Feature Extraction of Lubricating Oil Debris Signal Based on Segmentation Entropy with an Adaptive Threshold.

Ferromagnetic debris in lubricating oil, serving as an important communication carrier, can effectively reflect the wear condition of mechanical equipment and predict the remaining useful life. In practice application, the detection signals collected by using inductive sensors contain not only debris signals but also noise terms, and weak debris features are prone to be distorted, which makes it a severe challenge to debris signature identification and quantitative estimation. In this paper, a debris signature extraction method established on segmentation entropy with an adaptive threshold was proposed, based on which five identification indicators were investigated to improve detection accuracy. The results of the simulations and oil experiment show that the proposed algorithm can effectively identify wear particles and preserve debris signatures.

A Vascular Feature Detection and Matching Method Based on Dual-Branch Fusion and Structure Enhancement.

How to obtain internal cavity features and perform image matching is a great challenge for laparoscopic 3D reconstruction. This paper proposes a method for detecting and associating vascular features based on dual-branch weighted fusion vascular structure enhancement. Our proposed method is divided into three stages, including analyzing various types of minimally invasive surgery (MIS) images and designing a universal preprocessing framework to make our method generalized. We propose a Gaussian weighted fusion vascular structure enhancement algorithm using the dual-branch Frangi measure and MFAT (multiscale fractional anisotropic tensor) to address the structural measurement differences and uneven responses between venous vessels and microvessels, providing effective structural information for vascular feature extraction. We extract vascular features through dual-circle detection based on branch point characteristics, and introduce NMS (non-maximum suppression) to reduce feature point redundancy. We also calculate the ZSSD (zero sum of squared differences) and perform feature matching on the neighboring blocks of feature points extracted from the front and back frames. The experimental results show that the proposed method has an average accuracy and repeatability score of 0.7149 and 0.5612 in the Vivo data set, respectively. By evaluating the quantity, repeatability, and accuracy of feature detection, our method has more advantages and robustness than the existing methods.

Objective quantification of posterior capsule opacification after cataract surgery with swept-source optical coherence tomography.

PURPOSE: To evaluate the application of swept-source optical coherence tomography (SS-OCT) and pentacam scheimpflug tomography in posterior capsule opacification (PCO) severity assessment. METHODS: The posterior capsule image region segmentation and adaptive threshold algorithm are used to process the SS-OCT scanned image to obtain the posterior capsule thickness (PCT). Scheimpflug tomography reconstructed and analysized by image J software can obtain the average gray value and evaluate the effectiveness with the two methods. RESULT: One hundred sixty-two IOL eyes of 101 patients were divided into two groups, laser group (65 eyes) with the mean PCT was 8.0 ± 2.7 pixel unit and the mean gray value of the eyes was 66 ± 33 pixel unit. However, these figures in the control group (97 eyes) were 5.0 ± 0.9 and 11 ± 17. The sensitivity, specificity and area under curve(AUC) of SS-OCT PCT were 85%, 74% and 0.942,the sensitivity, specificity and AUC of Pentacam gray value were 91%, 76% and 0.947, respectively. After using the multivariable model of generalized estimation equation to corrected the dependence of subjects' eyes, it was found that SS-OCT PCT, Pentacam gray value, low vision quality of life questionnaire (LVQ questionnaire) for distance vision, and mobility and lighting dimension were significantly correlated with the PCO score (P = 0.012, P = 0.001, P = 0.005, respectively). CONCLUSION: The region segmentation and adaptive threshold algorithm of posterior capsule image will accurately quantify the posterior capsule. Computer aided quantifications of posterior capsule are of great significance in the early surgical decision-making of PCO. The average occurrence time of most PCO was around 34 months, and the severity of PCO worsened with increasing postoperative time.

Weighted Sparseness-Based Anomaly Detection for Hyperspectral Imagery.

Anomaly detection of hyperspectral remote sensing data has recently become more attractive in hyperspectral image processing. The low-rank and sparse matrix decomposition-based anomaly detection algorithm (LRaSMD) exhibits poor detection performance in complex scenes with multiple background edges and noise. Therefore, this study proposes a weighted sparse hyperspectral anomaly detection method. First, using the idea of matrix decomposition in mathematics, the original hyperspectral data matrix is reconstructed into three sub-matrices with low rank, small sparsity and representing noise, respectively. Second, to suppress the noise interference in the complex background, we employed the low-rank, background image as a reference, built a local spectral and spatial dictionary through the sliding window strategy, reconstructed the HSI pixels of the original data, and extracted the sparse coefficient. We proposed the sparse coefficient divergence evaluation index (SCDI) as a weighting factor to weight the sparse anomaly map to obtain a significant anomaly map to suppress the background edge, noise, and other residues caused by decomposition, and enhance the abnormal target. Finally, abnormal pixels are segmented based on the adaptive threshold. The experimental results demonstrate that, on a real-scene hyperspectral dataset with a complicated background, the proposed method outperforms the existing representative algorithms in terms of detection performance.

Fault Diagnosis of Wind Turbine Generators Based on Stacking Integration Algorithm and Adaptive Threshold.

Fault alarm time lag is one of the difficulties in fault diagnosis of wind turbine generators (WTGs), and the existing methods are insufficient to achieve accurate and rapid fault diagnosis of WTGs, and the operation and maintenance costs of WTGs are too high. To invent a new method for fast and accurate fault diagnosis of WTGs, this study constructs a stacking integration model based on the machine learning algorithms light gradient boosting machine (LightGBM), extreme gradient boosting (XGBoost), and stochastic gradient descent regressor (SGDRegressor) using publicly available datasets from Energias De Portugal (EDP). This model is automatically tuned for hyperparameters during training using Bayesian tuning, and the coefficient of determination (R2) and root mean square error (RMSE) were used to evaluate the model to determine its applicability and accuracy. The fitted residuals of the test set were calculated, the Pauta criterion (3σ) and the temporal sliding window were applied, and a final adaptive threshold method for accurate fault diagnosis and alarming was created. The model validation results show that the adaptive threshold method proposed in this study is better than the fixed threshold for diagnosis, and the alarm times for the GENERATOR fault type, GENERATOR_BEARING fault type, and TRANSFORMER fault type are 1.5 h, 5.8 h, and 3 h earlier, respectively.

Displacement Monitoring of a Bridge Based on BDS Measurement by CEEMDAN-Adaptive Threshold Wavelet Method.

From the viewpoint of BDS bridge displacement monitoring, which is easily affected by background noise and the calculation of a fixed threshold value in the wavelet filtering algorithm, which is often related to the data length. In this paper, a data processing method of Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN), combined with adaptive threshold wavelet de-noising is proposed. The adaptive threshold wavelet filtering method composed of the mean and variance of wavelet coefficients of each layer is used to de-noise the BDS displacement monitoring data. CEEMDAN was used to decompose the displacement response data of the bridge to obtain the intrinsic mode function (IMF). Correlation coefficients were used to distinguish the noisy component from the effective component, and the adaptive threshold wavelet de-noising occurred on the noisy component. Finally, all IMF were restructured. The simulation experiment and the BDS displacement monitoring data of Nanmao Bridge were verified. The results demonstrated that the proposed method could effectively suppress random noise and multipath noise, and effectively obtain the real response of bridge displacement.

The recruitment of indirect waves within primary motor cortex during motor imagery: A directional transcranial magnetic stimulation study.

Motor imagery (MI) refers to the mental simulation of an action without overt movement. While numerous transcranial magnetic stimulation (TMS) studies provided evidence for a modulation of corticospinal excitability and intracortical inhibition during MI, the neural signature within the primary motor cortex is not clearly established. In the current study, we used directional TMS to probe the modulation of the excitability of early and late indirect waves (I-waves) generating pathways during MI. Corticospinal responses evoked by TMS with posterior-anterior (PA) and anterior-posterior (AP) current flow within the primary motor cortex evoke preferentially early and late I-waves, respectively. Seventeen participants were instructed to stay at rest or to imagine maximal isometric contractions of the right flexor carpi radialis. We demonstrated that the increase of corticospinal excitability during MI is greater with PA than AP orientation. By using paired-pulse stimulations, we confirmed that short-interval intracortical inhibition (SICI) increased during MI in comparison to rest with PA orientation, whereas we found that it decreased with AP orientation. Overall, these results indicate that the pathways recruited by PA and AP orientations that generate early and late I-waves are differentially modulated by MI.

DA-LSTM-VAE: Dual-Stage Attention-Based LSTM-VAE for KPI Anomaly Detection.

To ensure the normal operation of the system, the enterprise's operations engineer will monitor the system through the KPI (key performance indicator). For example, web page visits, server memory utilization, etc. KPI anomaly detection is a core technology, which is of great significance for rapid fault detection and repair. This paper proposes a novel dual-stage attention-based LSTM-VAE (DA-LSTM-VAE) model for KPI anomaly detection. Firstly, in order to capture time correlation in KPI data, long-short-term memory (LSTM) units are used to replace traditional neurons in the variational autoencoder (VAE). Then, in order to improve the effect of KPI anomaly detection, an attention mechanism is introduced into the input stage of the encoder and decoder, respectively. During the input stage of the encoder, a time attention mechanism is adopted to assign different weights to different time points, which can adaptively select important input sequences to avoid the influence of noise in the data. During the input stage of the decoder, a feature attention mechanism is adopted to adaptively select important latent variable representations, which can capture the long-term dependence of time series better. In addition, this paper proposes an adaptive threshold method based on anomaly scores measured by reconstruction probability, which can minimize false positives and false negatives and avoid adjustment of the threshold manually. Experimental results in a public dataset show that the proposed method in this paper outperforms other baseline methods.

Automated quantification of vomeronasal glomeruli number, size, and color composition after immunofluorescent staining.

Glomeruli are neuropil-rich regions of the main or accessory olfactory bulbs (AOB) where the axons of olfactory or vomeronasal neurons and dendrites of mitral/tufted cells form synaptic connections. In the main olfactory system, olfactory sensory neurons (OSNs) expressing the same receptor innervate 1 or 2 glomeruli. However, in the accessory olfactory system, vomeronasal sensory neurons (VSNs) expressing the same receptor can innervate up to 30 different glomeruli in the AOB. Genetic mutation disrupting genes with a role in defining the identity/diversity of olfactory and vomeronasal neurons can alter the number and size of glomeruli. Interestingly, 2 cell surface molecules, Kirrel2 and Kirrel3, have been indicated as playing a critical role in the organization of axons into glomeruli in the AOB. Being able to quantify differences in glomeruli features, such as number, size, or immunoreactivity for specific markers, is an important experimental approach to validate the role of specific genes in controlling neuronal connectivity and circuit formation in either control or mutant animals. Since the manual recognition and quantification of glomeruli on digital images is a challenging and time-consuming task, we generated a program in Python able to identify glomeruli in digital images and quantify their properties, such as size, number, and pixel intensity. Validation of our program indicates that our script is a fast and suitable tool for high-throughput quantification of glomerular features of mouse lines with different genetic makeup.

Multilayer Network Approach in EEG Motor Imagery with an Adaptive Threshold.

The brain has been understood as an interconnected neural network generally modeled as a graph to outline the functional topology and dynamics of brain processes. Classic graph modeling is based on single-layer models that constrain the traits conveyed to trace brain topologies. Multilayer modeling, in contrast, makes it possible to build whole-brain models by integrating features of various kinds. The aim of this work was to analyze EEG dynamics studies while gathering motor imagery data through single-layer and multilayer network modeling. The motor imagery database used consists of 18 EEG recordings of four motor imagery tasks: left hand, right hand, feet, and tongue. Brain connectivity was estimated by calculating the coherence adjacency matrices from each electrophysiological band (δ, θ, α and ß) from brain areas and then embedding them by considering each band as a single-layer graph and a layer of the multilayer brain models. Constructing a reliable multilayer network topology requires a threshold that distinguishes effective connections from spurious ones. For this reason, two thresholds were implemented, the classic fixed (average) one and Otsu's version. The latter is a new proposal for an adaptive threshold that offers reliable insight into brain topology and dynamics. Findings from the brain network models suggest that frontal and parietal brain regions are involved in motor imagery tasks.

REMoDNaV: robust eye-movement classification for dynamic stimulation.

Tracking of eye movements is an established measurement for many types of experimental paradigms. More complex and more prolonged visual stimuli have made algorithmic approaches to eye-movement event classification the most pragmatic option. A recent analysis revealed that many current algorithms are lackluster when it comes to data from viewing dynamic stimuli such as video sequences. Here we present an event classification algorithm-built on an existing velocity-based approach-that is suitable for both static and dynamic stimulation, and is capable of classifying saccades, post-saccadic oscillations, fixations, and smooth pursuit events. We validated classification performance and robustness on three public datasets: 1) manually annotated, trial-based gaze trajectories for viewing static images, moving dots, and short video sequences, 2) lab-quality gaze recordings for a feature-length movie, and 3) gaze recordings acquired under suboptimal lighting conditions inside the bore of a magnetic resonance imaging (MRI) scanner for the same full-length movie. We found that the proposed algorithm performs on par or better compared to state-of-the-art alternatives for static stimulation. Moreover, it yields eye-movement events with biologically plausible characteristics on prolonged dynamic recordings. Lastly, algorithm performance is robust on data acquired under suboptimal conditions that exhibit a temporally varying noise level. These results indicate that the proposed algorithm is a robust tool with improved classification accuracy across a range of use cases. The algorithm is cross-platform compatible, implemented using the Python programming language, and readily available as free and open-source software from public sources.

Neurochemical balance and inhibition at the subacute stage after stroke.

Stroke is a leading cause of death and disability worldwide with many people left with impaired motor function. Evidence from experimental animal models of stroke indicates that reducing motor cortex inhibition may facilitate neural plasticity and motor recovery. This study compared primary motor cortex (M1) inhibition measures over the first 12 wk after stroke with a cohort of age-similar healthy controls. The excitation-inhibition ratio and gamma-aminobutyric acid (GABA) neurotransmission within M1 were assessed using magnetic resonance spectroscopy and threshold hunting paired-pulse transcranial magnetic stimulation respectively. Upper limb impairment and function were assessed with the Fugl-Meyer Upper Extremity Scale and Action Research Arm Test. Patients with a functional corticospinal pathway had motor-evoked potentials on the paretic side and exhibited better recovery from upper limb impairment and recovery of function than patients without a functional corticospinal pathway. Compared with age-similar controls, the neurochemical balance in terms of the excitation-inhibition ratio was greater within contralesional M1 in patients with a functional corticospinal pathway. There was evidence for elevated long-interval inhibition in both ipsilesional and contralesional M1 compared with controls. Short-interval inhibition measures differed between the first and second phases, with evidence for elevation of the former only in ipsilesional M1 and no evidence of disinhibition for the latter. Overall, findings from transcranial magnetic stimulation indicate an upregulation of GABA-mediated tonic inhibition in M1 early after stroke. Therapeutic approaches that aim to normalize inhibitory tone during the subacute period warrant further investigation.NEW & NOTEWORTHY Magnetic resonance spectroscopy indicated higher excitation-inhibition ratios within motor cortex during subacute recovery than age-similar healthy controls. Measures obtained from adaptive threshold hunting paired-pulse transcranial magnetic stimulation indicated greater tonic inhibition in patients compared with controls. Therapeutic approaches that aim to normalize motor cortex inhibition during the subacute stage of recovery should be explored.

Point and interval estimation in two-stage adaptive designs with time to event data and biomarker-driven subpopulation selection.

In personalized medicine, it is often desired to determine if all patients or only a subset of them benefit from a treatment. We consider estimation in two-stage adaptive designs that in stage 1 recruit patients from the full population. In stage 2, patient recruitment is restricted to the part of the population, which, based on stage 1 data, benefits from the experimental treatment. Existing estimators, which adjust for using stage 1 data for selecting the part of the population from which stage 2 patients are recruited, as well as for the confirmatory analysis after stage 2, do not consider time to event patient outcomes. In this work, for time to event data, we have derived a new asymptotically unbiased estimator for the log hazard ratio and a new interval estimator with good coverage probabilities and probabilities that the upper bounds are below the true values. The estimators are appropriate for several selection rules that are based on a single or multiple biomarkers, which can be categorical or continuous.

Primary motor cortex function and motor skill acquisition: insights from threshold-hunting TMS.

Modulation of GABA-mediated inhibition in primary motor cortex (M1) is important for the induction of training-induced plasticity. The downregulation of inhibition during acquisition may promote cortical reorganization, whereas an upregulation once performance has plateaued may promote consolidation of the newly acquired skill. GABA-related inhibition in human M1 is routinely assessed using the paired-pulse transcranial magnetic stimulation (TMS) paradigm of short-interval intracortical inhibition (SICI). However, modulation of SICI with motor skill learning is not a consistent finding and may be influenced by TMS parameters. The aim of this study was to compare the modulation of SICI by motor skill learning between conventional and adaptive threshold-hunting techniques with an anterior-posterior and posterior-anterior induced current. Sixteen participants (21-33 years) trained with their dominant (right) hand on a sequential visual isometric pinch task. Electromyographic recordings were obtained from the right first dorsal interosseous muscle. Corticomotor excitability and SICI were examined before and immediately after 12 blocks of training. Skill increased throughout the training, with performance plateauing before completion. Corticomotor excitability increased after motor training for both current directions. The amount of SICI was greater with anterior-posterior stimulation than posterior-anterior for both conventional and adaptive threshold-hunting techniques. SICI increased after motor training, but only for adaptive threshold-hunting with an anterior-posterior-induced current. The increased GABA-mediated inhibition evident after motor skill learning may promote consolidation of the newly acquired skill. The findings also support the notion that adaptive threshold-hunting SICI using an anterior-posterior current provides an effective assessment in interventional studies.

Design and analysis of biomarker-integrated clinical trials with adaptive threshold detection and flexible patient enrichment.

We propose a new adaptive threshold detection and enrichment design in which the biomarker threshold is adaptively estimated and updated by optimizing a trade-off between the size of the biomarker positive population and the magnitude of the treatment effect in that population. Enrichment is based on an enrollment criterion that accounts for the uncertainty in estimation of the threshold. Early termination for futility is allowed based on predictive success probability. Valid testing and estimation techniques for the treatment effect overall and inpatient subgroups are studied. Simulations and an example demonstrate advantages of the proposed design over existing designs.

Driver Identification System Using Normalized Electrocardiogram Based on Adaptive Threshold Filter for Intelligent Vehicles.

Driver-centered infotainment and telematics services are provided for intelligent vehicles that improve driver convenience. Driver-centered services are performed after identification, and a biometrics system using bio-signals is applied. The electrocardiogram (ECG) signal acquired in the driving environment needs to be normalized because the intensity of noise is strong because the driver's motion artifact is included. Existing time, frequency, and phase normalization methods have a problem of distorting P, QRS Complexes, and T waves, which are morphological features of an ECG, or normalizing to signals containing noise. In this paper, we propose an adaptive threshold filter-based driver identification system to solve the problem of distortion of the ECG morphological features when normalized and the motion artifact noise of the ECG that causes the identification performance deterioration in the driving environment. The experimental results show that the proposed method improved the average similarity compared to the results without normalization. The identification performance was also improved compared to the results before normalization.

An Adaptive Autogram Approach Based on a CFAR Detector for Incipient Cavitation Detection.

Cavitation failure often occurs in centrifugal pumps, resulting in severe harm to their performance and life-span. Nowadays, it has become crucial to detect incipient cavitation ahead of cavitation failure. However, most envelope demodulation methods suffer from strong noise and repetitive impacts. This paper proposes an adaptive Autogram approach based on the Constant False Alarm Rate (CFAR). A cyclic amplitude model (CAM) is presented to reveal the cyclostationarity and autocorrelation-periodicity of pump cavitation-caused signals. The Autogram method is improved for envelope demodulation and cyclic feature extraction by introducing the character to noise ratio (CNR) and CFAR threshold. To achieve a high detection rate, CNR parameters are introduced to represent the cavitation intensity in the combined square-envelope spectrum. To maintain a low false alarm, the CFAR detector is combined with the CNR parameter to obtain adaptive thresholds for different data along with sensor positions. By carrying out various experiments of a centrifugal water pump from Status 1 to 10 at different flow rates, the proposed approach is capable of cavitation feature extraction with respect to the CAM model, and can achieve more than a 90% detection rate of incipient cavitation and maintain a 5% false alarm rate. This paper offers an alternative solution for the predictive maintenance of pump cavitation.

Estimation of the resting motor threshold (RMT) in transcranial magnetic stimulation using relative-frequency and threshold-hunting methods in brain tumor patients.

BACKGROUND: Application of transcranial magnetic stimulation is often based on the resting motor threshold. The aim of this study was to validate recent findings on the advantage of resting motor threshold estimation using adaptive threshold-hunting algorithms over the Rossini-Rothwell method in a clinical sample and healthy subjects. METHODS: Resting motor thresholds in 115 patients with a brain tumor and 10 healthy subjects were assessed using the Rossini-Rothwell method and compared to an adaptive threshold-hunting algorithm. In healthy subjects, this measurement was repeated twice to capture test-retest reliability of both methods. Efficiency of both methods was assessed by comparing the number of pulses needed for resting motor threshold estimation. RESULTS: There was no significant difference between the Rossini-Rothwell method and the adaptive threshold-hunting algorithm in patients and healthy controls with limits of agreement between ± 12 V/m. There was a strong intraclass correlation and both methods showed a good test-retest reliability. However, the adaptive threshold-hunting algorithm was significantly faster. CONCLUSIONS: The adaptive threshold-hunting algorithm was more efficient in assessing the resting motor threshold, while reaching comparable results as the Rossini-Rothwell method. Thus, our results support the advantage of adaptive threshold-hunting algorithms to determine the resting motor threshold also in a clinical sample.

A Two-Dimensional Adaptive Target Detection Algorithm in the Compressive Domain.

By applying compressive sensing to infrared imaging systems, the sampling and transmitting time can be remarkably reduced. Therefore, in order to meet the real-time requirements of infrared small target detection tasks in the remote sensing field, many approaches based on compressive sensing have been proposed. However, these approaches need to reconstruct the image from the compressive domain before detecting targets, which is inefficient due to the complex recovery algorithms. To overcome this drawback, in this paper, we propose a two-dimensional adaptive threshold algorithm based on compressive sensing for infrared small target detection. Instead of processing the reconstructed image, our algorithm focuses on directly detecting the target in the compressive domain, which reduces both the time and memory requirements for image recovery. First, we directly subtract the spatial background image in the compressive domain of the original image sampled by the two-dimensional measurement model. Then, we use the properties of the Gram matrix to decode the subtracted image for further processing. Finally, we detect the targets by employing the advanced adaptive threshold method to the decoded image. Experiments show that our algorithm can achieve an average 100% detection rate, with a false alarm rate lower than 0.4%, and the computational time is within 0.3 s, on average.

Adaptive threshold hunting for the effects of transcranial direct current stimulation on primary motor cortex inhibition.

Primary motor cortex excitability can be modulated by anodal and cathodal transcranial direct current stimulation (tDCS). These neuromodulatory effects may, in part, be dependent on modulation within gamma-aminobutyric acid (GABA)-mediated inhibitory networks. GABAergic function can be quantified non-invasively using adaptive threshold hunting paired-pulse transcranial magnetic stimulation (TMS). The previous studies have used TMS with posterior-anterior (PA) induced current to assess tDCS effects on inhibition. However, TMS with anterior-posterior (AP) induced current in the brain provides a more robust measure of GABA-mediated inhibition. The aim of the present study was to assess the modulation of corticomotor excitability and inhibition after anodal and cathodal tDCS using TMS with PA- and AP-induced current. In 16 young adults (26 ± 1 years), we investigated the response to anodal, cathodal, and sham tDCS in a repeated-measures double-blinded crossover design. Adaptive threshold hunting paired-pulse TMS with PA- and AP-induced current was used to examine separate interneuronal populations within M1 and their influence on corticomotor excitability and short- and long-interval inhibition (SICI and LICI) for up to 60 min after tDCS. Unexpectedly, cathodal tDCS increased corticomotor excitability assessed with AP (P = 0.047) but not PA stimulation (P = 0.74). SICIAP was reduced after anodal tDCS compared with sham (P = 0.040). Pearson's correlations indicated that SICIAP and LICIAP modulation was associated with corticomotor excitability after anodal (P = 0.027) and cathodal tDCS (P = 0.042). The after-effects of tDCS on corticomotor excitability may depend on the direction of the TMS-induced current used to make assessments, and on modulation within GABA-mediated inhibitory circuits.