Analysis on unit maximum capacity of orthogonal multiple watermarking for multimedia signals in B5G wireless communications

Beyond-5G(B5G)aims to meet the growing demands of mobile traffic and expand the communication space.Considering that intelligent applications to B5G wireless communications will involve security issues regarding user data and operational data,this paper analyzes the maximum capacity of the multi-watermarking method for multimedia signal hiding as a means of alleviating the information security problem of B5G.The multiwatermarking process employs spread transform dither modulation.During the watermarking procedure,Gram-Schmidt orthogonalization is used to obtain the multiple spreading vectors.Consequently,multiple watermarks can be simultaneously embedded into the same position of a multimedia signal.Moreover,the multiple watermarks can be extracted without affecting one another during the extraction process.We analyze the effect of the size of the spreading vector on the unit maximum capacity,and consequently derive the theoretical relationship between the size of the spreading vector and the unit maximum capacity.A number of experiments are conducted to determine the optimal parameter values for maximum robustness on the premise of high capacity and good imperceptibility.

A high frequency vibration compensation approach for ultrahigh resolution SAR imaging based on sinusoidal frequency modulation Fourier-Bessel transform

Ultrahigh resolution synthetic aperture radar(SAR)imaging for ship targets is significant in SAR imaging,but it suffers from high frequency vibration of the platform,which will induce defocus into SAR imaging results.In this paper,a novel compensation method based on the sinusoidal frequency modulation Fourier-Bessel transform(SFMFBT)is proposed,it can estimate the vibration errors,and the phase shift ambiguity can be avoided via extracting the time frequency ridge consequently.By constructing the corresponding compensation function and combined with the inverse SAR(ISAR)technique,well-focused imaging results can be obtained.The simulation imaging results of ship targets demonstrate the validity of the proposed approach.

A complementary integrated Transformer network for hyperspectral image classification

In the past,convolutional neural network(CNN)has become one of the most popular deep learning frameworks,and has been widely used in Hyperspectral image classification tasks.Convolution(Conv)in CNN uses filter weights to extract features in local receiving domain,and the weight parameters are shared globally,which more focus on the highfrequency information of the image.Different from Conv,Transformer can obtain the long‐term dependence between long‐distance features through modelling,and adaptively focus on different regions.In addition,Transformer is considered as a low‐pass filter,which more focuses on the low‐frequency information of the image.Considering the complementary characteristics of Conv and Transformer,the two modes can be integrated for full feature extraction.In addition,the most important image features correspond to the discrimination region,while the secondary image features represent important but easily ignored regions,which are also conducive to the classification of HSIs.In this study,a complementary integrated Transformer network(CITNet)for hyperspectral image classification is proposed.Firstly,three‐dimensional convolution(Conv3D)and two‐dimensional convolution(Conv2D)are utilised to extract the shallow semantic information of the image.In order to enhance the secondary features,a channel Gaussian modulation attention module is proposed,which is embedded between Conv3D and Conv2D.This module can not only enhance secondary features,but suppress the most important and least important features.Then,considering the different and complementary characteristics of Conv and Transformer,a complementary integrated Transformer module is designed.Finally,through a large number of experiments,this study evaluates the classification performance of CITNet and several state‐of‐the‐art networks on five common datasets.The experimental results show that compared with these classification networks,CITNet can provide better classification performance.

Time-Frequency Bandwidth Product Estimation of Sinusoidal Non-Linear Chirp Keying Scheme

Sine Non-linear Chirp Keying(SNCK) is a kind of high-efficient modulation scheme, which provides a potential new beamforming method in communication and radar systems. It has been proved to have advantages in some parameter estimation issues over conventional modulation schemes. In this paper, a novel transform termed as Discrete Sinusoidal Frequency Modulation transform(DSFMT) is proposed. Then, the DSFMT of SNCK signal is deduced and classified into three types, based on which, the time-bandwidth product is estimated by the proposed algorithm. Simulation results show that the noise has a signifi cant impact on the localization of the peak value and the time-bandwidth product can be estimated by using local ratio values when.

Modulated Lapped Biorthogonal Transform for Non-orthogonal Narrowband Interference Excision in Spread Spectrum Communications

Traditional lapped transform domain excision techniques obtain good performance at the expense of increased processing delay. Extension of transform domain filtering techniques to the lapped biorthogonal transform domain can help solve the problem. By incorporating biorthogonality into the lapped transforms, more flexibility is obtained in the design of windows. Thus transform bases with better stopband attenuation can be generated by designing windows, but not by increasing the overlapping factor. In this paper, a new modulated lapped biorthogonal transform (MLBT) with optimized windows is introduced for efficient compression of multi-tone interfering signal energy. The bit error rate (BER) performance of the receiver employing the proposed MLBT excision technique is analyzed and compared with that of the lapped transform domain excision-based receivers. Simulation results demonstrate the improved performance and increased robustness of the proposed technique.

