The resulting interactive diagrams are complementary and allow the pulse source separation of pulses and clustering. The effectiveness of the proposed method and the developed Matlab application for separating PD sources is demonstrated with a practical laboratory experiment where various PD sources and pulse-type noise interferences were simultaneously measured.During the last two decades, on-line partial discharge (PD) measurements have been proven as a very efficient test to evaluate the insulation condition of high-voltage (HV) installations in service. Among the different PD-measuring techniques, the non-conventional electromagnetic methods are the most used due to their effectiveness and versatility. However, there are two main difficulties to overcome in on-line PD measurements when these methods are applied: the ambient electric noise and the simultaneous presence of various types of PD or pulse-shaped signals in the HV facility to be evaluated. A practical and effective method is presented to separate and identify PD sources acting simultaneously in HV systems under test. This method enables testers to carry out a first accurate diagnosis of the installation while performing the measurements in situ with non-invasive high-frequency current transformers (HFCT) used as sensors. The data acquisition in real-time reduces the time of postprocessing by an expert. This method was implemented in a Matlab application named PRPD-time tool, which consists of the analysis of the Phase-Resolved Partial Discharge (PRPD) pattern in combination with two types of interactive graphic representations. These graphical depictions are obtained including a feature parameter, effective time ( Developments in signal processing techniques achieved in recent years have positioned on-line partial discharges (PD) tests measuring with high-frequency current transformer (HFCT) sensors as an effective method for evaluation of the insulation condition of high-voltage (HV) electrical equipment such as power transformers . The posFiltering and signal denoising.Feature extraction.Individual patterns extraction.A practical and efficient computational tool named the phase-resolved partial discharge (PRPD)-time tool was developed in order to assist in the insulation diagnosis procedure. The PRPD-time tool is a PD analysis tool that makes possible the supervised source-pulse separation process using a powerful and interactive graphical interface. A source pulse separation and identification process allows the determination of the specific PD or noise sources present simultaneously in HV installations and is commonly implemented in three stages :Filterinφ—pulses amplitude mV—effective time efft). The effective time of different pulses in the ultra-high-frequency (UHF) range in combination with power ratio analysis have been previously used with success for the separation of several PD sources , while the high-frequency band is in the range  and tf is the highest frequency value to be analysed.Other methods use as feature parameter for separation, the power and energy of pulses, mathematically calculated after pulses were transformed into the frequency domain. The power distribution of signals in relevant frequency bands, in which there is higher dispersion in the accumulated power and are characteristic for different PD sources, allows separation of PD sources. Usually two frequency bands are considered and the power ratio (PD pulse :(4)%PRL=PRL and PRH can be used as feature parameters in a univariate representation for separating different sources on a 2D map. An enhancement of this method was presented in [PR map, for allowing the effective separation of sources with a similar power ratio. The third axis is the effective time that is defined as the duration of a rectangular pulse with the same peak as the squared signal whose integral gives the same energy as the original pulse [ented in  where a al pulse .cω) and the Root-Mean-Square (RMS) bandwidth (B) as feature parameters from the signal and its Fourier transform for generating 3D maps. Those maps allow separation of pulses from different sources such as corona, surface and internal PD measured using different inductive sensors [The chromatic technique  is anoth sensors ,20.peakI; the PD charge amplitude, Q and the energy, kE. The current peak value is calculated using the HFCT signal, kv and Q is calculated as the integral over the time duration of the PD current pulse. The energy is calculated as:R is the input resistance of the acquisition system, N is the number of samples, dT = 1/Fs, Fs is the sampling frequency. The equality in Equation (6) makes use of Parseval’s theorem. This approach has been also used for PD clustering applications under unsupervised conditions [peakI, Q and kE.In  a separanditions  using anIn automated separation and identification methodologies, after feature maps are obtained, clustering algorithms or correlated calculations using reference pulses can be employed in order to perform the pulse sources separation . DiffereCompared with the aforementioned separating methods, the developed PRPD-time tool allows a non-expert and low-computational cost procedure for PD source separation using the classical PRPD pattern and including a third axis corresponding to the effective time. The implementation of this processing application in Matlab is presented in -Internal PD appears slightly before the zero crossings and in the increasing intervals of the test voltage.-Surface PD appears in the increasing intervals of the test voltage and at the peak values. The PRPD pattern exhibits an asymmetry among positive and negative half-cycles.-Corona PD pulses have a similar amplitude and the PRPD pattern exhibits great density about the maximum values of the test voltage. The amplitude can vary on each half-cycle (according with the type of corona).-Electrical noise is usually not correlated with the voltage reference signal and depends on the type of the electrical noise.-Pulses from IGBTs in the resonant system are synchronized with the test voltage and appear at its zero-crossings.The classical PRPD patterns represent the amplitude or charge of the acquired pulses against the phase angle in which they occur. Thus, the pulses are seen super-imposed in one period of the test voltage. These patterns are widely used for the identification of PD sources, because each source features a different characteristic behaviour in the resulted image. Some expected patterns are :-Internaefft), is very useful. The implementation of this parameter, which is extracted from the measured pulses, enables the separation of the PD sources shown in the PRPD patterns using it as a third dimension.Therefore, PD pulses from the same source are positioned in defined regions when they are plotted on PRPD patterns. However, when several PD sources are simultaneously measured, their PD patterns together with the noise pulses can be overlapped. In these cases, the realization of accurate diagnosis is very difficult and sometimes even impossible. To solve this issue, the use of the proposed feature, called effective pulse width or effective time duration  selecting sources of similar efft.Containers are used in the PRPD-efft patterns.Its location in the PRPD-Its PRPD pattern.Pulse sources are separated using the following two criteria:The proposed method relies in the fact that pulses from different sources occupy specific zones in the PRPD-efft pattern is obtained by adding to the classical PRPD pattern, an axis corresponding to the effective time duration of each acquired pulse. To calculate this feature, in a first step the normalized pulse with time T (s) the time window in which each pulse The PRPD-efft that represents the equivalent time length of each signal. This parameter is related to the width of a rectangular pulse with the same peak value as a squared signal whose integral has the same energy as the original pulse, thus both signals have the same area. The efft (s) is expressed as follows [t (s), The normalized pulse with time is used to calculate, in a second step, the effective pulse width  follows :(8)teff=efft pattern is depicted using it. Both tasks can be performed in real time (during the PD test) and with very low processing requirements. The PRPD-time tool is programed based on the flowchart and steps shown in Once the diagnosis application is initialized a, it is Home, and the separation tools named Sep. by teff and Sep. by Clustering. The visualization related with Sep. by teff tab is shown in Position controls the centre of a range of effective time to be covered in order to analyse a specific source, whose limits will be controlled with the value of the slider Width.At the upper part of window in efft among different PD sources is small. For this reason, in order to improve the visualization and ease the classification procedure, a function, which assigns a scale of colours to the pulses’ representation considering their effective time magnitude in a specific range, was implemented. The tones of the colours in the PRPD-efft patterns is defined by a logarithmic function controlled by six states with different combinations for defining the RGB colour specification triplets in Matlab. Slope modifies the curve that controls the transition between the six states, producing the differences in tones among the different PD sources. However, taking into account that the PD duration is in the range of tens of nanoseconds to few microseconds , the varefft pattern for all pulses considered using an isometric representation of phase angle-amplitude- efft, Once the range of colours for the effective time is selected, it is possible to represent the corresponding PRPD-Slope, Width and Position variables in the Sep. by teff button, A first rough separation is possible adjusting the Sep. by Clustering, at the upper part of window in Sep by teff tab, For further analysis and in order to complete the PD sources separation process, by pressing the button efft patterns under study is possible by selecting 3D geometrical shapes, called “containers”. The “containers” shape can be parallelepipeds, ellipsoids or prisms.Using the cursors of the interface shown in efft visualization in The separation of pulse sources is performed by selecting with the containers, the clusters formed in the PRPD-Sep. by teff and Sep. by Clustering.Once an individual PRPD pattern has been selected using the PDCTC, a density plot is shown in The measurements performed for the validation of the proposed analysis tool were carried out in the HV laboratory LAT-UPM of the Universidad Politécnica de Madrid.An experimental setup was assembled with various PD defects characteristic of insulated cables. In the test performed an HFCT sensor and a commercial PD measuring instrument  were usePD sources are briefly described as follows. Internal discharges were generated due to a cavity at the end of the semi-conductive layer in a cable termination. On the other hand, surface discharges were produced adding traces of carbonized particles on the external surface of a cable termination. Finally, corona discharges were generated adjusting accurately the gap length in a needle-plane electrode configuration where the plane electrode was grounded. Furthermore, during the measurement, three types of impulsive noise sources were presented along with the PD pulse sources described above. The first source of impulsive noise was the resonant generator used to perform the HV test, which produces pulses due to the commutation of its IGBTs. The second was caused by a power electronic device connected to the electrical installation of the laboratory, which produces repetitive pulses due to the commutation of its transistors. Finally, random impulsive noise conducted by the grounding conductors, was also measured. A HV installation was implemented for the measurement of the PD and noise sources, shown in 2 in diameter and 1500 m in length. The HV source was a 36 kV resonant generator and the test voltage was 15 kV, 50 Hz. A capacitive divider  and random pulse-shaped noise. As can be seen in efft pattern for all pulses considered.When the third axis is added, efft patterns that can be displayed using the sliding list View at the lower left-hand side of Sep. by teff button allows users to distinguish clusters with different colours and tones depending on the magnitude of efft. This clear differentiation among pulses in the PRPD-efft pattern in When comparing efft pattern. For the case corresponding to efft axis. efft patterns where clusters of pulses, for sources considered in this study, can be found. Other sources as well other measurement scenarios will be considered in future works for a generalization.By analysing the cluster distribution from the different pairwise representations shown in efft patterns for each source. In addition, the PRPD patterns obtained for the PD pulses in containers are shown.In Taking into account the above analysis and the description of characteristics of PRPD pattern for pulse sources given at the beginning of efft patterns. It can be seen that corona and surface PD pulses have similar efft values and pulse magnitudes, however they can be differentiated by the phase of appearance. In addition, it can be seen that pulses corresponding to internal PD are overlapped in phase and magnitude with corona and surface PD pulses, however they have a different range of effective time that allows their effective separation. As can be seen from It can be seen that each PD source was effectively separated and identified. Analysing the individual PRPD patterns obtained, a proper diagnosis of the insulation condition can be performed. For all PD sources considered in this article, parallelepipeds were easily fitted around the clusters. However, other geometrical bodies as described in A novel and effective method for impulsive sources separation was proposed, which considers the effective time as a feature parameter along with the pulses’ amplitudes and the phase in which they are acting. In addition, in order to separate sources of pulses by clustering using the effective time parameter, a powerful and interactive interface tool was implemented in a Matlab application named PRPD-time. The method and the Matlab application were validated using experimental measurements considering six different impulsive sources acting simultaneously.efft patterns can be obtained with a dynamical colouring process that enhances the visualization of clusters. The second allows separating the clusters previously differentiated and analysing their PRPD pattern for identifying the corresponding impulsive source.In the PRPD-time tool, the successful source pulse separation is implemented using two complementary tools. The first, allows including the effective time as a feature parameter and PRPD-PRPD-time tool is a very interactive application that requires low-computational capabilities and gives competitive advantages over other methods and technologies present in the current market. The proposed method is very easy to implement, considers feature parameters with physical meaning and does not require previous training or expert knowledge.


Based on the laboratory results, the concentration profiles of individual ions along the membrane length in a single-pass industrial-scale nanofiltration (NF) unit was calculated, assuming the tanks-in-series flow model inside the membrane module. These calculations also propose a method for estimating the maximum achievable recovery before the occurrence of the calcium sulfate dihydrate scaling in a single-pass NF 40″ length spiral wound module, simultaneously allowing metastable supersaturation of calcium sulfate dihydrate. The performance of three membrane types  has been evaluated for the nanofiltration of mine water.Nanofiltration can be applied for the treatment of mine waters. One of the main problems is the risk of crystallization of sparingly soluble salts on the membrane surface . In this work, a series of batch-mode nanofiltration experiments of the mine waters was performed in a dead-end Sterlitech This may be caused by the different composition of each feed water. The water “A” contains less chloride, sodium, calcium, and magnesium ions, which causes a smaller diffusion driving force across the membrane, resulting in lower flux of these ions across the membrane; simultaneously, the water flux across the membrane is higher in the case of less saline water “A”, causing overall lower rejection coefficients of calcium and magnesium. This effect does not happen in the case of the sulfate, as both waters contain similar amounts of this ion. The rejection of sulfate was significantly higher than the rejection of calcium and magnesium, which was caused by the negative surface charge of the membrane at the experimental conditions (pH ≥ 5.7).The highest chloride rejection was observed when the NFDL membrane was used. Moreover, in most cases, NDFL membrane type demonstrates the highest rejection of both univalent cations and bivalent cations, while the NFX membrane generally shows the lowest rejection among all three types studied. The results are in line with the ionic rejection coefficients previously reported in the literature; for instance, Hilal et al.  reportedThe correlations obtained from laboratory data were then used to calculate the concentration profiles of the considered 5 ionic species along the membrane module length arranged in a single-pass industrial-scale NF unit, assuming the tanks-in-series reactor model of a flow inside the membrane module. These calculations allowed to estimate the maximum allowable recovery that would effectively prevent the membrane surface scaling phenomena in a single-pass NF module. Calculate the permeate recovery Y of the module by assuming the feed linear flow velocity refer to  ModelingAssume a point along the module membrane length.Calculate the bulk retentate ionic concentrations and retentate concentrations at the membrane surface for a chosen point using the previously discussed model.Calculate the theoretical time needed for the solution to flow from the chosen point to the retentate outlet. The gradual change in the volumetric flow along the membrane module length, due to the flow across the membrane is taken into account when calculating the time needed to leave the module has been taken into account by calculating the mean residence time in each of the elementary units separately and adding them.If the theoretical time needed for the solution is not at least six times higher than the induction time, the scaling risk is unacceptablely high.To assess the scaling risk on the membrane surface following methodology is proposed, based on the earlier research ,17:CalcuUsing the above assumptions, a maximum allowable recovery was calculated for each feed water and membrane type, defined as the maximum permeate recovery for which there is no unacceptable high risk of scaling at any point along the membrane module length—the results are presented in The results suggest that performing the nanofiltration at high permeate recovery (even above 90%) should be possible without the scaling on the membrane surface. One should remember, however, that in reality the supersaturated solution doesn’t leave the module precisely at the last point along the membrane and can stay for some time in the piping. In practical operation, a lower permeate recovery value (~ 85%) would be more recommended, as well as placing the precipitator for supersaturated retentate immediately after the pressurized device, to avoid unnecessary holdup in the piping.The scaling risk is the highest when applying the NFDL membrane and the lowest when applying the NFX membrane. This can be explained as a result of different chemistry of the obtained retentate: The NFDL membrane has shown the highest rejection coefficients of bivalent ions during the bench-scale tests, which means the retentate obtained using this membrane is the most supersaturated with calcium sulfate. On the other hand, NFDL is more hydrophobic than NF270, so it may show less tendency for scale layer growth in the same saturation conditions.Based on the permeate flux and sodium, magnesium, calcium, chloride, and sulfate ions concentration measurements in the dead-end experiments, the scaling risk of calcium sulfate dihydrate in the NF 40″ length spiral wound membrane module was estimated. The dead-end experiments showed that the nanofiltration process may be safely operated even at 80% recovery of permeate. A method of predicting the operational limits of nanofiltration modules working in high scaling risk situations, e.g., when the feed water is rich in calcium and sulfate, was proposed. Comparing the theoretical time needed for solution to flow through the module and calculated nucleation induction time of calcium sulfate dihydrate for a given final retentate concentration, it is possible to predict maximal safe recovery level Y for each specific process conditions; the established model, however, is valid only at given hydraulic pressure (40 bar) and would need an additional set of experiments to include pressure as a variable. Experimental tests clearly demonstrated that scaling-free operation of the 40″ length spiral wound NF module is possible at 75% to permeate recovery in the case of highly concentrated mine water and with 80% permeate recovery considering brackish water. A possibility of working at 75% to permeate recovery level should improve the performance of the integrated salt production systems using NF as pretreatment step, since they are limited in terms of overall recovery by the pretreatment (NF) recovery. The establishment of the reliable and mathematical model to simulate the nanofiltration in large-scale systems creates an opportunity for the investigation of NF applicability in several technologically important processes.


Despite recent advances, the myocardial microstructure remains imperfectly understood. In particular, bundles of cardiomyocytes have been observed but their three-dimensional organisation remains debated and the associated mechanical consequences unknown. One of the major challenges remains to perform multiscale observations of the mechanical response of the heart wall. For this purpose, in this study, a full-field Mueller polarimetric imager (MPI) was combined, for the first time, with an in-situ traction device. The full-field MPI enables to obtain a macroscopic image of the explored tissue, while providing detailed information about its structure on a microscopic scale. Specifically it exploits the polarization of the light to determine various biophysical quantities related to the tissue scattering or anisotropy properties. Combined with a mechanical traction device, the full-field MPI allows to measure the evolution of such biophysical quantities during tissue stretch. We observe separation lines on the tissue, which are associated with a fast variation of the fiber orientation, and have the size of cardiomyocyte bundles. Thus, we hypothesize that these lines are the perimysium, the collagen layer surrounding these bundles. During the mechanical traction, we observe two mechanisms simultaneously. On one hand, the azimuth shows an affine behavior, meaning the orientation changes according to the tissue deformation, and showing coherence in the tissue. On the other hand, the separation lines appear to be resistant in shear and compression but weak against traction, with a forming of gaps in the tissue. Each cardiomyocyte is surrounded by a thin layer of extra-cellular matrix, the endomysium. This defines the smallest scale of the tissue. Cardiomyocytes are bundled together, creating aggregates of typically 5–10 cells. These bundles have a typical size of around 100 2. At a larger scale, these aggregates are assembled to create the heart wall which is approximately one centimeter thick.Cardiac muscle has a complex, highly hierarchical organization composed of cardiomyocytes, extracellular collagenous matrix, vasculature, etc. This organization is deeply related to the macroscopic mechanical and morphological properties of the heart. The cardiac muscle or myocardium is mostly made of an assembly of cells, the cardiomyocytes, which are roughly cylindrical with a typical diameter around 10–20 3. It is now well established that the fiber orientation rotates in the thickness of the heart wall5. The spatial organization of the cell aggregates remains elusive. It is often considered that these structures create sheetlets, large layers of cardiomyocytes separated by a thick film of extra-cellular matrix7. However, this is not definitively proven and other organizations, such as globular clusters of dozens of cardiomyocytes, have also been proposed9. On top of this uncertainty on the mesoscopic organization of the myocardium, the physiological role of this mesostructure is not completely understood11.The cardiomyocytes are contractile cells, their main axis being the direction of contraction. These local preferential directions are thus the ones of the cardiac fibers12 or the organization of the perimysium and the endomysium9, showing clusters of cardiomyocytes. In these images, the perimysium appears to be much thicker and denser than the endomysium13. However, SEM has the major drawbacks of having a limited field of view and of requiring invasive sample preparation. Conversely, the Magnetic Resonance Imaging (MRI) has been used to investigate the local orientation of sheetlets  by measuring the magnetic relaxation15. While the MRI has the capability to observe the whole heart in vivo, it cannot be used to observe directly its mesostructure, which can only be inferred through theoretical assumptions. Intermediate scales have been investigated through confocal microscopy17. Here the collagen is specifically dyed to distinguish the endomysium and the perimysium from the cardiomyocytes. Millimeter-long lateral surfaces of rectangular pieces of the heart wall have been observed with this approach. It provides specificity, high resolution and a large field of view, but requires very long scanning times, making it incompatible with mechanical assays. Confocal microscopy images show large lines of collagen on the lateral surfaces of the sample. While some lines seem dense and thin, others appear broad and sparse. Sparse layers will tend to behave like lines of weakness, strong in compression but weak under traction, whilst denser layers will behave like stiff sheets. These contrasts raise questions about the mechanical properties of the collagen walls.The primary reason for this lack of knowledge of the mesostructural organization of the cardiomyocytes comes from the experimental difficulties to observe it, in particular in association with their mechanical role. Different methods have been used so far to observe the microstructure. The most resolving method is probably the Scanning Electronic Microscopy (SEM), which enables to observe, for example, the cellular shape20. The active response is due to the contraction of the cardiomyocytes, whilst the passive response arises from the whole tissue. Our focus is set on the latter. Various mechanical assays have been performed, such as uniaxial traction24, biaxial ones27 or shear experiments28 with the ultimate aim to determine the anisotropic mechanical response of the tissue. The anisotropy arises mainly from the cardiomyocyte orientation, but some experiments point towards a role of the cardiomyocyte bundles in the response28. However, to our best knowledge, no experiment has probed the mechanical response at the scale of the cardiomyocyte bundle . These questions require new experimental approaches, that allow to obtain mesostructural information for a large field of view and at a rate comparable to that of the mechanical loading.These mechanical properties, which have attracted considerable attention, can be generally split between active and passive properties30. The great advantage of this technique is that it can be used to observe, in few seconds, a macroscopic field of view , thus providing structural information on the explored tissue with a microscopic resolution  is used, for the first time, in this study. Over the past few years, Mueller polarimetric imaging has shown great potential for exploring the microstructure of biological tissues for different types of biomedical applicationsIn this study, we investigated the role of the myocardium mesoscale organization in its mechanical response, with a particular interest in the response of the perimysium—the collagen layer which surrounds cardiomyocyte bundles—as the role of this structure remains unclear. To do so, we used full-field MPI to investigate the microstructure of the myocardium under different stretch conditions. For this purpose, an original setup, combining a full-field MPI and a mechanical traction device was built in order to measure the evolution of the different polarimetric properties of myocardium during the stretching of strip samples of pig heart wall. The proposed approach enables to quantitatively investigate the structural modification of myocardial tissue on a mesoscopic scale, via the measurement of its polarimetric parameters, under different traction conditions. We observed an affine evolution of the tissue optical orientation, indicating that the mesostructure follows the local macroscopic deformation. However, the forming of gaps, perpendicular to the traction direction, indicated the presence of defect lines. These lines reflect weaknesses in the tissue, and are associated with a lower depolarization, denoting a less dense region which considerably reduces the light scattering. Our new imaging method gives a first evidence of the mechanical response of the collagen structure surrounding the sheetlet, which appears weak in response to traction, while being resistant to shear and compression.36 and were kept immersed in a physiological buffered saline solution (PBS) to prevent any drying of the tissue during measurements. The samples were subjected to traction, with pauses every 10% of stretch for MPI acquisition. Three types of data were collected for each sample: (1) the Mueller polarimetric matrix at every 10% of deformation, (2) the force and the displacement of the traction machine every second and (3) the local deformation every 3 s.Nine samples of dimensions approximately 37 is used to extract the birefringence, the depolarization and the dichroism matrices from measured Mueller Matrix. For the myocardium, the most relevant polarimetric parameters are the Depolarization (R) and the orientation of the fast axis, also named Azimuth . Specifically the orientation of these fibers can be visualized using the parameter The precise microstructural origin of the measured macroscopic polarimetric signal in the myocardium has yet to be identified. However, a very interesting study performed on the skeletal muscle proposes a simplified optical model which can be relevant also to describe the polarimetric effects observed for the myocardium28.We compared classical white-light images and polarimetric images on the same side of the same sample see Fig. . We obse24: after observing first a non-linear region, the curve becomes linear , it may play a role in the heart remodeling capacities. Developed models in the literature include both the cardiomyocyte and the perimysium layer43. However, they rely on the affine transformation assumption, which states that local kinematics of each constituent follow the local macroscopic deformation. To our knowledge, this hypothesis has never been experimentally tested, as it requests a simultaneous observation of the macroscopic deformation and the microstructure orientation. In this study, we investigate the question of the cardiomyocyte bundle mechanical response in the myocardial tissue by combining for the first time a traction assay with Mueller polarimetry imaging. These cardiomyocyte bundles group together a few dozen cells, and are around a few hundred of micrometers in thickness. They are often considered as organized in sheetlets7, although their spatial organization remains undetermined. Not surprisingly, their mechanical properties remain elusive as well8.In the recent years, the role of the myocardium mesostructure has attracted more and more attention. This is due to the fact that the physiological role of this spatial organization remains unclearMueller Polarimetric Imaging explores the response of the tissue with respect to different polarization states of the illuminating light. Therefore, it probes the tissue organization. Three parameters appear to be of interest in the myocardium: the Azimuth, the Linear Phase Retardance, and the Depolarization. We associate the azimuth entclass1pt{minimaWe observe “separation lines” in which the azimuth varies a lot on few micrometers, and in which the depolarization is smaller see Fig. . These lsee Fig. . SamplesTo confirm the mechanical role of these separation lines, it will be of interest to advance more complex mechanical loadings after having confirmed that they are really the borders of sheetlet. An improvement of the resolution of the Mueller polarimetric set-up will enable to achieve a more precise view of the microstructure. But most importantly, further developments will be strongly helped by an optical model of the signal including the cardiac microstructure, so that we can associate more precisely each element to a polarimetric parameter, as well as the size of probed volume.The obtained results prove the feasibility to measure the mechanical properties of the myocardium mesostructure. These experimental measures can be used as inputs for the mathematical models to determine myocardial mechanical properties, although our results show that affine-based models are unlikely to reproduce correctly our observations. With such approach one can compare healthy and remodeled or pathological myocardium samples, and see how the mechanical response is modified at a mesoscale—a scale not investigated so far. However direct clinical applications will request further studies.36  temporally modulates the polarization of the light impinging on the sample to be explored by consecutively generating four independent probing polarization states which are described by four Stokes vectors and regrouped together as the columns of a modulation matrix called A custom-made sequential multispectral Mueller polarimetric imager (MPI) in backscattering configuration, designed and constructed at LPICM, was used to analyze myocardial samples. It is an upgraded version of the one described inmentclass2pt{minim44, has been used here to calibrate the system. The PSG and PSA have been constructed by using ferroelectric liquid crystals as explained in45. The light is delivered by a 150 Watt halogen source . and guided to the MPI by means of a liquid light guide . Appropriate collimation optics was placed at the outlet of the liquid guide to produce a bright spot 6 cm in diameter at a working distance of about 30 cm corresponding to the sample position. A monochromatic CCD-camera  has been used as detector. It is provided with an electronics enabling to acquire 16-bit intensity images. The CCD-camera is provided of an objective of focal length f = 25mm and it has been positioned at a distance of about 20 cm from the sample in order to obtain a field of view of about The Eigenvalue Calibration Method, widely described in37. This decomposition enables to describe a Mueller matrix M as the product of three matrices:a, b and c are the eigenvalues of The Mueller matrix The phase retardance can be obtained by:33.Finally the azimuth is given by:36, equipped with load cells , on which the sample was attached , using a 8-bit CCD camera  46) to determine the displacement and stretch fields. The correlation was performed on positions separated by 100 px  was performed  In order to compare mechanical and polarimetric data, the information obtained on the two sides had to be extrapolated on the same location. To do so, we first registered the two images so that both were aligned in the same coordinate system. The registration was done using a “feature-based” technique, consisting in finding a few matching features on both images, and then computing a transformation matrix Once the images were registered, we determined the displacement Note that the positions Supplementary Figures.


This mini-review provides an updated summary of various analogs for adaptations of humans to the microgravity of space. Microgravity analogs discussed in this paper include dry immersion, wet immersion, unilateral lower-extremity limb suspension, head down tilt (HDT), and supine bed rest. All Earth-based analogs are imperfect simulations of microgravity with their own advantages and disadvantages. This paper compares these five frequently used microgravity analogs to offer insights into their usefulness for various physiological systems. New developments for each human microgravity analog are explored and advantages of one analog are evaluated against other analogs. Furthermore, the newly observed risk of Spaceflight Associated Neuro-Ocular Syndrome (SANS) is included in this mini review with a discussion of the advantages and disadvantages of each method of simulation for the relatively new risk of SANS. Overall, the best and most integrated analog for Earth-based studies of the microgravity of space flight appears to be head-down tilt bed rest. This mini-review serves as an updated look upon the various analogs to microgravity. All established microgravity analogs discussed in this paper , and supine bed rest) are imperfect simulations of microgravity with their own merits and disadvantages. This paper serves to discuss new developments for each human microgravity analog as well as to compare these simulation methods to actual microgravity conditions of spaceflight. Furthermore, the newly observed risk of Spaceflight Associated Neuro-Ocular Syndrome (SANS) is included in this mini review with a discussion of the advantages/disadvantages of each method of simulation for space flight for the SANS risk. Due to format limitations, some aspects, such as metabolism and countermeasures, cannot be considered in this mini-review.Five analogs are commonly used on Earth for simulating the microgravity of space . The welDry immersion is a microgravity simulation analog method developed in Russia in which the subject is encapsulated in a waterproof covering and submerged, from the neck down, in water. The water is held at a standard temperature of 32–34.5°C, which is thermoneutral, and the subjects are submerged just past their clavicles . SubjectDry immersion induces a head-ward fluid shift due to the hydrostatic compression of the subject. With increasing pressure with depth, lower limbs experience a higher level of head-ward fluid shift, (directing fluid to the upper body) similar to the shift seen in spaceflight. This fluid shift also decreases cardiovascular stress and heart rate decreases within hours of dry immersion. Heart rate drops by 5 bpm and blood pressure decreases by 5 mmHg in the first 4 h . HoweverBecause of the near-seated position of the subject, the gravitational forces upon the body vary. This is noted in the case of where torso movement is significantly more impaired than wrist movement, suggesting a difference in the reduction of gravitational forces across the body .Water immersion requires submersion in water from the neck down. However, extensive water immersion can induce subacute dermatitis in as little as 72 h, making this method unusable for long duration studies . The benUnilateral lower limb suspension is one of the most cost-effective methods to study the effects of microgravity and spaceflight on the human body . The metThis method has effects within 2–3 days of implementation, showing signs of muscle atrophy and deterioration. While this method cannot demonstrate global or cardiovascular changes from loss of hydrostatic pressures within the body, it is effective in demonstrating changes in the muscular and skeletal systems. The effects are localized to the lower leg, specifically affecting the postural muscles of the lower limb . The useThere is little risk to the subject aside from increased risk of venous thromboembolism relative to bed rest and spaceflight, which can be mitigated with the use of compression socks . It is rP = 0.05), which may counteract visual symptoms that might occur  when compared to normal HDT (P = 0.005) between grounded subjects and astronauts  by having subjects lie in the supine position . This meAll current analogs to microgravity are imperfect analogs and need to be compared to evaluate their utility for a given project or space maladaptation . ImmersiAs it stands, no studies utilizing any method other than HDT and supine bed rest to study SANS have been published. One of the possible causes of SANS may be the increased differential between intercranial pressure and intraocular pressure that occurs in a microgravity environment . MoreoveMP and AH contributed to the conception and creation of the manuscript. MP researched and wrote the initial draft of the manuscript. AH edited and advised on the manuscript. Both authors read and approved the submitted version of the manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Multilayer optical film plays a significant role in broad fields of optical application. Due to the nonlinear relationship between the dispersion characteristics of optical materials and the actual performance parameters of optical thin films, it is challenging to optimize optical thin film structure with the traditional models. In this paper, we present an implementation of Deep Q-learning, which suited for the most part for optical thin film. As a set of concrete demonstrations, we optimize solar absorber. The optimal program could optimal this solar absorber in 500 epoch (about 200 steps per-epoch) without any human intervention. Search results perform better than researchers’ manual searches. Sullivan and Dobrowolski implemented the needle optimization method to improve it much more flexible by defining a merit function that consists of quite complicated spectral quantities2. Chang and Lee applied the generalized simulated-annealing method (GSAM) for the thin-film system design and discovered that there would be no problem with the trapping local minimum that happened in the design3. Recently, the researchers worked in this field also have applied to the optical coating optimization methods with several models such as particle swarm optimization (PSO)4, genetic algorithm (GA)6, ant colony algorithm7 and deep learning algorithm12.Since 1970, with the development and application of computer technology, a variety of numerical data processing methods emerge in endlessly. Many numerical optimization algorithms, such as linear programming, simplex solution searching, least-square or damped-least-square error reductions, were studied and applied for the optical thin film design. Which the least-square error reduction was one of the most successful methods in the application. Those traditional methods often show the limitation of focusing only on the solution searching for the film system’s local optimum with a complicated multilayer film structure. The optical film researchers also developed a variety of global optimization methods used in the design of the film structure. Tikhonravov and Trubetskov developed an optical coating design software based on the needle optimization technique15. Although the above research has solved the related physical problems, it needs a lot of known data. This supervised learning model can not solve the problem well for some unknown combination problems of materials and structures. Especially in the optimization problem, because the search space is extensive, supervised learning can not adequately predict and optimize the optical thin film structure. The total optical film optimization method can be regarded as Markov Decision Process (MDP), which can be solved by reinforcement learning (RL). As one of the important reinforcement learning branches, deep Q-learning (DQN) has achieved a lot of success in the field of optimization and control and has made significant advantages in the confrontation with human beings17.In the traditional methods, as mentioned above, most of the optimization algorithms are based on the assumption that the film materials are not optically dispersive. i.e., the traditional algorithms only consider the parameters related to the refractive index and thickness of materials. With the increase of the spectral bandwidth, the dispersion feature of the material will significantly affect the film’s optical properties. Because of the broad bandwidth, the refractive indices of all materials vary as a function of wavelength. Extinction coefficients of the materials should be considered in some conditions, like that of the films in solar absorption applications. With the increase of the parameters that need to be optimized, the above classical algorithms’ optimization performance will significantly reduce or even be less able to work usually. An optimization algorithm framework with strong search ability and better robustness need to be used in the field of film optimization. Since 2014, with the rapid development of computer science, especially artificial intelligence, deep learning was widely applied in many research fields. Deep learning has been pioneeringly applied in many physical problems recentlyIn this work, a new multi-layered film optimization method is proposed with the DQN. The algorithm will automatically search the thickness of each layer of the film that minimize the objective function. Each layer of the film materials will be selected from a given set of pre-selected materials. In the following sections, we show the details of the algorithm’s formulation, and as a demonstration, we apply the deep Q-learning algorithm to the optimization for both a solar absorption film and an anti-reflection film structure. In the following sections, this paper will introduce in detail how to combine DQN with optical multilayer film optimization and show the performance of the algorithm in several typical film systems.For optical thin films, three main interrelated factors determine the optical properties of the film, respectively, transmission (T), reflection (R), and absorption (A). The relationship between transmission, reflection and absorption is determined by the conservation of energy, i.e.K layers material. In addition, in order to design multi-layer structure, we express the target spectrum which contains transmission, reflection and absorption as environment of reinforcement learning, and the improvement of multilayer film performance will be used as a condition of defining return. As for the actions of DQN agent will adjust the film thickness.For multi-layered optical thin film optimization, especially for broadband optimization,the target optical properties of the thin film are the optical performance constants of the material, as the reflective indices Here we explain the method briefly and discuss the implemention of deep Q-learning to solve the multilayer optical film problem. We use DQN to iteratively optimize the film parameters in the simulation environment, and get a better simulation film structure after a certain number of rounds of optimization. Figure 18. The efficiency of TMM is higher than in another optical film environment FDTD. In this environment, the film thickness A, T, and R.The environment of DQN is based on one kind of multilayer optical film simulation algorithm called the transfer matrix method (TMM)In this reinforcement learning, we input two kinds of information as states, respectively the optical properties of the film and the thickness of the film. The film’s optical properties represents the absorption, transmittance, and reflectivity in the target wavelength range. For example, because the optical parameters at each wavelength are different, the input shape of this part states is Actions represent the operation at each step in the process of optimizing the film structure. These actions adjust film thickness at different scales. As for a film with four layers of film thickness that need to be optimized, the minimum optimization scale is 0.1 nm. The actions space contains For more film layers, the actions space can be expressed by as follows.When transplanting DQN, we use a multi-input neural network instead of a full connection neural network as the backbone network of DQN. According to Fig. A in the state S. In our application, shows in Table R indicates whether the modification of the film structure is helpful to optimize the film parameters. Considering 1  and − 1  is the return value, it will not be easy to guide the agent to find the appropriate optimization direction. We have designed returns value in two patterns, binary and continuous, for reward shaping. The reward function is designed with the idea of reward shaping, to avoid sparse reward problem.The reward is essential feedback used to measure the performance of the agent in reinforcement learning. It represents the next gain in a single time step after acting which observation loss is represent improvement in last step.At first, the Q network is initialized randomly, and a series of predictions are also random. If we choose the action with the highest Q value, the action is naturally random. At this time, the agent is doing “exploration”. As the Q function converges, the returned Q value will tend to be the same. It allows the model to converge faster, and also convergence being wrongly affected in the local optimal search process. It can increase the convergence speed of the model, but it will also cause the model to fall into a local trap. A simple and effective solution is  where Combining the methods as mentioned above, the Q-network can be optimized by the algorithms as below:19.Solar selective absorber plays an essential role in the field of solar energy utilization. For the film, a full absorption bandwidth and higher absorption efficiency will be required to improve the film’s performance. Optimizing the film structure becomes very important, but the optimization method using a genetic algorithm or simplex method usually needs an excellent initial film structure in practice. We use the DQN algorithm to optimize the selective solar absorber to verify the effectiveness of the algorithm. We use materials optical constant fromFirst, we define the optimized objective function. The spectral range of solar radiation is extensive, while energy is mainly concentrated in the visible and near-infrared spectrum  4 layers; (2) 6 layers; and (3) 8 layers. The optimal thickness of each layer is shown in Table Figure We propose a novel algorithm to optimize the thickness of selective solar absorber, and this reinforcement learning method is easy to extend to other multi-layers films. This algorithm dramatically improves the optimization speed of the film thickness, and the researchers have not given any initialization of the film structure. The network structure using the 1D-Conv network, like backbones, features the observation from the simulation environment. We also notice that the total time-consuming optimization is very controllable, and the total number of iterations can search for a satisfactory result in 500 epochs as we test the result in a PC with AMD 3600X CPU and NVIDIA GTX-1080Ti GPU. The overall optimization time cost is about 20 min. Since the overall operation time is mainly concentrated in the system simulation stage, the overall optimization time may be further shortened, considering the introduction of multi-process simulation.


CyclicLoss, to impose our prior knowledge about the periodicity of the observed signal. The solution is investigated with a cohort of 25 ultrasound sequences acquired during the third-trimester pregnancy check, and with 1000 synthetic sequences. In the extraction of features, it is shown that a shallow CNN outperforms two other deep CNNs with both the real and synthetic cohorts, suggesting that echocardiographic features are optimally captured by a reduced number of CNN layers. The proposed architecture, working with the shallow CNN, reaches an accuracy substantially superior to previously reported methods, providing an average reduction of the mean squared error from 0.31 (state-of-the-art) to 0.09 The automatic analysis of ultrasound sequences can substantially improve the efficiency of clinical diagnosis. This article presents an attempt to automate the challenging task of measuring the vascular diameter of the fetal abdominal aorta from ultrasound images. We propose a neural network architecture consisting of three blocks: a convolutional neural network (CNN) for the extraction of imaging features, a convolution gated recurrent unit (C-GRU) for exploiting the temporal redundancy of the signal, and a regularized loss function, called  Fetal ultrasound (US) imaging plays a fundamental role in the monitoring of fetal growth during pregnancy and in the measurement of the fetus well-being. Growth monitoring is becoming increasingly important since there is epidemiological evidence that abnormal birth weight is associated with an increased predisposition to diseases related to cardiovascular risk  in young and adults , 2.Atherosclerosis and cardiovascular disease in the adult population is linked to an increase of stiffness and thickness of major vessels. Similarly, accruing evidence suggests that the abnormal endothelization of major vessels during intra-uterine growth is linked to the same pathophysiological mechanisms as in adults, and even that fetuses born with endothelial damage have higher risks of adverse events in adulthood –6. IntraThe intima-media thickness (IMT) and the stiffness of the abdominal aorta by means of ultrasound examination are the most promising non-invasive biomarkers of adverse cardiovascular remodeling in fetuses and newborns , 6–12. FObtaining reliable clinical metrics in IUGR is thus critically based on the accurate estimation of the diameter of the aorta over time. However, the poor signal to noise ratio of US data and the fetal movement makes the acquisition of a clear and stable US video challenging. Moreover, the measurements rely either on visual assessment at bedside during a patient examination, or on tedious, error-prone and operator-dependent review of the data and manual tracing at a later time. Very few attempts toward automated assessment have been presented , 15, allThe main contributions of the proposed method are as follows. First, we show that a shallow CNN is able to learn imaging features better than two other deep alternatives and outperforms classical methods as level-set for fetal abdominal aorta diameter prediction. Second, we add to the CNN a convolution gated recurrent unit (C-GRU)  for explThe interest in measuring the diameter and intima-media thickness (IMT) of major vessels has stemmed from its importance as a biomarker of hypertension damage and atherosclerosis in adults. Typically, these vascular biomarkers are assessed on the carotid or brachial arteries by identifying its lumen and the different layers of its wall on high-resolution US images, or the scaling factor that explain its changes . The impIn the prenatal setting, the lower image quality, due to the need of imaging deeper in the mother’s womb and by the movement of the fetus, makes the measurement of vascular biomarkers, although measured on the abdominal aorta, challenging. Methods that proved successful for adult carotid image analysis do not perform well on such data, for which only a handful of methods (semi-automatic or automatic) have been proposed, making use of classical tracing methods and mixture of Gaussian modeling of blood–lumen and media–adventitia interfaces , or on lThe solution developed in this work is inspired by recent works reported in the area of deep learning, where CNNs are outperforming classical methods in many medical tasks . The firThis study makes use of a dataset consisting of 25 ultrasound video sequences acquired during routine third-trimester pregnancy check-up from the Department of Woman and Child Health of the University Hospital of Padova . The local ethics committee approved the study and all patients gave written informed consent. The gestational age for the scans we used is 32 weeks and Fetal US data were acquired using a US machine  equipped with a 5 MHz linear array transducer, according to the guidelines in , 25, usiAfter the examination, the video of each patient was reviewed and a relevant video segment was selected for semi-automatic annotation considering its visual quality and length: All frames of the segment were processed with the algorithm described in  and thenThe 25 annotated segments in the dataset were then randomly divided into training , trying to capture the relevant appearance of patients’ data without a physics-based simulation. Images of US abdominal aorta are synthesised as illustrated in Fig. d calculated:In order to model the variability to be faced in clinical settings, each US sequence was created by first drawing the vessel lumen with average diameter Then, the vessel wall (intima-media thickness) is similarly calculated:Table t, without the explicit identification of the lumen or its walls, is described here and available in https://github.com/bionick87/DiameterNet.A solution to predict, from a sequence of ultrasound images, the diameter of the fetal abdominal aorta CyclicLoss, that better guides the learning through the redundancy between adjacent cardiac cycles.Our proposed deep learning solution consists of three main components see Fig. : a convos[t] has dimension t, with t the CNN extracts the feature maps x[t] of dimensions D is the number of maps, and Our input consists of a set of images of a sequence x[t] with an encoded representation h[t], the current state, at time t: This allows the exploitation of the temporal coherence in the data. The h[t] of the C-GRU layer is obtained by two- specific gates designed to control the information inside the unit: a reset gate, r[t], and an update gate, z[t], defined as follows:x[t] or the state r or update z).The feature maps are then processed by a C-GRU layer . The C-Gx[t], used to balance the new information represented by the feature maps x[t] derived by the current input data s[t] with the information obtained observing previous data h[t] is then passed on for updating the state All these matrices have a dimension of Under the assumption that the pulsatility of the aorta follows a periodic pattern with the cardiac cycle, the diameter of the vessel at corresponding instants of the cardiac cycle should ideally be equal. Assuming a known cardiac period CyclicLoss (CL), computed as t. y[t], and the average of all peak-to-peak detection distances define its value. Accordingly, y[t] signal divided by We call this regularization term y[t] is the target diameter at time point t.The loss to be minimized is therefore a combination of the classical mean squared error (MSE) with the CL, and the balance between the two is controlled by a constant It is worth noting that the knowledge of the period of the cardiac cycle is needed only during the training phase when the loss is being minimized. During the test phase, on an unknown image sequence, the trained network provides its estimate blind of the periodicity of the specific sequence under analysis.For our experiments, we chose AlexNet  as a feaCyclicLoss, takes the value of The loss function is optimized with the Adam algorithm , which iThe proposed solution is tested in the real datasets to evaluate the different architectural choices. In order to understand the behavior of different features extraction methods, we explored the performance of deeper network architectures, and so AlexNet was replaced by InceptionV4  and DensThe performance of each method was evaluated both with respect to the mean squared error (MSE) and to the mean absolute relative error (RE); all values are reported in Table Strikingly, we observed that deep CNNs are not able to outperform AlexNet on the dataset that was available for this work. One possible explanation was the lack of enough training data. The synthetic cohort is thus used to investigate if an unlimited source of imaging data could guide deeper CNNs to learn the required robust features.CyclicLoss and C-GRU. Results are consistent with the previous: as shown in Table The training and testing are conducted in the same manner as with the real data, and CNNs were enriched with both The regression line between predictions and ground truth reveals that estimated values are clustered around the regression line see Fig. , no mattThe deep learning (DL) architecture proposed shows excellent performance compared to traditional image analysis methods, both in accuracy and efficiency. This improvement is achieved through a combination of a shallow CNN and the exploitation of the temporal and cyclic coherence. Our results indicate that a shallow CNNs performs better than deeper CNNs such as DenseNet 121 and InceptionV4 and that this is not caused by the lack of training data.CyclicLoss is an efficient way to better guide the training of the DL solution in case of data showing some periodicity, as in cardiovascular imaging. Please note that the knowledge of the signal period is only required by the network during training, and as such it does not bring additional requirements on the input data for real clinical application. We interpret that the CyclicLoss is making the network to better extract the useful ultrasound features, and to learn to expect a periodic input and provide some periodicity in the output sequence.The exploitation of temporal coherence is what pushes the performance of the DL solution beyond current image analysis methods, reducing the MSE from Results in both the real and the synthetic datasets show that the sallow CNN outperforms two alternative deep CNNs. The synthetic dataset has a very simple set of features  without any confounding structure in the images. An ultrasound image is not as rich in features as a picture of a cat that is used in the design of deep CNNs such as InceptionV4 and Densenet121, and this may be the reason why the shallow CNN has outperformed the other two. Further experimentation is needed in order to generalize these findings.CyclicLoss for training. The other hint was the occurrence of this phenomena only with synthetic data, not with the real sequences, which indicates that this behavior originates from some feature of the idealized images. In any case, our choice for a C-GRU was motivated by two particular advantages compared to previous approaches [One surprising finding was the presence of a clustered regression line between predictions and ground truth values of diameter is shown in Fig. proaches , 23: firThis work assumes the presence of the vessel in the current field of view, and thus requires a preliminary solution to identify it will be required and may decrease the performance and throughput. Further research is thus required to evaluate how well the solution adapts to the scenario of lack of cyclic consistency during training, or when the vessel of interest can move in and out of the field of view during the acquisition, or to investigate the possibility of a concurrent estimation of the cardiac cycle and vessel diameter.


A wireless sensor network (WSN) deploys hundreds or thousands of nodes that may introduce large-scale data over time. Dealing with such an amount of collected data is a real challenge for energy-constraint sensor nodes. Therefore, numerous research works have been carried out to design efficient data clustering techniques in WSNs to eliminate the amount of redundant data before transmitting them to the sink while preserving their fundamental properties. This paper develops a new error-aware data clustering (EDC) technique at the cluster-heads (CHs) for in-network data reduction. The proposed EDC consists of three adaptive modules that allow users to choose the module that suits their requirements and the quality of the data. The histogram-based data clustering (HDC) module groups temporal correlated data into clusters and eliminates correlated data from each cluster. Recursive outlier detection and smoothing (RODS) with HDC module provides error-aware data clustering, which detects random outliers using temporal correlation of data to maintain data reduction errors within a predefined threshold. Verification of RODS (V-RODS) with HDC module detects not only random outliers but also frequent outliers simultaneously based on both the temporal and spatial correlations of the data. The simulation results show that the proposed EDC is computationally cheap, able to reduce a significant amount of redundant data with minimum error, and provides efficient error-aware data clustering solutions for remote monitoring environmental applications. Wireless sensor networks (WSNs) have been utilized for various applications such as facility monitoring, environmental monitoring, and military surveillance. Typically, these applications deploy plenty of sensor nodes, which are capable of communicating among themselves and to a base-station or external sink in WSNs . The senThe transmission of a high volume of time-series data from a source sensor to the sink is inefficient for energy-constraint sensor nodes . In addiData clustering is mostly utilized to reduce correlated data for achieving energy conservation in WSNs ,7,8,9. IIn this paper, a new error-aware data clustering (EDC) technique has been introduced and incorporated at the CHs in WSNs. The EDC is divided into three modules. The first module is histogram-based data clustering (HDC), which aggregates similar data into clusters based on the temporal correlation and provides a systematic data clustering for reducing data redundancy. The second module is recursive outlier detection and smoothing (RODS) with HDC module, which provides the error-bound guaranteed data clustering. This module is capable of detecting random outliers (sudden changes and those that lie outside of the regular data pattern) based on the temporal correlation and replaces them by the normal data. The final module is verification of RODS (V-RODS) with HDC module, which detects not only the random outliers but also frequent outliers (frequent changes and those that lie outside of the regular data pattern of the neighbour nodes) simultaneously. This module considers both the temporal and spatial correlations to detect outliers and replace the outliers with the normal data in order to provide more robust error-aware data clustering. The performance of the proposed EDC has been simulated using real-world sensor data.The rest of the paper is organized as follows: Data clustering has been considered as a useful technique, especially for applications that require scalability to handle a large amount of data collected from the sensor nodes. It also supports data aggregation by removing data redundancy before transmitting them to the sink to gain energy conservation. Recently, in ,8,29,30,Feature extraction-based technique transforms the original data into a set of data with a reduced number of variables, which contain the most discriminatory information. Usually, this technique is used to reduce data dimensionality by eliminating redundant or irrelevant information. Several feature extraction-based techniques have been explored in ,32,33,34Model-based techniques perform data prediction on the collected data and generate a model based on the historical data to reduce the volume of data. These techniques predict the actual reading at the source sensor based on historical recordings and compare predicted reading with the actual reading. If the error between the predicted and actual readings is less than the predefined threshold error, the predicted reading will not be sent to the CHs or sink and vice-versa. Some of the most recent techniques in ,39,40,41Feature selection-based technique is a process to select independent, informative and discriminating features  for preIn , the autApart from these, there are some other similar partitional clustering techniques in the literature, including K-medoids , and parFurthermore, a hierarchical clustering technique forms a hierarchy or tree of the clusters. There are two types of hierarchical processes: (i) Agglomerative and (ii) divisive. In the agglomerative procedure, a bottom-up strategy is implemented that initially considers data points into a group based on the temporal correlation of the data then the clustering process progresses upwards successively. Similarly, the top-down approach is a divisive process that initially collects all the data points into one group. Then, the algorithm progresses recursively based on similarity measure among the data points to partition the data into the groups. Balanced iterative reducing and clustering utilizing hierarchies (BIRCH)  is anothAnother technique named histogram-based data clustering has been incorporated in different field of applications ,51,52 toHowever, the main constraint of the partitioning data clustering is the determination of the number of clusters without any prior knowledge about the data. Thereby, the performance of many existing partitioning data clustering techniques are highly affected by the quality of data. The inconsistent, irregular and noisy, data can change the range of a data stream and influence the clustering accuracy. Most of the existing techniques only focus on clustering data that are falling within the user-defined threshold value instead of repairing the data, which are outbound of the predefined threshold. Moreover, the current partitional clustering algorithms ,16,46 maThe basic concepts and definitions utilized in this work are provided in this section. The key notations used in this paper are presented in Definition 1.(A Time-Series): A time-series is a set of numerical values that indicates the temporal characteristics of sensor node i : The range Equation :Definition 3.(Mean): Mean is a central value of a collection of data in a distribution . For exaEquation :where Definition 4.(Median): The median is the middle or central value of a collection of arranged data . If the where n is the number of possible arranged elements or values of a given distribution.Definition 5.(Standard Deviation): The standard deviation is a measure that is used to quantify the amount of variation or dispersion of a set of data values . It is aDefinition 6.(Data Redundancy): Redundancy is the provision of additional or duplicate resources, which can produce similar results. Usually, improving the consistency of observations provided by WSNs is to increase the redundancy, by either obtaining observations for a specific location from multiple neighbouring sensor nodes in a particular time , or by obtaining several observations for a specific location from the same sensor node over time  .Definition 7.: A set of data streams D is collected from the m sensor nodes hence, Definition 8.: A temporal data cluster A new error-bound guaranteed data clustering technique has been proposed to reduce the data redundancy by keeping the data reduction error within the user-defined threshold. This technique is mainly divided into three adaptive modules, namely histogram-based data clustering (HDC), recursive outlier detection and smoothing (RODS) with HDC, and verification of RODS (V-RODS) with HDC. The users are allowed to choose any of these modules for performing data clustering based on their requirements and quality of data. The proposed EDC technique has been implemented at each cluster-head (CH) of WSNs and data clustering is performed simultaneously to achieve in-network data reduction effectively. An example of a WSN scenario has been presented in m and n are the total number of sensor nodes of a cluster and the total number of readings, respectively.The sensor nodes with limited computational capacity and power supply are designed to be responsible for sampling the application surroundings and transmitting the sampled sensor data to their CH. However, we assume that CH is equipped with the powerful hardware capabilities to aggregate the data from multiple sensor nodes. In WSNs, each CH in a cluster collect the sensor readings from sensor nodes in a constant time interval Equation :(6)X⌢m×nD indicates "data matrix". Each collected reading of the sensor node i at time instant i is the spatial location identifier and t is the discrete-time identifier.During the collection of data, missing values can occur in periodic WSNs due to a variety of causes such as malfunctioning of sensor nodes, communication failure, interference, and unsynchronized sampling of the sensors. The missing values in a dataset could be appeared as: ‘Equation ) value oEquation :(7)Xit=Xm is the number of spatial locations, which is equal to the number of sensor nodes . Consequently, all sensor nodes are assumed to be synchronized according to the discrete-time model  where m accuracy . Furtherica2Dot)  measuresto noise , therebyThe HDC is a partitional-based data clustering technique broadly used in data mining to discretize continuous time-series variables ,52,59. AAlgorithm 1: Histogram-based data clustering and aggregationk is defined as an individual cluster) is an important parameter that is defined initially for HDC using two different rules, namely the square-root rule [W ::K=1,2,3,W to the previous term. For example, the difference between any two adjacent edges is W. The recursive definition is therefore as in Equation ak=aIn this phase, we determine the central value of each cluster using two different prototypes including averaging and medoids as well as computing the absolute deviation error of each data point of each cluster. Here, we consider two clusters of datasets as an example to calculate central values and absolute deviation errors. The clusters of datasets can be expressed as quations .The absolute deviation error (ADE) is a distance between a particular data point and the central value, which can be calculated for each data point of each cluster as in Equation :(11)ADEiThe mean absolute deviation of an individual cluster, as well as all clusters of a data stream, are computed as in Equations  and 13)13), respThe main advantage of the HDC technique is its ability to perform data clustering in a systematic way. There is no random initialization to determine the initial centroids for the number of clusters of a dataset as like existing data clustering techniques in ,46. HoweW. As a result, the data proximity may be reduced, the central values of the clusters may be shifted, and the deviation error may be increased. Therefore, in this paper, the RODS technique has been introduced with HDC to handle outliers to facilitate the process of determining the number of clusters.In WSNs, outliers can be defined as the sensor readings that significantly deviate from the normal pattern of the sensed dataset . The outThe main purpose of introducing the RODS with HDC is to consider noisy data during clustering in order to achieve error-aware data reduction. The proposed RODS processes the data stream according to Algorithm 2 for detecting and smoothing the outlier data points that exist in the data stream recursively. The RODS can be divided into two major phases, including initialization and recursion. In the initialization phase, the noisy data stream is detected and re-scaled by the standard score (Algorithm 2: Recursive outlier detection and smoothing (RODS) with HDCIn the RODS technique, a data stream Equation  and the Equation :(14)ifΔdThe raw sensor data stream Equation :(15)ξil=u indicates “updated data stream” is computed; (iv) it is assigned to In this phase, the maximum absolute standard score the mean . AfterwaThe RODS with HDC provides an error-bound guaranteed data clustering facility to reduce in-network data redundancy for the periodical sensing data in WSNs. However, the RODS detects outliers only by examining an individual data stream of a sensor node and performed outlier detection based on temporal correlation . TemporaThe V-RODS uses a modified standard score method where mean value is replaced with the median value of an outlier in data stream. Moreover, it considers the data of neighbouring nodes in a CH and utilizes the spatial correlation of the sensing data streams instead of only the temporal correlation of that particular data stream. Hence, the V-RODS in Algorithm 3 is able to detect and replace both types of outliers of a data stream more precisely. After detecting noisy data stream based on the Equation in , the V-RAlgorithm 3: Verification of RODS (V-RODS) with HDCIn this phase, initially outliers existing data stream Equation :(16)ξ⇀ilThen, an outlier of the Equation : (17)X^iThe absolute deviation In the recursion phase, the absolute deviation Equation :(18)ifDiIf the former outlier detection is examined as false detection, then we label the false outlier detected data stream Equation :(19)X⌣i=After de-standardization, the data points of the de-standardized data stream are replaced with the n is the number of observations of the stated phenomenon. The EDC is executed by the multiple CHs running simultaneously where all CHs are connected with a sink node, either in direct or multi-hop connection. For simplicity, each CH in each cluster is assumed to have equal number of sensor nodes connected to it. The proposed EDC has been implemented at CH to reduce the temporal correlated data points that are generated from the connected sensor nodes in each cluster. For example, The Intel Berkeley Research Lab (IBRL) dataset  has beenOur proposed EDC technique has been implemented in six cluster-based structures, namely C1–C6. Each cluster consists of eight sensor nodes, and thereby a total of 48 out of 54 sensors are chosen to evaluate the performance of the proposed technique. In our work, the simulations on IBRL dataset have been carried out on only the temperature data from 28 February to 8 March (10 days) collected by 48 sensor nodes. The collected data are in a matrix form at the CHs and all CHs contain a total of 1,382,400 measurements where each CH has 230,400 measurements. The missing values and invalid readings in the observations are replaced by the median value of the collected observations during data preprocessing stage for the continuity. To be realistic, measurements of each CH are segmented into 240 segments or time-interval where an individual segment has 960 measurements. The measurements of each segment are used as an input to our proposed algorithms to evaluate the performance of the proposed technique for each time-interval. Two different rules, namely square-root and Sturge’s, have been considered to specify the number of partitions or clusters for input measurements.Apart from this, the temperature data in the real world at two consecutive time points have a high degree of similarity, as mentioned in . This tyMean absolute deviation (MAD) measures the average deviation of each data point in a data stream from its central value. It describes the variability of a data stream and can be formulated as in Equation :(20)MADMean absolute percent error (MAPE) measures the mean absolute percentage deviation between each data point and the central value, which can be expressed as in Equation :(21)MAPAccuracy measures the retained originality of the real data after applying our proposed technique for data reduction. It is calculated by subtracting the mean absolute percentage error of a given dataset from the 100 percent and is defined as in Equation :(22)AccData reduction percentage (DRP) measures the difference between the original data Equation :(23)DRPThe performance evaluation of our proposed EDC technique has been accomplished using several performance metrics. The metrics are mainly mean absolute deviation, mean absolute percent error, accuracy, and data reduction percentage. The listed performance metrics are defined as follows:Declaration of predefined number of clusters K.Initialization of number of K randomly as the initial cluster centroids.Assignment of data points to their closest cluster centroid according to the Euclidean distance function.Computation of the centroids using the mean or median of all data points in each cluster.Repetition of steps 2–4 until the same data points are assigned to each cluster in consecutive rounds.The proposed EDC technique presented in In terms of in-network data reduction, a large number of data has been chosen as mentioned earlier, which were used to evaluate the reduction performance of the conventional and proposed techniques. It is presumed that the DRP is highly influenced by the variation of the number of clusters K values. Whereas, the data reduction accuracy is highly affected by regulating the K values in order to maximize the DRP. Thereby, two different K values were set based on two different rules such as square-root (K = 11) and Sturge’s (K = 8) for all evaluated techniques. Therefore, two different data reduction percentages were observed for two different rules. DRP values for all techniques are computed based on Equation  and plotIn this section, the HDC technique has been evaluated based on different criteria such as DRP, MAD and accuracy in order to provide in-network data reduction. The cluster C1 consisting of eight sensor nodes has been taken into account for identifying the factors that affect the data reduction accuracy of the proposed technique. Impact of the number of clusters on data reduction performance has been illustrated in e indicates “error”. This threshold value has been reported in [In orted in  for enviIn Indeed, it is claimed that accuracy is influenced not only by the number of K values but also by some other factors, especially in the case of the conventional techniques. In particular, the quality of the collected data can be one of those critical factors that can affect accuracy as well as the MAD. The following sections deal with the outcome of the investigation on the different quality of data collected from different sensor nodes.In this section, we choose 100 (from 140 to 240) segments of data from “sensor node 9” in C1 for simplicity, where the total number of data samples is 12,000. The data reduction performance is evaluated based on Sturge’s rule (K = 8). The selected samples are assumed as normal real data due to the closeness among their consecutive readings and because the overall data trend maintains stability over time. Afterwards, the proposed technique is applied to 12,000 real data samples, reduced them to 800 data samples and compared the performance of reduced data with normal real data.In this section, the same number of data samples are taken at the same time period as Hence, the data collection from faulty sensor nodes is considered as another critical factor, and will be investigated in this paper. The investigation will be carried out on the noisy data to improve the overall accuracy with the minimum deviation errors in the subsequent sections.This section discusses the performance of error-bound guaranteed proposed RODS with HDC-based technique based on two different rules. According to Equation , the datHowever, the RODS with HDC-based B-means prototype generates a slight higher deviation error than the B-medoids during a certain period, but it still remains within the predefined threshold value. This increment of MAD (B-means) can be occurred by the remaining outliers because the mean value is highly affected by the outliers .On the other hand, the accuracy of the proposed RODS with HDC-based technique was compared with the conventional techniques and shown in This section highlights the data reduction performance between the original and reduced data using the proposed RODS with HDC-based technique for the normal and noisy sensor data. The selected data streams for these two types of data and other selected parameters are kept the same as In In contrast, the reduced data stream obtained from the proposed RODS with HDC-based technique as depicted in However, two more uncovered factors are yet to be addressed. The first one is the reduced data of the RODS with HDC-based B-means prototype, which deviated from the normal data pattern of the real data. This deviation proves that the reduced data stream of the mean prototype is still affected by the outliers. Secondly, both the prototypes of the proposed RODS with HDC-based technique detect false outliers that can be discovered from This section discusses the performance of proposed V-RODS with HDC-based technique during severe noisy data collection that was extracted from a faulty “sensor node 6”. This section shows the outcome comparison of the MAD and accuracy between the error-bounded RODS with HDC and the false outliers handling V-RODS with HDC-based technique.The MAD was kept below According to the obtained MAD of the V-RODS with HDC-based technique, the accuracy was computed and presented in This section discusses the changes of MAD with respect to the DRP variation, which can be seen from However, the error-bound guaranteed proposed RODS with HDC and V-RODS with HDC techniques have proved the simultaneous improvement for both the MAD and DRP while eliminating the redundant data from the data streams. The statistical properties of the original, outlier-free and redundancy reduced data are illustrated in this section to evaluate the performance of our proposed technique. Moreover, it can be seen that the minimum and maximum data of the distribution are far away from The average results of all segments collected from C1  are computed based on different statistical parameters for all evaluated techniques as well as real data and represented in The performance evaluation of the proposed technique was carried out for all the CHs, which is highlighted in The mean absolute deviation of the original data was considered 0 as there was no data reduction and error. However, the MAD was introduced when the data reduction was achieved, which consequently increased the DRP. The MAD of all CHs was computed for all selected techniques by averaging of MAD obtained from each CH of all segments and then plotted in In On the other hand, the obtained accuracy for all techniques has been plotted in The error-bound guaranteed RODS with HDC technique retains more than 99% originality, which is almost equal to the obtained results of our third proposed V-RODS with HDC-based technique. The main reason is the absence of frequent outliers in the rest of the CHs over time.M is the number of sensors, and N is the number of data points. In contrast, the evaluated K-means algorithm is required higher than the proposed EDC as it is In this section, two types of complexity, including space and computational, have been analyzed. The estimated space complexity of EDC technique is N is the number of data points in each cluster, and K is the number of clusters. In the second phase, the RODS is implemented for detecting and smoothing outliers (if any) in the data stream before performing data clustering. The cost for RODS is d is the computational cost for detecting outliers and it is I is the number of iteration required for convergence.In the proposed technique, the computational complexity has computed in three phases individually. In the first phase, the complexity is required for data clustering, which can be estimated based on Algorithm 1. The cost is The proposed technique consists of three modules and the main advantage is that the user can select any of the three modules based on the user’s requirement and data quality. The parameters, namely MAD, accuracy and DRP, vary with the changes of the K value. Hence, the selection of the K value can be made based on the desired level of any of these parameters. The main claims of the proposed work are summarized as follows:Firstly, the investigation on the performance of the HDC-based technique was carried out by varying the number of clusters of the K value. At this stage, the MAD increases when the K value decreases and vice-versa. Based on our investigation, the HDC-based technique performs better than conventional techniques in Secondly, the RODS with the HDC technique has been introduced to provide error-aware data clustering by handling the noisy data. One of the most important benefits of this technique is to provide error-bound guaranteed redundancy reduction by maintaining the MAD within the predefined threshold error even under severe noisy data. Furthermore, in our prototypes (B-means and B-medoids), the MAD values are always maintained below the predefined threshold error as indicated by Thirdly, our proposed technique V-RODS with HDC can detect the false outlier and deviation of the reduced data in Finally, the complexity of our proposed three algorithms is analyzed in two different aspects and compared with the most adopted conventional algorithm . In the Our findings and analysis confirm the fact that data clustering is very useful for reducing in-network data transmission for energy conservation in large-scale periodic WSNs. The EDC was proposed to achieve error-bound guaranteed data clustering for in-network data reduction, hence making it useful for large-scale data generated applications in WSNs.Our HDC-based technique provides a systematic data clustering to minimize data redundancy. It does not require any random initialization for determining the cluster centroids and it converges the clusters without iteration. One of the most significant contributions of this paper is the development of error-aware data clustering (RODS with HDC) technique. In this technique, the data reduction error (MAD) is maintained below the predefined threshold value by diagnosing the random outliers instead of decreasing the DRP. The implementation of V-RODS with HDC on the other hand contributes to the error-bound guaranteed data clustering for minimizing the in-network redundant data in the case of both random and frequent outliers.Apart from this, the simulation results of our proposed EDC technique have proven that the data reduction error (MAD) is maintained below the predefined threshold over time. Moreover, it decreases further when the DRP increases. Furthermore, the proposed EDC diagnoses outliers correctly and reduces the data redundancy effectively without affecting the original properties of the data. In addition, the complexity analysis of our proposed EDC shows that both space and computational complexity are lower than the complexity of conventional techniques.In the future, an extensive performance evaluation of the V-RODS module with HDC will be tested on real-world data that are highly inconsistent, messy and noisy. Recent WSNs usually monitor multivariate data such as temperature, humidity, light and voltage. However, developing a data clustering technique for multivariate data is more challenging than for univariate data. Thus, we intend to conduct further study on multivariate data. Moreover, the energy-efficiency and latency of the proposed EDC technique will be analyzed in the extension of this paper.