This article focuses on determining how to double diffusion affects the non-Newtonian fourth-grade nanofluids peristaltic motion within a symmetrical vertical elastic channel supported by a suitable porous medium as well as, concentrating on the impact of a few significant actual peculiarities on the development of the peristaltic liquid, such as rotation, initial pressure, non-linear thermal radiation, heat generation/absorption in the presence of viscous dissipation and joule heating with noting that the fluid inside the channel is subject to an externally induced magnetic field, giving it electromagnetic properties. Moreover, the constraints of the long-wavelength approximation and neglecting the wave number along with the low Reynolds number have been used to transform the nonlinear partial differential equations in two dimensions into a system of nonlinear ordinary differential equations in one dimension, which serve as the basic governing equations for fluid motion. The suitable numerical method for solving the new system of ordinary differential equations is the Runge–Kutta fourth-order numerical method with the shooting technique using the code MATLAB program. Using this code, a 2D and 3D graphical analysis was done to show how each physical parameter affected the distributions of axial velocity, temperature, nanoparticle volume fraction, solutal concentration, pressure gradients, induced magnetic field, magnetic forces, and finally the trapping phenomenon. Under the influence of rotation  Peristaltic pumping of fluids through flexible tubes is defined as the movement of fluids within these channels utilizing pressure gradients that push them making them move along these tubes, such as the movement of blood within the veins and arteries, the movement of food within the flexible esophageal channel, as well as the movement of food within the small and large intestines. On the other hand, urine moves from the kidney to the urinary bladder through the peristaltic pumping of urine through the flexible ureteral canal, as well as the movement of semen from the testicle to the outside through peristaltic pumping through the canal of the carrier vessel and many other forms of peristaltic movement within living systems. Akram and Razia2 concentrated on the crossover impacts of warm and focus convection on the peristaltic stream of fourth-grade nanofluids in an inclined tapered channel. According to the findings of this study, as the Brownian motion coefficient values rise, so does the fluid's temperature distribution. Kothandapani et al.3 investigated magnetic field impact on the peristaltic flow of a fourth-grade fluid in a tapered asymmetric channel. The impact of the magnetic field on the axial velocity distribution is nonuniform and changes among increment and reduction, as indicated by the aftereffects of this study. Abdulhadi and Ahmed4 examined the peristaltic flow of Walters-B fluid through a porous medium in a tapered asymmetric channel in the present magnetic field. It was found through this study that the temperature distribution turned into a rising function affected by the thermophoresis coefficient and the Dufour coefficient while the solute concentration distribution became a decreasing function under the influence of the Brownian motion coefficient and the Soret coefficient. Asha and Deepa5 analyzed the Joule heating and magnetic field effect on the peristaltic flow of a third-grade fluid in an asymmetric channel. Ranjit et al.6 illustrated Joule heating and zeta potential effect on peristaltic blood flow through porous micro-vessels altered by electrohydrodynamics. It was found from this study that the effect of the Brinkman number on the temperature distribution was positive.The component of peristaltic pumping or the peristaltic movement of liquids inside living frameworks is perhaps the main strategy that certainly stands out to analysts in the field of biomechanics for its extraordinary significance and the significant job it plays inside the body of the creature. Latham7 used a numerical approach to solve the MHD peristaltic flow of a non-Newtonian power-law nanofluid through a non-Darcy-porous-medium-inside-non-uniform inclined channel. This work found that the Darcy number and Reynolds number had a negative effect on the axial velocity distribution. Sayed et al.8 studied the peristaltic transport of nano non-Newtonian fluid with heat transfer through an inclined asymmetric duct with slip and convective boundary conditions. It was observed that the axial velocity distribution changed irregularly between decreasing and increasing under the influence of heat Grashof number one of the outcomes of that study. Ali et al.9 investigated the impact of the Hartmann boundary layer in peristaltic flow for viscoelastic fluid. Nadeem et al.10 provided an elliptic cross-sectional illustration of the peristaltic flow of a heated Jeffrey fluid within a duct that study revealed that an increase in the axial velocity distribution was caused by the mean flow rate parameter. Akram and Saleem11 discussed the analysis of heating effects and different waveforms on the peristaltic flow of Carreau fluid in a rectangular duct. It was found that the effect of the mean flow rate coefficient on the axial velocity distribution is positive, one of the outcomes of that study. Abbas et al.12 debated the peristaltic flow of Casson fluid with heat and mass transfer in the presence of Soret and Dufour effects with Hall currents through a non-Darcy porous medium inside a vertical channel. According to the findings of this study, both the temperature distribution and the nanoparticles volume fraction distribution were affected negatively by the Grashof thermal number. A peristaltic blood flow of non-Newtonian Carreau fluid with heat transfer phenomenon through a curved channel was discussed by Tanveer et al.13. The results of this study indicate that the axial velocity distribution is positively influenced by the Darcy number and the heat Grashof number. He and Mostapha14 agitated the significance of the Hall current and Joule heating impacts on Darcy–Forchheimer's peristaltic flow of a Rabinowitsch fluid through a tapered tube.It is realized that non-Newtonian liquids are one of the kinds of liquids that have an extraordinary nature, which is that they consolidate consistency and flexibility in their properties together. The viscosity of non-Newtonian fluids, on the other hand, varies depending on other factors like pressure and temperature. As a result, the viscosity of each model of non-Newtonian fluid is unique because the relationship between stress and strain for these fluids is nonlinear. In actuality, non-Newtonian fluids respond to external factors in a significant way. At the point when this liquid is exposed to a bunch of outside drives, it abandons a fluid substance to an adaptable, rubbery strong and when these powers are debilitated, it gets back to what it was previously. Non-Newtonian liquids assume a significant part in most significant applications that rely upon their properties including non-Newtonian liquid erosion decrease, oil-pipeline grating decrease, surfactant applications to huge scope warming and cooling frameworks, and stream tracers. It is additionally utilized in numerous enterprises, for example, paints, toothpaste, glue, lubricants, car oils, and foodstuffs such as ketchup and others. As a result, numerous researchers attempt to investigate the flow issues of various non-Newtonian fluids; El-Dabe et al.15 demonstrated the hall currents effect on squeezing the flow of non-Newtonian nanofluid through a porous medium between two parallel plates. Mccash et al.16 elucidated entropy analysis of the peristaltic flow of a hybrid nanofluid inside an elliptic duct with sinusoidally advancing boundaries. Ahmed et al.17 explained the effects of nonlinear thermal radiation, convective boundary conditions, and heat generation/absorption on magnetohydrodynamic Maxwell nanofluid flow over a stretching surface through a porous medium. It was found from this study that the effects of the magnetic field coefficient and Darcy number on the velocity distribution is negative. Mohamed et al.18 analyzed the nonlinear thermal radiation and heat generation/absorption effect on MHD Jeffrey nanofluid flow over a stretching sheet through a porous medium. This study included that the nanoparticles volume fraction distribution was an increasing function under the influence of the temperature ratio coefficient, while it became a decreasing function under the influence of the Brownian motion coefficient. The influences of thermal radiation and slip conditions on MHD Casson nanofluid flow over a stretching surface through a porous medium was studied by Mohamed et al.19. The temperature distribution in this study has become in a state of continuous increase under the influence of both the absorption and heat generation coefficient, as well as the thermal transfer coefficient. Bouslimi et al.20 dissected MHD Williamson nanofluid flow over a stretching sheet through a porous medium under the effects of Joule heating, nonlinear thermal radiation, heat generation/absorption, and chemical reaction. The velocity distribution decreases with an increase in the magnetic field coefficient, while the temperature distribution increases with an increase in this parameter, and this is one of the outcomes of that study. Rashed and Ahmed21 studied the peristaltic flow of dusty nanofluids in curved channels. This study proved that the heat Grashof number affects positively on the temperature distribution and negatively on the nanoparticles volume fraction distribution, while the effect of the nanoparticles Grashof number is the opposite. Ahmed and Rashed22 investigated magnetohydrodynamic dusty hybrid nanofluid peristaltic flow in curved channels. Mohamed et al.23 analyzed the thermal radiation and MHD effects on the free convective flow of a polar fluid through a porous medium in the presence of internal heat generation and chemical reaction. Abd-Alla et al.24 studied the effects of rotation and initial stress on peristaltic transport of fourth-grade fluid with heat transfer and induced magnetic field. The study shows that the axial velocity distribution decreases with an increase in the rotation coefficient, and increases with the enhancement in the heat generation and absorption coefficient. Abd-Alla et al.25 investigated the influence of heat and mass transfer, initial stress, and radially varying magnetic fields on the peristaltic flow in an annulus with a gravity field. Abd-Alla et al.26 illustrated the effect of rotation on the peristaltic flow of a micropolar fluid through a porous medium with an external magnetic field. Abd-Alla and Abo-Dahab27 elaborated on a magnetic field and rotation effects on the peristaltic transport of a Jeffrey fluid in an asymmetric channel. Abd-Alla et al.28 illustrated the peristaltic flow of a Jeffrey fluid under the effect of a radially varying magnetic field in a tube with an endoscope.A substance with pores (voids) is known as a porous medium or porous material. The material's skeletal component is frequently referred to as the matrix or frame. The pores are normally loaded up with a liquid (fluid or gas). The skeletal material is typically strong, however, structures like froths are many times additionally helpfully investigated utilizing the idea of permeable media. A permeable medium is most frequently described by its porosity. Different properties of the medium  can in some cases get from the particular properties of its constituents (strong lattice and liquid) and the media porosity and pores structure, however, such a deduction is normally complicated. The idea of porous media, which is utilized in a lot of applied science and engineering fields: filtration, mechanics , designing , geosciences , science and