The aim of the present report is to review the literature addressing the methods developed for the purification of alpha1-antitrypsin (AAT) from the 1950s to the present. AAT is a glycoprotein whose main function is to protect tissues from human neutrophil elastase (HNE) and other proteases released by neutrophils during an inflammatory state. The lack of this inhibitor in human serum is responsible for the onset of alpha1-antitrypsin deficiency (AATD), which is a severe genetic disorder that affects lungs in adults and for which there is currently no cure. Being used, under special circumstances, as a medical treatment of AATD in the so-called “replacement” therapy (consisting in the intravenous infusion of the missing protein), AAT is a molecule with a lot of therapeutic importance. For this reason, interest in AAT purification from human plasma or its production in a recombinant version has grown considerably in recent years. This article retraces all technological advances that allowed the manufacturers to move from a few micrograms of partially purified AAT to several grams of highly purified protein. Moreover, the chronic augmentation and maintenance therapy in individuals with emphysema due to congenital AAT deficiency  is also presented. Alpha1-antitrypsin (AAT) is an acute-phase glycoprotein whose main function is to protect tissues from the enzymes of inflammatory cells, especially human neutrophil elastase (HNE) released by neutrophils during an inflammatory state ,3,4,5,6.AAT is a 52 kDa single-chain protein synthetized as a 418 amino acid precursor that migrates on SDS-PAGE as a single band with an approximate Mr of 52 kDa .The loss of the 24-aa signal peptide gives rise to a 394 amino acid mature protein containing three carbohydrate side chains linked by means of an N-glycosidic bond to three asparagine residues . Each carbohydrate side chain consists of N-acetylglucosamine, mannose, galactose, and sialic acid . GlycosyCrystallographic studies on the mature protein demonstrated that AAT displays a nearly 360-residue structure for a serpin domain folded into 3 beta-sheets and 9 alpha-helices see , Panel BAlthough AAT displays a reference range of concentration in blood of 0.9–2.3 g/L, this value can rise many fold upon acute inflammation ,12. Desp1-antitrypsin deficiency (AATD) is, most likely, one of the best characterized.The preservation of the tertiary structure is essential, as both misfolding and the intracellular polymerization of SERPIN members lead to the onset of a variety of serpinopathies , among wAATD is an autosomal co-dominant severe genetic disorder that affects lungs in adults and can be associated with liver disease in a small portion of affected children . A schemAlthough the exact AATD prevalence remains largely underestimated (being the diagnosis problematic due to the delayed onset of symptoms), it is believed to afflict millions of individuals worldwide ,15,16.There is currently no cure for this pathology, and the major goal of AATD management is slowing the progression of lung destruction. If the “conventional” treatments are essentially based on the use of bronchodilators, inhaled steroids, and antibiotics (when infections occur), augmentation (replacement) therapy, consisting of the intravenous infusion of the missing AAT protein, is presently being used, under special circumstances, as an efficient long-term medical treatment of AATD ,18,19.Based on the above considerations, AAT can be considered a “pharmaceutical form” that is administered as medication to AATD patients. Since AAT is typically purified from human blood, a remarkable impact on the biopharmaceutical industry has been caused by plasma shortage problems that have often disregarded the demand for a large-scale production of this protein. Consequently, the need to produce this therapeutic protein in a recombinant version has witnessed an enormous growth in recent years.It is the authors′ intention to retrace in the following paragraphs of this article all technological advances that allowed scientists to move from a few micrograms of partially purified AAT to several grams of highly purified protein and to present its current applications in the clinical setting.A literature search of articles published between the 1960s and the present day was performed via the MEDLINE database. The following search terms were used: ‘Biochemical characteristics of Alpha-1 antitrypsin’; ‘Methods of purification of Alpha-1 antitrypsin’; ‘Recombinant Alpha-1 antitrypsin’; ‘Alpha-1 antitrypsin deficiency treatment’; ‘Augmentation therapy’.Systematic reviews, as well as classical articles from the 1960s, were included for review. With the above-mentioned search terms, a total of 350 articles were found. The full text of these papers was analyzed and repeated articles and those with redundant content were eliminated. Finally, a total of 90 papers were selected.Protein purification may range from a simple one-step (precipitation) procedure to complex approaches that involve more than one step. Although most purification schemes currently include several steps, the key to an efficient protein purification is to minimize the number of steps required. This is accomplished by selecting the most appropriate techniques, optimizing their performance and combining them in a logical sequence to maximize yield. Thus, the optimization of protein purification is a multifactorial exercise that requires specific responses to questions such as (i) which source is the most appropriate, (ii) how many components must be removed, (iii) what are the purity issues in relation to the starting material and the end-use of the product, and (iv) what will be the final scale of purification. Unfortunately, in the early 1950s, when interest in the purification of AAT started to grow , due to a variety of reasons, most of these questions could not be answered. First of all, the only AAT source practically used was human plasma, and second, the basic scenario of available methods was essentially restricted to protein precipitation by addition of ammonium sulfate. Finally, the use of this single procedure did not allow achieving the desired amount/purity level of protein. Not surprisingly, in fact, the results were poor in terms of both protein purity and quantity, the small amounts of AAT recovered being still heavily contaminated by interfering proteins. However, the aim of the researchers was to investigate the important biochemical features of AAT in view of its use as a therapeutic tool in reducing the tissue degradation caused by proteolytic enzymes. Thus, to reach the targets for yield and purity, additional strategies were requested. The emergence in the 1960s of chromatographic procedures turned out to be very advantageous in terms of the progression of AAT purification. The primitive process was complemented/replaced by powerful, sophisticated methods. All purification schemes currently used to purify AAT involve some form of chromatography. Used singularly or in combination, different chromatographic techniques with different selectivities have become attractive technological tools for the recovery of good amounts of highly purified material, suitable for therapeutic use.The following paragraphs give a brief overview of the recent technological advances in AAT purification showing what has been the “historical” evolution of this process and how methodological choices may affect the physico-chemical characteristics of the final product.As stated in the previous paragraph, one of the first attempts to isolate AAT from human plasma was performed by Duthie and Lorenz  in 1949.They added ammonium sulfate following the simple procedure developed a few years before by Schmitz . The yieThe common trait of all these approaches was the huge volume of fluids used as starting material i.e., up to 1000 L of urine or 10 L of plasma. Evaluated on the basis of the current criteria, it can wrongly be assumed that this “primordial” method was not very productive, the poor resolution being the most obvious characteristic.However, if you look back, it is easy to see that despite their intrinsic limitations, these first efforts enabled the recovery of small amounts of active, although partially purified AAT, which allowed a better biochemical characterization of the protein.All projects were not successful, nevertheless these attempts played a crucial role in pioneering the development of more sophisticated techniques that led to a large-scale production of AAT at a good level of purity.The logical evolution in AAT purification was the combination of the ammonium sulfate fractionation with other procedures that could take advantage of the physico-chemical properties of AAT. To make easier the separation of the large pool of proteins  into several smaller pools, one (or more) of which was enriched in AAT, the first parameter considered by investigators was the protein charge. The shape and charge on the protein surface are in fact well-known parameters that affect its binding to different matrices.While Moll et al.  and BundSince the objective of all authors was to obtain the largest amount of the functional AAT with the fewest contaminants, the abundance of albumin in human plasma/serum was indeed the challenging obstacle to overcome. The role played by dye affinity chromatography in the specific removal of this protein is certainly worth emphasizing. The possibility to use albumin-free plasma as the starting material was a decisive step forward for AAT purification. It is no coincidence that all the authors mentioned in the continuation of this article have introduced dye affinity chromatography in their purification scheme.This is what Whitley and Liener  did befoA modification of Pannell’s procedure was applied by Sugiura et al. , who useIn addition, Plancot et al.  purifiedWith hindsight, it would be easy to state that in the above cases, the authors did not observe the dogma “minimum number of steps and the simplest possible design” to reach the targets for yield and purity. However, while considering that AAT purification is not a simple job, it is obvious that the need to add one step to another (and repeat the same step) was strictly connected to the poor quality of chromatographic media available on the market in the 1970s that strongly determined the efficiency of the process.Most likely to overcome these problems, Laurell et al.  isolatedThe original thiol-exchange method was also applied by Vaughan et al. , who isoThe success in the purification level observed with the advent of a wide range of sophisticated materials in the ion-exchange and affinity chromatography fields was slow but steady and encouraging. Although the purification strategy was not completely transformed , by taking advantage of the peculiar features of these novel matrices, researchers explored the effects of their combination in planning purification schemes of AAT that would otherwise be time-consuming. This allowed obtaining the protein under the most stabilizing conditions and to maximize the yield. The following examples attest to the progress that has been made in this difficult field.P. aeruginosa elastase.One of the first significant applications of these techniques was that of Morihara et al. , who purAn intriguing exercise in which the differences in interaction strengths between the different biomolecules within a mobile phase and the stationary phase were exploited was done by Dubin et al. . They haThe growing popularity captured by the chromatographic media led Finette et al.  to explon-butyl cholate treatment (to inactivate enveloped viruses) and to a further cation chromatography step. The whole recovery of AAT was around 64–70%, and its purity was above 95% by SDS analysis.The purification of AAT from Cohn Fraction IV was described also by Chen et al. , who folThe AAT source chosen by Kumpalume et al.  was the Given the importance of using AAT as a drug for the potential cure of lung emphysema induced by congenital AAT deficiency, a strategy for the isolation of large amounts of highly pure AAT was evaluated by Zhang et al. . This goInstead of ammonium sulfate, Huangfu et al.  tested tAnother procedure noteworthy for its peculiarity is that adopted by Fan et al. , who expUntil a few years ago, the only source to support the therapies for the diseases mentioned in the above paragraphs was plasma AAT. However, as underlined in previous paragraph, due to recurrent plasma shortage (limited supply of donated blood), the frequent difficulty of native AAT to meet the market demand caused a remarkable impact on the biopharmaceutical industry. Consequently, the need to produce this therapeutic protein via recombinant routes became urgent. The remarkable scientific advances of the last decades, which made protein overexpression and purification quite easy, allowed this alternative way to witness an enormous growth that resulted in the development of different production routes to be alternative/join the conventional eukaryotic systems. Obviously, once the problem relative to the production of good amounts of AAT was solved, the second key issue addressed by researchers was that of its quality. In fact, as previously stated, AAT is a glycosylated protein in which the level of glycosylation plays important roles in modulating the plasma half-life/stability and the immune modulatory properties of the protein. Thus, as plant cell cultures offer a number of advantages over other expression systems , a major question for the authors suggesting the use of transgenic plants as source of recombinant AAT was the level of glycosylation of the protein ,46.Among the numerous reports that attest to the feasibility of several approaches in this area, the most interesting will be presented in this section.Escherichia coli. This indicated that the N-linked glycosylation of this form was high mannose-type. The authors also observed that glycosylation conferred the yeast-produced AAT an enhanced kinetic stability toward heat inactivation.Kwon et al.  were forRecombinant AAT with a modified synthetic gene was expressed in transgenic tomato plants at a very high level  by Jha and Agarwal . The ammOryza sativa) endosperm. The powder obtained from brown rice was extracted, and the supernatant was loaded on a DEAE column. The eluted material was applied to a column containing a mixed-mode support of ceramic hydroxyapatite followed by a multimodal strong anion exchanger. Affinity chromatography performed on a Con A column was the last step that allowed producing 2.24 g of AAT (with a 97% purity) per kg of brown rice. Several analytical tests  demonstrated that the biochemical characteristics and biological activity of recombinant protein were equivalent to those of plasma AAT, both being heterogeneous proteins with the diversity of glycan modification and the absence of the N-terminal peptide. These results allowed the authors to demonstrate the potency of rice endosperm as a promising system to express a protein with biopharmaceutical interest.Zhang et al.  successfDue to the high production capabilities of the mammary gland, also the prospect of producing large quantities of AAT in the milk of transgenic livestock has attracted a great appeal. Considering the simplicity of accessing the expressed protein and the relatively low operating costs, this was suggested as a simple and efficient expression system for recombinant AAT .From the fusion of the ovine β–lactoglobulin gene promoter to the human AAT genomic sequences, Wright et al.  have beeTaken together, the data from SDS-PAGE; HPLC- and N-terminal analyses did not reveal any contamination with sheep AAT and showed that the transgenic protein was >95% pure.Kluyveromyces marxianus in directing the secretion of AAT from Saccharomyces cerevisiae. A yeast expression vector for AAT was constructed by placing the coding sequence of human AAT fused with the INUIA signal sequence downstream of the GAL10 promoter. About 70% of the total human AAT synthesized in the recombinant yeast was secreted into the extracellular medium by the inulinase signal peptide. The majority of intracellularly-retained AAT was unglycosylated, suggesting that once the human AAT enters into the yeast ER and is glycosylated, the protein is very rapidly transported through the secretion pathway to the outside cells. The secreted protein had high mannose-type glycosylation.Kang et al.  evaluateProteins present in the culture supernatant were precipitated by the addition of ammonium sulfate (80% saturation); the pellet was collected, dissolved, and applied to a DEAE–Sephacel column. Fractions containing AAT activity were pooled and further fractionated by Con A agarose chromatography. Affi-Gel Blue and Mono Q HR were the last two chromatographic steps that allowed obtaining the highly purified protein.GAL10 promoter and the inulinase signal sequence is a useful tool to produce large quantities of correctly folded human AAT protein into culture media.These data demonstrated that the yeast system using the The methods described, together with the yield of AAT recovered, the level of purity, the fold purification, and the reference number of the related article have been schematized in Since 1987, AAT augmentation therapy is being used in the treatment for AATD in some countries. This therapy involves the intravenousadministration to the patients of plasma-purified human AAT to normalize their AAT serum concentration to above the protective threshold of 11 µM. Interesting considerations about the goals of this therapy  as well as of the clinical experience gained in its use for AATD during the past 25 years are finely presented in a variety of excellent reviews by different authors ,52,53,54An alternative route to intravenous injections is the aerosol therapy, also known as inhaled AAT therapy, which has the major advantage of not requiring an intravenous access. While not being a big problem when AAT is administered for brief periods, the risk of thrombosis and infection is high when repeated intravenous accesses with weekly administration for many years (as recommended for AATD) are needed. This inhaled therapy entails the inhalation of human plasma-derived AAT, which is directly deposited into the lung.An exciting review article focused on this topic was published by Usmani O.S. . The autIn addition to the above cited reviews, other interesting articles focused on some of the highlights arising from the research on AAT augmentation therapy.A randomized controlled trial (RCT) was carried out by Chapman et al.  on 180 nDuring a meta-analysis carried out by the same group  on 1509 In the open-label extension trial , McElvaney et al.  surveyedCampos et al.  showed iAlthough the outcomes of the studies above discussed apparently prove the efficacy and effectiveness of AAT augmentation therapy, this argument is questioned by some authors ,61 who aAs a result of this dispute and of the fact that the effect of the AAT augmentation therapy on the protease–antiprotease balance is still incompletely understood, the treatment has only been implemented in a few countries .Nevertheless, various studies into different types of AAT therapy are being carried out.Gene therapy is another alternative that is being investigated using different methods of altering gene expression. One of these is transfecting the M-AAT gene to the patients by means of a vector. Currently, different clinical trials are being performed to discover the best administration route. The findings by Mueller et al.  suggest Other promising approaches involving gene transfection that are being explored are the use of a transposon to correct the point mutations in SERPINA1. In a very appealing article, Yusa et al.  show thaFinally, in a recent article, Turner et al.  evaluateAs the studies into these therapies are all still in an early phase, implementation will probably take a while.Alpha1-antitrypsin deficiency (AATD) is a genetic disorder characterized by increased risk for developing both early-onset lung emphysema and chronic liver disease due to the reduced serum levels of alpha1-antitrypsin (AAT). To protect the lungs from the proteolytic damage caused by proteases, replacement  therapy with the missing AAT protein is an innovative treatment for this condition that may be used under special circumstances. While being particularly attractive, this treatment requires the production of good amounts of highly purified AAT from human plasma. It is apparent that this process would benefit from the availability of procedures efficient enough for the routine production of this precious material. This article aims to provide a critical review of the methodological steps that have marked progress in the purification of this protease inhibitor. It has been planned to provide an overview of the literature concerning the strategies developed over the years in AAT production and the most recent practical applications. The numerous publications discussed in this report underline the enormous progression achieved in these years regarding the laboratory procedures developed for the purification of AAT. The jump from small quantities of raw material to large amounts of highly purified protein was achieved thanks to the improved experimental design/sample preparation together with the development of novel platforms. Nevertheless, the constantly growing appearance on the market of excellent chromatographic materials and the rapid pace of technological advances have also given impetus to explore the use of increasingly efficient techniques. Taken together, all these implementations have attracted increasing attention from numerous groups, and this has greatly facilitated the efforts devoted to this research. A significant impact in this area is still expected in coming years.Since AATD is classified as a rare disease , some may question the need for such comprehensive efforts put in a direction that benefits relatively few patients. However, in our opinion, this view is short-sighted, since it seems plausible that efforts to define better the pathogenesis of AATD, by studying in depth the effects of augmentation therapy, may serve to gain further insight also into other correlated disorders. Thus, the application of sophisticated techniques capable of purifying AAT at a very high level and/or of producing this protein from sources different from human plasma is expected to make the improvement of knowledge regarding this disease easier.


To solve the problem of the weak knock characteristic extraction for a port-injected two-stoke spark ignition (SI) unmanned aerial vehicle (UAV) engine burning aviation kerosene fuel, which is also known as the Rocket Propellant 3 (RP-3), the Intrinsic modal Functions Energy (IMFE) method is proposed according to the orthogonality of the intrinsic modal functions (IMFs). In this method, engine block vibration signals of the two-stroke SI UAV engine are decomposed into a finite number of intrinsic modal function (IMF) components. Then, the energy weight value of each IMF component is calculated, and the IMF component with the largest energy weight value is selected as the dominant characteristic component. The knock characteristic frequency of the two-stroke SI UAV engine is obtained by analyzing the frequency spectrum of the dominant characteristic component. A simulation experiment is designed and the feasibility of the algorithm is verified. The engine block vibration signals of the two-stroke SI UAV engine at 5100 rpm and 5200 rpm were extracted by this method. The results showed that the knock characteristic frequencies of engine block vibration signals at 5100 rpm and 5200 rpm were 3.320 kHz and 3.125 kHz, respectively. The Wavelet Packet Energy method was used to extract the characteristics of the same engine block vibration signal at 5200 rpm, and the same result as the IMFE method is obtained, which verifies the effectiveness of the IMFE method. Two-stroke piston engines are widely used in UAV and light aircraft due to their simple structure, small size, and good fuel economy . Most avAs an alternative heavy fuel to SI engines, aviation kerosene has similar physical and chemical properties . CompareAs shown in Knock is defined as abnormal combustion induced by auto-ignition in the combustion chamber. It is generally accepted that knock is caused by the auto-ignition of the end-gas . In the In the study, the research team of the author found that the anti-knock performance of RP-3 kerosene fuel was poor ,18. StroWhen the knock occurs, high frequency oscillation pressure waves will be created within the combustion chamber and induce high frequency vibration of the cylinder block . The popSignal transform techniques are useful tools for knock characteristic extraction methods, such as Fast Fourier Transform (FFT ), Short-In addition, how to find a suitable wavelet basis function as the knock characteristic generating function is also a difficult problem for wavelet transform. At present, the number of wavelet basis functions is infinite. Different wavelet basis functions are selected to process the same signal, and different results will be obtained. The proper selection and optimization of the wavelet basis function are worth discussing, but, so far, there is no systematic method to summarize. Thus, the selection of the wavelet basis function is influenced by human factors to a great extent. After selecting the wavelet basis function and a decomposition scale, the result of wavelet transform is a certain fixed frequency band signal. Therefore, the frequency range of wavelet transform cannot adaptively follow the change of the signal, which results in a clear signal redundancy. These problems of wavelet transform seriously affect the accuracy of weak knock characteristic extraction.In order to accurately and effectively extract the weak knock characteristic frequency from engine block vibration signals, a new characteristic extraction method, namely the IMFE method, is adopted in this paper. The IMFE method is an adaptive nonlinear non-stationary signal time-frequency analysis method, which is a major breakthrough for linear and steady-state spectral analysis based on Fourier Transform. The IMFE method can decompose signals into the sum of several orders of IMFs, according to different scales or trends . Each IMThe IMFE method has been gradually applied in the fields of rolling bearing fault recognition , plungerThe IMFE method is based on an Empirical Mode Decomposition (EMD) method. The EMD method can decompose the non-stationary and non-linear signals into a set of steady and linear data series, namely IMFs. The IMFs must satisfy two conditions ,34: (1) The EMD method uses the envelope of local maximum and minimum to obtain IMF components. Once all extremum points are obtained, all local maxima are interpolated using cubic spline interpolation to form an upper envelope of data . SimilarThe average value of the upper envelope and lower envelope is denoted as The process is repeated Using Equation (6) can be obtained when IMF components at different levels contain different frequency components. The lower the order of the IMF component, the more high-frequency components it contains. For the IMF component of the same order, different types of signals have different frequency components. Therefore, IMF component energy can be used as a feature for classification. The indicators of signal energy are defined as follows .(7)E0=∫In the process of the IMFE method, if the energy error exceeds the allowable range, the boundary extension and fitting algorithm are modified. The decomposition is then redone, and the process is repeated until To verify the effectiveness of the IMFE method in this paper to extract the knock characteristic frequency, a simulation calculation is set up. The knock simulation signal The simulation signal component FFT is used to analyze the simulation signal component The simulation signal component To analyze the time-frequency characteristic of the simulation signal In As shown in In conclusion, the IMFE method can be used to decompose the knock simulation signal into a series of IMF components adaptively in the frequency domain. According to the different energy proportion of each IMF component, the IMF component with the largest energy proportion is selected as the dominant characteristic component. The characteristic frequency value of the knock simulation signal can be obtained by FFT. The validity and feasibility of the IMFE method to extract the characteristics of knock simulation signals are verified.A two-cylinder two-stroke spark ignition piston engine burning RP-3 kerosene fuel was used to conduct the experiments in this study. One of the important characteristics of knock is the high frequency oscillation of cylinder pressure signals. When knock occurs, the pressure and temperature in the cylinder suddenly increase. The pressure shock wave repeatedly impacts the cylinder wall, the top of the piston, and the cylinder head at an extremely high rate, which causes high-frequency pressure oscillation in the cylinder. For certain spark ignition engines, the frequency of high frequency pressure oscillation caused by knock is within a certain range. The amplitude of high frequency pressure oscillation after the peak of the pressure signal in the cylinder is related to the strength of the knock. During the test, the inlet temperature is 301 K and the engine operating condition are 5100 rpm and 5200 rpm. In order to avoid spectrum aliasing, the sampling frequency of the signal is selected as 200 kHz in order to maintain the high-frequency characteristics of the signals. The DH5902 high-speed data acquisition instrument is adopted to synchronously collect cylinder pressure signals and engine block vibration signals. The operational conditions of the test are shown in The engine block vibration signal under weak knock condition shown in Since the IMFE method does not require setting the base function, the extraction of IMF components in each order is determined by the signal characteristics, so it is adaptive. From In addition, the Index of Orthogonal (on below .(13)IO=The orthogonality of IMF components obtained by the IMFE method was tested, and the The energy weight value of IMF components in order 1–7 is calculated, and the results are shown in FFT is used to analyze the IMF2 component in order to calculate the frequency of the component, and the result is shown in The time-domain diagram of the cylinder pressure signal and the engine block vibration signal under a weak knock condition collected synchronously at 5200 rpm is shown in Using the IMFE method, the engine block vibration signal in The energy weight value of seven IMF components shown in FFT is used to calculate the frequency of the IMF3 component, and the result is shown in To verify the effectiveness of the IMFE method, Wavelet Packet Energy method was used to identify the weak knock characteristics from the engine block vibration signal at 5200 rpm, as shown in As can be seen from Calculate the energy percentage for the reconstructed signal of eight nodes in three layers, as shown in The reconstructed signal of node S  was analyzed by FFT, and its frequency characteristics were obtained, as shown in In this paper, the IMFE method is used to extract the knock characteristic frequency of engine block vibration signals of a two-stroke SI UAV engine burning RP-3 kerosene fuel under a weak knock condition. In this way, engine block vibration signals are decomposed into a series of IMF components in the frequency domain adaptively. Calculate the energy weight value of each IMF component, and select the component with the largest energy weight value as the dominant characteristic component. By analyzing the spectral characteristics of the main IMF component, the knock characteristic frequency of the vibration signal can be obtained. The results show that the weak knock characteristic frequencies of the engine block vibration signal at 5100 rpm and 5200 rpm are 3.320 kHz and 3.125 kHz, respectively.A Wavelet Packet Energy method is used to extract the characteristics of the engine block vibration signal of the two-stroke SI UAV engine at 5200 rpm, and the same results are obtained with the IMFE method. This verifies the effectiveness of the IMFE method in extracting the characteristic frequency of an engine block vibration signal under a weak knock condition. The IMFE method does not need to set a fixed basis function, and the extraction of IMF components in each order is determined by the signal characteristics. It is adaptive and has higher accuracy when compared with wavelet transform. It has a certain application value for extracting a characteristic frequency of the engine block vibration signal under a weak knock condition and provides effective theoretical basis for realizing real-time knock control of the two-stroke SI UAV engine.


At the time of writing, the coronavirus disease COVID-19) has affected 212 countries and territories across the globe. According to the world health organization (WHO), a total number of 4,735,622 confirmed cases, including 316,289 deaths was reported [9 has affUsing robotic technology  and teleTo win the battle, health and wellness of every healthcare worker has to be guaranteed . From a Moreover, even with PPE, healthcare workers can still be infected in some special cases . TherefoBased on the needs mentioned above, a telerobotic system for remote care operation in isolation ward is developed and introduced here. The research and development of this system are mainly focused on the robotic design, motion capture, mapping algorithm development, telepresence software development, and control strategy design.teleoperation system consists of a wearable initial motion capture device and a dual-arm collaborative robot . Using the motion capture device, upper limb motion data of the healthcare worker can be obtained and used to control the robot arm motion remotely. A pair of data gloves are used to capture the finger motions and teleoperate the grippers or other end effectors of YuMi.The telepresence system is achieved by a tablet computer attached to the front of the teleoperated robot. A Multi-Users Audio/Video Conference System for remote medical consultation is developed and deployed based on Web Real-Time Communication (WebRTC). A voice wake-up function is developed to facilitate patient’s operation, and it reduces the chance of contact between patient and the robot. Furthermore, a deep neuron network is used to monitor the patient’s emotional states by deploying on the tablet computer.The The proposed telerobotic system Figure  has two The proposed system aims to block infections by reducing the chance of contact between the patients and the healthcare workers. With the two subsystems, the teleoperated robot can assist or even replace the medical staffs to take care of patients in the isolation ward. In other words, the teleoperated robot becomes the healthcare worker’s eyes, ears, and body in the isolation ward.There are four main parts of the self-designed teleoperated robot Figure : omnidirFor robot control, a wearable motion capture suit consists of 18 inertial measurement units (IMUs) is chosen to capture the healthcare worker’s motion data. Teleoperation is achieved using incremental motion mapping between the operator and robot, which is intuitive and convenient. It allows healthcare workers to reproduce their professional skills through the robot more effectively with lesser training needed. A pair of data gloves are used to collect the bend angle of the healthcare worker’s finger during the teleoperation. Various gesture instruction rules are designed to control the end effectors.This research is approved by the Clinical Research Ethics Committee of FAHZU. Currently, the proposed telerobotic system is undergoing clinical trials. Functional verification has been completed at the Teaching and Research Center of FAHZU and the proposed teleoperated robot has been tested in the emergency center’s Intensive Care Unit (ICU) of FAHZU.Remote auscultation is achieved using a Doppler Ultrasound Stethoscope attached to the end of the robot arm, which enables the healthcare workers to auscultation outside of the isolation ward Figure . To achiTo summarize, this newly designed telerobotic system combines the strengths of healthcare workers (expert knowledge for patient care) with the strengths of robotics  to give the best outcome to patients and healthcare workers. Implementation of the robot in the battle against COVID-19 has obtained positive feedback from healthcare workers for its potential in blocking infection and effectiveness in relieving medical workers from repeated tasks. In addition, this teleoperated robot also has potentials in other areas like space exploration, bomb disposal, disaster rescue and any other fields with a need of remote operation.


This article focuses on an underwater acoustic target recognition method based on target radiated noise. The difficulty of underwater acoustic target recognition is mainly the extraction of effective classification features and pattern classification. Traditional feature extraction methods based on Low Frequency Analysis Recording (LOFAR), Mel-Frequency Cepstral Coefficients (MFCC), Gammatone-Frequency Cepstral Coefficients (GFCC), etc. essentially compress data according to a certain pre-set model, artificially discarding part of the information in the data, and often losing information helpful for classification. This paper presents a target recognition method based on feature auto-encoding. This method takes the normalized frequency spectrum of the signal as input, uses a restricted Boltzmann machine to perform unsupervised automatic encoding of the data, extracts the deep data structure layer by layer, and classifies the acquired features through the BP neural network. This method was tested using actual ship radiated noise database, and the results show that proposed classification system has better recognition accuracy and adaptability than the hand-crafted feature extraction based method. Underwater acoustic target recognition is the technique of identifying the type of target through the analysis of underwater acoustic signal. The hardware basis of underwater acoustic target recognition is sonar equipment, which converts underwater acoustic wave into digital signal through hydrophones, and obtains underwater target information through various signal processing methods . UnderwaThe digital signals recorded by sonar through analog-to-digital conversion contain a lot of information about underwater acoustic targets ,14,15,16Because of the short-time stationary characteristic of underwater acoustic signal, LOFAR spectrum is an effective representation of signal. LOFAR is a typical method of passive sonar signal processing. It is based on the short-time Fourier transform (STFT) to obtain the time-frequency spectrum of the signal and uses the time accumulation of the low-frequency line spectrum to detect the line spectrum. Each row of data in LOFAR spectrum corresponds to the original power spectrum of a frame of underwater acoustic signal data. The original power spectrum can be directly used as the input of a recognition system for pattern recognition. However, the amount of original power spectrum data is very large, which brings great pressure to the training and work of recognition system. To reduce the complexity of classification, a lot of work has been carried out to further reduce the data dimension. Common methods are KL transformation  and prinHuman auditory system also has a good ability to identify ship radiated noise. To simulate the human ear hearing, people have deeply studied the human ear hearing system. The processing of sound by auditory system mainly includes the decomposition of acoustic signal by cochlea of inner ear, energy conversion from sound wave to nerve by hair cell vibration, and analysis of acoustic spectrum characteristics by inhibition side network. . In the From the perspective of information theory, hand-crafted feature extraction is actually a process of subjective screening of original information to reduce the amount of information . When thUnder the noise of the marine environment, the SNR of the underwater acoustic signal is relatively low. An important flaw in the realization of underwater acoustic target recognition through hand-crafted features is that it is difficult to adaptively adjust the feature extraction scale when the SNR is low. The clustering model needs to be modified according to the actual environment to obtain good classification effect. In addition, because the actual underwater acoustic signal samples are extremely diverse and difficult to obtain, the generalization ability of the hand-crafted features is poor, and it is difficult to achieve a consistent effect on various signals . FeatureThe recent development of deep neural networks (DNN) has given a better method than traditional hand-crafted feature extraction for extracting deep structures of complex data. Using DNN to extract features is widely used in various fields . There aThe deep Boltzmann machine is an undirected graph-based depth generation algorithm proposed by Hinton , which iIn this paper, an auto-encoder based on Boltzmann machine is constructed to perform adaptive feature extraction on the input signal. After layer-by-layer greedily pre-training, the overall optimization is performed by Markov chain Monte Carlo. The result of feature extraction can be used to classify and recognize signals through neural network after dimensionality reduction. It can obtain recognition performance better than hand-crafted features for ordinary noise signals. Experiments show that the system is also suitable for underwater acoustic signals. The important symbols involved in the description of the algorithm principle are shown in The remaining content of the article is structured as follows. The generation mechanism of ship radiated noise is very complicated. The sources of these noises including propellers, rotating and reciprocating machinery, various pumps, etc. According to different noise sources, noise can be divided into several components, including mechanical noise, propeller noise, and hydrodynamics noise etc. .Ship radiated noise is approximately stable in a short time, thus it can be described by power spectrum. The power spectrum of the signal contains the characteristics of the ship and reflects its sailing status. Radiated noise power spectrum and pictures of ships  are showThis section mainly introduces the principles of hand-crafted underwater acoustic feature extraction methods, including GFCC and LOFAR, and proposes an auto-encoding feature extraction method based on improved deep Boltzmann machine.The information removal process of hand-crafted feature extraction methods such as GFCC and LOFAR are based on the information extraction process judged by experts.The LOFAR spectrogram is a classic time-frequency analysis method, which is widely used in underwater acoustic target recognition. Because the noise has short-term stability characteristics, the signal is sampled by the short-window function sliding method, and the power spectrum estimation of the signal at different moments can be obtained through the short-time Fourier transform. The three-dimensional graph of time, frequency, and power can be drawn, that is, the LOFAR spectrum. The LOFAR spectrum contains two-dimensional information of time and frequency, and the information is redundant. Classification using the original LOFAR spectrum will put a lot of pressure on the recognition system. Therefore, reduction of the amount of information is necessary. Common methods are KL transformation and principal component analysis. The main features that help target recognition in LOFAR spectrogram include line spectrum frequency, power spectrum shape, and frequency line trajectory. Line spectrum information and line spectrum track are the reflection of narrow band components in target signal, which are vulnerable to multi-target interference. The shape of power spectrum is also vulnerable to the interference of environmental noise and multi-target. Therefore, it is difficult to obtain satisfactory results by using the features alone. The LOFAR spectrogram retains the original information of the underwater acoustic signal to a great extent, but it ignores sonarist’s hearing in recognition. Therefore, many scholars try to study the underwater acoustic target recognition algorithm based on auditory perception.GFCC uses Gammatone filters to divide the acoustic signal over a period of time into different frequency band components. The Gammatone power spectrum is taken as a logarithm. Finally, the GFCC features are extracted by discrete cosine transforms (DCT). The Gammatone power spectrum is taken as a logarithm. It is a signal processing structure that imitates the human cochlea, and its performance will be close to the best sonarist’s resolution effect theoretically, but frequency components outside the hearing range will be discarded. The hand-crafted feature is actually an expert system. GFCC is a method based on human auditory model, which has a good theoretical basis and is easy to implement. This method has good performance for speech signal. However, the hand-crafted features subjectively discard some features that help identify the target. Therefore, GFCC method is often not effective in real marine environments.Deep Boltzmann machine transforms low-level input data into high-level features through unsupervised layer-by-layer training. These high-level features represent the complex dependencies implied in the data. The deep Boltzmann machine can be decomposed into a deep neural network formed by stacking multiple restricted Boltzmann machines (RBM). Each RBM implements auto-encoding, and each auto-encoding can extract features that are more abstract. The basic structure of RBM is shown in The learning process of Boltzmann machine is to reduce the energy function of the whole system in the simulated annealing process, so that the system tends to be ideal. The energy function of the binary RBM system is:To correspond the low energy to the ideal system state, the energy-based joint probability distribution of the visible layer and the hidden layer is defined as:Based on the structure of RBM, there is no connection between the units of each layer, and their distributions are independent of each other, thus the distribution of visible units and hidden units in RBM can be summarized by the following formula.Substituting the system energy function and probability distribution function into the above formula, the simplification yields:Substituting Equations (1)–(4) into the random gradient, the log-likelihood gradient of the binary RBM is obtained.Gibbs sampling is usually used to reduce the computational complexity of sample estimation. The Gibbs sampler of In RBM, Although Gibbs sampling is an effective method to solve approximate expectation and update RBM weights through Markov chain Monte Carlo method, it needs continuous computation until the Markov chain is stable. The contrast divergence (CD) algorithm proposed by Hinton  can effeAlthough CD algorithm is widely used, it is found that CD algorithm only performs well in the early training period. In the late training period, the computational efficiency of CD algorithm decreases because the ergodicity of Markov chain decreases. The persistent contrast divergence (PCD) algorithm proposed by Tieleman  has bettIn the design of underwater acoustic target recognition system, the convergence speed and recognition performance of the algorithm are important, which requires the algorithm to make a trade-off between high precision and low delay. In this paper, cd-k algorithm is used to train RBM auto-encoder, and the convergence speed and performance of the model are adjusted by variable learning rate.The main application of RBM in neural network algorithm is pretraining. Each layer of the network is trained by greedily training, and the parameters are copied into the neural network as initial values. Training from these initial parameters can make the network converge quickly.The RBM described in To extract features that are more abstract, we stack the auto-encoders to obtain the auto-encoder shown in The multi-layer RBM used in the RBM feature extraction system in this paper is actually a stack-type auto-encoder. The two-layer structure of each layer of RBM takes the previous layer as an input, obtains its probability model, and uses the Gibbs sampler for greedily training to reach the final refactor accurately.When RBM auto-encoder model is used in actual signal processing, a noticeable problem is that the signal needs to be matched with the model through preprocessing. The energy of underwater acoustic signal is mainly concentrated in low frequency, and high frequency signal is often submerged in background and interference signal. Through the observation of data, the first 8 kHz of signal is used in underwater acoustic signal processing. At the same time, the underwater acoustic signal has a longer time stability than the voice signal, thus this paper adopts the data frame length of 100 ms, which is more than the tens of milliseconds in speech signal analysis, and uses 50% sliding framing.On the other hand, the hyperparameters of the model need to be manually set to adapt to the actual underwater acoustic signal. The number of units in the visible layer should be consistent with the number of sampling points of the normalized spectrum after the effective frequency is intercepted. The number of units in each hidden layer needs to be set decrementally layer by layer on the basis of the visible layer until it reaches a low dimension. Other hyperparameters such as learning rate, minibatch size, and number of model layers need to be found through experiments to find suitable values for performance and convergence speed.Algorithm 1 RBM Auto-encoder1: Configure the input, adjust the input dimension of RBMs and input structure2: Randomly initialize the entire system parameters, including connection weights and offsets3: Preprocess the acoustical signal get Spectrum4: for i = 1:layer5:  for j = 1:epoch[i]6:   for batch = 1:batch_num7:    Complete the forward process according to Equation (6), and calculate the hidden unit state8:    Complete the reconstruction process according to Equation (6), and calculate the state of the visible cell for reconstruction9:    Update model parameters according to Equation (5)10:   end11:  end12: endThe function of the RBM auto-encoder is to extract the probability characteristics of the data distribution through layer-by-layer unsupervised training. The Boltzmann energy equation (Equation (1)) based on the undirected graph can evaluate the energy function of the current state of the network. The energy function reflects the probability of occurrence of the current network state through the probability-energy equation (Equation (2)). Conditional probability equation (Equation (4)) uses the current network parameters and input unit state to calculate the state of the hidden layer unit, and reconstructs the state of the input unit through the state of the hidden layer unit, which is the basis for the realization of the Gibbs sampler. Gibbs sampling (Equation (6)) is used to solve the Markov chain to obtain the mathematical expectation including all input and hidden layer unit required by the stochastic gradient parameter update formula (Equation (5)). The detailed implementation process of the RBM auto-encoder is shown in Algorithm 1.The clustering model mainly includes two components, a L-layer RBM auto-encoder and a BP neural network classifier, and the output node of the Boltzmann machine is used as the input of the neural network. The structure of the entire deep clustering system is shown in The input to the system is the normalized spectrum of the acoustic signal. The deep Boltzmann machine acts as an auto-encoder in the clustering system to extract deep data features from the original input. With the increase of the levels of deep Boltzmann machines, the output features become more abstract. Abstract features with certain dimensions are conducive to the subsequent neural network clustering.BP neural network is the most commonly used neural network classifier. BP neural network with many hidden layers is also called multi-layer perceptron or deep neural network. The data flow of BP neural network always flows from the input layer through the hidden layer to the output layer.The learning algorithm of BP neural network is the process of learning the network parameters under the supervision of data and labels. The network parameters include the connection weight matrix The sigmoid function maps the input to Calculate the value of each neuron layer by layer according to the above formula to complete the forward propagation, which is also the process of classifying a new, unlabeled data input during the test.The back propagation process is the process of modifying the model according to the difference between the forward propagation and the actual label. For this reason, we must design a reasonable optimization algorithm to make the model converge as quickly as possible and improve its performance on the training set and test set. At present, there is no theoretical hyperparameter setting method. Most training algorithms are based on the least squares optimization of the loss function The parameter update needs to calculate the partial derivative of each parameter to the loss function. Since the commonly used models generally have a larger number of parameters, to simplify the derivation process, use chain derivation:The advantage of BP neural network is that it can perform non-linear mapping for a large number of samples, which has a good effect on underwater acoustic data with a larger sample size. In addition, the BP neural network has self-learning and generalization capabilities, and can effectively process samples that are quite different from the training data. The disadvantage of BP neural network is that the gradient descent algorithm converges slowly to samples with higher dimensions. When the network scale is large, the convergence speed of the BP neural network is unacceptable. In addition, the training of BP network highly depends on the number of labeled samples. When the number of samples is small, it is difficult for the BP neural network to achieve better training results.In summary, the RBM auto-encoder-BP classifier proposed in this paper mainly has the following advantages. BP neural network can effectively complete the nonlinear mapping problem and has a good effect in the clustering of RBM auto-encoders. The RBM auto-encoder can effectively compress the data and extract its abstract features, which is beneficial to speed up the convergence of the BP neural network. The generalization ability of BP neural network supplements the defect that RBM auto-encoder is difficult to effectively reconstruct untrained labels. Both BP neural network and RBM auto-encoder are self-learning algorithms, which can complete underwater acoustic target recognition tasks without manual intervention in practical applications. The data generation capability of the RBM auto-encoder can create identically distributed samples by modeling the probability distribution of the original data. When the number of samples is insufficient, the data generation capability of the RBM auto-encoder can be used to supplement the samples to complete the training of the BP neural network. Finally, RBM suppresses random noise based on the characteristics of the probability distribution of the data, which can effectively prevent the BP neural network from overfitting.Two noise databases were used to test the effectiveness of above-mentioned underwater acoustic target recognition system. Two comparison methods were used to verify the superiority of the proposed system in aquatic target recognition.The experiment process was divided into three stages. First, the original audio data were preprocessed according to the needs of different algorithms, and the original audio data were divided into parameterized samples of equal length. Then, samples were randomly selected to construct training set and test set, and the number ratio was 6:1. Finally, the dataset was used to train and test the underwater acoustic target recognition system to evaluate the performance of different systems.The number of layers of RBM auto-encoder was set to 4, and the number of units in each layer was 784, 500, 200, and 50. The learning rate during training was set to 0.001. The value of the weight matrix was initialized randomly at , the offset value was initialized to 0, and the number of iterations for each layer was set to 100. The Boltzmann machine took the normalized spectrum of the segmented signal as input and completed the feature extraction through the four-layer RBM auto-encoder. BP neural network obtained the output features of RBM auto-encoder to complete target recognition.As the control group, GFCC extracted the features of audio signals after the same preprocessing. The number of gammatone filters using in GFCC was 64, and the first 24 dimensions were taken as classification features for BP neural network.To evaluate the performance of the feature extraction algorithm in reducing the original signal’s dimensionality and improving the data separability, we set up a comparison algorithm to directly put the normalized spectrum of the original data into the BP neural network for recognition.N is the number of sampling points of a frame of data, w(n) is the Hanning window function, and X(k) is the norm of the spectrum.The data samples processed in this study are single channel audio signal. The signal was divided into 100 ms frames with 50% overlap between frames, and Hanning window was added to the signal to suppress high frequency interference and energy leakage. After adding Hanning window and FFT, spectrum samples of a frame data were obtained. The formula is as follows:The normalized spectrum At this time, To evaluate the performance of above-mentioned underwater acoustic target recognition system, the proposed classification system was used to carry out multiclass classification experiments on two databases, NOISEX-92 and ShipsEar. After preprocessing each signal, many samples were obtained. Six of the of the samples were randomly selected as the training set, and the remaining one was used as the test set. The training set was used to train the RBM auto-encoding BP neural network classification system, and then the test set was used to count the classification accuracy. To quantitatively describe the performance of underwater acoustic recognition system, adjusted Rand index (ARI) and Maximum Value of the Clustering Rate (MVCR) were introduced. ARI was used to evaluate the overall recognition performance. MVCR was used to evaluate the recognition performance of a single class. Assuming that the dataset of N classes is clustered into K clusters, ARI is defined as:MVCR analyzes the clustering effect of each class, which is defined as:The RBM auto-encoder algorithm was compared with five different clustering systems, including the two feature extraction methods GFCC and original spectrum. The clustering methods are BP neural network and GMM. The experimental results of data sets 1 and 2 are summarized in the next two sections respectively.The NOISEX-92 database was used as Dataset1 to test the performance of the classification system. NOISEX-92 is a group of noises with obvious category characteristics recorded in the air. We used NOISEX-92 to test the proposed classification system to ensure that the system can work properly. The NOISEX-92 database contains 14 types of noise. The data sample is a single channel audio signal with a duration of about 4 min, recorded with sampling rate of 19.98 kHz and bit depth of 16. The experiment used 10 types of noise in NOISEX-92. The information of each type is in The ShipsEar database was used as Dataset 2 to test the performance of the proposed classification system. ShipsEar database is widely used in underwater acoustic target recognition, ship noise detection and other research fields. The database contains a large number of ship noise and marine environmental noise samples, which were recorded in autumn 2012 and summer 2013 in different parts of the Spanish Atlantic coast in northwest Spain. The schematic diagram of ShipsEar database acquisition is shown in The amplifier used a 100 Hz high-pass filter to suppress marine background noise, the hydrophone sampling rate is 52,734 Hz, and the AD converter bit depth is 24 bits.This experiment used all the files in the ShipsEar database. Similar to the authors of , we manuIn this paper, a depth clustering algorithm combining RBM automatic encoder and BP neural network is used for underwater acoustic target recognition. This method can extract features and classify targets without manual intervention. Using a RBM auto-encoder to perform layer-by-layer self-supervised auto-encoding on the original signal can extract high-level abstract information that is beneficial to clustering. Two real acoustic databases were used to test the recognition effect of the proposed system. In two database tests, the proposed system shows better performance than the classification system based on hand-crafted features, which proves the effectiveness of the proposed system. The proposed method provides a good technical support for the target classification and recognition function of sonar system. The feature extraction and target classification of the proposed method under the condition of small sample size and low SNR is worthy of further study.


Deep anterior lamellar keratoplasty (DALK) is a highly challenging procedure for cornea transplant that involves removing the corneal layers above Descemet’s membrane (DM). This is achieved by a “big bubble” technique where a needle is inserted into the stroma of the cornea down to DM and the injection of either air or liquid. DALK has important advantages over penetrating keratoplasty (PK) including lower rejection rate, less endothelial cell loss, and increased graft survival. In this paper, we successfully designed and evaluated the optical coherence tomography (OCT) distal sensor integrated needle for a precise big bubble technique. We successfully used this sensor for micro-control of a robotic DALK device termed AUTO-DALK for autonomous big bubble needle insertion. The OCT distal sensor was integrated inside a 25-gauge needle, which was used for pneumo-dissection. The AUTO-DALK device is built on a manual trephine platform which includes a vacuum ring to fix the device on the eye and add a needle driver at an angle of 60 degrees from vertical. During the test on five porcine eyes with a target depth of 90%, the measured insertion depth as a percentage of cornea thickness for the AUTO-DALK device was  Blindness is a global public health issue and the worldwide blind population is estimated to be 36 million with an estimated 1.5–2.0 million new cases of unilateral blindness every year . DALK prThe goal of DALK is to remove most stromal tissue, leaving only the endothelium, DM, and a small amount of residual stromal tissue. Some authors have argued that removal of all stromal tissue is the goal, which is referred to as “baring of DM” . HoweverTherefore, there is a need for novel DALK technologies that could decrease the rate of perforation and conversion to PK. The key to a successful full big-bubble formation is the precise depth control of the needle insertion. Many surgeons believe the needle must get as close as possible to DM, which could be interpreted as 90+% corneal depth without perforation ,15,16. Smain OCT . The intmain OCT . Kang etmain OCT ,27. B-momain OCT ,29,30. Bmain OCT  and Shinmain OCT . HoweverIn this paper, we successfully designed and evaluated the OCT distal sensor integrated needle for the big bubble technique for DALK. We also used OCT guidance for micro-control of an ocular robot, the AUTO-DALK device. This device allows for the autonomous insertion of a needle for pneumo-dissection based on the depth-sensing results from the OCT system. An earlier prototype of AUTO-DALK was introduced by the authors in . In contTo acquire the depth information from the single-mode fiber , we utilized the home-built common-path swept source OCT system (CP-SSOCT), as shown in As shown in ectively . The typvertical ,9,16 forn, y is the A-scan image after filtering and h is the Gaussian filter with the length of The processed A-scan image is passed to the next step that detects the back boundary of the cornea, which is the DM and endothelium layer in our case. At this preliminary test, the AUTO-DALK device employs a fairly simple tracking and closed-loop control system. The 1 by 5 Gaussian averaging filter is used to smooth the A-scan image and reduce the noise. To apply the filter across the whole A-scan image, this was achieved by convolving the 1D Gaussian distribution with the original A-scan image and Equation (Then, we apply first-order derivation to the filtered A-scan image, which can be described as Equation . We threThis position information is updated and fed into the control loop. The updated measurement is compared with the preset distance, which determines how close the needle should get to the DM, usually around 100 To test the proposed sensor integrated needle’s ability to track the tissue layer during deformation, the experiment was conducted on ex-vivo porcine eyes, purchased from Wagner’s Meat in Mt Airy, MD. The test was done on the same day that the eyes were harvested. For better visualization, the cornea was carefully separated from the eye and fixed on a special cornea holder. The holder was designed based on the commercially available cornea holder with an additional opening on the side to be able to image the cross-sectional view of the cornea. This will allow us to match the A-scan image with the physical image of the cornea. The holder was 3D printed on an uPrint SE Plus ABS printer and it can hold the cornea in position during the test and preserve the normal curvature of the cornea. The needle was fixed on a vertical translational stage to approach the cornea precisely and the offset between the needle tip and OCT sensor reference plane was set to 550 To compare unassisted and assisted freehand insertion, the experienced surgeon performed the test with a normal 27 G needle and a 25 G distal sensor integrated needle on five porcine eyes for each test. The reason for the use of porcine cornea is because it shares many biomechanical properties with human cornea . The freTo validate the performance of the AUTO-DALK device, a similar insertion test was performed. One thing to notice is that the porcine eyes used for testing the AUTO-DALK device was two days after the eyes were harvested due to the problem of logistics. This resulted in the thickness of the eyes in these groups increased 100 to 200 p-value was h-value was The results from the three test groups were plotted in Once the needle was inserted into 90% above the thickness of the cornea during the standard DALK procedure, the air will be injected to separate the DM from the stroma to form the big bubble. However, during our tests on porcine eyes, the big bubble could not be achieved even when the insertion depth was above 90% of the cornea depth. The possible reason for this was that the porcine cornea we acquired from the local slaughterhouse exhibited cornea clouding indicating significant endothelium cell loss ,39. AlsoThis feasibility experiment was conducted on five bovine eyes consecutively with the assisted manual insertion. The insertion depth was recorded as 93.01%, 90.12%, 92.41%, 90.81% and 82.84%, respectively. A 10 ml syringe was connected to the needle for air injection due to the larger size of the bovine cornea. Big bubbles were achieved and DM was fully separated as  side up c and theIn this paper, we successfully implemented the OCT distal sensor integrated needle and demonstrated the AUTO-DALK device for autonomous needle insertion on the porcine cornea. As a significant determiner of pneumo-dissection success , the AUTCompared to the previous M-mode OCT integrated needle approach , they prNotably, there are some limitations to this work. Due to the weight of the device and air leakage at the eye interface of the device, the suction mechanism did not provide enough holding force to fixate the device on the eye, requiring manual stabilization during insertions. One might expect a decreased perforation rate in the assisted freehand insertion and AUTO-DALK trail, however, we only used five eyes for each test and there was no perforation in our case. Also, the average time used to complete the needle insertions for the three groups was similar. One reason for the relatively slow speed for the autonomous insertions was that the motor was set to the lowest speed for better monitoring during this feasibility study. Another reason is during the initial insertion, the tracking algorithm did not work as well due to relatively low SNR of the DM layer on the A-scan image and lower speed can help us avoid perforations. Lastly, the ultimate proof of success is autonomous needle insertion and full pneumo-dissection, which we had to test separately due to limitations of the tested eyes.The future work will be focused on designing a more compact device, improving the suction, and reducing the weight for the whole device. Currently, we used a DC brushed motor for the insertion task and the size and weight of this motor took a great part in the total size and weight. This could be lower by using a compact motor or the advanced piezo motor. With the decreased total weight, the suction will be improved and the main body of the device can be printed in a glossy style to improve the airtightness at the interface. Also, signal processing and control methods can be further improved by including PID control and data filtering methods to achieve faster insertion speed and reduce depth variability. Although PID control requires significant computing power for the integrative part and this could slow down the whole system speed, this problem can be addressed by using GPU for data processing. Another possible method could be the use of a Kalman filter, which has been applied to the previous CP-SSOCT system for motion compensation and has the ability to catch up with the sensing speed . Future In summary, our AUTO-DALK platform offers real-time distal sensing and improved needle insertion depth in a porcine eye DALK model. We believe this can be used for improving the DALK success rate and thereby lower the graft rejection rate. This could lead us to a more efficient and economical cornea treatment in the future.


Although the individuality of whole-body movements has been suspected for years, the scientific proof and systematic investigation that individuals possess unique movement patterns did not manifest until the introduction of the criteria of uniqueness and persistence from the field of forensic science. Applying the criteria of uniqueness and persistence to the individuality of motor learning processes requires complex strategies due to the problem of persistence in the learning processes. One approach is to examine the learning process of different movements. For this purpose, it is necessary to differentiate between two components of movement patterns: the individual-specific component and the discipline-specific component. To this end, a kinematic analysis of the shot put, discus, and javelin throwing movements of seven high-performance decathletes during a qualification competition was conducted. In total, joint angle waveforms of 57 throws formed the basis for the recognition task of individual- and discipline-specific throwing patterns using a support vector machine. The results reveal that the kinematic throwing patterns of the three disciplines could be distinguished across athletes with a prediction accuracy of up to 100% (57 of 57 throws). In addition, athlete-specific throwing characteristics could also be identified across the three disciplines. Prediction accuracies of up to 52.6% indicated that up to 10 out of 19 throws of a discipline could be assigned to the correct athletes, based on only knowing these athletes from the kinematic throwing patterns in the other two disciplines. The results further suggest that individual throwing characteristics across disciplines are more pronounced in shot put and discus throwing than in javelin throwing. Applications for training and learning practice in sports and therapy are discussed. In summary, the chosen approach offers a broad field of application related to the search of individualized optimal movement solutions in sports. Most of us are familiar with the experience of identifying friends or colleagues by their walk , even frIn human movement science, anecdotal evidence has made claims of “individuality” for years . AlthougIn the past, the term individuality most often has been normatively applied in three ways: (1) when no classification criteria could be found , (2) to While movement and sports science still struggle to balance the demands of group-oriented science and individual athlete- and patient-oriented practice, forensic science remains primarily concerned with individual cases that must lend legal validity. Therefore, the field of forensic science has developed specific methods and criteria for the identification of individuals (disjunct separation) . In this–17). This outcome is far below the magnitude of common probabilities used, first, in the statistical model based on the work of The first steps toward such criteria took place in the analysis of everyday and sports movements and revealed the identification of individual people based on gait , runningMeanwhile, the uniqueness and persistence of individual movement patterns could be validated for versatile whole-body movements such as walking , 2019, pIn practice, the identification of athletes based on their individual movement patterns and their corresponding fine structure does not require individually tailored learning or training methods. If we assume that individual differences exist from birth, then theoretically, the same learning and training content could have led to individually distinguishable movement patterns at a later age. However, this awareness is subject to the assumption that everyone responds in the same way to intervention measures. To shed more light on these questions, further studies on individual learning behavior should be conducted.Indications for individual responses on a similar intervention came from physiological  and biomWhile the first criterion could be tested indirectly via the degree of learning progress each participant achieves, testing the criterion of persistence is more complicated. The simplest way to prove the persistence of individual learning characteristics would be to allow athletes to learn the same skill several times after wash-out phases. However, a limitation with this approach has been raised by re-learning studies . Once a This pilot study aims to analyze the three throwing disciplines in the decathlon  and to search for athlete- and discipline-specific similarities in the kinematic throwing patterns of these disciplines using automatic classification by means of machine learning. Instead of merely testing the individuality of athletes in a single throwing discipline, the present classifications are used to test whether the kinematic throwing patterns will be assigned to the correct throwing discipline and whether the knowledge of, for example, an individual athlete’s shot put movement patterns predicts the individual’s discus or javelin movement patterns. For this objective, high-performance athletes competing in a national decathlon qualification competition were selected. This high performance level served to guarantee sufficient stability for all three throwing movements. A competition was selected for this study because it is a setting in which athletes often demonstrate their best performances, which we assume can increase the expression of the individuality in their movement patterns .Seven right-handed, male decathletes (18.9 ± 0.4 years), who were members of the German junior national team with at least 5 years of experience in the decathlon, were recorded during a national decathlon qualifying competition. The final throwing phases of 19 shot puts, 19 discus, and 19 javelin throws were analyzed . For rig3 was covered for the analysis using a calibration cube with 25 marker points. Due to the official competition rules, no marker points were allowed on the athletes. Data of both cameras were synchronized using an electric impulse transmitted from the master camera to the slave camera during each throw. Neither the athletes nor the experienced digitizers were informed about the aim of the study. The following anatomical body landmarks were digitized: the manubrium sterni, the left and right acromion, the epicondylus lateralis, the processus steyloidus ulnae, the spina illiaca anterior superior, the trochanter major, the lateral end of the femur (knee), the patella, the articulatio tibo fibulare talare, the calcaneus, the phalanx distalis, and the hallux.The recordings were taken using two high-frequency video cameras , which were positioned orthogonal to each other, one facing toward the flight direction of the throwing object. A space of 3 × 3 × 3 mThe digitization of these points allowed the estimation of three-dimensional joint angles of the right and left shoulder, elbow, hand, hip, knee, and ankle. All videos were manually digitized with SIMI Motion Software 5.0 . Data were filtered using a recursive, second-order Butterworth filter with a cutoff frequency of 14 Hz. All trials were digitized for an additional 10 video frames at both phase boundary ends because of filter effects at the beginning and end of each signal.For javelin, the duration of the final throwing phase lasts about 130 ms; for discus throwing, about 400 ms; and for shot put, about 200 ms . Despite–5, 2–4.75, …, 215 was conducted to determine C experimentally before the training and testing of the SVM models. An athlete-classification using a leave-discipline-out cross-validation and a discipline-classification using a leave-athlete-out cross-validation were performed. This processing means that data from one discipline (in the case of the athlete-classification) or from one athlete (in the case of the discipline-classification) were used either as training or as test data during the cross-validation of the SVM models. A schematic overview of the entire approach with data acquisition, processing, and classification is depicted in Time- and amplitude-normalized data formed the input vectors for the classifications using a support vector machine (SVM) . The claIn the case of athlete-classification, the kinematic data of one discipline were used in the cross-validation, either as training or as test data  in all three throwing disciplines. The remaining waveforms of the one athlete were used to test the performance of the SVM models for classification into one of the three possible throwing disciplines.The results of the athlete-classification are shown in Similar results were found when the performance of SVM models was determined using the kinematic patterns of shot puts as test data. While the prediction accuracy is slightly lower when all variables are considered, the lowest prediction accuracy of 31.6% is larger than for the discus split and is achieved when all variables and only the lower-body variables are used as test data.The lowest prediction accuracies are generally found when the SVM models are tested based on javelin throwing patterns (15.8–31.6%). When the SVM models for athlete-classification are trained on discus and shot put data, it seems to be more difficult to assign the movement patterns of javelin throwing to the individuals. Similar results can be observed in the pairwise cross-validations, which are also shown in In The results of this study reveal that the kinematic patterns of the three throwing disciplines in the decathlon  could be distinguished independently of the athlete with a prediction accuracy of up to 100% (57 of 57 throws) using an automatic classification using machine learning . In addition, prediction accuracies of up to 52.6% (10 of 19 throws) also indicate the persistence of individual throwing characteristics of athletes across different throwing disciplines. The results further suggest that individual throwing characteristics across disciplines are more pronounced in shot put and discus throwing than in javelin throwing. This finding demonstrates that the approach of classifying movement patterns using machine learning methods allows for the identification of athlete- and discipline-specific similarities in throwing patterns across different disciplines in high-performance athletes and suggests new ways to explore sports training in different disciplines.In the following sections, we discuss the results in more detail. In the athlete-classification, the highest prediction accuracies by SVM models based on all variables except the throwing arm variables mean that every second shot put kinematic pattern was correctly assigned to the corresponding individual athlete when the SVM model was trained on the kinematic throwing patterns of all athletes in javelin and discus throw. Prediction accuracies over ∼50%, which are well above the random baseline of 14.3%, provide a strong indication that individual movement signatures can be detected in different movements . The present findings reinforce previous studies that showed the uniqueness and persistence of individual movement patterns within various movements and support the call for a stronger focus on individual athletes or patients in sports and movement science e.g., .Note that the highest prediction accuracy is achieved using SVM models that consider all joint angle waveforms except the angles of the throwing arm. Comparatively lower prediction accuracies using SVM models that take into account all joint angle waveforms (including the ones of the throwing arm) might be traced to a slightly reduced individuality and a predominant expression of the discipline specificity in the throwing-arm, joint-angle waveforms. However, further research is needed to determine whether this lower prediction accuracy is due to the specificity of the disciplines or due to the variability in throwing arm movements. In this regard, a joint angle-wise classification and determination of movement variability could provide further clarification.Higher prediction accuracies for SVM models based on the joint angles waveforms of the upper body without the throwing arm in shot put and discus throwing provide evidence for increased individuality of the movement of the left arm, trunk, and head in comparison to the waveforms of the lower-body joint angles, which are more restricted by their contact to the ground. Whether lower prediction accuracies of the SVM models based on lower-body joint angles are only due to the comparably coarse biomechanical data acquisition without anatomical markers or due to the small geometric differences in the leg movements cannot be resolved satisfactorily here.Considerably lower prediction accuracies of SVM models for athlete-classification that were trained with the kinematic patterns of shot put and discus throw and tested with javelin throws are in line with findings of national  and inteIn discipline classification, a prediction accuracy of 100% for most cross-validation splits and combinations of considered variables implies an automatic and differentiated recognition of shot put, discus, and javelin throwing movement patterns. The results provide promising evidence for the ability of pattern recognition approaches using machine learning methods to distinguish between different qualities of whole-body movements .Finally, some specificities of this pilot study should be kept in mind. The chosen pattern recognition approach based on probabilities relative to the number of choices is distinguished from null-hypothesis-testing approaches. No claims for generalization are made. In addition, the demand for a relatively high level of performance in different sports disciplines limited the possibilities for empirical data collection enormously. Some limitations arise from selecting decathletes on their way from juvenile to adult competition classes as the object for this pilot study. The athletes’ age suggests that some may not have completed puberty, and ongoing physical growth could have an additional influence on the consistency of their movement patterns. To what extent incomplete physical growth influences throwing patterns and throwing consistency requires further research.The results offer evidence for the possibility of automatic recognition of kinematic movement patterns originating from different sports disciplines and confirm the assumption of a strong and cross-disciplinary importance of individuality in at least two of the throwing disciplines investigated. That certain individual movement characteristics can be identified in the kinematic patterns of both shot put and discus throwing is intriguing. This finding must be distinguished from the recognition of an individual athlete within a single discipline, as shown for discus  and javeA further criterion for individuality, which could be summarized by homomorphism, could be added to the necessary criteria of uniqueness and persistence. Different from static biometric measures such as fingerprints, facial characteristics, or ear shapes, which are frequently directly related to static genetics, movement-based biometry is subject to dynamic changes and uncertain associations to the genome. While it is difficult to find a common underlying basis for the biometrics of finger, face, or ear apart from genetics, the comprehension of individual commonalities in different movements  could provide access to the underlying individuality of central nervous physiology and structure. Future applications of this approach could investigate the extent to which the central nervous system or the muscle physiology are modifiable beyond an individual’s range.Against this backdrop, the probability of finding a single (time-independent) optimal movement pattern for an individual athlete is more than challenging. Instead, rethinking the understanding of an optimal movement pattern is promising. An extension of the term “optimal” by situation-optimal, as the currently optimal solution for an individual athlete, may be initially tempting. However, an optimal solution would only serve as a theoretical model and could never be realistically achieved. Because the motor system of an individual is constantly changing and adapting, the model of a situation-optimal movement pattern would also have to constantly change and adapt. Alternatively, the assumption of a situation-optimal model that is constantly changing could be more advantageous for motor learning than for the pursuit of an insurmountable goal.The study showed that an applied pattern recognition approach based on a machine learning classification provides an alternative and holistic approach for the analysis of biomechanical movement data. This approach is closely connected to a statistical method based on the original concept of probabilities and may help to circumvent some of the limitations connected with the Taken together, the findings of human movement science regarding the uniqueness and persistence of individual movement patterns based on machine learning methods and the insights into the influencing factors indicated in this study suggested that we are still at the beginning of understanding the individuality of moving and learning human beings.The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation, to any qualified researcher.Ethical review and approval was not required for the study on human participants in accordance with the local legislation and institutional requirements. Written informed consent for participation was not required for this study in accordance with the national legislation and the institutional requirements.All authors contributed to the article, critically revised the manuscript, and approved the final version. WIS designed the experiment. DJ, HB, and WIS conducted the acquisition and processing of the data. FH, DJ, and HB analyzed the data. FH, DJ, and WIS designed the figure. FH and WIS interpreted the data and wrote 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.


Trans-esophageal echocardiography (TEE) is a miniatured intra-operative ultrasound system, widely used in routine diagnosis and interventional procedure monitoring, to assess cardiac structures and functions. As a way to assist the operation of TEE remotely, we have developed an add-on robotic system to actuate a commercial TEE probe. For the proposed robot, understanding the inverse kinematics (IK) which relates the probe pose to the joint parameters is the fundamental step towards automatic control of the system. Rather than using conventional numerical-based techniques which may have problems with speed, convergence, and stability when applying to the TEE robot, this paper explores a soft computing approach by constructing an Adaptive Neuro-Fuzzy Inference System (ANFIS) to learn from training data generated by the forward kinematics (FK) and then computing the inverse kinematics in order to control the orientation of the TEE probe. With 1900 training data over 40 epochs, the minimum training error for each joint parameter was found to be less than 0.1 degree. Validation using a separate data set has indicated that the maximum error was less than 0.3 degree for each joint parameter. It is therefore concluded that the ANFIS-based approach is an effective way, with acceptable accuracy, to compute the inverse kinematics of the TEE robot. TEE Working towards automation, understanding the kinematics of the TEE robot is a fundamental step. In our previous work, a model for forward kinematics (FK), which relates the joint parameters to the location of the probe tip, i.e., its position and orientation, was descripted and validated experimentally. While the forward kinematic model is established, the inverse kinematics (IK) problem, which aims to calculate the joint parameters when giving a target probe tip location, is found to be a challenging task, since the mapping between Cartesian space and joint space is non-linear in nature and involves redundancy problem with multiple solutions caused by the combination use of handle rotation axis and bi-directional bending axes of the TEE robot in adjusting the orientation of the probe tip.While the exact IK solution of the TEE robot is seldom covered in the literature, several existing works have been presented for the similar types of robot where the actuated device is similar to the TEE probe mechanically, such as the intra-cardiac ultrasound catheter robot , the endIn this paper, we aim to explore a soft computing approach by constructing an Adaptive Neuro-Fuzzy Inference System (ANFIS) –11 to le2The developed add-on TEE robot  holds anv, which can be described by a standard rigid transformation. The bi-directional bending of the probe tip has been previously described in [x,φ which controls the bending tip pitch in the posterior-anterior plane; and the rotation angle of the second bending knob, y,φ which controls the bending tip yaw in the left-right plane. The geometric illustration of the FK of the TEE robot is shown in f is the length of the bending section of the probe tip, and Lu is the length of the rigid section of the probe tip. In the matrix, Sx and Cx, respectively, denote sin(x) and cos(x).With the translation of the probe modelled by an interpolation of the esophagus’s centre line, the probe base coordinates could be easily decided mathematically. The FK then models rotational movements of the TOE probe handle with the rotation angle ribed in . The inp3Fuzzification: Every node in this layer adapts to a function parameter. The output from each node is a degree of membership value that is given by the input of the membership functions.Inference: Every node in this layer is a fixed or non-adaptive node. In each node, the result of multiplying of signal coming into the node is firstly calculated, which represents the firing strength for each rule. Then the ratio between the i-th rules firing strength and the sum of all rules’ firing strengths, known as the normalized firing strength, is the output from each node.Implication: Every node in this layer is an adaptive node to an output. The output from each node is decided by the output membership function.Aggregation: The single node in this layer is a fixed or non-adaptive node that computes the overall output as the summation of all incoming signals from the previous node.ANFS is a combination of two soft-computing methods of artificial neural network (ANN) and fuzzy inference system (FIS), which constructs an adaptive network that uses supervised learning on learning algorithm. By using ANN to automatically adjust parameters of membership functions (MFs) of fuzzy inference systems, the network brings learning capabilities of neural networks to fuzzy inference systems. Using ANFIS, the inputs are mapped through input MFs and associated parameters, and then through output MPs and associated parameters to outputs. The parameters associated with the MFs changes through the learning process. The computation of these parameters is facilitated by back propagation algorithm alone or in combination with a least squares type of method. The structures of the ANFIS network can be divided into fuzzification, inference, implication, and aggregation:v and the rotation angle of the bending knobs xφ and yφ. In total, the ANFIS architecture consists of three ANFIS networks, with one for each joint parameter. In the network, four input membership functions are utilized based on our experimental trials. Accordingly, 64 numbers of fuzzy rules and output membership functions are constructed. The structure of the considered ANFIS network is shown in In the specific application of TEE, where the probe is constrained by the esophagus (less than 2 cm in diameter) and orientated towards the heart for ultrasound imaging, the translation movement along the esophagus can be easily decoupled and compensated from the kinematics model mathematically. As for the remaining Cartesian parameters of the probe tip location: Trans-X, Trans-Y, Rot-X, Rot-Y, and Rot-Z, the control of the orientation of the probe tip is important for visualizing the heart, while the translation offsets due to the orientation change are small and have less impacts to the ultrasound visualization with the constraint from the esophagus. Therefore, the inputs of the considered ANFIS network are set to be the three rotation angles representing the orientation of the probe tip and the outputs are the joint parameters, the handle rotation angle 4Using the FK solution described by formula (1), training data (1900 sets) are generated for different combinations of the robot joint parameters  at certain intervals within the working space and the resulted probe tip orientation (three decomposed rotation angles). After our experimental trials, 40 training epochs were selected for training of each ANFIS network. The change of the training error after each epoch is shown in To validate the performance of the ANFIS-based IK after each network is trained, a separate validation data set is created using the FK model with 100 randomly generated probe tip locations within the workspace. The according joint parameters were recorded and treated as the desired outputs of the IK solution. The three decomposed rotation angles were fed into each ANFIS network and the combined outputs will result in a set of output joint parameter. The differences between the desired joint parameters and the resulted output joint parameter from the ANFIS networks are treated as the errors of the proposed method, which are summarized in 5This paper introduces an ANFIS-based IK solution of a newly developed TEE robot, aiming at control the orientation of the TEE probe to visualize the heart. Three ANFIS networks were established with orientation of the probe tip as the input and the three joint parameters as the outputs. The networks were successfully trained using the data set generated by the FK solution with errors converged to the values less than 0.1 degree. A separate randomly generated data set was used for validation with maximum errors found to be less than 0.3 degree for all the joint parameters. The ANFIS-based IK solution has shown a reliable performance with an acceptable error range. Compared with the conventional numerical-based IK solutions, the ANFIS-based soft computing approach do not require heavy interactive computing for calculation of the joint parameters once the networks are trained. Therefore, such solution would be ideal for real-time applications, such as the feedback control to automatically adjust the TEE probe in order to keep track of a moving structures or interventional devices. Compared with geometry-based analytical IK solutions which are usually difficult to derive, the ANFIS-based solution is easy to implement with the training data generated using the FK, thus providing a more generic solution. This paper has verified the effeteness and accuracy for the ANFIS-based IK method used for a TEE robot. Considering the similar mechanical configuration, it is believed that several other types of endoscopic robots could use the same solution for calculating IK.Based on the results from the training and the validation experiments, it is concluded that the ANFIS-based IK solution is feasible and accurate for control the orientation of the TEE robot. Future works will focus on performing more task-driven experiments to further validate the method and also incorporating the additional DOF of ultrasound image steering controlled by the ultrasound system into the ANFIS networks to provide an extended IK solution to the image space.