Deep learning models are efficient in learning the features that assist in understanding complex patterns precisely. This study proposed a computerized process of classifying skin disease through deep learning based MobileNet V2 and Long Short Term Memory (LSTM). The MobileNet V2 model proved to be efficient with a better accuracy that can work on lightweight computational devices. The proposed model is efficient in maintaining stateful information for precise predictions. A grey-level co-occurrence matrix is used for assessing the progress of diseased growth. The performance has been compared against other state-of-the-art models such as Fine-Tuned Neural Networks (FTNN), Convolutional Neural Network (CNN), Very Deep Convolutional Networks for Large-Scale Image Recognition developed by Visual Geometry Group (VGG), and convolutional neural network architecture that expanded with few changes. The HAM10000 dataset is used and the proposed method has outperformed other methods with more than 85% accuracy. Its robustness in recognizing the affected region much faster with almost 2× lesser computations than the conventional MobileNet model results in minimal computational efforts. Furthermore, a mobile application is designed for instant and proper action. It helps the patient and dermatologists identify the type of disease from the affected region’s image at the initial stage of the skin disease. These findings suggest that the proposed system can help general practitioners efficiently and effectively diagnose skin conditions, thereby reducing further complications and morbidity. The skin is the largest organ in the human body, consisting of the epidermis, dermis, subcutaneous tissues, blood vessels, lymphatic vessels, nerves, and muscles. Skin can prevent lipid deterioration in the epidermis with liquid such that the skin barrier feature can be improved. Skin diseases can arise because of fungal development over the skin, hidden bacteria, allergic reactions, microbes affecting the skin’s texture, or creating pigment . Skin ilThe inclination of skin diseases shows a multiplicity of forms, lack and misdistribution of qualified dermatologists, and the need for timely and accurate diagnosis calls for data-driven diagnosis. The advancement of lasers and photonics-based medical technology has made it possible to diagnose skin diseases much more quickly and accurately. However, the cost of such diagnosis is still limited and expensive. Deep learning models ,5,6,7 arThis study used a dataset consisting of seven skin diseases: Melanocytic nevi, Benign keratosis-like lesions, Dermatofibroma, Vascular lesions, Actinic keratoses, Intraepithelial carcinoma, Basal cell carcinoma, and Melanoma. This dataset contains more than 10,000 dermatoscopic images. A random (rand) function is applied to split the data into the training data (7224) and validation data (1255). The considered dataset is slightly imbalanced because some skin diseases are more, and some are less in number. To overcome such problems, we used data augmentation, and this technique balances the data and generates more images either by rotations or transformations from the existing data.The main objective of the article is to bring in the state of art technique, namely the MobileNet V2  with LSTThe rest of the article is organized as follows. Several existing approaches are mechanized to recognize and classify skin diseases. Most of the diagnosing methods rely on imaging technology, and the epidermal recognition of such skin diseases does not need radiological imaging technologies. They can recognize the condition based on the standard images through image processing techniques, including image transformation, equalization, enhancement, edge detection, and segmentation ,20,21. TSkin diseases are also classified through the necessary image processing approaches like morphological operations for skin detection ,23. MorpAlam et al.  automateArtificial Neural Networks (ANN)  and ConvThe Fine-Tuned Neural Network-based  skin disThe image is classified based on intensity though a statistical approach, namely Gray Level Co-occurrence Matrix (GLCM) extracts the features that appear in the acquired image, usually the textured-based parameters . GLCM deBayesian classification is among the approaches used in skin disease classification . The appK-Nearest Neighbor (KNN)  is the pSkin disease classification through the ensemble models  yields hCross correlation-based model for classification of the feature extraction , where bThe proposed model is associated with the mobile application, and there are many other such experimental applications designed for the ease of assessment of the diseases. Lee, H.Y. et al.  presenteIn this section, integrating the LSTM with the MobileNet V2 is explained with an architecture diagram. MobileNet V2 is used in classifying the type of skin disease, and LSTM is used to enhance the performance of the model by maintaining the state information of the features that it comes across in the previous generation of the image classification.As opposed to MobileNet V2 , MobileNMobileNet  architecThe feature vector map of size n. The value of the variable resolution multiplier identified by The multiplier value is context-specific, and for the experimental analysis in skin disease classification, the value of multiplier The proposed model incorporates the depth-wise, and point-wise convolutions are bounded by the depletion variable identified by the variable The two hyper-features width multiplier and the resolution multiplier help adjust the optimal size window for accurate prediction based on the context . In the The principle underneath the MobileNet architectures is to substitute complicated convolutional layers in which each layer comprises a convolutionary layer of size 3 × 3 that buffers the input data, accompanied by a convolutional layer of size 1 × 1 pointwise that incorporates these filtered parameters to build a new component as shown in The MobileNet V2 architecture comprises the residual layer with a stride of 1 and the downsizing layer with a stride of 2 alongside the ReLu component. The architecture of the same is represented in Both residual and downsizing layer encompass 3 sub-layers each.The 1 × 1 convolution with the ReLu6 is the first layer.Depth-Wise Convolution is the second layer in the architecture. The Depth-Wise layer adds a single convolutional layer that performs a lightweight filtering process.1 × 1 convolution layer without non-linearity is the third layer in the proposed architecture. In the third layer, the ReLu6 component is used in the output domain.ReLu6 is used to ensure the robustness used in low-precision situations and improvise the randomness of the model.All the layers have the same quantity of output channels within that overall sequence.The filter of size 3 × 3 is common for contemporary architecture models, and dropout and batch normalization are used during the training phase.There is a residual component to support the gradient flow across the network through batch processing and ReLu6 as the activation component.tanh) is the layer for the non-linearity layer. In LSTM  is the ct’. The weights From Equations (4)–(8), the variable Om designates the occurrence matrix of dimension m × m, where m represents the image’s grey levels:One strategy of texture attribute extraction is the Grey-Level Co-occurrence Matrix (GLCM)  approachIn the Equation (9) stated above, the variable By normalization, matrix components have a dimension scale from 0 to 1 that can be modified as a function of likelihood. The variable The GLCM approach is used in approximating the disease growth based on the obtained texture-based information. The GLCM is used in evaluating the skin disease of the proposed model.This experiment was performed on an online compiler named Kaggle  with an The libraries used in our model are NumPy, pandas, os, matplotlib. pyplot, shutil, seaborn, and torchvision as stated by Declan V. . The MatThe dataset plays a crucial role in the training of our proposed neural networks for automated diagnosis. The dataset named HAM10000 is the skin disease dataset that has been extracted from the Kaggle, which has served as a benchmark database downloaded from the source . The datIn this section, the results of