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Copyright of Systems Engineering & Electronics is the property of Journal of Systems Engineering & Electronics Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In the time-frequency analysis of the echoes from targets with composite micro-motions, there is a problem that the energy of time-frequency strip is difficult to focus, which affects the extraction and analysis of the target's micro-Doppler feature. To solve this problem, this paper proposes the concept of time-frequency harmonic stripes and corresponding time-frequency transformation methods. Based on the assumption that the composite micro-motions include central motion components and high-frequency motion components, firstly, spline function is used to fit the frequency curve of the central motion component, and therefore construct the time-frequency transform kernel is constructed. Then, parameterized time-frequency transform is used to convert the composite micro-Doppler signal in the time domain to harmonic stripes on the time-frequency plane. Compared with traditional methods such as short-time Fourier transform and pseudo Wigner-Ville distribution, the proposed method can significantly improve the energy aggregation and spur suppression of time-frequency transform of radar echoes from composite micro-motion targets. Simulation results verify the effectiveness of the proposed method and its adaptability to low signal-to-noise ratio (SNR) conditions. [ABSTRACT FROM AUTHOR]

AbstractThe optimal design of complex engineering systems requires tracing precise mathematical modeling of the system’s behavior as a function of a set of design variables to achieve the desired design. Despite the success of current tibial components of knee implants, the limited lifespan remains the main concern of these complex systems. The mismatch between the properties of engineered biomaterials and those of biological materials leads to inadequate bonding with bone and the stress-shielding effect. Exploiting a functionally graded material for the stem of the tibial component of knee implants is attractive because the properties can be designed to vary in a certain pattern, meeting the desired requirements at different regions of the knee joint system. Therefore, in this study, a Ti6Al4V/Hydroxyapatite functionally graded stem with a laminated structure underwent simulation-based multi-objective design optimization for a tibial component of the knee implant. Employing finite element analysis and response surface methodology, three material design variables (stem’s central diameter, gradient factor, and number of layers) were optimized for seven objective functions related to stress-shielding and micro-motion (including Maximum stress on the cancellous bone, maximum and mean stresses on predefined paths, the standard deviation of mean stress on paths, maximum and mean micro-motions at the bone-implant interface and the standard deviation of mean micro-motion). Then, the optimized functionally graded stem with 6 layers, a central diameter of 5.59 mm, and a gradient factor of 1.31, was compared with a Ti6Al4V stem for various responses. In stress analysis, the optimal stem demonstrated a 1.92% improvement in cancellous bone stress while it had no considerable influence on the maximum, mean, and standard deviation of stresses on paths. In micro-motion analysis, the maximum, mean, and standard deviation of mean micro-motion at the interface were enhanced by 24.31%, 39.53%, and 19.77%, respectively. [ABSTRACT FROM AUTHOR]

Multi-rotor aircraft have the advantages of a simple structure, low cost, and flexible operation in the unmanned aerial vehicle (UAV) family, and have developed rapidly in recent years. Radar surveillance and classification of the growing number of multi-rotor aircraft has become a challenging problem due to their low-slow-small (LSS) characteristics. Estimation of the blade number is an important step in distinguishing LSS targets. However, most of the current research on micro-motion parameters estimation has focused on the analysis of rotational frequency, length, and the initial phase of blades with a prior of blade number, affecting its ability to identify LSS targets. In this article, a micro-motion parameters estimation method for multi-rotor targets without a prior is proposed. On the basis of estimating the flashing frequency of the blades, a validation function is constructed through spectral analysis to judge the number of blades, and then the rotational frequency is estimated. The blade length is calculated by estimating the maximum Doppler shift. Moreover, the variational mode decomposition (VMD)-based atomic scaling orthogonal matching pursuit (AS-OMP) method is jointly applied to estimate the blade length when suffering from the low PRF and insufficient SNR conditions. Extensive experiments on the simulated and measured data demonstrate that the proposed method outperforms robust micro-motion parameter estimation capability in low PRF and insufficient SNR conditions compared to the traditional time-frequency analysis methods. [ABSTRACT FROM AUTHOR]

Motion characterization, including Doppler and micro-Doppler, is crucial for the detection and identification of high-speed underwater targets. Under high-frequency and short-range conditions, underwater targets cannot be simply regarded as single highlight targets as they exhibit a complex structure with multiple scattering centers accompanied by distinct micro-motions. To address this multi-highlight and multi-micro-motion scenario, a model is proposed to characterize the motion features of underwater targets. Firstly, a mathematical model is established to represent the micro-Doppler features based on the single-highlight model. Subsequently, considering the overlap of multiple highlight echoes caused by the high-speed translation of the target and the long pulse detection signal, precise representation is achieved by setting motion positions and calculating time delays within the model. The results represent the echoes of moving targets with multiple highlights and micromotions. Finally, a time-frequency analysis method is employed to extract motion features and estimate target parameters, thereby validating the accuracy and effectiveness of the proposed model. This research provides a theoretical foundation for the modeling of underwater moving targets. [ABSTRACT FROM AUTHOR]

Micro-Doppler time–frequency analysis has been regarded as an important parameter extraction method for conical micro-motion objects. However, the micro-Doppler effect caused by micro-motion can modulate the frequency of lidar echo, leading to coupling between structure and micro-motion parameters. Therefore, it is difficult to extract parameters for micro-motion cones. We propose a new method for parameter extraction by combining the range profile of a micro-motion cone and the micro-Doppler time–frequency spectrum. This method can effectively decouple and accurately extract the structure and the micro-motion parameters of cones. Compared with traditional time–frequency analysis methods, the accuracy of parameter extraction is higher, and the information is richer. Firstly, the range profile of the micro-motion cone was obtained by using an FMCW (Frequency Modulated Continuous Wave) lidar based on simulation. Secondly, quantitative analysis was conducted on the edge features of the range profile and the micro-Doppler time–frequency spectrum. Finally, the parameters of the micro-motion cone were extracted based on the proposed decoupling parameter extraction method. The results show that our method can effectively extract the cone height, the base radius, the precession angle, the spin frequency, and the gravity center height within the range of a lidar LOS (line of sight) angle from 20° to 65°. The average absolute percentage error can reach below 10%. The method proposed in this paper not only enriches the detection information regarding micro-motion cones, but also improves the accuracy of parameter extraction and establishes a foundation for classification and recognition. It provides a new technical approach for laser micro-Doppler detection in accurate recognition. [ABSTRACT FROM AUTHOR]

In the realm of ballistic target analysis, micro-motion attributes, such as warhead precession, nutation, and decoy oscillations, play a pivotal role. This paper addresses these critical aspects by introducing an advanced analytical model for assessing the Doppler power spectra of convex quadric revolution bodies during precession. Our model is instrumental in calculating the Doppler shifts pertinent to both precession and swing cones. Additionally, it extends to delineate the Doppler power spectra for configurations involving cones and sphere–cone combinations. A key aspect of our study is the exploration of the effects exerted by geometric parameters and observation angles on the Doppler spectra, offering a comparative perspective of various micro-motion forms. The simulations distinctly demonstrate how different micro-motion patterns of a cone influence the Doppler power spectra and underscore the significance of geometric parameters and observational angles in shaping these spectra. This research not only contributes to enhancing LIDAR target identification methodologies but also lays a groundwork for future explorations into complex micro-motions like nutation. [ABSTRACT FROM AUTHOR]

Radar echo modeling based on dynamics and kinematics serves as the theoretical basis for micro-Doppler characteristic analysis and projectile parameter extractions. First, the initial disturbance of a projectile in the straight-line ballistic segment is analyzed. Based on the dynamic equation of the projectile, an angular motion model of the projectile characterized by two circular motion modes is established. Moreover, the motion definitions of projectile spin, nutation, and precession are explained. Subsequently, the parameterized characterization of the micro-Doppler signal produced by the angular motion of the projectile is derived. Furthermore, the mapping relationship between the angular motion of the projectile and the radar echo is obtained at the signal level. Taking high-speed spin projectile and a low-speed spin tail projectile as examples, when the angular motion of the two targets are affected by the initial disturbance, the radar echo signal model of the two targets is simulated and time-frequency analysis is carried out. The validity of the theoretical analysis and the model is verified by comparing the simulation results with the measured data of the projectile. Therefore, the micro-Doppler effect theory of projectile is enriched and verified through theoretical analysis, simulation modeling, and experimental verification. This study provides theoretical and technical support for the identification and analysis of projectile motion characteristics.

In this paper, we study the micro-motion characteristics of multi-feature targets based on a double pulse coherent system under atmospheric conditions. The theoretical model for echo signal and micro-motion characteristics of a 3D target in double pulse coherent system is deduced. We discuss the influence of micro-motion characteristics, the relative size of light spot and target, target shapes, and incident direction on frequency shift. LRCS (Lidar cross-section), echo waveform, intensity and radiation energy distribution under different conditions are obtained additionally. Simulation results conclude that these parameters are of advantage to the inversion of target shape properties and motion types.

Space target classification based on micro-motion characteristics has become a subject of great interest in the field of radar, particularly when using deep learning techniques. However, in practical applications, the ability of deep learning is hampered by the available radar datasets. As a result, obtaining a sufficient amount of the training dataset is a daunting challenge. To address this issue, this paper presents a novel framework for space target classification, consisting of three distinct modules: dataset generation, the kinematically sifted module, and classification. Initially, the micro-motion model of cone-shaped space targets is constructed to analyze target characteristics. Subsequently, the dataset generation module employs a complex-valued generative adversarial network (CV-GAN) to generate a large number of time-range maps. These maps serve as the foundation for training the subsequent modules. Next, the kinematically sifted module is introduced to eliminate images that do not align with the micro-motion characteristics of space targets. By filtering out incompatible images, the module ensures that only relevant and accurate dataset is utilized for further analysis. Finally, the classification model is constructed using complex-valued parallel blocks (CV-PB) to extract valuable information from the target. Experimental results validate the effectiveness of the proposed framework in space micro-motion target classification. The main contribution of the framework is to generate a sufficient amount of high-quality training data that conforms to motion characteristics, and to achieve accurate classification of space targets based on their micro-motion signatures. This breakthrough has significant implications for various applications in space target classification. [ABSTRACT FROM AUTHOR]

Radar micro-motion signatures help to judge the target's motion state and threat level, which plays a vital role in space situational awareness. Most of the existing micro-motion feature extraction methods derived from time-frequency (TF) representation cannot simultaneously satisfy the requirements of high resolution and multiple component representation, which has limitations on processing intersected multi-component micro-motion signals. Meanwhile, as the micro-motion features extracted from the TF spectrograms only focus on the global characteristics of the targets and ignore the physical properties of micro-motion components, it leads to poor performance in micro-motion discrimination. To address these challenges, we empirically observed a decrease in the probability of intersection between the components within the time-frequency-frequency rate (TFFR) space, where components appeared as separated and non-intersecting spatial trajectories. This observation facilitates the extraction and association of multiple components. Given the differences in modulation laws among various micro-motions in the TFFR space, we introduced a novel micro-motion identification method based on scatterer-level TFFR representation. Our experimental evaluations of different targets and micro-motion types demonstrate the efficacy and robustness of this proposed method. This method not only underscores the separability of signal components but also expands the scope of micro-motion discrimination within the TFFR domain. [ABSTRACT FROM AUTHOR]

Spectrum aliasing occurs in signal echoes when the sampling frequency does not comply with the Nyquist Sampling Theorem. In this scenario, the extraction of micro-motion parameters becomes challenging. This paper proposes a spectral de-aliasing method for micro-motion signals based on a complex-valued U-Net network. Zero interpolation is employed to insert zeros into the echo, effectively increasing the sampling frequency. After zero interpolation, the micro-motion signal contains both real micro-motion signal frequency components and new frequency components. Short-Time Fourier Transform (STFT) is then applied to transform the zero-interpolated echo from the time domain to the time–frequency domain. Furthermore, a complex-valued U-Net training model is utilized to eliminate redundant frequency components generated by zero interpolation, thereby achieving the frequency reconstruction of micro-motion signal echoes. Finally, the training models are employed to process the measured data. The theoretical analysis, simulations, and experimental results demonstrate that this method is robust and feasible, and is capable of addressing the problem of micro-motion signal echo spectrum aliasing in narrowband radar. [ABSTRACT FROM AUTHOR]

The detection and recognition of marine targets can be improved by utilizing the micro-motion induced by ocean waves. However, distinguishing and tracking overlapping targets is challenging when multiple extended targets overlap in the range dimension of the radar echo. In this paper, we propose a multi-pulse delay conjugate multiplication and layered tracking (MDCM-LT) algorithm for micro-motion trajectory tracking. The MDCM method is first applied to obtain the conjugate phase from the radar echo, which enables high-precision micro-motion extraction and overlapping state identification of extended targets. Then, the LT algorithm is proposed to track the sparse scattering points belonging to different extended targets. In our simulation, the root mean square errors of the distance and velocity trajectories were better than 0.277 m and 0.016 m / s , respectively. Our results demonstrate that the proposed method has the potential to improve the precision and reliability of marine target detection through radar. [ABSTRACT FROM AUTHOR]

Introduction: Elliptical-shaped humeral head prostheses have recently been proposed to reflect a more anatomic shoulder replacement. However, its subsequent effect on micro-motion of the glenoid component is still not understood. Materials and methods: Six fresh-frozen, cadaveric shoulders (mean age: 62.7 ± 9.2 years) were used for the study. Each specimen underwent total shoulder arthroplasty using an anatomic stemless implant. At 15°, 30°, 45° and 60° of glenohumeral abduction, 50° of internal and external rotations in the axial plane were alternatingly applied to the humerus with both an elliptical and spherical humeral head design. Glenohumeral translation was assessed by means of a 3-dimensional digitizer. Micro-motion of the glenoid component was evaluated using four high-resolution differential variable reluctance transducer strain gauges, placed at the anterior, posterior, superior, and inferior aspect of the glenoid component. Results: The elliptical head design showed significantly more micro-motion in total and at the superior aspect of glenoid component during external rotation at 15° (total: P = 0.004; superior: P = 0.004) and 30° (total: P = 0.045; superior: P = 0.033) of abduction when compared to the spherical design. However, during internal rotation, elliptical and spherical heads showed similar amounts of micro-motion at the glenoid component at all tested abduction angles. When looking at glenohumeral translation, elliptical and spherical heads showed similar anteroposterior and superoinferior translation as well as compound motion during external rotation at all tested abduction angles. During internal rotation, the elliptical design resulted in significantly more anteroposterior translation and compound motion at all abduction angles when compared to the spherical design (P < 0.05). Conclusion: In the setting of total shoulder arthroplasty, the elliptical head design demonstrated greater glenohumeral translation and micro-motion at the glenoid component during axial rotation when compared to the spherical design, potentially increasing the risk for glenoid loosening in the long term. Level of evidence: Controlled Laboratory Study [ABSTRACT FROM AUTHOR]

Radar cross section (RCS) sequences, an easy-to-obtain target feature with small data volume, play a significant role in radar target classification. However, radar target classification based on RCS sequences has the shortcomings of limited information and low recognition accuracy. In order to overcome the shortcomings of RCS-based methods, this paper proposes a spatial micro-motion target classification method based on RCS sequences encoding and convolutional neural network (CNN). First, we establish the micro-motion models of spatial targets, including precession, swing and rolling. Second, we introduce three approaches for encoding RCS sequences as images. These three types of images are Gramian angular field (GAF), Markov transition field (MTF) and recurrence plot (RP). Third, a multi-scale CNN is developed to classify those RCS feature maps. Finally, the experimental results demonstrate that RP is best at reflecting the characteristics of the target among those three encoding methods. Moreover, the proposed network outperforms other existing networks with the highest classification accuracy. [ABSTRACT FROM AUTHOR]

As a stable and widely covered signal resource, a Global Navigation Satellite System (GNSS) plays an important part in micro-Doppler extraction in a near field. This paper aims at the problems associated with rotor blade target recognition and proposes a novel solution based on phase compensation. First, the mathematical mechanism of flicker distribution in a time-frequency field is analyzed, and phase compensation is used to achieve the Doppler focusing, and then the blade number can be estimated. Second, according to the that the minimum delta frequency principle between the center frequency and standard frequency, the rotation velocity of the rotor blade is obtained. Next, blade lengths can be calculated through the flicker bandwidth in the time-frequency domain. Finally, the simulation experiments validate the effectiveness of the proposed method.

A problem with synthetic aperture radars (SARs) is that due to the poor penetrating action of electromagnetic waves within solid bodies, the ability to see through distributed targets is precluded. In this context, indeed, imaging is only possible for targets distributed on the scene surface. This work describes an imaging method based on the analysis of micro-motions present in volcanoes and generated by the Earth's underground heat. Processing the coherent vibrational information embedded in a single SAR image, in the single-look-complex configuration, the sound information is exploited, penetrating tomographic imaging over a depth of about 3 km from the Earth's surface. Measurement results are calculated by processing a single-look-complex image from the COSMO-SkyMed Second Generation satellite constellation of Vesuvius. Tomographic maps reveal the presence of the magma chamber, together with the main and the secondary volcanic conduits. This technique certainly paves the way for completely new exploitation of SAR images to scan inside the Earth's surface. [ABSTRACT FROM AUTHOR]

In this study, we consider real observation scenarios and propose an efficient method to accurately distinguish drones from birds using features obtained from their micro-Doppler (MD) signatures. In the simulations conducted using a rotating-blade model and a flapping-wing model, the classification result degraded significantly due to the diversity of both drones and birds, but a combination of features obtained for longer observation times significantly improved the accuracy. MD bandwidth was found to be the most efficient feature, but sufficient observation time was required to exploit the period of time-varying MD as a useful feature.

It is of great significance to detect drones in airspace due to the substantial increase and regrettable misuse in the consumer market. In this paper, we establish a micro-motion theoretical model of a drone and analyze the micro-Doppler signature of rotor targets and the flicker mechanisms of the multi-rotor targets. Hence, for the target recognition problem of multi-rotor drones, a multi-rotor target micro-Doppler parameter estimation method is proposed. Firstly, a signal frequency domain segmentation method is proposed based on the complex variational mode decomposition (CVMD) to separate the high-frequency part of the high-frequency flicker in the frequency domain. Secondly, for the signal after frequency domain segmentation, a flicker time domain position method based on singular value decomposition (SVD) is proposed. Finally, by integrating CVMD frequency domain segmentation and SVD time domain positioning, the reconstruction of multi-rotor target scintillation at different speeds is realized, and the micro-motion parameters of rotor blades are successfully estimated. The simulation results show that the method has high accuracy in estimating the micro-motion parameters of a multi-rotor, which makes up for the shortage of the traditional method in estimating the micro-motion parameters of the multi-rotor target. [ABSTRACT FROM AUTHOR]

The micro-Doppler (m-D) signature can describe the motion state and structure of the target in detail. It can be considered as an effective method for ship target detection and recognition in the marine environment. In this paper, an effective strategy is proposed to simulate the m-D signature and extract the ship's motion parameters on the time-varying sea surface. For this purpose, a ship target micro-motion model based on six degrees of freedom is first established. Then, the public domain strip (PDSTRIP) method is used to realize real-time estimation of ship motion. Next, a high-precision electromagnetic scattering model is used to generate radar echoes of a ship on the rough sea. Finally, the m-D signature can be acquired and analysed based on the obtained radar echo. The numerical results show that the instantaneous frequency and motion parameters can be estimated well from the m-D signature. At the same time, the proposed model can also be used to calibrate the Inverse Synthetic Aperture Radar (ISAR) of ship targets in the marine environment. [ABSTRACT FROM AUTHOR]

Copyright of Systems Engineering & Electronics is the property of Journal of Systems Engineering & Electronics Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Summary: Objective: Controversy exists in treatment of thoracolumbar fractures without neurologic deficits. Percutaneous pedicle screw implantation with external frame is a less invasive procedures. This study was conducted to compared outcomes of percutaneous pedicle screws with a novel external fixation (micro-motional adjustable external fixator-MAEF) and open surgery for thoracolumbar fracture without neurologic deficits. Methods: Between October 2017 to October 2018, 42 patients of thoracolumbar fractures without neurologic deficits treated at our trauma center were analyzed retrospectively. The mean age of the patients was 42 years old (range 17–65). Pedicle screw and a novel external frame or open surgery were performed by senior surgeons. Perioperative and radiographic data were collected. Results: There was no significant statistically difference between MAEF and open surgery in demographics and preoperative data. There were statistically significant differences in blood loss intraoperative between the two groups (44.3 ± 15.1 vs 291.1 ± 175.9, p < 0.05). There were statistically significant differences in local kyphosis (LK) (4.9 ± 5.1vs 8.8 ± 5.4, p < 0.05), regional kyphosis (RK) (4.2 ± 3.6 vs 8.3 ± 6, p < 0.05) and segmental kyphosis (SK) (4.3 ± 4.2 vs 8 ± 5.3, p < 0.05) after 12 month between the two groups. There were statistically significant differences in visual analogue scale（VAS） postoperative but 12 month postoperative between the two groups (3.7 ± 1 vs 5.4 ± 0.8, p < 0.05). There were no statistically significant differences in Oswestry disability index（ODI） after 12 months between the two groups (2 ± 1.8 vs 2.7 ± 2.8, p > 0.05). There are 1 complication in MAEF while 3 in open surgery group. Conclusion: The MAEF is a less invasive procedure, which provides reduced bleeding and no necessity of secondary surgery. The technique is effective in the management of thoracolumbar fractures as well as maintaining reduction. It may be a good choice for thoracolumbar fractures without neurological deficits. The translational potential of this article: This study showed that the new spinal external frame was as effective as open reduction and internal fixation in the treatment of thoracolumbar fractures, but had more advantages in postoperative kyphotic deformity. We will continue to investigate its advantages in open fractures, multiple jumping spine fractures and postoperative recovery.

The non-contact vital sign detection of an individual in indoor environments using a radar sensor has been widely researched by recent advances in the IoT industry. Most of these studies have been conducted by analyzing the spectrum of extracted phase variations in the echo signal of a motionless individual that has only micro-motion (that is, vital signs) caused by both the lung and the heart. However, in the case of a walking individual, the performance of the vital sign detection using conventional methods may be rapidly degraded owing to extreme phase fluctuations caused by the relatively large movement of the individual compared to the vital sign motions. To address this problem, we propose a new motion compensation method for the vital sign detection of a walking individual. First, we conduct a modeling of the echo signal for a walking individual using a radar and discuss problems in vital sign detection. Next, a new algorithm for real-time motion compensation is suggested based on a moving averaging filter and mode in statistics. In the experiments using an IR-UWB (Impulse-radio ultra-wideband) radar, we observed that the proposed method performed a successful real-time vital sign detection of a walking individual. [ABSTRACT FROM AUTHOR]

Aiming at the problem of micro-motion signal separation and micro-Doppler extraction of the precession target, a new method based on singular value decomposition (SVD) and joint approximate diagonalization of eigen-matrices (JADE) is proposed in this paper. Firstly, the micro-motion model of space precession target is established, and the micro-Doppler and scattering characteristics are analyzed to establish the echo signal model of the target. Secondly, through simplifying the signal model of scattering point and building the signal matrix of different signal lengths, the singular value ratio sequence is constructed by the method of SVD to estimate the precession period of the target. Thirdly, the singular vectors of different observation periods are extracted, the observation matrix is constructed, and then the JADE algorithm is adopted to separate the micro-motion signal of each scattering point. Finally, the clustering analysis is used to denoise the time-frequency graph and the centroid calculation is employed to extract the micro-Doppler of each scattering point. Simulation results show that this method of good robustness can effectively separate and extract the micro-Doppler of the scattering points.

The aim of the paper is to highlight the advantages of the modularization of the femoral component of the Total Hip Prosthesis (THP), especially the tribological implications it presumes (fretting wear of the modular junctions, fatigue and corrosion by fretting, which can contribute to the increase of metal ions concentrations in the blood and the possibility of the femoral stem fracture at the femoral head junction). Experimental laboratory studies focused on the analysis of the tribological phenomena occurring at the conical junctions between the femoral head and neck, as well as the neck and femoral stem, highlighting the relevant qualitative tribological aspects. Femoral stems with two junctions allow even greater capacity for the independent fit of the proximal femoral, offset and limb length with the metaphysical dimension/ stem body. For this purpose, experimental junctions, tested on a universal MTS testing machine, were built. It emphasizes the importance of the materials used for the conical or trapezoidal joints components and the measures to reduce the micro-motion that lead to fretting phenomena and fretting corrosion. The influence of conicity on the tribological phenomena is analysed.

To solve the problem of space targets' trajectory and attitude data generation in system simulation, the characteristics of various types of ballistic missiles and their midcourse targets are analyzed deeply. The baseline ballistic is divided into four types and design methods are given respectively, based on which multiple independently reentry vehicle (MIRV) ballistic design method is established. This method doesn't need preparations and simplifies the system simulation. In addition, an attitude data generation method according to micro-motion characteristic quantity is proposed in order to simulate midcourse target's micro-motion. This method doesn't require numerical integral and speeds up the computation. Simulation results show that four types of baseline trajectory and MIRV trajectory that can accurately hit the target are generated in ten seconds. In addition, the attitude data can reflect the micro-motion characteristic, and the time for data generation is short. This method can meet the requirements of system simulation.

The micro-Doppler (m-D) effect caused by micro-motion degrades the readability of the inverse synthetic aperture radar (ISAR) image. To achieve well-focused ISAR image of the target with the micro-motion part, this paper proposes a novel approach for the removal of m-D effect of ISAR image. Note that the range profiles of the rigid body are similar to each other, making the respective data matrix low-rank. Those of the micro-motion part, in contrary, generally fluctuate in different range cells, whose data matrix is sparse. Therefore, the removal of m-D effect can be naturally solved by the robust principal component analysis (RPCA)–a convenient convex program to decompose an auxiliary matrix into a low-rank matrix and a sparse one. In RPCA, the rank of a matrix is described by the nuclear norm, which is convex but leads to a suboptimal solution. To address it, we utilize a nonconvex surrogate, i.e., the summation of logistic function of the singular values of a matrix, to approximate the rank. Moreover, the range profiles of the rigid body are generally locally similar. To capture this geometric structured information, we further introduce a Laplacian regularization into the model. Then, the Laplacian regularized nonconvex low-rank (LRNL) model is solved efficiently by the linearized alternating direction method (ADM). Extensive experimental results based on both simulated and measured data demonstrate the effectiveness of the proposed approach on the removal of m-D effect of ISAR image. [ABSTRACT FROM AUTHOR]

Objectives: Evaluate the effects of two different machined‐collar lengths and designs on peri‐implant healing. Material and Methods: An implant with a microtextured surface and 3.6mm‐long internal‐connection machined collar was compared to two implants that had an identical 1.2mm‐long external‐connection machined collar, but one had the microtextured surface while the other's was machined. Participants received the three implants, with microgap at the crest, alternately at five sites between mental foramen, and a full‐arch prosthesis. Peri‐implant bone levels were measured after 23 to 26 years of function. Keratinized tissue height, plaque, probing depth, bleeding, and purulence were also evaluated. Descriptive and mixed models for repeated\measures analyses were used, with Bonferroni correction for pairwise comparisons. Results: Twenty‐two participants (110 implants) were evaluated at the 25‐year examination. Microtextured implants with the longer machined collar had significantly greater mean marginal bone loss (−1.77mm ± 0.18, mean ± SE) than machined (−0.85mm ± 0.18, p <.001) and microtextured (−1.00 ± 0.18mm, p <.001) implants with the shorter machined collar. Keratinized tissue height was greater for internal‐connection (0.74mm ± 0.10) versus external‐connection (0.51 ± 0.08, p = 0.01) microtextured implants. No differences were observed for plaque (p = 0.78), probing depth (p = 0.42), bleeding (p = 0.07), and purulence (p = 1.00). Implant survival rate was 99%. Conclusions: Implants with the 1.2mm machined collar limited bone loss to 1mm, while those with the longer machined collar showed > 1.5mm loss after 25 years of function with microgap at the crest. Internal‐connection design and fixture surface microtexturing did not result in greater bone preservation. ClinicalTrials.gov Identifier: NCT03862482. [ABSTRACT FROM AUTHOR]

The micro-motional period is an important feature for spatial target discrimination. Firstly, the movement and wideband echo models of wobbling targets with bistatic observations are introduced. Then, the characteristics of the mono-/bi-static high-resolution range profiles (HRRPs) with different polarizations are analyzed based on the simulation echoes of the wobbling target via the electromagnetic calculations. Finally, the observed HRRP sequence is employed to construct a time-range distribution matrix, and an estimation method of the micro-motional period is proposed based on the time-range distribution matrix correlation, which is validated by the measurement experiments in the anechoic chamber.

Translational compensation is one of the key problems in parameter estimation of moving targets and radar imaging, and envelope correction is the basis of translation compensation. However, in inverse synthetic aperture imaging, the traditional translation compensation algorithms cannot be applied to micro-motion targets. Based on the characteristics of micro-motion targets and the advantages of the terahertz radar, a new method of envelope correction for micro-motion targets based on the multi-layer perceptron is proposed in this paper, which is verified by a radar system with a carrier frequency of 330 GHz. The experimental targets adopted in this paper are rotating corner reflectors and precession warhead. Finally, this paper proposes a measure based on inverse Radon transform and compares the performance of the proposed algorithm with that of the previous one, which fully verifies the effectiveness of the proposed method.

Aiming at the difficult problem of the classification between flying bird and rotary-wing drone by radar, a micro-motion feature classification method is proposed in this paper. Using K-band frequency modulated continuous wave (FMCW) radar, data acquisition of five types of rotor drones (SJRC S70 W, DJI Mavic Air 2, DJI Inspire 2, hexacopter, and single-propeller fixed-wing drone) and flying birds is carried out under indoor and outdoor scenes. Then, the feature extraction and parameterization of the corresponding micro-Doppler (m-D) signal are performed using time-frequency (T-F) analysis. In order to increase the number of effective datasets and enhance m-D features, the data augmentation method is designed by setting the amplitude scope displayed in T-F graph and adopting feature fusion of the range-time (modulation periods) graph and T-F graph. A multi-scale convolutional neural network (CNN) is employed and modified, which can extract both the global and local information of the target’s m-D features and reduce the parameter calculation burden. Validation with the measured dataset of different targets using FMCW radar shows that the average correct classification accuracy of drones and flying birds for short and long range experiments of the proposed algorithm is 9.4% and 4.6% higher than the Alexnet- and VGG16-based CNN methods, respectively.

Micromotion refers to the small and non-uniform motion of the target or several target components along the radar line of sight. Using the high-resolution three-Dimensional (3D) Inverse Synthetic Aperture Radar (ISAR) imaging, the structural information and motion status of micromotion targets can be obtained, providing essential features for the detection, tracking, identification, and classification, which play important roles in the space situation awareness and ballistic missile defense. Given the complex micromotion forms and the non-stationary radar echoes, the available parametric ISAR imaging methods are no longer applicable. To overcome this limitation, this study aims to propose a high-resolution 3D imaging method for micromotion targets based on the scattering center trajectory matrix decomposition. First, the Range Instantaneous Doppler (RID) image series is generated to extract the support region of scattering centers by the watershed method. Then, the scattering center association is achieved based on the minimum Euclidean distance criterion. Considering the insufficient accuracy in the instantaneous slant range estimation with limited range resolution, a method for refined estimation of the trajectory matrix based on the modern spectrum analysis is proposed. Finally, the high-resolution 3D imaging of the micromotion targets is obtained by the trajectory matrix decomposition with constraints. The simulation results have demonstrated that the proposed method could effectively obtain high-resolution 3D imaging of the targets in complex micromotions such as nutation.

The technique of radar feature extraction, imaging, and recognition of target with micro-motions has become one of the most potential research directions in the field of radar target accurate recognition. In this paper, the concept of micro-motion is first introduced briefly. Subsequently, the achievements of echo modeling, feature extraction, imaging, and identification of micro-motion targets are summarized. Several typical frontier applications are then introduced. Finally, the future development trends of the research are discussed.

With years of development and accumulation, a considerable amount of research has focused on micro-motion, an important auxiliary feature in radar target detection and recognition. With the recent rise of terahertz, micro-motion feature extraction in the terahertz region has increasingly highlighted its advantages. Herein, we systematically surveyed the recent research on terahertz radar micro-motion feature extraction and discussed micro-motion feature analysis, micro-motion feature extraction, and micro-motion target imaging. And then we emphatically introduced the work of our research team, including the theoretical and experimental research on micro-motion feature analysis, micro-motion feature extraction and high-resolution/high-frame micro-motion target imaging. Furthermore, we analyzed the growing trend of micro-motion feature extraction in the terahertz region, and pointed out the new technology directions worth to be studied further and the technical challenges to be solved.

Micro-motion parameters extraction is crucial in recognizing ballistic missiles with a wideband radar. It is known that the phase-derived range (PDR) method can provide a sub-wavelength level accuracy. However, it is sensitive and unstable when the signal-to-noise ratio (SNR) is low. In this paper, an improved PDR method is proposed to reduce the impacts of low SNRs. First, the high range resolution profile (HRRP) is divided into a series of segments so that each segment contains a single scattering point. Then, the peak values of each segment are viewed as non-stationary signals, which are further decomposed into a series of intrinsic mode functions (IMFs) with different energy, using the ensemble empirical mode decomposition with the complementary adaptive noise (EEMDCAN) method. In the EEMDCAN decomposition, positive and negative adaptive noise pairs are added to each IMF layer to effectively eliminate the mode-mixing phenomenon that exists in the original empirical mode decomposition (EMD) method. An energy threshold is designed to select proper IMFs to reconstruct the envelop for high estimation accuracy and low noise effects. Finally, the least-square algorithm is used to do the ambiguous phases unwrapping to obtain the micro-curve, which can be further used to estimate the micro-motion parameters of the warhead. Simulation results show that the proposed method performs well with SNR at −5 dB with an accuracy level of sub-wavelength.

Aiming at the problem that the existing method has high computational complexity and high SNR,a novel method of multi-component SFM signals parameter estimation based on EM algorithm was proposed as to spinning tar-get's narrow band micro-motion echoes.The echo model of spinning target and its micro-doppler measurements based on time-frequency analysis were given.The iterative parameter estimation steps based on Gaussian mixture model and EM algorithm to measurements were established.The simulation results demonstrate that when SNR is greater than -3 dB and the SFM components are equal or greater than 2，the method can estimate the target’s micro-motion parameters e.g.projection size accurately in narrow band condition.

Airborne target's micro-motion such as rotation or vibration causes phase modulation, termed as micro-Doppler effect, into radar signals. The feature of micro-motion is one of the most obvious features for radar recognition in mid-course phase. In traditional works, it is assumed that the micro-motion of the scatterer is the same as the ballistic target. However, with the variation of the aspect angle of ISAR, the position of the scatterer changes. In this paper, the movement of a ballistic missile in mid-course is modeled and analyzed. A false target jamming method is proposed by combining the micro-motion modulation and the electromagnetic scattering modulation. Compared with the methods using ideal point models, our method is able to generate a vivid false target with structural information, micro-motion and variation of the scatterer's RCS. The micro-motion effect of the false target is presented through ISAR imaging and time-frequency analysis. The effectiveness and correctness of the algorithm is verified by simulation.

This study considered parameter estimations for micro-motion targets embedded in non-Gaussian noise with a Single Input Multiple Output (SIMO) radar. A novel estimation algorithm based on mutual correntropy was presented and used to derive the micro-perturbation parameters by exploiting the second and higher-order knowledge of the return signals among multiple channels. Compared with a conventional Fourier Transform (FT) method, the method proposed herein had a much higher Signal to Noise Ratio (SNR) gain. In addition, the location was derived by employing the Phase-Comparison Monopulse (PCM) technique. Finally, several numerical results were provided and discussed.

Background. The most common problem associated with dental implants is the abutment screw loosening. This research aimed to investigate the effect of the type of connection on screw loosening, using a finite element method (FEM). Methods. Periosave system and different types of the implant-abutment connection were used for modeling. After being measured, CAD files were modeled using CATIA software and imported to the ANSYS analysis software, and the model was loaded. Results. A force of 100 N was applied at 0.1 second, and no force was applied at 0.42 second. The screw head deformation at 0.1 and 0.42 seconds was 8 and 3.8 µm, and 7.6 and 2.8 µm at morse taper and octagon dental implant connections, respectively. The displacement rate of the internal surface of the abutment at 0.1 and 0.42 seconds was 10.7 and 8.4 µm, and 5.7 and 5.6 µm in the octagon and morse taper dental implant connections, respectively. The displacement of the implant suprastructure-abutment interface from the screw head at 0.1 and 0.42 seconds was 9 and 7 µm, and 7 and 6 µm in the morse taper and octagon dental implant connections, respectively. At intervals of 0 to 0.1 seconds and 0.6 to 0.8 seconds, the octagon connection was separated at the maximum screw head displacement and the internal part of the abutment, but the morse taper connection did not exhibit any separation. In the above time intervals, the results were similar to the maximum state in case of the minimum displacement of the screw head and the internal part of the abutment. Conclusion. Screw loosening is less likely to occur in the morse hex connection compared to the octagon connection due to the lack of separation of the screw from the internal surface of the abutment. [ABSTRACT FROM AUTHOR]

In order to suppress the strong clutter component and separate the effective fretting component from narrow-band radar echo, a method based on complex variational mode decomposition (CVMD) is proposed in this paper. The CVMD is extended from variational mode decomposition (VMD), which is a recently introduced technique for adaptive signal decomposition, limited to only dealing with the real signal. Thus, the VMD is extended from the real domain to the complex domain firstly. Then, the optimal effective order of singular value is obtained by singular value decomposition (SVD) to solve the problem of under-decomposition or over-decomposition caused by unreasonable choice of decomposition layer, it is more accurate than detrended fluctuation analysis (DFA) and empirical mode decomposition (EMD). Finally, the strongly correlated modes and weakly correlated modes are judged by calculating the Mahalanobis distance between the band-limited intrinsic mode functions (BLIMFs) and the original signal, which is more robust than the correlation judgment methods such as computing cross-correlation, Euclidean distance, Bhattachryya distance and Hausdorff distance. After the weak correlation modes are eliminated, the signal is reconstructed locally, and the separation of the micro-motion signal is realized. The experimental results show that the proposed method can filter out the strong clutter component and the fuselage component from radar echo more effectively than the local mean decomposition (LMD), empirical mode decomposition and moving target indicator (MTI) filter.

The sparse frequency band (SFB) signal presents a serious challenge to traditional wideband micro-motion curve extraction algorithms. This paper proposes a novel two-dimension (2-D) joint sparse reconstruction and micro-motion parameter estimation (2D-JSR-MPE) algorithm based on compressive sensing (CS) theory. In this technique, the 2D-JSR signal model and the micro-motion parameter dictionary are established based on the segmented SFB echo signal, in which the idea of piecewise effectively reduces the model complexity of ballistic target. With the accommodation of the CS theory, the 2D-JSR-MPE of the echo signal is transformed into solving a sparsity-driven optimization problem. Via an improved orthogonal matching pursuit (OMP) algorithm, the high-resolution range profiles (HRRP) can be reconstructed accurately, and the precise micro-motion curves can be simultaneously extracted on phase accuracy. The employment of 2-D joint processing can effectively avoid the interference of the sparse reconstruction error caused by cascaded operation in the subsequent micro-motion parameter estimation. The proposed algorithm benefits from the anti-jamming characteristic of the SFB signal and 2-D joint processing, thus remarkably enhancing its accuracy, robustness and practicality. Extensive experimental results are provided to verify the effectiveness and robustness of the proposed algorithm.

In order to solve the problem of the short lifespan of the neural electrode caused by micro motion, this study designed a novel neural electrode based on lumped compliance compliant mechanism to control different modes of micro-motion in a more effective way. According to the mathematical modeling of the novel neural electrode, the equivalent bending stiffness and equivalent tensile (compression) stiffness were calculated. The results of the finite element analysis based on the mathematical modeling revealed that the novel neural electrode showed excellent micro-motion-attenuation capability. The static analysis results showed that the novel design dramatically reduced the maximum displacement of the brain in 51% and the maximum stress in 41% under longitudinal micro-motion environment. It also effectively reduced the 5.1% maximum stress while maintaining the maximum displacement under lateral micro-motion environment. The experimental results based on the tissue injury evaluation system also confirmed that the novel electrode is more effective in micro-motion attenuation than the reference one. In detail, the strain of the brain tissue caused by the implantation of the neural electrode was decreased by 1.26 to 27.84% at the insertion depth of 3 mm, and 0.522 to 17.24% at the insertion depth of 4.5 mm, which has convinced the effectiveness of the design. Graphical abstract The schematic of the novel neural electrode and evaluationsystem of tissue injury. [ABSTRACT FROM AUTHOR]

Background Context: Achieving a successful spinal fusion requires the proper biological and biomechanical environment. Optimizing load-sharing in the interbody space can enhance bone formation. For anterior cervical discectomy and fusion (ACDF), loading and motion are largely dictated by the stiffness of the plate, which can facilitate a balance between stability and load-sharing. The advantages of load-sharing may be substantial for patients with comorbidities and in multilevel procedures where pseudarthrosis rates are significant.Purpose: We aimed to evaluate the efficacy of a novel elastically deformable, continuously load-sharing anterior cervical spinal plate for promotion of bone formation and interbody fusion relative to a translationally dynamic plate.Study Design/setting: An in vivo animal model was used to evaluate the effects of an elastically deformable spinal plate on bone formation and spine fusion.Methods: Fourteen goats underwent an ACDF and received either a translationally dynamic or elastically deformable plate. Animals were followed up until 18 weeks and were evaluated by plain x-ray, computed tomography scan, and undecalcified histology to evaluate the rate and quality of bone formation and interbody fusion.Results: Animals treated with the elastically deformable plate demonstrated statistically significantly superior early bone formation relative to the translationally dynamic plate. Trends in the data from 8 to 18 weeks postoperatively suggest that the elastically deformable implant enhanced bony bridging and fusion, but these enhancements were not statistically significant.Conclusions: Load-sharing through elastic micro-motion accelerates bone formation in the challenging goat ACDF model. The elastically deformable implant used in this study may promote early bony bridging and increased rates of fusion, but future studies will be necessary to comprehensively characterize the advantages of load-sharing through micro-motion. [ABSTRACT FROM AUTHOR]

Radiofrequency multipole traps have been used for some decades in cold collision experiments and are gaining interest for precision spectroscopy due to their low micromotion contribution and the predicted unusual cold-ion structures. However, the experimental realisation is not yet fully controlled, and open questions in the operation of these devices remain. We present experimental observations of symmetry breaking of the trapping potential in a macroscopic octupole trap with laser-cooled ions. Numerical simulations have been performed in order to explain the appearance of additional local potential minima and be able to control them in a next step. We characterise these additional potential minima, in particular with respect to their position, their potential depth and their probability of population as a function of the radial and angular displacement of the trapping rods. [ABSTRACT FROM AUTHOR]