Hierarchical Filtering Research Articles

Filter transfer learning algorithm for nonlinear systems modeling with heterogeneous features

Transfer learning can handle the domain adaptation of different feature spaces in nonlinear systems. Most existing studies only focus on common features between heterogeneous scenes rather than specific features that account for latent similarities between them. To deal with this problem, a filter transfer learning algorithm for nonlinear systems modeling with heterogeneous features is proposed. First, nonlinear mapping is constructed to learn the potential relationships between different feature attributes, including common features and specific features. Then, source knowledge from different domains can be obtained in the form of process parameters according to the mapping relationship. Second, a hierarchical filter framework is presented to reconstruct source knowledge in different transfer phases. In the pre-transfer phase, a knowledge filter is designed to increase the diversity of knowledge through selection, crossover, and mutation operations. In the post-transfer phase, a guided filter is established to achieve a coupling balance between source knowledge and target domain by using target samples as guidance information. Third, a dynamic parameter learning strategy is given to promote the learning performance of heterogeneous tasks. Finally, the effectiveness of innovations and the superiority of this proposed algorithm are verified by the experimental results in nonlinear systems with heterogeneous features.

Statistical Learning of Incidental Perceptual Regularities Induces Sensory Conditioned Cortical Responses.

Statistical learning of sensory patterns can lead to predictive neural processes enhancing stimulus perception and enabling fast deviancy detection. Predictive processes have been extensively demonstrated when environmental statistical regularities are relevant to task execution. Preliminary evidence indicates that statistical learning can even occur independently of task relevance and top-down attention, although the temporal profile and neural mechanisms underlying sensory predictions and error signals induced by statistical learning of incidental sensory regularities remain unclear. In our study, we adopted an implicit sensory conditioning paradigm that elicited the generation of specific perceptual priors in relation to task-irrelevant audio-visual associations, while recording Electroencephalography (EEG). Our results showed that learning task-irrelevant associations between audio-visual stimuli resulted in anticipatory neural responses to predictive auditory stimuli conveying anticipatory signals of expected visual stimulus presence or absence. Moreover, we observed specific modulation of cortical responses to probabilistic visual stimulus presentation or omission. Pattern similarity analysis indicated that predictive auditory stimuli tended to resemble the response to expected visual stimulus presence or absence. Remarkably, Hierarchical Gaussian filter modeling estimating dynamic changes of prediction error signals in relation to differential probabilistic occurrences of audio-visual stimuli further demonstrated instantiation of predictive neural signals by showing distinct neural processing of prediction error in relation to violation of expected visual stimulus presence or absence. Overall, our findings indicated that statistical learning of non-salient and task-irrelevant perceptual regularities could induce the generation of neural priors at the time of predictive stimulus presentation, possibly conveying sensory-specific information about the predicted consecutive stimulus.

Hierarchical three-layered fibers for bioaerosol and CO2 capture, and antimicrobial performance

As the duration of indoor activities has increased owing to environmental issues and the prevalence of respiratory diseases, it has become important to reduce indoor air pollutants. In this study, air filters with structurally modified surfaces are fabricated to effectively capture and remove indoor air pollutants. High-temperature alkali treatment is applied to a smooth fiber filter to convert it into porous calcium silicate hydrate and create a hierarchical three-layered (H3L) fibrous structure. The outer, middle, and inner layers of the H3L fiber are designed for bioaerosol capture and disinfection, CO2 capture, and maintenance of mechanical strength, respectively. An antimicrobial agent ((3-(trimethoxysilyl)propyldimethyloctadecyl ammonium chloride, TPMMC) is covalently grafted onto the surface of H3L fibers to impart antimicrobial performance. The H3L-TPMMC filter outperforms the bare filter in capturing E. coli and S. aureus aerosols, displaying improved quality factors of 31.9 % and 63.9 %, respectively. It achieves a 99.999 % reduction and 99.790 % for the collected E. coli and S. aureus aerosols, respectively. The H3L-TPMMC filter demonstrates selective CO2 capture and maintains consistent performance and antimicrobial efficacy even in environments with bioaerosols and CO2. This hierarchical filter membrane provides an efficient approach for reducing various indoor air pollutants using a single filter system.

