Automatic modulation classification(AMC)aims at identifying the modulation of the received signals,which is a significant approach to identifying the target in military and civil applications.In this paper,a novel dat...Automatic modulation classification(AMC)aims at identifying the modulation of the received signals,which is a significant approach to identifying the target in military and civil applications.In this paper,a novel data-driven framework named convolutional and transformer-based deep neural network(CTDNN)is proposed to improve the classification performance.CTDNN can be divided into four modules,i.e.,convolutional neural network(CNN)backbone,transition module,transformer module,and final classifier.In the CNN backbone,a wide and deep convolution structure is designed,which consists of 1×15 convolution kernels and intensive cross-layer connections instead of traditional 1×3 kernels and sequential connections.In the transition module,a 1×1 convolution layer is utilized to compress the channels of the previous multi-scale CNN features.In the transformer module,three self-attention layers are designed for extracting global features and generating the classification vector.In the classifier,the final decision is made based on the maximum a posterior probability.Extensive simulations are conducted,and the result shows that our proposed CTDNN can achieve superior classification performance than traditional deep models.展开更多
Radio Frequency Fingerprint Identification(RFFI)technology provides a means of identifying spurious signals.This technology has been widely used in solving Automatic Dependent Surveillance–Broadcast(ADS-B)signal spoo...Radio Frequency Fingerprint Identification(RFFI)technology provides a means of identifying spurious signals.This technology has been widely used in solving Automatic Dependent Surveillance–Broadcast(ADS-B)signal spoofing problems.However,the effects of circuit changes over time often lead to a decline in identification accuracy within open-time set.This paper proposes an ADS-B transmitter identification method to solve the degradation of identification accuracy.First,a real-time data processing system is established to receive and store ADS-B signals to meet the conditions for open-time set.The system possesses the following functionalities:data collection,data parsing,feature extraction,and identity recognition.Subsequently,a two-dimensional TimeFrequency Feature Diagram(TFFD)is proposed as a signal pre-processing method.The TFFD is constructed from the received ADS-B signal and the reconstructed signal for input to the recognition model.Finally,incorporating a frequency offset layer into the Swin Transformer architecture,a novel recognition network framework is proposed.This integration can enhance the network recognition accuracy and robustness by tailoring to the specific characteristics of ADSB signals.Experimental results indicate that the proposed recognition architecture achieves recognition accuracy of 95.86%in closed-time set and 84.33%in open-time set,surpassing other algorithms.展开更多
The interferogram of multiple-beam Fizeau fringe technique plays an important role to investigate the optical properties of fiber because this interferogram provides us with useful information which can used to determ...The interferogram of multiple-beam Fizeau fringe technique plays an important role to investigate the optical properties of fiber because this interferogram provides us with useful information which can used to determine the dispersion curve of the fiber sample. A common problem in any interferogram analysis is the accuracy in locating fringe centers (fringe skeleton). There are a lot of computer-aided algorithms, which depend on the interferogram types, used to fringe skeleton extraction of various digital interferogram. In this paper, as far as I know, a novel algorithm for fringe skeleton extraction of double bright fringe of multiple-beam Fizeau fringe is presented. The proposed algorithm based on using the different order of Fourier transform and the derivative-sign binary image. Also the proposed algorithm has been successfully tested by using a computer simulation fringe and an experimental pattern. The results are compared with the original interferogram and shown a good agreement.展开更多
The aim of this paper is to exploit the existing Lexicon-Grammar(LG)tables,as well as to assess their relative importance vis-à-vis the concept of transformation and automatic paraphrasing.These operations includ...The aim of this paper is to exploit the existing Lexicon-Grammar(LG)tables,as well as to assess their relative importance vis-à-vis the concept of transformation and automatic paraphrasing.These operations include multiple processes at the lexical,morpho-syntactic,and semantic levels.Our proposal is to model highly productive phenomena of the Arabic language,such as pronominalization and passivization,dedicated to the both Arabic verb classes and Multiword Expressions(MWEs),in order to formalize the relation between structures and their semantic properties and thus to represent the symmetry and pairs between sentences that share a predicate that links the noun and a support verb.Furthermore,the automatic process of paraphrasing involves both the distributional and transformative features of each class of verbs or other structures such as Arabic MWEs.This research in progress outlines how to build Lexicon-Grammar tables for Arabic syntactic sentences by using automatic paraphrasing in a large transformational grammar on the one hand,and to implement it into both NooJ electronic dictionaries and local grammars on the other hand.展开更多
建筑平面图外墙的自动识别在建筑设计和施工的过程中具有重要意义,它不仅能提高设计效率,还能辅助设计审核,降低图纸错误风险。同时,自动识别外墙等构件为三维模型的快速生成提供有效数据,推动建筑信息建模(Building Information Modeli...建筑平面图外墙的自动识别在建筑设计和施工的过程中具有重要意义,它不仅能提高设计效率,还能辅助设计审核,降低图纸错误风险。同时,自动识别外墙等构件为三维模型的快速生成提供有效数据,推动建筑信息建模(Building Information Modeling,BIM)技术的发展。目前自动识别建筑平面图外墙的方法主要是基于规则的方法,且其效果仍有一定的提升空间。针对上述问题,本文提出了一种基于Swin Transformer和LSTM的CycleGAN算法实现建筑平面图外墙的自动识别,以辅助建筑平面图的设计、建筑平面图设计的质量稽查、三维模型的快速生成等。该算法基于循环一致性生成对抗网络(CycleGAN)框架,嵌入Swin Transformer Block模块和长短期记忆(Long Short-Term Memory,LSTM)模块,增强模型特征提取能力和网络学习能力。实验结果表明,改进的CycleGAN算法实现了非成对训练样本条件下建筑平面图外墙的识别,在结构相似性指数和峰值信噪比的两个指标上分别提高了22.4%和4.223,且生成的建筑外墙图具有清晰度高、细节特征完整的特点。展开更多
A novel method is proposed to automatically extract foreground objects from Martian surface images.The characteristics of Mars images are distinct,e.g.uneven illumination,low contrast between foreground and background...A novel method is proposed to automatically extract foreground objects from Martian surface images.The characteristics of Mars images are distinct,e.g.uneven illumination,low contrast between foreground and background,much noise in the background,and foreground objects with irregular shapes.In the context of these characteristics,an image is divided into foreground objects and background information.Homomorphism filtering is first applied to rectify brightness.Then,wavelet transformation enhances contrast and denoises the image.Third,edge detection and active contour are combined to extract contours regardless of the shape of the image.Experimental results show that the method can extract foreground objects from Mars images automatically and accurately,and has many potential applications.展开更多
This paper discusses the approaches for automatical searching of control points in the NOAA AVHRR image on the basis of data rearrangement in the form of latitude and longitude grid. The vegetation index transformatio...This paper discusses the approaches for automatical searching of control points in the NOAA AVHRR image on the basis of data rearrangement in the form of latitude and longitude grid. The vegetation index transformation and multi-level matching strategies have been proven effective and successful as the experiments show while the control point database is established.展开更多
Digitizing road maps manually is an expensive and time-consuming task. Several methods that intend to develop fully or semi-automated systems have been proposed. In this work we introduce a method, based on the Radon ...Digitizing road maps manually is an expensive and time-consuming task. Several methods that intend to develop fully or semi-automated systems have been proposed. In this work we introduce a method, based on the Radon transform and optimal algorithms, which extracts automatically roads on images of rural areas, images that were acquired by digital cameras and airborne laser scanners. The proposed method detects linear segments iteratively and starting from this it generates the centerlines of the roads. The method is based on an objective function which depends on three parameters related to the correlation between the cross-sections, spectral similarity and directions of the segments. Different tests were performed using aerial photos, Ikonos images and laser scanner data of an area located in the state of Parana （Brazil） and their results are presented and discussed. The quality of the detection of the roads centerlines was computed using several indexes - completeness, correctness and RMS. The values obtained reveal the good performance of the proposed methodology.展开更多
