Sliding Window Filter Research Articles

Iterative Laplace of Gaussian filter and improved Teager energy operator for bearing fault detection in gearboxes

Abstract The presence of cyclic impulses is considered to be a significant indicator of bearing defects. The extraction of fault-related information from gearboxes, however, poses a significant challenge due to the complex working conditions in gear transmission systems. These conditions often lead to fault-related impulse features being masked by inevitable background noise and vibration components. To address this issue, a novel iterative Laplace of Gaussian (LoG) filtering technique is proposed for the fault diagnosis field of bearings in gearboxes. An objective function incorporating Kurtosis statistics is used to iteratively update LoG coefficients instead of relying on fixed coefficients as in the original version, making the iterative LoG filtering technique better suited for handling fault-induced impact signals. Furthermore, an improved Teager energy operator (TEO) is developed to reduce the susceptibility of the original TEO to noise. The improvement involves incorporating a multi-resolution symmetric difference sequence into the discrete expression of TEO. This symmetric difference acts as a translation-invariant, sliding-window filter that, when combined with an energy operator, enables both energy detection and frequency filtering. The robustness against noise and sensitivity to frequency can be further improved by adjusting the multi-resolution parameter. The results of the experiments confirm the effectiveness of the proposed approach in identifying weak bearing fault features for gearboxes. Besides, the proposed method is also compared with the original LoG filter-based method and some competing methods, demonstrating its superior performance.

Delay-Tracking Loop for Frequency Estimation of Grid-Interfaced Inverter Under Distorted Conditions

Fast and accurate frequency estimation is difficult for grid-interfaced inverter under distorted grid conditions with off-nominal frequency. A popular solution considers the multiple second-order generalized integrator-based frequency-locked loop yet can hardly achieve a fast dynamic response under significant phase jump and voltage sag. Another solution uses an adaptive sliding window filter with an additional frequency estimator. However, it is challenging to handle numerical instability due to the adjusting marginally-stable resonator and the accumulation error. This letter proposes a delay-tracking loop to overcome these problems. This loop allows the delay length to track the frequency with an inherent harmonics rejection at off-nominal frequency. At the same time, the fast response speed is maintained, and the marginally stable resonator is avoided. Comparative experimental results validate the above claims.

Exploring style transfer algorithms in Animation: Enhancing visual

The necessity to computationally process human motion to produce realistic and dynamic animations is increasing with the fourth industrial revolution. Motion style transfer provides an appealing alternative to manually creating motions from start by utilizing already recorded motion data to automatically create realistic motion samples. Motion style transfer techniques have been transformed by deep learning algorithms, especially deep neural networks (DNNs). These algorithms are excellent for motion synthesis tasks because they can anticipate future motion styles. A style transfer method called CNN-BiLSTM-ATT (Convolutional Neural Network-Bidirectional Long Short-Term Memory with Attention) is put forth to analyze spatiotemporal features. The strategy tries to realistically synthesize and represent the intricacy of human motion by merging CNNs, BiLSTMs, and attention mechanisms. The difference between reference and source styles is converted to a novel motion that might include never-before-seen movements by extracting spectral intensity representations of each. A temporally sliding window filter is added to the method to do local analysis in time for the processing of heterogeneous motion, greatly improving it. As a result, the method can be used to enrich style databases by filling in missing actions and enhancing the effectiveness of earlier style transfer techniques. Through controlled user studies and quantitative trials, the effectiveness of the suggested strategy is assessed. The outcomes show a notable advancement over earlier studies, emphasizing the method’s capacity to produce thorough and accurate motion sequences.

High-temporal resolution functional PET/MRI reveals coupling between human metabolic and hemodynamic brain response

PurposePositron emission tomography (PET) provides precise molecular information on physiological processes, but its low temporal resolution is a major obstacle. Consequently, we characterized the metabolic response of the human brain to working memory performance using an optimized functional PET (fPET) framework at a temporal resolution of 3 s.MethodsThirty-five healthy volunteers underwent fPET with [18F]FDG bolus plus constant infusion, 19 of those at a hybrid PET/MRI scanner. During the scan, an n-back working memory paradigm was completed. fPET data were reconstructed to 3 s temporal resolution and processed with a novel sliding window filter to increase signal to noise ratio. BOLD fMRI signals were acquired at 2 s.ResultsConsistent with simulated kinetic modeling, we observed a constant increase in the [18F]FDG signal during task execution, followed by a rapid return to baseline after stimulation ceased. These task-specific changes were robustly observed in brain regions involved in working memory processing. The simultaneous acquisition of BOLD fMRI revealed that the temporal coupling between hemodynamic and metabolic signals in the primary motor cortex was related to individual behavioral performance during working memory. Furthermore, task-induced BOLD deactivations in the posteromedial default mode network were accompanied by distinct temporal patterns in glucose metabolism, which were dependent on the metabolic demands of the corresponding task-positive networks.ConclusionsIn sum, the proposed approach enables the advancement from parallel to truly synchronized investigation of metabolic and hemodynamic responses during cognitive processing. This allows to capture unique information in the temporal domain, which is not accessible to conventional PET imaging.