Individual tree segmentation from UAS Lidar data based on hierarchical filtering and clustering

ABSTRACT Accurate Individual Tree Segmentation (ITS) is fundamental to fine-scale forest structure and management studies. Light detection and ranging (Lidar) from Unoccupied Aerial Systems (UAS) has shown strengths in ITS and tree parameter estimation at stand and landscape scales. However, dense woodlands with tightly interspersed canopies and highly diverse tree species challenge the performance of ITS, and current research has not delved into the impact of mixed tree species and different leaf conditions on algorithm accuracy. Therefore, this study firstly evaluates the performance of open-source ITS methods, including both deep learning and non-deep learning algorithms, on data with mixed tree species and different leaf conditions, then proposes a hierarchical filtering and clustering (HFC) algorithm to mitigate the influence and improve the robustness. Hierarchical filtering consists of intensity filtering, Singular Value Decomposition (SVD) filtering, and Statistical Outlier Removal (SOR). Hierarchical clustering involves point-based clustering, cluster merging, and filtered point assignment. Through experiments on three distinct UAS Lidar datasets of forests with mixed tree species and different leaf conditions, HFC achieved the optimal segmentation results while maintaining high robustness. The variations of F1-score are 1–3 percentage points for mixed tree species and 1–2 percentage points for different leaf conditions across different datasets.

A Fast GPU Schedule For À-Trous Wavelet-Based Denoisers

Given limitations of contemporary graphics hardware, real-time ray-traced global illumination is only estimated using a few samples per pixel. This consequently causes stochastic noise in the resulting frame sequences which requires wide filter support during denoising for temporally stable estimates. The edge avoiding à-trous wavelet transform amortizes runtime cost by hierarchical filtering using a constant number of increasingly dilated taps in each iteration. While the number of taps stays constant, the runtime of each iteration increases in these usually memory-throughput bound shaders with increasing dilation, because the increasing non-locality negatively impacts cache hit rates. We present a scheduling approach that optimizes usage of the memory subsystem by permutating global invocation indices in such a way that each wavelet filter iteration is applied through undilated taps. In contrast to prior approaches, our method has identical performance characteristics in each iteration, effectively decreasing maintenance cost and improving performance predictability. Furthermore, we are able to leverage on-chip memory and hardware texture interpolation. Our permutation strategy is trivial to integrate into existing wavelet filters as a permutation before and after each level of the wavelet filter. We achieve speedups between 1.3 and 3.8 for usual wavelet configurations in Monte Carlo denoising and computational photography.

SU-VPDN: A scene understanding method for vehicle part detection

High-accuracy positioning and identification of vehicle parts are crucial for status detection, fault diagnosis, and the development of intelligent damage detection systems. However, detecting left–right and front-rear categories in vehicle parts remains challenging due to numerous categories and feature similarity. To address this, we propose the scene understanding vehicle part detection network (SU-VPDN). It comprises three subnetworks: vehicle orientation scene classification (VOSC), vehicle part instance segmentation (VPIS), and scene understanding (SU). VOSC performs preliminary classification of vehicle orientation scenes, while VPIS detects vehicle parts. SU fine-tunes VOSC results using the part logic correction module, fuses VOSC and VPIS results using the part replacement module, and obtains accurate VPIS results through confidence regulation and hierarchical filtering. SU-VPDN integrates results from multiple subnetworks, transforming the challenge of detecting similar objects into orientation scene detection for precise recognition. We evaluate the proposed method on a dataset containing 59 types of vehicle parts and compare it with advanced segmentation detection models. Experimental results demonstrate that SU-VPDN outperforms other models, achieving a 12.2% and 12.3% improvement in mean average precision (mAP) for box and mask, respectively, compared to the baseline model. Moreover, qualitative and quantitative experiments show that SU-VPDN significantly enhances the detection performance of objects with similar features. Overall, our proposed SU-VPDN offers a promising solution for detecting left–right and front-rear categories in vehicle parts. It surpasses existing models and can enable insurance companies to replace damaged parts and improve claims efficiency efficiently.

