In this paper, a novel mirror visual feedback-based (MVF) bilateral neurorehabilitation system with surface electromyography (sEMG)-based patient active force assessment was proposed for upper limb motor recovery and improvement of limb inter-coordination. A mirror visual feedback-based human–robot interface was designed to facilitate the bilateral isometric force output training task. To achieve patient active participant assessment, an sEMG signals-based elbow joint isometric force estimation method was implemented into the proposed system for real-time affected side force assessment and participation evaluation. To assist the affected side limb efficiently and precisely, a mirror bilateral control framework was presented for bilateral limb coordination. Preliminary experiments were conducted to evaluate the estimation accuracy of force estimation method and force tracking accuracy of system performance. The experimental results show the proposed force estimation method can efficiently calculate the elbow joint force in real-time, and the affected side limb of patients can be assisted to track output force of the non-paretic side limb for better limb coordination by the proposed bilateral rehabilitation system.  Hemiplegia, which is a common sequela after post unilateral stroke, always refers to the hemiparesis on the contralateral side of the upper limbs leading to disability on one side . Due to Due to a lack of medical sources and the increasing number of stroke patients, robot-aided rehabilitation is proposed to accelerate recovery processing based on the aforementioned neurological principle ,7. For hFurthermore, the surface electromyography (sEMG) is a muscle drive signal that contains the motor information of the central nervous system (CNS) and brain, which is always utilized for rehabilitation assessment , human iThe sEMG-based human active force estimation is discovered as it can intuitively reflect muscle motor unit action potentials (MUAPs) for active muscular force evaluation . ZonninoAs mentioned, a novel mirror bilateral neuro-rehabilitation system with sEMG-based real-time active force assessment is proposed in this paper and preliminarily tested for the upper limb elbow joint bilateral isometric force coordination. The conception diagram is shown in The paper structure is organized as follows: In this section, a powered variable stiffness exoskeleton device (PVSED), as the hardware platform, is reviewed in mechanical design and the visual feedback-based human–robot interface is introduced for a mirror bilateral neuro-rehabilitation.In this study, the PVSED was utilized for aiding the subject to finish the mirror bilateral rehabilitation training tasks. The PVSED was developed in our previous research, which not only can assist flexion and extension motion of the upper limb elbow joint, but also can independently regulate the stiffness via a variable stiffness actuator (VSA). The detailed information of the PVSED was introduced as the reference . For easEffective real-time visual feedback is crucial to enhance motor learning in physical and cognitive rehabilitation . For impIn this study, a mirror bilateral isometric force training with visual feedback has been designed for improving motor learning and regaining motor control skills in patients. There are three different phases in the training process, including the offline learning phase, online validation phase, and real-time assist phase. The process involves the subjects sitting on the chair comfortably and placing their forearms on the table, which ensures their hands naturally touch the force sensor. Two Ag/AgCl bipolar surface sEMG electrodes were placed on the biceps and triceps of both side limbs for sEMG signals collection through the Personal-EMG device . The bilateral force signals were then displayed by the two symmetric vertical bar models and the sEMG signals shown in graph models in the human–robot interface configuration. In the offline learning phase, the active isometric force estimation is established by a neural network method for muscular force assessment. The subject is instructed to perform an isometric force output against the force sensor by their healthy side. The force signals and sEMG signals are recorded in real-time for training the learning algorithm. After the learning algorithm training, the online validation phase begins for ensuring the efficiency and safety of the trained estimation model. The subject repeats the same motion in the online validation phase but the estimated force results are calculated by the trained model and shown on the screen in real-time. The poor performance estimation model is rejected and retrained for the safety consideration until the estimation performance is acceptable. The real-time assist phase is performed once the online validation phase is finished. As the one disability side of the hemiplegia patients, the trained force estimation model is unitized as the active force assessment of their affected side. The subject with the PVSED is instructed to perform the mirror bilateral motion with the equal force output against the force sensors to maintain the same height of the two symmetrical vertical scroll bar models. The bilateral isometric force information is recorded and the error between both sides is then calculated as the control input of the bilateral limb coordination controller Jmθj¨There are two healthy subjects with no muscular disorder history  involved in this study for mirror bilateral isometric force training. The PVSED was fixed to a metal structure and adjusted to fit each subject-specific height. Then, the sEMG electrodes will be placed on the subject’s arm for real-time sEMG collection and MVC test. The location of electrodes corresponding to the triceps was at 50% on the line between the posterior crista of the acromion and the olecranon at two-finger-width medial to the line.The reference electrodes were placed on the styloid process of the ulna of the wrist joint. The experimental setup is shown in (1) Estimation Performance: For sEMG-based force estimation, the BPNN model should be set up and trained at first. Each subject was instructed to perform the isometric force output process from the relaxing state to the max voluntary output state five times. During the subject increasing the output force of the bilateral limb, the sEMG signals were accordingly increasing from the 0.2 mV of the relaxing state to the 0.6 mV. The BPNN model training results are shown in (2) Real-time assistant performance: For evaluating the robot assist effect in the real-time assist phase, the output force signals of both side limbs have been compared in Considering the safety of the human–robot interactions, the parameter of the PID controller was set as relatively low, which may reduce the force tracking performance so that the bilateral output error could be over 5 N, as shown in This study’s main purpose is to mirror a bilateral neuro-rehabilitation robotics system with sEMG-based patient active participant assessment, which uses the mirror visual feedback and robot assistant to induce bilateral limb inter-coordination. Furthermore, the patient active participant assessment is integrated into the system for real-time rehabilitation training evaluation by an sEMG-based elbow joint force estimation method. Based on the experimental results, two main aspects are discussed in this section, including the sEMG-based elbow joint force estimation and the MVF-based human–robot interface for the bilateral neuro-rehabilitation robot system.To realize the EMG-based force estimation, Zhang et al.  used then et al.  studied n et al.  proposedAs the sEMG-based elbow joint force estimation method was proposed for the real-time patient active assessment, the real-time performance is the crucial factor for real rehabilitation scenarios. In this study, only two channels of sEMG signals and four time-domain features were utilized, but the average As the one-side disability of the hemiplegia patients, the inter-coordination of the bilateral limbs should be particularly considered in bilateral rehabilitation. In this study, the isometric output joint force of the bilateral limbs was selected as the rehabilitation training task to promote the inter-coordination of the bilateral limbs. In this task, the equal output force of the bilateral upper limb elbow joint was expected for better coordination. For hemiplegia patients, it is difficult to complete the equal bilateral output force of the elbow joint without any assistance or feedback. To induce the patient’s active adjustment of the bilateral limb inter-coordination, a mirror visual feedback-based human–robot interface was designed. To prove the efficiency of the MVF-based human–robot interface, a comparison experiment was conducted. The subjects were instructed to perform the bilateral isometric lifting task in three different conditions, including without MVF, with MVF, and robot-assisted with MVF. The comparison results are shown in From the experimental results, it is obvious that the subject performing the task with MVF has a smaller average error than the condition without MVF. As the isometric force is limited, the absolute equal bilateral output force is difficult to realize even for healthy subjects. However, it may be caused for the isometric output force task. If the training task is an isotonic lifting task, e.g., lifting a stick and keeping it horizontal, the good completion of this task can be estimated for healthy subjects. This phenomenon can also be used to explain that assistance or feedback is necessary for hemiplegia patients. When the MVF human–robot interface was provided to the subjects, the average error declined significantly. Benefiting from the MVF, the output force of bilateral limbs can be clearly obtained by the visual feedback, which means the subject can voluntarily regulate the output force of bilateral limbs for better inter-coordination. Considering the one-side disability of hemiplegia, the PVSED was utilized to assist the patients in real-time according to the different errors of bilateral output force. In the comparison experiment, the subjects were asked to perform the same task wearing the PVSED and the smallest error was obtained among three conditions. It might be caused by the control strategy of the PVSED. Due to the force error that was used as the input of the PID controller, the PVSED will be quickly activated once the error is too significant. The assistance of the PVSED will not be delivered to the subject if the force error is too small. However, the too quick regulation of assistance may lead to the high instantaneous contact force, which must be forbidden in rehabilitation scenarios. Therefore, the parameters of the PID controller were set as a relatively low value .As the mirror neurons of brains, bilateral rehabilitation training is considered a promising way to induce brain plasticity for hemiplegia patients. In this paper, a mirror bilateral neuro-rehabilitation training system with sEMG-based patient active participation assessment was proposed for the bilateral isometric force output coordination of the upper limb elbow joint. With the mirror visual feedback of the human–robot interface, the hemiplegia patients could perform bilateral isometric lifting tasks with modulated robotic assistance intuitive cognition of motor control of bilateral limbs. To realize fast and adaptive real-time active force assessment, a backpropagation neural network was utilized to map the relationship of the sEMG signals and elbow joint output force by a time-domain multi-feature vector. This active force estimation enables the therapists and patient to observe the patient’s active participation effort during the rehabilitation training for quantitative motor recovery evaluation. Considering the one side disability of the hemiplegia patients, the PVSED rehabilitation robotics was employed in this system for real-time assistance of bilateral rehabilitation. The dynamics of the PVSED were analyzed and adapted for the isometric lifting task requirements. Furthermore, a PID controller was implemented in the robotic control framework for precise and fast output force tracking.Preliminary experiments were carried out to evaluate the feasibility of the real-time active force estimation and bilateral isometric force output assistance. As the three phases for BPNN model training, validation, and testing, the feasibility and effectiveness of the sEMG-based active force estimation method have been proven with good real-time performance. In the five experimental trials of two healthy male volunteers, the experimental results showed that the proposed mirror bilateral neuron-rehabilitation system allowed the patients to perform bilateral equal isometric output force with robotic assistance. Furthermore, a comparison experiment was conducted to validate the effect of the MVF and robot assistance on the isometric force inter-coordination of bilateral limbs. The future work will mainly focus on involving hemiplegia patients to carry out the controlled clinical trials.


