The simultaneous acquisition of multi-spectral images on a single sensor can be efficiently performed by single shot capture using a mutli-spectral filter array. This paper focused on the demosaicing of color and near-infrared bands and relied on a convolutional neural network (CNN). To train the deep learning model robustly and accurately, it is necessary to provide enough training data, with sufficient variability. We focused on the design of an efficient training procedure by discovering an optimal training dataset. We propose two data selection strategies, motivated by slightly different concepts. The general term that will be used for the proposed models trained using data selection is data selection-based multi-spectral demosaicing (DSMD). The first idea is clustering-based data selection (DSMD-C), with the goal to discover a representative subset with a high variance so as to train a robust model. The second is an adaptive-based data selection (DSMD-A), a self-guided approach that selects new data based on the current model accuracy. We performed a controlled experimental evaluation of the proposed training strategies and the results show that a careful selection of data does benefit the speed and accuracy of training. We are still able to achieve high reconstruction accuracy with a lightweight model. Multi-spectral imaging systems have a broad range of applications in the area of remote sensing, computer vision, camera-based security systems, etc. Such applications include image enhancement, dehazing, quality inspection, object classification, and the characterization of materials.The near-infrared band  is part of the active, or reflected infrared band, with wavelengths closest to the visible light spectrum (400–700 nm). The photometric properties of these two spectral bands are distinct. An image obtained at a specific wavelength represents different information compared to another image captured at another wavelength. While the edges between objects in images captured in the visible and NIR bands match, the intensities and intensity differences are different and depend on the object material. These properties make NIR complementary to the visible light range in numerous applications.In terms of image acquisition, the similarities between NIR and visible light are that both can be acquired through the same optical path and that silicon-based sensors are sensitive to both bands. Recently, single-sensor cameras for simultaneous acquisition of multi-spectral data have gained popularity in the research community. Using a single optical system and a single sensor for both RGB and NIR modalities alleviates the burden of precise optical calibration and misalignment correction, in contrast to multi-camera, spatial scanning or multi-shot approaches. In our work we will focus on the simultaneous acquisition of RGB and NIR images by a single sensor.The generalization from RGB to multi-spectral imaging can be achieved by placing an array of different wavelength-selective band pass filters in front of the sensor, denoted as a color filter array (CFA) or a multi-spectral filter array (MSFA). In the case of RGB-NIR imaging, the most practical modification is to replace one of the green pixels of the Bayer pattern  by a NIRRegardless of the filter configuration, extending the sensitivity range results in the decreased spatial sampling density of one or more spectral channels. Demosaicing is a crucial step for the recovery of missing information in the imaging pipeline and involves accurate color reconstruction and the alignment of edges. Since each band is sampled at a different spatial location, interpolation artifacts are typically prominent around edges. Looking at wider regions along the edges allows for sophisticated, edge-aware interpolation algorithms ,4. In ouDue to the complexity of deep neural networks, learning to model accurate data representation requires substantial amounts of training samples. However, using complete images creates a lot of information redundancy in the training samples. For example, natural images contain significant portions of flat regions of low variability. Through uniform random sampling, that proportion is reflected in the training datasets.Such data redundancy negatively influences the learned representation by introducing bias, a shortcoming that is well known in the image processing literature. Numerous dictionary learning algorithms for sparse representation as well as dimensionality reduction techniques have been focusing on learning compact and informative data representations ,6. AnothIn this paper we aim to perform data selection for the purpose of training a generative CNN for demosaicing. Some of the principles in this paper are inspired by the tools used in the compressed sensing and dictionary learning literature, thus making a connection between these topics. This focus is on modifying the uniform random sampling scheme to a selective or weighted random sampling so as to achieve a comparable performance and lower training complexity compared to training with a full dataset.In our prior work  we propoOur first proposed idea is to form clusters in the space of training samples and to train the network only with selected samples that represent all samples in the clusters. With this approach the variability among the selected samples will be high, therefore carrying a lot of information, while ignoring redundant samples. The training data consists of four-channel square patches extracted from a set of training images.The second approach presented in this paper performs a selection of training samples adaptively, based on the reconstruction error. More specifically, we propose to create a training dataset at each training epoch by statistical sampling and favoring samples from critical regions with a large reconstruction error.Moreover, we experimented with the objective of demosaicing. Instead of focusing merely on improving the peak signal-to-noise ratio (PSNR) as a standard metric in literature, we simultaneously optimize the structural similarity index (SSIM) as a perceptive quality measure. Some sources in the literature suggest that the combination of metrics yields superior results compared to optimizing each metric alone , and ourIn this paper we also decreased the number of network coefficients compared to our prior work  by 65% tIn For decades, numerous research efforts have been dedicated to accurate, full-resolution reconstruction from color mosaic images. Many of these works were designed to restore images within the RGB domain. However, the same principles are applicable for the multi-spectral case.Image demosaicing is an image interpolation (or up-sampling) problem, and therefore shares many of the challenges present in the problem of single-image super-resolution (SISR) . For exaModern super-resolution methods based on deep learning models, such as SRCNN  and VDSRIn a recent work , three pIn spectral reflectance reconstruction from trichromatic RGB samples, the goal is to reconstruct high-dimensional reflectance vectors from low-dimensional camera responses. Usually this involves both spatial and spectral interpolation, based on a transformation learned from large collections of training samples. The data redundancy problem has been addressed with different sample optimization techniques ,18,19, wTo mitigate the negative influence of data redundancy, Birodkar et al.  studied Gharbi et al.  proposedThe goal of instance selection algorithms ,25 is toHowever, the problem of over-fitting to difficult examples is commonly observed in the literature and is referred to as the fixation problem in . The “onInspired by these conclusions, we explore the idea of sampling difficult samples more densely and adaptively, while at the same time including easy samples in the training set to prevent over-fitting. Unlike most of the methods explained above, we construct a new training dataset before each epoch. Therefore, we are restricted to a simple and fast instance selection technique to avoid a significant increase in the training time. Our goal is to keep the overall training time lower compared to training with a large set without instance selection.In terms of training objectives, the most popular choices for image restoration problems are minimizing the mean squared error (MS-SSIM) , with ceThe authors of the study conducted in paper  experimex (SSIM) . Their cReconstructing full-resolution color images from sub-sampled outputs is one of the fundamental processing steps in modern digital cameras. As discussed above, typically a selective color filter array is used to spatially sample different wavelengths. In RGB-NIR imaging, four spectral components are spatially sub-sampled. A reconstruction of the full resolution color components requires interpolating the missing values from the sampled ones.We will assume a general image observation model that involves a CFA for sampling different light bands ,30 formuEach of these three operations, as well as the noise In this paper we focus on the problem of an accurate reconstruction of full-resolution images Equation . With thThe matrix Following our previous work , we focuIn our prior work  the demoIn this paper, we retain the same concept, with small modifications in the architecture. The current architecture is presented in Training deep neural networks with millions of parameters imposes the need for substantial amounts of data. To achieve generalization, the training data needs to be versatile, which is typically accomplished by acquiring huge training datasets and/or performing data augmentation. However, the fundamental issues with that approach are data redundancy and training time complexity. Our aim is to reduce the training dataset and retain a smaller, highly informative training subset.In the domain of demosaicing, the main challenges arise in image regions with strong edges and repetitive textures. Smooth regions do not require sophisticated interpolation methods. For textured regions, neural networks offer stronger modeling advantage over classical methods. Therefore, textured training samples can be considered more informative than smooth ones for discovering the true properties of the data.In this paper we propose a data sampling scheme in order to improve the training time and/or algorithm performance, as illustrated in Training with data selected based on a certain criterion has similarities with the principles of active learning . Namely,Let t in the training set with equal probability. The samples that are included into the training set are the ones with a positive outcome of Bernoulli sampling:t, and p is the probability for p is fixed for all samples and determines the sampling density i.e., the proportion of samples added from Uniform random sampling is a basic strategy, where the training set Uniform random sub-sampling of data is a fast technique that results in a subset that carries the same amount of information as the original data. It enables representing the original data distribution with fewer samples. However, by sampling uniformly regardless of the locations from the image manifold where data is more difficult to model, the subset may contain many redundant samples that do not contribute towards finding the optimal solution.i.Our first proposed strategy creates an optimal