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 ...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.展开更多
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 abil...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.展开更多
If the degree distribution is chosen carefully, the irregular low-density parity-check (LDPC) codes can outperform the regular ones. An image transmission system is proposed by combining regular and irregular LDPC cod...If the degree distribution is chosen carefully, the irregular low-density parity-check (LDPC) codes can outperform the regular ones. An image transmission system is proposed by combining regular and irregular LDPC codes with 16QAM/64QAM modulation to improve both efficiency and reliability. Simulaton results show that LDPC codes are good coding schemes over fading channel in image communication with lower system complexity. More over, irregular codes can obtain a code gain of about 0.7 dB compared with regular ones when BER is 10 -4. So the irregular LDPC codes are more suitable for image transmission than the regular codes.展开更多
The act of transmitting photos via the Internet has become a routine and significant activity.Enhancing the security measures to safeguard these images from counterfeiting and modifications is a critical domain that c...The act of transmitting photos via the Internet has become a routine and significant activity.Enhancing the security measures to safeguard these images from counterfeiting and modifications is a critical domain that can still be further enhanced.This study presents a system that employs a range of approaches and algorithms to ensure the security of transmitted venous images.The main goal of this work is to create a very effective system for compressing individual biometrics in order to improve the overall accuracy and security of digital photographs by means of image compression.This paper introduces a content-based image authentication mechanism that is suitable for usage across an untrusted network and resistant to data loss during transmission.By employing scale attributes and a key-dependent parametric Long Short-Term Memory(LSTM),it is feasible to improve the resilience of digital signatures against image deterioration and strengthen their security against malicious actions.Furthermore,the successful implementation of transmitting biometric data in a compressed format over a wireless network has been accomplished.For applications involving the transmission and sharing of images across a network.The suggested technique utilizes the scalability of a structural digital signature to attain a satisfactory equilibrium between security and picture transfer.An effective adaptive compression strategy was created to lengthen the overall lifetime of the network by sharing the processing of responsibilities.This scheme ensures a large reduction in computational and energy requirements while minimizing image quality loss.This approach employs multi-scale characteristics to improve the resistance of signatures against image deterioration.The proposed system attained a Gaussian noise value of 98%and a rotation accuracy surpassing 99%.展开更多
With the rapid development of artificial intelligence and the widespread use of the Internet of Things, semantic communication, as an emerging communication paradigm, has been attracting great interest. Taking image t...With the rapid development of artificial intelligence and the widespread use of the Internet of Things, semantic communication, as an emerging communication paradigm, has been attracting great interest. Taking image transmission as an example, from the semantic communication's view, not all pixels in the images are equally important for certain receivers. The existing semantic communication systems directly perform semantic encoding and decoding on the whole image, in which the region of interest cannot be identified. In this paper, we propose a novel semantic communication system for image transmission that can distinguish between Regions Of Interest (ROI) and Regions Of Non-Interest (RONI) based on semantic segmentation, where a semantic segmentation algorithm is used to classify each pixel of the image and distinguish ROI and RONI. The system also enables high-quality transmission of ROI with lower communication overheads by transmissions through different semantic communication networks with different bandwidth requirements. An improved metric θPSNR is proposed to evaluate the transmission accuracy of the novel semantic transmission network. Experimental results show that our proposed system achieves a significant performance improvement compared with existing approaches, namely, existing semantic communication approaches and the conventional approach without semantics.展开更多
In this paper, we propose a novel Unequal Error Protection (UEP) scheme with two levels for image transmission using Multilevel Codes (MLC). By providing the best protection for the most important data, the final reco...In this paper, we propose a novel Unequal Error Protection (UEP) scheme with two levels for image transmission using Multilevel Codes (MLC). By providing the best protection for the most important data, the final recovered image quality is remarkably improved both in visual effect and in Peak Signal to Noise power Ratio (PSNR) performance.展开更多
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 cont...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.展开更多
