attacks on the data encryption standard （DES） cryptosystem for minimal cost encryption scheme is presented in this article. In the attack, timing information on encryption processing is...The timing and Hamming weight attacks on the data encryption standard （DES） cryptosystem for minimal cost encryption scheme is presented in this article. In the attack, timing information on encryption processing is used to select and collect effective plaintexts for attack. Then the collected plaintexts are utilized to infer the expanded key differences of the secret key, from which most bits of the expanded secret key are recovered. The remaining bits of the expanded secret key are deduced by the correlations between Hamming weight values of the input of the S-boxes in the first-round. Finally, from the linear relation of the encryption time and the secret key＇s Hamming weight, the entire 56 bits of the secret key are thoroughly recovered. Using the attack, the minimal cost encryption scheme can be broken with 2^23 known plaintexts and about 2^21 calculations at a success rate a 〉 99%. The attack has lower computing complexity, and the method is more effective than other previous methods.展开更多
文摘Shadow and variable illumination considerably influence the results of image understanding such as image segmentation, object tracking, and object recognition. The intrinsic image decomposition is to separate the reflectance and the illumination image from an observed image. The intrinsic image decomposition is very useful to remove shadows and then improve the performance of image understanding. In this paper, we present a new shadow removal method based on intrinsic image decomposition on a single color image using the Fisher Linear Discriminant (FLD). Under the assumptions-Lambertian surfaces, approximately Planckian lighting, and narrowband camera sensors, there exist an invariant image, which is 1-dimensional greyscale and independent of illuminant color and intensity. The Fisher Linear Discriminant is applied to create the invariant image. And further the shadows can be removed through the difference between invariant image and original color image. The experimental results on real data show good performance of this algorithm.
基金supported by Natural Sciences and Engineering Research Council of Canada(NSERC)grants with reference numbers RGPIN-2023-05578,DGECR-2023-00336,and RGPIN-2019-04220.
文摘In the context of global efforts to mitigate climate change by pursuing sustainable energy sources, wind energy has emerged as a critical contributor. However, the wind energy industry faces substantial challenges in maintaining and preserving the integrity of wind turbine blades. Timely and accurate detection and classification of blade faults, encompassing issues such as cracks, erosion, and ice buildup, are imperative to uphold wind turbines' ongoing efficiency and safety. This study introduces an inventive approach that amalgamates hyperspectral imaging and 3D Convolutional Neural Networks (CNNs) to augment the precision and efficiency of wind turbine blade fault detection and classification. Hyperspectral imaging is harnessed to capture comprehensive spectral information from blade surfaces, facilitating exact fault identification. The process is streamlined through Incremental Principal Component Analysis (IPCA), reducing data dimensions while maintaining integrity. The 3D CNN model demonstrates remarkable performance, achieving high accuracy in detecting all fault categories in full-band hyperspectral images. The model retains high accuracy even with dimensionality reduction to 20 spectral bands. The reduced processing time of the 20-band image enhances the practicality of real-world applications, thereby reducing downtime and maintenance expenditures. This research represents a significant advancement in wind turbine blade inspection, contributing to the sustainability and dependability of wind energy systems and furthering the cause of a cleaner and more sustainable energy future as part of the broader fight against climate change.
基金Supported in part by the National Science Foundation CAREER Award (No. CNS-0347686)US Department of Energy (DoE) (No. DE-FG02-04ER46136)
文摘A Prioritized Medium Access Control （P-MAC） protocol is proposed for wireless routers of mesh networks with quality-of-service provisioning. The simple yet effective design of P-MAC offers strict service differentiation for prioritized packets. A Markov model is developed to yield important performance matrices including the packet blocking probability due to queue overflow and the packet reneging probability due to delay bound. It is further proved that the service time of P-MAC approximates exponential distribution, and can be effectively estimated. The analytic models with preemptive and non-preemptive schemes, validated via simulations, show that P-MAC can effectively support traffic differentiation and achieve very low packet dropping （both reneging and blocking） probabilities when the traffic load is below the channel capacity. When the network is overloaded, P-MAC can still maintain extremely stable and high channel throughput. Moreover, it is demonstrated that P-MAC performs superior in multihop networks, further proving the advantages of the proposed protocol.
基金supported by the National Natural Science Foundation of China(Grants No.11974047 and No.12374100)the Fundamental Research Funds for the Central Universities.
文摘Nonlinear spin torque nano-oscillators have received substantial attentions due to their important applications in microwave communication and neuromorphic computing.Here we investigate the dynamical behaviors of directly coupled skyrmion oscillators in a synthetic ferrimagnet.We demonstrate through the micromagnetic simulation and Thiele’s equation that the skyrmion oscillators can present either synchronization or frequency comb,depending on the strength of interactions between the skyrmions.The underlying physics of the transition between the two scenarios are unveiled based on a quantitative analysis of the effective potentials,which also successfully interprets the dependence of the transition on parameters.By further demonstrating the tunability of the nonlinear dynamics by the driving current of the oscillators,our work reveals the great potentials of ferrimagnetic-skyrmion-based interacting oscillators for nonlinear applications.
基金This work was supported by the National Natural Science Foundation of China （Grant Nos. 11674029, 11422430, and 11374035）, the National Basic Research Program of China （Grants Nos. 2014CB920903 and 2013CBA01603）, and the program for New Century Excellent Talents in University of the Ministry of Education of China （Grant No. NCET-13-0054）. L. He also acknowledges support from the National Program for Support of Top-notch Young Professionals.
文摘When electrons are confined in a two-dimensional （2D） system, typical quantum-mechanical phenonl- ena such as Landau quantization can be detected. Graphene systems, including the single atomic layer and few-layer stacked crystals, are ideal 2D materials for studying a variety of quantum-mechanical problems. In this article, we review the experimental progress in the unusual Landau quantized behav- iors of Dirac fernlions in monolayer and multilayer graphene by using scanning tunneling microscopy （STM） and scanning tulmeling spectroscopy （STS）. Through STS measurement of the strong mag- netic fields, distinct Landau-level spectra and rich level-splitting phenomena are observed in different graphene layers. These unique properties provide an effective method for identifying the number of layers, as well as the stacking orders, and investigating the fllndamentally physical phenomena of graphene. Moreover, in the presence of a strain and charged defects, the Landau quantization of graphene can be significantly modified, leading to unusual spectroscopic and electronic properties.
基金supported by the National Basic Research Program of China (2007CB807902, 2007CB807903)the Education Innovation Foundation of Institution and University of Beijing (2004).
文摘The timing and Hamming weight attacks on the data encryption standard （DES） cryptosystem for minimal cost encryption scheme is presented in this article. In the attack, timing information on encryption processing is used to select and collect effective plaintexts for attack. Then the collected plaintexts are utilized to infer the expanded key differences of the secret key, from which most bits of the expanded secret key are recovered. The remaining bits of the expanded secret key are deduced by the correlations between Hamming weight values of the input of the S-boxes in the first-round. Finally, from the linear relation of the encryption time and the secret key＇s Hamming weight, the entire 56 bits of the secret key are thoroughly recovered. Using the attack, the minimal cost encryption scheme can be broken with 2^23 known plaintexts and about 2^21 calculations at a success rate a 〉 99%. The attack has lower computing complexity, and the method is more effective than other previous methods.