training dataset Initially, a large pool of samples Equation ), since x in sample k is the number of clusters The sample patches are described by low level features including spectral intensity and texture. For clustering, we rely on the k-means method , which iTo confirm the hypothesis that the same amount of centroid samples carry more information than uniform random samples, we calculated the average per-feature entropy, based on the normalized features It is not surprising that the data sets generated by uniform random sampling have the same entropy, since in both cases the distribution of image patches is sampled in an identical manner. The clusters, on the other hand, are formed such that the variance between the them is high, and therefore they carry more information.i, a new training set The second data selection strategy we propose is closer to the idea of active learning. Before each training epoch Analogous to this approach in active learning is entropy-based uncertainty sampling for classification problems. The fundamental difference is that we do not make predictions of the future model outcomes, since ground truth is available, and we can calculate the past error as a criterion for mining new samples.t that follow the rule:y. The random variable t will be included in Creating the training dataset In this section we will explain the practical aspects of the implementation of the proposed methods with respect to computational complexity and dataset size. In our experiments we rely on the RGB-NIR Scene Dataset  and geneFor the sample clustering approach, the patches are described by low-level features of intensity and texture. The feature vectors consist of average intensities per channel in the patch , and the histogram of oriented gradients (HOG) obtained by a weighted gradient angle contributions by the corresponding magnitudes . The intensity information is important to accurately model the spectral properties of the data. Including samples with different patterns and edge orientations is beneficial for learning to perform more accurate interpolation around edges.Based on these features, a bisecting k-means strategy is carried out, recursively splitting the data into two sub-clusters, as long as the intra-cluster variance is larger than a variance threshold, or the number of samples in the cluster exceeds a cluster size threshold. Finally, the selected training samples are the ones closest to the centroids of the clusters in the feature space.Setting a threshold on the cluster variance serves to control the data compression strength. The cluster size limit is imposed to prevent data imbalance. We have experimentally chosen the stopping thresholds. In Based on these three properties we can make a tradeoff between processing time (number of clusters) and quality of the dataset . Since there is no global optimum, we chose a value around the knee point of the curvature in The motivation for top-down hierarchical k-means clustering is three-fold. First, the number of clusters does not need to be specified in advance, it can be controlled by constraining the cluster variance and cardinality.Moreover, the quality of the clusters obtained by hierarchical clustering tends to be higher than that of the original k-means . To compIn this experiment, the cluster variance, as an input parameter for hierarchical k-means, was gradually increased, resulting in fewer, larger, and more diverse clusters. For the original k-means, the number of clusters in each step was set to match the hierarchical approach. To make a fair comparison, we have not constrained the cluster size.The third motivation for hierarchical clustering is the time complexity. The complexity of the original k-means is In the second proposed sampling strategy, we adaptively selected the samples in x, corresponding sub-sampled mosaics y at pixel locations where the condition in Equation Lai={Lai={(7)LThe reconstruction error For illustration, In many image restoration problems, including super-resolution and demosaicing, the peak signal-to-noise ratio (PSNR) is one of the standard metrics for evaluating image quality. PSNR is calculated based on the mean squared error (MSE) between the reconstructed output image u is:As a metric for image quality, PSNR is not well correlated with the perceived reconstruction quality. On the other hand, the structural similarity index (SSIM) is a perceptually inspired metric for the structural correspondence between images. Typically, SSIM is calculated based on small windows at the same location in two images. The similarity between two windows Among the most popular training loss functions in image reconstruction literature are the MSE  Evaluation of the optimal distance metric for clustering-based instance selection ; (2) evaFor a controlled study, we attempted to make the experimental conditions equal, by minimizing differences in initialization and hyper-parameters. Therefore, the parameters of the neural network (convolution filters and biases) were randomly initialized and stored, with the goal of being re-used as an initial hypothesis for all experiments. Moreover, the number of training samples per epoch was fixed to 2500 patches with a size of ptimizer , with a The experiments performed in this study are small-scale, due to the limited number of images in the dataset. Training with more samples from the same set of images may add redundancy, without any significant added information. Moreover, the difference between the random samples and the optimized samples will become smaller, making the comparison less conclusive. Nevertheless, we expect that the conclusions from the small-scale experiments can be extrapolated for large-scale datasets and intend to investigate this in the future.As explained in Different datasets chniques ,18,19.In We suspect that the similarity in the results comes from the fact that the training process does not critically depend on the actual cluster properties, if the selected center samples are sufficiently diverse. Another reason could be that drawing samples from a limited number of training images results in overlap between the datasets. Furthermore, there is a close connection between the cosine and correlation distances with the Euclidean .Finally, using each of the two proposed approaches, a different model was trained until convergence. The hyperparameters were tuned for each model individually, to reach best performance. Based on the conclusions from the experiments explained above, the final strategies for the proposed approaches are the following:To evaluate the performances objectively, we compared the reconstruction quality with existing demosaicing methods for RGB and NIR images from the literature. The selection of algorithms for comparison was limited to methods with publicly available code that used the uniform pattern, so as to ensure the experiment could be controlled. However, we expect that with powerful deep learning algorithms, the reliance on the pattern layout will not be strong , if the In The best performances of the two approaches proposed in this paper are listed in the last two rows of Freiburg Forest Dataset contains 136 images with a size of For visual inspection, in Deep learning models are complex, with enormous number of trainable parameters. To train them robustly it is necessary to provide sufficient training data, with a lot of variability. This makes deep learning highly time-consuming, and the information redundancy can have adverse effects on training. Therefore, strategies for instance selection and data reduction are gaining increasing interest for numerous machine learning problems. In this paper we investigated different approaches for optimal dataset subsampling to train a deep learning model for multispectral demosaicing. Two general strategies were considered.The first proposed strategy optimized the training subset by maximizing the variability among the selected samples. To obtain a variable and representative training set, a hierarchical data clustering was adopted. The set of cluster centers obtained by k-means was a subset of high variance. Therefore, it maximized the magnitude of the model change with each parameter update and led to faster and more optimal convergence. Another benefit was that with large datasets, this technique could be carried out off-line and with pre-defined features based on domain knowledge.For efficiency and to find more optimal clusters, we performed hierarchical clustering, with k-means (k = 2) applied in each branch of the space-partitioning tree. Based on our experiments, we found this technique not overly sensitive to the distance metric used in clustering in terms of final dataset quality.The second proposed strategy was adaptive sampling and focused on self-guided training, where the samples were selected based on their reconstruction error. The model could choose the most interesting samples and improve the data representation faster and more precisely.In the experiments we evaluated different error metrics to measure the difference between the reconstructed and reference images, based on which samples were selected. The learning curves that were compared indicate that for optimizing PSNR, using SSIM as an error metric was not optimal. This may be due to the design of SSIM as a measure perceptual similarity and therefore not being consistent with metrics such as PSNR. Another explanation is that focusing entirely on the reconstruction quality of high-frequency details can lead to overfitting.In a controlled experiment, we compared the two proposed techniques against a baseline which is uniform random sampling. The results showed that both methods performed well, with adaptive sampling reaching a higher PSNR faster than the clustering-based approach.Additionally, motivated by the findings in the literature, we tested several loss functions for training. The experiments indicated that combining the mean squared error with a loss function based on the structural similarity index benefited the training even further.Finally, we trained a model based on the conclusions from the previous experiments and chose the best performing variants. Specifically, the new model was trained using the adaptive sampling strategy, and with a loss function which is a combination of MSE and MS-SSIM. Compared to our prior work, we achieved a comparable reconstruction accuracy on three public datasets, with a model reduced in size for 65%, retaining only 35% of the total number of parameters.In future work, we will focus on real multispectral data that will provide interesting opportunities to further investigate the proposed concepts. A more comprehensive solution in the image reconstruction pipeline will consider the spectral sensitivity of the sensor to each wavelength, as well as the spectral correlation between channels. Furthermore, we will experiment with combining the strengths of the proposed approaches, depending on the size of the available training image datasets. An interesting research direction could be to form partitions in a large data space and based on the reconstruction error of representative samples, to sample more densely in the more difficult partitions. Finally, we will focus on larger datasets and apply the findings in this paper to other deep learning-based computer vision problems.


In the past decade, big data has become increasingly prevalent in a large number of applications. As a result, datasets suffering from noise and redundancy issues have necessitated the use of feature selection across multiple domains. However, a common concern in feature selection is that different approaches can give very different results when applied to similar datasets. Aggregating the results of different selection methods helps to resolve this concern and control the diversity of selected feature subsets. In this work, we implemented a general framework for the ensemble of multiple feature selection methods. Based on diversified datasets generated from the original set of observations, we aggregated the importance scores generated by multiple feature selection techniques using two methods: the Within Aggregation Method (WAM), which refers to aggregating importance scores within a single feature selection; and the Between Aggregation Method (BAM), which refers to aggregating importance scores between multiple feature selection methods. We applied the proposed framework on 13 real datasets with diverse performances and characteristics. The experimental evaluation showed that WAM provides an effective tool for determining the best feature selection method for a given dataset. WAM has also shown greater stability than BAM in terms of identifying important features. The computational demands of the two methods appeared to be comparable. The results of this work suggest that by applying both WAM and BAM, practitioners can gain a deeper understanding of the feature selection process. Over the years, feature selection has become a fundamental preprocessing tool in data mining and machine learning. Otherwise known as attribute or variable selection, feature selection refers to the process of reducing the number of input variables for predictive modeling. The objective of feature selection is three-fold: improving the prediction performance of the features, providing faster and more cost-effective features and gaining a better understanding of the underlying process that generated the data. This objective is achieved through the characterization and elimination of irrelevant and redundant features, leaving only a subset of the most useful features to be used in further analysis. As such, features represent the individual independent variables typically used as predictors within a model. Throughout this paper, we use the terms variable and feature exchangeably.There are generally three classes of feature selection methods based on how the method interacts with the learning algorithm: filter, wrapper and embedded methods . Filter One of the most common challenges encountered in feature selection is choosing the most suitable method for a given problem. In general, there is no single feature selection method that outperforms all others across most applications. The three different classes of feature selection methods are frequently suitable under varying conditions. For instance, the greedy randomized adaptive heuristic (GRASP) filter technique introduced in  providesMeta-learning predicts the most appropriate feature selection method for a given dataset. In meta-learning, the feature selection task is treated as a supervised learning problem in which the datasets are objects and the target function maps a dataset to the feature selection method that shows the highest performance based on a certain criterion. With the increase in feature selection techniques and introduction of new methods, the implementation of meta-learning can provide a valuable way of weighing the most suitable feature selection methods. However, like other learning techniques, in order to make reliable recommendations, metalearning can be limited by its need for a suitable metadatabase that is representative of the given problem domain . An exceEnsemble methods were originally developed to enhance classification performance . EnsemblAlthough a number of aggregation techniques have been proposed in the literature, there is no clear rule to determine which one of them should be chosen for a specific feature selection task. However, the simple approach of mean-based aggregation seem to be efficient and compelling in most cases . In 15]15], KoldThe robustness or stability of the feature selection method is of paramount importance to reducing dimensionality and improving the performance of the learning algorithm. Stability measures the insensitivity of the feature selection method to variations in the training set. In other words, unstable feature selection methods can produce varied feature rankings when a single feature selection technique is applied to different training samples generated from the same dataset. Instability can also occur when very different feature selection techniques applied to the same dataset produce different feature rankings. The stability of feature selection has attracted a plethora of research in machine learning and data mining communities over the last decade . It has This paper introduces a general framework for a bootstrap ensemble in which feature selection results are aggregated within and between multiple feature selection methods. Multiple subsamples are generated from the original dataset by bootstrapping, and the feature selection techniques are applied on each subsample. The aggregation is thus two-fold; first, the single feature selection method is aggregated across subsamples, and second, different feature selection methods are combined into a single set. The mean-based aggregation rule is used where the generated importance scores are combined within and between the feature selection methods. Because filter feature selection methods are known for their computational efficiency, we chose four traditional filter techniques for our experimental framework, although wrappers and embedded methods are also applicable. Our experimental results with real data sets demonstrated that ensembling multiple feature selection methods improves the performance of the learning algorithm, guides the selection of the optimal feature subset and facilitates the identification of the most appropriate feature selection method for a given dataset. Furthermore, comparisons of the two folds of aggregation revealed that, on the whole, aggregating within a single feature selection method outperforms aggregating between multiple feature selection methods.The rest of this paper is organized as follows. In n observations and p features such that Y is the target variable . Moreover, i of the feature j. The goal of this work is to reduce the number of features in the dataset Let us consider a dataset -The Within Aggregation Method (WAM) refers to aggregating importance scores within a single feature selection method.-The Between Aggregation Method (BAM) refers to aggregating importance scores between different feature selection methods.Let Due to the simplicity and the efficiency of the arithmetic mean , it is uj represents the m bootstrap sample datasets. The final aggregated score for the feature j in Algorithm 1WAM Algorithm:p features, a testing dataset (i)For (ii)Based on (iii)Get the aggregated score set (iv)For the aggregated score set (v)Based on the rank vector (vi)Based on the selected feature set in (v), use the testing dataset Given a training dataset Let us consider a training dataset j represents the m bootstrap sample datasets. Then, each column in the score matrix Let us consider a training dataset j in the score matrix j between all considered feature selection methods Algorithm 2BAM Algorithm:p features, a testing dataset (i)For (ii)For each feature selection method (iii)Normalize the score matrices in (ii) as (iv)Use the arithmetic mean scores to combine the matrices in (iii) into one score matrix (v)Use the score matrix in (iv) to compute the aggregated scores (vi)Based on the aggregated scores in (v), compute the corresponding rank vector (vii)Based on the rank vector (viii)Based on the selected feature set in (vii), use the testing dataset Given a training dataset After the normalization, we use the arithmetic mean to combine the normalized aggregated scores across An importance scores vector A rank vector A subset of features represented by an index vector In order to measure the stability of feature rankings for each feature selection method in posed in . This apℓ into a rank vector m samples.Naturally, it is possible to transform any feature importance scores vector i.s in the score matrix Pearson’s correlation coefficient: In the case of similarity between two importance score vectors ii.Spearman’s rank correlation coefficient: With regard to the similarity between two rank vectors iii.p.Canberra’s distance: Another measure used to quantify the similarity between two rank vectors distance . This meiv.Jaccard’s index: Jaccard’s index measures the similarity between two finite sets; it is taken as the size of the intersection divided by the size of the union of the two sets. Given the index vectors Several similarity measures have been introduced in the literature . In thism bootstrap samples. Generally, a low average standard deviation would imply high stability, whereas a high average standard deviation would suggest lower stability.In addition to the stability scores based on similarity, one can compute the average standard deviation of the feature importance scores across all bootstrap samples for every feature selection method. By definition, the standard deviation measures the dispersion or instability of the feature selection scores under different training bootstraps. Similar to the work in , we defiWithout the loss of generality of the applicability of the proposed framework, the experimental evaluation in this study focuses on the binary classification conferred in the datasets illustrated in k thresholds were used, resulting in subsets containing the top k can be chosen. The existing literature in this regard provides some guidelines on how to choose k for some specific scenarios. For example, we refer interested readers to [k were chosen and the classification accuracy using cross-validations or independent test data was used to evaluate the quality of the chosen subsets [In this experiment, we compared the performance of the two aggregation methods: WAM and BAM. The experimental environment was Windows 10, 64-bit, 16 GB RAM, Intel(R) Xeon E-2124 (3.30 GHz). For the implementation of the proposed framework , we seleaders to ,23,24,25 subsets ,27.Logistic regression: A statistical model used to model the probability of the occurrence of a class or an event using a logistic (sigmoid) function. It is a widely used classification algorithm in machine learning. The objective of logistic regression is to analyze the relationship between the categorical dependent variable and a set of independent variables (features) in order to predict the probability of the target variable. The maximum likelihood estimation method is usually used to estimate the logistic regression coefficients.Naive Bayes: A probabilistic classifier based on the Bayes theorem . It assuRandom Forest: An ensemble model of decision trees in which every tree is trained on a random subsample to provide class prediction. The subsamples are drawn with replacements from the training dataset. The results from all the decision trees are then averaged to yield the model prediction . Random Support Vector Machine (SVM): A supervised learning algorithm in which each observation is plotted as a point in p-dimensional space (with p being the number of features). SVM aims to identify the optimal hyperplane which segregates the data into separate classes. The selected hyperplanes thus maximize the distance between data points of different classes .In the experiment, a five-fold cross-validation procedure was implemented in the testing phase. Accordingly, we divided the testing set into five stratified samples. Thus, the five folds were selected such that the distribution of the target variable Y was approximately equal in each of the folds. For instance, given a testing data with 500 observations such that the distribution of the binary target variable was 7:3, each fold in the stratified five-fold cross-validation would be expected to have 70 observations in one class and 30 in the other. Accordingly, stratification was used to ensure that each class was equally represented among the different folds. For every iteration, one of these five stratified samples was used as a testing set, while the remaining four samples were used to train the model. On each iteration, the testing sample was used to evaluate the performance of the selected feature subsets based on the following classification algorithms:The entire experimental framework was performed using the open-source statistical programming language R. Note that features of near-zero variance were removed prior to the analysis. After the final feature subsets were selected and utilized in building each classifier across the five-folds, we evaluated the performance of the classifiers through the estimation of the area under the receiver operator curve (ROC) , hereaftIn most datasets, we note that the ensembling of bootstrap samples and aggregation of feature importance scores within and between feature selection methods improved the baseline  method outperformed BAM in the Philippine and Scene datasets  or IonosFor the selection of the optimal It should be mentioned that the experimental results in this paper affirmed the fact that the performance of a given feature selection method is data-dependent. Overall, none of the feature selection methods produced the best AUC values across all datasets. For example, the aggregated MRMR showed the worst performance across multiple classifiers in the Scene, Spectrometer and Philippine datasets, whereas in the Optdigits dataset , it showIn summary, the analysis of the AUC results reveals that WAM can be used as a powerful tool to help practitioners to identify the most suitable feature selection method for a given data set. It can also guide the selection of the optimal level of feature reduction while achieving the maximum level of learning accuracy.The bolded scores in Contrasting the stability behavior of the BAM to that of the WAM, In summary, although BAM demonstrates a comparable stability behavior to that for each single feature selection method under WAM, it appears that, in 11 out of 12 datasets used in this paper, the stability of at least one individual method outperformed BAM. Interestingly, there appears to be a positive association between the classification performance of a feature selection method and its stability behavior. A feature selection method that outperforms others in terms of classification accuracy may also outperform them in terms of stability behavior.Over the years, datasets have grown increasingly large in terms of their size and dimensionality. As a result, feature selection has become a necessary reprocessing tool in machine learning applications and the focus of a wide range of literature and research across many domains. This study has explored the full potential of a bootstrap ensemble approach for feature selection in which the ensemble aggregation is performed within and between the multiple feature selection methods.The extensive experimental analysis of 13 different datasets selected from different domains has demonstrated that the Within Aggregation Method (WAM) is highly efficient in guiding the selection of the most suitable feature selection method for a given problem. As for reducing the dimensionality of the problem, our analysis showed that WAM and the Between Aggregation Method (BAM) were comparable in determining the optimal percentage reduction in the number of features. They are also comparable in terms of the computational costs. It important to emphasize that optimizing the feature subsets and the computational costs of the techniques depends largely on the dataset characteristics and learning algorithm implemented.In terms of stability, the WAM demonstrated better stability behavior than BAM in most datasets (11 out of 12). Overall, the BAM stability scores fell in the middle range of the computed values on each of the score-based and rank-based stability metrics, suggesting a desirable stability behavior of the method.The experimental analysis also showed that there exists a positive association between the classification performance of a feature selection method and its stability behavior. In other words, the feature selection method that outperforms others in terms of classification accuracy may also outperform them in terms of stability behavior. This association can, however, depend on the stability metric used, the learning algorithm and the characteristics of the dataset. Due to the extent to which the feature selection results in terms of learning accuracy and stability can depend on the data composition and learning algorithm, it is essential that both BAM and WAM methods be implemented in order to achieve a better understanding of and more useful insights into the underlying application domain.The observed association between learning accuracy and stability indeed merits further investigation in the future. Additionally, in future work, we will extend the application of the framework to other types of feature selection methods, such as wrappers. Particular interest will be given to the application using a mix of filter and wrapper feature selection methods and establish a wider scope of comparisons to better evaluate the ensemble framework. Furthermore, datasets with diverse performance and various learning algorithms will be implemented. Another line of future research would be the application and analysis of additional aggregation methods, such as the median or geometric mean, and the comparison of them against the mean aggregation used in this paper.