Predicting treatment response to ketamine in treatment-resistant depression using auditory mismatch negativity: Study protocol.

Ketamine has recently attracted considerable attention for its rapid effects on patients with major depressive disorder, including treatment-resistant depression (TRD). Despite ketamine's promising results in treating depression, a significant number of patients do not respond to the treatment, and predicting who will benefit remains a challenge. Although its antidepressant effects are known to be linked to its action as an antagonist of the N-methyl-D-aspartate (NMDA) receptor, the precise mechanisms that determine why some patients respond and others do not are still unclear. This study aims to understand the computational mechanisms underlying changes in the auditory mismatch negativity (MMN) response following treatment with intravenous ketamine. Moreover, we aim to link the computational mechanisms to their underlying neural causes and use the parameters of the neurocomputational model to make individual treatment predictions. This is a prospective study of 30 patients with TRD who are undergoing intravenous ketamine therapy. Prior to 3 out of 4 ketamine infusions, EEG will be recorded while patients complete the auditory MMN task. Depression, suicidality, and anxiety will be assessed throughout the study and a week after the last ketamine infusion. To translate the effects of ketamine on the MMN to computational mechanisms, we will model changes in the auditory MMN using the hierarchical Gaussian filter, a hierarchical Bayesian model. Furthermore, we will employ a conductance-based neural mass model of the electrophysiological data to link these computational mechanisms to their neural causes. The findings of this study may improve understanding of the mechanisms underlying response and resistance to ketamine treatment in patients with TRD. The parameters obtained from fitting computational models to EEG recordings may facilitate single-patient treatment predictions, which could provide clinically useful prognostic information. Clinicaltrials.gov NCT05464264. Registered June 24, 2022.

The Relationship Between Environmental Statistics and Predictive Gaze Behaviour During a Manual Interception Task: Eye Movements as Active Inference

AbstractHuman observers are known to frequently act like Bayes-optimal decision-makers. Growing evidence indicates that the deployment of the visual system may similarly be driven by probabilistic mental models of the environment. We tested whether eye movements during a dynamic interception task were indeed optimised according to Bayesian inference principles. Forty-one participants intercepted oncoming balls in a virtual reality racquetball task across five counterbalanced conditions in which the relative probability of the ball’s onset location was manipulated. Analysis of pre-onset gaze positions indicated that eye position tracked the true distribution of onset location, suggesting that the gaze system spontaneously adhered to environmental statistics. Eye movements did not, however, seek to minimise the distance between the target and foveal vision according to an optimal probabilistic model of the world and instead often reflected a ‘best guess’ about onset location. Trial-to-trial changes in gaze position were, however, found to be better explained by Bayesian learning models (hierarchical Gaussian filter) than associative learning models. Additionally, parameters relating to the precision of beliefs and prediction errors extracted from the participant-wise models were related to both task-evoked pupil dilations and variability in gaze positions, providing further evidence that probabilistic context was reflected in spontaneous gaze dynamics.

Research on Multi-Sensor Data Fusion Positioning Method of Unmanned Ships Based on Threshold- and Hierarchical-Capacity Particle Filter

