A Hybrid Cybersecurity Algorithm for Digital Image Transmission over Advanced Communication ChannelModels

The efficient transmission of images,which plays a large role inwireless communication systems,poses a significant challenge in the growth of multimedia technology.High-quality images require well-tuned communication standards.The Single Carrier Frequency Division Multiple Access(SC-FDMA)is adopted for broadband wireless communications,because of its low sensitivity to carrier frequency offsets and low Peak-to-Average Power Ratio(PAPR).Data transmission through open-channel networks requires much concentration on security,reliability,and integrity.The data need a space away fromunauthorized access,modification,or deletion.These requirements are to be fulfilled by digital image watermarking and encryption.This paper ismainly concerned with secure image communication over the wireless SC-FDMA systemas an adopted communication standard.It introduces a robust image communication framework over SC-FDMA that comprises digital image watermarking and encryption to improve image security,while maintaining a high-quality reconstruction of images at the receiver side.The proposed framework allows image watermarking based on the Discrete Cosine Transform(DCT)merged with the Singular Value Decomposition(SVD)in the so-called DCT-SVD watermarking.In addition,image encryption is implemented based on chaos and DNA encoding.The encrypted watermarked images are then transmitted through the wireless SC-FDMA system.The linearMinimumMean Square Error(MMSE)equalizer is investigated in this paper to mitigate the effect of channel fading and noise on the transmitted images.Two subcarrier mapping schemes,namely localized and interleaved schemes,are compared in this paper.The study depends on different channelmodels,namely PedestrianAandVehicularA,with a modulation technique namedQuadratureAmplitude Modulation(QAM).Extensive simulation experiments are conducted and introduced in this paper for efficient transmission of encrypted watermarked images.In addition,different variants of SC-FDMA based on the Discrete Wavelet Transform(DWT),Discrete Cosine Transform(DCT),and Fast Fourier Transform(FFT)are considered and compared for the image communication task.The simulation results and comparison demonstrate clearly that DWT-SC-FDMAis better suited to the transmission of the digital images in the case of PedestrianAchannels,while the DCT-SC-FDMA is better suited to the transmission of the digital images in the case of Vehicular A channels.

A semantic segmentation-based underwater acoustic image transmission framework for cooperative SLAM

With the development of underwater sonar detection technology,simultaneous localization and mapping(SLAM)approach has attracted much attention in underwater navigation field in recent years.But the weak detection ability of a single vehicle limits the SLAM performance in wide areas.Thereby,cooperative SLAM using multiple vehicles has become an important research direction.The key factor of cooperative SLAM is timely and efficient sonar image transmission among underwater vehicles.However,the limited bandwidth of underwater acoustic channels contradicts a large amount of sonar image data.It is essential to compress the images before transmission.Recently,deep neural networks have great value in image compression by virtue of the powerful learning ability of neural networks,but the existing sonar image compression methods based on neural network usually focus on the pixel-level information without the semantic-level information.In this paper,we propose a novel underwater acoustic transmission scheme called UAT-SSIC that includes semantic segmentation-based sonar image compression(SSIC)framework and the joint source-channel codec,to improve the accuracy of the semantic information of the reconstructed sonar image at the receiver.The SSIC framework consists of Auto-Encoder structure-based sonar image compression network,which is measured by a semantic segmentation network's residual.Considering that sonar images have the characteristics of blurred target edges,the semantic segmentation network used a special dilated convolution neural network(DiCNN)to enhance segmentation accuracy by expanding the range of receptive fields.The joint source-channel codec with unequal error protection is proposed that adjusts the power level of the transmitted data,which deal with sonar image transmission error caused by the serious underwater acoustic channel.Experiment results demonstrate that our method preserves more semantic information,with advantages over existing methods at the same compression ratio.It also improves the error tolerance and packet loss resistance of transmission.

On active synchronization of fractional-order Bloch chaotic system and its practical application in secure image transmission

Purpose-The purpose of this paper is to propose a novel and secure image transmission based on the unpredictable behavior of the chaotic systems.Design/methodology/approach-The proposed approach includes two main contributions:synchronization scheme and transmission scheme.The synchronization scheme benefits the advantage of the fractional-order active synchronization method.A new control law is derived to asymptotically synchronize the underlined fractional-order Bloch chaotic system.The validity of the proposed synchronization scheme is proved by the Lyapunov stability theorem.Then,a novel image transmission scheme is designed to transfer image data via chaotic signals,which modulates the encrypted data in the sender signals and demodulates it at the receiver side.Findings-Numerical simulations are provided to show the validity and effectiveness of the proposed image transmission system.Furthermore,the performance of the image transmission system is evaluated using some illustrative examples and their corresponding statistical tests.The results demonstrate the effectiveness of the proposed method in comparison with other proposed methods in this subject.Originality/value–A new chaos-based image transmission system is developed based on the synchronization of Bloch chaotic system.The introduced transmission system is interesting and could be applicable to any kind of secure image/video transmission.

SNR-adaptive deep joint source-channel coding scheme for image semantic transmission with convolutional block attention module

With the development of deep learning(DL),joint source-channel coding(JSCC)solutions for end-to-end transmission have gained a lot of attention.Adaptive deep JSCC schemes support dynamically adjusting the rate according to different channel conditions during transmission,enhancing robustness in dynamic wireless environment.However,most of the existing adaptive JSCC schemes only consider different channel conditions,ignoring the different feature importance in the image processing and transmission.The uniform compression of different features in the image may result in the compromise of critical image details,particularly in low signal-to-noise ratio(SNR)scenarios.To address the above issues,in this paper,a dual attention mechanism is introduced and an SNR-adaptive deep JSCC mechanism with a convolutional block attention module(CBAM)is proposed,in which matrix operations are applied to features in spatial and channel dimensions respectively.The proposed solution concatenates the pooling feature with the SNR level and passes it sequentially through the channel attention network and spatial attention network to obtain the importance evaluation result.Experiments show that the proposed solution outperforms other baseline schemes in terms of peak SNR(PSNR)and structural similarity(SSIM),particularly in low SNR scenarios or when dealing with complex image content.

Secret Image Communication Scheme Based on Visual Cryptography and Tetrolet Tiling Patterns

Visual cryptographic scheme is specially designed for secret image sharing in the form of shadow images.The basic idea of visual cryptography is to construct two or more secret shares from the original image in the form of chaotic image.In this paper,a novel secret image communication scheme based on visual cryptography and Tetrolet tiling patterns is proposed.The proposed image communication scheme will break the secret image into more shadow images based on the Tetrolet tiling patterns.The secret image is divided into 4×4 blocks of tetrominoes and employs the concept of visual cryptography to hide the secret image.The main feature of the proposed scheme is the selection of random blocks to apply the tetrolet tilling patterns from the fundamental tetrolet pattern board.Single procedure is used to perform both tetrolet transform and the scheme of visual cryptography.Finally,the experimental results showcase the proposed scheme is an extraordinary approach to transfer the secret image and reconstruct the secret image with high visual quality in the receiver end.

Full Image Inference Conditionally upon Available Pieces Transmitted into Limited Resources Context

In a context marked by the proliferation of smartphones and multimedia applications, the processing and transmission of images have become a real problem. Image compression is the first approach to address this problem, it nevertheless suffers from its inability to adapt to the dynamics of limited environments, consisting mainly of mobile equipment and wireless networks. In this work, we propose a stochastic model to gradually estimate an image upon information on its pixels that are transmitted progressively. We consider this transmission as a dynamical process, where the sender pushes the data in decreasing significance order. In order to adapt to network conditions and performances, instead of truncating the pixels, we suggest a new method called Fast Reconstruction Method by Kalman Filtering (FRM-KF) consisting of recursive inference of the not yet received layers belonging to a sequence of bitplanes. After empirical analysis, we estimate parameters of our model which is a linear discrete Kalman Filter. We assume the initial law of information to be the uniform distribution on the set [0, 255] corresponding to the range of gray levels. The performances of FRM-KF method have been evaluated in terms of the ratios in the quality of data image/size sent and in the quality of image/time required for treatment. A high quality was reached faster with relatively small data (less than 10% of image data is needed to obtain up to the sixth-quality image). The time for treatment also decreases faster with number of received layers. However, we found that the time of image treatment might be large starting from a image resolution of 1024 * 1024. Hence, we recommend FRM-KF method for resolutions less or equal to 512 * 512. A statistical comparative analysis reveals that FRM-KF is competitive and suitable to be implemented on limited resource environments.

Deep-Learning-Empowered 3D Reconstruction for Dehazed Images in IoT-Enhanced Smart Cities

With increasingly more smart cameras deployed in infrastructure and commercial buildings,3D reconstruction can quickly obtain cities’information and improve the efficiency of government services.Images collected in outdoor hazy environments are prone to color distortion and low contrast;thus,the desired visual effect cannot be achieved and the difficulty of target detection is increased.Artificial intelligence(AI)solutions provide great help for dehazy images,which can automatically identify patterns or monitor the environment.Therefore,we propose a 3D reconstruction method of dehazed images for smart cities based on deep learning.First,we propose a fine transmission image deep convolutional regression network(FT-DCRN)dehazing algorithm that uses fine transmission image and atmospheric light value to compute dehazed image.The DCRN is used to obtain the coarse transmission image,which can not only expand the receptive field of the network but also retain the features to maintain the nonlinearity of the overall network.The fine transmission image is obtained by refining the coarse transmission image using a guided filter.The atmospheric light value is estimated according to the position and brightness of the pixels in the original hazy image.Second,we use the dehazed images generated by the FT-DCRN dehazing algorithm for 3D reconstruction.An advanced relaxed iterative fine matching based on the structure from motion(ARI-SFM)algorithm is proposed.The ARISFM algorithm,which obtains the fine matching corner pairs and reduces the number of iterations,establishes an accurate one-to-one matching corner relationship.The experimental results show that our FT-DCRN dehazing algorithm improves the accuracy compared to other representative algorithms.In addition,the ARI-SFM algorithm guarantees the precision and improves the efficiency.