With the rise in cloud computing architecture, the development of service-oriented simulation models has gradually become a prominent topic in the field of complex system simulation. In order to support the distributed sharing of the simulation models with large computational requirements and to select the optimal service model to construct complex system simulation applications, this paper proposes a service-oriented model encapsulation and selection method. This method encapsulates models into shared simulation services, supports the distributed scheduling of model services in the network, and designs a semantic search framework which can support users in searching models according to model correlation. An optimization selection algorithm based on quality of service (QoS) is proposed to support users in customizing the weights of QoS indices and obtaining the ordered candidate model set by weighted comparison. The experimental results showed that the parallel operation of service models can effectively improve the execution efficiency of complex system simulation applications, and the performance was increased by 19.76% compared with that of scatter distribution strategy. The QoS weighted model selection method based on semantic search can support the effective search and selection of simulation models in the cloud environment according to the user’s preferences. The continuous evolution of complex systems  has had a tremendous impact on people’s daily life and social development. Due to the limitation of existing theoretical analysis methods and the difficulty of experimental analysis methods in some real-world complex systems , it is necessary to find and select the appropriate simulation model that accurately meets the user’s requirements in terms of function and can provide high QoS for building complex system simulation applications . ReusablIn order to solve the abovementioned problems in the existing studies, this paper proposes a service-oriented model encapsulation and selection method for complex system simulation based on cloud architecture. The novelty and contribution of this method includes that it designs a cloud-service-oriented reusable model development (C-RUM) specification to encapsulate the simulation model into a shareable simulation service in the cloud, and then devises a cloud-based simulation model service framework, which solves the problem of network communication in the former RUM specification. This method also uses a knowledge graph  to descrThe organization of this paper is as follows: The basic idea of HLA is to use an object-oriented method to design, develop, and implement object models of different levels and granularities and to obtain high-level interoperability and reusability of simulation models and simulation systems. The object model template (OMT) is a standardized description of the properties of simulation models and their interaction formats, but it is not a standard for establishing the object model. With the development of complex system simulation, there are higher requirements for the efficiency, flexibility, and openness of simulation model development. HLA-based simulation model development specification gradually exposes some problems in the application process, such as efficiency, ease of use, fault tolerance, dynamic compatibility, and so forth .In order to realize the interapplication and interplatform reuse of simulation models and the rapid development of simulation applications, many researchers have proposed reusable and composable development specifications and methods for simulation models. Lee et al. applied the product line engineering concept to the development of simulation model components . Feng etA patent for RUM specification  was propOntologies in the Semantic Web can describe simulation models at the semantic level. Web service ontology description language (OWL-S) was designed to make the Web service an entity which computers can understand based on the description of ontology. OWL-S describes Web services in three aspects: (1) service profile, (2) service model, and (3) service grounding ,15. OntoSimilar to Web services, QoS is a key factor in choosing simulation models that are stored in the cloud environment as a service . At presIn summary, current HLA-based simulation model development specification and RUM specification cannot support simulation models as a shared service to be invoked and operated on cloud architecture by developers. Existing OWL-based model search methods and QoS-based model selection methods cannot support users in searching for relevant models through the correlations between models and selecting simulation models that meet their QoS requirements to construct complex system simulation applications in the cloud environment.Complex system simulation applications usually contain some simulation models which have a large computational load. The operation of such a model requires an immense amount of computing resources, making other simulation models in the same process fall into a long queue, thus delaying the advancement of the simulation timing and reducing the execution efficiency of the simulation application. If such simulation models are encapsulated in the form of shared services and are distributed and stored in the cloud environment, the construction efficiency of complex system simulation applications and the utilization of related models could be improved. Cloud computing resources can be used to realize interoperability between heterogeneous simulation models and distributed collaborative computing of simulation models on multiple computing nodes, so as to improve the execution efficiency of simulation applications.The RUM specification can implement local invocation and communication of simulation models. However, in the cloud architecture, the cloud server where the simulation service model is located and the user terminal are interconnected through the network. The RUM specification does not support communication between the simulation model and the specific simulation engine framework on the network. This paper proposes C-RUM specification by transforming the RUM specification. The purpose is to invoke the simulation model encapsulated by C-RUM specification as a form of shared service in the cloud architecture, to make the model service transmit the data through the network protocol to interact with the simulation engine framework, and to implement distributed collaborative computing and heterogeneous execution of simulation applications. The original RUM specification specifies seven standard (service) interfaces for the simulation model to interact with the outside world—model initialization, parameter input, model status recovery, parameter and status adjustment, data output, model status acquisition, and model calculating interfaces—to provide seven standard operations, as shown in In the cloud architecture, the service interfaces in the original RUM specification cannot identify or parse the network data. Therefore, the C-RUM specification defines the network data input interface and network data output interface. These two interfaces are used to encapsulate the seven service interfaces in order to implement data conversion between the network and the original interfaces. According to the execution flow of the simulation model under the original RUM specification , the C-RNetwork data input interface. This interface is mainly used for parsing network data transmitted based on the socket network transmission protocol. The purpose of the parsing is to obtain the target standard service interface of the network data packet and convert the input data into the specified standard data format of the corresponding service interface. Finally, the parsed instructions or parameters are passed to the target standard service interface. The model invoke and calculate commands need to be analyzed by the network data input interface.Model invoke command: When the simulation system of the user terminal needs to be initialized, the terminal will send a model invoke command to the simulation model in the cloud server, and the distributed invocation system in the cloud server will mount the simulation service model into a process. Then, the model initialization command and related parameters are passed to the model initialization interface through the network data input interface to complete the initialization operation of the simulation service model.Model calculate command: When the simulation system in the user terminal needs the simulation service model to calculate, the model calculate command will be sent to the simulation model. After the command is parsed by the network data input interface, the model will (1) first recover its status through the model status recovery interface, (2) then check whether there is a working parameter adjustment instruction to adjust the working parameters and status, (3) then set the input data through the parameter input interface, and (4) finally start the simulation model calculating operation through the model calculating interface.Network data output interface. This interface is used to encapsulate the data output from the simulation model after calculation and the status information of the simulation model. That includes indicating the standard interface source of the data and the destination address of the transmission and converting them into the format for the socket network communication protocol. Before output to the network, the calculating result and model status need to be encapsulated by the network data output interface.Calculating result and model status output: After the simulation service model finishes its calculation, the network data output interface will obtain the data after calculating from the simulation model output interface and acquire the model status from the simulation model status acquisition interface and encapsulate the data into a socket communication protocol package. The package will be forwarded to the simulation system in the corresponding user terminal through the distributed architecture in the cloud environment.Most of the current complex simulation system application frameworks need to integrate or migrate simulation models into specific simulation platforms, making it difficult to separate the models from the platform. For different simulation platforms, the operation mechanism of the simulation engine is quite different, and it is not easy to bind the service simulation model of the cloud center to a specific simulation platform. In order to invoke the service-oriented simulation model in the cloud architecture without relying on the simulation platform, running under any simulation engine framework ,24, thisThe complex system simulation application consists of a large number of simulation object instances, and there are complex interactions and collaborative calculations between the instances. The simulation object framework is built on a specific simulation engine, and the simulation object instances are defined by the simulation object framework . Each object instance is implemented by a specific simulation model. The local simulation model can be directly mounted or integrated into the object framework, while the simulation model service stored in the cloud environment relies on the communication with its proxy model in the corresponding object framework. The proxy model does not have the specific function of the simulation service model; it only takes the place of the simulation service model in the entire simulation object framework, defining the interaction relationship with other models. When the simulation application needs to interact with the simulation service model in the cloud server or obtain its status, the simulation object framework will accept and transmit the corresponding parameters and data through the socket communication between the proxy model and the simulation service model. The cloud-based simulation model service framework is applicable to the invocation and operation of the C-RUM-encapsulated simulation model in the cloud environment and does not depend on a specific simulation platform.The traditional Web services description language (WSDL)-based  simulatiIn this study, a description method of cloud simulation model resources based on a knowledge graph  was usedUnder the search framework proposed in this paper, users input model attribute requirements as semantic search conditions stored in the array Attributes_conditions [M]. Each item of the array corresponds to 1 to M attribute requirements of the simulation model. The user can input one or more attribute requirements  as semantic search conditions to search for simulation models that meet the requirements of these attributes. The user can also input the required association model and specific association relationships  as semantic search conditions stored in the two variables Correlated model and Relationship, respectively, to search for simulation models that have a certain relationship with the correlated model. The input of correlated models is necessary in this search pattern. Algorithm 1 shows the semantic search algorithm.Data_Base represents a knowledge graph database that