To improve the positioning accuracy of unmanned ships, a multi-sensor system including ZigBee, a Global Positioning System (GPS), and BeiDou Navigation Satellite System (BDS) is constructed, and an adaptive multi-sensor data fusion positioning method based on the threshold and hierarchical capacity particle filter (TCPF) is designed. First, the ZigBee-GPS/BDS multi-sensor measurement data is preprocessed to achieve a consistent space–time reference and transformed into the same coordinate system by projection. Then, the fault data is weighted and corrected through the consistency inspection of ZigBee-GPS/BDS multi-sensor positioning data, and the corresponding confidence factor is given according to the confidence distance of the positioning data; furthermore, the confidence factor is associated with stratified sampling. After that, the multi-sensor positioning data is filtered and denoised using a basic particle filter. Finally, a TCPF data fusion algorithm is designed, and the navigation positioning data of the unmanned ship is fused and filtered to obtain its positioning information. Numerical tests show that compared with other filtering algorithms, the mean square root error and standard deviation of the proposed TCPF algorithm decrease by an average of 25.0% and 28.0%, respectively, which verifies its high filtering accuracy and its advantages in suppressing particle degradation and avoiding sample scarcity. The experimental tests show that compared with other fusion algorithms, the proposed TCPF algorithm can not only realize the precise positioning during unmanned ship navigation, but also in the positioning and fault tolerance test, the average positioning error, root-mean-square error, and standard deviation of the former decrease by 36.0%, 38.0%, and 37.0%, respectively, and the corresponding performance indicators of the latter decrease by an average of 20.0%, 19.5%, and 17.5%, which verifies that it has the advantages of high data reliability and good filtering fault tolerance, and helps to improve the positioning accuracy of unmanned ships.

Aberrant Hierarchical Prediction Errors Are Associated With Transition to Psychosis: A Computational Single-Trial Analysis of the Mismatch Negativity

BackgroundMismatch negativity reductions are among the most reliable biomarkers for schizophrenia and have been associated with increased risk for conversion to psychosis in individuals who are at clinical high risk for psychosis (CHR-P). Here, we adopted a computational approach to develop a mechanistic model of mismatch negativity reductions in CHR-P individuals and patients early in the course of schizophrenia. MethodsElectroencephalography was recorded in 38 CHR-P individuals (15 converters), 19 patients early in the course of schizophrenia (≤5 years), and 44 healthy control participants during three different auditory oddball mismatch negativity paradigms including 10% duration, frequency, or double deviants, respectively. We modeled sensory learning with the hierarchical Gaussian filter and extracted precision-weighted prediction error trajectories from the model to assess how the expression of hierarchical prediction errors modulated electroencephalography amplitudes over sensor space and time. ResultsBoth low-level sensory and high-level volatility precision-weighted prediction errors were altered in CHR-P individuals and patients early in the course of schizophrenia compared with healthy control participants. Moreover, low-level precision-weighted prediction errors were significantly different in CHR-P individuals who later converted to psychosis compared with nonconverters. ConclusionsOur results implicate altered processing of hierarchical prediction errors as a computational mechanism in early psychosis consistent with predictive coding accounts of psychosis. This computational model seems to capture pathophysiological mechanisms that are relevant to early psychosis and the risk for future psychosis in CHR-P individuals and may serve as predictive biomarkers and mechanistic targets for the development of novel treatments.

A hierarchical adaptive extended Kalman filter algorithm for lithium-ion battery state of charge estimation

A hierarchical adaptive extended Kalman filter algorithm for lithium-ion battery state of charge estimation

Artifact Suppression of Back-Projection Algorithm Under Multiple Buried Objects

Focusing on the problem that objects with weak reflected energy cannot be detected caused by the poor performance of eliminating artifacts under the circumstances of a large amount of buried objects, this article proposed a new algorithm for eliminating artifacts after analyzing the causes of artifacts and the characteristics of artifacts in the presence of multiple buried objects. Under the premise of ensuring that the artifacts generated by different targets are roughly the same, the problem of that targets with weak reflected energy disappearing from the image is solved first. Second, the back-projection (BP) algorithm is utilized for imaging the reflection area of the buried objects. Finally, the hierarchical filter is used to eliminate the artifacts according to their characteristics. To verify the effectiveness of the novel algorithm proposed in this article, the simulation environment and physical experimental environment are built based on GPRmax3.0 and software radio radar, respectively. The results showed that when the reflected area and the relative dielectric constant of the buried objects differ greatly, the proposed algorithm can accurately locate the position of the buried objects and image the shape of the reflective surface, and it can also remove the artifacts to improve the performance of imaging.