A large part of our daily lives is spent with audio information. Massive obstacles are frequently presented by the colossal amounts of acoustic information and the incredibly quick processing times. This results in th...A large part of our daily lives is spent with audio information. Massive obstacles are frequently presented by the colossal amounts of acoustic information and the incredibly quick processing times. This results in the need for applications and methodologies that are capable of automatically analyzing these contents. These technologies can be applied in automatic contentanalysis and emergency response systems. Breaks in manual communication usually occur in emergencies leading to accidents and equipment damage. The audio signal does a good job by sending a signal underground, which warrants action from an emergency management team at the surface. This paper, therefore, seeks to design and simulate an audio signal alerting and automatic control system using Unity Pro XL to substitute manual communication of emergencies and manual control of equipment. Sound data were trained using the neural network technique of machine learning. The metrics used are Fast Fourier transform magnitude, zero crossing rate, root mean square, and percentage error. Sounds were detected with an error of approximately 17%;thus, the system can detect sounds with an accuracy of 83%. With more data training, the system can detect sounds with minimal or no error. The paper, therefore, has critical policy implications about communication, safety, and health for underground mine.展开更多
基金supported in part by the National Natural Science Foundation of China under Grant(62171045,62201090)in part by the National Key Research and Development Program of China under Grants(2020YFB1807602,2019YFB1804404).
文摘Automatic modulation classification(AMC)aims at identifying the modulation of the received signals,which is a significant approach to identifying the target in military and civil applications.In this paper,a novel data-driven framework named convolutional and transformer-based deep neural network(CTDNN)is proposed to improve the classification performance.CTDNN can be divided into four modules,i.e.,convolutional neural network(CNN)backbone,transition module,transformer module,and final classifier.In the CNN backbone,a wide and deep convolution structure is designed,which consists of 1×15 convolution kernels and intensive cross-layer connections instead of traditional 1×3 kernels and sequential connections.In the transition module,a 1×1 convolution layer is utilized to compress the channels of the previous multi-scale CNN features.In the transformer module,three self-attention layers are designed for extracting global features and generating the classification vector.In the classifier,the final decision is made based on the maximum a posterior probability.Extensive simulations are conducted,and the result shows that our proposed CTDNN can achieve superior classification performance than traditional deep models.
基金supported by the National Key Research and Development Program of China(No.2022YFB4300902)。
文摘Radio Frequency Fingerprint Identification(RFFI)technology provides a means of identifying spurious signals.This technology has been widely used in solving Automatic Dependent Surveillance–Broadcast(ADS-B)signal spoofing problems.However,the effects of circuit changes over time often lead to a decline in identification accuracy within open-time set.This paper proposes an ADS-B transmitter identification method to solve the degradation of identification accuracy.First,a real-time data processing system is established to receive and store ADS-B signals to meet the conditions for open-time set.The system possesses the following functionalities:data collection,data parsing,feature extraction,and identity recognition.Subsequently,a two-dimensional TimeFrequency Feature Diagram(TFFD)is proposed as a signal pre-processing method.The TFFD is constructed from the received ADS-B signal and the reconstructed signal for input to the recognition model.Finally,incorporating a frequency offset layer into the Swin Transformer architecture,a novel recognition network framework is proposed.This integration can enhance the network recognition accuracy and robustness by tailoring to the specific characteristics of ADSB signals.Experimental results indicate that the proposed recognition architecture achieves recognition accuracy of 95.86%in closed-time set and 84.33%in open-time set,surpassing other algorithms.
文摘The interferogram of multiple-beam Fizeau fringe technique plays an important role to investigate the optical properties of fiber because this interferogram provides us with useful information which can used to determine the dispersion curve of the fiber sample. A common problem in any interferogram analysis is the accuracy in locating fringe centers (fringe skeleton). There are a lot of computer-aided algorithms, which depend on the interferogram types, used to fringe skeleton extraction of various digital interferogram. In this paper, as far as I know, a novel algorithm for fringe skeleton extraction of double bright fringe of multiple-beam Fizeau fringe is presented. The proposed algorithm based on using the different order of Fourier transform and the derivative-sign binary image. Also the proposed algorithm has been successfully tested by using a computer simulation fringe and an experimental pattern. The results are compared with the original interferogram and shown a good agreement.
