Editorial: Special issue on “Internet of things and edge computing in the new 5G era”

1.Introduction Recently,edge computing has evolved from concept to early-stage implementations because new use cases require a more remote computing and networking approach than a conventional,fully cloud-based model.Many companies,network providers and cloud companies are testing and launching early commercial products in the developed markets of the United States,Europe,China and Asia Pacific.Although progress in edge trials continues,key issues remain to be resolved around the most viable location of the edge,the scale of the required edge investment,and the actual business models.

Effect of porous surface layer on wave propagation in elastic cylinder immersed in fluid

To study the damage to an elastic cylinder immersed in fluid, a model of an elastic cylinder wrapped with a porous medium immersed in fluid is designed. This structure can both identify the properties of guided waves in a more practical model and address the relationship between the cylinder damage degree and the surface and surrounding medium. The principal motivation is to perform a detailed quantitative analysis of the longitudinal mode and flexural mode in an elastic cylinder wrapped with a porous medium immersed in fluid. The frequency equations for the propagation of waves are derived each for a pervious surface and an impervious surface by employing Biot theory. The influences of the various parameters of the porous medium wrapping layer on the phase velocity and attenuation are discussed. The results show that the influences of porosity on the dispersion curves of guided waves are much more significant than those of thickness,whereas the phase velocity is independent of the static permeability. There is an apparent “mode switching” between the two low-order modes. The characteristics of attenuation are in good agreement with the results from the dispersion curves.This work can support future studies for optimizing the theory on detecting the damage to cylinder or pipeline.

Surface lattice resonance of circular nano-array integrated on optical fiber tips

As metallic nanoparticles are arranged to form a 2D periodic nano-array,the coupling of the localized surface plasmonic resonance(LSPR)results in the well-known phenomenon of surface lattice resonances(SLRs).We theoretically investigate the SLR effect of the circular nano-array fabricated on the fiber tips.The difference between the 2D periodic and circular periodic arrays results in different resonant characteristics.For both structures,the resonant peaks due to the SLRs shift continuously as the array structures are adjusted.For some specific arrangements,the circular nano-array may generate a single sharp resonant peak with extremely high enhancement,which originates from the collective coupling of the whole array.More interestingly,the spatial pattern of the vector near-field corresponding to the sharp peak is independent of the polarization state of the incidence,facilitating its excitation and regulation.This finding may be helpful for designing multifunctional all-fiber devices.

Capacity of a Class of Dirty-Tape Gaussian Orthogonal Relay Channel with State Information at the Source and Relay

A Gaussian channel with additive interference that is causally known to the transmitter is called a Dirty-Tape Channel(DTC).In this paper,we consider a state-dependent dirty-tape Gaussian relay channel with orthogonal channels from the source to the relay and from the source and relay to the destination.The orthogonal channels are corrupted by two independent additive interferences causally known to both the source and relay.The lower and upper bounds of the channel capacity are established.The lower bound is obtained by employing superposition coding at the source,Partial Decode-and-Forward(PDF)relaying at the relay,and a strategy similar to that used by Shannon at the source and relay.The explicit capacity is characterised when the power of the relay is sufficiently large.Finally,several numerical examples are provided to illustrate the impact of additive interferences and the role of the relay in information transmission and in removing the interference.

Reversible Watermarking Method with Low Distortion for the Secure Transmission of Medical Images

In telemedicine,the realization of reversible watermarking through information security is an emerging research field.However,adding watermarks hinders the distribution of pixels in the cover image because it creates distortions(which lead to an increase in the detection probability).In this article,we introduce a reversible watermarking method that can transmit medical images with minimal distortion and high security.The proposed method selects two adjacent gray pixels whose least significant bit(LSB)is different from the relevant message bit and then calculates the distortion degree.We use the LSB pairing method to embed the secret matrix of patient record into the cover image and exchange pixel values.Experimental results show that the designed method is robust to different attacks and has a high PSNR(peak signal-to-noise ratio)value.The MRI image quality and imperceptibility are verified by embedding a secret matrix of up to 262,688 bits to achieve an average PSNR of 51.657 dB.In addition,the proposed algorithm is tested against the latest technology on standard images,and it is found that the average PSNR of our proposed reversible watermarking technology is higher(i.e.,51.71 dB).Numerical results show that the algorithm can be extended to normal images and medical images.

