Mode Filter Research Articles

Shrub height estimation for habitat conservation in NW Iberian Peninsula (Spain) using UAV LiDAR point clouds

ABSTRACT This study aimed to develop and validate a method of estimating 3D parameters from DJI Zenmuse L1 LiDAR on DJI Matrice 300 RTK UAV data to characterise and monitor the structure and conservation status of dense shrub formations. The shrub heights were estimated using Progressive Morphological Filter (PMF) and the Ground Filter module of the FUSION/LDV software. A digital terrain model (DTM) was interpolated with RMSE 0.23 and 0.27 m, respectively, and a normalised canopy height model (nCHM) was calculated by subtracting it from the LiDAR data and the best DTM obtained. The reliability of the estimates was evaluated against georeferenced field data. In addition, the study examined the impact of vegetation characteristics and return reduction in the original point cloud on the accuracy of LiDAR-data derived . Significant differences were found in the correlations between observed and estimated data for the DTM (R2 = 0.9998) and nCHM heights (R2 = 0.51/0.54). The corresponding RMSE values were 0.23 and 0.34 m. Moreover, no significant differences in the reliability were found for different vegetation types, whereas reduction point cloud density (up to 25–50 returns/m2) did not significantly affect accuracy. In conclusion, lightweight UAV LiDAR can effectively detect sub-metric scale vegetation 3D structure, useful for fine-scale habitat conservation.

Switchable anisotropic diffraction mode filter based on fork-like liquid crystal gratings

The interaction of liquid crystal (LC) fork-like gratings (FLGs) with a Laguerre–Gaussian beam of the TEM00 mode is described. It is experimentally shown that when the direction of the light polarization vector differs from the direction of the LC eigenvectors, the medium behaves as an FLG with a controlled modulation depth, and in the opposite case, it is isotropic and homogeneous. Distributions of the intensities of the longitudinal section of the beam in the infinitely far field are given depending on the FLG rotation angle in the plane perpendicular to the direction of beam propagation. It is shown that the LC FLG functions as a switchable anisotropic diffraction mode filter.

A flow rate estimation method for gas–liquid two-phase flow based on filter-enhanced convolutional neural network

Accurate estimation of flow rate in gas–liquid two-phase flow is crucial for various industrial processes. How to accurately estimate flow rate remains a challenging problem. Previously, deep learning-based methods focused on a few human-set points with single task learning. In addition, the data were not denoised. In this study, a flow rate estimation method based on a filter-enhanced convolutional neural network (FECNN) is proposed for gas–liquid two-phase flow. The method leverages multimodal data from a Venturi tube and an electrical capacitance tomography (ECT) sensor as input, utilizing multilayer perceptron (MLP) to fuse data. Subsequently, a learnable filter module is employed to attenuate noise adaptively, followed by multiscale convolutional neural network (MSCNN) extraction of flow rate features at different scales. Finally, the method enables estimate each single-phase flow rate simultaneously through multi-task learning (MTL). The adaptive noise attenuation capabilities of the learnable filter module are demonstrated, and the ability of the proposed MSCNN to capture multiscale flow rate features through multiple comparative experiments is shown. Additionally, a qualitative comparison with recent flow rate estimation methods is provided. Overall, this study demonstrates the effectiveness and superiority of the proposed FECNN in flow rate estimation.

A New Filter Based on Sliding Mode Method with Discrete‐Time Implementation for Noise Attenuation and Differentiation

AbstractThis paper presents a novel filter based on the sliding mode method for filtering noise and extracting reliable signals from noisy signals by enhancing Levant's sliding mode filter. Specifically, the proposed sliding mode filter takes advantage of the generalized signum function to adjust the gain in different regions of phase space, thereby decreasing the overshoot during the response phase. Additionally, the discrete‐time implementation of the proposed sliding mode filter is achieved by utilizing the implicit‐Euler algorithm, which effectively eliminates chattering in the output. The effectiveness of the presented sliding mode filter is substantiated via numerical simulation cases.

Multi-modal vibration control method combining modal decoupling and active disturbance rejection for wind tunnel models

When excited by broadband wind load, the wind tunnel model support system with low stiffness and damping is easy to produce multi-modal coupling vibration. It not only leads to inaccurate experimental data but also has potential safety hazards. Therefore, to ensure the reliable development of wind tunnel tests, a multi-modal vibration control method with multi-damping is proposed. Firstly, a multi-damping vibration suppression structure is designed based on the vibration analysis results. Next, the modal filter is designed by identifying the modal decoupling matrix, which can get the independent feedback signals of each mode. Then, considering the external disturbance of the environment and the internal disturbance between dampers, a multi-modal linear active disturbance rejection controller is designed to control the vibration and total disturbance. Finally, the multi-modal vibration suppression system of the UAV model is built, and the sweep frequency identification and hammering experiment are carried out. The results show that the proposed method can reduce the power spectral density of the first and second modes by 42.500 dB/Hz and 12.442 dB/Hz respectively, and increase the damping ratio by 14.659 times and 2.898 times respectively. Compared with the traditional control methods, the proposed method possesses obvious advantages in multi-modal vibration control. Therefore, ensured that wind tunnel tests were conducted smoothly.

Integrated membranes system for water application in microbiology/molecular biology

An integrated membrane filtration system was developed to make water purity suitable for microbiology/molecular biology research. Water samples were collected from outlets in different buildings of the National Research Center and analyzed for their composition before filtration. An integrated membrane system was developed based on mathematical modeling. Flat sheet membranes were produced, including microfiltration, ultrafiltration, and nanofiltration membranes. The flat sheet membranes were converted to a spiral wound module filter to simulate the local market filters and applied in the integrated membrane system that was designed and installed. The produced water was analyzed and compared to molecular-grade water used in different molecular biology/microbiology applications. Both PCR, agarose gel electrophoresis, bacterial liquid cultures, and viral propagation indicated that treated water using the herein-developed system exhibited comparable performance to the molecular grade water provided with imported reagent kits. So, this research can offer a promising solution for producing high-quality water suitable for sensitive laboratory applications.

SF-GPT: A training-free method to enhance capabilities for knowledge graph construction in LLMs

Knowledge graphs (KGs) are constructed by extracting knowledge triples from text and fusing knowledge, enhancing information retrieval efficiency. Current methods for knowledge triple extraction include ”Pretrain and Fine-tuning” and Large Language Models (LLMs). The former shifts effort from manual extraction to dataset annotation and suffers from performance degradation with different test and training set distributions. LLMs-based methods face errors and incompleteness in extraction. We introduce SF-GPT, a training-free method to address these issues. Firstly, we propose the Entity Extraction Filter (EEF) module to filter triple generation results, addressing evaluation and cleansing challenges. Secondly, we introduce a training-free Entity Alignment Module based on Entity Alias Generation (EAG), tackling semantic richness and interpretability issues in LLM-based knowledge fusion. Finally, our Self-Fusion Subgraph strategy uses multi-response self-fusion and a common entity list to filter triple results, reducing noise from LLMs’ multi-responses. In experiments, SF-GPT showed a 55.5% increase in recall and a 32.6% increase in F1 score on the BDNC dataset compared to the UniRel model trained on the NYT dataset and achieved a 5% improvement in F1 score compared to GPT-4+EEF baseline on the WebNLG dataset in the case of a fusion round of three. SF-GPT offers a promising way to extract knowledge from unstructured information.

BiLSTM-Filt: Neural network for radar word segmentation

Radar word extraction is the analysis foundation for multi-function radars (MFRs) in electronic intelligence (ELINT). Although neural networks enhance performance in radar word extraction, current research still faces challenges from complex electromagnetic environments and unknown radar words. Therefore, in this paper, we propose a promising two-stage radar word extraction framework, consisting of segmentation and recognition. To fill the vacancy of radar word segmentation, we establish the mathematical model from the time series analysis viewpoint and design a novel segmentation neural network based on Bi-direction Long Short-Term Memory with a filter module (BiLSTM-Filt). Specific radar word structure characteristics are extracted by training the network and applied for detecting radar words in the pulse train. To further improve segmentation performance, a bounding box regression method is designed to merge information from sub-region structures. Simulation experiments on a typical MFR, Mercury, reveal that the proposed method can outperform the baseline methods within complex electromagnetic environments, containing corrupted environments, various pulse backgrounds, and variable pulse train lengths. Due to the artificial design structure, the proposed method can also make a trial on unknown radar word segmentation.

Multi-feature fusion and multi-attention deep network for enhancing road extraction in remote sensing images

ABSTRACT Road detection in remote sensing (RS) images plays a critical role in applications ranging from urban planning to autonomous navigation systems. However, accurate road extraction remains a challenging task due to the presence of textual-similar objects that can be visually confused with roads, and shadows that can obscure road features. For this regard, we present a novel end-to-end network that leverages multi-feature fusion and multi-attention. Multi-level dual residual blocks are introduced to capture multi-scale and multi-level road features. A channel attention feature fusion module fuses road features from the decoder while suppressing coarse-grain noises. Moreover, a difference channel feature fusion module filters out the interfering texture-similar features and irrelevant background features. Subsequently, a spatial channel feature enhancement module is designed to identify the mis-segmented regions and reclassify these pixels. We conducted extensive experiments on diverse datasets, demonstrating the effectiveness of our approach in improving road detection accuracy, especially in areas with shadows and textual-similar objects. The results indicate that our approach outperforms state-of-the-art methods in handling textual-similar objects and shadow-occluded roads, making it a valuable contribution to the field of road extraction in remote sensing images.

"Seeing" ENF From Neuromorphic Events: Modeling and Robust Estimation.

Most artificial lights exhibit subtle fluctuations in intensity and frequency in response to the influence of the grid's alternating current, providing the potential to estimate the Electric Network Frequency (ENF) from conventional frame-based videos. Nevertheless, the performance of Video-based ENF (V-ENF) estimation largely relies on the imaging quality and thus may suffer from significant interference caused by non-ideal sampling, scene diversity, motion interference, and extreme lighting conditions. In this paper, we show that the ENF can be extracted without the above limitations from a new modality provided by the so-called event camera, a neuromorphic sensor that encodes the light intensity variations and asynchronously emits events with extremely high temporal resolution and high dynamic range. Specifically, we formulate and validate the physical mechanism for the ENF captured in events and then propose a simple yet robust Event-based ENF (E-ENF) estimation method through mode filtering and harmonic enhancement. To validate the effectiveness, we build the first Event-Video ENF Dataset (EV-ENFD) and its extension EV-ENFD+ with diverse scenarios, including static, dynamic, and extreme lighting scenes. Comprehensive experiments have been conducted on our proposed datasets, showcasing that our proposed E-ENF significantly outperforms the V-ENF in extracting accurate ENF traces, especially in challenging environments.

FilterGNN: Image feature matching with cascaded outlier filters and linear attention

The cross-view matching of local image features is a fundamental task in visual localization and 3D reconstruction. This study proposes FilterGNN, a transformer-based graph neural network (GNN), aiming to improve the matching efficiency and accuracy of visual descriptors. Based on high matching sparseness and coarse-to-fine covisible area detection, FilterGNN utilizes cascaded optimal graph-matching filter modules to dynamically reject outlier matches. Moreover, we successfully adapted linear attention in FilterGNN with post-instance normalization support, which significantly reduces the complexity of complete graph learning from O(N2) to O(N). Experiments show that FilterGNN requires only 6% of the time cost and 33.3% of the memory cost compared with SuperGlue under a large-scale input size and achieves a competitive performance in various tasks, such as pose estimation, visual localization, and sparse 3D reconstruction.

Efficient time series adaptive representation learning via Dynamic Routing Sparse Attention

Time series prediction plays a crucial role in various fields but also faces significant challenges. Converting original 1D time series data into 2D data through dimension transformation allows capturing more hidden features but incurs high memory consumption and low time efficiency. We have designed a sparse attention mechanism with dynamic routing perception called Dynamic Routing Sparse Attention (DRSA) to address these issues. Specifically, DRSA can effectively handle variations of complex time series data. Meanwhile, under memory constraints, the Dynamic Routing Filter (DRF) module further refines it by filtering the blocked 2D time series data to identify the most relevant feature vectors in the local context. We conducted predictive experiments on six real-world time series datasets with fine granularity and long sequence dependencies. Compared to eight state-of-the-art (SOTA) models, DRSA demonstrated relative improvements ranging from 4.18% to 81.02%. Furthermore, its time efficiency is 2 to 5 times higher than the baseline. Our code and dataset will be available at https://github.com/wwy8/DRSA_main.

Vehicle trajectory prediction based on cross-attention and multilevel spatio-temporal features

Accurately predicting vehicle trajectories is crucial for motion planning in autonomous driving. Some existing trajectory prediction models employ Long Short-Term Memory (LSTM) networks to encode trajectory information, but this approach may lead to information loss. Additionally, the sparsity of the grid representation may limit the model’s ability to extract vehicle interaction features. To address these issues, this paper proposes a vehicle trajectory prediction model based on cross-attention and multilevel spatio-temporal features. The proposed model incorporates a spatio-temporal feature extraction module designed to capture the dynamic evolution of vehicle states across both time and space. Furthermore, it employs a temporal cross-attention module that reuses hidden states to select spatio-temporal features closely related to the target vehicle’s historical state, thus compensating for the temporal information loss during the encoding process. Additionally, a spatial cross-attention module, informed by social context, is deployed to analyze the dynamic interactions among vehicles. This module filters spatio-temporal interaction features with a significant impact on the target vehicle’s trajectory. The model is trained and tested using the NGSIM dataset and the highD dataset. Compared with other models, the model presented in this paper shows higher prediction accuracy in long-term trajectory prediction.