the proposed model are discussed in detail. The proposed MobileNet V2 with LSTM performance is evaluated through the hyperparameters like training and validation loss measures that determine the proposed model’s capabilities. The proposed model’s learning rate at various training levels is discussed in the current section. The performance evaluation with other existing approaches in terms of Sensitivity, Specificity, Accuracy, Jaccard Similarity Index (JSI), and Mathew Coefficient Correlation (MCC) are presented. The proposed model’s computational time is evaluated as a part of performance evaluation and compared against the existing approaches on performing the classification over similar data.The experiment is carried out on the dataset discussed in To make a reasonable contrast among various approaches concerning the implementation configurations, the authors decided to standardize pivotal parameters throughout all the studies. At first, the experiment was performed over several images, and the type of disease is assessed through the proposed MobileNet V2 with the LSTM approach. The outcome of the experiment is shown in The graphs represented in The fact we observed is that the accuracy in predicting the input skin images is slightly distorted. The right graph represents the learning rate versus loss obtained. This non-linear graph resulted in lower values at specific points, challenging, leading to higher epochs and increasing the time complexity.To overcome the drawbacks mentioned above, we reduced the batch size to a much smaller size to have faster convergence, resulting in better-optimized results. We increased the learning rate, which resulted in getting better outputs at training fewer epochs. The The values are evaluated on repeated execution of the proposed model with a varied training level. The performance of the proposed model is compared against a Heuristic Approach for Real-Time Image Segmentation (HARIS) , a Fine-In evaluating the proposed model’s performance, the experimentation is repeatedly executed over the auxiliary computer on repeated execution of the model. The evaluations are done in concern to the number of times the proposed model accurately classifies the skin disorder that is considered the True Positive and correctly identifies that the image is not of that particular skin category as True Negative. The number of times the proposed model recognizes the disease correctly is considered the False Positive. The number of times the proposed model misinterprets the skin disease is assumed as the False Negative. The approximated values of the True Positive, True Negative, False Positive, and False Negative are considered in evaluating the metrics like Sensitivity, Specificity, and the Accuracy of the proposed model.The values of the various evaluation metrics like Sensitivity (Sen), Specificity (Sep), Accuracy(Acc), Jaccard Similarity Index (JSI), and Matthews Correlation Coefficient (MCC) are presented through the Equations (12)–(16) with respect to the obtained True Positive, True Negative, False Positive, and False Negative values on experimentation. The metrics determines the preciseness of the model in correctly classifying the class of the skin disease.The performance of the proposed model is compared against the various other approaches concerning the parameters like Accuracy, Sensitivity, and Specificity of each of the approaches like Decision Tree and Random Forest approaches, Lesion Index Calculation Unit (LICU) approach, Fuzzy Support Vector Machine with probabilistic boosting the segmentation, Compact Deep Neural Network, SegNet model, U-Net model, respectively ,83,84,85The proposed model outperformed compared to the various existing approaches. All the approaches are examined against the five classes of skin diseases. The proposed model is implemented against seven skin diseases classes as evaluations presented in The confidence mean in The incorporation of the LSTM component has enhanced the accuracy of the proposed approach. It can be observed from Training loss and validation loss are two significant hyper-parameters that determine the preciseness of the proposed model. The training accuracy and the validation accuracy of the proposed model are evaluated against the similar parameters of other models considered in this study. In the process of evaluating the performance of the proposed model, the execution time of the validation phase is presented in The computational time of the proposed MobileNet V2 with LSTM is reasonably good, as shown in The proposed model based on MobileNet V2 with LSTM is associated with the mobile application for ease of use for the patients/doctors to classify diseases based on the image fed as the input shown in one such application . Figure netscope and netron architecture. The information can either be transferred through the XML/JSON, or the model can be implemented in an iOS platform without separate space for the model. A flask framework can be used in web/mobile-based data access with a set of available libraries. The LSTM can be imported from the Keras libraries that are available for incorporating into the model; the integration of the LSTM is almost the same as the Recurrent Neural Network architecture.The proposed model is quite helpful for both the patients and the doctor in classifying the type of skin disease. The image captured using the mobile device is fed as the input for the interface. The interface then uses the MobileNet V2 with LSTM for processing the data. The MobileNet V2 can be implemented in an iOS platform through The proposed framework for the practical implication involves multiple phases in the process of classifying the type of the skin disease presented through The performance of the proposed Mobilenet V2 with LSTM is evaluated through various assessment metrics, and the implemented results are presented along with the graphs of the hyperparameters. It is evident from the obtained results that the proposed model’s performance for lesion classification is reasonably fair with minimal computational time than the other approaches. The proposed model needs a considerable lesser computational effort in performing the classification of the images, which makes it suitable to deploy in mobility devices. The prospectus application that works with the proposed model can precisely identify the skin disease for the image that is captured.The proposed model based on the MobileNet V2 and LSTM approach proved efficient for skin disease classification and detection with minimal computational power and effort. The outcome is promising, with an accuracy of 85.34% when experimented with and compared with other methods over the real-time images acquired from Kaggle . The MobThe proposed model is computationally efficient as it is designed to work on top of lightweight capability devices. The proposed MobileNet V2 with the LSTM model needs a more significant number of parameters for better accuracy. The considered input image and the MobileNet V2 with LSTM model’s resultant outputs have no significant randomness to explore all possible patterns in the assessment process. Alongside the bottleneck in residual connections in the proposed architecture, the model yields higher accuracy with minimal effort. The model can be further improved by incorporating the self-learning capability and knowledge acquisition from its previous experiences. The efforts on training the model can be considerably reduced. However, the model must be mechanized to assess the impact of features extracted for each strategy, and the incorporation of randomizing components is necessary. The researchers recommend that future research be performed to examine the feature extraction actions based on biomarkers, even though there is ample data, depending on the specific findings. Biomarkers effectively identify the disease from the supplementary data like the genomic, protein sequences, and pathological data in addition to the imaging data. It is recommended to consider lightweight security when transmitting physiological and biological data in health networks, and a user-friendly smart device app, which can display alarms and communicate between patients and physicians in eHealth and telehealth environment to securely exchange and transmit data ,97.


