Motion Noise Research Articles

Object Pre-processing using Motion Stabilization and Key Frame Extraction with Machine Learning Techniques

Video information processing is one of the most important application areas in research and to solve in various pre-processing issues. The pre-processing issues such as unstable video frame rates or capture angle, noisy data and large size of the video data prevent the researchers to apply information retrieval or categorization algorithms. The video data itself plays a vital role in various areas. This work aims to solve the motion stabilization, noise reduction and key frame extraction, without losing the information and in reduced time. The work results into 66% reduction in key frame extraction and nearly 6 ns time for complete video data processing.

Application of Semi-Airborne Frequency Domain Electromagnetic Data Based on Improved Ant-Colony-Optimized Wavelet Threshold Denoising Method

The semi-airborne frequency domain electromagnetic detection method is a popular electromagnetic detection method in which the transmission system is excited on the ground and the reception system coil is in the air to measure the vertical magnetic field signals in the frequency domain. In the field exploration, the semi-airborne frequency domain electromagnetic detection system is vulnerable to low-frequency motion noise, power-line interference, industrial noise, astronomical noise, and many other interferences, which leads to a very low signal-to-noise ratio of the detection data. Owing to the overlap between the effective signal and several types of noise in the time-frequency domain in the semi-airborne frequency domain of the detected signal, the conventional denoising methods are limited and cannot meet practical needs. Therefore, an integrated denoising method based on the improved ant-colony-optimized wavelet threshold is proposed. The method first separates the low-frequency noise of motion using the wavelet’s high-scale component for noise reduction. Other interference noise types, such as power-line interference, industrial noise and astronomical noise, are also reduced using the improved ant-colony-optimized wavelet threshold method. The experimental results show that the proposed integrated denoising method exhibits good suppression effects on several types of noise, can effectively improve the signal-to-noise ratio of the data, and improve the field data inversion accuracy.

Constructing Floor Plan through Smoke Using Ultra Wideband Radar

Floor plan construction has been one of the key techniques in many important applications such as indoor navigation, location-based services, and emergency rescue. Existing floor plan construction methods require expensive dedicated hardware (e.g., Lidar or depth camera), and may not work in low-visibility environments (e.g., smoke, fog or dust). In this paper, we develop a low-cost Ultra Wideband (UWB)-based system (named UWBMap) that is mounted on a mobile robot platform to construct floor plan through smoke. UWBMap leverages on low-cost and off-the-shelf UWB radar, and it is able to construct an indoor map with an accuracy comparable to Lidar (i.e., the state-of-the-art). The underpinning technique is to take advantage of the mobility of radar to form virtual antennas and gather spatial information of a target. UWBMap also eliminates both robot motion noise and environmental noise to enhance weak reflection from small objects for the robust construction process. In addition, we overcome the limited view of single radar by combining multi-view from multiple radars. Extensive experiments in different indoor environments show that UWBMap achieves a map construction with a median error of 11 cm and a 90-percentile error of 26 cm, and it operates effectively in indoor scenarios with glass wall and dense smoke.

A Novel Motion Noise Suppression Method for SQUID Sensor Based on VME

A Novel Motion Noise Suppression Method for SQUID Sensor Based on VME

Models of monitoring of self-like traffic of information and communication networks for attack detection systems

Autoregressive, fractal and multifractal models of network self-similar traffic are con-sidered, which allow to form an adequate reference model (template) of "normal" traffic and to detect traffic anomalies in attack detection and prevention systems. Models of fractal Brownian motion and fractal Gaussian noise were considered as models of fractal motions, because they have self-similarity and long-term dependence properties that correspond to the properties of experimental data, as well as the possibility of their analytical interpretation. When evaluating and identifying processes for the implementation of autoregressive models use adaptive filters-approximators, among which there are neural network and neuro-wavelet. The following were used as multifractal models: a multifractal wavelet model with a beta distribution and a hybrid multifractal wavelet model in which the beta distribution is used on a coarse scale and the dis-tribution of point masses on an accurate scale By modeling as a result of adaptation and learning of models, autocorrelation functions, spectra and variances of model signals qualitatively correspond to the graphs of the experimental signal. In addition, the qualitative and numerical values of the characteristics of the model signals generally correspond to the characteristics of the experimental signal. In this case, beta multifractal wavelet models have a smaller error of determination of characteristics than hybrid multifractal wavelet models, and the relative root mean square error of approximation of the experimental signal using a neural network adaptive filter approximator does not exceed 0.046. Statistical verification by non-parametric criterion of signs allowed to establish the adequacy of experimental and model signals with a significance level of 0.01. Further research should be aimed at developing and using predictive models of self-similar traffic in attack detection and prevention systems, which will increase the efficiency of attack detection.

Benchmarking automated detection of the retinal external limiting membrane in a 3D spectral domain optical coherence tomography image dataset of full thickness macular holes

In this article, we present a new benchmark for the segmentation of the retinal external limiting membrane (ELM) using an image dataset of spectral domain optical coherence tomography (OCT) scans in a patient population with idiopathic full-thickness macular holes. Specifically, the dataset used contains OCT images from one eye of 107 patients with an idiopathic full-thickness macular hole. In total, the dataset contains 5243 individual 2-dimensional (2-D) OCT image slices, with each patient contributing 49 individual spectral-domain OCT tagged image slices. We display precise image-wise binary annotations to segment the ELM line. The OCT images present high variations in image contrast, motion, brightness, and speckle noise which can affect the robustness of applied algorithms, so we performed an extensive OCT imaging and annotation data quality analysis. Imaging data quality control included noise, blurriness and contrast scores, motion estimation, darkness and average pixel scores, and anomaly detection. Annotation quality was measured using gradient mapping of ELM line annotation confidence, and idiopathic full-thickness macular hole detection. Finally, we compared qualitative and quantitative results with seven state-of-the-art machine learning-based segmentation methods to identify the ELM line with an automated system.

Probabilistic orbits and dynamical masses of emission-line binaries

ABSTRACT The observed orbits of emission-line stars may be affected by systematics owing to their broad emission lines being formed in complex and extended environments. This is problematic when orbital parameter probability distributions are estimated assuming radial-velocity data are solely comprised of Keplerian motion plus Gaussian white noise, leading to overconfident and inaccurate orbital solutions, with implications for the inferred dynamical masses and hence evolutionary models. We present a framework in which these systems can be meaningfully analysed. We synthesize benchmark data sets, each with a different and challenging noise formulation, for testing the performance of different algorithms. We make these data sets freely available with the aim of making model validation an easy and standardized practice in this field. Next, we develop an application of Gaussian processes to model the radial-velocity systematics of emission-line binaries, named marginalized $\mathcal {GP}$. We benchmark this algorithm, along with current standardized algorithms, on the synthetic data sets and find our marginalized $\mathcal {GP}$ algorithm performs significantly better than the standard algorithms for data contaminated by systematics. Finally, we apply the marginalized $\mathcal {GP}$ algorithm to four prototypical emission-line binaries: Eta Carinae, GG Carinae, WR 140, and WR 133. We find systematics to be present in several of these case studies; and consequently, the predicted orbital parameter distributions, and dynamical masses, are modified from those previously determined.

Hilfer fractional stochastic system driven by mixed Brownian motion and Lvy noise suffered by non-instantaneous impulses

This paper discusses the existence result for a class of non-instantaneous impulsive Hilfer fractional stochastic systems (NIHFSS) driven by mixed Brownian motion and Lvy noise. Sufficient conditions are obtained by utilizing Mnch fixed point theorem (FPT) for the existence of solution of non-instantaneous impulsive Hilfer fractional stochastic system of order and of type The existence result is derived by adapting stochastic analysis techniques, the measure of non-compactness, semigroup theory, and fractional calculus. The discussed theory is illustrated through an example.

The Longitudinal Effect of Meditation on Resting-State Functional Connectivity Using Dynamic Arterial Spin Labeling: A Feasibility Study.

We aimed to assess whether dynamic arterial spin labeling (dASL), a novel quantitative MRI technique with minimal contamination of subject motion and physiological noises, could detect the longitudinal effect of focused attention meditation (FAM) on resting-state functional connectivity (rsFC). A total of 10 novice meditators who recorded their FAM practice time were scanned at baseline and at the 2-month follow-up. Two-month meditation practice caused significantly increased rsFC between the left medial temporal (LMT) seed and precuneus area and between the right frontal eye (RFE) seed and medial prefrontal cortex. Meditation practice time was found to be positively associated with longitudinal changes of rsFC between the default mode network (DMN) and dorsal attention network (DAN), between DMN and insula, and between DAN and the frontoparietal control network (FPN) but negatively associated with changes of rsFC between DMN and FPN, and between DAN and visual regions. These findings demonstrate the capability of dASL in identifying the FAM-induced rsFC changes and suggest that the practice of FAM can strengthen the efficient control of FPN on fast switching between DMN and DAN and enhance the utilization of attentional resources with reduced focus on visual processing.

KONİK IŞINLI BİLGİSAYARLI TOMOGRAFİDE HAREKET VE GÜRÜLTÜ ARTEFAKTLARINDA PHOTOSHOP PROGRAMI KULLANIMI

Background: Patient movement and noise artifacts are quite common during the CBCT exposure in the head and neck region. Among the underlying causes are the long duration of the device, uncontrolled movements due to the general health of the patients, and excessive mobility of the patients such as children. In this study, various filter methods and software features will be examined to remove artifacts by using Photoshop program.Methods: Five different images with noise and motion artifact were selected. These sections were followed by specific paths with Photoshop filters. Images collected and corrected in the program were evaluated by specialist dentists. Before and after the filtering in the image evaluations, 1 to 5 criterions were evaluated by the experts. Each section was scored as 1 very inadequate, 2 inadequate, 3 normal, 4 adequate, 5 very good.Results: As a result of the evaluation of 50 pieces of noise artifacts evaluated between 1 and 5, the average score was 4.12 points and the results were 1.62 for motion artifacts. According to the results of the Wilcoxon test, a statistically significant difference was found between the rate of postoperative correction of the sections with motion artifact and noise artifact.Conclusion: Motion artifact and noise artifact which may occur due to many reasons are frequently encountered in CBCT examinations. In such cases, with the Photoshop program, the resulting artifacts can be reversed and can be evaluated from diagnostic point of view.KEYWORDSCBCT, Artifact, Noise, Photoshop

A signal quality assessment–based ECG waveform delineation method used for wearable monitoring systems

Identifying transient and nonpersistent abnormal electrocardiogram (ECG) waveforms by continuously monitoring the high-risk populations is of great importance for the diagnosis, treatment, and prevention of cardiovascular diseases. In recent years, fabric electrodes have been widely used in wearable devices because of their non-irritating properties and better comfort than traditional AgCl electrodes. However, the motion noise caused by the relative movement between the fabric electrodes and skin affects the quality of ECGs and reduces the accuracy of diagnosis. Therefore, delineating the ECG waveforms that are recorded from wearable devices with varying levels of noise is still challenging. In this study, a signal quality assessment (SQA)-based ECG waveform delineation method that is used for wearable systems was developed. The ECG signal was first preprocessed by a bandpass filter. Five indices, including the multiscale nonlinear amplitude statistical distribution (adSQI1, adSQI2), the proportion of energy-related to T wave (ptSQI), and heart rates computed from R waves and T waves (rHR and tHR, respectively), were then calculated from the preprocessed ECG signal. The signals were classified as good, acceptable, and unacceptable ECGs by combining these indices through the use of a neural network. Subsequently, the R waves or/and T waves were identified for the corresponding feature interpretations based on the SQA results. ECGs that were recorded from the chest belts from 29 volunteers at different activity statuses were divided into 4-s segments. A total of 7133 manually labeled segments were used to derive (4985 segments) and validate (2148 segments) the algorithm. The adSQI1, adSQI2, tHR, and rHR characteristics were significantly different among the good, acceptable, and unacceptable ECGs. The ptSQI value was considerably higher in the good ECGs than in the acceptable and unacceptable ECGs. The ECG segments of different quality levels were classified with an accuracy of 96.74% by using the proposed SQA method. The R waves and T waves were identified with accuracies of 99.95% and 99.57%, respectively, for segments that were classified as acceptable and/or good. The SQA-based ECG waveform delineation method can perform reliable analysis and has the potential to be applied in wearable ECG systems for the early diagnosis and prevention of cardiovascular diseases.

Comparative Study of Gesture Recognition Based on Accelerometer and Photoplethysmography Sensor for Gesture Interactions in Wearable Devices

With the goal of implementing gesture interaction applications on wearable devices, this paper presents a comparative study on hand gesture recognition based on accelerometer (ACC) and photoplethysmography (PPG) sensors. Research is conducted on 14 hand gestures involving wrist and finger movements under four motion scenarios: sitting, walking, jogging, and running. When the participants perform the hand gestures, four channels of PPG signals and 3D ACC signals are collected. To effectively extract gesture repetitions from the continuous movements, a segmentation method based on PPG signal and motion noise is employed. The experimental results verify that the PPG signal is more suitable for implementing gesture interactions on wearable devices. At first, the signal-to-noise ratio analysis demonstrates that the ACC signals are much more affected by motion noise than the PPG signals. Then, the hand gesture recognition results in the four different motion scenarios demonstrate that the accuracies of both the ACC and PPG signals decrease with the intensity of body movement; however, the decrease of the PPG signals is significantly lower than that of the ACC signals, especially for finger-related gestures. A hand gesture recognition experiment with ratios of the samples between the training set and test set from 1:9 to 8:2 demonstrates that the PPG signal also has advantages in regards to reducing user's training burden. Moreover, this research verifies that the green PPG signal is not the best choice for the implementation of gesture interactions. The research results provide a reliable foundation for promoting the development of gesture-based interactions in wearable devices.

Robust Heart Rate Monitoring by a Single Wrist-Worn Accelerometer Based on Signal Decomposition

Monitoring heart rate (HR) by a single wrist-worn accelerometer would provide many advantages over electrocardiogram (ECG) or photoplethysmography (PPG), such as wearing comfortability, saving power, tiny footprint and automatic motion artifact removal. However, like ECG, accelerometry was mostly implemented on the chest, named seismocardiogram (SCG), and PPG was dominating the wrist worn format. Pulse condition detection in Traditional Chinese Medicine or sphygmography was engineered into a wearable format in this work, and a wrist-worn HR monitor by a single accelerometer was demonstrated. A major limitation of wrist-worn device is that motion artifacts and noise severely corrupt the signal integrity. In this study, with raw data segments including hundreds of random finger or hand motions, several signal decomposition algorithms were compared, such as independent component analysis (ICA), variable mode decomposition (VMD), wavelet synchrosqueezed transform (WSST), and singular spectrum analysis (SSA), and Kalman smoothing was implemented to track HR continuously. Our method was tested on 20 subjects during random finger tapping or hand swinging. Without knowing properties of noise or motion beforehand or requirement of an extra sensor as reference, our method provides a significant removal of motion artifacts and noise from three-dimensional acceleration signals, with 95% of HR estimation within ±8.86 bpm, much longer battery life and better wearing comfort than PPG. It would broaden the working conditions, and thus provide a more holistic assessment of HR during everyday life.

Robust Inertial Motion Tracking through Deep Sensor Fusion across Smart Earbuds and Smartphone

IMU based inertial tracking plays an indispensable role in many mobility centric tasks, such as robotic control, indoor navigation and virtual reality gaming. Despite its mature application in rigid machine mobility (e.g., robot and aircraft), tracking human users via mobile devices remains a fundamental challenge due to the intractable gait/posture patterns. Recent data-driven models have tackled sensor drifting, one key issue that plagues inertial tracking. However, these systems still assume the devices are held or attached to the user body with a relatively fixed posture. In practice, natural body activities may rotate/translate the device which may be mistaken as whole body movement. Such motion artifacts remain as the dominating factor that fails existing inertial tracing systems in practical uncontrolled settings. Inspired by the observation that human heads induces far less intensive movement relative to the body during walking, compared to other parts, we propose a novel multi-stage sensor fusion pipeline called DeepIT, which realizes inertial tracking by synthesizing the IMU measurements from a smartphone and an associated earbud. DeepIT introduces a data-driven reliability aware attention model, which assesses the reliability of each IMU and opportunistically synthesizes their data to mitigate the impacts of motion noise. Furthermore, DeepIT uses a reliability aware magnetometer compensation scheme to combat the angular drifting problem caused by unrestricted motion artifacts. We validate DeepIT on the first large-scale inertial navigation dataset involving both smartphone and earbud IMUs. The evaluation results show that DeepIT achieves multiple folds of accuracy improvement on the challenging uncontrolled natural walking scenarios, compared with state-of-the-art closed-form and data-driven models.

Reliability on multiband diffusion NODDI models: A test retest study on children and adults

Neurite Orientation Dispersion and Density Imaging (NODDI) and Bingham-NODDI diffusion MRI models are nowadays very well-known models in the field of diffusion MRI as they represent powerful tools for the estimation of brain microstructure. In order to efficiently translate NODDI imaging findings into the diagnostic clinical practice, a test-retest approach would be useful to assess reproducibility and reliability of NODDI biomarkers, thus providing validation on precision of different fitting toolboxes. In this context, we conducted a test-retest study with the aim to assess the effects of different factors (i.e. fitting algorithms, multiband acceleration, shell configuration, age of subject and hemispheric side) on diffusion models reliability, assessed in terms of Intra-class Correlation Coefficient (ICC) and Variation Factor (VF). To this purpose, data from pediatric and adult subjects were acquired with Simultaneous-MultiSlice (SMS) imaging method with two different acceleration factor (AF) and four b-values, subsequently combined in seven shell configurations. Data were then fitted with two different GPU-based algorithms to speed up the analysis. Results show that each factor investigated had a significant effect on reliability of several diffusion parameters. Particularly, both datasets reveal very good ICC values for higher AF, suggesting that faster acquisitions do not jeopardize the reliability and are useful to decrease motion artifacts. Although very small reliability differences appear when comparing shell configurations, more extensive diffusion parameters variability results when considering shell configuration with lower b-values, especially for simple model like NODDI. Also fitting tools have a significant effect on reliability, but their difference occurs in both datasets and AF, so it appears to be independent from either misalignment and motion artifacts, or noise and SNR. The main achievement of the present study is to show how 10 min multi-shell diffusion MRI acquisition for NODDI acquisition can have reliable results in WM. More complex models do not appear to be more prone to less data acquisition as well as noisier data thus stressing the idea of Bingham-NODDI having greater sensitivity to true subject variability.

Intelligent Recognition of Hospital Image Based on Deep Learning: The Relationship between Adaptive Behavior and Family Function in Children with ADHD.

Chronic diseases are gradually becoming the main threat to human health. By designing an efficient hospital management platform to quickly identify the corresponding chronic diseases, it can effectively reduce the labor cost, improve the accuracy of disease identification, and improve treatment efficiency. ADHD is a common behavioral disorder in school-age children, and it is also one of the most common chronic health problems in this period. The internationally recognized prevalence of ADHD is 3%–9%. ADHD often brings adverse effects on children's life and studying and at the same time increases difficulties for their families. Therefore, this paper designs an intelligent management platform for public hospitals based on a deep learning algorithm, evaluates the current situation and influencing factors of ADHD children through the child adaptive behavior scale and the family function assessment scale, and designs its intelligent platform by using a new technology of fNIRS. According to the nonlinearity and unsteadiness of the fNIRS signal, this paper proposes a motion noise removal method based on EMD algorithm methods: to automatically identify children with ADHD and improve the cognitive function of children with ADHD by intervention technology. The data are from the outpatients of the Department of Child Psychology of the First People's Hospital of Tianshui City in Gansu Province in 2018. The results showed that there were significant differences in the adaptive behavior scale (CABS) and fad scores between the two groups. In the seven dimensions of family function, there were significant differences between the two groups (P < 0.01). fNIRS management platform can effectively identify ADHD patients with high recognition accuracy. The intelligent management platform can significantly reduce the number of physical examination personnel, prolong the diagnosis and treatment time, reduce a lot of repetitive work, and improve the efficiency of diagnosis and treatment. At the same time, this technology also provides great help for better research and improvement of ADHD patients and provides a reference for the information intelligent construction of modern hospitals.

Design, static analysis and development of a new three-legged shake table

In this paper, an alternative design is proposed based on a family of three-legged manipulators. Such manipulators have two actuators (one vertical and one horizontal) in each leg, unlike the standard UP̅S Stewart platform, which has one actuator in each leg. The arrangement of the two actuators is such a way that, to have vertical motion of the shake table only the Vertical Motion Actuators (VMA) are actuated and for longitudinal or lateral motion, the Horizontal Motion Actuators (HMA) alone are moved. Due to its inherent features such as simplified kinematics, control and distributed loading, a study is carried out to determine the performance of such three-legged manipulators as a shake table. Sinusoidal motion and white noise motions are given to the actuators and shown that the VMA forces have linear relationship with the platform forces. The translational stiffness and the torsional stiffness are studied separately for the manipulators. In the dynamic analysis, it is highlighted that the gravity load of the legs is borne by the Vertical actuators, irrespective of the motion being spatial or planar. Hence, this topology provides scope for lighter electromechanical actuation. The performance analysis of the 3 legged configuration is accomplished using simulation results, in comparison to a 7-UP̅S configuration of shake table. A prototype of the shake table is fabricated and tested with earthquake data of El Centro.

Suppression of motion noise based on a linear‐homomorphic filtering algorithm in airborne electromagnetic survey

ABSTRACTThe movement of the receiver coil in a magnetic field produces motion noise in the airborne electromagnetic field. With an amplitude that is several orders of magnitude higher than that of other noise sources, this motion noise is one of the main low‐frequency (&lt;1 kHz) noise sources. Especially in the late decay period, the residual motion noise produces false anomalous information, which is a key problem of low‐base‐frequency airborne electromagnetic surveys. In this paper, a method for calculating the motion noise is proposed. This method takes into account both the earth's magnetic field and the active secondary magnetic field and introduces additive and multiplicative motion noise. Then, a linear‐homomorphic filtering algorithm is proposed to handle the additive and multiplicative motion noises. In the linear filtering part of our algorithm, the minimum noise fraction transform is applied to suppress additive motion noise. It provides sets of components ordered by the signal‐to‐noise ratio by deriving the eigenvalues and eigenvectors for the signal and uncorrelated noise covariance matrices. In the homomorphic filtering part, the multiplicative motion noise is removed. To do this, the adaptive‐width smoothing algorithm is applied to the logarithm of the filtered with the linear filtering process profile data. The results of synthetic and field data demonstrate that the linear filtering process based on the minimum noise fraction transform is effective for very low signal‐to‐noise ratios of the late‐time data, and the homomorphic filtering process can help distinguish small anomalies of these channels.

Asymptotic behavior analysis of Markovian switching neutral-type stochastic time-delay systems

A new integral inequality method is put forward to analyze the general decay stability for Markovian switching neutral stochastic functional differential systems. At first, in order to get around the dynamic analyses difficulty induced by the coinstantaneous presence of neutral term, Markovian switching and Brownian motion noise, an new integral inequality as a powerful tool is gained. Then, based on the integral inequality, general decay stability in the sense of pth(p>0) moment and the almost sure can be taken out by utilizing the nonnegative semimartingale convergence theorem and Lyapunov stability theory. The obtained results can be especially applied to two special types of neutral stochastic differential systems that have been studied in the literature. Finally, an example has been performed to verify the obtained analytical results.

Stable Bioelectric Signal Acquisition Using an Enlarged Surface-Area Flexible Skin Electrode

Numerous wearable devices were developed to measure bioelectric signals for continuous healthcare monitoring. The electrode, which interconnects electronics and the human body, significantly affects the signal quality. Although Ag/AgCl electrodes have been commonly used, noble-metal electrodes are more promising in terms of long-term reusability and flexibility. However, the signal-to-noise ratio (SNR) of noble metals is still insufficient for highly accurate biosignal acquisition. In this study, we propose an approach to enhance the electrical characteristics of a noble-metal skin electrode by surface modification using gold nanoparticles. The process parameters for nanoparticle deposition were optimized to maximize the surface area, thereby significantly improving the SNR of the electrode. The SNR value was increased by 51% in electrocardiogram and by 63% in electromyogram (EMG). We also propose an approach to quantify the motion artifact by spectral analysis, and the high flexibility of our electrode reduced the motion noise by 95% compared to the conventional Ag/AgCl electrode. The enhanced electrode interface paves the way for analyzing complex biosignals such as EMG and electroencephalogram in wearable applications.