文摘The aim of this paper is to exploit the existing Lexicon-Grammar(LG)tables,as well as to assess their relative importance vis-à-vis the concept of transformation and automatic paraphrasing.These operations include multiple processes at the lexical,morpho-syntactic,and semantic levels.Our proposal is to model highly productive phenomena of the Arabic language,such as pronominalization and passivization,dedicated to the both Arabic verb classes and Multiword Expressions(MWEs),in order to formalize the relation between structures and their semantic properties and thus to represent the symmetry and pairs between sentences that share a predicate that links the noun and a support verb.Furthermore,the automatic process of paraphrasing involves both the distributional and transformative features of each class of verbs or other structures such as Arabic MWEs.This research in progress outlines how to build Lexicon-Grammar tables for Arabic syntactic sentences by using automatic paraphrasing in a large transformational grammar on the one hand,and to implement it into both NooJ electronic dictionaries and local grammars on the other hand.
基金Supported by the National 973 Program of China(No.2007CB310804)the National Natural Science Foundation of China(No.61173061).
文摘A novel method is proposed to automatically extract foreground objects from Martian surface images.The characteristics of Mars images are distinct,e.g.uneven illumination,low contrast between foreground and background,much noise in the background,and foreground objects with irregular shapes.In the context of these characteristics,an image is divided into foreground objects and background information.Homomorphism filtering is first applied to rectify brightness.Then,wavelet transformation enhances contrast and denoises the image.Third,edge detection and active contour are combined to extract contours regardless of the shape of the image.Experimental results show that the method can extract foreground objects from Mars images automatically and accurately,and has many potential applications.
基金Project supported by the National Oommission of Defense Science and Technotocjy(No.Y96-10)
文摘This paper discusses the approaches for automatical searching of control points in the NOAA AVHRR image on the basis of data rearrangement in the form of latitude and longitude grid. The vegetation index transformation and multi-level matching strategies have been proven effective and successful as the experiments show while the control point database is established.
文摘Digitizing road maps manually is an expensive and time-consuming task. Several methods that intend to develop fully or semi-automated systems have been proposed. In this work we introduce a method, based on the Radon transform and optimal algorithms, which extracts automatically roads on images of rural areas, images that were acquired by digital cameras and airborne laser scanners. The proposed method detects linear segments iteratively and starting from this it generates the centerlines of the roads. The method is based on an objective function which depends on three parameters related to the correlation between the cross-sections, spectral similarity and directions of the segments. Different tests were performed using aerial photos, Ikonos images and laser scanner data of an area located in the state of Parana （Brazil） and their results are presented and discussed. The quality of the detection of the roads centerlines was computed using several indexes - completeness, correctness and RMS. The values obtained reveal the good performance of the proposed methodology.
文摘A large part of our daily lives is spent with audio information. Massive obstacles are frequently presented by the colossal amounts of acoustic information and the incredibly quick processing times. This results in the need for applications and methodologies that are capable of automatically analyzing these contents. These technologies can be applied in automatic contentanalysis and emergency response systems. Breaks in manual communication usually occur in emergencies leading to accidents and equipment damage. The audio signal does a good job by sending a signal underground, which warrants action from an emergency management team at the surface. This paper, therefore, seeks to design and simulate an audio signal alerting and automatic control system using Unity Pro XL to substitute manual communication of emergencies and manual control of equipment. Sound data were trained using the neural network technique of machine learning. The metrics used are Fast Fourier transform magnitude, zero crossing rate, root mean square, and percentage error. Sounds were detected with an error of approximately 17%;thus, the system can detect sounds with an accuracy of 83%. With more data training, the system can detect sounds with minimal or no error. The paper, therefore, has critical policy implications about communication, safety, and health for underground mine.