biophysics, material science, and so on. Liquid course through permeable media is a subject of most normal interest and has arisen in a different field of study. On the other hand, any natural and industrial areas are very interested in the flow through porous media. Models remember the progression of hydrocarbons for oil wells, groundwater courses through beds of rocks, as well as the vehicle of minerals and pollutants through the ground. One may likewise find this stream peculiarity straightforwardly significant in vaporous or fluid synergist and idle pressed bed reactors moves through separates channels, geothermal intensity the executives, softening, or cementing of double composites. Abou-zeid and Ouaf29. They are a basic fluid that is permeated with tiny metal particles called nanoparticles which are measured in nanometers. On the other hand, nanofluids play an important role in many modern technological applications as they are the most intelligent type of liquid. In the past few years, many researchers in the field of nanofluids have worked hard to understand the behavior of these fluids. From previous investigations, nanofluids have been found to possess enhanced thermophysical properties such as thermal conductivity, thermal diffusivity, viscosity, and convective heat transfer coefficients compared to those of base fluids like oil or water. Nanofluids can be used in a plethora of engineering applications ranging from use in the automotive industry to the medical arena to use in power plant cooling systems as well as computers. Ibrahim and Anbessa30 examined the three-dimensional MHD mixed convection flow of Casson nanofluid with hall and ion slip effects. Hayat et al.31 examined the impacts of slip in radiative MHD peristaltic flow of fourth-grade nanomaterial with chemical reaction. It was noted from the consequences of this study that the impact of the magnetic field coefficient on the axial velocity is negative, the temperature distribution has turned into a diminishing function affected by the non-linear thermal radiation coefficient, and the nanoparticles volume fraction diminishes with an expansion in the thermophoresis coefficient. Akram and Razia32 analyzed the hybrid effects of thermal and concentration convection on the peristaltic flow of fourth-grade nanofluids in an inclined tapered channel. A portion of the consequences of this study incorporated that the impact of the magnetic field coefficient on the axial velocity distribution was negative, and the temperature dispersion was a rising function affected by the thermophoresis coefficient. Kothandapani et al.33 discussed the effect of a magnetic field on the peristaltic flow of a fourth-grade fluid in a tapered asymmetric channel. It has been observed that the axial velocity distribution fluctuates between an increase and a decrease under the influence of the magnetic field coefficient. Haroun34 explained the non-linear peristaltic flow of a fourth-grade fluid in an inclined asymmetric channel. Akram et al.35 illustrated slip impact on double diffusion convection of magneto fourth-grade nanofluids with peristaltic propulsion through an inclined asymmetric channel. Hayat and Noreen36 investigated the peristaltic transport of fourth-grade fluid with heat transfer and an induced magnetic field. A portion of the consequences of this study showed that the heat Grashof number affects irregularly the axial velocity distribution and the induced magnetic field distribution. Mohamed et al.37 studied the MHD three-dimensional flow of a couple of stress nanofluids over a stretching sheet through a porous medium in the presence of heat generation/absorption and nonlinear thermal radiation. Abo-Dahab et al.38 discussed the Double-diffusive peristaltic MHD Sisko nanofluid flow through a porous medium in the presence of non-linear thermal radiation, heat generation/ absorption, and Joule heating. Abd-Alla et al.39 examined the rotation and initial stress effect on MHD peristaltic flow of reacting radiating fourth-grade nanofluid with viscous dissipation and Joule heating. Some results of this study showed that the distribution of axial velocity changes its effect between increase and decrease under the influence of rotation, initial stress, and magnetic field coefficient. Mohamed et al.40 analyzed the effects of nonlinear thermal radiation and heat generation/absorption on magnetohydrodynamic Carreau nanofluid flow on a nonlinear stretching surface through a porous medium. Raja et al.41 elaborated the integrated intelligent computing application for the effectiveness of Au nanoparticles coated over MWCNTs with velocity slip in curved channel peristaltic flow. Abdelhafez et al.42 illustrated the influence of an inclined magnetic field and heat and mass transfer on the peristaltic flow of blood in an asymmetric channel. Rafiq et al.43 studied the theoretical exploration of thermal transportation with Lorentz force for a fourth-grade fluid model obeying peristaltic mechanism. Abbas et al.44 investigated the rheology of peristaltic flow in a couple of stress fluids in an inclined tube. Abbas et al.45 analyzed the peristaltic transport of a Casson fluid in a non-uniform inclined tube with Roseland approximation and wall properties. Abbas and Rafiq46 examined the numerical simulation of thermal transportation with viscous dissipation for a peristaltic mechanism of micropolar-Casson fluid. Ghazanfari et al.47 sicussed the numerical study on the thermal performance of the shell and tube heat exchanger using twisted tubes and Al2O3 nanoparticles. Manh et al.48 elaborated the investigation of nanomaterial flow through non-parallel plates. Sheikholeslami et al.49 illustrated hybrid nanoparticles dispersion into water inside a porous wavy tank involving magnetic force. Awais et al.50 examined the MHD effects on ciliary-induced peristaltic flow coatings with rheological hybrid nanofluid, Ali et al.51 explained the entropy generation on MHD peristaltic flow of Cu–water nanofluid with slip conditions. Rooman et al.52 investigated the entropy generation analysis of magnetized radiative Ellis (Cu–TiO2/Engine Oil) nanofluid flow using Cattaneo–Christov heat flux model with viscous dissipation and Joule heating effects. Jameel et al.53 elaborated the statistical and entropy optimization modeling for radiative hybrid nanofluid flow with Hall effect over exponential stretching/shrinking plate. Asghar et al.54 analyzed the dual solutions of convective rotating flow of three-dimensional hybrid nanofluid across the linear stretching/shrinking sheet.Nanofluids are a new class of fluids that was discovered by Choi and EastmanThe double-diffusion of peristaltic flow of a non-Newtonian fourth-grade nanofluid through a vertically symmetric flexible channel through a porous medium was the subject of our investigation in this paper. The primary objective of this numerical study is to investigate the effects of rotation, initial stress, and induced magnetic field in the presence of non-linear thermal radiation, heat generation/absorption, and Joule heating with viscous dissipation. For the system of equations governing the peristaltic flow process, it is a set of partial differential equations that consist of the continuity equation, the momentum equation, the energy equation in addition to the solute concentration equation, the nanoparticles volume fraction equation, and the magnetic forces equation, and induced magnetic field equation. The limitations of the long-wavelength approximation were used to facilitate the solution of the aforementioned system of differential equations and thus the system of these differential equations was converted to the system of ordinary differential equations which was solved numerically using the fourth-order Runge–Kutta method with the shooting technique by MATLAB code. The effect of all important physical parameters resulting from the current study on important distributions such as velocity, temperature, solute concentration, nanoparticle volume fraction, stream function, and induced magnetic field was studied employing the graphs using the MATLAB program and the results are discussed with the physical meaning of the phenomenon.55. Consider the magnetohydrodynamic (MHD) peristaltic steady flow of an incompressible fourth-grade nanofluid through a porous medium in a vertical two-dimensional symmetric channel of uniform thickness At the outset, it should be noted that the current numerical study is represented in the peristaltic motion of fluid inside the vertical symmetrical flexible channel called a fourth-grade nanofluid, A fourth-grade fluid is defined as a category of non-Newtonian fluids with very high viscosity and describe the shear thinning and shear thickening phenomena which cannot be expressed by the classical Navier–Stokes equations, and this study is a simulation of the movement of food and liquids from the mouth to the stomach through the flexible esophageal channel, food, and liquids work on the contraction and relaxation of the esophageal channel walls, which is what happens to the fourth-grade fluid movement inside the flexible channel used in the study additionally, the study of the model's non-Newtonian fluid peristaltic motion's natural properties in general. It is worth noting that the peristaltic movement of the fluid inside the channel occurs in the presence of a suitable porous medium with permeable properties that acts as a resistance force that affects the movement of the fluid inside the channel. The porous medium contains a group of holes or voids permeated by the fluid during its movement, In the simulation or applied study, it corresponds to the membrane lining the walls of the flexible esophageal channel, as it is a semi-permeable membrane, or food can be considered as a porous medium permeated with fluids also the size of the pores of the porous medium is appropriate to the size of the nanoparticles and that the mechanism of action of the porous medium is subject to Darcy's law, seementclass2pt{minim56;The geometry of the symmetric channel can be written as followsThe velocity field 56;The governing equations of the non-Newtonian nanofluid motion can be written as follows24:56:Cauchy stress tensor 24;In magnetic fluid mechanics Maxwell’s equations in the absence of displacement current are presented as follows55Modified Darcy’s law obeys24;Now, 48;Now, the basic governing equations of MHD non-Newtonian fourth-grade nanofluid can be written mathematically as followsUsing non-dimensional quantities as follows;The stream function Substituting Eq.  in Eqs. It is important to note that the values of each  in Eqs. –13) res res\docu in Eqs. –30) is  is \docuThe boundary conditions for the dimensionless in the wave frame are given as follows;31:Now, the extra stress tensor for fourth-grade fluid can be written as followsThus, the final form of Eq.  becomes By Eqs.  and 40)40) we geThe dimensional rate of fluid flow in the laboratory frame isAnd in wave frame, the above equation is as followsBy using Eqs. , 33), a, a33), aThe time-mean flow over a period After using Eq.  the abovThe dimensionless time-mean flow n of the first-order equations, then using the bvp4c function  to solve these first-order equations. The governing system of ordinary differential equations can be written in simplest form as follows;The Runge–Kutta numerical method is one of the most important numerical analysis methods that are used in solving a system of ordinary differential equations, there are different formulas for the solution of the numerical method of Runge–Kutta method; for example, there is the Runge–Kutta method of the fourth order and also the Runge–Kutta method of the fifth order, and the most used method is the Runge–Kutta method of the fourth-order with shooting technique because it gives accurate results and is easy to use, its derivation depends on the Euler method, and the error rate in the solutions is very small and this is greatly required for the credibility of the results and also for their ease. Accordingly, the system of Eqs. –35) and and35) anditions  and 37)\documentThe system of differential Eqs. –52) has has52) hThe boundary conditions can be written as followsTaking into considerationTable The effects of the rotation entclass1pt{minimaTake a look at Figs. mentclasspt{minimaThe pressure gradient plays an important role in the movement of the fluid within the elastic channel, as it is like a machine that works to pump the fluid and its flow at different speeds. Figure The magnetic force is one of the four known forces in nature and is considered the product of the electromagnetic force, or in other words, it is the result of the electromagnetic force, and the main cause of the magnetic force is the movement of charges. For example, when two bodies contain the same charge and have the same direction of motion, a magnetic attraction force is generated between them, while if the direction of motion is opposite, then a magnetic repulsion force is generated. Accordingly, Figs. Trapping is a process that can be seen in the transfer of liquids and can be visualized by drawing streamlines, which is a characteristic of peristaltic motion. Streamlines profiles also play an important role in the study of trapping. A bolus is encapsulated by dividing a streamline when certain conditions are met, and it is carried along with the wave in the wave frame as the wave moves. This practice is referred to as trapping. As a result, the behavior of the fluid flow lines inside the flexible channel under investigation and its connection to the trapping phenomenon are depicted in some significant graphic figures as follows, Fig. 24 in the absence of a porous medium, nonlinear thermal radiation, heat generation/absorption, viscous dissipation, and Joule heating, and there is no solutal concentration equation and nanoparticle volume fraction equation with the difference in the geometric shape of the channel and the boundary conditions. In contrast, the graphs in Fig. 24 investigation of the impact of the electric Reynolds number coefficient The reader ought to focus on; the current work is due to Abdalla et al.Under the influence of the rotation Under the influence of the Darcy number The heat Grashof number The temperature distribution is a decreasing function under effects of the nonlinear thermal radiation The distribution of solutal concentration The solutal concentration The pressure gradients distribution The magnetic forces distribution The behavior of the induced magnetic field distribution The subject of this study is the double-diffusion peristaltic flow of a non-Newtonian fourth-grade nanofluid through a symmetric vertical elastic channel in the presence of a suitable porous medium. The flow process is also subject to the strength of the induced magnetic field and is affected by rotation, initial stress, nonlinear thermal radiation, heat generation/absorption, viscous dissipation, and Joule heating. The flow process is controlled by a system of nonlinear partial differential equations with boundary conditions that have been transformed into a system of nonlinear ordinary differential equations with a new form of boundary conditions using the long wavelength approximation and ignoring the wave number and the lower Reynolds number. The new system of ordinary differential equations was mathematically solved numerically using the fourth-order Runge–Kutta strategy with the shooting technique in MATLAB code. Finally, graphs were made using MATLAB software to examine the influence of the relative polymorphism of the important physical parameters due to the constant focus on the main distributions namely axial velocity, temperature, solutal concentration, nanoparticle volume fraction, magnetic forces, induced magnetic field, and streamlines profiles. The following is a list of the most important findings of the study:


Deep neural networks have made great achievements in remote sensing image analyses; however, previous studies have shown that deep neural networks exhibit incredible vulnerability to adversarial examples, which raises concerns about regional safety and production safety. In this paper, we propose an adversarial denoising method based on latent representation guidance for remote sensing image scene classification. In the training phase, we train a variational autoencoder to reconstruct the data using only the clean dataset. At test time, we first calculate the normalized mutual information between the reconstructed image using the variational autoencoder and the reference image as denoised by a discrete cosine transform. The reconstructed image is selectively utilized according to the result of the image quality assessment. Then, the latent representation of the current image is iteratively updated according to the reconstruction loss so as to gradually eliminate the influence of adversarial noise. Because the training of the denoiser only involves clean data, the proposed method is more robust against unknown adversarial noise. Experimental results on the scene classification dataset show the effectiveness of the proposed method. Furthermore, the method achieves better robust accuracy compared with state-of-the-art adversarial defense methods in image classification tasks. The development of deep learning has led to a revolution in remote sensing image (RSI) analysis. With their excellent feature extraction capabilities and end-to-end training mode, deep neural networks can provide more accurate and efficient solutions for environmental monitoring , land usAdversarial training (AT) is considered to be a very effective method of adversarial defense ,13,14. TThe above two types of methods are designed with the goal of enhancing the generalization ability of the DNN model itself. In addition, there are some methods to reduce the harmfulness of adversarial noise by modifying the input RSI, which are called adversarial preprocessing (AP). In early exploration, some studies used traditional enhancement operations  to change the visual characteristics of the image in order to blur out the adversarial noise. However, new noises or style differences may bring new challenges to deep learning models and may easily destroy the spatial features and spectral information of RSIs. Later, Gu et al.  used advIn addition, through the extensive research and continuous improvement of self-supervised representation learning, researchers have found that this technology can provide new ideas for the research on adversarial defense. Hendrycks et al.  trained In order to improve the robustness of RSI scene classification models, we propose an adversarial denoising method based on latent representation guidance. This method takes full advantage of the label-independence of self-supervised representation learning, only uses clean data to train variational autoencoders (VAEs)  in the tWe introduce self-supervised representation learning into the study of adversarial defense methods and design an adversarial denoising method. Because only clean data are used in the training phase, the proposed method is label-independent and model-independent, which is beneficial for improving the model’s defense ability against unknown adversarial noise.At test time, we use NMI to measure the quality of the reconstructed image and iteratively update its latent representation. Because the adversarial denoising operation is indirectly completed in the latent space, the proposed method has less impact on the quality and spatial information of images.We conduct attack and defense tests on various architectures of RSI scene classification datasets. The results show that the proposed method can effectively reduce the impact of adversarial noise on the model and protect the highly vulnerable RSI scene classification models in the real world.To test the performance of our method, we chose state-of-the-art adversarial defense methods in the field of computer vision for comparative experiments, and the results show that the proposed method exhibits a competitive defense performance. Furthermore, the proposed method can be combined with other adversarial defense methods as an additional plugin.In summary, we provide the following contributions:In this section, we first briefly introduce the adversarial attack methods that are involved in our experiments and then review the classic adversarial defense methods; finally, we introduce the principle and development of VAEs.Although physical adversarial attacks are not a threat in most remote sensing applications, the ultimate goal of studying adversarial attacks is to increase models’ resilience to overfitting and increase their generalization ability to complex data spaces. Most of the existing adversarial attack methods used in the field of remote sensing extend from the research results in the field of computer vision, such as the fast gradient sign method (FGSM) , projectThe FGSM method is a gradient-based adversarial attack method; it generates adversarial examples by computing the gradient of the model to the input data and by adjusting the input data according to the direction of the gradient. It is a fast and relatively simple adversarial attack method. The PGD method is an iterative gradient descent adversarial attack method; it generates adversarial examples by applying FGSM on the input data in multiple iterations. At each iteration, PGD will slightly perturb the input data within a certain range to increase the effect of the attack. The C&W method is an optimization-based adversarial attack method; by performing optimizations in the input space, it aims to find adversarial examples that maximize the objective function. This method usually has a high attack success rate but has a high computational cost. The DeepFool method is an iterative linearization adversarial attack method; it uses a linear approximation in the input space to find the direction of the smallest perturbation and slightly perturbs the input data in this direction. DeepFool aims to minimize the magnitude of the perturbation. The AutoAttack method is a comprehensive adversarial attack evaluation framework that integrates a variety of adversarial attack methods; in addition to the above four common methods, it also includes the methods of square attack, boundary attack, etc. It aims to provide users with a one-stop adversarial attack evaluation tool, which can help researchers more fully understand the weaknesses and vulnerabilities of models. The BPDA+EOT method is an adversarial attack method based on back-propagation; it approximates discrete gradient values by using different transformation functions in the forward and back-propagation stages. This approach can be computationally more efficient and relatively effective for attacking.AT is considered to be one of the most effective defense strategies in the field of deep learning. Its idea is to supplement adversarial examples into the training set as outlier samples that are close to the decision surface so that the model can explore the decision space more comprehensively and capture complex decision boundaries. The higher the quality of adversarial examples during AT, the better the generalization of the model. The following are several classic AT methods. Xu et al.  introducAdversarial preprocessing is the operation of detecting or purifying the data to be tested with the aim of eliminating or modifying samples that contain adversarial noise so as to ensure the accuracy of the model to the greatest extent . AdversaResearch on purification methods is more diverse. Researchers treat adversarial noise as ordinary noise and adopt traditional denoising methods to deal with it. For example, Tabacof et al.  studied z and VAEs ,48 usualTo