Fault diagnosis of mechanical equipment is mainly based on the contact measurement and analysis of vibration signals. In some special working conditions, the non-contact fault diagnosis method represented by the measurement of acoustic signals can make up for the lack of contact testing. However, its engineering application value is greatly restricted due to the low signal-to-noise ratio (SNR) of the acoustic signal. To solve this deficiency, a novel fault diagnosis method based on the generalized matrix norm sparse filtering (GMNSF) is proposed in this paper. Specially, the generalized matrix norm is introduced into the sparse filtering to seek the optimal sparse feature distribution to overcome the defect of low SNR of acoustic signals. Firstly, the collected acoustic signals are randomly overlapped to form the sample fragment data set. Then, three constraints are imposed on the multi-period data set by the GMNSF model to extract the sparse features in the sample. Finally, softmax is used to as a classifier to categorize different fault types. The diagnostic performance of the proposed method is verified by the bearing and planetary gear datasets. Results show that the GMNSF model has good feature extraction ability performance and anti-noise ability than other traditional methods. Rotating machine plays a crucial part in steam turbine, electric generator, and engine ,2. Bearil1/2 norm to the objective function of SF to realize intelligent fault diagnosis under velocity fluctuation. Han et al. [With the development of deep learning, intelligent fault diagnosis methods have achieved remarkable success . Wang etn et al.  stacked Acoustic signal adopts non-contact measurement method, which is more widely used than vibration signal and has lower cost . TherefoIn the process of feature extraction , we can In order to overcome the above shortcoming, a novel non-contact fault diagnosis method based on the generalized matrix norm sparse filtering (GMNSF) is proposed in this paper. In this method, the generalized matrix norm is introduced into the sparse filtering to seek the optimal sparse feature distribution to overcome the defect of low SNR of acoustic signals. First of all, the acoustic signals collected are randomly overlapping to form the sample fragment data set. Then, three constraints are imposed on the multi-period data set by the generalized matrix norm sparse filtering model to extract the sparse features in the sample. Finally, Softmax classifier is used to output bearing health states corresponding to different acoustic signals.The remainder of this paper is organized as follows. W is a filter applied to each sample. Concretely, let jth feature value (row) for the ith example (column). As shown in In practice, the activation function ith row and jth column in characteristic matrix, and The features of each sample are normalized so that the features of all samples fall on the unit sphere of L denotes the sample dimension.Then, the matrix norm penalty is used for sparsity constrained optimization. Suppose a data set has The generalized matrix norm can ensure that all the inactive eigenvalues will be approximately uniformly distributed in the eigenspace. In the feature matrix, the lifetime sparsity of the feature has been optimized in the optimization process of population sparsity and high dispersity.L-BFGS algorithm. In particular, the gradient of cost function for In this paper, the cost function As shown in W.Model training: The GMNSF model is trained through the original acoustic dataset. Set the training sample set as Feature extraction: The discriminant features can be obtained by the optimal weight matrix Fault recognition: The learned features of all samples are combined with the labels and then trained through the softmax classifier. First, Z-Score is normalized to activate training and test data. The calculation method is as follows:M denotes the training sample size, and D denotes the category number. λ is the parameter of the weight decay term.Then, the softmax regression model is trained by the learned characteristic set Rolling bearings and gears are the major components of rotating machinery, and their health conditions are closely related to the normal operation of the equipment. In this paper, the rolling bearing data set and planetary gear data set are taken as the research objects to evaluate the effect of the proposed method.The bearing data set is from Shandong University of Science and Technology (SDUST). Experimental equipment includes motor, coupling, planetary gearbox, bearing seat, and turntable. r and s are set between 1 and 10. As can be seen from r = 8 and then gradually decreased. According to the above analysis, r = 8 and s = 1 are the best and the average accuracy is 97.7%.The input and output dimensions of the model as well as the size of the data set have a great impact on the algorithm. In order to study the performance of GMNSF under different parameter settings, On the basis of the above-mentioned analysis, the average accuracy of the proposed method on bearing dataset is 97.9%, the error is 0.96%, and the average calculation time is 12 s. In addition, a confusion matrix is used to display the classification result as shown in To better demonstrate the advantage of GMNSF, we make a comprehensive comparison with several existing methods as shown in In order to simulate the actual working conditions, acoustic data are collected in the gear speed random fluctuation state, so as to verify the diagnostic performance of the proposed generalized matrix norm sparse filtering method. The experimental data in this section are collected by the planetary gearbox failure test bench. In order to show the speed of the collected sound signal, the speed fluctuation diagram under five health conditions is shown in Additional diagnostic information can be seen in To further describe the differentiation of learning features by the proposed method, T-SNE  is used To further illustrate the sparse feature extraction performance of GMNSF. The standard SF is compared with the proposed GMNSF feature vector by using the rolling bearing data set, as shown in In addition, 9 row vectors of weight matrix trained by GMNSF are randomly selected as shown in The current study proposes a generalized matrix norm sparse filtering to improve the feature learning ability of raw sparse filtering. The generalized matrix norm is introduced into the standard sparse filtering to obtain the optimal sparse distribution. The diagnosis results of rolling bearing dataset and planetary gearbox dataset show that GMNSF model has higher test accuracy and more stable weight quality for acoustic signals. In addition, the selection of sparsity parameter is beneficial in improving diagnostic performance, and the diagnostic accuracy of the proposed method is better than that of existing methods.However, the hyperparameters of the proposed method still need to be manually adjusted. The automatic selection of the parameters may make GMNSF easier to be used in practice. Therefore, the next step is to adaptively adjust these parameters by some optimization techniques, so as to improve the intelligence of the model and raise its performance.