Conversion of adverse data corpus to shrewd output using sampling metrics

An imbalanced dataset is commonly found in at least one class,which are typically exceeded by the other ones.A machine learning algorithm(classifier)trained with an imbalanced dataset predicts the majority class(frequently occurring)more than the other minority classes(rarely occurring).Training with an imbalanced dataset poses challenges for classifiers;however,applying suitable techniques for reducing class imbalance issues can enhance classifiers’performance.In this study,we consider an imbalanced dataset from an educational context.Initially,we examine all shortcomings regarding the classification of an imbalanced dataset.Then,we apply data-level algorithms for class balancing and compare the performance of classifiers.The performance of the classifiers is measured using the underlying information in their confusion matrices,such as accuracy,precision,recall,and F measure.The results show that classification with an imbalanced dataset may produce high accuracy but low precision and recall for the minority class.The analysis confirms that undersampling and oversampling are effective for balancing datasets,but the latter dominates.

Effect of viscosity on stability and accuracy of the two-component lattice Boltzmann method with a multiple-relaxation-time collision operator investigated by the acoustic attenuation model

A two-component lattice Boltzmann method(LBM) with a multiple-relaxation-time(MRT) collision operator is presented to improve the numerical stability of the single relaxation time(SRT) model. The macroscopic and the momentum conservation equations can be retrieved through the Chapman–Enskog(C-E) expansion analysis. The equilibrium moment with the diffusion term is calculated, a diffusion phenomenon is simulated by utilizing the developed model, and the numerical stability is verified. Furthermore, the binary mixture channel model is designed to simulate the sound attenuation phenomenon, and the obtained simulation results are found to be consistent with the analytical solutions. The sound attenuation model is used to study the numerical stability and calculation accuracy of the LBM model. The simulation results show the stability and accuracy of the MRT model and the SRT model under different viscosity conditions. Finally,we study the influence of the error between the macroscopic equation of the MRT model and the standard incompressible Navier–Stokes equation on the calculation accuracy of the model to demonstrate the general applicability of the conclusions drawn by the sound attenuation model in the present study.

Underwater Data-Driven Positioning Estimation Using Local Spatiotemporal Nonlinear Correlation

Dear Editor,A global and local canonical correlation analysis(GLCCA)based on data-driven is presented for underwater positioning.Underwater positioning technology can help the underwater targets move predetermined destinations for specific tasks[1].Since using different sensor,underwater positioning can be divided into three types:inertial navigation,hydroacoustic positioning and geophysical navigation.

Pseudopotential multi-relaxation-time lattice Boltzmann model for cavitation bubble collapse with high density ratio

The dynamics of the cavitation bubble collapse is a fundamental issue for the bubble collapse application and prevention. In the present work, the modified forcing scheme for the pseudopotential multi-relaxation-time lattice Boltzmann model developed by Li Q et al. [ Li Q, Luo K H and Li X J 2013 Phys. Rev. E 87 053301] is adopted to develop a cavitation bubble collapse model. In the respects of coexistence curves and Laplace law verification, the improved pseudopotential multi-relaxation-time lattice Boltzmann model is investigated. It is found that the thermodynamic consistency and surface tension are independent of kinematic viscosity. By homogeneous and heterogeneous cavitation simulation, the ability of the present model to describe the cavitation bubble development as well as the cavitation inception is verified. The bubble collapse between two parallel walls is simulated. The dynamic process of a collapsing bubble is consistent with the results from experiments and simulations by other numerical methods. It is demonstrated that the present pseudopotential multirelaxation-time lattice Boltzmann model is applicable and efficient, and the lattice Boltzmann method is an alternative tool for collapsing bubble modeling.

Electric characteristic and cavitation bubble dynamics using underwater pulsed discharge

Underwater pulsed discharge is widely applied in medicine, machining, and material modification.The induced cavitation bubble and subsequent cavitation collapse are considered the major motivations behind these applications. This paper presents an underwater pulsed discharge system.The experimental setup is established to induce and investigate the cavitation bubble assisted with a high-speed camera. Three aspects, including the characteristic of the discharge with different applied voltages and conductivities, the evolution of the cavitation bubble profile, and the energy efficiency of cavitation bubble inducing, are investigated, respectively. Especially, the mechanism of pre-discharge time delay in the low field intensity case is explained using the Joule heat effect.The results show the validity of the underwater pulsed discharger and experimental setup. The present underwater pulsed discharger is proved to be a simple, portable, and easy-to-implement device for the investigation of cavitation bubble dynamics.

Highly twisted M-line of a vortex beam due to the coupling of ultrahigh-order modes

In modern optics, particular interest is devoted to the phase singularities that yield complicated and twisted phase structures by photons carrying optical angular momentum.In this paper, the traditional M-line method is applied to a vortex beam(VB) by a symmetric metal cladding waveguide chip, which can host numerous oscillating guided modes via free space coupling.These ultrahigh-order modes(UOMs) result in high angular resolution due to the high finesse of the resonant chip.Experiments show that the reflected pattern of a VB can be divided into a series of inner and outer rings, whilst both of them are highly distorted by the M-lines due to the UOMs’ leakage.Taking the distribution of the energy flux into account, a simple ray-optics-based model is proposed to simulate the reflected pattern by calculating the local incident angle over the cross section of the beam.The theoretical simulations fit well with the experimental results, and the proposed scheme may enable new applications in imaging and sensing of complicated phase structures.

Generative adversarial network-based atmospheric scattering model for image dehazing

This paper presents a trainable Generative Adversarial Network(GAN)-based end-to-end system for image dehazing,which is named the DehazeGAN.DehazeGAN can be used for edge computing-based applications,such as roadside monitoring.It adopts two networks:one is generator(G),and the other is discriminator(D).The G adopts the U-Net architecture,whose layers are particularly designed to incorporate the atmospheric scattering model of image dehazing.By using a reformulated atmospheric scattering model,the weights of the generator network are initialized by the coarse transmission map,and the biases are adaptively adjusted by using the previous round's trained weights.Since the details may be blurry after the fog is removed,the contrast loss is added to enhance the visibility actively.Aside from the typical GAN adversarial loss,the pixel-wise Mean Square Error(MSE)loss,the contrast loss and the dark channel loss are introduced into the generator loss function.Extensive experiments on benchmark images,the results of which are compared with those of several state-of-the-art methods,demonstrate that the proposed DehazeGAN performs better and is more effective.

Image Denoising with Adaptive Weighted Graph Filtering

Graph filtering,which is founded on the theory of graph signal processing,is proved as a useful tool for image denoising.Most graph filtering methods focus on learning an ideal lowpass filter to remove noise,where clean images are restored from noisy ones by retaining the image components in low graph frequency bands.However,this lowpass filter has limited ability to separate the low-frequency noise from clean images such that it makes the denoising procedure less effective.To address this issue,we propose an adaptive weighted graph filtering(AWGF)method to replace the design of traditional ideal lowpass filter.In detail,we reassess the existing low-rank denoising method with adaptive regularizer learning(ARLLR)from the view of graph filtering.A shrinkage approach subsequently is presented on the graph frequency domain,where the components of noisy image are adaptively decreased in each band by calculating their component significances.As a result,it makes the proposed graph filtering more explainable and suitable for denoising.Meanwhile,we demonstrate a graph filter under the constraint of subspace representation is employed in the ARLLR method.Therefore,ARLLR can be treated as a special form of graph filtering.It not only enriches the theory of graph filtering,but also builds a bridge from the low-rank methods to the graph filtering methods.In the experiments,we perform the AWGF method with a graph filter generated by the classical graph Laplacian matrix.The results show our method can achieve a comparable denoising performance with several state-of-the-art denoising methods.

Binaural Speech Separation Algorithm Based on Long and Short Time Memory Networks

Speaker separation in complex acoustic environment is one of challenging tasks in speech separation.In practice,speakers are very often unmoving or moving slowly in normal communication.In this case,the spatial features among the consecutive speech frames become highly correlated such that it is helpful for speaker separation by providing additional spatial information.To fully exploit this information,we design a separation system on Recurrent Neural Network(RNN)with long short-term memory(LSTM)which effectively learns the temporal dynamics of spatial features.In detail,a LSTM-based speaker separation algorithm is proposed to extract the spatial features in each time-frequency(TF)unit and form the corresponding feature vector.Then,we treat speaker separation as a supervised learning problem,where a modified ideal ratio mask(IRM)is defined as the training function during LSTM learning.Simulations show that the proposed system achieves attractive separation performance in noisy and reverberant environments.Specifically,during the untrained acoustic test with limited priors,e.g.,unmatched signal to noise ratio(SNR)and reverberation,the proposed LSTM based algorithm can still outperforms the existing DNN based method in the measures of PESQ and STOI.It indicates our method is more robust in untrained conditions.

Modeling for Collapsing Cavitation Bubble near Rough Solid Wall by Mulit-Relaxation-Time Pseudopotential Lattice Boltzmann Model

Cavitation bubble collapse near rough solid wall is modeled by the multi-relaxation-time (MRT) pseudopotential lattice Boltzmann (LB) model. The modified forcing scheme, which can achieve LB model’s thermodynamic consistency by tuning a parameter related with the particle interaction range, is adopted to achieve desired stability and density ratio. The bubble collapse near rough solid wall was simulated by the improved MRT pseudopotential LB model. The mechanism of bubble collapse is studied by investigating the bubble profiles, pressure field and velocity field evolution. The eroding effects of collapsing bubble are analyzed in details. It is found that the process and the effect of the interaction between bubble collapse and rough solid wall are affected seriously by the geometry of solid boundary. At the same time, it demonstrates that the MRT pseudopotential LB model is a potential tool for the investigation of the interaction mechanism between the collapsing bubble and complex geometry boundary.

Guided Intra-Patch Smoothing Graph Filtering for Single-Image Denoising

Graph filtering is an important part of graph signal processing and a useful tool for image denoising.Existing graph filtering methods,such as adaptive weighted graph filtering(AWGF),focus on coefficient shrinkage strategies in a graph-frequency domain.However,they seldom consider the image attributes in their graph-filtering procedure.Consequently,the denoising performance of graph filtering is barely comparable with that of other state-of-the-art denoising methods.To fully exploit the image attributes,we propose a guided intra-patch smoothing AWGF(AWGF-GPS)method for single-image denoising.Unlike AWGF,which employs graph topology on patches,AWGF-GPS learns the topology of superpixels by introducing the pixel smoothing attribute of a patch.This operation forces the restored pixels to smoothly evolve in local areas,where both intra-and inter-patch relationships of the image are utilized during patch restoration.Meanwhile,a guided-patch regularizer is incorporated into AWGF-GPS.The guided patch is obtained in advance using a maximum-a-posteriori probability estimator.Because the guided patch is considered as a sketch of a denoised patch,AWGF-GPS can effectively supervise patch restoration during graph filtering to increase the reliability of the denoised patch.Experiments demonstrate that the AWGF-GPS method suitably rebuilds denoising images.It outperforms most state-of-the-art single-image denoising methods and is competitive with certain deep-learning methods.In particular,it has the advantage of managing images with significant noise.

Simple Raman scattering sensor integrated with a metallic planar optical waveguide: effective modulation via minor structural adjustment

We report experimental realization of Raman spectra enhancement of copper phthalocyanine, using an on-chip metallic planar waveguide of the sub-millimeter scale. The oscillating ultrahigh order modes excited by the direct coupling method yield high optical intensity at resonance, which is different from the conventional strategy to create localized ＂hot spots.＂ The observed excitation efficiency of the Raman signal is significantly enhanced,owing to the high Q factor of the resonant cavity. Furthermore, effective modulation of the Raman intensity is available by adjusting the polymethyl methacrylate（PMMA） thickness in the guiding layer, i.e., by tuning the light–matter interaction length. A large modulation depth is verified through the fact that 10 times variation in the enhancement factor is observed in the experiment as the PMMA thickness varies from 7 to 23 μm.