improve the visual quality of the images that are generated by VAEs, there have been many variants. Some change a et al.  introduca et al.  embeddeda et al.  proposeda et al.  relied oa et al.  proposeda et al.  proposedThe architecture of the classical VAE consists of two main components: an encoder and a decoder. The encoder can capture the basic features of the input data and map it to a compact cluster of latent space, namely the multivariate Gaussian distribution. In particular, to sample from the latent space in a differentiable way, VAEs employ reparameterization techniques to shift the problem into a more general distribution. Meanwhile, the decoder generates new data samples that are similar to the original data based on the sampled latent representation. These representations contain most of the important information that is needed by the data. Analyzing latent representations provides insight into the patterns or structural similarities between data.When training the VAE, minimize the reconstruction error to ensure that the generated new data are similar to the distribution of the original data and optimize the relative entropy of the latent vector to learn a compact and smooth latent representation. After properly training with clean data, the encoder’s mapping to normal data deviates from its mapping to abnormal data; that is, the latent distribution that is obtained by encoding clean samples and adversarial examples is different.To fix this “bias”, we adjust the latent distribution during the denoising phase by iteratively updating the latent features. In addition, because the pictures generated by the VAE are usually blurry and cannot meet the needs of practical applications, we chose the derived NVAE  model asIn this paper, we choose the deep hierarchical NVAE model. We focus on the orthogonal direction of neural architectures for hierarchical VAEs, divide the latent variables into vector groups x, the encoder performs multi-level encoding on the sample x and obtains a top-level feature vector For the input sample Z is sampled from a normal distribution that is determined by Because each conditional in the prior h to obtain a deterministic feature map. For other layers, the decoder combines the deterministic feature map output using the upper layer with the sample taken from x.The decoder first calculates latent variables When training NVAE, for each sample, we wish to maximize its variational lower bound on Given m et al. :(4)logpθx.The above inequality illustrates that the multi-scale architecture provides a tighter variational lower bound. Such a design can better fit complex continuous distributions. When the NVAE model is properly trained, the sample As shown in x is first fed into both the reconstruction module and the DCT denoising module.Next, we introduce the four modules of the adversarial denoising model sequentially. An input sample x to obtain a latent Gaussian distribution as determined by the latent representations i-th  loss After that, we keep the weights of the NVAE model unchanged and use quations  and 8)..Lcal to In this way, the latent representation is iteratively updated, thereby changing the latent distribution of the original samples. After repeated adjustments and sampling and filtering operations, the structure of the perturbations is gradually destroyed, and the reconstructed samples are less deceptive to the target model.In this section, we first test the effectiveness of the proposed method for defending against adversarial noise on the UC Merced (UCM) land use dataset . Then, wUCM is a remote sensing image dataset for land-use classification and recognition tasks; it covers 21 different land use categories, such as urban areas, farmlands, forests, grasslands, lakes, rivers, highways, etc. Each category consists of 100 high-resolution aerial images with a resolution of 256 × 256 pixels. We randomly split the UCM into the training set and test set at a ratio of 8:2. Therefore, the training set and test set contain 1680 and 420 remote sensing images, respectively. To this end, the experimental results on the UCM dataset are based on the average accuracy obtained from five independent experiments.CIFAR-10 is a computer vision dataset for image classification tasks. It covers 10 different object classes, i.e., airplanes, cars, birds, cats, deer, dogs, frogs, horses, boats, and trucks. These images have certain complexity and variability, which is challenging for the training and evaluation of deep learning models. Each category consists of 6000 images with a resolution of 32 × 32 pixels.When training target models to be attacked, we first perform preprocessing operations such as random cropping, scaling, and horizontal flipping on the training set and then perform 200-epoch training. During this phase, a cosine annealing learning rate adjustment algorithm with a maximum period of 200 and batch normalization techniques are used to speed up the optimization of the model.We compare the proposed method against widely-used AT and AP methods on a variety of Next, we introduce the experimental settings of the proposed algorithm. When training NVAE, the main parameter settings are listed in We conduct experiments on four classic image classification models, i.e., LeNet with a standard accuracy of 70.00%, VGG16 with a standard accuracy of 77.14%, AlexNet with a standard accuracy of 77.86%, and ResNet-18 with a standard accuracy of 85.95%. The accuracy of the target model is equal to the ratio between the number of recognition results that are consistent with the true label and the total number of the test images. We can see that when the input is clean data, the standard accuracy of the target model on the reconstructed data is slightly reduced. This is a common problem that is often associated with adversarial defense methods. Adversarial purification methods may lead to the loss of meaningful features, and adversarial training methods may lead to the overfitting of adversarial examples. Meanwhile, when the input is an adversarial example, the classification accuracy of the target model on the reconstructed data is significantly improved. In In this part, we first use classic adversarial attack algorithms to attack advanced object classification models in the CIFAR-10 dataset. Then, we test the effectiveness of the proposed adversarial denoising method for defending against adversarial attacks, as shown in The following points can be seen from the value and the second best by a bold value.In this part, we test the adversarial attacks and adversarial purification methods on CIFAR-10 using WideResNet-28 as the target model, as shown in The adversarial attack algorithms that we chose were BPDA+EOT and AutoAttack. BPDA+EOT is the main attack method for evaluating adversarial purification algorithms ,46. AdveThese methods only require clean data to participate in training. The performance comparison of these method are shown in Because SOAP is trained on classification tasks and self-supervised tasks, we separately tested the performance of combining the proposed adversarial cleaner as a plugin with the SOAP framework, as shown in In addition, we tested a classic AT method, TRADES, and conducted experiments that combined the proposed denoising method with TRADES. When training TRADES, we set the step size for perturbations to 0.007, the weight decay to As shown in Due to the limitation of GPU memory, it is inconvenient for us to use the UCM dataset to test the larger-scale VAE. Therefore, we first used the CIFAR-10 dataset to test the optimal parameter settings of the NVAE part and then used the UCM dataset to test the parameters involved in the proposed denoising method.Although Nie et al. have concluded through experiments that NVAE cannot purify adversarial examples , we founAfter determining the parameters of NVAE, we tested the parameters involved in the proposed adversarial denoising model on the UCM dataset. We tested the influence of the number of iterations, assessment metrics in the screening module, and level of DCT denoising, as shown in Remote sensing images have the characteristics of high spatial resolution, rich spectral information, and unclear foregrounds and backgrounds. It is difficult for traditional image processing methods to analyze them effectively. Deep learning has the ability to learn complex feature representation and pattern recognition. This technology has achieved remarkable results in RSI analysis tasks, such as classification, semantic segmentation, and the detection of change of land use. However, in the field of remote sensing, factors such as the atmosphere, clouds, noise, and motion artifacts may reduce the quality of RSIs, thereby affecting the analysis of DL models. In particular, the existence of adversarial examples poses a serious threat to the reliability and security of RSIs.To improve the robustness and reliability of DL models, we proposed an adversarial denoising method based on latent representation guidance for remote sensing image scene classification. Our method does not involve the target model and adversarial examples during the training phase and thus is robust to unknown types of adversarial noise. On the classic remote sensing dataset UCM, we used a variety of mainstream adversarial attack methods to conduct attack and denoising tests. The experimental results show that the proposed adversarial denoising method is effective, especially when defending against L2-bounded attacks such as C&W and DeepFool. Additionally, from the noisy image in the examples shown in The VAE assumes that the latent space is continuous, which means that samples corresponding to adjacent points in the latent space should also be similar in the data space. This may not be true for data in domains such as natural language or drug molecules.The VAE may encounter challenges in sampling and reconstruction when processing high-dimensional data.This paper is a study on the robustness of deep learning models, which is suitable for slight adversarial noise removal tasks but not for cloud removal  and imagThe idea of the proposed method is to first train a self-supervised generative model and then adjust its latent distribution by updating the latent representation of the data to achieve the purpose of filtering adversarial noise. Within the process, the deep hierarchical variational autoencoder is an important part of our method, which may have the following potential limitations:Adversarial denoising technology can remove noise and interference in images by learning the noise distribution or characteristics of perturbations, thereby improving the effect and reliability of the RSI analysis. In the future, adversarial denoising technology has broad application prospects in the generalizability and robustness of DL models, super-resolution reconstruction, multi-modal image purification, medical image processing, etc.In this paper, we designed an adversarial denoising method for RSI scene classification using a VAE with a multi-scale architecture. During the training phase, we learned the intrinsic features and meaningful representations of the clean data in the form of self-supervised learning. At test time, we first measured the quality of the reconstructed image output from the decoder according to the NMI. Then, this image was reconstructed under the guidance of the latent representations. Several iterations were performed to gradually weaken or eliminate the influence of the adversarial noise. The experimental results show that the proposed method is effective in RSI scene classification tasks and achieves adversarial robustness that is competitive with advanced computer vision adversarial defense methods. In the future, we will continue to seek theoretical innovations in self-supervised learning-based adversarial defense methods within the field of remote sensing.