Spectral X-ray computed tomography (SCT) is an emerging method for non-destructive imaging of the inner structure of materials. Compared with the conventional X-ray CT, this technique provides spectral photon energy resolution in a finite number of energy channels, adding a new dimension to the reconstructed volumes and images. While this mitigates energy-dependent distortions such as beam hardening, metal artifacts due to photon starvation effects are still present, especially for low-energy channels where the attenuation coefficients are higher. We present a correction method for metal artifact reduction in SCT that is based on spectral deep learning. The correction efficiently reduces streaking artifacts in all the energy channels measured. We show that the additional information in the energy domain provides relevance for restoring the quality of low-energy reconstruction affected by metal artifacts. The correction method is parameter free and only takes around 15 ms per energy channel, satisfying near-real time requirement of industrial scanners. Spectral X-ray computed tomography (SCT) is an emerging technique for enhanced non-destructive investigation of inner features of objects. In SCT, the energy dependence of the linear attenuation coefficient (LAC) of materials is measured with the aid of detectors able to discriminate the energy of the incoming photons in a discrete number of energy channels. It has been demonstrated that using this technique yields enhancement of the contrast-to-noise ratio (CNR) ,2,3,4, tThe goal of a MAR algorithm is to improve the quality of the reconstructions by removing the streaks while preserving the real features. This can be conducted in a pre-reconstruction approach by applying a correction algorithm in the sinogram domain. This is typically conducted by adaptive interpolation of the photon-starved detector pixels in the projections. Alternatively, MAR can be conducted in a post-reconstruction step, by directly attempting to remove the streaks and restore the LAC values of the materials. However, these traditional correction methods typically require a high amount of prior knowledge for iterative regularization, are tailored ad-hoc for certain applications, and fail to offer a generalizable solution. Convolutional neural network (CNN) architectures have shown a lot of success recently in fields such as computer vision and image classification and segmentation ,14,15. IIn order to include spectral domain information, we adopt a CNN architecture designed for biomedical image segmentation, namely U-Net . This isThe importance of the information in the spectral domain has been confirmed in related work. A CNN-based SCT method that includes information in the spectral domain is that of Wu et al. . This meThe overarching goal of the method presented in this manuscript is to remove the metal streaking artifacts from energy-resolved reconstructions measured using SCT. In SCT, the level of photon starvation depends on the LAC properties of the materials. It is thus an energy dependent phenomenon and there may be samples for which the artifacts appear in the low-energy channels but not in the high-energy ones. This is of great value as the information regarding geometrical features, shapes and material physical properties can be extracted from the artifact-free high-energy domain and used to restore the reconstruction in the artifact-affected low-energy domain. In our approach, the MAR is performed in a post-reconstruction step with the aid of a spectral CNN architecture. The CNN is trained using a data pair consisting of the metal artifact-affected and ground truth (artifact-free) reconstructions. Once trained, the CNN can directly correct metal artifacts from reconstructions. One of the key factors to the success of the CNN corrections is the quality of the training data, i.e., how well the samples generalize the problem to be solved. With our architecture, an image set for training is a pair of distorted and ground truth spectral images, which, in a real acquisition, correspond to the measured and desired corrected images. Depending on the difficulty  of the problems that we aim at solving using a CNN, the quantity and variety of the training data must be adjusted. In the literature, as a rule of thumb, at least a few thousand sets of training data are required to model realistic, non-trivial imaging problems. Such a large number is generally achieved by relatively simple data augmentation steps, in which elementary operations such as Gaussian noise filtering and affine transformations are applied to the input data. The training data can either be generated starting from real experimental data or simulation data. Alternatively, efficient solutions have been found by a first step training with synthesized simulation data, followed by a second training step with experimentally measured data. For most applications, experimental training data are hard and slow to generate in sufficient amounts. For metal artifact corrections, for example, the ground truth would be the results of iterative correction algorithms or manual correction of the measured data.In the following, we present a method for generating training data based on physical models of photon-matter interactions  and thenIn this work, the ground truth training data are generated with the aid of a software tool for CT phantoms presented by Kazantsev et al. . Using tThe metal artifact-affected reconstruction are produced via modeling the forward projection with the sample component for SCT simulations presented by Busi et al. . The tooOnce the full set of the training data designed for the CNN learning is generated, the dataset pairs are split and grouped into training data, validation data and test data. The training data consists of the samples that are used by the network to fit the parameters at each epoch. The validation data are excluded from the training of the network and used after the fitting at each epoch to tune hidden-layer parameters, avoid overfitting and evaluate the convergence of the training. Similarly, the test data consists of samples that are excluded from the training and are only used at the end of the overall training of the network to analyze the performance of the trained CNN architecture in solving the assigned problem. Experts in the field typically recommend to randomly divide the dataset into 64% training data, 16% validation data and 20% test data . The maiAs this work focuses on spectral X-ray CT, the U-Net architecture present in literature required an adaptation that enables the extraction of the additional information in the energy domain as well as to support three-dimensional data. This is a technical and conceptual challenge because the spectral images are represented as large 3D matrices , which expresses how the input values L represents the difference between the network’s prediction and the target Conv. 3D: These are layers of the network consisting of a defined number of 3D convolutional filters with defined size. These filters are the main units (or neurons) of the network as they contain all the coefficients being tuned during the training process. Note that the number of filters at each step defines the fourth dimension of the incoming dataset in Dropout: During training, some training entities  are randomly chosen and ignored or, in other terms, dropped. This step is introduced to avoid overfitting, as suggested by Srivastava et al. .MaxPooling: Max pooling refers to the step of applying a maximum filter to  non-overlapping sub-regions of the initial representation, and it is necessary to reduce the dimension of the data while preserving the features. For example, a 3D MaxPooling with size UpSampling: Works as opposite of the MaxPooling operation. By default, this is conducted by padding the matrix cells with zero values. Alternatively, the upsampling can be carried out with interpolation.Concatenate: This step concatenates datasets along a chosen dimension. For example, the concatenation in the first layer of the CNN architecture concatenates the data preceding the MaxPooling and the data connected to the 4 filters convolutional layers, both with a size of The network contains several layers of parameters that are recursively updated by loss function optimization. The update of the parameters Adam  was emplIn this work, the initial spectral image was with size The drawback of a 3D network like ours, and the limiting factor for applications to physically measured data with large image sizes, is the exponential computation requirement. Existing hardware limitations govern the maximal depth of a 3D U-Net implementation, which prohibits the learning of higher-level logic and global data relations across the image. The hardware limitations can be overcome in trivial scenarios by increased pooling sizes, leading to a more aggressive downsampling of the input image data