Multiset Tag Searching Protocol for Multicategory RFID Systems

RFID has been widely deployed in large-scale supply-chain and warehouse scenarios to achieve effective inventory tracking and theft prevention. In such a scenario, there is a practical need for searching for a particular subset with a large number of tags, which can help the retailer to determine whether the current inventory matches the needs of a specific order. The current solution usually searches for the target tags in the entire inventory through single-set matching, which generally takes too much time and is hard to meet given the strict delay requirements of users. To address this problem, this paper proposes a technique called multiset tag matching, which applies a hierarchical space-efficient bloom filter variation to aggregate multiple search queries and searches them by category. We show that communication overhead can be optimized further by exploiting the distribution among different categories for grouping similar categories. Thus, each category can be assigned a personalized length of filter to trade-off between accuracy and cost. Moreover, our results also show that our approach provides a fair degree of accuracy across different categories. We further propose a combination technique for further aggregating category groups with similar sizes to reduce the search and switching costs.

RadarPDR: Radar-Assisted Indoor Pedestrian Dead Reckoning

Pedestrian dead reckoning (PDR) is the critical component in indoor pedestrian tracking and navigation services. While most of the recent PDR solutions exploit in-built inertial sensors in smartphones for next step estimation, due to measurement errors and sensing drift, the accuracy of walking direction, step detection, and step length estimation cannot be guaranteed, leading to large accumulative tracking errors. In this paper, we propose a radar-assisted PDR scheme, called RadarPDR, which integrates a frequency-modulation continuous-wave (FMCW) radar to assist the inertial sensors-based PDR. We first establish a segmented wall distance calibration model to deal with the radar ranging noise caused by irregular indoor building layouts and fuse wall distance estimation with acceleration and azimuth signals measured by the inertial sensors of a smartphone. We also propose a hierarchical particle filter(PF) together with an extended Kalman filter for position and trajectory adjustment. Experiments have been conducted in practical indoor scenarios. Results demonstrate that the proposed RadarPDR is efficient and stable and outperforms the widely used inertial sensors-based PDR scheme.

Segmentation-based hierarchical interpolation filter using both geometric and radiometric features for LiDAR point clouds over complex scenarios

To enhance the filtering accuracy in complex environments, a segmentation-based hierarchical interpolation filter using both geometric and radiometric features is proposed in this paper. Specifically, raw point cloud is first segmented using DBSCAN with both geometric and radiometric features. Then, initial ground seeds are selected from the set of segments with the consideration of terrain features. Finally, all ground points are detected using an enhanced multiresolution hierarchical filter based on three reference ground surfaces of different attributes coupled with slope-adaptive thresholds. Four plots with complex landscapes were adopted to evaluate the results of the proposed method, and its accuracy was compared with those of seven state-of-the-art filtering methods. Results demonstrate that the proposed method obviously outperforms the classical filtering methods, with the reduction of average type I, II, and total errors by at least 15.1%, 10.0%, and 19.4%, respectively, and the improvement of the kappa coefficient by at least 2.9%.

Performance Analysis of Coal Gangue Recognition Based on Hierarchical Filtering and Coupled Wrapper Feature Selection Method

Performance Analysis of Coal Gangue Recognition Based on Hierarchical Filtering and Coupled Wrapper Feature Selection Method

A Fusional Intrusion Detection Method Based on the Hierarchical Filtering and Progressive Detection Model

A Fusional Intrusion Detection Method Based on the Hierarchical Filtering and Progressive Detection Model

Data-assimilated time-lapse visco-acoustic full-waveform inversion: Theory and application for injected CO2 plume monitoring

Continuous seismic monitoring for quantifying CO2 plume migration and detection of any potential leakages in the subsurface is essential for the security of long-term anthropogenic carbon dioxide geologic storage. Traditional time-lapse full-waveform inversion (TLFWI) methods aim to map the CO2 distribution by estimating seismic velocity changes, but recent studies find that CO2-induced attenuation is an important complement to seismic velocity for tracking the CO2 plumes and even quantifying the CO2 saturation. We have developed a novel data-assimilated TLFWI method to construct high-resolution time-lapse velocity and attenuation changes from dense time-lapse monitoring data. This method consists of two theoretical developments: visco-acoustic full-waveform inversion (QFWI) and multiparameter hierarchical matrix-powered extended Kalman filter (mHiEKF). The method is capable of (1) posing temporal constraints to retrieve time-lapse information from dense monitoring data by using mHiEKF, (2) accurately recovering high-spatial-resolution velocity and attenuation perturbations using first-order equation system-based QFWI, and (3) providing the model uncertainty by estimating their model standard deviation. With numerical examples, we first find the effectiveness of the new QFWI on estimating accurate velocity and attenuation models simultaneously. Then, a CO2 leakage case and a realistic Frio-II CO2 monitoring case are presented to find the advantages and applicability of our data-assimilated QFWI method for estimating time-lapse changes using dense time-lapse monitoring surveys. By assimilating time-lapse seismic monitoring data over time, our data-assimilated QFWI method can improve the resolution of velocity and attenuation changes and decrease their model uncertainties.

Belief Updating in Subclinical and Clinical Delusions.

Current frameworks propose that delusions result from aberrant belief updating due to altered prediction error (PE) signaling and misestimation of environmental volatility. We aimed to investigate whether behavioral and neural signatures of belief updating are specifically related to the presence of delusions or generally associated with manifest schizophrenia. Our cross-sectional design includes human participants (n[female/male] = 66[25/41]), stratified into four groups: healthy participants with minimal (n = 22) or strong delusional-like ideation (n = 18), and participants with diagnosed schizophrenia with minimal (n = 13) or strong delusions (n = 13), resulting in a 2 × 2 design, which allows to test for the effects of delusion and diagnosis. Participants performed a reversal learning task with stable and volatile task contingencies during fMRI scanning. We formalized learning with a hierarchical Gaussian filter model and conducted model-based fMRI analysis regarding beliefs of outcome uncertainty and volatility, precision-weighted PEs of the outcome- and the volatility-belief. Patients with schizophrenia as compared to healthy controls showed lower accuracy and heightened choice switching, while delusional ideation did not affect these measures. Participants with delusions showed increased precision-weighted PE-related neural activation in fronto-striatal regions. People with diagnosed schizophrenia overestimated environmental volatility and showed an attenuated neural representation of volatility in the anterior insula, medial frontal and angular gyrus. Delusional beliefs are associated with altered striatal PE-signals. Juxtaposing, the potentially unsettling belief that the environment is constantly changing and weaker neural encoding of this subjective volatility seems to be associated with manifest schizophrenia, but not with the presence of delusional ideation.

Metal-organic frameworks decorated wood aerogels for efficient particulate matter removal

The severe indoor/outdoor air contamination caused by hazardous particulate matters (PMs) has a devastating impact on the environment and human health. Herein, air filters fabricated from delignified wood aerogels and metal–organic frameworks (MOFs) were used for PM removal. Two specially designed MOFs (i.e. Zn-based ZIF-L and Zr-based UiO-66-NH2) were deposited into a highly porous wood matrix through in situ growth approach. The MOF crystals are attached tightly to the highly available cellulose surface groups and distributed uniformly. The integration of MOFs can regulate the aerogel morphology and surface property, leading to enhanced PM removal performance. Bare wood aerogels beard removal efficiencies of 70.7% and 75.0% for PM2.5 and PM10, with a low-pressure drop of 15 Pa. By contrast, ZIF-L decorated wood aerogels exhibited PM2.5 and PM10 removal efficiencies of 97.6% and 99.5%, respectively, which are ascribed to the increased interception/impaction of PMs with the positively charged ZIF-L layer. While UiO-66-NH2 functionalized wood aerogels demonstrated PM2.5 and PM10 removal efficiencies of 96.4% and 98.9% because of the hierarchical filter matrix and the presence of polar –NH2 groups. Moreover, MOF modified wood aerogels can be easily regenerated and superior reusability can be achieved. This work provides an effective and economic strategy for the preparation of air filters from renewable materials.