Know What You Don't Know: Consistency in Sliding Window Filtering With Unobservable States Applied to Visual-Inertial SLAM

Estimation algorithms, such as the sliding window filter, produce an estimate and uncertainty of desired states. This task becomes challenging when the problem involves unobservable states. In these situations, it is critical for the algorithm to “know what it doesn't know”, meaning that it must maintain the unobservable states as unobservable during algorithm deployment. This letter presents general requirements for maintaining consistency in sliding window filters involving unobservable states. The value of these requirements for designing navigation solutions is experimentally shown within the context of visual-inertial SLAM making use of IMU preintegration.

Observability Analysis and Keyframe-Based Filtering for Visual Inertial Odometry With Full Self-Calibration

Camera–inertial measurement unit (IMU) sensor fusion has been extensively studied in recent decades. Numerous observability analysis and fusion schemes for motion estimation with self-calibration have been presented. However, it has been uncertain whether the intrinsic parameters of both the camera and the IMU are observable under general motion. To answer this question, by using the Lie derivatives, we first prove that for a rolling shutter (RS) camera–IMU system, all the intrinsic and extrinsic parameters, camera time offset, and readout time of the RS camera are observable with an unknown landmark. To our knowledge, we are the first to present such a proof. Next, to validate this analysis and to solve the drift issue of a structureless filter during standstills, we develop a keyframe-based sliding window filter (KSWF) for odometry and self-calibration, which works with a monocular RS camera or stereo RS cameras. Though the keyframe concept is widely used in vision-based sensor fusion, to our knowledge, the KSWF is the first of its kind to support self-calibration. Our simulation and real data tests have validated that it is possible to fully calibrate the camera–IMU system using the observations of opportunistic landmarks under diverse motion. Real data tests confirmed previous allusions that keeping landmarks in the state vector can remedy the drift in standstill and showed that the keyframe-based scheme is an alternative solution.

Smartphone-Based Unconstrained Step Detection Fusing a Variable Sliding Window and an Adaptive Threshold

Step detection for smartphones plays an important role in the pedestrian dead reckoning (PDR) for indoor positioning. Aiming at the problem of low step detection accuracy of smartphones in complex unconstrained states in PDR, smartphone-based unconstrained step detection method fusing a variable sliding window and an adaptive threshold is proposed. In this method, the dynamic updating algorithm of a peak threshold is developed, and the minimum peak value filtered after a sliding window filter is used as the adaptive peak threshold, which solves the problem that the peak threshold of different motion states is difficult to update adaptively. Then, a variable sliding window collaborative time threshold method is proposed, which solves the problem that the adjacent windows cannot be contacted, and the initial peak and the end peak are difficult to accurately identify. To evaluate the performance of the proposed unconstrained step detection algorithm, 50 experiments in constrained and unconstrained states are conducted by 25 volunteers holding 21 different types of smartphones. Experimental results show: The average step counting accuracy of the proposed unconstrained step detection algorithm is over 98%. Compared with the open source program Stepcount, the average step counting accuracy of the proposed algorithm is improved by 10.0%. The smartphone-based unconstrained step detection fusing a variable sliding window and an adaptive threshold has a strong ability to adapt to complex unconstrained states, and the average step counting accuracy rate is only 0.6% lower than that of constrained states. This algorithm has a wide audience and is friendly for different genders and smartphones with different prices.