The deformation of a thick-walled cylinder under pressure is a classic elastic mechanics problem with various engineering applications. In this study, the displacement of a viscoelastic thick-walled cylinder under internal pressure is investigated via analytical as well as numerical modelling. The fractional Maxwell model is initially introduced to describe the creep deformation of high-strength Q460 steel. Subsequently, an analytical solution to the creep deformation of the thick-walled cylinder under both internal and external pressures is deduced with the corresponding principle. The analytical solution is examined with a numerical simulation that incorporates the fractional Maxwell model by a user-defined subroutine. The numerical simulation agrees well with the analytical solution. The limitations of the current study are also discussed. Thick-walled cylinders are widely used in the petrochemical industry, in natural gas, in high-pressure hydraulic systems and in other structures, such as high-pressure oil pipes, petrochemical pressure vessels, heat exchange tubes, storage vessels, nuclear reactor pressure vessels, cannon barrel, steam pipelines and functionally graded materials ,2. TheseThe creep deformation of thick-walled cylinders has been intensively investigated. Shinozuka  studied Thus, in this study, a fractional rheological model is first introduced to describe the creep deformation of Q460 steel under different uniaxial tensile stresses. With this fractional creep model, the creep deformation of a thick-walled cylinder subjected to internal pressure is analyzed through both analytical and numerical modelling.Until the first application of fractional calculus in the solution of an integral equation by Abel, it has been a long time since the discussion of fractional calculus between L’Hôpital and Leibniz in 1695 . NowadayThe traditional integer Maxwell model is composed of a spring element and a Newtonian dashpot in series connection, as illustrated in E is the elastic modulus of the spring; t is the time; According to the constitutive relationship of the Abel dashpot and Hooke spring, and their series connection in the fractional Maxwell model, the constitutive equations of this fractional model can be expressed as follows: the total strain of the FMM is composed of the elastic strain represented by the Hooke spring and the viscous strain represented by the Abel dashpot. With the elastic constitutive equation of the Hooke spring and the fractional constitutive equation of the Abel dashpot, the constitutive relation of the FMM is expressed asAfter simplification, Equation (1) can be rewritten ass, Equation (2) can be rewritten with the Laplace transform technique as follows :(4)Js=εsThus, Equations (3) and (4) giveApplying the inverse Laplace transform to Equation (5) yieldsIf the fractional order in Equation (6) is 1, then Equation (6) reduces toThe expression of To verify the practicability of the fractional Maxwell model introduced above, a group of strain–time curves obtained from creep tests of high-strength Q460 steel at various stress levels  are digiTo show the superiority of the fractional Maxwell model, the traditional integer Maxwell model is also utilized to fit the creep curves of Q460 steel, as shown in From a and b are the inner and outer radius of the cylinder, respectively; G is the shear modulus and r is the distance from any point within the cylinder to the center of the cylinder. When it comes to the plane strain condition, the radial displacement can be obtained by replacing K, G are volume and shear modulus, respectively. The viscoelastic generalization of Hooke’s law can be expressed asD is interpreted as representing partial differentiation with respect to time; f, g, D. The Laplace transforms of the viscoelastic generalization of Hooke’s law areHooke’s law is decomposed into isotropic and deviatoric parts asK with G with Suppose that the solution to a certain problem in elasticity is available; then, the associated viscoelastic solution in which the same stresses are applied at Now, suppose that the material is elastic in hydrostatic compression and follows the fractional Maxwell model in shear. Equation (3) can be rearranged asComparing Equations (13) and (14), the shear modulus is expressed asG with Equation (15), the displacement in the Laplacian domain is expressed asReplacing With Equation (17), Equation (16) can be rewritten asThen, the radial displacement in the time domain can be obtained with the inverse Laplace transform of Equation (18) asIt can be seen from Equation (19) that the radial displacement is composed of two parts—the elastic part and the time-dependent part. When G in Equation (10) is replaced by the elastic modulus E of the fractional Maxwell model in Equation (20), which can be explained by the basic assumption that the time-dependent deformation relies only on the shear stress, as stated previously.Equation (20) is almost the same as Equation (10), with only the slight difference that the shear modulus Though the history of fractional calculus is long, it is still somewhat new to non-mathematicians and ordinary engineers. Fortunately, as a general finite element platform, ABAQUS offers users great flexibility to build their own models with a secondary development function. More specifically, when ABAQUS fails to provide an appropriate model to deal with a certain time-dependent material, user subroutine CREEP can be used to define the rheological behavior of this material .If user subroutine CREEP is used to define a material’s behavior, the subroutine aims to offer the “uniaxial” creep laws, which will be incorporated into a general time-dependent material formulation. It can be coupled with temperature and electricity analysis. It also should be noted that the meaning of variables defined in this subroutine depends on the specific model chosen. Taking the variables DECRA(1), for example, it may represent the equivalent uniaxial deviatoric/cohesion/compressive creep strain increment when the subroutine CREEP coupled with the metal/capped Drucker–Prager/gasket model. Details of the numerical implementation of the fractional Maxwell model can also be found in a previous study  and the To check the reliability of the built-in creep subroutine of the fractional Maxwell model in ABAQUS, numerical creep experiments of high-strength Q460 steel at various stress levels are conducted and compared with the laboratory experiments carried out by Wang  et al.The geometrical model of the steel specimen is illustrated in Repeating the simulation process mentioned above, with the mechanical parameters listed in Form A plain strain model is established to simulate the creep deformation of a thick-walled cylinder under internal pressure. The inner and outer radii of the cylinder are 1 cm and 6 cm, respectively. The elastic modulus of cylinder is 10 GPa and the Poisson’s ratio is 0.3. The viscosity of the cylinder is The theoretical radial displacement at the inner surface of the cylinder is plotted according to Equation (19) and radial displacement by the numerical simulation is also plotted with the data obtained at each step, and they are both plotted and compared in In line with the previous section, it should be noted that there are also limitations of the current study. Firstly, only the viscoelastic deformation is considered in this study and the plastic deformation is not taken into consideration. This is a requirement of the elastic–viscoelastic correspondence principle . This isCompared to the traditional integer Maxwell model, the fractional Maxwell model is an efficient nonlinear model to capture the essence of creep deformation of steels both in the transient and the steady-state stages.In analogy with the analytical models based on traditional integer creep models, the analytical solutions based on fractional models can also be established with the help of the elastic–viscoelastic correspondence principle.The implementation of the fractional Maxwell model with the help of the second development function enriches the material libraries of the general-purpose finite element software and enables the capability of finite element software to conduct more realistic numerical simulations.The numerical simulation results validate the creep displacement of a thick-walled cylinder under constant internal pressure predicted by the analytical solution based on the fractional Maxwell model. Hence, the analytical solution of creep displacement of a thick-walled cylinder under constant internal pressure based on the fractional Maxwell model can be used with confidence to predict the time-dependent deformations.The viscoelastic deformation of a thick-walled cylinder under internal pressure is investigated via analytical modelling and numerical simulation. Based on the study, the following conclusions are made:


The growing demands on compact and high-definition single-photon avalanche diode (SPAD) arrays have motivated researchers to explore pixel miniaturization techniques to achieve sub-10 μm pixels. The scaling of the SPAD pixel size has an impact on key performance metrics, and it is, thereby, critical to conduct a systematic analysis of the underlying tradeoffs in miniaturized SPADs. On the basis of the general assumptions and constraints for layout geometry, we performed an analytical formulation of the scaling laws for the key metrics, such as the fill factor (FF), photon detection probability (PDP), dark count rate (DCR), correlated noise, and power consumption. Numerical calculations for various parameter sets indicated that some of the metrics, such as the DCR and power consumption, were improved by pixel miniaturization, whereas other metrics, such as the FF and PDP, were degraded. Comparison of the theoretically estimated scaling trends with previously published experimental results suggests that the scaling law analysis is in good agreement with practical SPAD devices. Our scaling law analysis could provide a useful tool to conduct a detailed performance comparison between various process, device, and layout configurations, which is essential for pushing the limit of SPAD pixel miniaturization toward sub-2 μm-pitch SPADs. Single-photon avalanche diodes (SPADs) have been widely recognized for having unique features, such as single-photon sensitivity and picosecond timing resolution. In recent decades, SPAD arrays fabricated with the silicon-based complementary-metal-oxide-semiconductor (CMOS) process have been extensively studied for a number of scientific and industrial applications. To explore the emerging applications of SPAD image sensors, researchers have developed large-scale SPAD pixel arrays in compact sensor formats. Continuous efforts in SPAD research and development have led to the exponential growth of the array size and dramatic shrinkage of the pixel dimension; the SPAD array size has reached a milestone of 1 megapixel .In addition, 3D-stacking approaches have enabled the physically isolation of pixel circuits from the SPAD array while ensuring electrical connection via pixel-level bonding, which provides a promising solution for pixel miniaturization below 10 μm ,5,6. SucIn this paper, we present an in-depth study of scaling laws in SPADs to clarify the underlying tradeoffs in SPAD design and to give perspectives for the future design of multi-megapixel SPAD arrays. The formulation of scaling laws with regard to the pixel size is performed based on the assumption that the pixel circuit can be located outside of the SPAD array and does not impact the pixel layout. Based on the introduced equations, the scaling behavior of the SPAD performance is exemplified in the plots. The scaling law equations are then compared with previously published experimental results with various SPAD sizes. A good agreement between the theoretical fitting and experimental data validates the scaling law analysis.The paper comprises four sections. To proceed with the theoretical analysis of the scaling laws, some assumptions must be made. First, the SPAD pixel array configuration is assumed to be a square grid, while it is not difficult to generalize the discussion to other configurations, e.g., honeycomb structure . Second,In a non-3D-stacked FSI or BSI configuration, SPAD and pixel circuits coexist in the same plane. In a given pixel pitch, the SPAD and circuit have to share the limited area, and the circuit complexity can affect the size of the SPAD active area and its performance. The main focus of this analysis is to formulate the scaling laws of SPAD performance, and, hence, the SPAD array without circuit components is desired for more systematic and quantitative analysis. Fourth, the active-to-active distance is assumed to be fixed at a certain dimension, irrespective of the scaling parameter. This is justified by the following discussion.z-axis for each implantation layer is unchanged, and the breakdown voltage of the p-n junction in the SPAD remains in the same range. This implies that, unlike scaling in MOS transistors, where a lower supply voltage is adopted for the smaller devices, the power supply voltage for the SPAD does not scale as a function of its dimensions. Another premise in SPAD pixel design is that the guard-ring width is sufficiently large to avoid premature edge breakdown. Given the fact that the lateral diffusion length of doped ions cannot readily be controlled, the electrostatic potential distribution around the guard ring is not dependent on the active diameter. The optimum guard-ring width ensuring no edge breakdown in the operating condition is defined by the following equation [For analysis of the scaling laws in the SPAD pixel, it is natural to assume that the doping profile along with the equation :(1)VBgrPDP=Qmeter Lp .The discrepancy between the designed and actual active size stems from two possible reasons: nonideality in the process fabrication and nonideality in the device design. One example of the process nonideality is the lateral diffusion of doped ions . The latOn the other hand, the device design nonideality is caused by a lateral electric field near the guard ring. Photocharges generated in the neutral region of the SPAD randomly move around due to thermal diffusion until they reach the nearby depletion region and are drifted to an electrode. If the photocharges reach the main p-n junction with a high electric field, they induce avalanche multiplication, thereby generating a photon detection signal. However, photocharges close to the border of the active area can reach the depletion region toward the guard ring before reaching the main junction. In such a case, the carriers do not cause avalanche multiplication, and no photon detection signal is observed. This so-called “border effect” ,24 causeFor both process- and device-originated nonidealities, PDP correction can be performed by introducing an inactive radius e region . The corPDE is another indicator of single-photon sensitivity. Unlike PDP, where the sensitivity is normalized by the active area, PDE is defined as the single-photon sensitivity normalized to the pixel area. The following equation holds :(6)PDE=PBased on the previous equations, PDE can be explicitly formulated as:DCR has several different causes, such as band-to-band tunneling, trap-assisted tunneling, trap-assisted thermal generation, and the diffusion current ,27. ExpeBased on the assumption that premature edge breakdown is suppressed, the tunneling components at the edge of the active region can be neglected. Contributions of the thermal generation and diffusion current are also negligible in the depletion region to the guard ring due to an insufficient electric field for avalanche triggering by the generated carriers. Therefore, the contribution from the main p-n junction of the SPAD dominates over that from the edge of the active region. Interestingly, all the aforementioned DCR components are proportional to the “effective” active area.The tunneling current, regardless of being band-to-band or trap-assisted, is proportional to the total volume of the region with a highly concentrated electric field, which is clearly proportional to the active area. Thermal generation and diffusion carriers are detected only when those carriers are generated in the vicinity of the active region. Assuming that thermal generation and the diffusion current are spatially uniform around the active region, those components are also naturally assumed to be proportional to the active area. The scaling law for DCR can be formulated as follows:R is defined by the DCR normalized by the drawn active area and is often used for comparison of the SPAD process quality between devices fabricated in different processes [R on The DCR density rocesses . As withAt larger Note that the above discussion is based on the assumption that the guard-ring width is optimized to avoid edge breakdown for the entire range of the pixel pitch. In the actual device design, sometimes an abrupt increase of DCR and DCR density is observed at a smaller pixel pitch even with fixed active-to-active distance. To the best of our knowledge, no systematic analysis has been conducted for this phenomenon. One possible reason is the enhanced curvature at the edge of the active region inducing a high electric field near the guard ring. Analogously to antennas, the electric field tends to increase in regions of high curvature, which may induce premature edge breakdown when scaling down the pixel. Another possible explanation is the nonideality in the photoresist formation process.In most SPAD devices, the diffusion regions for the p-n junction, guard ring, or isolation are formed by well doping where high energy doping is employed. In such a process, a thicker photoresist is desired to avoid penetration of the accelerated ions through the resist. The opening size of the photoresist for such a thick resist  requires careful calibration to match the actual shape and size to the designed layout. The layout for well doping is usually supported only for 0 or 90 degree lines, whereas a SPAD layout often involves a circular or ring shape with arbitrary angles. This could cause the deviation of the actual resist opening size from the design especially in the smaller pixel dimension, leading to unwanted edge breakdown.Correlated noise, such as afterpulsing and crosstalk, is critical for certain applications where the temporal and spatial correlations of photon detection signals play key roles ,33,34. Aximation .e is the elementary charge; A is the temperature-, bias-, and process-dependent coefficient; Assuming the spatially uniform distribution of the deep trap states, Note that the dead time is assumed to be constant for all Crosstalk is another type of correlated noise in SPAD pixels. Unlike afterpulsing, where only a single pixel is involved, crosstalk involves two or more pixels. When avalanche multiplication is triggered in a pixel, thousands to millions of electrons and holes are generated. When those carriers are recombined with counterpart charges, either photons or phonons can be emitted to preserve the energy conservation law. Silicon is a material with an indirect bandgap, and hence the probability to emit photons is very low. For photon energy higher than the silicon bandgap, only several to tens of photons are emitted out of the one million avalanche-generated carriers ,37. HoweB is a coefficient that will be explained later, r is the distance from one SPAD of interest to the other, and r in the above equation corresponds to the pixel pitch Similar to afterpulsing, the crosstalk probability mated by :(12)Pc=BB is dependent on both the emitter and receiver characteristics. Considering the emitter, B should depend on the total number of emitted photons, which is proportional to B should also be correlated with the sensitivity of the receiver. The probability of detecting an emitted photon is proportional to the PDP and the active area, which coincide with the PDE times The coefficient To suppress crosstalk, several countermeasures can be considered. First, lowering The avalanche-originated power consumption in large-scale SPAD arrays is a key parameter as it grows proportionally to the number of pixels. The total power consumption in a SPAD array depends on the incident photon flux. For a systematic comparison, the following discussion focuses on the energy consumption per single avalanche event, The timing jitter in the SPAD is determined by multiple factors, such as the device configuration, doping profile, detection threshold, excess bias, and temperature, and it is not straightforward to formulate the scaling law for this. Qualitatively, a larger pixel pitch produces a higher timing jitter for several reasons: first, the spatial expansion of the avalanche multiplication process takes more time in the larger velocity . Second,In the above sections, the scaling laws of the key SPAD characteristics with pixel dimensions were investigated. Miniaturization of the SPAD pixel improves the DCR, afterpulsing, power consumption, and timing jitter, whereas it has an adverse effect on the fill factor, PDP, and PDE. The equations for the scaling laws are summarized in To demonstrate the applicability of the scaling law analysis to practical situations, we performed a theoretical fitting with experimental results from the literature. The extracted fitting parameters indicate that the maximum PDP reaches 22.8% for larger pixels with this device configuration and bias condition, whereas the effective photon-insensitive region with the width of 1.06 μm reduces the maximum PDP for smaller pixels. The fitting result implies that the PDP will go down to zero at In We investigated the theoretical expressions of the scaling laws for the major performance metrics in SPAD pixels. The analysis showed that SPAD pixel miniaturization improved the DCR, afterpulsing, power consumption, and timing jitter, whereas it had an adverse effect on the FF, PDP, and PDE. The scaling law equations for PDP and DCR were then applied to the experimental data in the literature, showing good agreement with the measured trends. The extracted fitting parameters were used to extrapolate the expected pixel size dependence of PDP and DCR, which implied that a pixel size smaller than 10 μm cannot be achieved without modification of the current process conditions and design rules.Our scaling law analysis has three potential applications: the prediction of SPAD performance based on existing measurement data, extraction of model parameters to quantify the pixel-size-independent metrics, and systematic comparison of SPAD performance tradeoffs for different process, device, and layout configurations. The first approach can be useful for designers to understand the underlying tradeoffs and decide the optimal pixel size for applications of interest. The second approach can be critical to understanding the limiting factors of SPAD performance.In-depth study of the extracted model parameters provides rich information of SPAD pixels, such as the inactive radius, parasitic capacitance, effective depletion width, and effective decay length of the avalanche-induced photons, which cannot be directly measured with existing measurement techniques. The third approach can be employed for clarifying the pros and cons of one SPAD device configuration to the other, which is essential for the correct choice of process conditions and device structure. Combining these approaches will provide a promising tool for further pushing the limit of SPAD pixel miniaturization toward sub-2 μm-pitch SPADs.The extracted models are focused on pixel size dependence. Further generalization of the models to fully account for the voltage and temperature dependence of the metrics remains to be verified in future work.


To automatically generate accurate and meaningful textual descriptions of images is an ongoing research challenge. Recently, a lot of progress has been made by adopting multimodal deep learning approaches for integrating vision and language. However, the task of developing image captioning models is most commonly addressed using datasets of natural images, while not many contributions have been made in the domain of artwork images. One of the main reasons for that is the lack of large-scale art datasets of adequate image-text pairs. Another reason is the fact that generating accurate descriptions of artwork images is particularly challenging because descriptions of artworks are more complex and can include multiple levels of interpretation. It is therefore also especially difficult to effectively evaluate generated captions of artwork images. The aim of this work is to address some of those challenges by utilizing a large-scale dataset of artwork images annotated with concepts from the Iconclass classification system. Using this dataset, a captioning model is developed by fine-tuning a transformer-based vision-language pretrained model. Due to the complex relations between image and text pairs in the domain of artwork images, the generated captions are evaluated using several quantitative and qualitative approaches. The performance is assessed using standard image captioning metrics and a recently introduced reference-free metric. The quality of the generated captions and the model’s capacity to generalize to new data is explored by employing the model to another art dataset to compare the relation between commonly generated captions and the genre of artworks. The overall results suggest that the model can generate meaningful captions that indicate a stronger relevance to the art historical context, particularly in comparison to captions obtained from models trained only on natural image datasets. Image captioning refers to the task of generating a short text that describes the content of an image based only on the image input. This usually implies recognizing objects and their relationships in an image. Those descriptions should be meaningful and accurate in relation to the image content. In resolving this task, significant progress has recently been made using multimodal deep learning models. However, most of the research in this field is performed on datasets of natural images, while the specific aspects of generating captions for artwork images have not yet been systematically explored.A common prerequisite for training deep neural captioning models are large datasets of semantically related image and sentence pairs. In the domain of natural images, several well-known large-scale datasets are commonly used for this task, such as the MS COCO , Flickr3A more interesting, as well as more challenging, task would be to generate “iconographic” captions that describe the contextual aspect of the subject matter. Creating a dataset for such a complex task is difficult because it requires expert knowledge in the process of collecting sentence-based descriptions of images. Several such art datasets of image-text pairs exist, but those mostly consist of only a few thousand examples and are therefore not suitable for training deep neural network models in the current state-of-the-art setting for image captioning. However, there are several existing large-scale artwork collections that associate images with textual descriptions in the form of keywords and specific concepts. In particular, a large-scale artwork dataset, published under the name “Iconclass AI Test Set” , represeThe work presented in this paper is an extension of a previous work that represents one of the first attempts in generating captions for artworks . The metThe availability of large collections of digitized artwork images fostered research initiatives in the intersection of artificial intelligence and art history. Most commonly, research in this area focuses on addressing problems related to computer vision in the context of art data, such as image classification ,12,13, vRecently, there has been a surge of interest in topics related to jointly exploring both visual and textual modalities of artwork collections. Pioneering works in this research area addressed the task of multimodal retrieval. In particular, Ref.  introducApart from multimodal retrieval, another recently emerging topic of interest is visual question answering (VAQ). In , the autThe task of image captioning has not been significantly studied in the context of art images. A limited number of studies contributed to the task of generating descriptions of artwork images using deep neural networks. For example, Ref.  proposesMotivated by the success of utilizing large-scale pretrained language models such as the BERT  model  for diffwww.arkyves.org (accessed on 21 June 2021). It includes images of various types of artworks such as paintings, posters, drawings, prints, manuscripts pages, etc. Each image is associated with one or more codes linked to labels from the Iconclass classification system. The authors of the “Iconclass AI Test Set” provide a json file with the list of images and corresponding codes, as well as an Iconclass Python package to perform analysis and extract information from the assigned classification codes. To extract textual descriptions of images for the purpose of this work, the English textual descriptions of each code associated with an image are concatenated. Further preprocessing of the descriptions includes removing text in brackets and some recurrent uppercased dataset-specific codes. In this dataset, the text in brackets most commonly includes very specific named entities, which are considered a noisy input in the image captioning task. Therefore, when preprocessing the textual items, all the text in brackets is removed, even at the cost of sometimes removing useful information.The main dataset used in this work is the “Iconclass AI Test Set”  dataset.The textual descriptions are represented as a concatenation of text phrases related to the Iconclass codes. One image in the dataset can be