Optical sensing has attracted more and more attention in recent years with the advance in planar waveguide fabrication processes. The photon, as a carrier of information in sensing areas, could have a better performance than electrons. We propose a novel end-to-end ring cavity to fabricate sensitive units of a strain sensor. We then propose a method of combining a flexible substrate with an end-to-end semiconductor nanowire ring cavity to fabricate novel strain sensors. We used a tuning resonant wavelength detected by a homebuilt excitation and detection system to measure applied strain. The resonant wavelength of the strain gauge was red-shift and linear tuned with increasing strain. The gauge factor was about 50, calculated through experiments and theory, and Q was 1938, with structural parameters L = 70 µm and d = 1 µm. The high sensitivity makes it possible to measure micro deformation more accurately. End-to-end coupling active nanowire waveguides eliminate the shortcomings of side by side coupling structures, which have the phasing shift with no minor optical density loss. This resonator in flexible substrates could be used not only as on-chip strain sensors for micro or nano deformation detecting but also as tunable light sources for photonic integrated circuits. Strain detection plays an important role in modern society, including for industrial fabrication, safety detection, scientific research, and human movement detection ,2,3. TheThe flexible strain sensor based on PDMS (polydimethylsiloxane) and semiconductor nanomaterials is an emerging technology that has huge application prospects due to its flexibility and ease of attachment to surfaces. A transfer-and-bond fabrication method  and a saIn this work, a photonic integrated circuit minor deformation sensor was fabricated by combining an end-to-end coupling CdS (Cadmium sulfide) based resonator cavity and a PDMS flexible and transparent substrate. Semiconductor nanowire has good mechanical and optical properties, and therefore been used here as a critical sensing element. Manipulating passive optical nanowires to function as different components such as a filter or resonator, etc. has been a mature method. Here, a CdS active semiconductor nanowire was manipulated to be an end-to-end resonator cavity through a fiber tip fixed on a precession stage. Then the manipulated cavity on a flexible substrate was put into a Paryline deposition system to package the sensing element. The fabricated sensor was put under a home-built excitation and detection system to test the performance. The detected results show a novel strain sensor based on the optical cavity.CdS was purchased from Sichuan Xinlong Tellurium Technology Development Co., LTD. .The PDMS substrate was fabricated by mixing the PDMS solution and hardening agent in a ratio of 10:1, then stirring the mixed solution for 8 min. The mixed solution after stirring had bubbles, which can cause harm to the mechanical properties. We placed the solution in a vacuum tank for some time to remove the bubbles from the solution. We then poured the solution into the mold and heated it to solidify. We used the self-made temperature and humidity sensor to control the temperature and humidity of the experimental environment .The collection and detection system was built based on an Olympus BX53 microscope . A Thorlabs  made working distance, 50 × 0.65 N.A. objective lens was used. The CCD (Charge Coupled Device) camera was produced by Princeton Instruments , the model used was Pixis, 2K and a spectrometer with an 1800 grooves/cm grating was used.The CdS nanowire was fabricated using the well-known method of chemical vapor deposition , which pAfter the fabrication process of nanowire active waveguide and PDMS substrate was finished, the nanowire was grown directly on a silicon substrate, and a PDMS substrate showed good mechanical properties. The nanowire could be transferred to other substrates freely through a centrifuge in solution and then subsided. In practice, when the PDMS is stretched, the shape longitudinal variables in the middle of the resonator are larger than those on both sides. Strain will cause the two ends to sag inward, so we chose the structure with a certain outward angle in the initial state to prevent damage to the nanowire (The inward sag angle will cause nanowire fracture and other problems). Moreover, the shape of the nanowire also prevented the coupling efficiency from dropping sharply due to the change of the angle. When the strain disappeared, the PDMS substrates returned to their initial shape. Because nanowires were embedded in PDMS substrates, the nanowire also returned to its initial shape as the PDMS substrate recovered. Therefore, the sensor is reversible and reusable.For semiconductor materials with direct bandgap, the material absorbed photon energy There are two main factors that cause the tuning of a wavelength: one is the change of loop cavity length, the other is the change of the nanowire refractive index. The resonant wavelength of the nanowire microring cavity can be calculated by using the formula: λ = (L/m)n. The effect of change in the nanowire waveguide cross-section dimensions caused by the strain is so small that it was neglected. Therefore, the change of the wavelength is given by:The tuning wavelength and sensitivity of the sensor could be calculated by using a change of the cavity length and nanowire refractive index with different strains applied.sub =~1000hwav, the rigidity of the substrate is much larger than the nanowire waveguide, the relationship between them is Dsub=~109Dwav [The horizontal length (Δnn)When light is propagating in the direction perpendicular to the strain direction, the change of refractive index is :(4)(Δnn)Therefore, the change of cavity length in theory is:The nanowire ring cavity sensitive unit with high sensitivity is high enough to make the sensor sense the micro or nano deformation more accurately than the previous sensor.The coupling geometry of the nanowire resonator is end-to-end, and the narrowest air gap of two end facets is about 30 nm. There is a coupling angle of the two end facets that could be defined as the deviation angle between two normal nanowire ends. The nanowire coupling structure has a small calculation dimension and the finite-difference time-domain is a proper numerical method used to obtain highly accurate simulation results that could guide experimental parameter design and explain experimental phenomena. By using the FDTD (finite-difference time-domain) method, the coupling efficiency of light transfer between two nanowires with a deviation angle and air gap could be obtained . Two cylIn the experiment, the semiconductor nanowire was cleaved by using a fiber tip, and the facet after incision could be considered as smooth. The diameter of nanowire was 1 µm. Because the diameters of the two ends of nanowires were the same, the light conduction mode between the two ends could match well. Thus, the ring nanowire cavity had a perfect ability to limit light, meaning the coupling efficiency between the two ends was higher . But a bWhen we finished packaging the resonant cavity into a PDMS substrate, a homebuilt photoluminescence spectroscopy collection and detection system was built, based on an Olympus BX53 microscope. This detection system is shown in We observed the single nanowire placed on the surface of the silicon wafer under the microscope with white light illumination, as shown in After finishing the packaging of the resonant cavity in a PDMS substrate, we put the fabricated sensor on the homebuilt testing system to verify the performance. As shown in The nanowire packaged in PDMS is approximately a circular shape deformed with substrate, as the diagram illustrates in Two triaxial micrometer precession moving stages were used to apply strain. Two ends of the sensor were fixed at two triaxial micrometer precession moving stages, respectively. The stretching percentage ε was defined by the elongation length of the PDMS substrate with packaging and an end-to-end coupling structure resonator cavity. Before measuring the response, we pre-pulled the sensor, and then defined the walking distance as the variable. The strain of nanowires was very small , and this tiny deformation can be tuned to the spectrum without much influence on the overall shape of nanowires. The photoluminescence wavelength of the CdS nanowire under excitation was related to the laser power, so the fixed pumping power and constant position of the pumping spot in the experiment avoided the wavelength tuning induced by the carrier density effect. The testing schematic diagram is shown in After we measured the peak shift of the spectra to the walking distance, we obtained the following conclusion: the stretching percentage and spectral drift is a linear relationship, as shown in 11 and p12 in this equation are stress–optic coefficients, and they are 0.144 and 0.066, respectively [The theoretical sensitivity can be calculated by the parameters of the CdS nanowire ring cavity, using Equations (1)–(4). pectively . The PoiThe shape of the loop nanowire is not exactly circular, which could influence the resonate wavelength and cause differences between the experimental sensitivity and theoretical sensitivity calculation. In addition, the uncertain deviation angle and the uncertain shape change of the ring cavity will also cause differences between the theory and experiment.The nanowire resonator provides a way to measure micro deformation more accurately. The end-to-end coupling active nanowire waveguide eliminates the phasing shift and not the minor optical density loss of side by side coupling structure. This resonator in flexible substrates could be used not only as on-chip strain sensors for micro or nano deformation detecting, but also as tunable light sources for photonic integrated circuits.In this paper, an end-to-end coupling structure resonator cavity combined with the PDMS flexible transparent substrate was fabricated to sense micro outside deformation. The end-to-end coupling structure easily achieved a stable efficiency compared to the micro loop resonator, whose coupling efficiency was not stable, and changed with the length of the coupling area and the angle at which the two ends of a nanowire waveguide cross. A finite difference time domain calculation method was used to investigate the coupling efficiency of an end-to-end coupling at different coupling structures. There is a high coupling efficiency when the two end faces have a small interspace and cross angle. The simulation result ensured the technical feasibility of fabricating end-to-end CdS nanowire resonators. The experimental results verified the simulation results: There are significant resonator peaks in the recorded spectra. In the stretching experiment, the resonator peak was sensitive to the applied strain when the calculated strain gauge factor is ~50. This provides a new way to fabricate an on-chip strain sensor.