Atrial Fibrillation Detection Using a Feedforward Neural Network

PurposeIn this study, we aimed to develop an automatic atrial fibrillation detection technique for the early prediction of atrial fibrillation, that can be used with wearable devices.MethodsAn effective deep learning-based technology is proposed to automatically detect atrial fibrillation. First, novel preprocessing algorithms, wavelet transform and sliding window filtering, are introduced to reduce the noise and outliers, respectively, from ECG signals. Then, a robust R-wave detection algorithm is developed. In addition, we proposed a feedforward neural network to detect atrial fibrillation based on ECG records.ResultsExperiments verified using a tenfold cross-validation strategy showed that the proposed method achieves competitive detection performance, and can be applied to wearable detection devices. The proposed R-wave detection algorithm achieved a detection sensitivity of 99.22%, a positive recognition rate of 98.55%, and a deviance of 2.25% on the MIT-BIH arrhythmia database. The proposed atrial fibrillation detection model achieved an accuracy of 84.00%, a detection sensitivity of 84.26%, a specificity of 93.23%, and an area under the receiver operating curve of 89.40% on a mixed dataset composed of the Challenge2017 database and the MIT-BIH arrhythmia database.ConclusionThe analysis demonstrated that the proposed atrial fibrillation detection method could automatically detect atrial fibrillation with high accuracy and efficiency, could be applied to wearable devices, and has great value in the early detection of atrial fibrillation. We believe that our work will make a valuable contribution to the area of atrial fibrillation.

Metabolic low-frequency oscillation and abbreviated protocol for estimating REE by indirect calorimetry in healthy adults.

The aim of this study is to propose a new wave protocol to identify low-frequency oscillations for evaluating resting energy expenditure (REE) and compare its performance with the 5-min interval abbreviated protocol and standard protocol. Consecutive 20-min indirect calorimetry (IC) was used to collect metabolic data from 23 women and 37 men (between 23 and 43 yr old). Sliding window filter algorithms were used to eliminate noise. Three protocols were used to evaluate REE: averaging the data between two consecutive waves (wave protocol), averaging the second 5-min intervals (interval protocol), and averaging the last 15-min REE (standard protocol). Based on 60 healthy participants' metabolic data, compared with the interval protocol, the wave protocol showed better consistency with the standard protocol. The mean bias (limits of agreement) using the wave protocol was 0.3458% (-7.817% to 8.509%), and that using the interval protocol was -1.720% (-16.06% to 12.62%). The time required to evaluate REE with the wave protocol and interval protocol was measured. The measurement time for the interval protocol was 10 min, while the average measurement time for the wave protocol was 9.75 min. We recommend the wave protocol for estimating REE in healthy people. This abbreviated protocol can identify low-frequency oscillations and consider individual differences to more accurately reflect the baseline REE compared with the interval protocol. When compared with the standard protocol, the measurement time of the wave protocol was reduced by nearly half [from 20 min (standard protocol) to 9.75 min].NEW & NOTEWORTHY For estimating REE by indirect calorimetry, to shorten the measurement time, a consideration of low-frequency oscillations is essential. Based on that we proposed a new abbreviated protocol named wave protocol, which showed accuracy approaching that of the standard protocol with a measurement time close to that of the interval abbreviated protocol.

Relative Position Estimation Between Two UWB Devices With IMUs

For a team of robots to work collaboratively, it is crucial that each robot have the ability to determine the position of their neighbors, relative to themselves, in order to execute tasks autonomously. This letter presents an algorithm for determining the three-dimensional relative position between two mobile robots, each using nothing more than a single ultra-wideband transceiver, an accelerometer, a rate gyro, and a magnetometer. A sliding window filter estimates the relative position at selected keypoints by combining the distance measurements with acceleration estimates, which each agent computes using an on-board attitude estimator. The algorithm is appropriate for real-time implementation, and has been tested in simulation and experiment, where it comfortably outperforms standard estimators. A positioning accuracy of less than 1 meter is achieved with inexpensive sensors.

Solutions to mitigate the impact of measurement noise on the air pollution source strength estimation in a multi-zone building

Indoor contaminants jeopardize people’s health and may even cause serious consequences under extreme conditions. Therefore, the prompt and accurate identification of indoor airborne contaminant characteristics is significant for indoor health and safety. In this paper, we used an inverse Markov chain model, combined with the regularization proposed in our previous research, to identify periodic source strength under steady airflow in a multi-zone building. The impact of different measurement noise (0.05%, 0.1%, 0.2%) on the inverse results was investigated. The results showed that the greater the noise, the greater the oscillation of the inverse result. Furthermore, we also investigated the effect of adjusting the calculation time step (5 s, 10 s, 20 s, 30 s) and adding digital filters (Sliding window filter and Butterworth low pass filter) on the inverse source release rate. The results showed that properly increasing the calculation time step can reduce the impact of measurement noise. The root mean square error (RMSE) of the inverse source strength with 0.1% noise decreased from 22.89 under a 5-s time step to 0.9793 under a 30-s time step. It was also found that adding digital filters could reduce the oscillation of the inverse source results, and the performance of the filters also depends on the calculation time steps.

A Bluetooth-Low-Energy-Based Detection and Warning System for Vulnerable Road Users in the Blind Spot of Vehicles.

Blind spot road accidents are a frequently occurring problem. Every year, several deaths are caused by this phenomenon, even though a lot of money is invested in raising awareness and in the development of prevention systems. In this paper, a blind spot detection and warning system is proposed, relying on Received Signal Strength Indicator (RSSI) measurements and Bluetooth Low Energy (BLE) wireless communication. The received RSSI samples are threshold-filtered, after which a weighted average is computed with a sliding window filter. The technique is validated by simulations and measurements. Finally, the strength of the proposed system is demonstrated with real-life measurements.

Incremental inference of collective graphical models

We consider incremental inference problems from aggregate data for collective dynamics. In particular, we address the problem of estimating the aggregate marginals of a Markov chain from noisy aggregate observations in an incremental (online) fashion. We propose a sliding window Sinkhorn belief propagation (SW-SBP) algorithm that utilizes a sliding window filter of the most recent noisy aggregate observations along with encoded information from discarded observations. Our algorithm is built upon the recently proposed multi-marginal optimal transport based SBP algorithm that leverages standard belief propagation and Sinkhorn algorithm to solve inference problems from aggregate data. We demonstrate the performance of our algorithm on applications such as inferring population flow from aggregate observations.

Research on Fault Diagnosis Technology of Thermal Test Measurement and Control System Based on Data Driven

Temperature data, as one of the most important parameters in spacecraft vacuum thermal test, is also an important characteristic parameter for fault detection of spacecraft thermal test system. From the perspective of numerical heat transfer, the physical and mathematical models of thermal test specimens are established by using finite difference method. Based on the data driving of specimen measurement temperature and heating power, the mathematical model coefficients are fitted by the combination of least squares and sliding window filtering, and then realize the on-line prediction of temperature data and fault detection of thermal test measurement and control system. Based on the thermal test data of the solar wing of a certain type of spacecraft, the above-mentioned method was verified by physical experiments. The experimental result shows that the method can accurately and effectively realize the temperature prediction not less than 2 periods, and the prediction accuracy is less than ±2°C. It can provide an effective fault detection means for the thermal test measurement and control system.

Real-Time 3D Motion Tracking and Reconstruction System Using Camera and IMU Sensors

Jointly using the data of visual and inertial sensors to achieve higher accuracy and robustness is a problem of high computational complexity. In this work, this paper presents a 3D motion tracking and reconstruction system on a mobile device using camera and inertial measurement unit (IMU) sensors. The mobile device has very small active infrared projection depth sensors with high-performance IMU and wide field of view cameras. We utilize visual-inertial odometry (VIO) to integrate visual and IMU data. However, traditional VIO method is too complex to apply to a mobile device with limited computing resources in real time. We employ sliding window filter (SWF) for the proposed system to achieve accurate 3D motion tracking and high-quality reconstruction models in real time based on delayed state marginalization. Moreover, we also extensively evaluate in the dataset and real world. Many experiments show the accurate trajectories and high quality 3D reconstruction models by the proposed system. To the end, the qualitative and quantitative experimental results indicate the proposed system based on SWF has much better performance than existing methods in motion tracking and 3D reconstruction models.

Image-based modelling of the skin-friction coefficient in compressible boundary-layer transition

We develop a model of the skin-friction coefficient based on scalar images in the compressible, spatially evolving boundary-layer transition. The images are extracted from a passive scalar field by a sliding window filter on the streamwise and wall-normal plane. The multi-scale and multi-directional geometric analysis is applied to characterize the averaged inclination angle of spatially evolving filtered component fields at different scales ranging from a boundary-layer thickness to several viscous length scales. In general, the averaged inclination angles increase along the streamwise direction, and the variation of the angles for large-scale structures is smaller than that for small-scale structures. Inspired by the coincidence of the increasing averaged inclination angle and the rise of the skin-friction coefficient, we propose a simple image-based model of the skin-friction coefficient. The model blends empirical formulae of the skin-friction coefficient in laminar and fully developed turbulent regions using the normalized averaged inclination angle of scalar structures at intermediate and small scales. The model prediction calculated from scalar images is validated by the results from the direct numerical simulation at two Mach numbers, 2.25 and 6, and the relative error can be less than 15 %.

Sliding Window Filtering for Ground Moving Targets with Cross-Correlated Sensor Noises

This paper reports a sliding window filtering approach for ground moving targets with cross-correlated sensor noise and uncertainty. In addition, the effect of uncertain parameters during a tracking error on the model performance is considered. A distributed fusion sliding window filter is also proposed. The distributed fusion filtering algorithm represents the optimal linear combination of local filters under the minimum mean-square error criterion. The derivation of the error cross-covariances between the local sliding window filters is the key to the proposed method. Simulation results of the motion of the ground moving target a demonstrate high accuracy and computational efficiency of the distributed fusion sliding window filter.

Learnable despeckling framework for optical coherence tomography images.

Optical coherence tomography (OCT) is a prevalent, interferometric, high-resolution imaging method with broad biomedical applications. Nonetheless, OCT images suffer from an artifact called speckle, which degrades the image quality. Digital filters offer an opportunity for image improvement in clinical OCT devices, where hardware modification to enhance images is expensive. To reduce speckle, a wide variety of digital filters have been proposed; selecting the most appropriate filter for an OCT image/image set is a challenging decision, especially in dermatology applications of OCT where a different variety of tissues are imaged. To tackle this challenge, we propose an expandable learnable despeckling framework, we call LDF. LDF decides which speckle reduction algorithm is most effective on a given image by learning a figure of merit (FOM) as a single quantitative image assessment measure. LDF is learnable, which means when implemented on an OCT machine, each given image/image set is retrained and its performance is improved. Also, LDF is expandable, meaning that any despeckling algorithm can easily be added to it. The architecture of LDF includes two main parts: (i)an autoencoder neural network and (ii)filter classifier. The autoencoder learns the FOM based on several quality assessment measures obtained from the OCT image including signal-to-noise ratio, contrast-to-noise ratio, equivalent number of looks, edge preservation index, and mean structural similarity index. Subsequently, the filter classifier identifies the most efficient filter from the following categories: (a)sliding window filters including median, mean, and symmetric nearest neighborhood, (b)adaptive statistical-based filters including Wiener, homomorphic Lee, and Kuwahara, and (c)edge preserved patch or pixel correlation-based filters including nonlocal mean, total variation, and block matching three-dimensional filtering.

Probabilistic cooperative mobile robot area coverage and its application to autonomous seabed mapping

There are many applications that require mobile robots to autonomously cover an entire area with a sensor or end effector. The vast majority of the literature on this subject is focused on addressing path planning for area coverage under the assumption that the robot’s pose is known or that error is bounded. In this work, we remove this assumption and develop a completely probabilistic representation of coverage. We show that coverage is guaranteed as long as the robot pose estimates are consistent, a much milder assumption than zero or bounded error. After formally connecting robot sensor uncertainty with area coverage, we propose an adaptive sliding window filter pose estimator that provides a close approximation to the full maximum a posteriori estimate with a computation cost that is bounded over time. Subsequently, an adaptive planning strategy is presented that automatically exploits conditions of low vehicle uncertainty to more efficiently cover an area. We further extend this approach to the multi-robot case where robots can communicate through a (possibly faulty and low-bandwidth) channel and make relative measurements of one another. In this case, area coverage is achieved more quickly since the uncertainty over the robots’ trajectories is reduced. We apply the framework to the scenario of mapping an area of seabed with an autonomous underwater vehicle. Experimental results support the claim that our method achieves guaranteed complete coverage notwithstanding poor navigational sensors and that resulting path lengths required to cover the entire area are shortest using the proposed cooperative and adaptive approach.

Spectral style transfer for human motion between independent actions

Human motion is complex and difficult to synthesize realistically. Automatic style transfer to transform the mood or identity of a character's motion is a key technology for increasing the value of already synthesized or captured motion data. Typically, state-of-the-art methods require all independent actions observed in the input to be present in a given style database to perform realistic style transfer. We introduce a spectral style transfer method for human motion between independent actions, thereby greatly reducing the required effort and cost of creating such databases. We leverage a spectral domain representation of the human motion to formulate a spatial correspondence free approach. We extract spectral intensity representations of reference and source styles for an arbitrary action, and transfer their difference to a novel motion which may contain previously unseen actions. Building on this core method, we introduce a temporally sliding window filter to perform the same analysis locally in time for heterogeneous motion processing. This immediately allows our approach to serve as a style database enhancement technique to fill-in non-existent actions in order to increase previous style transfer method's performance. We evaluate our method both via quantitative experiments, and through administering controlled user studies with respect to previous work, where significant improvement is observed with our approach.