and a reduction in the number of learning filters. This approach has limitations in practical implementations when a given minimum observation size for imaged features is required and when a minimum number of features is to be extracted for the image correction. We observed a significant loss of detail in the image correction due to hardware resources restricting the 3D network to be relatively shallow  and operating at excessively coarse resolution. A possible solution to this would be the integration and subsequent combination of alternative architectures able to preserve resolution and detail with respect to the spatial patterns as well as the spectral signal response of the data.The main issue faced when including highly attenuating materials in the training data is the logarithmic nature of the LAC of materials. Since the metals have a value range that is significantly superior to organic compounds, there is a biased training of the CNN architecture. That is because the training relies on the tuning of convolutional filters by minimizing a loss function, which is the difference between the two input images. Due to photon statistic noise, the difference between the simulated and ground truth image is significantly higher for the metal when compared with organic compounds. Consequently, low-attenuation materials vanish from the images, as they are seen by the architecture as a noisy void, hence replaced with zero values. On the other hand, in most cases the overarching goal of the correction is not a mere streaking reduction but a restoration of the image features of the low attenuating materials to efficiently perform further analysis routines. The solution proposed and used in this work is a high attenuation thresholding, which consists in substituting LAC values greater than the LAC of silicon with the corresponding values of silicon. This step is carried out directly after the tomographic reconstruction and is justified by the fact that in most SCT application the metals are not the objective of the data analysis but an intrusive material that leads to artifacts. The CNN architectures are optimized to work with data normalized in the value range A second concern in the data generation process is that the geometric shapes have great impact on the performance of the correction itself. Depending on the application of the CT itself, this imposes different conditions on the type of training data. For security screening scanners, ideally the CNN should have enough training data to learn all the possible geometries. Of course, this is limited by the storage and computational time to generate enough training data. In this work, the sample variation is restricted to relatively simple geometries and the few entries for the number of projections preceding the reconstruction. In an application focusing on luggage screening, objects could be scanned separately  to provide the simulation inputs with realistic shapes for objects made of different materials.Lastly, the aim of our MAR method is not only to restore the image quality by removing streaks and enhancing contrast between different material (as in most existing work). Our goal is also quantitative material characterization from physical properties. The preservation and restoration of the effective LAC values is therefore important too. The correctness of the physical models for generation of the training data thus becomes crucial as incorrect models may introduce offsets in the material LACs after the MAR correction has been applied.In this section, we present our MAR experiments with simulation data  and withFor the benchmarking of the spectral MAR correction using CNN, the training data was generated using McXtrace with the following procedure. The number of individual unique phantoms was set to 10, and a SCT scan of each was conducted simulating randomly 120, 90, 72, 60 or 52 projections with a cone-beam geometry collimation of the source and a  toolbox  and eachThe training was carried out for 50 and 250 epochs, for a total time of approximately 2 and 8 h respectively, to analyze the training performance against the number of training epochs. Once trained, each metal artifact-affected slice is corrected in approximately Root Mean Squared Error . Structural Similarity (SSIM) is a method for measuring the similarity between two images presented by Wang et al. . This meThe performance of the correction is quantified in terms of the following metrics for image quality:These are calculated for each uncorrected and corrected (for both 50 and 250 training epochs) test data and reported in The MSSIM increases significantly for both the 50 and 250 epochs trained CNN corrections, meaning that the streaks are successfully corrected in most cases. The worse In these figures, the top image is consisting of metal artifact-affected (Input), corrected (CNN Correction) and reference input (Ground Truth) image slices at four of the thirtytwo energy channels, corresponding to The first example  is a sliThe same CNN architecture that was trained with MC simulation data was then tested with real metal artifact-affected datasets. A suitcase composed of several metal parts was filled with objects commonly found in luggage and scanned with the spectral X-ray CT technique. The geometry and source settings were set to resemble the one in the simulations to generate training data and 75 projections were taken. As opposed to the simulations, the source was collimated into a fan-beam geometry to have a reduced amount of scattering noise while still having streaking artifacts due to metals. Each vertical slice of the object was measured by vertically translating the sample stage. The difference between the two reconstruction geometries is that vertical slices from the cone-beam reconstruction may have artifacts that are caused by metals belonging to adjacent slices, whereas this is not found for fan-beam geometry. The measured data processing routine followed the SCT procedure described by Busi et al. , rebinniThe drawback of our correction technique (in its current version) is a slight degradation of feature resolution, which manifests as blurring and dampening of LAC values of small metallic objects, and in some cases  as cupping artifacts in homogeneous materials. This could potentially be prevented by including more training data with objects of more diverse shapes and material compositions. Alternatively, the introduction of an objective function in the loss function minimization during the CNN training could identify and segment geometric features that are typically less artifact-prone in the intermediate energy channels and project them into the lower energy channel images.Note that, as opposed to the simulated data, the high-energy channels are dominated by noise due to low photon statistics, indicating an eventual mismatch in the spectrum modeling. Interestingly, a circle shaped artifact (marked with a red arrow) that appears in the first energy channel image of The comparison of the 3D volumes of the input and corrected suitcases in We have presented a method for metal artifact reduction in spectral X-ray CT reconstructions. As a part of our method, we introduced the concept of a spectral CNN architecture. Our approach learns features from the energy domain and thus extends the range of the problems that we can successfully correct for. The main advantage of our method is that it can be generalized and adapted for the problem at hand as the a priori information required for the correction is intrinsically incorporated into the simulated training data. The correction step for real measured data is then parameter-free and with near real-time speed, satisfying the requirements of real scanners. The performance of our method depends highly on the quality of the training data and how well its variety generalizes the problem. We found that training data generation can be a slow and in some cases complex procedure that needs to be tailored according to the desired application. However, this aspect becomes simpler in cases where the diversity of the objects under inspection decreases. In our examples mimicking use cases in luggage scanning for airport security, our correction always successfully improved the results. The current limitation of our correction method is the introduction of mild blur in the reconstruction, which is mainly due to computational hardware limitations. Due to the extremely large size of spectral reconstructions (3- or 4-D), the input data was downsampled in size losing high-resolution features. Likewise, the spectral CNN architecture was relatively limited in the layer depth and in the number and size of the convolutional filters (weight tensors). For these reasons, high-resolution scanners with a large data size may require further adaptations of the architecture or fragmentation of the datasets into smaller patches followed by a successive merging.