stores simulation model description information and correlation relationships. Algorithm 1 Semantic_SearchInput:Attributes_conditions [M], the vector for storing model attribute requirements;   Correlated model; Relationship, the relationship with correlated model;Output:1: Boolean if (Attributes_search_conditions ≠ null)||(Relationship_search_conditions ≠ null) then2: for eachdo // Loop traversal of the simulation model3:  fortoMdo // Loop traversal of model attribute requirement condition4:    ifthen5:     end for6:    if7:    if        then8:     else9:     if (flag1 & flag2) thenpush_into_list  is determined by the computation of the model. A simulation model with more computations has lower model performance.Model performance (QC) reflects the communication capability of the link between the user’s terminal node and the cloud server.Communication capability (QA) indicates the probability that the simulation model can be called and used. It is defined by the mean time between failures and the mean time to repair.Availability (QR) is defined by the execution success rate of the service, which refers to the probability of obtaining the correct response to the user’s requirements within the maximum expected time range.Reliability (QS) is measured by the data management capability of a model service, which mainly depends on the user’s historical experience. Terminal users should be given a  range to score the service  after using it. Then, the value of QS is the average score; with the increase and accumulation of evaluations, this value becomes reliable.Security  are all positive metrics; that is, the higher the value, the higher the quality. In order to eliminate the gap between the different QoS indices, we used the following formula [QoS weighted-based selection algorithm. The above five attributes  models. The airliner model records the delay time and has three statuses: taking off, landing, and waiting. The airport runway model records the idling and queuing status of runway. The ATC model needs to do many complex calculations based on relevant strategy, queue waiting of runways, and delay time of airliners, and then schedule the relevant airliners to wait on specified runways. Therefore, it takes much more time to calculate than the other two models. The abovementioned airport operation control system simulation was used as an experimental case to analyze the efficiency and practicability of the service-oriented model encapsulation and selection method for complex system simulation based on cloud architecture proposed in this paper. The simulation platform used in the case study was SUPE, and all experiments were run on two computing nodes with a Linux (centos7) operating system. Each node was equipped with a 3.40 GHz Intel (R) Core (TM) i7-6700 quad core CPU processor. Docker (version 1.13.0) container technology was used as a virtualization method to build a two-node cloud architecture, in which the distributed operation of the airport operation control simulation system was implemented. In the experiment, a simulation time was set up corresponding to the physical time of 10 min to study the actual system of 1 week (7 days), so each simulation promoted the logical simulation time of 1008. The time that was measured in the test was the execution time when the simulation application finished the 1008 simulation steps . Each piece of experimental data in the analysis chart was the average value after 10 test runs. The experimental configuration is shown in In order to test the performance of the CDS , this experiment tested the execution time of the airport operation control simulation application under three operation modes: serial simulation on a single process in a single node (S-1P), traditional distributed simulation  on two processes , and CDS on two processes  based on the above experimental parameter settings. TDS-2P and CDS-2P used the scatter distribution method (each type of simulation model was distributed to each process in turn), and each process ran in one node.As shown in This experiment packaged the simulation models with large computational requirements into a shareable simulation service through the C-RUM specification. The service model ran in parallel on a single process of a cloud server node, participating in the execution of a complex system simulation application in the cloud-based simulation model service framework . In order to study its performance, the case study encapsulated the ATC model, which has a greater number of calculations than the other models, into a service model based on the C-RUM specification. Then, we tested the effect of using scatter and MS distribution methods under CDS (Scatter-CDS and MS-CDS) on simulation execution time. The specific operation and distribution modes are shown in The results of the test are shown in Through the experimental results and analysis, we found that packaging models with large computational loads into a shareable service, on the one hand, can provide support for quickly constructing the simulation system in the form of service combination. On the other hand, it can effectively improve the execution efficiency of the simulation system. Also, when the total calculation of the remaining models is gradually increased, the degree of parallelism should be increased to ensure load balancing, so as to maximize the effect of MS on increasing the execution efficiency of simulation applications.In order to prove the practicability of the simulation model selection method based on semantic search proposed in this paper, five kinds of ATC service models with different QoS attribute characteristics were constructed by C-RUM specification. A simulation model description method based on a knowledge graph  was usedUnder the simulation model semantic search framework, the five ATC service models  could be accurately found by correlation with the airliner or runway model or by the attributes of the ATC model. These five models were added to the initial model set, and then based on the QoS index values of the five simulation models and QoS index weight vector, the target QoS value Q of each model could be obtained. The simulation model candidate set that was obtained by sorting Q was available for users to select. The experiment assumed that the user wants to select the service model that can optimize the execution efficiency of the simulation application. Directed at two operation modes, two QoS index weight vectors for different experimental methods were provided to select simulation models. The effectiveness of the semantic search framework and the QoS weighted-based model selection method was verified by running and testing the performance of the simulation application that was assembled by the selected ATC models M dominates the effect (the other three QoS indices may have little effect on the execution efficiency), so the QoS index weight was set to W = , and the specific selection process is shown in (1) When the entire simulation system is running on a single node, the external network communication capability Qc does not affect the execution efficiency of the simulation application. The model performance QM, and the optimized candidate model set {ATC-C, ATC-A, ATC-E, ATC-D, ATC-B} could be obtained through calculation. The five ATC service models were assembled, respectively, to the same five airport operation control simulation systems, and the airliner instance was set to 100. The execution times of the five simulation applications operated by S-1P are shown in The QoS weight vector gave a large weight to QThese applications could run effectively, and it can be seen that in S-1P operation mode, the order of the ATC models corresponding to the execution efficiency of the five simulation applications was consistent with the optimization order in the model candidate set. The simulation application assembled by the model ATC-C, which ranked first in the candidate set, had the shortest running time (3234 s).C and QM of the simulation service model affect the execution efficiency of the simulation application, so the QoS preference weight vector was set to W = . The specific selection process is shown in (2) In cloud architecture, shareable simulation services are often stored in the cloud center. The service model and simulation engine framework need to communicate through network interconnection. Both QC and QM. After calculation, the optimized candidate model set could be obtained as {ATC-A, ATC-C, ATC-D, ATC-E, ATC-B}. The five ATC service models were assembled, respectively, to the same five airport operation control simulation systems, and the airliner instance was set to 100. The execution times of the five simulation applications operated by MS-CDS-2P are shown in The QoS weight vector assigned the same weight to QM and network communication capability QC affected the entire execution efficiency of the simulation application.These applications could run effectively, and it can be seen that in MS-CDS-2P operation mode, the order of the ATC models corresponding to the execution efficiency of the five simulation applications was not completely consistent with the optimization order in the model candidate set. Because the QoS weight vector W was set merely according to the experimental architecture without detailed analysis, it was impossible to accurately quantify the extent to which the model performance QThe experimental results show that the searched model can work together with other models and implement the simulation task, which verifies the effectiveness of the semantic search framework. Further, the proposed QoS-based simulation model selection method can support users in selecting the model which has the biggest target QoS index (Q) according to their QoS preference. However, it cannot always give the optimum solution that could optimize a certain performance (execution efficiency) of a complex simulation system.The experiment first tested the performance of the simulation application under three patterns: S-1P, TDS-2P, and CDS-2P. The results prove that, compared with TDS, CDS can also effectively improve the execution efficiency of the simulation application with little performance loss, which demonstrates the practicability of CDS. Experiment 2 encapsulated the models with large computational loads into shareable services in the cloud environment by the C-RUM specification proposed in this paper. Then, by comparing the performance of MS-CDS and Scatter-CDS, the results prove that the MS distribution mode is better than the traditional scatter distribution mode at improving the execution efficiency of complex system simulation. This demonstrates the feasibility of C-RUM specification in cloud networking architecture and the effectiveness of the method, making the models with large computational loads into shared services, proposed in this paper. In experiment 3, the required ATC models were found under the proposed semantic search framework by attributes or correlation searching in the model description knowledge graph. The experiment assembled the searched model into the simulation application of the case study and verified that it can work together with other models and effectively implement the simulation task, which verified the correctness of the semantic search framework. Then, the model ranking based on the QoS weighted selection method was compared with the ranking of the execution time of actual simulation systems assembled by the models in the candidate set. This proved that the proposed QoS weighted-based simulation model selection method can select simulation models according to users’ customized requirements, but the solution is not always the optimum one that could optimize the performance of the complex system simulation.A service-oriented model encapsulation and selection method for complex system simulation was proposed in this paper. This method first promotes the original RUM specification and puts forward C-RUM specification, which solves the problem of network communication in RUM specification. Models with large computational requirements are encapsulated into shareable services in the cloud architecture. The experimental results showed that model servitization can effectively improve the execution efficiency of complex system simulation applications. Then, the model semantic search framework is built in the simulation model description knowledge graph, which increases the correlation search ability compared with other semantic search methods. The QoS weighted-based model selection method supports users in customizing the weight of QoS indices and obtaining the ordered candidate model set by weighted comparison. This mechanism can support the selection of the required simulation model that satisfies users’ QoS preference under the cloud architecture.Future work should further improve the QoS weighted-based simulation model selection method, considering the limitation that it cannot assist users in selecting the optimal simulation service model directed at a specific simulation application or its certain performance. Also, future research should confirm the metric of the model QoS indices and study how to assign the corresponding QoS index weights.