Images and videos provide a wealth of information for people in production and life.Although most digital information is transmitted via optical fiber,the image acquisition and transmission processes still rely heavil...Images and videos provide a wealth of information for people in production and life.Although most digital information is transmitted via optical fiber,the image acquisition and transmission processes still rely heavily on electronic circuits.The development of all-optical transmission networks and optical computing frameworks has pointed to the direction for the next generation of data transmission and information processing.Here,we propose a high-speed,low-cost,multiplexed parallel and one-piece all-fiber architecture for image acquisition,encoding,and transmission,called the Multicore Fiber Acquisition and Transmission Image System(MFAT).Based on different spatial and modal channels of the multicore fiber,fiber-coupled self-encoding,and digital aperture decoding technology,scenes can be observed directly from up to 1 km away.The expansion of capacity provides the possibility of parallel coded transmission of multimodal high-quality data.MFAT requires no additional signal transmitting and receiving equipment.The all-fiber processing saves the time traditionally spent on signal conversion and image pre-processing(compression,encoding,and modulation).Additionally,it provides an effective solution for 2D information acquisition and transmission tasks in extreme environments such as high temperatures and electromagnetic interference.展开更多
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 accordi...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.展开更多
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 fo...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.展开更多
<span style="font-family:Verdana;">In a context marked by the proliferation of smartphones and multimedia applications, the processing and transmission of images </span><span style="font-...<span style="font-family:Verdana;">In a context marked by the proliferation of smartphones and multimedia applications, the processing and transmission of images </span><span style="font-family:Verdana;">ha</span><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;">ve</span></span></span><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;"> become a real problem. Image compression </span></span></span><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;">is</span></span></span><span><span><span style="font-family:;" "=""><span style="font-family:Verdana;"> 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 </span><span style="font-family:Verdana;">information</span><span style="font-family:Verdana;"> on its pixels that are transmitted progressively. We consider this transmission as a </span><span style="font-family:Verdana;">dynamical</span><span style="font-family:Verdana;"> process, where the sender </span><span style="font-family:Verdana;">push</span></span></span></span><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;">es</span></span></span><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;"> 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 </span></span></span><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;">of</span></span></span><span><span><span style="font-family:;" "=""><span style="font-family:Verdana;"> recursive inference of the not yet received layers belonging to a sequence of bitplanes. After empirical analysis, we estimate </span><span style="font-family:Verdana;">parameters</span><span style="font-family:Verdana;"> 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 ha</span></span></span></span><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;">ve </span></span></span><span><span><span style="font-family:;" "=""><span style="font-family:Verdana;">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. </span><span style="font-family:Verdana;">A high</span><span style="font-family:Verdana;"> 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 </span><span style="font-family:Verdana;">number</span><span style="font-family:Verdana;"> of received layers. However, we found that the time of image treatment might be large starting from </span><span style="font-family:Verdana;">a image</span><span style="font-family:Verdana;"> resolution of 1024 * 1024. Hence, we recommend </span><span style="font-family:Verdana;">FRM-KF</span><span style="font-family:Verdana;"> method for resolutions less or equal to 512 * 512. A statistical comparative analysis reveals that FRM-KF is competitive and suitable to be implemented </span><span style="font-family:Verdana;">on</span><span style="font-family:Verdana;"> limited </span><span style="font-family:Verdana;">resource</span><span style="font-family:Verdana;"> environments.</span></span></span></span>展开更多
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 o...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.展开更多
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 configuratio...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.展开更多
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...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.展开更多
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 tetragon...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.展开更多
Traditional one-way imaging methods become invalid when a target object is completely hidden behind scattering media. In this case, it has been much more challenging, since the light wave is distorted twice.To solve t...Traditional one-way imaging methods become invalid when a target object is completely hidden behind scattering media. In this case, it has been much more challenging, since the light wave is distorted twice.To solve this problem, we propose an imaging method, so-called round-trip imaging, based on the optical transmission matrix of the scattering medium. We show that the object can be recovered directly from the distorted output wave, where no scanning is required during the imaging process. We predict that this method might improve the imaging speed and have potential application for real-time imaging.展开更多
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 challe...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.展开更多
基金the Deanship of Scientific Research,Princess Nourah bint Abdulrahman University,through the Program of Research Project Funding After Publication,Grant No.(44-PRFA-P-131).
文摘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.
基金supported in part by the Tianjin Technology Innovation Guidance Special Fund Project under Grant No.21YDTPJC00850in part by the National Natural Science Foundation of China under Grant No.41906161in part by the Natural Science Foundation of Tianjin under Grant No.21JCQNJC00650。
文摘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.
文摘If the degree distribution is chosen carefully, the irregular low-density parity-check (LDPC) codes can outperform the regular ones. An image transmission system is proposed by combining regular and irregular LDPC codes with 16QAM/64QAM modulation to improve both efficiency and reliability. Simulaton results show that LDPC codes are good coding schemes over fading channel in image communication with lower system complexity. More over, irregular codes can obtain a code gain of about 0.7 dB compared with regular ones when BER is 10 -4. So the irregular LDPC codes are more suitable for image transmission than the regular codes.
文摘The act of transmitting photos via the Internet has become a routine and significant activity.Enhancing the security measures to safeguard these images from counterfeiting and modifications is a critical domain that can still be further enhanced.This study presents a system that employs a range of approaches and algorithms to ensure the security of transmitted venous images.The main goal of this work is to create a very effective system for compressing individual biometrics in order to improve the overall accuracy and security of digital photographs by means of image compression.This paper introduces a content-based image authentication mechanism that is suitable for usage across an untrusted network and resistant to data loss during transmission.By employing scale attributes and a key-dependent parametric Long Short-Term Memory(LSTM),it is feasible to improve the resilience of digital signatures against image deterioration and strengthen their security against malicious actions.Furthermore,the successful implementation of transmitting biometric data in a compressed format over a wireless network has been accomplished.For applications involving the transmission and sharing of images across a network.The suggested technique utilizes the scalability of a structural digital signature to attain a satisfactory equilibrium between security and picture transfer.An effective adaptive compression strategy was created to lengthen the overall lifetime of the network by sharing the processing of responsibilities.This scheme ensures a large reduction in computational and energy requirements while minimizing image quality loss.This approach employs multi-scale characteristics to improve the resistance of signatures against image deterioration.The proposed system attained a Gaussian noise value of 98%and a rotation accuracy surpassing 99%.
基金supported in part by collaborative research with Toyota Motor Corporation,in part by ROIS NII Open Collaborative Research under Grant 21S0601,in part by JSPS KAKENHI under Grants 20H00592,21H03424.
文摘With the rapid development of artificial intelligence and the widespread use of the Internet of Things, semantic communication, as an emerging communication paradigm, has been attracting great interest. Taking image transmission as an example, from the semantic communication's view, not all pixels in the images are equally important for certain receivers. The existing semantic communication systems directly perform semantic encoding and decoding on the whole image, in which the region of interest cannot be identified. In this paper, we propose a novel semantic communication system for image transmission that can distinguish between Regions Of Interest (ROI) and Regions Of Non-Interest (RONI) based on semantic segmentation, where a semantic segmentation algorithm is used to classify each pixel of the image and distinguish ROI and RONI. The system also enables high-quality transmission of ROI with lower communication overheads by transmissions through different semantic communication networks with different bandwidth requirements. An improved metric θPSNR is proposed to evaluate the transmission accuracy of the novel semantic transmission network. Experimental results show that our proposed system achieves a significant performance improvement compared with existing approaches, namely, existing semantic communication approaches and the conventional approach without semantics.
文摘In this paper, we propose a novel Unequal Error Protection (UEP) scheme with two levels for image transmission using Multilevel Codes (MLC). By providing the best protection for the most important data, the final recovered image quality is remarkably improved both in visual effect and in Peak Signal to Noise power Ratio (PSNR) performance.
文摘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.
文摘Images and videos provide a wealth of information for people in production and life.Although most digital information is transmitted via optical fiber,the image acquisition and transmission processes still rely heavily on electronic circuits.The development of all-optical transmission networks and optical computing frameworks has pointed to the direction for the next generation of data transmission and information processing.Here,we propose a high-speed,low-cost,multiplexed parallel and one-piece all-fiber architecture for image acquisition,encoding,and transmission,called the Multicore Fiber Acquisition and Transmission Image System(MFAT).Based on different spatial and modal channels of the multicore fiber,fiber-coupled self-encoding,and digital aperture decoding technology,scenes can be observed directly from up to 1 km away.The expansion of capacity provides the possibility of parallel coded transmission of multimodal high-quality data.MFAT requires no additional signal transmitting and receiving equipment.The all-fiber processing saves the time traditionally spent on signal conversion and image pre-processing(compression,encoding,and modulation).Additionally,it provides an effective solution for 2D information acquisition and transmission tasks in extreme environments such as high temperatures and electromagnetic interference.
基金This work was supported in part by the National Natural Science Foundation of China(62293481)in part by the Young Elite Scientists Sponsorship Program by CAST(2023QNRC001)+1 种基金in part by the National Natural Science Foundation for Young Scientists of China(62001050)in part by the Fundamental Research Funds for the Central Universities(2023RC95).
文摘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.
文摘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.
文摘<span style="font-family:Verdana;">In a context marked by the proliferation of smartphones and multimedia applications, the processing and transmission of images </span><span style="font-family:Verdana;">ha</span><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;">ve</span></span></span><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;"> become a real problem. Image compression </span></span></span><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;">is</span></span></span><span><span><span style="font-family:;" "=""><span style="font-family:Verdana;"> 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 </span><span style="font-family:Verdana;">information</span><span style="font-family:Verdana;"> on its pixels that are transmitted progressively. We consider this transmission as a </span><span style="font-family:Verdana;">dynamical</span><span style="font-family:Verdana;"> process, where the sender </span><span style="font-family:Verdana;">push</span></span></span></span><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;">es</span></span></span><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;"> 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 </span></span></span><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;">of</span></span></span><span><span><span style="font-family:;" "=""><span style="font-family:Verdana;"> recursive inference of the not yet received layers belonging to a sequence of bitplanes. After empirical analysis, we estimate </span><span style="font-family:Verdana;">parameters</span><span style="font-family:Verdana;"> 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 ha</span></span></span></span><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;">ve </span></span></span><span><span><span style="font-family:;" "=""><span style="font-family:Verdana;">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. </span><span style="font-family:Verdana;">A high</span><span style="font-family:Verdana;"> 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 </span><span style="font-family:Verdana;">number</span><span style="font-family:Verdana;"> of received layers. However, we found that the time of image treatment might be large starting from </span><span style="font-family:Verdana;">a image</span><span style="font-family:Verdana;"> resolution of 1024 * 1024. Hence, we recommend </span><span style="font-family:Verdana;">FRM-KF</span><span style="font-family:Verdana;"> method for resolutions less or equal to 512 * 512. A statistical comparative analysis reveals that FRM-KF is competitive and suitable to be implemented </span><span style="font-family:Verdana;">on</span><span style="font-family:Verdana;"> limited </span><span style="font-family:Verdana;">resource</span><span style="font-family:Verdana;"> environments.</span></span></span></span>
基金supported in part by the National Natural Science Foundation of China under Grant 61902311in part by the Japan Society for the Promotion of Science(JSPS)Grants-in-Aid for Scientific Research(KAKENHI)under Grant JP18K18044.
文摘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.
基金supported by National Natural Science Foundation of China(NSFC No.61876108)the National Key Research&Development Program of Ministry of Science and Technology of the People's Republic of China(Grant Nos.2018YFC2002300,2018YFC2002303).
文摘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.
基金supported by the National Natural Science Foundation of China(Nos.41974064,42174076 and U1865206)Young Elite Scientists Sponsorship Program by CAST(No.2019QNRC001).
文摘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.
基金supported by the National Natural Science Foundation of China(No.11475086)
文摘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.
基金supported by the National Natural Science Foundation of China(Nos.61535015,61275149,and 61275086)the Special Scientific Research Plan from Education Department of Shaanxi Provincial Government(No.16JK1083)
文摘Traditional one-way imaging methods become invalid when a target object is completely hidden behind scattering media. In this case, it has been much more challenging, since the light wave is distorted twice.To solve this problem, we propose an imaging method, so-called round-trip imaging, based on the optical transmission matrix of the scattering medium. We show that the object can be recovered directly from the distorted output wave, where no scanning is required during the imaging process. We predict that this method might improve the imaging speed and have potential application for real-time imaging.
基金National Natural Science Foundation of China(Nos.22072150,22172168)CAS Project for Young Scientists in Basic Research,China(No.YSBR-022)+1 种基金CAS Youth Innovation Promotion Association,China(No.2019190)Innovative Research Funds of Dalian Institute of Chemical Physics,China(No.DICPI202013).
文摘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.