We propose a wafer-type ion energy monitoring sensor (IEMS) that can measure the spatially resolved distribution of ion energy over the 150 mm plasma chamber for the in situ monitoring of the semiconductor fabrication process. The IEMS can directly be applied to the semiconductor chip production equipment without further modification of the automated wafer handling system. Thus, it can be adopted as an in situ data acquisition platform for plasma characterization inside the process chamber. To achieve ion energy measurement on the wafer-type sensor, the injected ion flux energy from the plasma sheath was converted into the induced currents on each electrode over the wafer-type sensor, and the generated currents from the ion injection were compared along the position of electrodes. The IEMS operates without problems in the plasma environment and has the same trends as the result predicted through the equation. The plasma process has become widely used in manufacturing semiconductors, displays, and electronic devices as semiconductor manufacturing technology has advanced. Plasma has an anisotropic property that helps it form narrow and deep patterns . As a rePlasma etching is essential in semiconductor fabrication to reduce device size and increase the aspect ratio of etched features. Plasma uniformity has become the most critical variable in ensuring the reproducibility and stability of the process and increasing yield as plasma has become the leading technology of the semiconductor manufacturing process. The ion current in the plasma parameters affects etching uniformity, and the ion energy on the substrate surface determines etching selectivity and rate ,3,4,5. PThe importance of technology for diagnosing process state grows in direct proportion to the difficulty of the process and the degree of integration required. Several studies have been conducted to assess the current state of the semiconductor process. The most common process diagnostic techniques are optical, electrical, and chemical methods. One of the optical diagnostic methods, optical emission spectroscopy, can monitor the plasma state by separating the plasma state into wavelengths ,10,11. IBy inserting the LP’s probe directly into the chamber, plasma parameters can be measured position by position ,21,22. T® and Impedans’ Semion RFEA are two current on-wafer sensor vendors . As the importance and demand for semiconductor process diagnosis sensors grow, research is being conducted on an on-wafer platform that combines two or more types of sensor technologies, rather than just one.A wafer-type sensor, called on-wafer sensor, has been researched and developed for this purpose to measure process uniformity on the wafer surface . A sensoWe fabricated a wafer-type ion energy-monitoring sensor (IEMS) in this study. The wafer-type IEMS is a sensor that monitors ion energy and is designed in the form of a wafer. By inserting it into semiconductor manufacturing equipment, it is possible to monitor the ion energy generated in the same environment as the actual semiconductor manufacturing process. Furthermore, unlike previous probe-type measurement equipment, it can be inserted into a plasma bulk to improve plasma interference and diagnose a substrate on which an actual process occurs.The wafer-type IEMS is a sensor that detects plasma states by converting ion current generated during the plasma process into ion energy. The current flowing on the substrate is detected by the magneto-impedance (MI) sensor when current is generated in the plasma process with the data compared using the LP, a sensor that can calculate the ion current, to calculate the ion current from the current measured in the IEMS .Ion energy may be calculated by measuring electron density and ion density in the bulk plasma through the Langmuir probe (LP). Measure the current by placing the sensing pad and LP’s tip of IEMS on the same x-axis. Through this process, a constant for converting the current generated in the plasma into an ion current is obtained.The ion current measured by the IEMS is as follows:/In collisionless RF plasma sheaths, the shape of the IED is determined by the ratio of ion transfer time to the RF cycle. The ion transfer time is the time it takes for the ion to cross the sheath, which can be calculated using the Child–Langmuir law. First, to calculate the ion transfer time, the thickness of the coating is calculated as follows :(2)s¯=23equation ,27:(3)τiCombining the above formulas, the ion energy can be calculated as follows :(4)∆E=2eTo minimize plasma interference, a wafer-type IEMS was designed as a printed circuit board (PCB) of the same size as a 6-inch wafer and consists of four layers. This improves the issue of being inserted directly into plasma and does not damage the measuring component. Since the IEMS is located in the chuck where the plasma process occurs, it does not directly affect plasma properties and conditions. Furthermore, if it meets the same thickness and impedance requirements as silicon wafers, it is possible to collect data in the same environment as the actual process.IEMS includes a sensing section, a control section, and a charging section. When data measurement is initiated, the current signal is detected by the MI sensor in the sensing section. A built-in amplifier and analog-to-digital converter convert the current signal detected by the sensing unit into a digital signal. The microcontroller unit (MCU) controls the sensor signal via the I2C protocol of the sensing unit, acquires the data, and stores them in the flash memory. After the data collection is completed, the IEMS can be removed from the process chamber and the flash memory data can be analyzed via PC communication. We utilized the Origin application to visualize measured data using line graphs and contour maps. The IEMS block diagram is depicted in The IEMS was created utilizing the layout tools of Autodesk’s EAGLE PCB design software. The EAGLE logic tool was employed to design the IEMS circuit. Before drawing the circuit diagram, the actual environment and operational functions were considered when selecting the components. This includes an MCU for system-wide operation control, a charging circuit for charging the battery, and a data transmission and reception circuit. IEMS employs EMI sheets to ensure stable operation within the plasma chamber. To protect circuits from electromagnetic waves, EMI sheets are inserted into each layer. To prevent metal oxidation and increase conductivity, the surface of the sensing component’s circuit is coated with gold.Here, 19 sensing units are evenly distributed to ensure uniform measurement of the ion flux on the PCB’s surface. Six additional sensing units were placed around the central sensing unit on the PCB to provide information on the measurement uniformity of the current. In addition, as the size of the wafer increases, controlling the ion flux in the edge area has a substantial effect on the process yield; therefore, 12 sensing units are positioned to measure the ion flux in the edge area.As the sensing component, coreless current sensors are typically shielded to prevent measurement errors caused by external magnetic fields. Because the two sensors built into the sensor can simultaneously detect the magnetic field and cancel out the external magnetic field, only the target magnetic field can be detected. The structure of the sensing unit for measuring the ion energy distribution is depicted in Using the proposed wafer-type IEMS, ion currents were measured, and data were compared with LP for sensor reliability and validation. In the experiment, the results were compared based on radial position changes while adjusting the LP’s position, as depicted in As depicted in The results of ion current measurement according to the changes in radial position using IEMS and LP are similar. Moreover, as the radial position moved away from the center, the measured ion current tended to decrease. These results are identical to those of prior studies . This deTo confirm that IEMS can detect changes in ionic current caused by pressure and RF power, additional experiments were conducted. The pressure impact experiment was conducted by maintaining the RF power at 100 W and the Ar gas flow rate at 30 sccm, while increasing the Ar gas pressure from 30 mTorr to 70 mTorr in 20 mTorr increments. In addition, the experiment was conducted by increasing the RF power from 100 W to 400 W at 50 mTorr pressure and 30 sccm Ar flow rate. As shown in As a result of the experiment, the IEMS can detect the change in ion current according to the change in pressure and RF power. We explain the calculation of the ion energy in A wafer-type IEMS capable of measuring the spatial distribution of ion energy in a plasma chamber measuring 150 mm in diameter has been developed. IEMS can measure the distribution of ion current on a substrate, and the measurement result can be visually analyzed to determine the distribution of the ion current.The IEMS was fabricated on a PCB. The IEMS does not necessitate any modifications to the chamber or chuck. Moreover, the system design does not necessitate semiconductor manufacturing processes. This results in relatively low manufacturing costs. In addition, it is simple to customize and expand the circuit.In this experiment, the distribution of ion current in relation to varying Ar gas pressure and RF power was monitored and measured. To confirm the validity of the proposed sensor, the trends were compared to the LP. Validity was thus confirmed. In addition, IEMS functions normally in a plasma environment and detects ion current according to the process conditions. Through previous experiments, we confirmed the stability and effectiveness of this sensor. We will improve the completeness by supplementing some problems, and we will expand it to 300 mm wafer size so that it can be applied to various processes. If this sensor is applied in the actual manufacturing process, it will bring positive results to yield improvement and process prediction.