Scale reduction Transformer-based soft measurement of oil–water two-phase flow

In addressing the challenge of parameter measurement in high-water-content oil–water two-phase flow, we independently develop a dual-helix microwave sensor measurement system. Using this system, we conduct vertically upward experiments on oil–water two-phase flow and collect time-series signals characterizing microwave attenuation. Based on signal characteristics, we design a scale reduction Transformer soft measurement model (SRSM-Transformer). The time filter module effectively facilitates the aggregation and embedding of channel features with diverse physical significance. Additionally, the scale reduction temporal module models global information from a multi-resolution perspective, enabling the model to flexibly extract multi-scale feature information from fluid signals. Demonstrating pronounced superiority over existing models, our proposed model achieves recognition accuracies of 93.63% for total flow rate and 95.73% for water content. This research provides a new direction for modeling complex industrial oil–water two-phase flow systems and measuring multi-coupled key parameters.

Deep learning approach for skin melanoma and benign classification using empirical wavelet decomposition.

Melanoma is a malignant skin cancer that causes high mortality. Early detection of melanoma can save patients' lives. The features of the skin lesion images can be extracted using computer techniques to differentiate early between melanoma and benign skin lesions. A new model of empirical wavelet decomposition (EWD) based on tan hyperbolic modulated filter banks (THMFBs) (EWD-THMFBs) was used to obtain the features of skin lesion images by MATLAB software. The EWD-THMFBs model was compared with the empirical short-time Fourier decomposition method based on THMFBs (ESTFD-THMFBs) and the empirical Fourier decomposition method based on THMFBs (EFD-THMFBs). The accuracy rates obtained for EWD-THMFBs, ESTFD-THMFBs, and EFD-THMFBs models were 100%, 98.89%, and 83.33%, respectively. The area under the curve (AUC) was 1, 0.97, and 0.91, respectively. The EWD-THMFBs model performed best in extracting features from skin lesion images. This model can be programmed on a mobile to detect skin lesions in rural areas by a nurse before consulting a dermatologist.

The Blossoming of Ultrasonic Metatransducers.

Key requirements to boost the applicability of ultrasonic systems for in situ, real-time operations are low hardware complexity and low power consumption. These features are not available in present-day systems due to the fact that US inspections are typically achieved through phased arrays featuring a large number of individually controlled piezoelectric transducers and generating huge quantities of data. To minimize the energy and computational requirements, novel devices that feature enhanced functionalities beyond the mere conversion (i.e., metatransducers) can be conceived. This article reviews the potential of recent research breakthroughs in the transducer technology, which allow them to efficiently perform tasks, such as focusing, energy harvesting, beamforming, data communication, or mode filtering, and discusses the challenges for the widespread adoption of these solutions.

A Plug-In Graph Neural Network to Boost Temporal Sensitivity in fMRI Analysis.

Learning-based methods offer performance leaps over traditional methods in classification analysis of high-dimensional functional MRI (fMRI) data. In this domain, deep-learning models that analyze functional connectivity (FC) features among brain regions have been particularly promising. However, many existing models receive as input temporally static FC features that summarize inter-regional interactions across an entire scan, reducing the temporal sensitivity of classifiers by limiting their ability to leverage information on dynamic FC features of brain activity. To improve the performance of baseline classification models without compromising efficiency, here we propose a novel plug-in based on a graph neural network, GraphCorr, to provide enhanced input features to baseline models. The proposed plug-in computes a set of latent FC features with enhanced temporal information while maintaining comparable dimensionality to static features. Taking brain regions as nodes and blood-oxygen-level-dependent (BOLD) signals as node inputs, GraphCorr leverages a node embedder module based on a transformer encoder to capture dynamic latent representations of BOLD signals. GraphCorr also leverages a lag filter module to account for delayed interactions across nodes by learning correlational features of windowed BOLD signals across time delays. These two feature groups are then fused via a message passing algorithm executed on the formulated graph. Comprehensive demonstrations on three public datasets indicate improved classification performance for several state-of-the-art graph and convolutional baseline models when they are augmented with GraphCorr.