Due to its significance in astrobiology, assessing the amount and state of liquid water present on Mars today has become one of the drivers of its exploration. Subglacial water was identified by the Mars Advanced Radar for Subsurface and Ionosphere Sounding (MARSIS) aboard the European Space Agency spacecraft Mars Express through the analysis of echoes, coming from a depth of about 1.5 km, which were stronger than surface echoes. The cause of this anomalous characteristic is the high relative permittivity of water-bearing materials, resulting in a high reflection coefficient. A determining factor in the occurrence of such strong echoes is the low attenuation of the MARSIS radar pulse in cold water ice, the main constituent of the Martian polar caps. The present analysis clarifies that the conditions causing exceptionally strong subsurface echoes occur solely in the Martian polar caps, and that the detection of subsurface water under a predominantly rocky surface layer using radar sounding will require thorough electromagnetic modeling, complicated by the lack of knowledge of many subsurface physical parameters. Higher-frequency radar sounders such as SHARAD cannot penetrate deep enough to detect basal echoes over the thickest part of the polar caps. Alternative methods such as rover-borne Ground Penetrating Radar and time-domain electromagnetic sounding are not capable of providing global coverage. MARSIS observations over the Martian polar caps have been limited by the need to downlink data before on-board processing, but their number will increase in coming years. The Chinese mission to Mars that is to be launched in 2020, Tianwen-1, will carry a subsurface sounding radar operating at frequencies that are close to those of MARSIS, and the expected signal-to-noise ratio of subsurface detection will likely be sufficient for identifying anomalously bright subsurface reflectors. The search for subsurface water through radar sounding is thus far from being concluded. Most of the ice present on Mars today is located in the polar regions, which are covered by ice sheets extending for millions of square kilometers and possessing a thickness of thousands of meters. These deposits are constituted by several geologic units differing in origin, composition, and age. The most recent and dynamic ones are the seasonal deposits of COne meter . The resne meter , while tne meter , and abone meter . The SHAthe SPLD . There athe SPLD  seems toIce is also present in mid-latitudes landforms such as lineated valley fills and lobate debris aprons, which are thought to be the remains of glaciers from a recent ice age . In addiThe total volume of ice currently present at Mars has been estimated to be equivalent to a layer of 34 m over the entire surface of the planet   occurrence of liquid water at the surface of Mars was first reported in , in whicdeposits . The forussed in , morpholHigh-resolution imaging has recently revealed the presence of the so-called recurring slope lineae, which are narrow (a few meters wide), dark streaks occurring on Sun-facing steep slopes close to the equator. Appearing and gradually growing during warm seasons, they fade in cold seasons , and havEvidence for surface liquid water in the current Martian climate is inconclusive, but water could be present underground below the cryosphere. In the early warm Mars, water would naturally percolate into the ground until it reached an impermeable layer, thus forming an aquifer similarly to what happens on Earth. As the mean surface temperature decreased over the ages, a global cryosphere would form, which would effectively seal groundwater in place . There iRecently, evidence for subglacial liquid water beneath the South polar cap has been obtained through orbital radar sounding . Quantit present . Modelin present . In spit present .Subglacial water was detected by the MARSIS  radar soAn electromagnetic wave encountering a discontinuity in the medium through which it is propagating is partially reflected, while the remainder is transmitted. In the ideal case of a plane parallel geometry in which the wave is perpendicular to the discontinuity, the partition between reflected and transmitted power is described by the reflectance  R, and the transmittance  T at normal incidence :1)R=ε1−R=ε1−(1)RG is the radar antenna gain, H the spacecraft altitude, and The equation describing the amount of power received by a radar illuminating some target is known as the radar equation. A specialized form of this equation for an orbiting radar sounder over a plane surface has been presented in :(3)Ps=Ptz is the depth of the subsurface dielectric discontinuity, f the radar frequency, z and subsurface echo delay c is the speed of light in vacuo and The term Equation  implies pression :(4)Pss=PThe identification of liquid water in a radar signal is based on its different electromagnetic response compared to ices and other geomaterials. The surface of Mars is constituted predominantly by igneous rocks , while Most materials listed in v is the volume fraction of inclusions in the mixture, Materials on the surface of Mars can consist of mixtures of different substances, as in the case of the ground ice found outside the polar caps , or the According to Equation , materiaThe identification of liquid water on Mars through radar sounding is thus based on the detection of areas of strong subsurface echoes. Indeed, in the case of  it was fSubsurface echoes can be stronger than surface echoes because of the higher relative permittivity of water-bearing materials. This can be verified computing the ratio between subsurface and surface echo power by dividing Equation  by EquatEquation  and the Equation  because ootprint . AlthougThe relative permittivities of surface and subsurface materials have to be defined to compute values of Equation . The diession in ). Once tEquation  can be cIt can be seen in material . However of Mars , and it Attenuation, as measured by the loss tangent, is the other key factor determining the relative power of surface and subsurface echoes. By setting Equation  can be is, as in .A comparison of loss tangent values in f, then attenuation in pure water ice is frequency-independent, while it increases with frequency in all other materials. A decrease of Because the dielectric constant of materials listed in Equation  includesnoted in ; this phnoted in  operatinThe discussion above is based on the implicit assumption that the surface layer is homogeneous down to the depth of the interface producing subsurface echoes. This assumption is not verified even in the case shown in Equation  , it is The search for strong basal echoes beneath the Martian polar caps is far from being complete, however. As discussed in , the smaThe Mars Express spacecraft was launched in 2003 and it is thus expected that MARSIS will continue collecting data on the Martian polar caps for no more than a few years. Fortunately, the Chinese mission to Mars to be launched in 2020, Tianwen-1, will carry the Mars Orbiter Subsurface Investigation Radar (MOSIR), which will operate in the 10–15 MHz, 15–20 MHz, and 30–50 MHz frequency ranges. The lowest band is at frequencies that are intermediate between those of MARSIS and those of SHARAD . As showIn spite of its limitations, orbital radar sounding is currently the only technique that allows a global search of subsurface water from orbit. Ground Penetrating Radars (GPR) will be carried by NASA’s Perseverance  and ChinThe exploration of the Martian subsurface is critical in the search for life on Mars . Stable


The aim of this study is to examine the extreme return spillovers among the US stock market sectors in the light of the COVID-19 outbreak. To this end, we extend the now-traditional Diebold-Yilmaz spillover index to the quantiles domain by building networks of generalized forecast error variance decomposition of a quantile vector autoregressive model specifically for extreme returns. Notably, we control for common movements by using the overall stock market index as a common factor for all sectors and uncover the effect of the COVID-19 outbreak on the dynamics of the network. The results show that the network structure and spillovers differ considerably with respect to the market state. During stable times, the network shows a nice sectoral clustering structure which, however, changes dramatically for both adverse and beneficial market conditions constituting a highly connected network structure. The pandemic period itself shows an interesting restructuring of the network as the dominant clusters become more tightly connected while the rest of the network remains well separated. The sectoral topology thus has not collapsed into a unified market during the pandemic. The abrupt outbreak of coronavirus disease 2019 (COVID-19) and its quick spread throughout the world, especially the US, in early 2020 have cast the world into an unprecedented global health crisis. Declared a pandemic by the World Health Organization (WHO) on March 11, 2020, COVID-19 has been a major factor in hundreds of thousands of deaths, led to the suffering of millions of people, and put the lives of billions across more than 193 countries at risk.1The COVID-19 outbreak is more than a health crisis, it is a catastrophic event that has led the global economy into delirium and, for this reason, will likely make it into (not only) economic history textbooks as the Great Lockdown recession. With its overwhelming magnitude, the COVID-19 outbreak represents one of the steepest few recessions since the Great Depression of the 1930s. For example, US GDP decreased by 5% from Q4 2019 to Q4 2020 due to the COVID-19 outbreak.In such an environment of heightened economic and financial uncertainty, during the extreme catastrophic event related to the COVID-19 lockdown, investors and portfolio managers are very keen to understand the transmission of shocks across US equity sectors for the sake of portfolio diversification and risk management.While several studies examine return spillovers across the US sector indices within the system, they often use mean-based measures of connectedness . A first strand of literature considers inter-country stock market linkages at the aggregate levels and reveals evidence that the spillover effects across a large set of stock markets vary under different market conditions such as bearish, normal, and bullish  bilateral interactions among an m-vector of variables, Equation  is estimEquation  is rewriH is computed as:jth variable to the variance of the forecast error of the ith variable at horizon H, ∑ is the variance matrix of the vector of errors, jth diagonal element of the Σ matrix, and ith element and 0 otherwise.The variance decomposition is conducted using orthogonal innovations while the VAR innovations are generally correlated contemporaneously. To overcome the variables ordering issue, we follow Diebold and Yilmaz  and use Each entry of the variance decomposition matrix is scaled as follows:Using the quantile GFEVD, we define various measures of connectedness at the τth conditional quantile.i at quantile τ, which is denoted by “TO”; i to all stock indices at quantile τ, which is denoted by “FROM”; The above quantile connectedness measures are estimated suing a VAR lag order of 1 (selected based on the Bayesian information criterion) and a 10-step-ahead GFEVD. To reflect the time variation in the spillover measures, a rolling-window approach is adopted . Notably, logarithmic return series are used in the analysis, and the related summary statistics and preliminary tests are reported in Table As the estimation of quantile regressions and thus also quantile connectedness can be problematic in a system of equations with cross-section correlations, we follow Ando et al.  and remoThis kind of factorization is likely to affect the network properties of the system. Following Greenwood-Nimmo et al. , we presAs a baseline case of the system topology, we inspect the network properties and connectedness at the conditional mean (estimated by OLS) and at the conditional median (estimated by LAD—the least absolute deviations estimator). Note that the former is based practically on an average effect over all possible quantiles, whereas the latter presents the results at the median, i.e. the 50th quantile. Comparison of these topologies can be seen as a first indication of quantile connectedness differences. Figures When studying the possible quantile dependence in the financial markets, be it correlations, regression, or, as in our case, spillovers, we are primarily interested in the actual existence of the quantile dependence of these measures and secondarily in the (a)symmetry of this quantile dependence. Figure The detailed inspection of the network spillover structure in the case of extreme events, as shown in Fig. We have already shown that the connectedness between the US sectors materializing in the network topology of sectoral stock returns possesses a profound non-linearity reflected in much stronger spillovers in the tails of the underlying distributions, i.e. during extreme events. To further investigate the temporal stability of such dynamics, we inspect the dependence using a sliding window of 200 trading days.Figure As the COVID-19 period seems to have imprinted itself mostly in spillovers at the distribution bulk, which might be due to a rather swift resurgence of the stock market after the monetary injection, we look closely at whether the network topology has also changed. Figures From a big picture perspective, the network has changed structurally during the COVID-19 critical period. To see whether this specific situation, likely leading to global risk aversion  and the GRA index.Overall, the relationships between global risk aversion and the spillovers are not linear. The energy and material sectors are not driven by global risk aversion. The role of fear and uncertainty in the dynamic connectedness structure of financial markets is highlighted by recent studies . Even though the origin of the pandemic can be seen as mostly exogenous, which makes it unique compared to the other recent crises, its spread is inherent and endogenous to the ever-more interconnected and intertwined global economy. Still, its unambiguous distinction from the other economic critical events makes it attractive for a detailed analysis of its effects on the functioning of financial markets and how these differ from more standard, economically or specifically business cycle induced crises.We provide a detailed, network-based interconnectedness study that focuses on the dynamics in tails of the distribution, i.e., during the critical periods the market is undergoing. By focusing on the sectoral structure of the US stock market, we reveal several interesting findings. Firstly, controlling for a common factor mostly affects the highest connections and spillovers. Omitting this filtration would lead to overrepresentation of the strong links in the system and not only would this bias the interpretation of the systemic structural changes, it would also misrepresent the split between systematic and idiosyncratic risk in the sectoral network leading to inefficient portfolio signals. Secondly, we show that the network structure of sectoral links is well-established with three rather tightly connected clusters, with the IT sector set as a central hub of the network. Thirdly, the overall sectoral spillovers increase markedly for extreme events and market conditions. In addition, the network topology changes its structure from well-defined clusters to an almost completely connected graph. The sector-driven dynamics of the network thus disappears during extreme market movements and the sectors tend to move together strongly. The tail dependence of the spillovers during extreme market conditions is symmetric, i.e., we do not find a significant difference between adverse and beneficial market conditions. Fourthly, the temporal connectedness is rather stable both in the bulk and the tails of the distribution, even though we find a mildly increasing trend in connectedness in the bulk and a mildly decreasing trend in the tails, suggesting a slow convergence. This is further reflected in the topology reorganization during the COVID-19 pandemic which, however, does not copy the general behaviour for the bulk versus tails network structure. We do not observe an implosion of the topology into a fully connected graph, but rather find more strongly connected original clusters, with real estate clearly detaching from the network. The financial sector also shows an interesting change in dynamics as it becomes a clear information receiver rather than a leader, as observed during the financial crisis of a decade ago, which only highlights the different roots of the last two episodes of global financial and economic turmoil. Fifthly, we find that changes in perception of market uncertainty, as reflected in global risk aversion, lead to structural changes in the sectoral network topology, both in calm and extreme market states. From the other side, the network topology changes only due to extreme events leading to changes in global risk aversion, making the relationship asymmetric.Overall, the sectoral network structure of the US stock market has several intuitive features such as well-defined clusters with logical links of similar members (sectors) and an almost collapsed structure, in the network topology sense, during critical times. Our main contribution lies in revealing that the COVID-19-induced market turmoil stands out from other critical events in the market as there is a clear market reaction and a sectoral restructuring in the network connections but the structural changes occur mostly in the bulk of the distribution rather than the tails.Our results open several interesting avenues for future research. Firstly, a study of high-frequency data would reveal the structural changes in more detail. Reactions to announcements of the pandemic spreading could be traced with higher precision. Secondly, focusing on the reaction of the connections, and the network structure in general, to monetary stimuli would reveal the efficiency of such policies. In addition, such an analysis could focus on similar policies in the preceding crises and how these decisions affected the market topology and its fragility or stability overall. As the network properties of the financial markets come to light as crucial characteristics of the system, moving from systematic to systemic risk, they remain highly relevant during the lockdown recession.Although our analysis reveals some important insights into the quantile return spillovers across sectors in the US stock market which is the largest and most appealing market for global stock investors, it disregards quantile spillovers among sectors returns in other leading stock markets in Europe and Asia. This limitation can be addressed in future studies to see whether the results revealed in the US stock market during the COVID-19 outbreak hold in other stock markets. Some studies (Kou et al.


Accurate spasticity assessment provides an objective evaluation index for the rehabilitation treatment of patients with spasticity, and the key is detecting stretch reflex onset. The surface electromyogram of patients with spasticity is prone to false peaks, and its data length is unstable. These conditions decrease signal differences before and after stretch reflex onset. Therefore, a method for detecting stretch reflex onset based on empirical mode decomposition denoising and modified sample entropy recognition is proposed in this study.The empirical mode decomposition algorithm is better than the wavelet threshold algorithm in denoising surface electromyogram signal. Without adding Gaussian white noise to the electromyogram signal, the stretch reflex onset recognition rate of the electromyogram signal before and after empirical mode decomposition denoising was increased by 56%. In particular, the recognition rate of stretch reflex onset under the optimal parameter of the modified sample entropy can reach up to 100% and the average recognition rate is 93%.The empirical mode decomposition algorithm can eliminate the baseline activity of the surface electromyogram signal before stretch reflex onset and effectively remove noise from the signal. The identification of stretch reflex onset using combined empirical mode decomposition and modified sample entropy is better than that via modified sample entropy alone, and stretch reflex onset can be accurately determined. Muscle spasticity is an intermittent or persistent involuntary excessive movement of the skeletal muscle caused by an upper motor neuron injury . SpasticThe tonic stretch reflex threshold is currently recognized as the most effective and consistent value for assessing spasticity . The strAt present, the commonly used stretch reflex onset detection method is based on experience or the standard deviation (SD) of surface EMG signal , 15. ExpSample Entropy is a method based on approximate entropy that is used to measure the complexity of time series. Sample entropy has been applied in evaluating the complexity of physiological time series and in diagnosing pathological state . Zhou etEmpirical mode decomposition is the adaptive decomposition of a signal into several intrinsic mode functions in accordance with the time-scale characteristics of data without presetting any basis function . The empGiven the pathological symptoms of patients with spasticity, abnormal overexcitement leads to discharges of motor units, and consequently poor quality of surface EMG signals. The surface EMG of patients with spasticity is prone to unconscious and involuntary false peaks, which cause a slight difference in surface EMG signal before and after the stretch reflex onset. Moreover, a false EMG peak is mixed into the mutant signal after the stretch reflex onset . TherefoR), and the correlation coefficient (P).The semi-synthetic surface EMG signals of 10 patients with spasticity were selected and compared with those of empirical mode decomposition denoising via the wavelet threshold denoising method. Among these signals, the wavelet basis selected the sym4 basis function, which is relatively similar to the surface EMG signals. Three evaluation indices were cited to compare the denoising effects of the two methods, namely, SNR, root mean square error  is the original signal, yi(t) is the signal after de-noising, and N is the number of sampling points. Cov is the covariance function, D is the variance function.Where SNR, R is smaller, the coincidence degree between the denoised and original signals is higher; when the value of P is larger, the correlation between the denoised and original signals is stronger. The semi-synthetic surface EMG signals of the 10 subjects were selected, and the evaluation indices were obtained using the two denoising methods  The empirical mode decomposition algorithm is better than the wavelet threshold algorithm in denoising surface EMG signals because it can retain useful information and effectively remove noise from surface EMG signals. 2) Gaussian white noise with different SNRs was added to the original signal, and the results showed that empirical mode decomposition exhibits good anti-noise performance and its denoising capability is helpful in the stretch reflex onset detection. The stretch reflex onset recognition rate of combined empirical mode decomposition denoising and modified sample entropy recognition is considerably higher than that of the modified sample entropy alone. The stretch reflex onset recognition rate under the optimal parameters of the modified sample entropy can reach up to 100% and the average recognition rate is 93%. In summary, the empirical mode decomposition algorithm can effectively remove noise from surface EMG signals, and the stretch reflex onset detection rate of combined empirical mode decomposition and the modified sample entropy is high. Thus, accurate and reliable stretch reflex onset detection can be realized, and a new method for the objective and accurate evaluation of spasticity is provided.The surface EMG signals of biceps brachii during the passive extension of the upper limbs were collected from patients with spasticity. The subject sat upright while the doctor passively stretched the upper limb of the affected side of the subject, allowing the upper limb to adapt to the traction speed. Sudden traction was prevented from causing tension in the subjects that might affect experimental data. At the end of the preparatory activity, the doctor collected the surface EMG signals of each subject at an appropriate uniform speed on the basis of experience, and the process was repeated four times at 2 min intervals. Reacquisition was performed after 1 day. The elbow joint was fixed with one hand, and the wrist joint was held with the other hand. The passive traction of the subject from the maximum bending angle to the maximum extension angle was completed. Spasticity is velocity-dependent; thus, the fluctuation of the stretching speed must be maintained within a certain range during the experiment.The EMG signal sensor used in the experiment was based on the hardware circuit and software design of a single-chip microcomputer STM32). The collected analog signals were A/D converted and then transmitted to the upper computer. The data sampling frequency was 1000 Hz. The positive and negative electrodes of the electrode sheet were attached to the biceps muscle at 2 cm intervals. The reference end was attached to the side of the biceps. The experimental procedure is illustrated in Fig. 2. The coA total of 25 eligible stroke survivors from the Anhui Provincial Hospital were selected for the experiment. The study was approved by the Anhui Provincial Hospital Ethics Committee, and all the participants provided informed consent. Among the subjects, 11 belonged to the modified Ashworth scale 1, 7 to the modified Ashworth scale 1+, and 7 to the modified Ashworth scale 2.Inclusion criteria: patients with upper limb flexor spasticity caused by stroke; can sit independently; an elbow flexion and extension range of at least 90°; clear consciousness, without serious cognitive and audiovisual impairment, can cooperate with the examination to complete simple instructions.Exclusion criteria: patients with central nervous system disorders, such as multiple sclerosis and other diseases that may cause spasticity of the limbs; patients with diseases that can affect the movement of the elbow joints of the upper limbs, such as fracture of the upper limbs; patients with the modified Ashworth scale grades of 0, 3, and 4.The empirical mode decomposition method proposed by N.E. Huang is based on the time-scale characteristics of data for signal decomposition without setting any basis function . The oriThe number of extreme points and zero crossings in the entire dataset should not be more than one.The mean values of the upper and lower envelopes should be zero at any point.Each intrinsic mode function should meet the following conditions :The numAny signal is composed of several intrinsic mode functions.Each intrinsic mode function can be linear or nonlinear. The numbers of local zeros and extreme points of each intrinsic mode function are the same, and the upper and lower envelopes are locally symmetric with respect to the time axis.Each signal can contain several intrinsic mode functions at any time. If the modal functions overlap with one another, then a composite signal is formed.However, actual signals are complex, and thus they do not satisfy the aforementioned conditions. Therefore, Huang made the following assumptions :Any sigs(t) are extracted. Then the upper envelope e+(t) and the lower envelope e−(t) are fitted by a cubic spline function. Lastly, the mean values of the upper and lower envelopes are calculated as follows:The empirical mode decomposition algorithm assumes that a signal is composed of several intrinsic mode functions. First, all the maximum and minimum points in the original signal The difference between the original signal and the mean envelope is calculated as follows:h1(t) meets the preceding intrinsic mode function conditions, then h1(t) can be used as the first-order intrinsic mode function. If the preceding intrinsic mode function conditions are not met, then h1(t) is used as the input and the aforementioned steps are repeated. Assume that h1k(t) meets the preceding intrinsic mode function conditions after k times. Then, h1k(t) is used as the first-order intrinsic mode function of the original signal as follows:If s(t) and the first-order intrinsic mode function can be calculated as follows:The difference between the original signal c1(t) is derived repeatedly to enable r1(t) to obtain a second-order component, i.e., c2(t). Empirical mode decomposition stops when the N-th order intrinsic mode function component cn(t) or the remaining amount rn(t) is less than a preset value, or when the residual component rn(t) is a monotonic function or a constant.x(t) as follows:Lastly, empirical mode decomposition decomposes the original signal into η (IMFij) denotes the j-th value in the i-th-order intrinsic mode function component after denoising, IMFi represents the i-th-order intrinsic mode function component that should be preprocessed, λi indicates that each intrinsic mode function is filtered with two SDs of the surface EMG signal at rest, and (∙)+ designates the positive portion.Before the stretch reflex onset surface EMG signal of patients with spasticity exhibits involuntary muscle activities and is mixed with noise during the acquisition process. Consequently, the difference in EMG signal before and after the stretch reflex onset is reduced, and the probability of the stretch reflex onset misjudgment is increased. To eliminate the baseline activity and high-frequency noise components of surface EMG signal, the surface EMG signals of the subjects with non-autonomous activities in resting state were set as the soft threshold. Inspired by the soft threshold function in , the sofIMFi after the stretch reflex onset is twice greater than that of the intrinsic mode function in resting state. Therefore, pretreatment can effectively eliminate involuntary muscle activities generated during spasticity evaluation, reduce the baseline activity and noise interference of the surface EMG signals before the stretch reflex onset, and expand signal difference before and after the stretch reflex onset.The SD of surface EMG signals is twice larger than that of baseline signals in the stretch reflex onset . The SD s(t) is calculated, and the effective components are selected in accordance with the correlation results for filtering. The specific processes are as follows:IMFi.First, the original signal is normalized, and then the signal is decomposed via empirical mode decomposition to obtain The correlation between intrinsic mode function and the original signal of each order is calculated. In accordance with the magnitude of the correlation, the effective component is found with the soft threshold of one-tenth of the maximum correlation coefficient, and the pseudo-component is eliminated.Filtering is performed on the effective component, and the signal is reconstructed after filtering to reduce the noise of the original signal.First, the correlation between each intrinsic mode function component and the original signal r is discarded, and only the number of distances less than or equal to the similarity tolerance r is retained. The similarity of vectors is based on the Heaviside function , 1 ≤ i ≤ N–m + 1.The continuous Xm(i) and Xm(j)  is defined as follows:The distance Xm(i) and Xm(j) can be calculated by providing a similar tolerance r as follows:The similarity The function m to m + 1 as follows:Similarly, the function Lastly, the modified sample entropy (mSampEn) can be defined asN, m, and r. In accordance with the empirical value  is selected on the basis of the stretch reflex onset that is set in advance. If the detection result is within the interval, then it is considered true detection (TD). If it is beyond the interval, then it is considered false detection (FD). The recognition rate is defined as:


The fifth generation (5G) cellular communication system is designed to support Time Difference of Arrival (TDOA), Round-Trip Time (RTT), and Direction of Arrival (DOA) measurements for indoor positioning. To mitigate the positioning error caused by non-line-of-sight (NLOS), existing base station selection methods identify channel conditions and only use line-of-sight (LOS) signals for positioning. However, different selected base station combination would lead to a different geometric dilution of precision (GDOP), base station selection based only on channel condition is not fully applicable for the hybrid positioning. This paper derives the GDOP for the hybrid TDOA, RTT, and DOA positioning, and proposes a GDOP-assisted base station selection method, which is based on both channel conditions and GDOP value changes. The simulation shows that using the proposed base station selection method could lead to higher positioning accuracy than base station selection based only on channel condition. In the simulation, in the side region of the scenario, where the change of selected base station combination causes a notable increment in GDOP value, the positioning accuracy improvement caused by the proposed method is greater than that in the center region.  Indoor positioning plays an important role in the location-based service, the Internet of Things (IoT), and health care ,2. As onThe major error sources of positioning measurement in wireless positioning include system noise and propagation error . The sysIdentifying the LOS/NLOS signals and selecting LOS BSs to estimate position has become a major approach to mitigate the effect of NLOS caused measurement errors on positioning results inside and outside buildings. Existing LOS/NLOS condition-based BS selection methods can be divided into coherence criterion-based methods and channel characteristics-based methods. In coherence criterion-based BS selection method, the criterion is the residual between the positioning measurement and position result estimated by the measurements is used as an indicator of the coherence to select BSs . The basNot only the measurement error of positioning signals, but also the layout of the positioning BSs and number of measurements have great effect on positioning error . The geoIn this paper, we propose a GDOP-assisted BS selection method in LOS and NLOS mixed environment. We first derive the GDOP for the hybrid TDOA, RTT, and DOA positioning. Using the derived GDOP, the proposed BS selection method is an enhancement on LOS/NLOS condition-based selection, not only a channel condition, the GDOP changes are also considered. The LOS BSs and the NLOS BSs, that have a better GDOP, would be selected for positioning. The simulation shows that the proposed method can lead to a higher positioning accuracy than the selection based only on LOS/NLOS condition.This paper is organized as follows: M BSs and obtain TDOAs with the number of M minus 1. For the model shown in M is equal to 4. The serving BS is denoted as BS 1, and the rest BSs are sorted by the distance to UE, the farthest is numbered M. Only the serving BS could receive uplink signal from UE and obtain RTT and DOA.The system model of 5G cellular system based indoor positioning is shown in In the following description, the position of UE is denoted by The real distance from UE to BS The real distance from projection of UE to BS The RTT measurement result between BS 1 and UE is expressed asi to UE, and The TDOA ranging result is expressed asThe DOA measurement from UE to BS 1 is expressed asGDOP is defined as the ratio of the accuracy limitation of a position fix to the accuracy of measurements . The GDOM BSs, the DOA measurement equations can be written asGDOP for the hybrid TDOA, RTT, and DOA positioning is derived in the following. In the indoor environment, the antennas of BSs are usually deployed under the ceiling and have same height, which lead to a high vertical dilution of precision and makes the system cannot provide reliable vertical positioning results . TherefoThe RTT measurement equations can be written asThe TDOA measurement equations can be written asTo achieve the GDOP for hybrid TDOA, RTT and DOA positioning, we first need to linearize the measurement equations. Using the first order Taylor series to approximate When there are no measurement errors, the position estimation result When the measurement errors are not equal to zero and use Let position estimation error vector For RTT measurement error For DOA measurement error For TDOA measurement errors After linearization, measurement equations in Equations (6)–(8) can be formulated as the following matrix form:In the cellular communication system, the three different type measurements, TDOA, RTT and DOA are measured independently by different reference signals and the errors of those different type measurements can be assumed as mutually independent differently zero-mean Gaussian distribution ,28. For tributed . For all elements in the error vector Standard deviations of different kind measurement error have the following statistical properties:According to the weighted least square (WLS), the solution to Equation (16) is given bySince The covariance matrix of estimated position error Therefore, according to the definitions, GDOP is defined as the ratio of positioning error to positioning signal measurement error . For theIdentify the channel condition of BSs and classify LOS BSs as the selected BSs.Estimate an initial position result.Find the unselected BS which could reduce GDOP most.Determine whether to mark the BS found in the step 3 as selected. If yes, jump back to step 2.Utilizing the GDOP derived in the above section, a BS selection method based on channel condition and GDOP changes is proposed for the hybrid positioning in this section. The proposed method is used for selecting BSs and corresponding measurements to positioning after UE has already measured RTT, DOA and TDOAs. The selection procedure consists of the following four steps:The procedure is shown as the flowchart in In the step 1, a channel characteristics-based LOS/NLOS condition identification method, as proposed in , is usedondition .In the step 2, an initial position, In the step 3, the GDOP decrement rate caused by each element in In the step 4, determine whether to add the BS, which reduce GDOP value most, into To find the threshold the data . Using lTo analyze the GDOP for the hybrid TDOA/RTT/DOA positioning under different BS combinations and to verify the effectiveness of the proposed GDOP-assisted BS selection method, we have built a simulation scenario according to the 3GPP TR 38.901 indoor office scenario , as showAt first, the GDOP distribution of the hybrid TDOA, RTT and DOA positioning is calculated and shown in In After the analysis from In the comparison of the four different BS selection combinations in According to the above analyses, to more intuitively show the change of the GDOP value in the side area of the simulation scene, we counted the GDOP value in the BS 1 serving area with different BS selection combinations, which are shown in As shown in Based on the above analysis about GDOPs, the different BS selection combinations for positioning could cause a great difference in positioning accuracy. It can be seen from this that if the channel condition-based BS selection method is used, it would greatly increase the GDOP value, which can be several tens of times at the maximum, when the serving BS is in the NLOS condition. According to the definition of GDOP, the increase in GDOP value represents the increase in the lower bound of positioning error. Therefore, the change of the GDOP value caused by BS selection is especially important for positioning.I and, as measured in [To show the effectiveness of the proposed GDOP-assisted BS selection method, we computed positioning results, which are based on different BS selection methods, in the simulation scenario with 10,000 Monte Carlo simulations. In each Monte Carlo runs, a UE test point is generated at a random position and the TDOA, RTT, and DOA measurements without errors are calculated. Then, for each BS, the channel condition is randomly determined according to the LOS probability for indoor office scenario in the 3GPP TR 38.901 . For eacsured in , in NLOSThe cumulative distribution functions (CDFs) of the positioning errors are shown in Through the analysis of GDOP in In summary, the proposed GDOP-assisted BS selection method can lead to higher positioning accuracy than the LOS/NLOS condition-based selection method. This improvement is larger in the side region of the simulation scenario and is relatively smaller in the center region.In this paper, we propose a GDOP-assisted BS selection method for the hybrid TDOA, RTT and DOA positioning in mixed LOS and NLOS environment. In order to implement the method, we first derived the GDOP for the hybrid positioning. The proposed selection method is an extension of LOS/NLOS channel condition identify and the selection method based only on channel condition . In the


In the last years, the commercial drone/unmanned aerial vehicles market has grown due to their technological performances (provided by the multiple onboard available sensors), low price, and ease of use. Being very attractive for an increasing number of applications, their presence represents a major issue for public or classified areas with a special status, because of the rising number of incidents. Our paper proposes a new approach for the drone movement detection and characterization based on the ultra-wide band (UWB) sensing system and advanced signal processing methods. This approach characterizes the movement of the drone using classical methods such as correlation, envelope detection, time-scale analysis, but also a new method, the recurrence plot analysis. The obtained results are compared in terms of movement map accuracy and required computation time in order to offer a future starting point for the drone intrusion detection. Considering their variety and the boundless applications in which they can be involved, the research interest in the drone field has grown exponentially in recent years. Nowadays, drones, also known as unmanned aerial vehicles (UAV), are widely used in both civilian and military environments representing an important data source for inspection, surveillance, mapping, and modeling applications ,2,3,4,5.The drones increasing cost-effectiveness, along with their improved capabilities, has also led to their transformation into a threat. Being suitable for offensive and intelligence, surveillance, and reconnaissance (ISR) applications, the attackers pay most attention to the low, slow and small UAV (LSS UAV) category that is difficult to detect . The US The state-of-the-art solutions on drone detection are classified in the function of technologies involved: Visual, acoustic, radio-frequency (RF), and multimodal detection . Drone detection using images or videos captured by camera sensors is based on computer vision algorithms and can be performed by traditional machine learning algorithms ,10 or deEmploying the sound produced by a flying drone, the acoustic detection eliminates the possibility of confusing birds or planes with drones , which rConsidering the increased robustness to weather and illumination conditions, the RF drone detection methods are widely used because they can ensure a larger coverage area than the previous approaches. The RF signals related techniques are classified in active (involving radar sensors) and passive surveillance techniques. Typically, between drones and their controller exists the uplink and downlink transmissions that use the industrial, scientific, and medical (ISM) frequency spectrum. The same bandwidth is used by an enormous number of Wi-Fi and Bluetooth devices that can interfere and cover the target transmission. Based on the bandwidth and modulation feature of the RF detected signal, in  it is usThe surveillance systems based on radar sensors represent a reliable solution for autonomous UAV detection. Considering the LLS UAV dimensions and the fact that they are mostly made of plastic, the radar cross-section is very small. The new approaches try to improve the detection capabilities using enhanced solutions based on the multi-static radar system , empiricConsidering the LSS UAV intrusion challenges and the different methods previously presented, this paper proposes a new approach for the UAV/drone movement detection and characterization based on ultra-wide band (UWB) sensing. This sensing system is composed of two UWB sensors and uses different signal processing methods in order to obtain an accurate detection and characterization of the movement. The experiments are carried out indoor, but the presented approach can also be extended for outdoor situations.To the best of our knowledge, no UWB sensing approach has been proposed so far for drones intrusion detection, but only for their pose estimation in cooperative mode ,30. ConsOur approach has the big advantage to sense a small dimension moving target built from heterogeneous materials  which makes it a suitable candidate for the detection of such COTS-commercial of the shelf UAVs. Another advantage is the UWB technology capability to detect the movement even in non-line-of-sight  scenarioThe paper is organized as follows: 1 and S2, are placed at the same height. The choice of this minimum number of sensors is justified by the fact that the drone movement is carried out in a horizontal or vertical plane. The experiment was performed using two PulsON 440 UWB sensors  Figure a connect1): Left-right movement, up-down movement, and forward-back movement .Correlation is a linear signal processing method that measures the degree of similarity of two signals when no other different parameters are known, such as relative position or phase change. When the two signals overlap, the correlation is maximum and it decreases as one signal is translated from the other with the time shifting lag When the translation is large enough and no similar patterns are found in the signal, no correlation can be found and its value becomes almost zero. If repetitive patterns are found in the signal, the correlation function provides secondary maxima .In this experiment, the correlation is calculated between the difference of each successive scan lines and the emitted signal from The correlation movement map is represented by performing the difference between each two successive correlation operations according to the equation:The analysis based on the correlation is often used in the time series analysis and signal processing, and is very useful in applications where there are similar signals present. Its main advantage is that it is the optimal detector of a signal embedded in noise .In the field of radio communications, one of the elementary mathematical notions is given by the analytical signal. To obtain this signal, the Hilbert transform is applied to a real signal l signal .In order to detect the envelope, the Hilbert transform is applied on the result obtained from the difference between each two successive scan lines, As it can be seen, tion 1πt .Using this information, we conclude that the analytical signal has the following expression:The envelope detection involves creating the analytic signal by using the Hilbert transform. Mathematically, it is defined as :(6)enviThe envelope detection movement map is given by the equation:This approach has the advantage to demand low computational resources, but it is noise sensitive.One of the most used methods in the field of signal detection consists of the linear transformation of signals from the time domain into the frequency domain to measure the instantaneous frequency of the studied signal. Hereby, the short-time Fourier transform (STFT) is applied on a time sliding window an lines :(8)SiPi∫−∞On this foundation, the idea that proposes the creation of an orthonormal base elaborated from this wavelet and from its dilated and delayed variants is created. In this process, the wavelet is dilated with the scale parameter Considering the difference between each two successive scan lines erformed .(13)Wim-dimensional phase space through a two-dimensional representation of the pair-wise distance, a representation called the recurrence plot. Mathematically, this is expressed as the distance matrix, a form of representation of the distances between all pair-wise trajectory points and provides a two-dimensional alternative to represent the trajectory [This method  allows iajectory ,44:(16)movement .Next, the time-distributed recurrence (TDR) is defined in order to highlight the sudden changes in the trajectory :(17)TDRThe TDR RPA movement map is given by the equation:The advantage of this method is that it is a non-parametric method and it can be analyzed for many types of signals ,44. In this section, the results obtained  being significantly reduced.1 and a distance of 1.6 m from sensor S2, at a varying height range. Hereby, the movement of the drone relative to both sensors is similar. In this configuration, the movement is repeatedly performed at a distance of 2.2 m from sensor S1 acquires the signals in a higher proportion, given the different distances from the drone to the sensors. The motion is also oscillatory, but less deep than in the case of the forward-back configuration relative to the sensor S2. Relative to both sensors, the movement is similar, therefore, as expected, the movement maps corresponding to both sensors resemble a lot. The major difference between the recordings for each sensor is that sensor SVisually, the performances of each method remain classified in the same order as in 1, but on the direction of sensor S2 in a forward-back movement. The movement of the drone relative to the sensor S1 is a left-right movement. Still, during the experiment the movement of the drone is not purely left-right relative to sensor S1, it was combined with a forward movement. In this case, the movement is performed at a distance of 2.5 m from sensor S2 senses the forward-back movement through spike-motion which reduces their amplitude meaning that the drone distances from the sensor, whereas the UWB sensor S1 perceives this movement as an oscillatory motion .As presented in The wavelet transform provides similar results with the envelope detection method. It needs an appropriate dictionary, otherwise its performance can be limited. Moreover, the computation time is higher than the envelope detection, but still usable for on-site applications.From The time distributed recurrence based on the recurrence plot analysis (TDR RPA) method mostly improves the quality of the movement map: It highlights the trajectory of the moving LSS UAV and minimizes the salt-and-pepper effect ; also, tThis paper presents the actual context of the LSS UAV presence detection and characterization, and proposes a new alternative for their movement detection and characterization using advanced signal processing methods and the UWB sensing system.The choice of the UWB sensing system is especially argued by the fact that this technology is able to detect small-dimension moving targets built from heterogeneous materials. Therefore, it surpasses the LoS limitations that the visual sensing system have and it does not interfere with other existing communication technologies. With a perpendicular positioning of the longitudinal axes of the UWB sensors and the use of several signal processing methods, our approach is able to detect and characterize the movement of a LSS UAV. The experiment presented in this paper emphasizes the fact that our approach detects indoor LSS UAVs intrusions, which represent a potential threat for classified or public areas with a special status. For example, given the audio/video recording capabilities of today’s LSS UAVs, possible specific use cases could be related to the unauthorized surveillance in areas such as: Window governmental office intrusion or balcony/window private building intrusion. Another possible application is the use of the LSS UAV in the context of “internet of everything” (IoE) and our proposed sensing system can be explored for the LSS UAV presence alert to the existing users .The correlation method provides fast results, but a low contrast between the movement trajectory and static sensed objects. The spectrogram movement map provides better results for the LSS UAV trajectory, but it requires higher computation time and the trajectory quality is limited by the time-frequency resolution of the method.The envelope detection provides better significant results than the latter two mentioned methods, pointing out the movement map trajectory in a clearer manner. The second advantage of this method is the reduced computation time/resources required.With similar results as the envelope detection method, the wavelet transform approach is able to highlight the movement trajectory of the LSS UAV. The limitation of this method is the appropriate choice of the mother wavelet function and the required higher computation time/resources (but still usable for such applications).The TDR RPA method provides the best results for the movement trajectory representation, clearly highlighting it and, by definition, filtering the salt-and-pepper noise. The proposed quantification measure, TDR, has the advantage to augment the sudden changes in the phase space and to minimize the noise effect. For our application, these sudden changes represent exactly the UWB pulses corresponding to the object’s movement. Although the required computation time is comparable with the wavelet transform method, processing is performed fast enough in order to trigger an alarm in real time for the surveyed system. Therefore, this drawback can be ignored, considering that the TDR RPA method emphasizes the trajectory of the LSS UAV the most. Therefore, our work describes a new approach for the LSS UAV indoor/office intrusion detection and tracking, proposing the UWB sensing system, and analyzing the results with advanced signal processing methods. Moreover, we discuss the trade-off between the movement map quality and computation time, which may be of interest depending on the application. The limitations of our approach are related to the UWB technology power standardization which makes it difficult to sense an object on long ranges. However, it is our belief that this approach can provide a good start for indoor LSS UAV detection and tracking.Future developments of our work foresee the use of the presented approach on an automatic UWB sensing system based on machine learning methods, that is capable of discriminating between LSS UAVs and other moving targets.


Composite materials are widely used in many engineering applications and fields of technology. One of the main defects, which occur in fiber-reinforced composite materials, is delamination. It manifests itself in the separation of layers of material and the damaged structure once subjected to mechanical loads degrades further. Delamination results in lower stiffness and the decrease of structure’s carry load capability. Its early detection is one of the tasks of non-invasive structural health monitoring of layered composite materials. This publication discusses a new method for delamination detection in fiber-reinforced composite materials. The approach is based on analysis of energy signal, calculated with Teager–Kaiser energy operator, and comparison of change of the weighted instantaneous frequency for measurement points located in- and outside of delamination area. First, applicability of the developed method was tested using simple models of vibration signals, reflecting considered phenomena. Next, the authors’ weighted instantaneous frequency was applied for detection of deamination using signals obtained from FEM simulated response of the cantilever beam. Finally, the methods effectiveness were tested involving real experimental signals collected by the laser Doppler vibrometer (LVD) sensor measuring vibrations of the delaminated glass-epoxy specimens. Fiber-reinforced polymer (FRP) composite materials have found application in a wide range of engineering structures—from parts of vehicles, through structures like pedestrian bridges to wind turbine blades. The main cause of their popularity is very good strength to mass ratio.Both, during manufacturing as well as use, FRP structures are subjected to a number of degradation processes, such as cracks or delamination. Degradation can result from loading the structure with force over accepted level, e.g., as a result of impulse, or due to violating construction’s fatigue strength on cyclic load. According to present state of technology, the origination of a defect does not preclude further use of a structure. However, further operation of an object, in accordance with a given level of safety, implies the need for early detection, identification, and localization of the fault as well as controlling fault development. Detection of an early phase of a fault allows to change operation conditions or to plan maintenance and repairs ,2.Delamination is the separation of part of a material of FRP which causes local change of its stiffness. Further loading of a structure, will effect, in the growth of delamination area or, in an extreme case, its rapid development causing destruction or irreversible loss of operation ability of a structure, e.g., like in the case of helicopter rotor blade delamination which took place in Israel in 2009 . Due to Those methods are based on observations of different physical phenomena such as: ultrasonic waves ,5, acousDue to economical and practical reasons, the methods based on observation and analysis of vibrations have gained big popularity, mainly in structural health monitoring applications. It is caused by the fact that the vibration signal can be easily measured by relatively cheap equipment . There iLiterature presents many techniques, which are dedicated to dealing with vibrational response of a structure and aim for detection and/or identification of the structural damages, such as delamination. A great number of methods are based on inverse problem analysis ,20. For In , the autAnother group of methods identify delamination by the use of so-called damage indicators. The most popular damage indicators are based on time domain signals or modal characteristics extracted from the vibrational signal. The example of such an indicator is the probabilistic delamination indicator (PDI) presented in . PDI is The methods based on the use of damage indicators are significantly simpler and less time consuming then the inverse analysis methods, but there are still areas that need to be improved. Many indicators exhibit sensitivity problems, need a reference state, and do not provide the possibility of detecting false alarms, reducing their reliability.Intelligent signal processing-based algorithms can be pointed as the next group of delamination identification methods. An interesting example of signal processing-based identification is presented in . The autAs the delamination is a very serious damage and can result in catastrophic failure, there is a very high concern in the scientific community in finding the effective methods allowing for its identification. The papers mentioned above represent only a small fraction of the published literature resources in the field, showing the diversity of scientific tools adopted for delamination identification purposes. In the more broad discussion on the vibration-based delamination assessment methods, their potential and classification are a job for a self-contained review article rather than for the introductory part of the research paper; a general conclusion coming from at least few review papers ,30,31 caIn the context of the above, the proposed method should not be considered as a stand-alone solution, being a perfect remedy, but as one of possible assisting methods in the vibration-based SHM system. Nonlinear dynamics of breathing deformation, observed in the finite element model of the vibrating specimen as well as in experimental tests, points to the need of selecting proper signal processing and analysis methods allowing to detect this phenomena.The presented approach, is based on the Teager–Kaiser energy operator (TKEO). The analysis of the time domain signal using the Teager–Kaiser energy operator enables observation of transient disturbances of the signal’s instantaneous frequency (IF) . Such diIn contrast to many present vibration-based methods, the original TKEO-based delamination detection method does not require knowledge of dynamic behavior of the structure in reference, i.e., healthy state, nor referring to the numerical model. In the considered case of breathing delamination, comparison of instantaneous frequency of vibration signals recorded in measurement points located within and outside delamination allows to create a useful failure indicator.The paper is organized in the following order: Present-day publications ,34 as weTo reflect nonlinear dynamic behavior of the structure with breathing delamination and to perform preliminary analysis, the dedicated numerical model of a vibration signal was created. The model must be considered simplified because it is not reflecting any particular breathing delamination of any specific structure. Additionally, it assumes that change of instantaneous frequency takes place in a contiguous way without rapid transitions.For the preliminary tests, it was assumed that half-periods of the created signal will differ by 2 Hz and instantaneous frequency will continuously vary from 9 to 11 Hz and back. In the first approach, indicated as Model 1, the numerical model was created by combining half-periods of two single harmonic signals with different frequencies using the cubic spline interpolation function St , smoothlTo build the numerical signal, the upper half-period of 9 Hz waveform and the bottom half-period of 11 Hz waveform were joined without interval. Due to different curvature of half-periods, the connection point was smoothed with spline interpolation. Due to spline smoothing, the zero-crossing point is shifted from the original zero-crossing of 9 and 11 Hz waveforms. In the The Model 2 ) and instantaneous frequency (Equation (5)) of the analyzed signal:In the considered case of the nonlinear signal with half-period frequency variation, the Hilbert transform demodulation does not allow to properly detect changes occurring in the signal. According to this approach, the investigated signal has weak amplitude and frequency modulations. The spectrum of the obtained instantaneous frequency signal reveals existence of the 9.95 Hz component as well as the contribution of its higher harmonics.The modeled discrepancy of frequency is not correctly represented in the instantaneous frequency . The HilThe Teager–Kaiser energy operator is a differential operator and was presented in  while itTeager–Kaiser energy In , the autFor a certain class of AM-FM signals, described as (Equation (8)):ion (9)) :(9)ETKt=Extending this concept, the TKEO (Equation (6)) can be applied for determination of the envelope (Equation (10)) and instantaneous frequency (Equation (11)):The demodulation procedure based on Equations (10) and (11) is known as the energy separation algorithm (ESA) and was presented in ,48. DiscIt is important to emphasize here, that because in the general case, the base formula (9) allows for determination of approximate value of Teager–Kaiser energy, Equations (10) and (11) are accurate for harmonic signals only. In the case of signals with AM and FM modulations, both for CESA and DESA, the additional high frequency components arise in related Teager–Kaiser energies. In consequence, some additional conditions have to be imposed on the signal to minimize discrepancy, such as : the anaSeveral DESA algorithms exist  and althOne must be aware of the limitations of this approach. The vibration signal coming from breathing delamination is not an AM-FM class signal and does not fulfill the conditions allowing for proper demodulation. As a result, it is not possible to fully correctly estimate changes occurring in the signal. The value of the instantaneous frequency is overestimated. Additionally, as  discusseComparison of analyzed methods clearly show, that only the one based on TKEO demodulation allowed for estimation of modeled changes of IF and revealed phenomena on interest. The spectrum analysis as well as Hilbert demodulation did not make it possible to identify variation of instantaneous frequency.To define applicability of the described Teager–Kaiser weighted instantaneous frequency (Equation (14)), a number of test cases were investigated. Both steady-state and decaying signals (Equation (15)) were analyzed:The reason for performing such tests was the higher ease of FE modeling of the structure with breathing delamination producing decaying signals, which were later used as input data for analysis of the method effectiveness in the case of the cantilever beam with delamination . HoweverSignals with amplitude decaying factor from 1 (no decaying) to 0.1 (fastest decaying) were analyzed to identify whether decaying increases estimation error. For each considered case, the investigated signal was limited to the time range from the beginning till the moment when the signal’s absolute envelope reached the value of 0.2. Statistical parameters for each case, including the signal’s duration, are presented in The quality of the instantaneous frequency estimation was strongly related to the time length of the signal taken to the analysis. For the signals with low values of ux  and uy . Besides, the CF boundary conditions scheme was applied to the whole delaminated beam, meaning that the beam was fixed in one end. The standard surface-to-surface contact interaction introduced between two faces of the delamination, allowed neither penetration nor separation among the sublaminate structures.The finite element modeling for simulation of a delaminated beam behavior was performed with the use of the commercial finite element software ANSYS. The delaminated beam was composed of four blocks, as it is presented in Four-nodal plane elements with a plane strain condition defined in the width direction were used. This is a rather rarely used practice in analysis of beams upon out of plane loading, but this approach was verified by the comparison of the eigenvalues obtained from the discussed model with the eigenvalues of full 3D model. There were no meaningful frequency differences for the first three modes for considered geometry. The use of four-nodal plane elements allows to reduce time cost of computation in comparison to use of 3D brick elements. The additional asset is, that those elements allow to create more realistic conditions at the delamination tips than standard beam elements.As delamination splits part of the beam into two distinct segments, which are connected in the delamination tips and can come into interaction during the vibrations, the additional boundary conditions, called contact conditions ,53,54, hTo enforce maintaining the constraints in contact modeling, the penalty method was used with contact status defined as it is shown in The value of contact stiffness The number of equilibrium equations in this method, for most of the cases, is much smaller compared to other methods and the time of the computation can be shortened.For the purpose of demonstrating the breathing delamination effect, transient analysis were performed with the initial condition imposed as the displacement field. Those conditions recreated deformation of the beam, caused due to application of static, perpendicular force in the beam tip. Delamination of the length The numerical FE model described in To obtain the band-limited signal, low-pass filtration was applied in order to remove influence of higher frequencies. As it can be seen in It can be observed that the deviation of For validation purposes, the signals were also analyzed with the Hilbert transform demodulation approach. In contrast to the TKEO-based method, which allowed to identify variation of 2 and epoxy resin ARALDITE LY3505  with hardener XB 3403 , by the use of vacuum-bag method. During the hand lay-up of the fabric, three 0.0001 m thick Teflon inserts of different dimensions were placed between the 6th and 7th layer of the composite. Four individual test specimens of nominal dimension 0.18 m × 0.02 m × 0.0014 m were cut from the laminate after resin solidification and the inserts were removed after cutting out specimens. The inserts allowed to create a partially separated layer with controlled length and edges. The created delamination span through the whole width of the beams. Throughout vibration tests, specimens were clamped along one of the tips with 0.03 m distance and active length was equal to 0.15 m. The center of delamination was located at 0.075 m from the free end, i.e., in the center of the active part of the specimen. During the experiment, four specimens were tested: one without delamination  of basis weight 163 g/mThe schematic diagram is presented in The measurements were conducted in two steps. In the first one, all specimens were excited with pseudo-random noise signal and modal analysis was performed in the range  Hz. This band covered the first five bending modes of the specimens. The results are presented in It must be pointed out, that in the case of the first two modes, the differences between modal frequencies of the individual specimens can be related to its weight and width variation, as well as influence of mounting errors. First modal frequencies might not be an objective criterion for technical state assessment.In the second step, the specimens were excited with the harmonic signal with the frequency equal to the identified first modal frequency. During the test of a single specimen, the Polytec vibrometer recorded signals from measurement points: M1 (center of specimen) and M2 (free end of specimen—out of delaminated area). For each measurement point, the vibrometer recorded the simultaneously time domain vibration signal of specimen and reference vibration of mounting clamp. The sampling frequency was equal to 12,800 Hz. Duration of the recorded waveforms was 5.12 s.Because fluctuation of instantaneous frequency, discussed in the TSA displacement signals from measurement points M1 and M2 of the successive specimens as well as reference signal, measured by the sensor located on mounting clamp, were processed with the use of the DESA-2 algorithm to obtain The weighted instantaneous frequency of reference signal was oscillating close to the assumed frequency of the excitation signal as it is presented in For comparison of the presented signals and evaluation of delamination, statistical parameters can be used such as: standard deviation (Equation (17)), coefficient of variation (Equation (18)), or values span (Equation (19)) of the weighted instantaneous frequency Values of those parameters computed for all the samples are presented in S and coefficient of variation V. Change of standard deviation between samples with increasing size of delamination is presented in For the performed measurements, the most informative parameters were standard deviation fw caused by opening and closing of breathing delamination. Based on the measures like standard deviation, it is possible to build a failure indicator.The experimental work confirmed periodic changes of weighted instantaneous frequency numerical models were created, reflecting breathing delamination phenomena in vibration signal;the conducted experimental tests confirmed half-period variation of instantaneous frequency resulting from breathing delamination;the weighted instantaneous frequency a failure indicator and a diagnostic method were proposed for detection of breathing delamination type failure.The paper discussed the application of the original Teager–Kaiser weighted instantaneous frequency The main findings of the conducted research can be summarized as:the use of the Teager–Kaiser operator for the construction of the new weighted instantaneous frequency parameter gives much better prospects for delamination detection than in the case of use of instantaneous frequency obtained using demodulation with the Hilbert transform;standard deviation of the method is efficient for the analysis of the real-world vibrational signals, when proper signal processing is applied.The performed research allowed to identify several limitations of the method, e.g., that the presence of noise in the signal causes that the calculation of The main benefit of The proposed approach can be useful for monitoring of simple beam-like structures e.g., composite bridge decks. However, the structure’s boundary conditions must allow for delamination breathing. This occurs for at least some of the boundary condition schemes, such as CF (clamped-free) or CC (clamped-clamped). As it was shown, in the case of a simple cantilever beam, the first mode was sufficient for delamination detection. In the case of more complicated structures, the proper excitation must be implemented to force breathing delamination phenomena to occur in all suspected components of the structure. In such a situation, multimodal testing can be necessary.The experimental research presented in the paper was limited to the case of unidirectional arrangement of reinforcing layers in the specimens. In order to verify the method’s effectiveness for other stacking sequences, future research work will cover testing of specimens with various orientation of layers and the ratio of the stiffness of the separated part (upper sublaminate in The definition of the applicability scope of the method to other types of failures and development of the algorithm, allowing for identification of size of delamination, is an open issue and will be the subject of future work.