As healthcare costs continue to rise, finding affordable and non-invasive ways to monitor vital signs is increasingly important. One of the key metrics for assessing overall health and identifying potential issues early on is respiratory rate (RR). Most of the existing methods require multiple steps that consist of image and signal processing. This might be difficult to deploy on edge devices that often do not have specialized digital signal processors (DSP). Therefore, the goal of this study is to develop a single neural network realizing the entire process of RR estimation in a single forward pass. The proposed solution builds on recent advances in video recognition, capturing both spatial and temporal information in a multi-path network. Both paths process the data at different sampling rates to capture rapid and slow changes that are associated with differences in the temperature of the nostril area during the breathing episodes. The preliminary results show that the introduced end-to-end solution achieves better performance compared to state-of-the-art methods, without requiring additional pre/post-processing steps and signal-processing techniques. In addition, the presented results demonstrate its robustness on low-resolution thermal video sequences that are often used at the embedded edge due to the size and power constraints of such systems. Taking that into account, the proposed approach has the potential for efficient and convenient respiratory rate estimation across various markets in solutions deployed locally, close to end users. In recent years, there has been an unprecedented surge in demand for remote healthcare services. Apart from the most evident cause of this change, i.e., the pandemic , this teGiven all of the above, it is of the utmost importance to develop a solution that would enable remote medical diagnosis that is both precise and convenient. The latter is an important aspect, as a convenient solution is used more frequently and thus can provide necessary feedback to help shape a healthier lifestyle and identify anomalies faster. Unarguably, one of the core metrics of vital signs estimation is respiratory rate (RR) . HoweverThe issue of vital sign estimation has recently become an area of increased interest from researchers. While proposed solutions are often contactless, they usually rely on the visible light spectrum (380 to 700 nm electromagnetic radiation wavelength) , test su1.Slow and Fast branches are adapted to capture breathing signals by changing the sampling frequencies. Specific rates are selected based on the extensive grid search of the network parameters .2.Convolutional filters used for feature extraction and for capturing temporal dependencies between signal frames are modified to increase the receptive field and capture more distant dependencies between features .3.Finally, the classification head is replaced by a regression layer to output RR values instead of the recognized action category .In view of the foregoing, this study focuses on the feasibility of an end-to-end convolutional model for an RR estimation task without the need for additional calibration, signal extraction, or data conversion to the frequency domain that often requires higher numerical precision. In addition, the deep learning model used in the study operates directly on low-resolution thermal images, which usually have lower resolutions than their visible light counterparts and it is not uncommon to see resolutions of 160 × 120 or even 80 × 60, without the need for applying additional image enhancement techniques. Therefore, the proposed method has a potential for deployment in practical systems, because such low-resolution sensors are smaller and much more affordable, making them suitable for embedded edge processing in home monitoring, epidemiological diagnostics, and other contactless health state estimation systems, e.g., at airports. Specifically, this study builds on top of the SlowFast model [The rest of the work is structured as follows: Thermal imaging has already been successfully used to evaluate human RR from a sequence of facial images; however, most of the methods are still multi-step procedures, requiring tuning of each phase separately, which is time-consuming and may affect final performance both in terms of accuracy and computational overhead . Such syMost of the later works used a thermal camera positioned collinearly to the subject’s face. The surface temperature changes are observed near the nostrils or/and the mouth during inhalation and exhalation of air. Typically, the respiratory signal is extracted using aggregation of the useful data from each frame into a sequence of values that form an estimated respiratory signal. In , a multiIn fact, most methods vary in the selection and tracking of the Region of Interest (ROI), as the rest of the algorithm, the RR estimation, is most commonly solved using similar algorithms based on analysis of the frequency spectrum. The proper identification of the ROI as a source of the thermal-based respiratory signal is key in such solutions. The expert-based, manual method can be used for a few cases , but canIn addition to ROI detection, it is also crucial to track the nostril area in case of head movements. Various tracking methods have been already proposed in the literature, also to extract an optimized respiratory-related signal e.g., ,33,34). ,34. 33,3The main objective of RESCUED is to verify if the convolutional model originally designed for action recognition from visible light video sequences could be repurposed for the task of RR estimation from low-resolution thermal sequences. It is important to note that the characteristics of data from both domains significantly differ, and thus a direct application of such a network might be challenging and require topology modification. Visible light data contain high-frequency components, such as sharp edges and higher contrast changes in adjacent regions. On the other hand, thermal data are characterized by blurriness, and changes are less dynamic in values of the spatial representation due to heat flow in objects, as can be seen in The motivation for sampling rate modification is explained by introducing the details of SlowFast architecture first. The SlowFast model was designed for action recognition tasks, where the Slow branch is responsible for detecting objects involved in the action, given the fact that the type of objects is usually persistent across visible changes, and the Fast branch is used for action recognition with a relatively high sampling rate to be able to capture fast and sudden movements. Respiratory events have different characteristics. First, the respiratory signal is periodic, meaning that the dynamics of respiration are consistent across the window used to capture the signal. Secondly, the respiratory signal’s period is longer than the length of the activity stored in the dataset for the action recognition models .A breathing episode is a term used to refer to a complete cycle of inhalation and exhalation that occurs during the process of breathing. The duration of a breathing episode may vary depending on a number of factors, including the individual’s RR, lung capacity, and physical activity level. Thermal vision sensors allow for capturing of breathing episodes by analysis of temporal changes in pixel values corresponding to differences in temperatures inhaled and exhaled air, as presented in Therefore, the original window of 30 frames with a sampling rate of 8 would only capture around 3 s of the recording, making it impossible to capture one full breathing cycle. Taking this into account, both the width of the fusion kernel and the sampling rates of both branches were increased to enable RR estimation using a single convolutional model. A detailed analysis of the different configurations is presented in the Experimental Analysis section .In the study, the adjustment of various parameters was examined, including the receptive field of the kernels in the CNN backbone network. As mentioned previously, thermal images have less defined features than those captured in visible light due to heat flow in objects. To address this issue, this work evaluates different sizes of the receptive field. A larger receptive field allows the network to capture more global information about the input image, which can improve the network’s ability to recognize complex patterns and structures. For example, in image classification tasks, a larger receptive field can help the network recognize objects in the context of their surroundings and reduce the impact of noise in the input image. By considering a larger region of the input image, the network can average out small variations and focus on more significant features.D residual blocks there are d skip connections are selected, the number of layers (L) in the CNN can be approximated as:q indicates the number of pathways in each block . As described in [l is the layer number, s is the stride size. Given qTo make the base model suitable for the regression task, some necessary modifications were made to allow the model to predict continuous output values.The first and most significant change involved replacing the final layer of the network, which in the original model used a softmax activation function to produce a probability distribution over classes. Working on a regression task, the final layer was replaced with a linear activation function. This allowed the model to predict continuous values instead of discrete classes.w is an adjustable parameter, n is the number of samples. The intuition behind using the weighted MSE was to give more importance to prediction errors obtained for samples with the higher standard deviation, and thus to obtain the model that would be able to cover a wider range of RR values. Finally, once the architecture modifications and loss function selection were complete, the adapted model was trained on the RR regression task.In addition to changing the activation function, an appropriate loss function to measure the difference between the predicted values and the actual values has to be selected. Various loss functions were evaluated, including Mean Absolute Error (MAE), Mean Squared Error (MSE), Weighted MSE (WMSE) Mean, and Root Mean Squared Error (RMSE). The WMSE function was defined as:® thermal camera  was utilized. It is a portable sensor that can be connected to a smartphone or tablet, making it suitable for use in various scenarios, such as remote medical diagnosis. The camera can capture images at an 8.7 Hz frame rate with a resolution of 80 × 60 pixels.Thermal imaging technology captures electromagnetic radiation intensities as precise digital values, usually with a 14-bit resolution. These digital values are then translated into temperature data by assigning color values to each one, which creates the final thermal image. To gather additional data for this research, a FLIR ONETo validate the reliability of the proposed RESCUED model, a collection of 77 thermal video sequences was used. Of these sequences, 40 were taken from the SC3000 dataset , and theDuring the experimental analysis, various parameters were analyzed in a set of experiments to find the best-performing configuration. Initially, an extensive grid search was performed to select the model and training hyperparameters. The default configuration is presented in N is the number of data recording, In the performed experimental analysis, the RMSE error was used for comparing the estimated Equation .(4)RMSEIn this step, the evaluation of different depths of the model backbone was performed. Given the sufficient training data, using deeper architectures should theoretically lead to better accuracy. The results obtained for ResNet50 and ResNet101 backbones are presented in As can be seen, the smaller model performed better, which may be the result of a small dataset and/or model overfitting (increasing variance).Following , the incWhen evaluated on the test set, the network trained with a larger receptive field (configuration 2) outperformed the one with a smaller receptive field (configuration 1) by In this step, the influence of different sampling rates for the Slow  and Fast (beta) branches was compared. The sampling rates were applied to the input sequence with the default window length of 128 frames. In addition, different widths of the fusion kernel combining both paths were compared. In experiments, the impact of the choice of the loss function and reduction method (aggregating loss across samples in a batch) used during training on the final results was investigated. The results shown in Finally, to ensure the reliability of the study, a five-fold cross-validation technique was employed to train and validate the model. The dataset is first divided into five parts of equal size. Subsequently, the model is trained on four of these parts, and the fifth part is kept aside for validation. The process of splitting data has a constraint of sorting recordings of the same volunteer in a single fold. This process is repeated five times to ensure that each part of the dataset is used as a validation set once. This technique helps us to assess the model’s performance more accurately and minimize the possibility of overfitting the training data. The achieved RMSE was averaged across all runs and compared with previous state-of-the-art methods .RRJR is a method proposed in  based onThe presented work focused on evaluating the possibility of RR estimation from low-resolution thermal image sequences using a one-shot convolutional-based method. Such a solution possesses some important advantages over the previous methods that were based on a set of various image and signal processing algorithms offering only a semi-automated solution. First of all, according to the achieved results, the error of RR estimation was reduced. Secondly, the use of a fully convolutional-based method which does not require higher-precision signal analysis algorithms allows for its deployment on machine learning accelerators  supportiThe performed in-depth analysis of various parameters of the model showed the importance of increasing the receptive field and fusion kernels when dealing with the thermal data which have different characteristics than visual light samples. This study aligns with similar research in the field . It highHowever, although the presented results are promising, they are only preliminary, and further work is required for supporting the presented findings. First of all, the respiration sequences used in the study were recorded only for normal breathing rates. It would be interesting to evaluate whether the model can be applied to abnormally slow or fast RR. Such a solution would potentially allow for detecting emergency situations, such as asthma, anxiety, pneumonia, congestive heart failure, or drug overdose, which would have a real practical value . Such anAnother limitation of the presented RESCUED model is the large input size (two branches of a size 224 × 224 × 3 × 32 and 224 × 224 × 3 × 8) and the increased receptive fields, leading to more parameters and, as a result, a bigger model. Such a network may be difficult to deploy on resource-constrained devices with limited memory. Additionally, evaluated input window lengths of 64 and 128 combined with a sampling rate of 3 may introduce additional latency to fill up the input buffer. While all methods used to evaluate RR need to accumulate sequence data in a buffer before extracting a signal, these parameters should be taken under consideration, especially when using the model to estimate RR from a live stream. Taking it into account, the future work will focus on reducing the network size by using smaller inputs and deployment of better techniques for increasing the receptive field, i.e., atrous convolutions , or deptThe presented work introduces a novel approach to respiratory rate estimation, one of the key parameters in patient monitoring. The RESCUED method uses a robust machine learning algorithm with two concurrent pathways (Slow and Fast branches) to accurately estimate the respiratory rate from thermal sequences captured by a small form factor thermal camera. Combination of two paths covering expandable depths of thermal data for vital sign estimation has not been done in the literature before. The experimental results demonstrate that the proposed innovative approach outperforms the state-of-the-art methods in terms of accuracy and robustness, eliminating the need for converting data to the frequency domain, which might not be supported on ML accelerators. Thus, the proposed method has great potential for deployment in real-world scenarios, such as hospital and home-based patient monitoring. Further research could investigate the feasibility of integrating our approach with wearable devices and developing real-time monitoring systems to aid in the early detection and treatment of respiratory disorders. Overall, this work provides a promising direction for improving the accuracy and reliability of respiratory rate monitoring.


Wheat seed classification is a critical task for ensuring crop quality and yield. However, the characteristics of wheat seeds can vary due to variations in climate, soil, and other environmental factors across different years. Consequently, the present classification model is no longer adequate for accurately classifying novel samples. To tackle this issue, this paper proposes an adaptive domain feature separation (ADFS) network that utilizes hyperspectral imaging techniques for cross-year classification of wheat seed varieties. The primary objective is to improve the generalization ability of the model at a minimum cost. ADFS leverages deep learning techniques to acquire domain-irrelevant features from hyperspectral data, thus effectively addressing the issue of domain shifts across datasets. The feature spaces are divided into three parts using different modules. One shared module aligns feature distributions between the source and target datasets from different years, thereby enhancing the model’s generalization and robustness. Additionally, two private modules extract class-specific features and domain-specific features. The transfer mechanism does not learn domain-specific features to reduce negative transfer and improve classification accuracy. Extensive experiments conducted on a two-year dataset comprising four wheat seed varieties demonstrate the effectiveness of ADFS in wheat seed classification. Compared with three typical transfer learning networks, ADFS can achieve the best accuracy of wheat seed classification with small batch samples updated, thereby addressing new seasonal variability. Wheat is a crucial staple crop worldwide, and its quality and yield directly impact global food security. The accurate identification of wheat seed varieties holds immense significance for enhancing the quality of wheat varieties and ensuring the market order . HoweverTraditional methods for identifying wheat seed variety rely on time-consuming and subjective visual inspection by experts . The advRecently, deep learning combined with hyperspectral images has been widely used in the field of crop seed variety recognition ,9,10. DaTransfer learning is a method for improving the performance of deep learning models on hyperspectral image data by transferring relevant knowledge from a source domain to a target domain, where training and test data exist in different feature spaces . Yang etGenerally speaking, the tasks of the source domain and the target domain are the same, and the samples are also similar, so their distribution in the feature space will be relatively close. However, interactions between genotype and environment can lead to changes in wheat seed spectral characteristics, especially due to environmental differences across different years. This interference will cause certain deviations in the distribution of the source wheat seeds and the target wheat seeds in the feature space. These biases are the main reasons that lead to the decline of wheat seed classification performance under different scenarios. Domain adaptation is a research area that focus on transfer learning models in the computer vision field . When thMost of the domain adaptive transfer learning strategies focus on learning domain feature representation, aiming to learn a feature that is specific to the category but not related to the domain ,24. In oThe remainder of this paper is organized as follows. In order to investigate the proposed model’s detection ability for wheat seeds from different years, this study collected four different varieties of wheat seeds harvested in 2019 and 2020, namely Bainong4199 (BN4199), Jimai44 (JM44), Weilong169 (WL169) and Zhoumai33 (ZM33). These four types of wheat seed are all semi-winter varieties with strong gluten and high-yield characteristics. All wheat samples were provided by Sinochem Modern Agriculture Co., Ltd. in Beijing, China. For each wheat, 1000 visually undamaged seeds with intact appearance were selected for hyperspectral image sample collection. A total of 8000 hyperspectral images of wheat seeds were collected. The specific information is shown in This study utilized a line-scan hyperspectral imaging system operating in the visible/NIR range (400–1000 nm) to collect hyperspectral images of wheat seeds. In this study, the spectral resolution of the hyperspectral camera was set to 6.38 nm, the slit range of the spectrometer was 25 μm, the exposure time was set to 250 ms, and the power of the light source was set to 60 w. Each batch of wheat seeds contained 100 samples, and the resulting hyperspectral image data cube had dimensions of 1000 × 1392 × 94, with 94 representing the waveband dimension. After correcting the hyperspectral images, the wheat seed of interest needed to be isolated from the background. For facilitating subsequent modeling, zeros were added around the seed to ensure that each wheat image had a consistent size of 60 × 80 × 94. The details regarding the hyperspectral imaging system and image pre-processing can be found in the paper [Domain adaptive is a novel transfer learning strategy. By mapping inconsistent source domain data and target domain data into a feature space and aligning their distributions in feature space, the knowledge from the source domain can be transferred to the target domain, thereby improving the detection accuracy in the target domain .G, nG, cG and dG represent feature extraction modules; nF, cF and dF denote the feature map; h1, h2 and h3 are classifier modules. The specific structures of each feature extractor and classifier in the model are shown in This study introduces the idea of domain feature separation based on domain adaptation to mitigate negative transfer and enhance the model’s detection accuracy. G of the model to obtain mappings of both source and target domain samples in the feature space.In the first stage, the samples are collected for hyperspectral image, image preprocessing, and zero-fill reconstruction to form a dataset consisting of sample data with the same data size. Then, the samples in the dataset are divided into source domain and target domain according to the year, which are input into the feature extractor nG and two private feature extractors cG, dG. After abstracting the samples into low-dimensional features, the total features are refined into shared features nF, class-specific features cF, and domain-specific features dF. The shared features nF refer to the similar features between samples from source domain and target domain. While the class-specific features cF may shift with domain changes, they contain knowledge helpful for classification tasks within their respective domains. dF includes information that reflects the difference between the source domain and the target domain. On the other hand, the domain-specific features dF includes information reflecting differences between the source domain and target domain. They are not helpful to the learning task and may have a negative effect on the target domain task, so the features are not transferred during learning. In the proposed structure, transfer training is performed on nF and cF that are domain-irrelevant features.In the second stage, the feature separation module is divided into a shared feature extractor nF is domain-irrelevant and class-irrelevant, the features from the source domain samples should have a similar distribution to that from the target domain samples. Therefore, cosine distance is used as a loss function to minimize the distribution distance between the shared features from source and the target domains in the feature space. The alignment process of nF is illustrated in the red box in sX and target domain sample tX; the sample pair is input into the feature extractor G and nG at the same time. Then, the adversarial training is carried out to calculate the Cosine distances between source sample sX and target sample tX, that is the Since the knowledge represented by the shared features cF contains features that can distinguish different seed varieties, it is suitable to be used as the input for the class classifier when combined with nF. Therefore, both are input into the class classifier h2 in series and trained using the cross-entropy loss function. Similarly, dF and nF are connected in series into the domain classifier h3 and trained using the cross-entropy loss function as the same. In order to further ensure that the refined features have their corresponding feature knowledge, the cF, dF and nF are connected in series and input into domain-class classifier h1 for domain-class combination prediction and trained as same as h2 and h3. The cross-entropy loss function used for Finally, the classifiers are constructed by combining the separated features. Since There are differences in the feature space of wheat seed classification from different years. The main research directions of feature-based transfer methods include adversarial domain alignment and reconstruction-based domain alignment. Hyperspectral images are 3D images with a spectral dimension. The reconstruction-based domain alignment methods have significant variations and are not applicable to wheat seed classification. Therefore, we chose a representative method of adversarial transfer learning called domain Adversarial Neural Network (DANN) for comparison. Additionally, pre-training and fine-tuning, which are commonly used transfer methods in the spectral domain, were also employed.DANN: DANN was proposed by Yaroslav Ganin et al. . It consPre-training model: The model first used the source domain sample set for pre-training, and then used a small number of samples from the target domain for updating all the parameters so as to transfer the knowledge from source domain to target domain. The model structure is shown in Fine-tuning model: The method was similar to the pre-training model, as it also utilized the source domain samples for training. In contrast, the parameters of the shallow feature extraction layer of the convolutional neural network are frozen. Then, a small number of samples from the target domain are used for updating to achieve the purpose of transfer learning. The model structure is shown in The pre-training model and fine-tuning model are based on the same initial CNN that was trained using the source domain samples. The updatable block represents the modules initialized with parameters from the initial model and then updated using target domain samples. The fixed block represents the modules with fixed parameters loaded from the initial model.In order to enhance the robustness of the detection model in the open environment, this study proposed a novel transfer strategy called ADFS. The wheat seeds harvested in two different years were used as experimental samples. The proposed ADFS was compared with three common transfer methods, and experiments were carried out for transferring from 2019 to 2020 as well as from 2020 to 2019.−3, a gradient decay rate The structure of initial CNN model is shown in The comparison of classification results is summarized in Interestingly, when wheat seeds from different years were used for cross-year prediction, the classification accuracies were less than 50%. The poor results were due to inconsistent data distribution in different years. It is difficult to meet the detection requirements by relying solely on the generalization performance of the CNN model itself. When using samples from the source domain, the model cannot learn the feature information of the target domain. It is apparent that WL169 has the worst classification accuracy in cross-year prediction. As can be seen from There were two years of wheat seed samples, and cross-year predictions for different years were compared. In the cross-year predictions, the source target samples were randomly divided into training sets and test sets according to 4:1. The 20% of the target samples with labels were used to update the model for pre-training and fine-tuning models. For DANN and ADFS, 10% of the target domain samples with labels and 10% of the target domain samples without labels were used to optimize the model parameters; the remaining 80% of the target domain samples were tested. The parameter The comparison of cross-year predictions output by different transfer learning models is shown in By introducing domain adaptive, the DANN and ADFS can achieve more than 90% classification accuracy after semi-supervised learning. At the same time, target domain labels are used less than the pre-training and fine-tuning model. The core of the adversarial domain adaptive method adopted by DANN and ADFS is to learn a good enough representation to reduce the difference of different data distributions at the representation level. The proposed ADFS model combines the idea of domain self-adaptation, uses different modules to extract class-specific features, and refines the distribution difference between the source domain and target domain. Adversarial training is used to ensure that the shared parameter spaces are aligned. After the feature is refined, some features that will produce negative transfer and affect the classification effect of the model in the target domain are discarded. Overall, ADFS outperforms DANN in terms of accuracy for wheat seed classification. The transfer performance of ADFS with 10% labeled samples and 10% unlabeled samples is better than 50% of the target sample training model.The confusion matrix of the classification results for the target test set by ADFS is shown in nF, which are alignment features across scenarios. It is apparent from this subgraph that the shared features are jumbled together and do not shift in distribution with the change in the scenario. The middle subgraph is a visualization of the combined shared features nF and class-specific features cF; these features are input into the class classifier. These features are domain-irrelevant, so the source domain and target domain cannot be distinguished. However, there is a difference between different categories. After adding domain-specific features dF, the visualization of the features is shown in the right subgraph. Samples of different domains and different categories can be well clustered together. However, the cluster is relatively scattered and some cluster boundaries overlap.The shared module captures some shared features across scenarios, the private module captures class-specific features. In order to provide richer judgment for the network, T-Distribution Stochastic Neighbour Embedding (T-SNE)  was usedIn order to further study the influence of labeled sample proportion on the stability of the ADFS, experiments of ADFS and DANN with different proportion samples were conducted in this section. The sample division is shown in The classification results of wheat seed varieties after adding different proportions of labeled target samples are shown in From nF and all feature set nF + cF + dF. The classification accuracies are shown in The feature extraction of the ADFS network is carried out through three different modules, which respectively extract shared features class-specific features and domain-specific features. In order to further verify the effectiveness of feature separation and extraction, ablation experiments were performed. The ablation experiment compares the classification results of different separated feature sets, using only the aligned shared feature nF constructed in the ASDF network does not include features of classes and domains. Therefore, the classification accuracy using nF is the worst. As shown in nF and class-specific cF can obtain the best classification accuracy. Further domain-specific feture dF is added and the results become worse. It is precisely due to that dF is useless for classification, thus causing negative transfer to reduce the classification accuracy.The shared feature In order to address the issue of hyperspectral data transfer for wheat seeds from different years, an ADFS network based on shared and private features is proposed. The network differentiates the feature space into a shared feature space that captures features shared across domains and two private feature spaces that capture features unique to each domain or each variety. After differentiation, the effect of reducing negative transfer is achieved by removing the domain-specific features. Wheat varieties from two different years are used for experimental verification. The results demonstrate that ADFS achieves a classification accuracy of 93.51% and 91.69% when a small number of target domain samples is added. Compared to fine-tuning, pre-training, and DANN approaches with the same target domain sample size, ADSF performs the best in terms of cross-year classification accuracy. The overall results showed that the ADSF network was feasible and could provide accurate cross-year wheat seed variety identification, which would help to develop a system for quality detection of wheat seed under different scenarios.This study focused on developing a transfer learning network to identify wheat seeds to address data drift across different years. However, the ADSF network still requires a small number of labeled samples for cross-year transfer. In order to better adapt to practical cross-year prediction, future research will concentrate on the unsupervised transfer network with high accuracy.