One of the major shortcomings of variational autoencoders is the inability to produce generations from the individual modalities of data originating from mixture distributions. This is primarily due to the use of a simple isotropic Gaussian as the prior for the latent code in the ancestral sampling procedure for data generations. In this paper, we propose a novel formulation of variational autoencoders, conditional prior VAE (CP-VAE), with a two-level generative process for the observed data where continuous  Variational autoencoders (VAEs) ,2 are deThough powerful and theoretically elegant, the VAEs in their basic form suffer from multiple deficiencies that stem from the mathematically convenient yet simplistic distributional assumptions. Multiple strategies have been proposed to increase the richness or interpretability of the latent code ,10,11,12In this paper, we reason that for generating realistic samples of data originating from complex distributions, it is the prior that lacks expressiveness. Accordingly, we propose a new VAE formulation, conditional prior VAE (CP-VAE), with two-level hierarchical generative model combining categorical and continuous (Gaussian) latent variables.The hierarchical conditioning of the continuous latent variable on the discrete latent component is particularly suitable for modelling multimodal data distributions, such as distributional mixtures. Importantly, it also gives us better control of the procedure for generating new samples. Unlike in the standard VAE, we can sample data from specific mixture components at will. This is particularly critical if the generative power of VAEs shall be used in conjunction with methods requiring the identification of the distributional components, such as in continual learning ,14.As recently shown ,15, withWe formulate the corresponding variational lower bound on the data log-likelihood and use it as the optimization objective in the training. In the spirit of empirical Bayes, we propose estimating the parameters of the conditional priors from the data together with the parameters of the variational posteriors in a joint learning procedure. This ensures that the inferred structure of the latent space can be exploited in data generations.Variational autoencoders (VAEs) ,2 are deIn their basic form, they model the unknown ground-truth Computing Variational Inference involves the optimization of an approximation to the intractable posterior. In Variational Inference, we specify a family of tractable distributions Reordering the terms of Equation , we haveThe first term is the initial KL divergence we want to minimize in VI. Since the Kullback-Leibler divergence is always greater than or equal to zero, the second term In VAEs, the typical assumptions are of a simple isotropic Gaussian prior The VAEs employ the strategy of amortized variational inference. They approximate the intractable posteriors The encoder and decoder networks are trained end-to-end by stochastic gradient-based optimization maximizing the sample estimate of The Evidence Lower Bound A in Equation ≈p(z) ,16. ThisThe posterior collapse and, consequently, the mismatch between the true and the approximate posterior motivates a direct improvement of variational inference by assuming/learning a more flexible posterior approximation for variational inference ,5, or anEven though the prior in the VAEs is usually modelled by a simple isotropic Gaussian distribution, this assumption is a source of over-regularization, and is one of the causes of the poor density estimation performance .To derive the optimal prior, we reformulate the VAE objective Equation . By maxiEquation .(7)Lθ,ϕThe maximizing solution is equal to the negative entropy of the data distribution, S, and it can been seen as Mixture of Gaussians (MoG) over all the data. However, this extreme case leads to over-fitting as this prior essentially memorizes the training set. Moreover, it is computationally inefficient, since it is very expensive to compute at every training iteration.In this optimal case, the marginal approximate posterior A natural approximation of the marginal approximate posterior prior can be a Mixture of Gaussian (MoG) prior in a random subset of the data, Alternatively, marginal approximate posterior can be modelled while using a mixture of posteriors over learned virtual observations (pseudo-inputs) with a fixed number of components (z|u(k)) . Hence, Since their introduction in 2014 [First, it has been argued that optimizing the variational bound Equation  instead  InfoVAE ,10, or w InfoVAE ,20,21. A InfoVAE ,15.For our model, the analysis presented in Second, the simplifying conditional independence assumptions for the data dimensions factored into the simple Gaussian decoder PixelVAE , or sequPixelVAE . In our Third, the insufficient flexibility of the variational posterior variable ,5. WhileThis has been recognised and addressed by the fourth group of improvements that focuses on the model prior and that our method pursues. These build on the observation that overly-simple priors can be source of excessive regularization, limiting the success of the VAE models ,19. For More related to ours are methods that suggest to learn the prior. The VLVAE  uses theThe VampPrior  proposesThe continuous-discrete decomposition of the latent space similar to ours have been used for data clustering through generative model presented in ,26. The Options for freeing the distributional class of the latent representations through Bayesian non-parametrics have been explored, for example, in ,27,28. TThe mathematically and practically convenient assumption of the factorial Gaussian approximate posterior Even though various methods have been proposed for enriching the posterior distributions, as we mention in We propose a new VAE formulation, conditional prior VAE (CP-VAE), with a conditionally structured latent representation that encourages a better match between the prior and the posterior distributions by jointly learning their parameters from the data.We consider a two-level hierarchical generative process for the observed data where two latent variables K categories We assume a uniform categorical as a prior distribution for the discrete component The compositional prior we propose is well suited for generations of new samples from multimodal data distributions mixing multiple distributional components. In contrast to sampling from a simple isotropic Gaussian prior that concentrates symmetrically around the origin, we can sample the latent code from discontinuous parts of the latent space. These are expected to represent data clusters corresponding to the originating distributional mixing.In addition, the variations encoded into the continuous part of the latent space are also sampled conditionally and therefore are better adapted to represent the important factors of data variations within the distributional clusters. This is in contrast to the single common continuous distribution of the basic VAE  or VAEs The data conditional There is no straightforward way to fix the parameters Equation  for eachechnique .As explained in As in standard VAEs, we employ amortized variational inference to learn the unknown data distribution. We use the approximate posterior distributionOur approximate posterior replicates the two-level hierarchical structure of the prior. In this way, we ensure that the latent samples are structurally equivalent both during inference and new samples generations. This is not the case in other hierarchical latent models that rely on simplifying mean field assumptions for the posterior inference ,26.We use encoder network with a gated layer As customary in variational inference methods, our optimization objective is the maximization of the lower bound on the data log-likelihoodA and B terms. Using the prior and posterior distribution decompositions from Equation . As theFor the decoder parameters For the encoder parameters timators  based on–Softmax c=gϕ(c˜) Finally, the gradients with respect to the parameters B of the objective Equation EpThe regularizer maximizes the entropy of this distribution, thus encouraging the model to use evenly all of the categories of the discrete latent code counteracting the pathological case of the first point above.Finally, the middle term pushes the marginalized conditional posteriors of the continuous latent variable We unify and analyse the objective under different assumptions for the joint distribution of continuous and discrete latent variables As in the vanila VAE, the different variations of VAE with continuous and discrete latent variables jointly optimize the generative and the inference model. Using discrete latent variables we impose a categorical distribution as the output of the encoder. We first perform a decomposition of the objective given by Equation  and thenTo better understand the various modifications of the VAE objective with continuous and categorical latent variables, we review the possible independence assumptions for the inference and generative models as summarized in As we show in A is the negative reconstruction cost. Term The terms CP-VAEIn the proposed model where we do not make any independence assumption about the approximate posterior, The term The term see also .INDq modelIn INDq model, we assume conditional independence between the continuous and discrete latent variables, These assumptions are made by the Variational Deep Embedding (VaDE) paper , where tINDp modelIn INDp model, we do not make any independence assumption about the approximate posterior In this model, similarly to our model, the continuous latent variable In this model, none of the terms ensure that the discrete latent variable The INDp assumptions are used in the semi-supervised model by Kingma et al. in , where tINDqp modelINDqp model assumes conditional independence between the continuous and discrete latent variables and marginal independent priors. In this case, the continuous latent variable also see . The C1 In contrast to our proposed model, in INDqp, none of the terms in the objective prevent the model from ignoring the discrete latent variables or guarantees samples from a specific category.This was also experimentally found in , where tK components In this section, we examine the special case where instead of inferring the categorical posterior as in the models above, we assume that it follows a uniform distribution over A term. Given that we do not infer the categorical posterior, this extra Assuming that the categorical posterior follows the uniform distribution, the marginal categorical posterior Equation  are equahttps://bitbucket.org/dmmlgeneva/cp-vae/) over synthetic data and three image datasets  model through experiments . We coms  may not be known or even easy to estimate:In Because of the simplicity of this set-up, even methods that do not adjust their priors to the disjoint learned representation, such as the simple VAE is able to recover the multimodal structure of the data at generation time. However, VAE in contrast to CP-VAE, We further explore how our model handles the excess capacity within the categorical latent variable. For this, we focus on the 150-category case and generate data by sampling the discrete latent variable (a) from the marginal posterior When comparing the two in Equation .For the real-data experiments, we use three image datasets, MNIST , FashionWe examine the ability of our model to generate new examples from the underlying distributional clusters. For this, we trained our model (CP-VAE) over the FashionMNIST data with 150 categories in the discrete latent variable and compared it to the INDq, INDp, and INDqp models.We show  that theThe discrete latent variables seems to discover the true labels in an unsupervised manner as the major source of variability and therefore we confirm this by examining the conditional marginal categorical posteriors. This is implemented by training our model without any supervision and, at the end, we use the the true labels to compute the marginal categorical posterior condition on each label. In If the true class label is available at the training data, then CP-VAE is also able to generate samples from specific labels. This can be done by computing the marginal categorical posterior for each class, Repeating the analysis  using the MNIST data set we observe the same behaviour for our model. It uses the vast majority of the discrete latent variable with high probability allowing to discover a lot of different clusters among the data, Repeating the analysis  using the MNIST data set we observe the same behaviour for our model. It uses the vast majority of the discrete latent variable with high probability allowing to discover a lot of different clusters among the data, Unlike MNIST and FashionMNIST, which have a small number of labels with many images of each label and a large amount of data, the Omniglot dataset  consistsFinally, we compare the performance of our CP-VAE to a number of standard baselines varying also the size of the categorical variable. We experiment with ods from : VAE witWe summarize the numerical results in terms of the negative variational lower bound calculated over the test data in We present the new data examples generated by the various methods in In this paper, we introduce CP-VAE, an unsupervised generative model that is able to learn the multi-modal probabilistic structure of the data. We propose a conditionally structured latent representation that enables our model to discover the modes in the training data distribution. This is achieved by decomposing the latent representation into a continuous and a discrete component and through a better matching between prior and posterior distributions by jointly learning their parameters from the data. The experimental results demonstrate that our approach is able to recover the modes over the original data in an unsupervised manner with a performance similar to that of a human annotator and that CP-VAE is able to conditionally generate new samples from the individual modes of the underlying distribution. In addition, we conduct a theoretical and experimental analysis of various independence assumptions on the continuous and discrete latent representations adopted in the related literature and argue in favour of our more general model formulation.


The paper presents an investigation and analysis of the electromechanical and thermal characteristics of the carbon fiber alone as single tow and embedded in host materials such as polymer e.g., acrylonitrile butadiene styrene (ABS) using 3D printing. While carbon fibers can partially reinforce the structure, they can act as sensors to monitor the structural health of the host material. The piezo-resistive behavior was examined without any pretreatment of the carbon fiber under tensile test in both cases. Special focus on the filaments clamping types and their effects was observed. An auxetic behavior was exhibited; otherwise, the free part shows elastic and yielding ranges with break point at high resistance. An induced temperature of the carbon fiber was measured during the tensile test to show low variation. The carbon fiber can provide strength contribution to the host material depending on the percentage of filling the material in 3D printing. The relative variation of the electrical resistance increases by 400% while embedded in the host material, but decreases as the tows filament density increases from 1 to 12 K.  Ensuring the operational safety and long-term stability of structures are essential requirements in designing and improvement of new materials. Integrating a structure with a sensory system that mimics the nerve system in its ability to sense, locate, and heal damages is an ultimate goal that researchers are taking steps toward its realization . This isSuperior mechanical properties of carbon fibers make them of particular importance in structural reinforcement. The most prominent characteristic of the carbon fibers (CFs), in spite of their low density, are their relatively high strength and stiffness compared with steel. In addition, the aspect size ratio boosts their ability to be reinforced within different polymer-based materials. Such reinforcement was utilized to enhance the strength properties of the hosting structures in various applications. For instance, aerospace applications are widely using CF-reinforced composite polymers ,3,4.Moreover, the carbon fibers possess excellent sensing ability due to their linear piezo-resistive behavior. In recent years, researchers realized that variation in the fibers’ electrical properties can be used as a mean to quantify mechanical strain . The pieThe relative resistance change δR/R can be described by Equation (2) ,13:(2)δRCarbon fibers in the elastic region exhibit different tensile modulus behavior i.e., non-Hookean behavior ,15,16. TCharacterizing the behavior of carbon fibers under tensile testing is crucial to understand the structural flaws that lead to fibers failure According to the authors, large flaws in the carbon fibers can significantly deteriorate the fiber strength. It is also suggested that, in absence of large flaws, the disordered region is most likely contributing to the tensile strength more than the crystallites. Few papers where cited that discussed the tensile failure phenomena in carbon fibers from micro- and nano-structural points of view. Possible causes of failure in carbon fibers are crystallite shear failure, failure of wrinkled crystallites, rupture of the basal planes within misaligned crystallites, and yielding of the disordered structure . Using nThe resistivity change during tensile condition contributes to the piezo-resistivity behavior of CFs and the gauge factor’s value reflects the resistivity behavior i.e., negative or positive behavior. With the absence of resistivity change effect, and for the typical value for Poisson’s ratio of 0.28, the limit value of the gauge factor is 1.56 . The incMoreover, there are attempts to embed carbon fibers on other non-polymeric-based materials for health monitoring applications. Horoschenkoff embedded carbon fibers on the surface of glass fiber reinforced polymer specimens and pressure vessels to monitor crack density ,38. BaseMore recently, Xi and Chung reported that carbon fibers have piezoelectric behavior . It was In tensile testing of fiber tows, care must be taken to reduce slippage and inhomogeneous stress levels. While the engineering standards recommended the use of end tabs or adhesive at the end of the specimen, no explicit procedure was identified to specify the tab materials, grip pressure, or grip alignments. Sawada and Shindo emphasized the importance of the clamping method during tensile testing of fiber tows . The useThis paper will consider the characterization of the bare carbon fiber in its initial form without any pre-treatment e.g., adding resin, with a particular emphasis on the clamping and port attachment with its effect on the overall behavior. This study will be followed by the characterization of the same carbon fiber under the same condition of no pretreatment while embedded in a host material e.g., polymer. The characterization will include the mechanical, electrical, and thermal behavior under tensile test in normal and cyclic modes. The results will be discussed with respect to the literature review.The experimental setup is described in detail in In this experiment, the typical challenges are to secure vertical straightness of the CF with perfect gripping of the two ends avoiding slippage, bending moment, and stress concentration. In addition, the CF needs the electrodes to be connected for the real-time measurement of electrical resistance during the test. Two flat plates of Perspex material have been used to hold the CF and aluminum foil in sandwich. This has proved to be efficient in securing complete electrical conductivity by keeping the CF smooth, straightened, and free of stress concentration after several initial tests.The second sample was built by embedding the CF inside ABS polymer using shapes shown in The gripping system significantly contributes to the resistance measurements during the tests. Connections of the CF with copper ports using C-Solder from the market helped to form strong bonds that are thermally and electrically conductive, see As a preliminary test, the three different configurations shown in The “free CF length” ends by a high jump in the resistance due to the sudden fracture of the fiber called the “breakage” region. The disparity between the three configurations in The tests were conducted with simultaneous records of the load applied in tension to the bare fiber while measuring the electrical resistance variations for characterization. The typical setup of the tests is shown in The four categories 1, 3, 6, and 12 K are considered in a standalone test. Based on the observation made previously in the preliminary results for the gripping, a cyclic tensile test has been conducted for the CF. The results are discussed later. The 1 K filament exhibits a leading strain to start almost immediately the free CF length region, followed by 3, 6, and 12 K, respectively, with different behaviors. Based on the idea that the through-thickness phenomena is closely related to the number of filaments in one tow, the number of fibers contributes significantly to that behavior, where the probability of the contacts among the fibers increases in the 1, 3, 6, and 12 K filaments, respectively.The higher number of fiber filaments requires high strain to overcome the effect of the through-thickness. The response of the carbon fiber for the tensile load can be represented by the change of the electric resistance. Theoretically and based on Equations (1) and (2), these changes may happen due to either geometrical changes which are summarized by Poisson’s ratio and the strain, or material properties, precisely the resistivity change or piezo-resistivity. The opposite behavior of the resistance measurements at the beginning of the graph is being attributed to either the through-thickness resistance phenomena, which appears wherever the fibers are in contact, but this may not be the case since we apply a pretension force that can eliminate this cause or at least reduce its effects. In addition, the usual resin application is absent. No such observation was found in the literature since results with this type of fiber clamping were not published before. The cause may be due only to gripping forces. However, Wang et al. observed this auxetic behavior for CF in epoxy-matric composite under compression . As it hThe first part is the critical region in the gripping. It was noticed that the variation of the resistance is almost negative due to possibly the compressive stress developed under the clamps as shown in The breakage progress of the carbon fiber under tensile test is shown in In the second phase of the work, the aim is to investigate the fiber embedded inside a host material, the tensile tests have been carried out on the ABS samples. Standard samples were prepared with the host ABS polymer  using a 1 and L2. The relative resistance is not straightforward due to the gripping inside the polymer at the clamping area. The relative resistance starts with a constant towards a negative slope and then positive as seen previously with standalone tow carbon fibers. Originally, the 1 K tow filament and due to its relatively small size i.e., less contact between the fibers, are not much affected by the through-thickness phenomena and also the embedding process, where the negativity of the relative resistance is very low. The previous results for the bare fiber filaments show that increasing filament’s number leads to an increase in the effect of the through-thickness. Accordingly, the negative ratio for resistance variation will appear; for instance, in the 3 K filament tow shown in We investigated the load cycling effect on the gripping region and outside in the free length. The cyclic tests have been carried out within the load limits for those regions. The load between (0–10) N was applied for the inside gripping region in However, if we compare the hysteretic behavior captured for both cases in This characteristic can be used to monitor structural damage of the host material if the CF is embedded. The negative behavior of the resistance during the inside region has been approved again in Since the carbon fiber material is subject to a pulling force under tensile test, Equation (2) shows the components of the relative variation of the resistance depending on the strain and resistivity. The variation of the resistivity can also be governed by the effect of temperature as in Equation (3):On the temperature contribution to the electromechanical characteristics, it is evident that there is a variation in the temperature during the tensile test. Although the variation is negligible for low number of filaments, it is noticeable in large tows density. This increase of temperature may affect the precision of measurements in applications e.g., damages in cement or concrete structures or when subject to harsh environment. Further analysis is needed to explore this effect. Currently, CFs are embedded manually into host material during 3D printing. There is a need of a well-defined process of embedding where the embedded fibers are fed in the host material with prescribed initial tension. For the sensing precision measurement, further investigations are needed to check the effects of this generated temperature with respect to the host material temperature on the electrical resistance measurements.This paper investigated the electromechanical characteristics with measurement of an induced temperature variation during the tensile test of non-pretreated bare carbon fibers and embedded in host ABS material using 3D printing. A particular emphasis on the clamping of the carbon fiber for the tensile tests has been taken into consideration since this has been discussed in the literature and we face it in the current tests.Careful attention has to be considered when designing the clamping of the ports for reading to avoid any secondary issue that may affect free length measurement. With this type of clamping selected, the carbon fiber has exhibited two regions with different behaviors; one related to the clamping with negative electrical resistance variation and positive loading since it is pre-loaded. The second one was related to the free effective length of the fiber. To overcome this gripping region, a minimum value of strain should be applied. This value depends on the number of fiber filaments; for instance, a strain of 0.18%, 0.23%, and 0.4% was required for 1, 3, and 6 K filaments, respectively. Similar behavior has been noticed when CF was embedded in ABS polymer. Abnormally, the 12 K filaments exhibited a difference for both bare CF or ABS embedded CF. A relatively minimum high strain of 1.3% is needed to overcome the gripping region in bare CF.The negative behavior of the resistance has affected the cycling tests where an opposite behavior of deformation to the relative resistance variation appears inside the gripping region. However, good agreement for the actual behavior of deformation was depicted outside the gripping region. Linear behavior was observed for CFs embedded inside ABS material in that region. Adherence of the ABS to CF is key. This might be promising since it will allow the embedding of fibers without the need of pretreatment process, hence, reducing fabrication time and cost. Several more composites that have been investigated, e.g., polyactide PLA, have shown that with a secured adherence at the interface with the CF and showing sensitive tensometric properties, little shifted from ABS in tensile and bending are suitable for measurement. The group of composites includes fiber-reinforced composites e.g., ABS, polycarbonate (PC) and polyphenylsulfone (PPSU), as well as the epoxy resin composites. The CF seemed to behave like an auxetic material with negative Poisson ratio under the preload of the grips. This indicates the need for proper fixing mechanisms for the fibers when embedded in polymeric materials. Moreover, initial pre-tension is essential to avoid the initial gripping region and allow the fibers to operate in the linear range with improved sensitivity. However, there is a need to examine the effectiveness of fibers under cyclic load since breakage of fibers is inevitable.


Our proposed model attained a Jaccard Index of 0.772 on the ISIC 2017 test set and 0.854 on the PH2 dataset, which are comparable results to the current available state-of-the-art techniques.Clinical treatment of skin lesion is primarily dependent on timely detection and delimitation of lesion boundaries for accurate cancerous region localization. Prevalence of skin cancer is on the higher side, especially that of melanoma, which is aggressive in nature due to its high metastasis rate. Therefore, timely diagnosis is critical for its treatment before the onset of malignancy. To address this problem, medical imaging is used for the analysis and segmentation of lesion boundaries from dermoscopic images. Various methods have been used, ranging from visual inspection to the textural analysis of the images. However, accuracy of these methods is low for proper clinical treatment because of the sensitivity involved in surgical procedures or drug application. This presents an opportunity to develop an automated model with good accuracy so that it may be used in a clinical setting. This paper proposes an automated method for segmenting lesion boundaries that combines two architectures, the U-Net and the ResNet, collectively called Res-Unet. Moreover, we also used image inpainting for hair removal, which improved the segmentation results significantly. We trained our model on the ISIC 2017 dataset and validated it on the ISIC 2017 test set as well as the PH Computer-aided technologies for the diagnostic analysis of medical images have received significant attention from the research community. These are efficiently designed and modified for the purposes of inter-alia segmentation and classification of the region of interest (ROI) , which iAmong the different types of skin cancers, melanoma is not only the most dangerous and aggressive in nature due to its high metastasis rate, but also has the greatest prevalence . MelanomFrom the above details pertaining to the incidence and mortality rate associated with melanoma, timely diagnosis becomes all the more necessary for providing effective treatment to the affected. Insofar as the detection and segmentation of lesion boundaries, there are two streams of methodologies: first, traditional methods that usually resort to visual inspection by the clinician, and second, semi-automated and automated methods, which mostly involve point-based pixel intensity operations ,10, pixeBe that as it may, most of the methods being used today are not semi-automated because the accuracy associated therewith is generally prone to errors due to the following reasons: the inherent limitations of the methods , and chaIn this paper, we intend to exploit the properties and model architectures based on CNN for skin lesion boundary delimitation and segmentation. In addition, we propose our own novelty within the already available techniques which greatly increases the segmentation accuracy, which is image inpainting. Image inpainting, together with other image preprocessing techniques such as morphological operations, is used to remove the hair structures contained within the dermoscopic images that otherwise handicap the architecture because of complexities present in the images.This research examines the accuracy of the proposed technique together with the adoption of the proposed preprocessing method. We also benchmark our proposed scheme with other available methods by way of results through network accuracy, Jaccard Index, Dice score, and other performance metrics that aid us in comparison.This section delineates and chalks-out the relevant work done on the issue of segmentation of skin lesions. It is done with an added emphasis and focus on the recent studies that have incorporated deep-learning methods for the aforementioned purpose of lesion segmentation.At the outset, it is contended that accurate segmentation and delimitation of skin lesion boundaries can aid and assist the clinician in the detection and diagnosis process, and may later also help toward classification of the lesion type. There has been a gamut of studies done for the purposes of segmentation and classification of skin lesions, and for a general survey of these, the reader can refer the following two papers authored by Oliveira et al. , and RafWe hereinafter present a review of the literature vis-à-vis two aspects . Both aspects have a direct effect on the outcome of the results (the prediction) and therefore, both are catered into the broader scheme of methodology presented in this paper. Additionally, since dermoscopic images have varying complexities and contain different textural, intensity, and feature inhomogeneity, it becomes necessary to apply prior preprocessing techniques so that inhomogeneous sections can be smoothened out.Researchers encounter complications while segmenting skin lesions due to low brightness and the noise present in the images. These artifacts affect the accuracy of segmentation. For better results, Celebi et al.  proposedImage inpainting is a preprocessing technique used for both removing parts from an image and for restoration purposes, so that the missing and damaged information in images is restored. It is of vital importance in the field of medical imaging and through its application, unnecessary structures or artifacts from the images  can be removed ,29,30.Most image segmentation tasks use traditional machine learning processes for feature extraction. The literature explains some of the important techniques used for accurate segmentation. Jaisakthi et al.  summarizPennisi et al.  introducThis paper combines Otsu’s thresholding and CNN for better results. U-Net based architecture was used to extract more complex features. Chengyao Qian et al.  proposed2 [We trained and tested our CNN model on dermoscopic skin images acquired from two publicly accessible datasets , the la2 dataset with its 200 dermoscopic images including 40 melanoma, 80 common nevi, and 80 atypical nevi images.We compared our model in the task of Lesion Segmentation, part 1 of the 2017 ISBI Skin Lesion Analysis Toward Melanoma Detection challenge. We evaluated our model on the ISIC-17 test data consisting of 600 images to compare its performance with state-of-the-art pipelines. Additionally, our model was also tested on the PHIn this section, we introduce our devised methodology, which was trained and tested on the datasets (details presented later), and the subsequent results are reported and discussed. At the outset, it is pertinent to mention that we proposed a method that out-performed other similar available methods, both in terms of model accuracy and in pixel-by-pixel similarity measure, also called the intersection over union overlap . We herein proceed to describe, point by point, the various subsections of the proposed method.Images are preprocessed using resizing, scaling, hair removal and data centering techniques before being given as input to the CNN model. For noise removal, morphological operations are applied. We obtained promising results by applying preprocessing practices, which are as follows.Image Resizing: It is good practice to resize images before they are fed into the neural network. It allows the model to convolve faster, thereby saving computational power and dealing with memory constraints. Dermoscopic images vary in size and to overcome such individual differences, the images and their corresponding ground truths are down sampled to 256 × 256 resolution. All the RBG images are in the JPEG file format while the respective labels are in the PNG format.Image Normalization and Standardization: Images are normalized before training to remove poor contrast issues. Normalization changes the range of pixel values, rescaling the image between 0 and 1 so that the input data is centered around zero in all dimensions. Normalization is obtained by subtracting the image from its mean value, which is then divided by the standard deviation of the image.Conversion of RGB a to grayBlack top-hat filter ,46 is apInpainting algorithm is implemented on the generated binary mask; andInpainting of the hair occupied regions with neighboring pixels.Hair Removal: Dermoscopic images contain hair-like artifacts that cause issues while segmenting lesion regions. A series of morphological operations are applied to the image to remove these hair-like structures. The inpainting algorithm  is then A 17 × 17 cross shaped structuring element is defined, as shown in Closing morphological operation is the erosion of the dilation of set A and B. Closing fills small holes in the region while keeping the initial region sizes intact. It preserves the background pixels that are like the structuring element, while eliminating all other regions of the background.The image obtained after applying the closing operation on a grayscale image is subtracted from the image itself to obtain hair like structures. Binary mask of the hair elements is obtained by applying a threshold value of “10” on the image obtained from the black top-hat filter. Images obtained from the black top-hat filter and after thresholding, respectively, are highlighted in The image based on the fast marching method was employed . The inpDeep learning architectures are currently being used to solve visual recognition and object detection problems. CNN models have shown good impact over semi-automated methods for semantic segmentation. The U-Net architecture, which is based on an encoder–decoder approach, has revealed significant results in medical image segmentation. The output of these networks are binary segmentation masks.In general, CNN models are the combination of layers . CNN architectures have been widely used in computer assisted medical diagnostics.For this purpose, a CNN architecture was trained on an ISIC 2017 dataset. The network architecture  curve was used to evaluate the performance binary classifiers. ROC is a plot between the true positive rate (Sensitivity) as a function of false positive rate (Specificity) at different thresholds. This study emphasizes segmenting the lesion region, with 1 representing the lesion region and 0 representing the black region of the image. The ROC curve is the best evaluation technique that defines separability between classes. Each datapoint in a curve shows the values at a specific threshold. The ROC curve dictates the model’s capability to distinguish between classes accurately. The higher the area under the curve, the higher the network’s ability to distinguish two classes more precisely . The AUC of our proposed model was 0.963, illustrating the model’s remarkable competence of differentiability.The model was tested both with and without preprocessing on the ISIC-17 dataset to ascertain the efficiency of the hair removing algorithm. A Jaccard of 0.763  the ISIC 2017 test group; (2) ISIC 2017 validation group; and the (3) PHDifferent methods has been employed for segmentation purpose. The second ranked participant Matt Berseth et al.  obtained2 dataset and compared our segmentations with the existing state-of-the-art techniques. The results are listed below in 2 dataset.To evaluate the robustness of our proposed model, we further tested the architecture on the PH2 dataset, and the Jaccard index obtained thereof was 0.772 and 0.854, respectively. Our proposed method achieved promising results compared with the state-of-the-art techniques in terms of the Jaccard index. Furthermore, our CNN model was tested on a PH2 dataset along with the ISIC-17 test set and produced better segmentation and performed better than the existing methods in the literature. Empirical results show that the combination of the U-Net and ResNet shows impressive results.Skin lesion segmentation is a vital step in developing a computer aided diagnosis system for skin cancer. In this paper, we successfully developed a skin lesion segmentation algorithm using CNN with an advanced hair-removal algorithm that effectively removed hair structures from the dermoscopic images, improving the accuracy considerably. We tested our model architecture on the ISIC-2017 dataset and PHThe limited training data used requires extensive augmentation to prevent the model from overfitting. A large dataset is therefore needed for better accuracy and generalization of the model. Furthermore, for it to achieve state-of-the-art results, the model was made to be complex and efficient, which takes more time to train as opposed to the conventional U-Net.Our future work includes using a larger dataset to reduce overfitting problems and hyper tuning the parameters for more effective training. Additionally, a conditional random field (CRF) application can also be applied to refine the model output.


The development of magnetic field sensors for biomedical applications primarily focuses on equivalent magnetic noise reduction or overall design improvement in order to make them smaller and cheaper while keeping the required values of a limit of detection. One of the cutting-edge topics today is the use of magnetic field sensors for applications such as magnetocardiography, magnetotomography, magnetomyography, magnetoneurography, or their application in point-of-care devices. This introductory review focuses on modern magnetic field sensors suitable for biomedicine applications from a physical point of view and provides an overview of recent studies in this field. Types of magnetic field sensors include direct current superconducting quantum interference devices, search coil, fluxgate, magnetoelectric, giant magneto-impedance, anisotropic/giant/tunneling magnetoresistance, optically pumped, cavity optomechanical, Hall effect, magnetoelastic, spin wave interferometry, and those based on the behavior of nitrogen-vacancy centers in the atomic lattice of diamond. Nowadays, the rapid progress of micro- and nano-technology exerts its influence on the huge number of scientific developments resulting in improvements in the quality of everyday life . ContempGenerally, the application of magnetic field sensors in biomedicine is limited by the high cost of device fabrication and often demands a special environment for the procedure. Additionally, the required limit of resolution of these sensors for the detection of relatively low biomagnetic signals from organs and tissues is an important issue . TherefoA survey of new trends in the magnetic sensing of human tissue and organs  shows that there are different restrictions not only on the noise level but also on the frequency range. Biomagnetic sensing covers all requirements more efficiently than many commonly used techniques and has a range of obvious advantages. These include measurements on small areas, 3D probe arrangements, and reduction of the background noise signal. In some applications, they are also less invasive than other alternatives. It is important to note that detection of biomagnetic signals gives the possibility of obtaining reference-free and coherent measurements that do not depend on the dielectric properties of biological systems. For example, magnetocardiography gives the possibility of obtaining earlier information about cardiac conduction abnormalities in the fetal period of human development . MagnetoCapture, preconcentration, and separation of analytes bonded with the specifically functionalized surface of the particles;Mixing of lateral flows;Creation of contrast in magnetic susceptibility of the biological medium for future sensing.Some recent studies have been dedicated to the development of point-of-care devices for biomedical diagnostics . AlthougThis set of features opens doors for completely new applications as well as for the significant improvement of existing methods. For instance, the lateral flow immunoassay is a well-established method of biodetection which is very prominent because of the immunochromatographic strip test for ascertaining pregnancy . This caIn this review, we focus on current developments of magnetometry in biological diagnostics. First, we introduce the types and working principles of existing magnetometers. Following that, recent noteworthy studies of magnetometers suitable for biomagnetic signal detection and point-of-care technologies are summarized.The development of highly sensitive and highly localized magnetic field sensors is a rapidly expanding topic due to the progress in micro- and nano-fabrication technologies. However, their sensitivities decrease with the linear dimensions of sensing elements, and some of them have a rather complicated working scheme. Thus, there is no ideal magnetic sensor as yet that would suit all the possible applications. The list of magnetic field sensors suitable for biomedical applications includes direct-current superconducting quantum interference devices (dc SQUIDs), search coils, fluxgate, magnetoelectric, giant magneto-impedance (GMI), anisotropic/giant/tunneling magnetoresistance (AMR/GMR/TMR), optically pumped, cavity optomechanical, Hall effect, magnetoelastic, spin wave interferometry magnetometers, and magnetometry based on nitrogen-vacancy centers in diamonds. A list of magnetic sensors depending on the type of application  is presented in The list of magnetic sensors was constituted from their potential use in systems for sensing many kinds of biomagnetic signals including point-of-care devices. In this section, the working principles of these sensors are presented. For point-of-care, there will necessarily be other factors involving cost and convenience, but in this paper we are primarily concerned with magnetic sensitivity.The oldest type of magnetic field sensor is a search coil magnetometer consisting of several induction coils. It allows the measurement of an alternating external magnetic field without the use of an internal energy source and has a detection limit down to 100 fT/√Hz ,36. Depeext, passes through the turns of the coil, it induces a voltage Usig which is directly proportional to the amplitude and rate of change of the magnetic field .Depending on the geometry, mode of use, and required sensitivity, a search coil magnetometer may have a ferrite core . The numf1 and high f2), the output voltage (modulated by both frequencies) can be measured at the combinatorial frequencies in a way that reduces the noise level [Recently, search coils were used as biosensors to detect magnetic labels which are characterized by non-linear magnetization processes . If an ese level .Nowadays, sensors based on dc SQUIDs  are the mod smaller than a critical value flows through the junctions [ext influences the SQUID, it changes the phase difference and the current–voltage characteristic of the superconducting loop shows an oscillating behavior allowing measurement of the external magnetic field amplitude as a function of Usig which is the voltage variation.These sensors are widely used in the detection of low magnetic fields starting from the magnetic properties of materials to ultra low-field sensing in magnetic resonance imaging ,53. The unctions . This soc SQUIDs (not requiring liquid helium) provide the possibility of placing sensing elements close to the system to be measured, unfortunately, also followed by a higher noise level [However, this method is complicated to achieve in practice, because it relies on the use of a magnetically shielded room and cooling of the sensing element with liquid nitrogen or helium ,56. The se level .Fluxgate magnetometry has been known since around 1930 . It is aexc) causing the magnetization to flip rapidly from one direction to the other. An applied dc field Hext (to be detected) causes an imbalance in the “gated” output and is detected by a sensing coil (Usig). In parallel fluxgate magnetometers, Hexc is created with induction coils wound up around the core, while in modern orthogonal fluxgate magnetometers based on microwires, Hexc is created by the biasing alternating current Iexc in a microwire. This simplifies the sensor design replacing the driving induction coils in the conventional parallel fluxgate magnetometer scheme. The pick-up coils are being used for measuring the double-frequency voltage signal Usig induced by the breaking of symmetry in the presence of Hext. Schematic diagrams illustrating the sensing elements of the parallel fluxgate magnetometer and microwire based orthogonal fluxgate magnetometer are shown in ext, so the voltage induced in the pick-up coils has the frequency of the excitation current. Thus, this method obtains information about the amplitude of Hext applied perpendicularly to the saturation field.Fluxgate magnetometers are divided into two categories named parallel  and orthext, a double frequency signal is detected. This configuration makes it possible to achieve a very small equivalent magnetic noise level of 100 fT/√Hz [Another proposal involves using a rotating magnetic field applied in the plane of a single-domain ferromagnetic disc with the disc serving as a core. In this case, no domain displacements are involved so Barkhausen noise is eliminated, and the core reversal is achieved by magnetization rotation. In the presence of H0 fT/√Hz ,70.2, and the detection limit was 2 nT/√Hz. This can be  improved up to 1 fT/√Hz.Recent developments of the fluxgate magnetometer have been directed towards miniaturization based on planar thin-film structures with a minimal magnetic noise level is about 0.1 nT/√Hz . Also, KAnother way of measuring small magnetic fields is based on the magnetoresistance effect ,75,76. GAlthough being a well-established technology, magnetoresistance magnetometers continue to advance. The equivalent noise level has reduced down to a few pT/√Hz ,81,82 inexc flowing in the thin film perpendicularly to the applied external magnetic field Hext is modulated [ext. The resulting change in the voltage arising from the external field is detected in the axial direction of magnetization vectors of the GMR system and is proportional to Hext. In TMR devices, an insulator separates ferromagnetic layers and the voltage is detected perpendicularly to the axis of the magnetization vectors of the structure. Current flow through the very thin insulator is provided by the electron tunneling effect. The output of a GMR sensor is four times that of an AMR sensor but that of a TMR sensor is twenty times bigger—although TMR is technically much more difficult to achieve. An essential feature of this technology is that it can be implemented on the microscale as part of an electronic ‘chip’. Thus, it can be used in lab-on-a-chip applications related to biomedicine such as in the detection of magnetic nanoparticles used as biomarkers. We discuss this application in more detail in The working principle of the AMR magnetometer is that when the magnetic vector direction in a magnetic thin film (such as NiFe) is changed , then the ohmic resistance to a current Iodulated . To achiTunneling MR and to some extent GMR has also been explored in the context of particilate materials. The principle of the effects is broadly the same and relies on the partial contact between magnetic particles or between magnetized regions of a semi-continuous magnetic matrix. An example of GMR is in fine cobalt grains inside a copper matrix in the core of glass-coated magnetic microwires . These mThere has been significant research on what was termed ‘Colossal’ magnetoresistance—which centered around manganese-based perovskite oxides. Orders of magnitude changes in MR sensitivity have been observed. Although research on this has largely subsided, it still offers the possiblility of new and advanced materials not necessarily requiring thin-film deposition. We include a reference on this which gives a summary of the state of the art at the time of publication . We haveOne of the promising candidates for implementation in point-of-care and biomagnetic signal-sensing devices are magnetoelectric magnetic field sensors . At roomexc is applied, the magnetostrictive material changes its linear dimensions and causes mechanical stress in the piezoelectric material. This stress polarizes the piezoelectric material, producing an output proportional to the applied field. One of the crucial parameters describing the magnetoelectric coupling and magnetostriction process is the ratio between the output voltage and the applied biasing field. This is called the direct magnetoelectric voltage coefficient. The coefficient depends on the materials and operational mode . If the magnetostriction is not in the saturation state and the external alternating magnetic field Hext is applied to the system, the change of the measured induced voltage Usig is directly proportional to the applied magnetic field. The measured component of the Hext also depends on the position of the electric contacts on the surface of the magnetoelectric composite.When the excitation magnetic field generated by the dc voltage UThere many possible ways of attaining high performance in magnetoelectric magnetic field sensors. These depend on a variety of parameters such as the composition of the material, the shape of the sensing element and biasing magnetic field. Future improvements in the limit of detection in a magnetically unshielded environment may involve the possibility of measuring simultaneously the orientation and the magnitude of the magnetic field as well as improvement in the frequency bandwidth. Also, implementation of gradiometry with a small phase shift between individual magnetoelectric sensors helps to make the influence of environmental noise levels lower. Advances may also lie in the use of new three-component elastomers based on a polymer matrix, since they show a significant magnetoelectric effect while possessing mechanical flexibility ,96.The magnetoimpedance effect, which refers to a large change in the high-frequency voltage measured across a magnetic conductor subjected to the application of a dc magnetic field, was originally observed back to 1935–1936 in NiFe wires with high permeability . Howevereff on the magnetic properties. When the ac current Iexc is applied to a ferromagnetic material, it is restricted to a small area of the sample determined by δeff. Its value depends not only on frequency but also on the dc magnetization and dynamic permeability. If an external magnetic field Hext is applied, it influences the magnetic parameters and δeff leading to the change in the complex-valued resistance (or impedance) of the material. This results in a change in the output voltage Usig.The mechanism of the impedance change is related to the skin effect and to the dependence of the skin depth δGiant MI has rapidly become a very popular research topic, and there is a significant growth of works devoted to the enhancement of sensitivity , improvisig is measured in the coil which is similar to the orthogonal fluxgate depicted in b and the applied dc magnetic field Hext, the ac current changes both circular and longitudinal ac magnetizations and generates an output voltage in the coil. The voltage Usig depends on the applied magnetic field (measured field Hext) which influences the helical magnetization. Importantly, when Hext is reversed, the phase of Usig (relative to the excitation current) shifts by 180° which makes it possible to identify the measured field direction. In this sense, the off-diagonal GMI sensor provides a linear and vectorial response. The other advantage of the off-diagonal GMI is the possibility of improving the output voltage sensitivity and noise characteristics by optimizing the number of turns on the coil as well as the excitation frequency [Considering the voltage change caused by the external magnetic field, GMI can be compared with AMR and GMR  use a geometry with both pump and probe beams. The circularly polarized pump beam is used for polarizing the state of spins of alkali atoms and the rotation of polarization of the linearly polarized probe beam is proportional to the magnitude of Hext. In this case, Hbias is applied longitudinally to the pump beam and perpendicularly to the probe beam.Optically pumped magnetometers (a) are devices that use the spin-dependent optical properties of a resonant medium in the form of a vapor cell of alkali atoms. Depending on the mechanism and principle, detection models are divided into the detection of the intensity (i) or the polarization ((ii) and (iii)), of transmitted light. When the circularly polarized “pump” laser polarizes spins of atoms in a vapor cell, they start to oscillate at the Larmor frequency determined by the transverse bias dc magnetic field HThese optically pumped magnetometers, working in a spin-exchange relaxation free regime are the best candidates to replace SQUID magnetometry. It is shown, that in some cases they have benefits in sensitivity, spatial resolution and cost-effectivity compared with SQUID-based magnetometers ,137,138 Due to the modern tendency of device miniaturization, there is a high demand for highly localized magnetic field sensors. One type of magnetic field sensor having linear dimensions of tenths of micrometers , and theThe change of linear dimensions of the magnetostrictive material under an applied external magnetic field moves one of the mirrors of the optical resonator. This causes the optical path length and the resonance frequency of the optical resonator to change in proportion to the applied magnetic field. The information about the external magnetic field is contained in the phase shift of the probe light and can be read-out with the optical phase measurement technique. In other words, the cavity optomechanical magnetometer is based on the detection of magnetostriction using an optical resonance technique. In the Although achieving a pT/√Hz  limit ofOne of the latest achievements in the field of magnetic field sensing is the development of magnetometers based on the behavior of nitrogen-vacancy (NV) centers in diamonds ,147,148.s = 0, ±1. The singlet state has ms = 0. If an external magnetic field Hext is applied to the system, the ms = ± 1 spin state splits due to the Zeeman Effect. Because of the splitting, the electrons on the excited state with ms = ±1 are likely to decay via a metastable singlet state, while electrons on the excited state (with ms = 0) are decaying through the radiative transition to the ground state. Thus, depending on the magnitude of Hext one can detect the raising or lowering of the spin-dependent intensity of the reflected probe or luminescent light from the nitrogen vacancy. This technique is known as “optically detected magnetic resonance”.A nitrogen vacancy center in diamond is a defect in the crystal lattice of diamond ,158. SucThe advantages of this technology include unprecedented spatial resolution, relative technical simplicity and biocompatibility. Nevertheless, there are also some challenges, such as the not strictly determined triplet-singlet energies, sample inhomogeneities and high refractive index. These will be overcome in future. Detection limits will be improved by the use of thinner diamond layers and improved positioning of the samples to be measured . These iMagnetometers based on the Hall effect  are suitsig) arising from the distortion of an ac current flowing at right angles to both of these [The working principle of Hall effect magnetometer lies in the change in the transverse  voltage  . If a maUnfortunately, the stresses, induced in the material affects its measuring characteristic, so future prospects may lay in redesigning the measuring scheme and adjusting of the material’s composition. Also, the miniaturization of such sensors will greatly help the development of multi-dimensional sensors.One more way of developing a highly sensitive, highly localized magnetic field sensor proposed for magnetoencephalography is based on the use of spin electronics . The mosbias. When spin waves are excited by a generator on the input antennas, they propagate through the cross-section to the output contacts connected to the detectors. If spin waves are out of phase, the output voltage is minimum and otherwise, the output signal reaches its maximum. This is due to the phenomenon of destructive and constructive interference, respectively. The phase shift of spin waves depends on the external out-of-plane component of the external magnetic field Hext. Thus, the output signal from the device is proportional to the applied field.The principle of operation is based on the interference of spin waves in the crossed antennas made from a suitable material placed on the top of magnetic material providing the dc bias in-plane magnetic field HBeing a relatively new kind of magnetometry, the spin wave-based magnetic field sensor has a huge potential for optimization—such as in the geometry of the sensing element. This could provide a wider overlap between the magnetostatic surface spin wave and backward volume magnetostatic spin wave signals. There is also scope for more research in the field of noise suppression techniques, adjustment of the bias magnetic field and on the choice of operational frequency.(i)measuring a magnetic field produced by human organs(ii)detecting magnetically labelled biomolecules.Applications of magnetic sensing technology in biomedical fields can be subdivided into two main categories:Biomagnetic signals from a human body provide a lot of useful information about the heart, nervous system, brain or muscle activity. However, magnetic fields generated by most biosystems have a low amplitude in comparison with noise sources . The aduIn the field of biomagnetic signal detection, there are some well-established as well as new state-of-the-art magnetometry techniques worth mentioning. Nowadays, the most used technique is SQUID magnetometry . One of 87Rb vapor cell. The optimization of the working scheme and noise reduction makes it is possible to achieve the detection limit of 15 fT/√Hz which is very promising for biomagnetic signals detection.Optically pumped magnetometers achieving measurements less than 1 fT/√Hz  have greTo achieve a limit of detection of the order of fT/√Hz the sensing elements of both SQUID and optically pumped magnetometers need to be relatively large (in the milli- or micrometer size). One of the newest and most promising types of magnetometers providing nanometer spatial resolution is based on the behavior of nitrogen vacancy (NV) centers in the atomic structure of diamond. Though these magnetometers commonly have a nT/√Hz ,195 or pThe previously discussed sensors have restrictions on the operating temperature or on the optical excitation power, and there is a demand on the magnetic field sensor operating at room temperature whilst maintaining optimum performance. One of the most promising candidates for this purpose is the cavity optomechanical magnetometer. This currently has low energy consumption, high spatial resolution, and requires no cooling systems. In a recent work by Li et al.  developi3Fe2(FeO4)3 sensing element (area 1.8 mm2) predicts the value of the detection limit of 1 pT/√Hz. Experimental results for different orientations of bias magnetic field were performed and compared with the theoretical calculations. Better adjustment of the bias magnetic field can be done with the use of the magnetic materials having an out-of-plane magnetization and/or with the use of antiferromagnetic materials. However, a lack of experimental data on spin wave interference in such materials prevent a quantitative analysis.An additional type of magnetometer having low energy consumption, wide temperature range and competitive sensitivity is the spin wave interferometry-based magnetometer. One of the latest works devoted to the advantages of this new technique is done by the group of Balynsky . The micThe highly sensitive and localized magnetic field sensors already discussed are ether technically sophisticated or cost expensive. The topic of relatively simple and cheap magnetic field sensors having sensitivities at a level suitable for magnetocardiography or magnetomyography is expanding. One relatively cheap and available magnetic field sensor able to measure biomagnetic signals is the fluxgate magnetometer. Recently, a new type of flux-gate sensor which utilizes epitaxial iron garnet films with a specially designed edge profile as a core was developed, claiming a reduction of noise level at room temperature down to 0.1 pT/√Hz. This magnetometer was successfully applied in magnetocardiography experiments on animals .Magnetic field sensors competing with the fluxgate for magnetocardiography are those based (separately) on the magnetoimpedance, magnetoelectric and magnetoresistive effects. With the use of GMI gradiometers in a magnetically unshielded environment, the magnetic heart signal from a human subject was detected at nine spatially separated points demonstrating the possibility of obtaining more information in comparison with electrocardiography ,208. For® ribbon in the form of a tuning fork. The suppression of acoustic and thermal noise allowed an enhancement of the detection limit up to 3 pT/√Hz at a frequency of 318 Hz.Another good candidate for simple and reliable biomagnetic signals detection is the magnetoelectric magnetic field sensor. Turutin et al.  proposedAmong magnetoresistive sensors, TMR-based sensors are good candidates for measuring fields from the heart and, possibly, the brain. TMR magnetometers for measurement of these signals  achieveA future challenge for the low cost, portable magnetic MR sensors is to non-invasively monitor the heart of an unborn baby in the womb, being an area presently covered by SQUID magnetometers. The signals from the electrical activity of the brain, however, are much smaller, so to monitor these a sensitivity in the femto-Tesla range is required which is currently beyond the range of MR sensors.The requirements of point-of-care diagnostics combine portable and fast working measurement devices providing different physiological parameters and performing bioanalysis on a cellular or molecular level. Subsequently, important points are mobility, the speedy obtaining of results and low cost of fabrication. These devices provide information and clinical diagnosis relating to the basic parameters of human health. Magnetic field sensors in such devices are used for detection of drugs, cellular proteins or other biomarkers which are usually labelled with magnetic particles as shown in 40Ni38Mo4B18) was embedded into an artificial bone made from polylactic acid to study its hydrolytic degradation. The output power from the sensor decreased from 0.04 ± 0.004 dBm to 0.2 ± 0.04 dBm when the polylactic acid bone diameter was changed from 15 ± 0.05 mm to 14.92 ± 0.02 mm under an external force of 100 N. This result coincided with the theoretical analysis. In Reference [76Cr2Mo6B15Cu1 microwires as magnetoelastic sensors is made by the Kozejova et al. [Magnetoelastic magnetic field sensors are used for the monitoring of glucose concentration, growth of bacteria, pH, biliary stent monitoring and other noteworthy in vitro applications. Two recent articles in the field of biophysical analysis concern the monitoring of artificial bone degradation under external stresses. These were published by Tan et al. ,222. In eference . An invea et al. .4 cells/mL. Recently, AMR-based microstructures [Escherichia coli O157:H7 (E. coli O157:H7) bacteria with a limit of detection of 100 cfu/mL (colony forming units per mL). At the same time, there is a demand for the simultaneous detection of several biomarkers. The latest article (showing a way to develop a portable multiplex and sensitive biosensing platform based on the GMR effect) is published by Klein et al. [2 with a detection limit of 8 nT/√Hz. To find more information about magnetoresistivity in biosensing technologies we refer to the reviews made by Giouroudi [In the field of microfluidics and lateral flow bioanalysis, there is a need for high sensitivity and fast response. A lot of proposed biosensing platforms meeting these needs are based on the magnetoresistive effect. One of the noteworthy articles is published by Barroso et al.  and is dructures ,226,227 ructures . Biosensructures . This aln et al. . Anothern et al. . In theiiouroudi , Huang eiouroudi , and Zheiouroudi .−1 and working in the 0.1 ng/mL–20 ng/mL biomarker concentration range is given in [Detection of biomarkers can also be performed with magnetic field sensors based on the magnetoimpedance effect. Work on the detection of a-fetoprotein bioconjugates with a GMI magnetometer (with the 100 fg/mL limit of detection) is made by Zhu et al. . Two papgiven in . Such chgiven in . Some prgiven in .When detecting the magnetic fields produced by magnetized magnetic beads a problem occurs due to the presence of a large demagnetizing field although the fringe field from the particles is relatively large. For example, a standard bead of 2.8 microns in diameter having a magnetic susceptibility of about unity can be magnetized by a field of ~10 mT and its stray field is around 20 nT. Previously discussed detection methods based on applying the magnetizing field orthogonal to the sensitive direction of the sensor is shown in Another principle of “magnetic beads” detection relies on their nonlinear magnetization which results in higher-order harmonics in the output voltage signal. An impressive example of the implementation of this method with the use of search coils was performed by Nikitin et al. ,216. An An outstanding advantage of magnetic sensors is their realization on flexible platforms . In poinThis paper presents the latest research dedicated to well-established and new magnetometry techniques able to be implemented in the field of biomedicine. Because they are generally non-invasive, highly sensitive, and can be easily localized, these techniques have many perspectives in biomagnetic signals’ sensing and point-of-care devices.In the field of biomagnetic signal detection, SQUID magnetometry remains the most common tool for magnetocardiography, magnetomyography, magnetoneurography or magnetoencephalography while having the best sensitivity approaching fT/√Hz. In spite of the usefulness of SQUID magnetometers, the requirement of low operating temperature and magnetically shielded room makes it relatively hard to use in practice. The future prospects for SQUID magnetometers may lay in miniaturization and, also, in the reduction of the operational noise values whilst remaining at a low limit of detection.2. In comparison with SQUIDs, these magnetometers produce less operational noise and need heating of vapor cells to a temperature up to around 400 K, which is easier to realize. Still, optically pumped magnetometers require a real-time precise magnetic shielding system and some construction issues for the implementation of such sensors in biomedicine are yet to be solved [Among all magnetic field sensors, the most promising candidate for replacing SQUID-based sensors are optically pumped magnetometers (particularly ones based on the spin relaxation free regime). Their potential lays in the ability to achieve a limit of detectivity of several fT/√Hz while keeping the sensing area (restricted by linear dimensions of vapor cells) to a few tenths of mme solved ,246,247.In addition, great progress has been achieved in magnetometry using a detection system based on nitrogen-vacancy centers in diamond. Though the sensitivity of such devices commonly does not extend beyond tenths of pT/√Hz, they operate at the room temperature while allowing nanoscale sensing and providing a way to develop novel magnetic field sensors. Due to these prospects, further and current research is mostly aiming to extend the sensitivity and resolution of magnetometers of this type.Due to the developments in nano- and microfabrication techniques, new approaches to miniaturize magnetic field sensors with competitive sensitivities may be realized in spin wave interferometry or cavity optomechanical magnetometers. However, these magnetometers are still technically complicated, so magnetometers based on well-established and more simply fabricated AMR/GMR/TMR, GMI, fluxgate, and magnetoelectric technologies are enough for many biomagnetic field measurements.Concerning point-of-care technologies, sensor integration into lab-on-a-chip, and microfluidic technologies could lead to the replacement of many diagnostic systems currently used in laboratories and clinics. As the sensitivity requirements are determined by a particular device, the path to the miniaturization of diagnostic systems lies in overall design improvements. New advances in the fields of micro- and nano-fabrication will also help to overcome current limitations of the usage of magnetic sensors in this field because of issues concerning high power consumption, single-target detection and system complexity. The most promising magnetic sensors to overcome these limitations are GMR/TMR and GMI magnetometers due to the fact of their flexibility and the convenience of the low-cost integration process. This means that not only the sensor but the complete signal processing system can be built on the same chip using closely related technologies.


In this paper, we present challenges and achievements in development and use of a compact ultrasonic Phased Array (PA) module with signal processing and imaging technology for autonomous non-destructive evaluation of composite aerospace structures. We analyse two different sets of ultrasonic scan data, acquired from 5 MHz and 10 MHz PA transducers. Although higher frequency transducers promise higher axial (depth) resolution in PA imaging, we face several signal processing challenges to detect defects in composite specimens at 10 MHz. One of the challenges is the presence of multiple echoes at the boundary of the composite layers called structural noise. Here, we propose a wavelet transform-based algorithm that is able to detect and characterize defects . This algorithm uses a smart thresholding technique based on the extracted statistical mean and standard deviation of the structural noise. Finally, we use the proposed algorithm to detect and characterize defects in a standard calibration specimen and validate the results by comparing to the designed depth information. This work is part of the EU-H2020 FET-OPEN CompInnova project  that aimThere is an increasing use of ultrasonic phased array ,10,11,12The use of autonomous systems for non-destructive testing utilising a wide range of sensing techniques has been explored by several researchers in the literature. For instance, robotic platforms have been designed and developed for the inspections of long welded lines . A relatHowever, when the inspected material is not homogeneous, conventional ultrasonic imaging techniques are not effective, and we need to employ signal processing techniques to improve the temporal resolution of the ultrasonic signal . Some stSome other works assume that back-scattered ultrasonic echoes can be modeled as superimposition of multiple Gaussian echoes and try to estimate multiple unknown parameters of such model from the received signals ,27. ThisWavelet analysis is another method that has been widely used in many signal processing applications for signal estimation, classification, compression and de-noising. . Due to In general, phased arrays are able to electronically control their beam to scan, steer or sweep their beams. One of the main advantages of the PA inspection with full waveform data capture and storage is the ability to extract the depth information using sliding gate (window) analysis. Depth information is crucial in the assessment of the defect, the state of the component and to decide whether repair, replacement or no-action is needed. In addition, for the CompInnova concept, the depth information is very valuable because it allows an accurate calculation of the volume and area of material that the laser will remove during repair process.Dead zone and limited axial (depth) resolution are the main drawbacks related to the use of lower frequency PA ultrasonic transducers for defect detection and characterization of thin composite structures . The phyThere are fundamentally two resolutions in two different scanning axes which determine the imaging performance of a PA C-scan image. One is the lateral resolution, and another is the axial resolution. Furthermore, for thin composite structures, near surface resolution is also considered as an important factor in PA transducer selection for estimating the defect depth accurately. Near surface resolution depends on the extent of the dead zone of the transducer which in turn depends on the pulse-width. The lateral resolution of the transmitted ultrasound beam depends upon the centre frequency, bandwidth, and active aperture, near field distance from the transducer, material thickness and material attenuation properties. Having a larger aperture and higher frequencies results in a smaller beam width, which in turn causes a higher lateral resolution and sharper image ,39. The structural noise and has the same spectral characteristic as the defect echoes [However, increasing the frequency of PA transducer results in the increase of the ultrasonic attenuation and scattering noise from the material. The scattering noise is due to wavelength being much closer to ply thickness, which causes internal reflections at each resin-ply interface and results in a train of continuous noise-like signatures between the front wall and back wall of the specimen . This not echoes ,42, and t echoes  in the pAnother concern is about the interpretation of the PA data. Although manual interpretation is the de facto standard for composite inspections, it is time-consuming, expensive, and represents the major bottleneck of a PA inspection process. Advances in PA, full waveform data acquisition and ease of application of signal processing during post-processing, have led to a new area of research in semi-automated defect detection. Using this approach vast amount of data collected can be processed efficiently using dedicated signal processing algorithms with only the most important information presented to the NDT inspector. Additionally, 3D visualisation of defect in composite structures can lead to better defect characterisation. One further challenge of post-processing PA ultrasonic data are that the acquired phased array data of a larger area is presented by multiple strips of data. In the case of manual phased array inspection, an experienced technician spends significant amount of time for merging, aligning, stitching and adjustment of the individual strips before displaying a C-scan image of the large structure. In manual inspection, re-scanning is sometimes required, due to lack of couplant, variation of load applied on the probe or not capturing data along a straight line. However, scanning the structure by a manipulator or robot is less prone to these problems since required pressure on the wedge and uniform distribution of couplant is ensured. Thus, post processing of PA data can help to automate or eliminate some of the manual post processing needs.In this paper, we propose an algorithm based on wavelet transform for detection and localization of defect echoes during the inspection of composite components using the high frequency ultrasonic phased array. The proposed solution offers improved axial resolution while maintaining sensible performance. We analyse two sets of scan data using 5 MHz and 10 MHz PA transducers, and address the benefit of using higher frequency ultrasonic PA transducers. Subsequently, the signal processing challenges of this choice is presented. Then, we propose solutions for the challenges and smooth the way toward semi-automated PA inspection of composite structures at high frequency 10 MHz. Finally, the paper is concluded in 12 as stacking sequence with uni-directional pre-preg IMS-977-2-34-24KIMS-196 material . Therefore, for a 64-element transducer and choosing 8 elements as an active sub-aperture, the total number of A-scans will be The material used for the experiment consists of a carbon reinforced epoxy resin laminate of size 200 × 200 mm which is manufactured through a hand layup process using cross-ply (0/90)reference echo model based on back wall echoes from a scanned area with no defects . Then, with the help of wavelet transform, it searches for all echoes having an absolute amplitude above a certain threshold. Next, it finds the actual phase information of the echo and calculates exact location of the defect by analysing the phase information from the defect echo and the reference echo model. For the threshold, we use the following definition:N is the total number of samples of the original signal The baseline echo localization algorithm extracts the peak information for all echoes, following the stages according to the flowchart shown in Compute the complex continuous wavelet transform of the signal The maximum value in the scalogram An echo estimate is created by scaling the reference echo model and shifting to The echo estimate is removed from the original signal and the maximum amplitude (absolute) value within the residue  is found and compared to the threshold. If this is larger than the threshold, the resulting signal will become the input of the next iteration until the maximum value of the residue is smaller than the threshold.Having the echo reference model, the algorithm operates as follows:All echoes with an amplitude above the defined threshold are detected and localized considering their phase information. In comparison, the conventional C-scan image generation method used only one maximum peak using an absolute value of the rectified signal in the defined gate. In the best case, this gate covers the whole distance between front wall and back wall echoes. When multiple echoes are present in this gate, manual processing extracts only the strongest echo, other echoes are missed. Furthermore, phase information of the strongest echo is missed.Overlapped echoes are also localized by the algorithm. The defect depth can be obtained by using The above algorithm offers the following benefits:In order to provide a clearer view for the inspector on the defect profile over the material depth, visualization of the localization results has been implemented. The use of 10 MHz ultrasonic transducers promises higher resolution of defect (depth) detection, which will be useful in thin skin-stiffer panel and step-repaired panel to separate the echoes and characterize damages.It was found, however, that post-processing of inspection data using 10 MHz transducer is more challenging in comparison with inspection data at 5 MHz. This is mostly due to structural noise, affected also by higher sensitivity to distribution of couplant (sprayed water) and surface roughness. A previously proposed algorithm was not performing well for this data from transducer with 10 MHz and required some modifications to address the above issues.The first challenge is the existence of multiple echoes between front wall and back wall, i.e., structural noise, corresponding to the boundaries of layers. Due to this noise, detection of defect echoes is much harder compared to the 5 MHz case, and we can only detect echoes stronger than the structural noise .Another issue is the higher attenuation of the propagating wave at a higher frequency. As a result, defect echoes closer to the back wall have lower amplitudes. One practical solution found in the literature is to apply a time gain compensation that increases signal amplification with depth.With the 10 MHz PA transducer, the amount of couplant (water) should be as small as possible and it should be evenly distributed. Furthermore, a constant pressure should be applied to the wedge. Excess of couplant causes reverberation of the signals and increases the front wall echo length and hence the dead zone.Due to increased sensitivity to a surface roughness at 10 MHz, we can see from To address this issue in our signal processing, prior to the echo localization algorithm, all A-scans are aligned in time domain and amplified using time gain compensation (0.85 to 0.95 dB per mm). This is to compensate the higher attenuation we observe in 10 MHz ultrasonic wave. A threshold to ignore structural noise,Search Window width,Better echo-fit search methodology.In this section, we present a modified echo localization algorithm that is better suited to work with 10 MHz ultrasonic data. It is worth highlighting that, prior to our echo localization algorithm, we use data pre-processing steps . Successively, the output data are fed to the localization algorithm for further processing. The following changes have been implemented in the modified algorithm:The algorithm searches for best echo fit is changed as follows. First, the position of the strongest echo is found from the wavelet, similar to the baseline algorithm. Then, we define a window over the max position with a width that corresponds to envelope width where its values are above the threshold. Next, we use cross-correlation between the reference echo model and the windowed signal followed by search for a combination of strongest echoes. These echoes are removed from the signal resulting in the lowest residual signal in this window. After that, the algorithm analyses the rest of A-scan signal  that has a maximum of its envelope above the threshold.Calculate wavelet transform of the signal,Find the location of strongest peak from the wavelet scalogram,Define a window at the location of the peak showing the area where envelope is above the threshold,Calculate cross-correlation of the windowed signal Define two echo models for the signal from the cross-correlation peaks information by applying coefficients and time-lags to the reference model, i.e., Subtract The procedure in steps 1–6 is iteratively applied for all remaining peaks above the threshold.Flowchart of the modified algorithm is presented in We note that the reference echo model is a normalized version of the back wall echo that is obtained by averaging over multiple back wall echoes taken from a scan area with no defect. This procedure continues iteratively until the peak echo amplitude values after subtraction in the analysis window fall below the threshold. This is presented in An important factor here is the right value of the threshold. If it is too low, we will see wrong peaks detected due to having structural noise above the threshold. On the other side, if we select a too high threshold, we may miss some defect echoes.In order to optimise the detection process, we are looking for a solution that gives us a threshold that is adaptive with respect to the structural noise level to avoid being wrongly detected as defect echoes. We note that the structural noise should have the same shape as front wall and back wall echoes except having lower amplitudes.In this study, we select threshold following a data driven strategy. To realise this approach, we analyze the distribution of the structural noise of the A-scans in the part of material with minimal influence from the defect-related echoes. This allows for effective threshold selection due to various conditions imposed by different amplitudes of the wave packet propagating in the media and variations in the structure of the inspected material.In order to analyze the distribution of peak absolute value amplitudes of the structural noise, each A-scan is divided into three zones see : (i) ZonObserving the shape of the distribution in Our algorithm extracts depth information of defects (based on time of flight information) for all A-scans and visualize the results in 3D plot. It is also capable of slicing the depth information into several zones and customizing each slice size which can be set, conveniently, equal to thickness of the composite layer. Detection is performed in each slice separately, for example, based on amplitude information. The depth of the zone is calculated according to time of flight information and a 3D plot containing detected defects from each zone (slice) is created. Using this technique, it is possible to quantify the defects layer-by-layer.We believe that the observed error in detected defect sizes is due to different reflection properties of different insert materials. We address this issue by removing a lower 40th percentile of the cumulative distribution function (CDF) of absolute peak values of detected defect echoes shown in In this paper, we analyzed signal processing challenges of NDT of aerospace grade composite using an ultrasonic PA module in an autonomous inspection platform designed for the EU-H2020 FET-OPEN CompInnova project. The post processing of the PA data with the proposed algorithm leads to resolving overlapped echoes and improving defect detection and axial (depth) resolution. We demonstrated an approach that allows achieving good results at low hardware and computational costs. We addressed the signal processing challenges for the use of high frequency 10 MHz PA transducers, and proposed solutions to overcome these problems. The proposed hardware solution is relatively inexpensive due to a conservative selection of the frequency (10 MHz), and it is going to be deployed using the robotic platform developed within the CompInnova project. This solution allows for eliminating some of the adverse effects of manual scanning related to non-uniform pressure distribution and couplant application, and increased repeatability of the scanning process.The proposed modification of the resolution enhancement algorithm allows for robust detection of the defects close to either front or back walls of the inspected structure and resolution improvement up to the ply thickness of composite material while remaining simple in implementation. The algorithm also benefits from the smart thresholding technique that allows for keeping good performance in terms of peaks due to structural noise overlapping with defect echoes, while keeping defect detection sensitivity at or above the level of conventional immersion-based ultrasonic scanning. Another advantage of the proposed approach is intuitive 3D visualization of the inspected material volume that is highly suitable for semi-automated data analysis and interpretation, where routine post-processing tasks are automated and an inspector is only evaluating results presented in easy to perceive 3D form.


Nickel–Titanium (NiTi)-based shape-memory alloys (SMA) are utilized in automotive, biomedical, microsystem applications because of their excellent shape memory effect, biocompatibility and super elastic properties. These alloys are considered difficult to cut—especially with conventional technologies because of the work hardening and residual stresses. Laser-machining is one of the most effective tools for processing of these alloys especially for microsystem applications. In this work, a thorough investigation of effect of process parameters on machining of microchannels in NiTi SMA is presented. In addition, a multi-objective optimization is carried out in order to find the optimal input parameter settings for the desired output performances. The results show that the quality of microchannels is significantly affected by input parameters. Layer thickness was found to have a significant effect on taper angle of the microchannel. Scan speed, layer thickness and scan strategy were found to have significant effects on both spatter thickness and top-width error, but in opposite directions. The multi-objective optimization-minimizing taper angle and spatter thickness revealed an optimal solution that was characterized by high frequency, moderate speed and low layer-thickness and track displacement. Shape-memory alloys have the distinctive ability to withstand large recoverable strains and regain their original shape after deformation either instantaneously or upon heating . Among vNonconventional technologies like laser-beam machining (LBM) , electriMicrochannels in NiTi alloys have various applications in microactuators, sensors , biomediIn this work the effect of laser-process parameters on the quality of microchannels is investigated systematically using experimental design methodology. After the analysis and interpretation of the results, a multi objective optimization is carried out in order to find the optimal parameter setting which results in microchannels with minimum taper and spatter.00) with a maximum average power of 10 W. The setup used in this work is the Lasertech 40 from DMG/Sauer  equipped with the Q-switched Nd:YAG laser operating with a wavelength of 1064 nm. The aperture size and laser pulse width were kept constant at 30 µm and 10 µs, respectively. The beam was operated in fundamental Gaussian mode . It is the difference between the actual measured width at the top of the channel and the designed width. Scan speed, layer thickness and scan strategy were found to have a significant effect on TWE. Whereas frequency and track displacement were found to have little or no effect on the TWE.The taper angle of the microchannel is the average of the angles made by the vertical side walls of the microchannel with the perpendicular. The results show only layer thickness to be a significant factor effecting the taper angle. The frequency and scan strategy were found to have a little effect on the taper. Whereas speed did not have any effect on the taper.Spatter is the ejected molten material that adhere to the periphery of the microchannel. It was measured using the top view of the microchannel at various places and the average was analyzed. The results show that scan speed, layer thickness and scan strategy had a significant effect on spatter. Frequency and track displacement were found to have a little or no effect on spatter.The effect of process parameters on the spatter thickness is shown in Material removal rate is the actual volume of material removed over machining time. The machining time is the sum of individual layer scan time in second, which is influenced by scan speed and track displacement. The analysis of results showed scan speed, layer thickness, track displacement and scan strategy had a significant effect on the material removal rate. In contrast, frequency was had a little or no effect on MRR. Scan speed, track displacement and layer thickness had a similar trend with the MRR. As these factors increased the MRR also increased; the increase was however significantly higher in case of layer thickness as shown in In order find the effect of laser processing on the mechanical properties of the material, microhardness tests were carried out beneath the microchannel surface as shown in ® software , the optimization workflow is shown in Multi objective optimization was performed in order to find the optimal parameters minimizing taper angle and spatter around the periphery of the microchannel while keeping the TWE and MRR as constraints within a range as shown in MOGA-II is an effective algorithm that uses smart elitism for multi-objective search. The elitism operator prevents convergence to local optima. The algorithm considers a total of number of evaluations which are DOE runs multiplied by number of generations. In the current work, DOE runs were 65 and number of generations were set to 1000 in MOGA-II. Therefore, about 65,000 evaluations were carried out during optimization. Of these, about 38% of the results were found to be unfeasible due to violation of constraints. The design points from the original DOE matrix were considered real, whereas the points predicted from RSM were called virtual. The optimization results solution space is shown using 3D-bubble graph in As the objective function is minimization of both taper and spatter, the optimal solution lies at the lower end of the solution space in The laser machined microchannels were associated with dimensional errors, tapered side walls and spatter. However, with suitable selection of process parameters these defects could be minimized;In general, higher values of speed and layer thickness, along with scan strategy S1 were found to produce microchannels with least dimensional error;Layer thickness was found to have a significant effect on the taper angle of the microchannel. Low layer-thickness resulted in microchannels with least taper angle;Spatter thickness was found to be significantly affected by scan speed, layer thickness and scan strategy. Low levels of speed and layer thickness along with scan strategy S1 was found to produced microchannels with least spatter;MRR was mostly influenced with scan speed, layer thickness and track displacement. Higher values of speed, layer thickness and track displacement result in higher MRR;Multi-objective optimization was successfully carried out using MOGA-II algorithm. Optimal solution was characterized by high frequency, moderate speed and low layer-thickness and track displacement;Optimization provides the decision-makers with the resourceful and efficient results. Optimal solutions could be selected from within these results depending upon the requirement;Optimal solution resulted in a minimum taper of 12.5° and a spatter of 30.8 µm. Additional experiments and optimization algorithms could be tested for further minimization of taper and spatter;Microhardness tests beneath the microchannel surface showed the absence of any detrimental effect of laser processing on the material properties. However, a detailed analysis of the effect of laser processing on the functional properties of the NiTi SMAs was essential for actual applications.In this work, laser-machining of microchannels in NiTi SMAs was investigated by varying five different process parameters: frequency, speed, layer thickness, track displacement and scan strategy. Optimal design IV-based experimental design methodology was used in order to investigate the effect of process parameters on the microchannel performance measures such as TWE, taper, spatter and MRR. A multi-objective optimization was performed in order to minimize the taper angle and spatter thickness using a combination of RSM and genetic algorithm. Based upon the analysis of results the following conclusions were drawn: