Decoding of the neural representation of the visual RGB color model.

RGB color is a basic visual feature. Here we use machine learning and visual evoked potential (VEP) of electroencephalogram (EEG) data to investigate the decoding features of the time courses and space location that extract it, and whether they depend on a common brain cortex channel. We show that RGB color information can be decoded from EEG data and, with the task-irrelevant paradigm, features can be decoded across fast changes in VEP stimuli. These results are consistent with the theory of both event-related potential (ERP) and P300 mechanisms. The latency on time course is shorter and more temporally precise for RGB color stimuli than P300, a result that does not depend on a task-relevant paradigm, suggesting that RGB color is an updating signal that separates visual events. Meanwhile, distribution features are evident for the brain cortex of EEG signal, providing a space correlate of RGB color in classification accuracy and channel location. Finally, space decoding of RGB color depends on the channel classification accuracy and location obtained through training and testing EEG data. The result is consistent with channel power value distribution discharged by both VEP and electrophysiological stimuli mechanisms.

Reconstructing sources location of visual color cortex by the task-irrelevant visual stimuli through machine learning decoding.

Visual color sensing is generated by electrical discharges from endocranial neuronal sources that penetrate the skull and reach to the cerebral cortex. However, the space location of the source generated by this neural mechanism remains elusive. In this paper, we emulate the generation of visual color signal by task-irrelevant stimuli to activate brain neurons, where its consequences over the cerebral cortex is experimentally tracked. We first document the changes to brain color sensing using electroencephalography (EEG), and find that the sensing classification accuracy of primary visual cortex (V1) regions was positively correlated with the space correlation of visual evoked potential (VEP) power distribution under machine learning decoding. We then explore the decoded results to trace the brain activity neural source location of EEG inversion problem and assess its reconstructive possibility. We show that visual color EEG in V1 can reconstruct endocranial neuronal source location, through the machine learning decoding of channel location.

An Anti-Turbulence Self-Alignment Method for SINS under Unknown Latitude Conditions.

For the alignment problem of strapdown inertial navigation system (SINS) under the complex environment of unknown latitude, angular oscillation interference, and line interference, the ant colony simulated annealing algorithm of gravity vector optimization is proposed to obtain the gravity apparent motion vector optimization equation, and the polynomial fitting method is proposed to simultaneously perform latitude estimation and self-alignment in combination with the alignment principle of SINS. Simulations and experiments show that the proposed method has more robust anti-interference capability than the traditional interference-based alignment method, the latitude estimation accuracy is improved by six times, the self-alignment yaw angle error RMSE value after obtaining the latitude is within 0.7°, and the roll angle and pitch angle error values are within 0.1°.

Optimal Rotational Angular Velocity Determination Method Based on Compound Rotary Semi-Strapdown Inertial Navigation System.

Single-axis rotation modulation (SRM) still accumulates errors in the roll axis direction, which leads to the navigation accuracy not meeting the requirements of guided missiles. Compound rotation modulation (CRM) superimposes one-dimensional rotation on the basis of SRM, so that the error of the projectile in the direction of the roll axis is also modulated. However, the error suppression effect of CRM is not only affected by the error of the IMU itself, but also related to the modulation angular velocity. In order to improve the accuracy of rotary semi-strapdown inertial navigation system (RSSINS), this paper proposes an optimal rotation angular velocity determination method. Firstly, the residual error in CRM scheme is analyzed; then, the relationship between the incomplete modulation error and the modulation angular velocity in CRM is discussed; finally, a method for determining the optimal modulation angular velocity is proposed (K-value method). The analysis of the results shows that the navigation accuracy of the guided projectile is effectively improved with the rotation scheme set at the modulation angular velocity determined by the K-value method.

A Low-Cost MEMS Missile-Borne Compound Rotation Modulation Scheme.

Rotation modulation (RM) has been widely used in navigation systems to significantly improve the navigation accuracy of inertial navigation systems (INSs). However, the traditional single-axis rotation modulation cannot achieve the modulation of all the constant errors in the three directions; thus, it is not suitable for application in highly dynamic environments due to requirements for high precision in missiles. Aiming at the problems of error accumulation and divergence in the direction of rotation axis existing in the traditional single-axis rotation modulation, a novel rotation scheme is proposed. Firstly, the error propagation principle of the new rotation modulation scheme is analyzed. Secondly, the condition of realizing the error modulation with constant error is discussed. Finally, the original rotation modulation navigation algorithm is optimized for the new rotation modulation scheme. The experiment and simulation results show that the new rotation scheme can effectively modulate the error divergence of roll angle and improve the accuracy of roll angle by two orders of magnitude.

Roll Angular Rate Measurement for High Spinning Projectiles Based on Redundant Gyroscope System.

Precision-guided projectiles, which can significantly improve the accuracy and efficiency of fire strikes, are on the rise in current military engagements. The accurate measurement of roll angular rate is critical to guide a gun-launched projectile. However, Micro-Electro-Mechanical System (MEMS) gyroscope with low cost and large range cannot meet the requirement of high precision roll angular rate measurement due to the limitation by the current technology level. Aiming at the problem, the optimization-based angular rate estimation (OBARS) method specific for projectiles is proposed in this study. First, the output angular rate model of redundant gyroscope system based on the autoregressive integrated moving average (ARIMA) model is established, and then the conventional random error model is improved with the ARIMA model. After that, a Sage-Husa Adaptive Kalman Filter (SHAKF) algorithm that can suppress the time-varying process and measurement noise under the flight condition of the high dynamic of the projectile is designed for the fusion of dynamic data. Finally, simulations and experiments have been carried out to validate the performance of the method. The results demonstrate the proposed method can effectively improve the angular rate accuracy more than the related traditional methods for high spinning projectiles.

A New In-Flight Alignment Method with an Application to the Low-Cost SINS/GPS Integrated Navigation System.

The optimization-based alignment (OBA) methods, which are implemented by the optimal attitude estimation using vector observations-also called double-vectors-have proven to be effective at solving the in-flight alignment (IFA) problem. However, the traditional OBA methods are not applicable for the low-cost strap-down inertial navigation system (SINS) since the error of double-vectors will be accumulated over time due to the substantial drift of micro-electronic- mechanical system (MEMS) gyroscope. Moreover, the existing optimal estimation method is subject to a large computation burden, which results in a low alignment speed. To address these issues, in this article we propose a new fast IFA method based on modified double-vectors construction and the gradient descent method. To be specific, the modified construction method is implemented by reducing the integration interval and identifying the gyroscope bias during the construction procedure, which improves the accuracy of double-vectors and IFA; the gradient descent scheme is adopted to estimate the optimal attitude of alignment without complex matrix operation, which results in the improvement of alignment speed. The effect of different sizes of mini-batch on the performance of the gradient descent method is also discussed. Extensive simulations and vehicle experiments demonstrate that the proposed method has better accuracy and faster alignment speed than the related traditional methods for the low-cost SINS/global positioning system (GPS) integrated navigation system.

Optimization of a New High Rotary Missile-Borne Stabilization Platform.

The passive semi-strapdown roll stabilized platform is an inertial platform, which can isolate the rolling of a projectile body by a special mechanical device. In the passive semi-strapdown roll stabilized platform, the bearing device plays an important role in isolating the rolling of the projectile body. The smaller the friction moment of bearing, the smaller the swing angular velocity of the platform, the smaller the range of inertial sensors required, the higher the accuracy of the navigation solution. In order to further reduce the swing angular velocity of the platform and improve the navigation accuracy, the bearing nested structure that could reduce the friction torque is proposed. Combined with the working principle of the passive semi-strapdown roll stabilized platform, the mechanical calculation model of friction at the moment of bearing the nested structure was established. A series of simulation analysis and tests showed that the output stability value of the friction moment was 47% that of a single bearing; the roll rate of the platform based on the bearing nested structure decreased to 50% of that based on the single bearing structure; the position and attitude errors measured of the platform based on the bearing nested structure decreased to more than 50% of that based on the single bearing structure. It showed that the bearing nested structure could effectively reduce the friction moment, improve the axial reliability of the bearing, and provide a more stable working environment for the passive semi-strapdown roll stabilized platform.

A Novel Rotation Scheme for MEMS IMU Error Mitigation Based on a Missile-Borne Rotation Semi-Strapdown Inertial Navigation System.

In previous research, a semi-strapdown inertial navigation system (SSINS), based on micro-electro-mechanical system (MEMS) sensors, was able to realize over-range measurement of the attitude information of high-rotation missiles by constructing a single axis "spin reduction" platform. However, the MEMS sensors in SSINS were corrupted by significant sensor errors. In order to further improve SSINS measurement accuracy, a rotational modulation technique has been introduced to compensate for sensor errors. The ideal modulation angular velocity is changed sharply to achieve a constant speed, while in practical applications, the angular rate of the rotating mechanism's output needs to go through an acceleration-deceleration process. Furthermore, the stability of the modulation angular rate is difficult to achieve in a high-speed rotation environment. In this paper, a novel rotation scheme is proposed which can effectively suppress the residual error in the navigation coordinate system caused by the modulation angular rate error, including the acceleration-deceleration process and instability of angular rate. The experiment results show that the position and attitude accuracy of the new rotation scheme was increased by more than 56%. In addition, the proposed scheme is applicable to navigation accuracy improvement under various dynamic conditions.

Reference markers of bone turnover for prediction of fracture: a meta-analysis.

OBJECTIVE: To explore whether bone turnover biomarkers (BTMs), i.e., C-terminal telopeptide of type I collagen (CTX) and procollagen type I aminoterminal propeptide (PINP), are associated with fracture. METHODS: We searched electronic database including PubMed, Embase and Cochrane Library, and the reference lists of relevant articles published from inception to August 22, 2018. An updated meta-analysis was performed to assess the prediction value of CTX and PINP in fracture. RESULTS: Nine articles met our inclusion criteria and were included in the meta-analysis. The crude and adjusted effect size between PINP and fracture were extracted from two and five studies, respectively. PINP was not associated with fracture incidence without adjusting covariates (crude GR, 1.03; 95% CI, 0.91-1.17). After adjusting for potential confounders, PINP demonstrated a significant positive association with fracture (adjusted GR, 1.28; 95% CI, 1.15-1.42). In the subgroup analysis of studies after adjusting covariates, there were significant associations in women. Both the crude (1.16, 95%CI, 1.04-1.20) and adjusted GR (1.20, 95%CI, 1.05-1.37) shown positive relationships between CTX and fracture, which were extracted from four and six studies, separately. The sensitivity analysis confirmed the stability of the results. In the subgroup analysis of studies after adjusting covariates, there were significant associations in the subgroups of elderly, female, and hip fracture patients. CONCLUSIONS: Our results indicate a statistically significant but modest association between BTMs (s-PINP or s-CTX) and future fracture risk after adjusting for BMD and clinical risk factors. The causal relationship between the two clinical conditions requires future validation with more standardized studies. REGISTRATION NUMBER: CRD42018107879.

Serum 14-3-3η Could Improve the Diagnostic Rate of Rheumatoid Arthritis and Correlates to Disease Activity.

OBJECTIVES: Serum 14-3-3η is a novel joint-derived proinflammatory mediator associated with rheumatoid arthritis (RA). This study aimed to evaluate the diagnostic capacity of serum 14-3-3η and its correlation with clinical variables in patients with RA. METHODS: A total of 94 patients with RA and 80 age- and sex-matched controls, including 40 healthy subjects, were included. Serum 14-3-3η levels were assessed by quantitative enzyme-linked immunosorbent assay. Receiver-operating characteristic (ROC) curves analysis was used to determine the sensitivity and specificity of 14-3-3η. Spearman's rank correlation coefficient was used to assess the relationship between 14-3-3η and other clinical measures in patients with RA. RESULTS: Median (interquartile range) of serum 14-3-3η concentration (ng/ml) in RA patients (2.34 [1.56-3.39]) was significantly higher than that in healthy subjects (0.17[0.11-0.30]) and disease controls (1.66[1.21-2.74]; P<0.05). ROC curve analysis comparing patients with RA with all controls demonstrated a significant (P<0.001) area under the curve (AUC) of 0.81 (95% confidence interval: 0.74-0.88). At a cutoff of 1.44 ng/mL, the ROC curve yielded a sensitivity of 78.7% and specificity of 73.8%. The sensitivity of anti-citrullinated protein antibody (ACPA) and rheumatoid factor (RF) were 84.0% and 72.3%, respectively. Adding 14-3-3η to ACPA and/or RF discriminated more than 96% of patients with RA. The positive rate of at least one of the three markers was up to 99%, with a specificity of about 70%. The results of correlation analyses revealed that serum levels of 14-3-3η protein positively correlated with C-reactive protein (r=0.250, P<0.05), erythrocyte sedimentation rate (r=0.294, P<0.01), and 28-joint disease activity score (r=0.275, P<0.05) in patients with RA. CONCLUSIONS: 14-3-3η protein is a novel marker that can apparently enhance the detection rate of patients with RA. The level of serum 14-3-3η protein correlates to some degree with disease activity.

The Optimal Design of Modulation Angular Rate for MEMS-Based Rotary Semi-SINS.

In previous studies, the semi-strapdown inertial navigation system (SSINS), based on microelectromechanical system (MEMS) sensors, had realized cross-range measurement of attitude information of high-spinning projectiles through construction of a "spin reduction" platform of the roll axis. However, further improvement of its measurement accuracy has been difficult, due to the inertial sensor error. In order to enhance the navigational accuracy, a periodically rotating method is utilized to compensate for sensor error, which is called rotation modulation. At present, the rotation scheme, as one of the core technologies, has been studied by a lot of researchers. It is known that the modulation angular rate is the main factor affecting the effectiveness of error modulation. Different from the long-endurance and low-dynamic motion characteristics of ships, however, the short-endurance and high-dynamic characteristics of the high-spinning projectile not only require the modulation angular rate to be as fast as possible but, also, the influence of the rotation speed error caused by rotating mechanism errors cannot be ignored. Combined with the rotation speed error of the rotating mechanism, this paper explored the relationship between modulation angular rate, device error, and the navigation error, and then proposed a design method for optimal modulation angular rate. Experiments were carried out to validate the performance of the method. In addition, the proposed method is applicable for rotation modulation systems with different types of motors as the rotating mechanism.

An Improved Strong Tracking Cubature Kalman Filter for GPS/INS Integrated Navigation Systems.

The cubature Kalman filter (CKF) is widely used in the application of GPS/INS integrated navigation systems. However, its performance may decline in accuracy and even diverge in the presence of process uncertainties. To solve the problem, a new algorithm named improved strong tracking seventh-degree spherical simplex-radial cubature Kalman filter (IST-7thSSRCKF) is proposed in this paper. In the proposed algorithm, the effect of process uncertainty is mitigated by using the improved strong tracking Kalman filter technique, in which the hypothesis testing method is adopted to identify the process uncertainty and the prior state estimate covariance in the CKF is further modified online according to the change in vehicle dynamics. In addition, a new seventh-degree spherical simplex-radial rule is employed to further improve the estimation accuracy of the strong tracking cubature Kalman filter. In this way, the proposed comprehensive algorithm integrates the advantage of 7thSSRCKF’s high accuracy and strong tracking filter’s strong robustness against process uncertainties. The GPS/INS integrated navigation problem with significant dynamic model errors is utilized to validate the performance of proposed IST-7thSSRCKF. Results demonstrate that the improved strong tracking cubature Kalman filter can achieve higher accuracy than the existing CKF and ST-CKF, and is more robust for the GPS/INS integrated navigation system.

Analysis and Compensation of Modulation Angular Rate Error Based on Missile-Borne Rotation Semi-Strapdown Inertial Navigation System.

The Semi-Strapdown Inertial Navigation System (SSINS) provides a new solution to attitude measurement of a high-speed rotating missile. However, micro-electro-mechanical-systems (MEMS) inertial measurement unit (MIMU) outputs are corrupted by significant sensor errors. In order to improve the navigation precision, a rotation modulation technology method called Rotation Semi-Strapdown Inertial Navigation System (RSSINS) is introduced into SINS. In fact, the stability of the modulation angular rate is difficult to achieve in a high-speed rotation environment. The changing rotary angular rate has an impact on the inertial sensor error self-compensation. In this paper, the influence of modulation angular rate error, including acceleration-deceleration process, and instability of the angular rate on the navigation accuracy of RSSINS is deduced and the error characteristics of the reciprocating rotation scheme are analyzed. A new compensation method is proposed to remove or reduce sensor errors so as to make it possible to maintain high precision autonomous navigation performance by MIMU when there is no external aid. Experiments have been carried out to validate the performance of the method. In addition, the proposed method is applicable for modulation angular rate error compensation under various dynamic conditions.

Correction: A New Quaternion-Based Kalman Filter for Real-Time Attitude Estimation Using the Two-Step Geometrically-Intuitive Correction Algorithm. Sensors 2017, 17, 2146.

The authors wish to make the following corrections to their paper [...].

A New Quaternion-Based Kalman Filter for Real-Time Attitude Estimation Using the Two-Step Geometrically-Intuitive Correction Algorithm.

In order to reduce the computational complexity, and improve the pitch/roll estimation accuracy of the low-cost attitude heading reference system (AHRS) under conditions of magnetic-distortion, a novel linear Kalman filter, suitable for nonlinear attitude estimation, is proposed in this paper. The new algorithm is the combination of two-step geometrically-intuitive correction (TGIC) and the Kalman filter. In the proposed algorithm, the sequential two-step geometrically-intuitive correction scheme is used to make the current estimation of pitch/roll immune to magnetic distortion. Meanwhile, the TGIC produces a computed quaternion input for the Kalman filter, which avoids the linearization error of measurement equations and reduces the computational complexity. Several experiments have been carried out to validate the performance of the filter design. The results demonstrate that the mean time consumption and the root mean square error (RMSE) of pitch/roll estimation under magnetic disturbances are reduced by 45.9% and 33.8%, respectively, when compared with a standard filter. In addition, the proposed filter is applicable for attitude estimation under various dynamic conditions.