Adaptive Video Dual Domain Watermarking Scheme Based on PHT Moment and Optimized Spread Transform Dither Modulation

To address the challenges of video copyright protection and ensure the perfect recovery of original video,we propose a dual-domain watermarking scheme for digital video,inspired by Robust Reversible Watermarking(RRW)technology used in digital images.Our approach introduces a parameter optimization strategy that incre-mentally adjusts scheme parameters through attack simulation fitting,allowing for adaptive tuning of experimental parameters.In this scheme,the low-frequency Polar Harmonic Transform(PHT)moment is utilized as the embedding domain for robust watermarking,enhancing stability against simulation attacks while implementing the parameter optimization strategy.Through extensive attack simulations across various digital videos,we identify the optimal low-frequency PHT moment using adaptive normalization.Subsequently,the embedding parameters for robust watermarking are adaptively adjusted to maximize robustness.To address computational efficiency and practical requirements,the unnormalized high-frequency PHT moment is selected as the embedding domain for reversible watermarking.We optimize the traditional single-stage extended transform dithering modulation(STDM)to facilitate multi-stage embedding in the dual-domain watermarking process.In practice,the video embedded with a robust watermark serves as the candidate video.This candidate video undergoes simulation according to the parameter optimization strategy to balance robustness and embedding capacity,with adaptive determination of embedding strength.The reversible watermarking is formed by combining errors and other information,utilizing recursive coding technology to ensure reversibility without attacks.Comprehensive analyses of multiple performance indicators demonstrate that our scheme exhibits strong robustness against Common Signal Processing(CSP)and Geometric Deformation(GD)attacks,outperforming other advanced video watermarking algorithms under similar conditions of invisibility,reversibility,and embedding capacity.This underscores the effectiveness and feasibility of our attack simulation fitting strategy.

An approach for parameter estimation of combined CPPM and LFM radar signal

In this paper, the problem of parameter estimation of the combined radar signal adopting chaotic pulse position modulation （CPPM） and linear frequency modulation （LFM）, which can be widely used in electronic countermeasures, is addressed. An approach is proposed to estimate the initial frequency and chirp rate of the combined signal by exploiting the second-order cyclostationarity of the intra-pulse signal. In addition, under the condition of the equal pulse width, the pulse repetition interval （PRI） of the combined signal is predicted using the low-order Volterra adaptive filter. Simulations demonstrate that the proposed cyclic autocorrelation Hough transform （CHT） algorithm is theoretically tolerant to additive white Gaussian noise. When the value of signal noise to ratio （SNR） is less than 4 dB, it can still estimate the intra-pulse parameters well. When SNR = 3 dB, a good prediction of the PRI sequence can be achieved by the Volterra adaptive filter algorithm, even only 100 training samples.

Self-Sparse Generative Adversarial Networks

Generative adversarial networks(GANs)are an unsupervised generative model that learns data distribution through adversarial training.However,recent experiments indicated that GANs are difficult to train due to the requirement of optimization in the high dimensional parameter space and the zero gradient problem.In this work,we propose a self-sparse generative adversarial network(Self-Sparse GAN)that reduces the parameter space and alleviates the zero gradient problem.In the Self-Sparse GAN,we design a self-adaptive sparse transform module(SASTM)comprising the sparsity decomposition and feature-map recombination,which can be applied on multi-channel feature maps to obtain sparse feature maps.The key idea of Self-Sparse GAN is to add the SASTM following every deconvolution layer in the generator,which can adaptively reduce the parameter space by utilizing the sparsity in multi-channel feature maps.We theoretically prove that the SASTM can not only reduce the search space of the convolution kernel weight of the generator but also alleviate the zero gradient problem by maintaining meaningful features in the batch normalization layer and driving the weight of deconvolution layers away from being negative.The experimental results show that our method achieves the best Fréchet inception distance(FID)scores for image generation compared with Wasserstein GAN with gradient penalty(WGAN-GP)on MNIST,Fashion-MNIST,CIFAR-10,STL-10,mini-ImageNet,CELEBA-HQ,and LSUN bedrooms datasets,and the relative decrease of FID is 4.76%-21.84%.Meanwhile,an architectural sketch dataset(Sketch)is also used to validate the superiority of the proposed method.