This paper presents the novel concept of structuring a planar coil antenna structured into the outermost stainless-steel layer of a fiber metal laminate (FML) and investigating its performance. Furthermore, the antenna is modified to sufficiently work on inhomogeneous conductive substrates such as carbon-fiber-reinforced polymers (CFRP) independent from their application-dependent layer configuration, since the influence on antenna performance was expected to be configuration-dependent. The effects of different stack-ups on antenna characteristics and strategies to cope with these influences are investigated. The purpose was to create a wireless self-sustained sensor node for an embedded structural health monitoring (SHM) system inside the monitored material itself. The requirements of such a system are investigated, and measurements on the amount of wireless power that can be harvested are conducted. Mechanical investigations are performed to identify the antenna shape that produces the least wound to the material, and electrical investigations are executed to prove the on-conductor optimization concept. Furthermore, a suitable process to fabricate such antennas is introduced. First measurements fulfilled the expectations: the measured antenna structure prototype could provide up to 11  In recent years, fiber compounds have increasingly been used in lightweight construction, which has led to increased research efforts on manufacturing processes, material improvement, and the development of structural health monitoring (SHM) systems. Often, fiber-reinforced plastic and thin metal layers are combined to form fiber metal laminates (FMLs), which have the benefits of both materials for application. In particular, the glass laminate aluminum reinforced epoxy (GLARE) has attracted attention as it is used as fuselage material for the Airbus A380, but other material combinations also provide promising properties.The use of FMLs in safety critical applications leads to increased efforts to predict or detect damages early on. Numerous methods for structural health monitoring, i.e., to detect damage inside composite structures, have been proposed. The work of Diamanti et al.  providedAlthough several external approaches exist, an integrated SHM system that can easily be read by a single handheld device would present a major advancement, as it immensely reduces the time and effort for maintenance. Therefore, a sensor node must be embedded into the FML by hybrid integration  without Passive radio frequency identification (RFID) technology is suited for this application, as energy harvested from the reader field can power the sensor node inside the material while data are simultaneously transferred. Therefore, the embedded sensor node is represented as the transponder part of the RFID system, i.e., commercially available readers can be used to power the sensor node and read out sensor data.This article proposes a novel concept for the integration of the antenna into the FML by structuring its outer metal layer. Methods to decouple the antenna from the underlying, potentially inhomogeneous, conductive fiber compound material are discussed. The mechanical influence of the antenna on the load-bearing material layer could be minimized by optimizing the antenna geometry.Different concepts for RFID antennas on conductive substrates were investigated ,6. Most This is the starting point for the following investigations. However, the carbon-fiber-reinforced polymer (CFRP) layers used here were more complex than metal layers used by Ohmura. In particular, the orientation of the carbon fibers inside the composite material influenced the strength of coupling to the antenna, complicating analysis in comparison to isotropic materials such as metals. Here, a thin layer of a ferromagnetic material directly beneath the RFID antenna structure redirected the electromagnetic field, which decoupled the antenna from the substrate and allowed for optimizing the antenna independent of the particular configuration of the FML.The proposed antenna was cut by lasers into the outer steel layer of an FML structure consisting of CFRP and stainless-steel layers. The sensor electronics were embedded inside the material stack. The passive RFID-based sensor node approach used here had the advantage of a minimized construction space and maximized lifetime, since a large energy storage, e.g., a battery, was not needed for operation.However, the conductive CFRP material used as a substrate material complicated the design of the antenna ,9, and tHigh-permeability layers, also known as ferrite sheets, inserted between conductive materials and RFID antennas reduce detuning and attenuation effects . The reaApart from providing the data interface for sensor measurements, the antenna must be able to provide enough energy for the whole sensor node to function properly during data collection. Therefore, the power consumption of the planned electronic system was estimated, and the energy-harvesting capability of the system was measured to show that it is possible to realize sufficient energy and data transfer at RFID standardized The promising results of the measurements carried out for this paper are provided in The design of planar spiral coils for high-frequency RFID antennas is well-known, at least for antennas used in nonconductive environments. Manufacturers of RFID ICs deliver the information on the range of internal tuning capacity There are several approaches that can be used to approximate the inductance of an antenna. Wheeler’s equation  is the bTuning capacity Equation .(2)LcoiThis gives a theoretical value of Equation  was the The geometry of the antenna was designed in CAD software using the determined parameters as described above. The remaining areas of stainless steel within and around the antenna structure itself were not included in the above calculations. Due to this approach, the resulting shift in resonance frequency due to parasitic capacitances was compensated by iterative changes on the antenna geometry. A top view of the antenna design is presented in Femtosecond laser ablation was used to cut contours into a 100 The number of repetitions was optimized, so that a minimally thin residual metal layer remained and mechanically stabilized the flatness of the antenna geometry. If the turns were completely released, mechanical stresses in the material would eventually deform the antenna. Then, heat-resistant adhesive tape was manually applied onto the antenna surface to hold the antenna turns in place. Consecutively, the interturn material was removed by manually lifting it up using tweezers and a little out-of-plane force.For the first measurements, the antenna structure remained on the adhesive tape applied to it. For later applications, the idea was to transport the structure with this tape, electrically contact it, and then establish an adhesive bond with the ferrite sheet and insert the stack into an exposed CFRP region of the FML. After this process, the adhesive tape could be removed.Two different methods were tested to permanently contact the ends of the stainless-steel antenna structures. Soldering to stainless steel did not work well, presumably because of the relatively large surface of the coil combined with good temperature conduction properties. Very good results were obtained by using electrically conductive glue (Elecolit 327 by Panacol). For antenna handling, a thin coverage with UV hardening glue is recommended to increase the connections’ mechanical stability. For later in-material-contacting, the UV glue step is not necessary.Since the antenna structure was initially fabricated without the underlying magnetic foils and CFRP layers, a fixture depicted in For impedance measurements, an impedance analyzer (HP 4194A) combined with a clamping fixture (16047D) was used. The instrument was calibrated using its internal CAL function for OPEN and SHORT with the attached clamping device and the contacting wires leading to the antenna. During OPEN calibration, connecting wires must be placed similar to their positions during measurement to consider their additional parasitic capacitance when placed close to each other. A photograph of the measurement setup is shown in The CFRP plates used here were chosen to represent a selection of different CFRP configurations that may occur beneath the antenna in dependence of the particular application of the FML. The chosen configuration mainly depends on the desired mechanical properties of the FML component that is produced. If loads on the components are expected to mainly act in one direction, a unidirectional approach is advantageous. For most applications, however, a multidirectional layer configuration is chosen because expected loads are also multidirectional. Because various layer configurations are possible, three different layer stacks were chosen to investigate possible differences in the influence on an RFID antenna’s performance. The layer orientation definition is shown in Unidirectional CFRP—UDMultidirectional CFRP 0Multidirectional CFRP 0As a first step of our investigation, the process of the geometry selection by a mechanical FEM simulation is explained. After that, we present impedance measurements to determine the resonance frequency of the antenna and the antenna-ferrite-CFRP system in different configurations, and design adaptations due to application-specific requirements and their effects. Lastly, the power requirement of the sensor node to realize the SHM system inside the material is