An efficient reliability algorithm is developed to transfer the system reliability problem to a single-component reliability problem, considering the uncertainty of loading cases and the material properties. The main difficulty is that femoral bone densities change after hip arthroplasty and, thus, the mechanical properties of the distinctive bone tissues and, therefore, the corresponding elasticity modulus and yield stress values change. Therefore, taking these changes into account during the hip prosthesis design process is strongly needed. As the bone possesses anisotropic behaviors, as the material properties in both radial and tangential directions in long bone  are almost similar, the bone anisotropy is represented in this study by transversal isotropy. Two optimized formulations for yield stress against the elasticity modulus relationship are first developed and then integrated into an efficient reliability algorithm. Thus, a coupling between reliability and optimization, so-called reliability-based design optimization (RBDO), is introduced in order to control the reliability level. The proposed RBDO algorithm using optimum safety factors (OSF) takes into account the material uncertainties and leads to new stem dimensions. An in-depth numerical analysis on a cementless hip prosthesis is implemented to demonstrate the appropriateness of the proposed algorithm with the consideration of many different loading cases. The results show that the studied model can be effectively used when compared to previous works, which concerns the changes in both geometry and material properties. Hip arthroplasty is an innovative subject in orthopedic surgery, as it permits a significant advancement in pain and functional capabilities, in addition to a high patient justification level with the embedded prosthesis. The primary goal of this process is the long-time continuation of the implant while maintaining steady obsession of the parts with negligible wear of the contact surfaces . The intAs a matter of fact, bone remodeling relies on mechanical and biological elements . The mecOn the one hand, the living tissue modeling difficulty is related to the change in the loading and bone material properties after prosthesis arthroplasty. Considering bone as an anisotropic material, the elastic and strength properties in the axial direction are different than those in the radial direction, in which the bone is found to have higher strength values when applied in the diaphyseal axis  compared to those values obtained in the radial or circumferential “transverse” directions. Relatively, the variations in the computed modulus and strength in the radial and circumferential directions have been found to be small, so human bone can be considered and modeled as transversely isotropic. A possible reason is that the growth and adaptation of the bone is most efficiently related to the uniaxial stresses that develop along the diaphyseal axis during habitual activities, such as gait . These pIn addition to that, the prosthesis design is considered a large-scale optimization problem due to the consideration of a large number of constraints and variables. A reliability analysis of such a large problem should be performed using a particular optimization procedure , and it This way, a direct assessment may lead to an unsuitable reliability level; therefore, it is very important to use the concept of the reliability-based design optimization (RBDO). In Kharmanda et al. , a RBDO In summary, for the hip prosthesis design process, there is a considerable need to take the changes of the material properties of the different bone tissues and the corresponding elasticity modulus and yield stress values into account. On the other hand, some muscles can be cut or harmed during surgery operation, and consequently, they cannot operate at their maximum capacity. Accordingly, it is important to introduce the uncertainty to the loading during the prosthesis design.In this study, optimized formulations for yield stress against the elasticity modulus relationship are developed to dynamically consider the changes in the material properties. The uncertainty to loading cases is studied in order to take the maximum loading capacity of the joint into account. In addition, an integrated reliability model that considers the variation effect of the limit state functions is developed to provide an integrated design solution to the addressed problem.The developed reliability strategy is applied to the hip prosthesis design. The main difference between the previous structural reliability method and current strategy is the integration of living tissues, where bone continues to remodel after the replaced hip implantation . The ageA 2D finite element model of the studied cementless hip prosthesis with different parts is shown in 2 = 0 at the lower cut in the cortical part, and with the following applied loads: The body (FB) and muscle forces (FM) in the x- and y-directions. The used components of different forces corresponding to the three loading cases: L1, L2, and L3 that are shown in In this work, three different day-by-day loading cases are implemented: one-legged standing (L1), abduction (L2), and adduction (L3) . Figure 2O3) with Young’s modulus The number of elements used for the presented model is 1542 non-linear elements with element type, 8-node/PLANE82, and with a total number of 5027 nodes. The materials of the ball and the stem are considered homogeneous and isotropic. The number of elements is 255 for the ball and 485 for the stem. The stem is assumed to be made of a titanium-based alloy (Ti-6Al-4V) with Young’s modulus  250 MPa . The cor 250 MPa .In several published works, the yield stress and also the Young’s modulus are heavily connected to bone density. In addition, bone has been modeled as isotropic and homogeneous materials; however, the anisotropy of bone is probably the most critical mechanical characteristic. For example, during the gait cycle, largely uniaxial stresses can be produced along the diaphyseal axis. In the context of bone anisotropy, Lotz et al.  computedSimilarly, the elastic modulus and compressive strength for cancellous bone in the axial direction were defined asKharmanda  used theAccording to Lotz’s model, Equation (11) in the cancellous bone can be written for the axial direction asTo obtain the optimum fitness of Equation (11), at least three experimental points should be considered in order to find the constant values. According to the previous work by Kharmanda , the optAccordingly, the three experimental results that are presented in The optimized formulations (Equations (16) and (17)) for the cortical bone in both axial and transversal directions are compared to Lotz’s model (Equations (12) and (13)). A graphical representation of the compressive stress values that are presented in Giesen et al.  tested tThe tensile yielding stress can be presented as a fraction of the compression strength for the axial direction, and can thus be written asDifferent values have been used for the ratio ous bone .The goal is to seek the most probable point (MPP) that is defined by the minimum distance between the source of the normalized space and the most important failure surface (limit state function). Related to the earlier stem-bone simulations, the von-Mises stress at the cancellous region has been found to be the most effective element of the limit state function. In the interest of resolving the reliability issue, for the given failure scenario, the reliability index Equations (18) and (19) can be integrated into the constraints in Equation (20). According to Hasofer and Lind , operatiHence, a normalized variable le xisee a. σi is The RBDO algorithm is carried out using the OSF method. The RBDO procedure contains three main steps:The first step is to obtain the failure point (MPP). The optimization process minimizes an objective function subjected to the design constraint without consideration of the safety factors. This way, the optimization problem for a single failure mode is simply written asThe second step is to compute the optimum safety factors. Here, the sensitivity values of the limit state function(s) with respect to all variables are needed. It has been demonstrated that the optimum values of the normalized vector ing form :(24)ui*=The final step is to calculate the optimum solution. This encompasses inclusion of the resulting values of the safety factors into the design variables in order to evaluate the optimum solution.The efficiency of the OSF method has been demonstrated relative to the hybrid one . RecentlThe yield stress is considered the limitation of failure for all layers. The failure scenario can occur in each layer. The stem and ball parts are made of isotropic materials and their failure scenarios are ties see . The comties see . The preties see b is callus works , where tCase 1:The MPP is found on the failure scenario of the cancellous tissue in the transversal direction where the reliability index is Case 2:The MPP is found on the failure scenario of the cancellous tissue in the transversal direction where the reliability index is Case 3:The MPP is found on the failure scenario of the cancellous tissue in the transversal direction where the reliability index is According to the Joint Committee on Structural Safety (JCSS) , the relJeppsson . ConsequThe current configuration of the studied stem, especially for the third loading case (L3), is less reliable than the codes of practice, such as Eurocodes (European norms) and ACI (American norms) . Thus, wAfter having performed the RBDO algorithm for the most critical loading case (L3), the resulting reliable configuration is shown in Therefore, the resulting RBDO configuration shown in In the following, the RBDO results of the three different loading cases L1, L2, and L3 are explained:Case 1:Case 2:Case 3:When comparing the failure point and the MPP in The different results can be discussed according to three aspects: Simulation, reliability, and reliability-based design optimization aspects. For the simulation aspect, when comparing the studied model to the previous works, two different changes can be noted: Geometry and material properties. In the works of Kharmanda  and KharIn this work, the studied model is analyzed considering the ball and the anisotropy of bone material properties. The effect of the ball was much more remarkable than the anisotropy of the bone material properties. The von-Mises stress distribution was very different especially when it concerns the maximum stress values. For the reliability aspect, two categories of reliability strategies can be distinguished in the literature: Dystem reliability analysis and component reliability. When considering a single failure scenario, the reliability algorithm leads to a single index, while when several failure cases are considered, the reliability algorithm leads to several indices . This walgorithm , the reslgorithm . After hThe developed reliability algorithm in this work deals with multiple failure modes, while the previous reliability algorithm in Kharmanda  treated ineering , there iineering  was carrIn order to overcome these drawbacks, the OSF method was efficiently used for several failure modes in this work. Here, three geometrical parameters concerning the stem were considered to be optimized in order to control the reliability level . The different results show that the failure scenario may happen in the cancellous tissue. Therefore, this tissue should be treated as a porous material in order to obtain more realistic results.


A joint demosaicing and denoising task refers to the task of simultaneously reconstructing and denoising a color image from a patterned image obtained by a monochrome image sensor with a color filter array. Recently, inspired by the success of deep learning in many image processing tasks, there has been research to apply convolutional neural networks (CNNs) to the task of joint demosaicing and denoising. However, such CNNs need many training data to be trained, and work well only for patterned images which have the same amount of noise they have been trained on. In this paper, we propose a variational deep image prior network for joint demosaicing and denoising which can be trained on a single patterned image and works for patterned images with different levels of noise. We also propose a new RGB color filter array (CFA) which works better with the proposed network than the conventional Bayer CFA. Mathematical justifications of why the variational deep image prior network suits the task of joint demosaicing and denoising are also given, and experimental results verify the performance of the proposed method. Nowadays, many digital imaging systems use a single monochrome sensor with color filter array (CFA) to capture a color image. Without color filters, the monochrome camera sensor would give only brightness or luminance information and could not recover the colors of the light that fall on each pixel. To obtain color information, every pixel is covered with a color filter that only lets through a certain color of light: red, green or blue. These sampled red, green and blue channels are then interpolated to fill in the missing information at the pixels for which certain colors could not be sampled. This procedure is called the demosaicing procedure, for which many methods have been proposed ,6,7,8,9.Current image sensors come in CCD (charge-coupled device) or CMOS  types, which are both sensitive to thermal noise. Therefore, CFA pattern images taken in low illumination suffer from noise of Poisson distribution. The noise in the CFA pattern image has a large effect on the reconstruction of the color image, as the noise in the noisy pixels spreads out to neighboring regions by the demosaicing process. The denoising is also a challenging task since at least two-thirds of the data are missing. Complex aliasing problems can occur in the demosaicing process if a poor denoising is applied beforehand. As most digital camera pipelines are sequential, quite often the demosaicing and denoising are also performed in an independent and sequential way. This leads to an irreversible error accumulation, since both the demosaicing and the denoising are ill-posed problems and the error occurring in one of the procedures cannot be undone in the other procedure. It has been shown that simultaneous coping with the errors in both the denoising and the demosaicing has advantages, and some joint demosaicing and denoising methods based on optimization techniques have been developed ,11,12. RIn this paper, we propose a deep image prior based method which needs only the noisy image as the training data for the demosaicing. The proposed method uses as the input a sum of a constant and varying noise. We give mathematical justifications as to why the added varying input noise results in the denoising of the demosaiced image. Furthermore, we propose a color filter array which suits the proposed demosaicing method and show experimentally that the proposed method yields good joint demosaicing and denoising results.The following works are related to the proposed method. The proposed method can be seen as a variation of the following works fitted to the joint demosaicing and denoising problem.D is a distance measure, which is normally set as the square of the L in Equation  for joint demosaicing and denoising. We denote by fined in ; i.e., aHere, k, then the reconstructed image For the steps f with p input channels and a convolution filter of size d, and Next, we give mathematical justifications on the joint demosaicing and denoising property of the variational DIP. First, the reason which explains why the DIP performs demosaicing can be found in , where ishown in , that a minf∈Rnf*shown in , since ishown in  the low minf∈Rnf*M multi-variable scalar-valued function Now, to consider the effect of adding the varying noise i-th (The Taylor expansion of the We can extend Equation  to fθ(z)This results inEquation  implies Furthermore, if we take the expectation of the different outputs The fact that different inputs of When Equation  is, withNext, we propose a CFA pattern, which we think works well with the proposed demosaicing method. The proposed CFA consists of randomly distributed pixels, where the pixels corresponding to the R, G and B channels take up 33% of the whole CFA pattern each. The design of the proposed CFA is not based on a rigorous analysis, as done in classical CFA designs ,24,25, bWe compared the proposed method with other deep-learning-based demosaicing methods on three different datasets. We added different noises generated from Gaussian distributions with different standard deviations of ethod in  as a repethod in ; the Demethod in  with twoethod in . We madeEquation  were setP in Equation (enoising  and the In this paper, we proposed a variational deep image prior for the joint demosaicing and denoising of the proposed random color filter array. We mathematically explained why the variational model results in a demosaicing and denoising result, and experimentally verified the performance of the proposed method. The experimental results showed that the proposed method is superior to other deep-learning-based methods, including the deep image prior network. How to apply the proposed method on the demosaicing of color filter arrays including channels other than the three primary color channels could be the topic of further studies.


In this study, we present the implementation of a neural network model capable of classifying radio frequency identification (RFID) tags based on their electromagnetic (EM) signature for authentication applications. One important application of the chipless RFID addresses the counterfeiting threat for manufacturers. The goal is to design and implement chipless RFID tags that possess a unique and unclonable fingerprint to authenticate objects. As EM characteristics are employed, these fingerprints cannot be easily spoofed. A set of 18 tags operating in V band (65–72 GHz) was designed and measured. V band is more sensitive to dimensional variations compared to other applications at lower frequencies, thus it is suitable to highlight the differences between the EM signatures. Machine learning (ML) approaches are used to characterize and classify the 18 EM responses in order to validate the authentication method. The proposed supervised method reached a maximum recognition rate of 100%, surpassing in terms of accuracy most of RFID fingerprinting related work. To determine the best network configuration, we used a random search algorithm. Further tuning was conducted by comparing the results of different learning algorithms in terms of accuracy and loss. Nowadays, radio frequency (RF) identification (RFID) is used in various applications such as: object identification , trackinThe methods based on the identification of individual transmitters/receivers or objects by using the EM signal characterization are called RF fingerprint identifications. These methods attempt to extract an identifier from the received signal. The RF fingerprint identification exploits the intrinsic characteristics related to hardware and manufacturing randomness, which can be extracted by processing the transient round-trip signal .Traditional passive UHF RFID systems can be adapted to authentication solutions. Some protocols are proposed to improve its security level . PhysicaChipless RFID technology is an intermediate technology between the barcodes and the UHF RFID . It combThe authentication using chipless RFID tags in ultra-wide band (UWB) (3.1–10.6 GHz) was initially demonstrated in . The conIn , variousWe propose chipless RFID tags operating in V-band, which are more sensitive to geometrical inhomogeneities than other bands at lower frequencies. In order to harness the fabrication randomness, EM signatures are employed to characterize each tag.We evaluate the capacity of a neural model to identify and authenticate the EM signatures of the tags in a supply chain scenario and we obtained a high recognition rate.Neural networks come in different shapes and forms mostly derived from architectures like: Multilayer Perceptron (MLP), Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN). These networks are tweaked and modified in order to solve a particular problem. As a consequence, there are a large variety of models to choose from and to analyze, with each one of them being suited for a narrow area of applications, such as pattern recognition, trend detection, and optimization problems . These nThe paper is organized as follows: The chipless tags can be seen as a radar target designed to scatter a specific electromagnetic signature. In chipless RFID applications, it is possible to authenticate such items with RFID chipless tags using a noninvasive method based on the exploitation of a distinctive signature, which paves the way to authenticate documents (printing the tag directly on paper) or even specific products. The tags’ structure is basically composed of metallic ink printed on a substrate; therefore, no components are needed. Nowadays, a chipless application designed to operate in the X-band (8–12 GHz) was reported .In the interest of increasing the sensibility due to manufacturing intrinsic errors, a chipless millimeter-wave E-shape resonator was designed and simulated for proof of concept. The fabricated tag is further used to validate the better distinctiveness in V-band. Contrary to X-band RF approaches, the V-band range (50–75 GHz) is chosen to grow the tag’s sensitivity related to uncertainties in the fabrication process. Then, for comparison purposes, the E-shaped V-band geometrical dimensions are scaled 5 times generating the X-band design, as shown in 21 is recovered by a far-field probe. As depicted in Considering the same manufacturing method, the same precision is imposed for both structures. For instance, for photolithograph methods, a precision around 35 o V-band . FurtherTherefore, as the randomness is the same for the fabrications and considering the operating range of each structure, the fabrication process imposes a manufacturing error on the structures. Consequently, the structure developed to operate on V-band is more sensitive to the physical error process, resulting in better differentiation between the analyzed signals on the V-band.Finally, to resume the better distinctiveness due to V-band operation, A set of 18 tags was fabricated on the same substrate, as shown in Q of this E-structure is 84. It is high when compared with [Q represents greater sensitivity due to structural variations. However, whatever its quality factor is, tags must have an RCS greater than −40 dB to be read correctly. Then, the E shape scatterer is a better candidate for authentication purposes, and a Furthermore, this increase in the number of resonators will carry out a coupling increment between the resonators that may grow the degree of randomness. Due to coupling, the multiplication of the number of scatterers does not carry a proportional augmentation of the RCS level; hence an optimization step is performed. The tag’s EM responses were obtained by EM simulation using CST Microwave Studio. The substrate is Rogers RT5880 with ure E in . The quared with ,34. It iThe dataset was generated in a laboratory as shown in 15 waveguide output connected to horn antennas in copolarization configuration. The tag is positioned inside a thin piece of foam. The dedicated foam substrate, as shown in The measurements were performed with an Agilent N5222A  PNA with Virginia Extensions (VDI modules) to operate from 65 GHz to 72 GHz. The VDI module is a frequency multiplier combined with a mixer with a WROne problem that arises is related to how the training of the neural system should be conducted in a real environment. Fitting the neural model should be done prior to deployment (after the manufacturing process) and it should use measurements that correspond to a real scenario where the tags are incorporated into products, like pharmaceuticals products, consumer goods, apparel, spare parts, luxury products, etc. This is essential in order to minimize the probability of misclassification, because integrating tags into products implies modeling the environment noise and interferences into the EM signature. The ideally measured tags are only used to validate the proposed concept. The maximum differentiation among the tags is affected mainly due to two factors: the uncertainty of the manufacturing process, that directly relates to the tag’s back-scattered signal, and the frequency differentiation that concerns the frequency operation of the chipless tag. Therefore, the millimeter-wave operation improves the frequency resolution of the approach . For instance, considering the UWB frequency band, the maximum frequency shift seen in the measurements is 80 MHz . On the The evaluated network is a 2-layer MLP with ReLU activations and one final dropout layer, as shown in radients . The regradients . The lasOur ML application was implemented in Python programming language, using the open-source neural network library Keras , a powerEach tag was measured 10 times in the manner discussed in Another important step before the training process is to feed normalized data to a network. In general, normalizing the data speeds up the learning process and leads to faster convergence. The evaluation of the ML approach is conducted on the database presented in In order to find the optimal number of hidden units for our model we used the random search (RS) optimization approach. RS is empirically and theoretically demonstrated to be more efficient for hyper-parameter optimization than grid search or manual search . A searcThe RS algorithm randomly chooses a candidate solution to minimize the cost function. Until a termination criterion is met, a new candidate is sampled from the search-space, given a radius surrounding the initial solution. If the evaluation of the new candidate solution leads to a lower value, the algorithm moves to that position and starts over the process. Having a relatively small search-space, RS quickly found that 96 hidden units per layer are sufficient to learn from our data. The neural network tested further in the study has 96 units per hidden layer.In the following section we firstly present the results of our best model that uses Adam  as stochThe LCs provide a representation of the learning process over time. It is common to visualize the dual LCs using the training and validation dataset. The training LC emphasizes how well the model is learning, while the validation curve shows how well the model is generalizing. A model is capable of generalization if it successfully classifies other data than the data from the training set. Both metrics imply how the model behaves after each iteration. The loss is calculated with respect to the SoftMax function. The accuracy is determined by comparing the predictions of the neural model to the true data. The interpretation of the LCs can determine if the model is underfitting or overfitting. Overfitting means that a model has learnt the training dataset too well. This has implications in its generalization power. The more specialized a model becomes, the less capable it is to generalize to new data. Overfitting can be detected by analyzing the gap between the training and validation LCs. If the training loss is much lower than the validation loss over time, the model learnt the training observations by heart and cannot respond accordingly to other unseen observations. On the other hand, underfitting refers to an inadequate learning of the dataset. This can imply that the model does not have the necessary capacity to learn the complexity of the dataset, thus further tuning is required. Underfitting can be observed if the training LC shows a flat line or relatively high loss/low accuracy.The model successfully learnt the dataset. The LCs have low fluctuations and a steady convergence, demonstrating an optimal fit. The validation steps performed at every epoch shows that the network is capable of generalization to unseen data. The validation accuracy goes up to 100% and demonstrates the ability of the ML approach to recognize the EM signatures of 18 different chipless tags.To further enhance the visualization of the model performance, a confusion plot is presented in The diagonal cells show the number and the percentage of correct classifications by the network with respect to the dataset used for creating the matrix. For example, the first diagonal cell states that 43 EM signatures are correctly classified as being part of the same tag class. This number corresponds to 6.0% of all 720 EM signatures used for evaluation. The sum of numbers along each row represents the total number of measurements for the corresponding tag class. In order to interpret the metrics mentioned in the previous paragraph, we need to define some auxiliary terms. A true positive (TP) is a correct prediction of the positive class (P). A false positive (FP) is an incorrect prediction of the positive class. Similarly, a false negative (FN) is an incorrect prediction of the negative class (N). The positive class refers to the ground truth tag class, while the negative class refers to the other tag classes. As an example, we consider the outcomes of the measurements from the first tag class. The TP value is 43, FP is 0 , and FN is 0 . As a result, using the above formulas, the precision is 100%, the FDR is 0%, the recall is 100%, and the FNR is 0%.Whenever we are designing ML algorithms, it is always a good practice to randomly split your data into training and testing sets. Still, this approach can miss valuable information about some examples because they were not used in training. To avoid losing important characteristics, it is useful to use k-folds cross validation . The conThe following subsection presents a performance comparison for our model when it uses different optimization algorithms. The final paragraph provides an overview of the achievements of the most common techniques used to authenticate chipless RFID tags.In In This paper presents the actual context of the chipless tags authentication methods and proposes a ML approach in the characterization of the V band operating tags that can be used to solve the counterfeiting phenomena encountered by many industries.Chipless RFID tags can generate a unique and unclonable identifier from its intrinsically random manufacturing process, thus making them suitable in authentication applications. The choice of using the V-band is argued by the fact that it better reflects the differences between EM signatures due to the smaller wavelength, which is comparable to the structural inhomogeneities of the designed tag. The inherent randomness of the fabrication process is visible in the EM spectrum in terms of phase and amplitude, therefore making them suitable to characterize the identity of each tag. This unique feature allowed us to successfully exploit the recognition power of ML.The neural network used in the study has two hidden layers, uses ReLU activations, dropout regularization, and SoftMax loss. This architecture is chosen due to its simplicity and fast implementation. Moreover, it demonstrated that it is capable enough to train with the given data and to authenticate all tags with maximum accuracy. Another important aspect is related to the development of new fingerprinting equipment, that can be supported by integrating the neural network in an IC.The proposed method is a low-cost and a high-level authentication approach in contrast to other available high secure authentication solutions , where the cost of the equipment to examine the authenticity is high. The chipless tag can be inserted (or hidden) into the product and thus, it can be considered as a highly-secure seal.Further study might include a generative adversarial network to extend our datasets, as opposed to the data augmentation technique. Moreover, instead of training with the EM signatures, modern instruments of nonlinear data analysis such as recurrence plot analysis (RPA) and recurrence quantification analysis (RQA) can lead to other unique characterizations of chipless RFID tags.