An adaptive feature mode decomposition-guided phase space feature extraction method for rolling bearing fault diagnosis

Abstract The extraction of fault features from rolling bearings is a challenging and highly important task. Since they have complex operating conditions and are usually under a strong noise background. In this study, a novel approach termed phase space feature extraction guided by an adaptive feature mode decomposition (AFMDPSFE) is proposed to detect subtle faults in rolling bearings. Initially, a new method using Kullback-Leiber divergence is introduced to automatically select the optimal mode number and filter length for the decomposition of vibration signals, facilitating the automatic extraction of optimal components and ensuring efficient screening. This eliminates the need for manual configuration of feature mode decomposition parameters. Furthermore, a criterion that could determine two crucial parameters to capture system dynamics characteristics in phase space reconstruction is embedded into AFMDPSFE algorithm. Subsequently, a series of high-dimensional independent components is derived. The envelope spectrum of the principal component exhibiting the highest kurtosis value is computed to achieve fault identification, consequently enhancing the separation of signal from noise. Both simulations and experimental results confirm the effectiveness of AFMDPSFE approach. A comparison analysis shows the excellent performance of AFMDPSFE in extracting fault features from significant noise interference.

A 0.00179 mm2/Ch Chopper-Stabilized TDMA Neural Recording System With Dynamic EOV Cancellation and Predictive Mixed-Signal Impedance Boosting.

This article presents a digitally-assisted multi-channel neural recording system. The system uses a 16-channel chopper-stabilized Time Division Multiple Access (TDMA) scheme to record multiplexed neural signals into a single shared analog front end (AFE). The choppers reduce the total integrated noise across the modulated spectrum by 2.4 × and 4.3 × in Local Field Potential (LFP) and Action Potential (AP) bands, respectively. In addition, a novel impedance booster based on Sign-Sign least mean squares (LMS) adaptive filter (AF) predicts the input signal and pre-charges the AC-coupling capacitors. The impedance booster module increases the AFE input impedance by a factor of 39 × with a 7.1% increase in area. The proposed system obviates the need for on-chip digital demodulation, filtering, and remodulation normally required to extract Electrode Offset Voltages (EOV) from multiplexed neural signals, thereby achieving 3.6 × and 2.8 × savings in both area and power, respectively, in the EOV filter module. The Sign-Sign LMS AF is reused to determine the system loop gain, which relaxes the feedback DAC accuracy requirements and saves 10.1 × in power compared to conventional oversampled DAC truncation-error ΔΣ-modulator. The proposed SoC is designed and fabricated in 65 nm CMOS, and each channel occupies 0.00179 mm2 of active area. Each channel consumes 5.11 μW of power while achieving 2.19 μVrms and 2.4 μVrms of input referred noise (IRN) over AP and LFP bands. The resulting AP band noise efficiency factor (NEF) is 1.8. The proposed system is verified with acute in-vivo recordings in a Sprague-Dawley rat using parylene C based thin-film platinum nanorod microelectrodes.

A frequency selection network for medical image segmentation

Existing medical image segmentation methods may only consider feature extraction and information processing in spatial domain, or lack the design of interaction between frequency information and spatial information, or ignore the semantic gaps between shallow and deep features, and lead to inaccurate segmentation results. Therefore, in this paper, we propose a novel frequency selection segmentation network (FSSN), which achieves more accurate lesion segmentation by fusing local spatial features and global frequency information, better design of feature interactions, and suppressing low correlation frequency components for mitigating semantic gaps. Firstly, we propose a global-local feature aggregation module (GLAM) to simultaneously capture multi-scale local features in the spatial domain and exploits global frequency information in the frequency domain, and achieves complementary fusion of local details features and global frequency information. Secondly, we propose a feature filter module (FFM) to mitigate semantic gaps when we conduct cross-level features fusion, and makes FSSN discriminatively determine which frequency information should be preserved for accurate lesion segmentation. Finally, in order to make better use of local information, especially the boundary of lesion region, we employ deformable convolution (DC) to extract pertinent features in the local range, and makes our FSSN can focus on relevant image contents better. Extensive experiments on two public benchmark datasets show that compared with representative medical image segmentation methods, our FSSN can obtain more accurate lesion segmentation results in terms of both objective evaluation indicators and subjective visual effects with fewer parameters and lower computational complexity.