estimated, and the maximal energy-harvesting capabilities of the designed antenna structure connected to the RFID IC using the IC ’s harvesting feature are validated.The antenna is intended to be structured into the outer material layer of the FML in the final application, so that there is a void inside the material in which the antenna structure is placed. An FEM simulation was performed investigating the impact of different geometric voids inside a 120 μm thick, 70 mm by 70 mm stainless-steel plate. The dimensions were chosen to comply with the plate size and steel foil thickness of the targeted prototype. This step investigates the influence of different void shapes on the stress distribution for one stainless-steel layer without CFRP layers beneath, since only the top layer contained a hole in the shape of the antenna. Therefore, the plate was first simulated without voids to obtain the maximal von Mises stress over the plate without an opening in the material. Plates with rectangular and circular voids were then modeled. The plate was fixed onto the left side and bent down on the opposite side. Von Mises stress is depicted for the investigation. The 0.2% proof strength for the investigated type of stainless-steel 1.4310 (X10CrNi18-8) is provided with 195 The simulated force on the right edge of the plate was iteratively increased to 52 On the basis of these results, a circular antenna geometry was selected to avoid structural damage to the metal surface. In general, when aiming for minimal material weakening, a circular geometry is expected to always be superior. Furthermore, the performance of rectangular antennas, especially if their length-to-width ratio is much different than 1, is expected to depend on their position relative to the carbon fibers inside the CFRP. Circular antennas are expected to not show this placement-dependent behavior.The antenna produced using the FS laser ablation technique as described above can be seen in The resonant frequency of the resulting RFID system was verified by impedance spectroscopy from 1 to 40 One measure to allow for the system to work on a CFRP substrate is to place a ferrite sheet inbetween. The dashed blue curve in As the frequency shift due to CFRP and ferrite under the antenna can be taken into account by changing the inductance value of the antenna geometry, for direct application on CFRP, it might be possible to shift the frequency back from 27 to The antenna structure not only enables data transfer from the sensor system to a reading unit, but also provides the sensor system with a sufficient amount of power for operation. The measurement for the antenna mounted on a ferrite sheet on the multidirectional CFRP plate showed that energy-harvesting capability is currently limited to 11 Results presented in this paper clearly demonstrate the validity of the new concept of using the outer stainless-steel layer of an FML as an antenna. Mechanical simulations confirmed that a circular antenna is best suited to be manufactured into the outer layer of a load-bearing structure composed of FML. The use of thin ferrite layers directly underneath the antenna provides two main advantages: electrical insulation and independence from the composite material used in the immediate vicinity of the antenna structure. This approach allows for embedding sensor nodes for SHM into the FML, and for using the existing outer metal layer to create an antenna, eliminating the necessity for external wiring or additional components outside the material itself.Additionally, results of this paper show that, even for a reader with a geometrically nonoptimized antenna, the harvested energy from the reader field is sufficient to meet the estimated energy requirements of a sensor node with comparably large amounts of required energy due to high analog-to-digital conversion rates. With these findings, it is also possible to provide enough energy for other sensor nodes requiring equal or less energy for multiple other applications.As the next steps, investigations will be performed regarding the miniaturization of the antenna structure and the effect on its energy-harvesting ability. After that, a demonstration of the antenna integrated into a real FML plate will be performed, and the influence of additional metal layers inside the material stack will be investigated.


There are many factors that affect athletes' sports performance in sports competitions. The traditional sports performance prediction method is difficult to obtain more accurate sports performance prediction results and corresponding data analysis in a short time, which is not conducive for coaches to formulate targeted and scientific training sprint plans for athletes' problems. Therefore, based on GA-BP neural network algorithm, this paper constructs a sports performance prediction model and carries out experiments and analysis. The experimental results show that GA-BP neural network algorithm has a faster convergence speed than BP neural network and can achieve the expected error accuracy in a shorter time, which overcomes the problems of the BP neural network. At the same time, different from the previous models, GA-BP neural network algorithm can get the athlete training model according to the relationship between quality training indicators and special sports training results, which can more intuitively show the advantages and disadvantages of athletes. In the final sports performance prediction results, GA-BP neural network prediction results have higher accuracy, better stability, better prediction effect, and higher application value than BP neural network. When people's basic material needs are met, they will continue to improve the quality and level of life and begin to expect a healthier physical and mental world. Healthy life and healthy physical condition have become the focus of people's pursuit and attention in recent years. More and more people are involved in healthy sports, and more and more sports have gradually come into the public's sight and been promoted. Scientific and correct sports methods have also become the concern of people in the process of sports . We shouIn view of the above problems existing in traditional sports performance prediction methods, some people propose to combine artificial neural network, big data mining, and other technologies to solve these problems. Among them, the application of artificial neural network in sports performance prediction has been widely concerned. With the development of artificial intelligence prediction and neural network algorithm, its application in the field of prediction has shown good results, which makes the research of sports prediction begin to change in the direction of more fitting the way of human thinking and more in line with the dynamic changes of sports prediction environment . The neuThe related research projects are systematically described, and their contributions and novelty are compared with the previous work. In view of the above problems existing in the traditional sports performance prediction methods, some people propose to combine artificial neural network, big data mining, and other technologies to solve these problems. Among them, the application of artificial neural network in sports performance prediction is widely concerned. In this paper, based on GA-BP neural network algorithm, the prediction model of sports performance is constructed, and the experiment and analysis are carried out. The results show the following: (1) GA-BP neural network algorithm has faster convergence speed than BP neural network. (2) GA-BP neural network algorithm can achieve the expected error accuracy in a short time and overcome the shortcomings of BP neural network. (3) Different from the previous model, GA-BP neural network algorithm can get the athletes' training model according to the relationship between the quality training index and the special sports training results, which can show the athletes' advantages and disadvantages more intuitively. (4) In the final sports performance prediction results, GA-BP neural network prediction results have higher accuracy, better stability, better prediction effect, and higher application value than BP neural network.In this section, the following is mainly based on the GA-BP neural network algorithm to build the sports performance prediction model. First, we will build a prediction model based on BP neural network algorithm and then introduce a GA algorithm to optimize the algorithm, so as to solve the problems of BP neural network algorithm.BP neural network algorithm is the most important basic algorithm in neural network algorithm. Its structure is topological and belongs to feedforward network structure in neural network structure classification , 20. In Formula  shows thW and threshold θ of each neuron 2 is defined as the mean square error between the actual output and the ideal output. That is to say, the smaller the value is, the more reliable the output is and the smaller the accuracy of the prediction of sports performance is. The flowchart of the GA-BP neural network algorithm is shown in In the GA algorithm, the middle population is represented as xi is the real value, and n is the number of participating samples.In order to predict sports performance more accurately, this paper uses the RMSE evaluation model to determine the standard error through the prediction results and real data of the GA-BP neural network algorithm, as shown in formula :(21)RMSEThis paper will take the long jump as the experimental