Transmissive multifocal laser speckle contrast imaging through thick tissue

Laser speckle contrast imaging(LSCI)is a powerful tool for monitoring blood flow changes in tissue or vessels in vivo,but its applications are limited by shallow penetration depth under reflective imaging configuration.The traditional LSCI setup has been used in transmissive imaging for depth extension up to 2l_(t)–3l_(t)(l_(t)is the transport mean free path),but the blood flow estimation is biased due to the depth uncertainty in large depth of field(DOF)images.In this study,we propose a transmissive multifocal LSCI for depth-resolved blood flow in thick tissue,further extending the transmissive LSCI for tissue thickness up to 12lt.The limited-DOF imaging system is applied to the multifocal acquisition,and the depth of the vessel is estimated using a robust visibility parameter V_(r)in the coherent domain.The accuracy and linearity of depth estimation are tested by Monte Carlo simulations.Based on the proposed method,the model of contrast analysis resolving the depth information is established and verified in a phantom experiment.We demonstrated its effectiveness in acquiring depth-resolved vessel structures and flow dynamics in in vivo imaging of chick embryos.

Imaging internal density structure of the Laoheishan volcanic cone with cosmic ray muon radiography

Muon radiography is a promising technique for imaging the internal density structures of targets such as tunnels,pyramids,and volcanoes up to a scale of a few hundred meters by measuring the flux attenuation of cosmic ray muons after they have traveled through these targets.In this study,we conducted experimental muon radiography of one of the volcanoes in the Wudalianchi area in Northeast China to image its internal density structure.The muon detector used in this study was composed of plastic scintillators and silicon photomultipliers.After approximately one and a half months of observing the crater and conduit of the Laoheishan volcano cone in Wudalianchi from September 23^(rd) to November 10^(th) 2019,more than 3 million muon tracks fulfilling the data selection criteria were collected.Based on the muon samples and high-resolution topography obtained through aerial photogrammetry using an unmanned aerial vehicle,a density image of the Laoheishan volcano cone was constructed.The results obtained in this experiment demonstrate the feasibility of using a radiography technique based on plastic scintillator detectors.To obtain the density distribution,we performed a detailed background analysis and found that low-energy charged particles dominated the background noise.Relatively higher densities were found near the surface of the volcanic cone,whereas relatively lower densities were found near the center of the volcanic cone.The experiment in this study is the first volcano muon tomography study performed in China.Our work provides an important reference for future research.

Structural and Piezoelectric Properties of Sr_(0.6)Ba_(0.4)Nb_2O_6 Micro-rods Synthesized by Molten-Salt Method

Sr0.6 Ba0.4 Nb2 O6 micro-rods are prepared by the molten-salt method with K2 SO4,KCl-K2 SO4,and KCl as fluxes.It reveals that the Sr0.6 Ba0.4 Nb2 O6 synthesized with KCl as a flux exhibits a single phase with tetragonal tungsten bronze structure.The measurement of X-ray diffraction indicates that the Sr0.6 Ba0.4 Nb2 O6 micro-rods synthesized at 1 300℃are anisotropic.The morphology of the powers is examined by transmission electron microscope.It reveals that the length-diameter ratio of Sr0.6 Ba0.4 Nb2 O6 micro-rods increases with increasing annealing temperature from 900℃to 1 300℃.At 1 300℃,the rod possesses a large length-diameter ratio of 8∶1.Moreover,the analysis of the piezoelectric properties of single micro-rods using apiezo-response force microscope indicates that the domains of the material are arranged along its radial direction.

Full Metal Species Quantification of Metal Supported Catalysts Through Massive TEM Images Recognition

For a practical high-loading single-atom catalyst,it is prone to forming diverse metal species owing to either the synthesis inhomogeneity or the reaction induced aggregation.The diversity of this metal species challenges the discerning about the contributions of specific metal species to the catalytic performance,and thus hampers the rational catalyst design.In this paper,a distinct solution of dispersion analysis based on transmission electron microscopy imaging specialized for metal-supported catalysts has been proposed in the capability of full-metal-species quantification(FMSQ)from single atoms to nanoparticles,including dispersion densities,shape geometry,and crystallographic surface exposure.This solution integrates two image-recognition algorithms including the electron microscopy-based atom recognition statistics(EMARS)for single atoms and U-Net type deep learning network for nanoparticles in different shapes.When applied to the C_(3)N_(4)-and nitrogen-doped carbon-supported catalysts,the FMSQ method successfully identifies the specific activity contributions of Au single atoms and particles in butadiene hydrogenation,which presents remarkable variation with the metal species constitution.This work demonstrates a promising value of our FMSQ strategy for identifying the activity origin of heterogeneous catalysis.