associated with one or more textual phrases. To better understand the configuration of the dataset, Due to this type of structure and having only one reference caption for each image, the Iconclass Caption dataset is not a standard image captioning dataset. However, having in mind the difficulties of obtaining adequate textual descriptions for images of artworks, this dataset can be considered as a valuable source of image-text pairs in the current context, particularly due to the large number of annotated images that enables training deep neural models. In the experimental setting, a subset of approximately 76,000 items is used for training the model, while around 5000 items are used for validation and 5000 for testing.www.wikiart.org (accessed on 1 February 2020), collection was used. Images in the WikiArt dataset are annotated with a broad set of labels ; therefore, one aspect of the evaluation process includes analysing how the generated captions relate to genre labels because genre labels indicate the category of the subject matter that is depicted . Furthermore, this dataset is used to explore the difference between captions generated using a model trained on artwork images and models trained on natural image datasets.In order to explore how the proposed approach works on another artwork dataset, a subset of 52,562 images of paintings from the WikiArt, https://github.com/EvaCet/Iconclass-image-captioning (accessed on 22 July 2021).For the purpose of training an image captioning model, in this work the unified vision-language pretraining model (VLP) introduced in  was emplThe evaluation of the model’s performance includes both quantitative and qualitative analyses of the generated captions. To quantitatively evaluate the generated captions, standard language evaluation metrics for image captioning and novel reference-free image captioning methods are used. The standard metrics include the four BLEU metrics , METEOR As the standard image captioning metrics measure the relation between generated and original captions, they do not address the relation between the image itself and the generated caption. Although translating images and text in a joint semantic space has been an ongoing research topic, the recently introduced CLIP model  achievesThe relation between the generated captions and the reference captions on the Iconclass Caption test set was evaluated using standard image captioning metrics. To evaluate the relation between the generated caption and the input image, the new CLIPScore metric was used, both in its original and reference-augmented versions. The results on the Iconclass Caption test set are presented in The current results cannot be compared with any other work because the experiments were performed on a new and syntactically and semantically different dataset. However, the quantitative evaluation results are included to serve as a benchmark for future work. In comparison with current state-of-the-art caption evaluation results on natural image datasets  , the BLEIt is questionable how adequate standard reference-based metrics are in assessing the overall quality of the captions in this particular context because they mostly measure the word overlap between generated and reference captions. They are not designed to capture the semantic meaning of a sentence and therefore it is particularly difficult to evaluate iconographic descriptions. Furthermore, they are not appropriate for measuring very short descriptions which are quite common in the IconClass Caption dataset. Moreover, because they do not address the relation between the generated caption and the image content, the standard image captioning score could be low even if the generated caption is semantically aligned with the image content. In In some examples within the Iconclass dataset, the generated caption is even more related to the image content than the ground-truth description (example image in row 3 in Qualitative analysis was performed by exploring examples of images and generated captions on two datasets. One is the test set of the Iconclass Caption dataset that serves for direct comparison between the generated captions and ground-truth descriptions. The other dataset is a subset of the WikiArt painting collection, which does not include textual descriptions of images but has a broad set of labels associated with each image. Therefore, this dataset is useful to explore how the generated captions relate to the genre categorization of the paintings.To gain a better insight into the generated image captions, in Analysis of the unsuccessful examples indicates that similarities between visual representations can result in generating analogous, but very misleading, iconographic captions. It also demonstrates underlying biases within the dataset. For instance, in the Iconclass Caption training test, there are more than a thousand examples that include the phrase “New Testament” in the description. Therefore, images that include structurally similar scenes, particularly from classical history and mythology, are sometimes wrongly attributed as depicting a scene from the New Testament. This signifies the importance of balanced examples in the training dataset and indicates directions for possible future improvements. Furthermore, by analysing various examples of generated captions, it becomes clear that recognizing fine-grained categories, e.g., exact names of saints or specific historical scenes, is still a very challenging task.The Iconclass dataset is a collection of very diverse images and apart from the Iconclass classification codes, there are currently no other metadata available for the images. Therefore, it is difficult to perform an in-depth exploratory analysis of the dataset and the generated results in regard to attributes relevant in the context of art history such as the date of creation, style, genre, etc. For this reason, the fine-tuned image captioning model was employed on another artwork dataset.The quality of the generated captions and the model’s capacity to generalize to new data are further explored by employing the model on another artwork dataset, a subset of the WikiArt dataset that includes labels related to the genre of the paintings. To understand the contribution of the proposed model in the context of iconographic image captioning, it is interesting to compare the Iconclass captions with captions obtained from models trained on natural images. For this purpose, two models of the same architecture but fine-tuned on the Flickr 30 i MS COCO datasets were used. The mean value of CLIP-S on the Iconclass captions of the WikiArt subset is 0.595, while the mean score of the Flickr caption is 0.684 and that of the Coco captions is 0.691. This result corresponds to the conclusion presented in , which sTo gain a better understanding of the CLIPScore in relation to the various types of image captions and the images themselves, The distribution of data points in This paper introduces a novel model for generating iconographic image captions. This is achieved by utilizing a large-scale dataset of artwork images annotated with concepts from the Iconclass classification system designed for art and iconography. Within the scope of this work, the available annotations were processed into clean textual descriptions and the existing dataset was transformed into a collection of suitable image-text pairs. The dataset was used to fine-tune a transformer-based vision-language model. For this purpose, object classification aware region features were extracted from the images using the Faster R-CNN model. The base model in our fine-tuning experiment is an existing model, called the VLP model, that was pretrained on a natural image dataset on intermediate tasks with unsupervised learning objectives. Fine-tuning pretrained vision-language models represents the current state-of-the-art approach for many different multimodal tasks.The captions generated by the fine-tuned models were evaluated using standard image captioning metrics and recently introduced reference-free metrics. Due to the specific properties of the Iconclass dataset, standard image captioning evaluation metrics are not very informative regarding the relevance and appropriateness of the generated captions in relation to the image content. The reference-free metric, CLIPScore, represents an interesting new approach for evaluating image captions based on the cosine distance between image and text embeddings from a joint feature space. This image captioning metric shows very promising results in evaluating the semantic relation of images and texts, particularly in the case of well-known iconographic image-text examples. However, it is still uncertain if it the best choice for assessing all iconographic image captions because it generally favours literal over non-literal image-text relations. In this context, one of the major directions for future research is related to exploring multimodal deep learning approaches in the context of non-literal relations between images and texts.The overall quantitative and qualitative evaluations of the results suggest that it is possible to generate iconographically meaningful captions that capture not only the depicted objects but also the art historical context and relation between subjects. However, there is still room for significant improvement. In particular, the unbalanced distribution of themes and topics within the training set results in often wrongly identified subjects in the generated image descriptions. Furthermore, the generated textual descriptions are often very short and could serve more as labels rather than captions. Nevertheless, the current results show significant improvement in comparison to captions generated from artwork images using models trained on natural image caption datasets. Further improvement can potentially be achieved with fine-tuning the current model on a smaller dataset with more elaborate ground-truth iconographic captions.


Ginkgo biloba extracts (GbE) using a combination of non-targeted and targeted analytical approaches [Herbal supplements, usually used as dry extracts, are multi-substance mixtures. For their characterization, it is essential to specify the drug-extract ratio and indicate the extraction solvent used. The National Toxicology Program (NTP) has evaluated a ginkgo-based herbal product for toxicity and carcinogenicity in short-term and long-term studies . The traproaches .Notwithstanding the great importance of this project, we consider the conclusions drawn from the results of the pilot study to be largely speculative and scientifically insufficiently substantiated.Extract characterizationG. biloba leaves is a multi-step process. The quality of the starting material and the production process decisively influence the quality and composition of the product (GbE). A minimum requirement for the comparison of herbal extracts is the indication of the drug-extract ratio (DER) and the information about which solvent was used for the extraction process.The production of a standardized extract of EGb 761® is an aqueous-acetonic dry extract DER 35–67:1) that contains 22 to 27% flavonol glycosides, 5.4 to 6.6% terpene trilactones, and less than 5 ppm ginkgolic acids, according to the European Pharmacopoeia [5–67:1 thFor the NTP test extract, the contents of flavone glycosides, terpene trilactones, and ginkgolic acids are listed, but the DER and the extraction solvent are not described. In our opinion, it is scientifically inappropriate to use an uncharacterized ginkgo extract as a reference extract for determining extract similarities.HPLC-ELSD analyticsA HPLC-ELSD method was used to generate chromatographic fingerprints of the studied extracts. Apart from the analytical limitations of this HPLC method, it is annoying that Figs. 1 and 2 in the publication of Collins and colleagues [Non-targeted NMRNMR spectroscopy is an excellent and indispensable analytical tool for the characterization of (complex) molecular structures. In contrast, NMR is not very suitable for the determination of similarities in the composition of complex substance mixtures, not least because of a lack of sensitivity and resolution. In this light, the assignment of compositional similarities of GbE based on dendrogram analysis of similarities of NMR spectra appears to be not very plausible.HPTLCHigh-performance thin-layer chromatography (HPTLC) was used to compare the similarities of the studied extracts. As described for the methods above, HPTLC is also limited concerning its resolution und sensitivity. Aside from the method limitations, no comparison of HPTLC lanes of the NTP test article and EGb 761® is presented in the results of the study, and thus, this data cannot contribute to a similarity comparison of the products.We base our criticism on four points in particular:The concerns described above, about the different specifications of the GbE products and hence well-known chemical differences between the extracts, the method limitations, the apparent differences in the presented HPLC data, the lack of direct NMR and HPTLC comparability, and the lack of methods to detect important substance classes beside the flavonoids and terpene trilactones, raise the question to what extent the conclusions can be supported. The assessment of chemical profiles of herbal extracts requires profound understanding of the compounds present in the different extracts investigated and the use of multiple tailored analytical methods to detect und compare substance patterns from polymers, and of major constituents down to compounds in trace amounts.For all the above reasons, we are more than critical of Collins’ claim to be able to easily determine a sample’s authenticity of a complex GbE using a simple analysis. Furthermore, we do not consider the methods presented to be suitable for making robust comparisons of the similarities of complex herbal extracts.


The Deterministic Network (DetNet) is becoming a major feature for 5G and 6G networks to cope with the issue that conventional IT infrastructure cannot efficiently handle latency-sensitive data. The DetNet applies flow virtualization to satisfy time-critical flow requirements, but inevitably, DetNet flows and conventional flows interact/interfere with each other when sharing the same physical resources. This subsequently raises the hybrid DDoS security issue that high malicious traffic not only attacks the DetNet centralized controller itself but also attacks the links that DetNet flows pass through. Previous research focused on either the DDoS type of the centralized controller side or the link side. As DDoS attack techniques are evolving, Hybrid DDoS attacks can attack multiple targets (controllers or links) simultaneously, which are difficultly detected by previous DDoS detection methodologies. This study, therefore, proposes a Flow Differentiation Detector (FDD), a novel approach to detect Hybrid DDoS attacks. The FDD first applies a fuzzy-based mechanism, Target Link Selection, to determine the most valuable links for the DDoS link/server attacker and then statistically evaluates the traffic pattern flowing through these links. Furthermore, the contribution of this study is to deploy the FDD in the SDN controller OpenDayLight to implement a Hybrid DDoS attack detection system. The experimental results show that the FDD has superior detection accuracy (above 90%) than traditional methods under the situation of different ratios of Hybrid DDoS attacks and different types and scales of topology. As the 5th Generation (5G) of mobile communication promoted telecommunication technologies across different domains and achieved initial success, the 3rd-Generation Partnership Project (3GPP) began investigating advanced features to pave the way for 6G . One of Hybrid DDoS means high malicious traffic not only attacks the DetNet centralized controller itself but also attacks the links that DetNet flows pass through. On the one hand, Hybrid DDoS follows the conventional DDoS attacking pattern that exhausts controller resources by using a high volume of legitimate traffic, causing the controller operation to be significantly delayed or interrupted. This type of DDoS attack is also called a Server-Exhausted Attack (SEA) ,14,15,16Numerous studies have explored how to detect a single type of DDoS attack, i.e., an SEA ,14,15,16This study, therefore, proposes a Flow Differentiation Detector (FDD) to detect Hybrid DDoS attacks in the DetNet. The FDD adopts a two-step detection scheme: (1) it first applies a fuzzy-based feature extraction methodology to determine the most valuable links for the DDoS link/server attacker, and then (2) it applies a heuristic approach to detect the network behaviors generated from the SEA and LFA. The FDD observes two common characteristics of the SEA and LFA: (1) they must traverse through the target links, and (2) their source IP address or destination IP address must be the servers located in the target area. When hybrid attacks occur, the number of DetNet flows that pass through the servers in the target area increases, while the number of flows that depart from the servers in the target area remains almost fixed. The FDD is also implemented in OpenDayLight, which is a typical Software-Defined Networking (SDN) controller to observe the performance of the FDD in detecting hybrid attacks in an SDN environment.The contributions of the FDD are threefold: (1) to implement a hybrid attack in OpenDayLight, which is a typical Software-Defined Networking (SDN) controller, to observe and ensure the existence of hybrid attacks and synergy in an SDN environment; (2) to provide a conceptual solution to detect hybrid attacks in the DetNet; and (3) to evaluate the performance of current solutions and discuss a possible development path to defend against hybrid attacks.This study is organized as follows. This section describes the various types of DDoS attacks, including SEAs, LFAs, and hybrid attacks, and the corresponding detection methods.SEAs exploit server vulnerability and generate a large number of requests to quickly exhaust the targeted server’s resources. Therefore, a server lacking resources cannot respond to legitimate requests and may even crash. A typical SEA includes SYN flooding attacks ,10,11, DSYN flooding attacks that transmit TCP connections and ACK packets are not returned, causing the server to store all these requests and, eventually, run out of resources. Therefore,  calculatDNS flooding attacks send a large number of nonexistent domain-name requests to the DNS, causing DNS server failure. To detect DNS flooding,  calculatAn HTTP flooding attack means that the clients interact with the server and send HTTP requests to compel the server to allocate as many resources as possible to serve the attack, thus denying legitimate users access to the server’s resources. Reference  uses serLFAs exploit vulnerabilities in topology by generating legitimate traffic nearby the target area to congest key links. If all target links are congested, the target area cannot receive requests from an external network, and the service in the target area is therefore unreachable. LFAs comprise two kinds of attacks, i.e., Coremelt attacks and Crossfire attacks, where the former represents having a single target link, while the latter represents having multiple target links. For example, Reference  uses endThe DetNet applies flow virtualization to satisfy time-critical flow requirements. The DetNet virtualizes heterogeneous data flows in order to (1) preserve the properties of these flows over a converged IP-based infrastructure and 2) be backward compatible with statistically multiplexed IP-based traffic. Therefore, two tasks of DetNet flow virtualization are designed. First, the DetNet duplicates and eliminates packets over noncongruent routing paths to achieve a sufficiently high delivery ratio and satisfy the application’s QoS requirement  be backw. SeveralMost studies focus on detecting a single type of DDoS attack, i.e., SEA or LFA, in a traditional network rather than DetNet. Reference  exploresThe comparison of the studies mentioned above is summarized in L) and switches of all flow tables in the network.The DetNet is a centralized network architecture for managing network node behavior and topology information. T represents the size of the topology, SW represents the switches, TA represents the servers in the target area, F represents all flows  they must traverse through the target links, and (2) their source IP address or destination IP address must be the servers located in the target area. Furthermore, when hybrid attacks occurred, we observed a phenomenon where the number of flows that passed through the servers in the target area increased, while the number of flows that departed from the servers in the target area was almost fixed. However, under normal traffic, the number of flows that departed from the servers increased when the number of flows that passed through the servers increased because the servers can provide normal services.As discussed above, this study proposes a Flow Differentiation Detector (FDD). The FDD adopts a two-step detection scheme to detect Hybrid DDoS attacks in the DetNet. First, the FDD uses a fuzzy-based feature extraction methodology to determine the most valuable links and locate the targeted area of the Hybrid DDoS attacker. Second, the FDD statistically evaluates and detects the network behaviors generated from the SEA and LFA based on the observation of the target links.W) represents the importance of the links enclosed in an area, For the first step, to locate the targeted links, the FDD calculates the link weight of each link according to Formula (1) and selects the top 20% links as the target links based on the Pareto principle, where roughly 80% of consequences come from 20% of causes . Link weAfter finding the target link, we selected the switch that was farthest from the target area at the end to the ends of the target link. It was necessary to use the end of the target link to measure the service performance considered attacked by an SEA and LFA. We merged the flow tables from these switches and divided these flows in the target link into input flows and output flows. The input flows with a destination IP equal to the target area represent an external request to access the target area, and the output flows with a source IP equal to the target area represent a service response from the target area. Regardless of the type of attack, the number of input flows will increase, and the number of output flows will maintain a value or increase slowly. Therefore, we calculated the frequency of the differentiation between the number of input flows and output flows.The OpenFlow protocol supports IP Flow Information Export (IPFIX) . We usedIn order to detect hybrid attacks, it was necessary to calculate the flow’s traffic attacking the target area. For this purpose, we used an SDN controller to complete the process and calculated the flow differentiation by the number of flows to be the service performance in the network ,12,14,15c is the degrees of freedom, and iO is the evaluated sample calculated by formula (3)iE is the i is the i-th data in the sampled time period, and k is used to moderate the sensitivity of testing. The k value is set to 1.0 in this study and can be modified by the network administrator based on their security policy. The chi-square test p-value that evaluates whether the test result is significantly a hybrid attack or not.The FDD statistically evaluates and detects the network behaviors generated from the SEA and LFA based on the observation of the target links in the second step. The FDD applies a chi-squared test, which is a statistical hypothesis test to determine whether there is a statistically significant difference between the influx of expected flows and the influx of observed flows. Influx refers to the difference between the traffic flow in the targeted area and the traffic flow outside the targeted area. If the testing result is significantly different, it can be determined that the target area is attacked, and an alert is sent to the network manager. The formula (2) for the chi-square statistic M. Third, we avoid causing measurement error, selecting existing flows. Other flows in the contrast source IP and the destination IP are measuring flows, and the remaining flows are deleted. Fourth, the large table (M) classifies the number of input (inputSP) and output (outputSP) flows. The last step applies a chi-squared test to determine whether there is a statistically significant difference between the observed The detailed procedure, as shown in The experiment of this study establishes a hybrid attack detection system based on the FDD in SDN. It is implemented in Java, deployed on OpenDayLight, which is a popular open-source SDN controller, and obtains flow information through OpenDayLight’s REST APIs so that it will not introduce additional loading overhead to the controller. There are two main modules in this system: (1) the Target Link Selection Module (TSM) and (2) the Service Performance Module (SPM).APIs1: /restconf/operational/network-topology:network-topology;APIs2: /restconf/operational/opendaylight-inventory:nodes/node/switch/table.The TSM is responsible for calculating which links will be selected as target links. It also calculates the link weight using Formula (1) in . The TSMThe SPM applies a chi-square test to evaluate the differentiation between input and output flows to obtain SP in the target link. If a significant difference exists, the system will send an alert that indicates the target area is under attack to the network manager. The SDN controller can obtain all the information from the network depending on the OpenFlow protocol. This implementation will focus on the SDN controller.The SDN controller obtains the topology of the network, such as the link between the switch and the switch, the link between the switch and the node, or the link between the switch and the controller, so we can use this API to determine all the distributions and positions of the links in the network. The SDN controller obtains the flow table from the links. The TSM obtains flows that pass the target area to calculate the link weight of each link, and the SPM obtains input and output flows that pass through the target area.This section compares our proposed FDD with other detection methods through experiments. We first introduce the experimental environments, scenarios, performance metrics, and methods to be compared. Then, the effects of detection performance on different parameters, such as the ratio of hybrid attacks, the number of servers in the target area, and the number of switches, are investigated.For comparison of the detection effect between the FDD and the current detection systems, therefore, the experimental group is the FDD, and the control group is LFADefender (LAD) , which dThis subsection explains how to set up the experimental environment, including the tools for simulating the network, attacks, and parameters, all experiment scenarios, evaluation of detection performance metrics, and the methods to be compared, are described.The experiment setup in  was usedThe SEA botnets with trafgen  send at R is the hybrid ratio and is defined as LB/SB, where LB is the number of LFA botnets, and SB is the number of SEA botnets. This research experimentally tested the effectiveness of one to five servers in the target area because the number of servers could be changed. This study randomly generated attacking and normal traffic based on a fuzzy testing methodology to ensure all experiments in this section have sufficient samples/scenarios regarding hybrid attacking and normal traffic.By default, we set up the topology with seven switches in the SDN, three servers in the target area, and five botnets as attackers in the SDN to combine SEAs and LFAs under different ratios to achieve the effect of hybrid attacks. Finally, we tested three types of topologies  where hyIn the evaluation effect part, three measures are considered, namely detection accuracy, false-positive rate (FPR), and false-negative rate (FNR), which are classified by the confusion matrix shown in We further explored the type I and type II errors of all studies by observing the false-positive rate (FPR) and false-negative rate (FNR). A type I error is the false rejection of the null hypothesis (FPR) such as “normal traffic is detected as a hybrid attack”, while a type II error is the false acceptance of the null hypothesis (FNR) such as “a hybrid attack is detected as normal traffic”. It should be noted that the accuracy rate of previous studies shown in This experiment attempts to explore the performance of all methods detecting the hybrid attack and the synergy effect of different numbers of servers in the target area. The environment setting is that the hybrid ratio is equal to 0.4 in the topology with seven switches.This section further explores the performance of all methods when facing different scales of topology. In this experiment, the hybrid ratio is equal to 0.4, and the three servers in the target area are under different numbers of switches. The results are shown in In this study, we proposed the FDD, an approach against Hybrid DDoS attacks that leverages SDN, to effectively detect Hybrid DDoS attacks according to the experimental results. In the detection system, we deployed the FDD on OpenDayLight and evaluated it on Mininet, which simulates a real network environment.The FDD first applies a fuzzy-based mechanism, Target Link Selection, to determine the most valuable links for the DDoS link/server attacker, and then it statistically evaluates the traffic pattern flowing through these links. Furthermore, the contribution of this study is to deploy the FDD in the SDN controller OpenDayLight to implement a Hybrid DDoS attack detection system. The experimental results show that the FDD has superior detection accuracy (above 90%) than traditional methods under the situations of different ratios of Hybrid DDoS attacks and different types and scales of topology.k in our proposed FDD. Furthermore, we will explore the possibility of developing an AI robustness test methodology to verify that the artificial intelligence model has been fully trained to ensure robustness and effectiveness.In future work, we plan to extend our work to Artificial Intelligence (AI) technology and explore the best setting of the moderator


The classification, phenotyping, and management of heart failure (HF) has been based on the left ventricular (LV) ejection fraction (LVEF) . In the HF is a very complex syndrome, and it is unrealistic to predicate its classification on a single biomarker . MoreoveMoreover, the controversies regarding the delineation of normal range show there is substantial discordance between the various imaging modalities in LVEF measurement among expert laboratories . ImportaEqually disturbing with the variability in measurements is the vague physiological significance of LVEF. The LVEF, which equals the LV stroke volume (LVSV) divided by the LV end-diastolic volume (LVEDV) and is usually expressed as a percentage, is inappropriately assumed to be an index of myocardial contractility . HoweverFinally, LVEF has long been used for the guidance of medical treatment in HF. In this regard, the lack of effective medications for HFpEF has represented for years a large and growing unmet need in cardiology . HoweverTo have a meaningful impact, classification systems should reflect current approaches in HF evaluation and management. Although the LVEF has served for a long time as the holy grail of HF, the time to be ejected as the primary classification system for this global pandemic has arrived ,16.