object of the sports performance prediction model based on the GA-BP neural network algorithm. The process of long jump is a complex dynamic process under the influence of many factors . TherefoWhen the correlation degree is higher than 0.6, it can be considered that the test result of the correlation degree is satisfactory. Therefore, according to the results of the above table, four quality training indexes with the highest correlation degree are selected to construct the neural network sports performance prediction model related to the special long jump performance. Therefore, the number of input neurons of the GA-BP neural network algorithm is set to 4, which, respectively, represents four quality training indexes with a high correlation degree. The number of output neurons is set to 1, which means the result of sports performance prediction . The numIn this paper, the quality training index and special performance of 10 excellent male long jumpers are selected as the learning samples of the GA-BP neural network algorithm, as shown in The convergence curves of the two neural networks in the same error range are shown in In conclusion, the BP neural network algorithm with a genetic algorithm can achieve convergence effect faster under the same conditions, effectively improve the efficiency of learning and training, and quickly get the optimal error accuracy and not fall into the local optimal solution. It has a better fitting and greatly improves the accuracy of sports performance prediction. Therefore, the sports performance prediction model based on GA-BP neural network algorithm is more advantageous than the single BP neural network model.After data normalization, the weights of GA-BP neural network connection can be obtained, which reflects the correlation between quality training indicators and special sports performance and special sports training model. In order to find out the technical problems and training weaknesses of each athlete, the coach can work out the training content for different athletes, so as to achieve the purpose of improving performance scientifically and effectively. As shown in As shown in The test of sports achievements can not only help athletes and students understand their own achievements and weaknesses but also help coaches to understand the strength level of athletes more comprehensively. According to the specific situation, formulate a scientific and effective sports training plan to improve the performance of athletes and students in a short time. At the same time, the prediction of the results of large sports events can help the coach to sprint the goals and plans more comprehensively. But the traditional sports performance prediction method is subjective and has higher requirements for the coach experience. There are many factors that affect the prediction method, which will cause a large prediction dismissal error. The prediction method of sports performance is more accurate, but the coach needs to have a certain calculation ability and level, which is not suitable for extensive application. Therefore, based on GA-BP neural network algorithm, this paper constructs the prediction model of sports performance and makes an experimental analysis of it. Compared with the traditional sports performance prediction method, the single BP neural network algorithm can reflect the nonlinear relationship between input value and output value more quickly and accurately and predict sports performance according to learning samples, but it still has problems such as slow convergence trend and easy to fall into local optimal solution, which leads to the decrease in the accuracy of prediction results. It needs to be further optimized. The experimental results show that GA-BP neural network trained by samples has a faster convergence speed than BP neural network under the same conditions and can achieve the optimal accuracy of error in a shorter time, which improves the accuracy and solves the problem of local optimal solution of BP neural network. In addition, the simulation results show that GA-BP neural network can reflect the correlation between quality training indicators and special sports performance, more intuitively show the advantages and disadvantages of athletes, and carry on the crosswise comparison between the athletes according to the data result, and help the coach to understand the gap between the athletes and the development of each aspect of the athletes so as to more accurately locate the current position of athletes and develop a targeted training plan. Compared with the single BP neural network, GA-BP neural network algorithm is closer to the real sports performance of athletes and students, and its prediction result is more stable, which reduces the error rate from the overall situation and shows a better prediction effect. Although the prediction model constructed in this paper overcomes some problems of BP neural network, there are still some problems, such as a large number of training samples and poor extensiveness. At the same time, in the actual situation, there are many other factors that affect the performance of athletes in all aspects, and these factors are not considered in this paper, so it needs to be further verified.


Ultrasonic measurements are used in civil engineering for structural health monitoring of concrete infrastructures. The late portion of the ultrasonic wavefield, the coda, is sensitive to small changes in the elastic moduli of the material. Coda Wave Interferometry (CWI) correlates these small changes in the coda with the wavefield recorded in intact, or unperturbed, concrete specimen to reveal the amount of velocity change that occurred. CWI has the potential to detect localized damages and global velocity reductions alike. In this study, the sensitivity of CWI to different types of concrete mesostructures and their damage levels is investigated numerically. Realistic numerical concrete models of concrete specimen are generated, and damage evolution is simulated using the discrete element method. In the virtual concrete lab, the simulated ultrasonic wavefield is propagated from one transducer using a realistic source signal and recorded at a second transducer. Different damage scenarios reveal a different slope in the decorrelation of waveforms with the observed reduction in velocities in the material. Finally, the impact and possible generalizations of the findings are discussed, and recommendations are given for a potential application of CWI in concrete at structural scale. The assessment of the residual safety and durability of reinforced concrete structures  is vital for the efficient planning of retrofitting strategies for modern-day infrastructure networks. Ultrasonic measurements are frequently used to monitor the structural health of such concrete structures with the objective of detecting changes as early as possible before devastating damages occur.Due to multiple scattering effects in heterogeneous media, the coda wave, i.e., the trailing diffuse part of wave signals, is especially sensitive to small changes in the apparent seismic velocity ,2. In adry (CWI)  has beenir rocks ,7, in coir rocks ,9. Furthir rocks .In addition to ultrasonic laboratory experiments on different-sized concrete specimen, numerical simulations have been used to explain and understand the coda signal and its sensitivity to various damage geometries ,11. In tconcrete . In thisTo study the effect of different damage scenarios on ultrasonic wave propagation and the analysis with CWI, simulations of the full elastic seismic wavefield were performed using a rotated staggered-grid finite-difference scheme . All simhttps://pycmg.readthedocs.io/en/latest/ accessed on 15 July 2021.At the mesoscale, the internal material structure of concrete is characterized by a large volume fraction of aggregates (up to 60–70%) of different sizes, embedded into a mortar matrix. Subjected to an external load, the mesoscopic heterogeneity in concrete contributes to highly complicated stress- and ultrasonic wave fields. Especially when interacting with the initial defects at finer scales, it results in a complex damage process ranging from diffuse microcracking to localized fracture . Thus, tIn general, a concrete aggregate can be characterized by its multiple faces, sharp corners/edges, and multiple non-convex surfaces. In our work, aggregates are modeled by means of irregular polyhedrons with concave depressions. The generation of a virtual aggregate involves the reduction of an initial cuboid to the polyhedron by applying multiple random cuts tangential to an imaginary inscribed ellipsoid. The ellipsoid geometry is a function of the maximum size and the elongation ratio (ratio of the longest dimension to the shortest dimension). The random orientation of an aggregate is realized by applying rotational transformation with respect to the global axes and the concave depressions are introduced in terms of a 2D Gaussian surface at random locations lying on the surface of the inscribed ellipsoid.After each aggregate or grain is generated, the next step is to embed it into a prescribed hosting mortar matrix . The main objective of the assembly step is to randomly pack aggregates that follow the statistical particle size distribution (PSD) of concrete, while fulfilling the non-overlapping condition between aggregates. The packing strategy begins with the pre-processing of input packing parameters such as the grading curve of concrete, and the total volume fraction. Each particle family is sorted according to their size and the largest particles are placed first, followed by smaller particles until the target volume fraction of each size is reached. The current aggregate is successfully embedded into the mesostructure only if it does not intersect with other aggregates as well as the number of attempts does not exceed an a priori defined value. The mesostructure is periodic. This is achieved by assembling the part of the aggregate lying outside the surface of the mesostructure domain to the opposite surface. This feature allows the generation of large-sized concrete models, for instance, at the structural scale.In the current study, we considered three concrete mesostructures with three different grain size ranges. The goal is to take into account the influence of grain size on the simulated Coda signals with increasing damage level. The three samples are denoted as sample 0, sample 1, sample 2  generateWith the concrete mesostructures at hand, we proceed to model damage of concrete using the Discrete Element method (DEM). In DEM, a granular material  is described as a packing of DEM-particles that are linked together by cohesive frictional forces. Within the material, the induced forces are transmitted via a contact network between particles and the dynamics of these particles is governed by Newton’s second law . The conThe inter-particle modeling in the DEM approach based on  is brief©XeonR©Gold 6148 CPUs running in parallel at The modeling of damage in concrete begins with the generation of a random densely packed ensemble of 661,830 DEM-particles with radius of 0.8mm  Left. Ne=0.3m in  of the vested in . Followiested in , DEM uni‘WooDEM’ . The sofd of particle i is evaluated by averaging the damage variable over all cohesive contacts associated with that particle. Particles/voxels with parameter of value 0 are in undamaged stage, while At each time increment, the damage parameter For each numerical sample, ten mesostructure snapshots at different damage levels are extracted for the wave propagation analysis. To simulate the ultrasonic wave field, the mesostructural models for concrete specimen described in the previous section are assigned appropriate parameters for P- and S-wave velocity and density. These parameters for the initially undamaged state are listed in d. This first set of models is denoted as “macro+microcrack” for the remainder of this study.The virtually generated mesostructure models  are subjected to tensile loading. Each of the samples 0, 1 and 2, subjected to uniaxial tension was analyzed by the DEM up to failure, covering the complete damage regime, from the intact state, to an initial diffuse growth of microcracks up to complete failure of the sample. For the wave propagation analysis described in the next subsection, local elastic velocities are assigned at the corresponding locations according to the local damage level A second set of models has been generated for samples 1 and 2 in which, for the wave propagation simulations, diffuse damage, i.e., damage levels corresponding to a lower-level load ”) and the grain phase (“diffuse damage (grains)”).In the following sections, the models described above are denoted as their aggregate compositions  plus their history of damage .Ultrasonic wave field simulations were performed with a model size of .1m×0.1m . The conIn this study, a rotated staggered-grid finite-difference scheme  is used The spacing between adjacent points of the evenly spaced finite-difference grid is To simulate the typical decline in amplitudes with time observed in the coda wave of ultrasonic measurements, a homogeneous intrinsic attenuation has been applied. At the central frequency of the source signal, the attenuation is For all simulations, a single source and a single receiver are used. Source and receiver are located at the center of the x-y-slice and separated in z-direction by 0.3m a. The so0.3m b,c. The velength .For the analyses in this study, the xx- and zz-component of the stress recorded at the receiver position are averaged d,e and uSince transducers have a discrete physical size, preliminary tests have been performed with implementing multiple sources in a localized area. The results did not show a significant difference as compared to the implementation of a single point source. Therefore, sources are implemented as point sources in this study.t representing the discrete simulation steps performed using the input models from each of the sets described in the previous section, T is the total recording time per simulation step and Following the approach outlined in , the strectively :(1)up(t′field up :(2)CC Diffuse damage implemented by reducing velocities in the complete sample and (2) localized crack opening during increasing tensile loading computed by the DEM model. In the following, results from these scenarios are investigated separately first and then compared.To simulate velocity changes corresponding to diffuse damage, i.e., induced by temperature or moisture variations, the P-wave and S-wave velocities have been reduced stepwise and the CWI workflow has been applied to every step. As described above, two scenarios are investigated in what follows: Reduction of velocities only in the matrix and reduction of velocities only in the grains.The velocities were manually reduced in six discrete steps. A reduction in the velocities of the matrix phase results in observed velocity changes  changes . This diA significant portion of the grains in sample 0 are smaller than 10% of the dominant wavelength used here  which haThe numerical models of the concrete samples have been provided from the DEM simulations at different levels of uniaxial tensile loading and used for wave propagation simulations. The different loading levels correspond to different levels of damage and different locally reduced wave velocities of the mortar matrix.t , a steep increase of In this set up with a single source and a single receiver, the location of the macrocrack (closer to the source or closer to the receiver) produced nearly identical results due to the source-receiver reciprocity of the elasto-dynamic wave equation.In the previous section, the capability of CWI to discriminate between different damage scenarios has been demonstrated. Further confirmation of the results of this study could be achieved by measuring temperature and moisture simultaneously with acoustic emission signals, i.e., with a system such as reported in . AlthougThe used approach to create mesostructural models has been demonstrated to produce very realistic results . SimilarIn the previous section, the recording length was fixed to  and 3ms . For com and 3ms . This isThe figure also shows that a recording length of In this study, the complete signal is used for the analysis. In practice it may be beneficial to exclude the first arrival and/or investigate different parts of the Coda signal separately to recover the velocity changes at different distances with respect to the transducers.In the previous simulations, vacuum boundaries are assumed around the sample to mimic finite-sized concrete specimen. If larger structures are monitored , the ultrasonic wave field travels only through parts of the structure. Depending on the transducer positions, the wave field is not reflected at the boundaries. Therefore, absorbing boundaries approximate this particular situation more adequately. Simulations for sample 2 with suppressed microcracking and the diffuse damage scenario in the mortar matrix are repeated with absorbing boundaries. It is hardly possible to distinguish the different damage scenarios . The decFurther studies need to be performed to enhance and update the CWI workflow to make it suitable for large-scale structures. Preliminary ideas involve the use of a set of optimally positioned transducers  and usinThe feasibility of CWI to detect small-scale velocity changes very early has been demonstrated.The decorrelation of wave forms indicates the spatial extent of the damage, enabling the detection of distributed microcracking in early stages of the loading, while a constant diffuse damage shows almost no change in the decorrelation coefficient.On the specimen level it was possible to differentiate between diffuse and localized damage using CWI.Comparing reflective and absorbing boundary conditions revealed with absorbing boundary conditions, it was no longer easily possible to distinguish localized and diffuse damage as the lack of reflections from the boundaries significantly reduces illumination of the damage.Applications of CWI on larger scale specimen should prioritize long recording lengths to monitor and discriminate multi-scale damages.The use of multiple optimally placed transducers seems to be crucial for structural health monitoring of infrastructures.The sensitivity of Coda Wave Interferometry to different damage scenarios has been investigated numerically. Realistic numerical concrete models were created with three different grain size ranges. The response of concrete specimen subjected to uniaxial tension has been simulated by means of the discrete element method to generate a set of virtual mesostructural concrete models with evolving microcracks, which are eventually coalescing into a macrocrack. Simulations of the ultrasonic wave propagation were performed on these models for all stages during tensile loading and compared to scenarios, where a constant diffuse damage has been assumed in the mortar matrix and the grains, respectively. The main conclusions of this study are as follows: