Channel Dimensions Research Articles

A dual-stream temporal convolutional network for remaining useful life prediction of rolling bearings

Abstract Remaining useful life (RUL) prediction plays an indispensable role in the reliable operation and improved maintenance of rolling bearings. Currently, data-driven methods based on deep learning have made significant progress in RUL prediction. However, most of such methods only consider the correlation between channels, ignoring the importance of different time steps for RUL prediction. In addition, it is still challenging to effectively fuse the degradation features of rolling bearings to improve the model RUL prediction performance. To address the above issues, this paper proposes a novel data-driven RUL prediction method named dual-stream temporal convolution network (DSTCN). First, a hybrid attention temporal convolution block (HATCB) is designed to capture the correlation of degraded features on the channel dimension and temporal dimension. Second, a one-dimensional attention fusion module is designed. This module is capable of weight recalibration and assignment to adaptively fuse different degraded features. Afterward, the Hilbert Marginal spectrum is obtained using the Hilbert Huang Transform and used as the input to one stream. Meanwhile, vibration signals are used as the input of the other stream, thus building a dual-stream temporal convolution network to realize RUL prediction. The effectiveness of the proposed method is validated with two life-cycle datasets, and the results show that the method has lower prediction error than other methods for RUL prediction and prognostic analysis.

Lightweight Detection of Broccoli Heads in Complex Field Environments Based on LBDC-YOLO

Robotically selective broccoli harvesting requires precise lightweight detection models to efficiently detect broccoli heads. Therefore, this study introduces a lightweight and high-precision detection model named LBDC-YOLO (Lightweight Broccoli Detection in Complex Environment—You Look Only Once), based on the improved YOLOv8 (You Look Only Once, Version 8). The model incorporates the Slim-neck design paradigm based on GSConv to reduce computational complexity. Furthermore, Triplet Attention is integrated into the backbone network to capture cross-dimensional interactions between spatial and channel dimensions, enhancing the model’s feature extraction capability under multiple interfering factors. The original neck network structure is replaced with a BiFPN (Bidirectional Feature Pyramid Network), optimizing the cross-layer connection structure, and employing weighted fusion methods for better integration of multi-scale features. The model undergoes training and testing on a dataset constructed in real field conditions, featuring broccoli images under various influencing factors. Experimental results demonstrate that LBDC-YOLO achieves an average detection accuracy of 94.44% for broccoli. Compared to the original YOLOv8n, LBDC-YOLO achieves a 32.1% reduction in computational complexity, a 47.8% decrease in parameters, a 44.4% reduction in model size, and a 0.47 percentage point accuracy improvement. When compared to models such as YOLOv5n, YOLOv5s, and YOLOv7-tiny, LBDC-YOLO exhibits higher detection accuracy and lower computational complexity, presenting clear advantages for broccoli detection tasks in complex field environments. The results of this study provide an accurate and lightweight method for the detection of broccoli heads in complex field environments. This work aims to inspire further research in precision agriculture and to advance knowledge in model-assisted agricultural practices.

Algorithms and software for processing images of the structure of metallic materials for the purpose of determining creep characteristics

The paper contains a description of the approach, algorithms, and software tool for image analysis of deformed material structures. The developed approach is based on the analysis of the microstructure of the material to identify important factors that affect high-temperature deformation during creep in a heat-resistant nickel-based alloy, namely, the dimensions of the channels and their orientation. The developed software tool uses algorithms for converting images into binary black and white using the Otsu method. The intensity of the gradient at each point of the image is visualized using the Sobel operator. Fragment boundaries are determined using the Canny edge detector. Straight line segments are found using the Hough transformation. The software tool is implemented in the Python programming language using the OpenCV library. The main components are described and a flow chart of the program is provided. With the use of the developed software, the transformation of the known experimentally obtained images of the structures of the specimens from heat-resistant nickel-based alloy CMSX-4 deformed at a temperature of 1273K and in a wide range of stresses at different time moments was performed. The results of the analysis of the dimensions of the g-phase channels in the alloy using quantitative evaluation of transformed binary images are discussed. The found characteristics were matched to the value of the creep strain rate, which was determined by calculation based on known experimental data. The possibility of determining the transition from the secondary creep to the stage of avalanche-like growth of strains and hidden damage is shown. For the considered example, the location of the channels in the representative image was determined. A technique for correcting creep curves with the involvement of material structure image processing data is proposed.

Unsupervised Multi-Scale Hybrid Feature Extraction Network for Semantic Segmentation of High-Resolution Remote Sensing Images

Generating pixel-level annotations for semantic segmentation tasks of high-resolution remote sensing images is both time-consuming and labor-intensive, which has led to increased interest in unsupervised methods. Therefore, in this paper, we propose an unsupervised multi-scale hybrid feature extraction network based on the CNN-Transformer architecture, referred to as MSHFE-Net. The MSHFE-Net consists of three main modules: a Multi-Scale Pixel-Guided CNN Encoder, a Multi-Scale Aggregation Transformer Encoder, and a Parallel Attention Fusion Module. The Multi-Scale Pixel-Guided CNN Encoder is designed for multi-scale, fine-grained feature extraction in unsupervised tasks, efficiently recovering local spatial information in images. Meanwhile, the Multi-Scale Aggregation Transformer Encoder introduces a multi-scale aggregation module, which further enhances the unsupervised acquisition of multi-scale contextual information, obtaining global features with stronger feature representation. The Parallel Attention Fusion Module employs an attention mechanism to fuse global and local features in both channel and spatial dimensions in parallel, enriching the semantic relations extracted during unsupervised training and improving the performance of unsupervised semantic segmentation. K-means clustering is then performed on the fused features to achieve high-precision unsupervised semantic segmentation. Experiments with MSHFE-Net on the Potsdam and Vaihingen datasets demonstrate its effectiveness in significantly improving the accuracy of unsupervised semantic segmentation.

Development and performance evaluation of hydrophobic association plugging agents for sealing formation prior to cementing

This study introduces a novel blocking agent composed of polymerized hydrophobic particles to address the challenge of sealing formations with heterogeneous permeability. These particles display high viscosity and adsorption capabilities, essential for sealing highly permeable formations effectively. Hydrophobic associative polymers (AMSN) were prepared using free radical micellar polymerization, grounded in hydrophobic association principles and molecular design. Optimal monomer ratios and synthesis procedures were identified through the observation of the polymers’ apparent viscosity in aqueous solutions. Characterization of the polymers involved Fourier transform infrared spectroscopy, thermogravimetric analysis, and fluorescence analysis to assess their temperature and salt resistance, reduction in filtrate loss, and blocking abilities. The polymers, containing hydrophobic groups at 300 mg/L concentrations, demonstrated excellent temperature resistance, maintaining 50% of their original viscosity at 95 °C, and sustained effectiveness in 50 g/L salt solutions, surpassing conventional polymers like HPAM. The sealing effect of this hydrophobic conjugated polymer on cracks with different openings can effectively solve the leakage problem. It addresses the need for effective plugging in heterogeneous formation with uncertain leak channel dimensions. Therefore, this study is of great significance for solving cementing leakage, especially for oil and gas reservoir protection in non-homogeneous reservoirs.

Multi-scale sensing and multi-dimensional feature enhancement for surface defect detection of hot-rolled steel strip

ABSTRACT Surface defect detection of hot-rolled steel strip is a crucial process in its production. Aiming at the problem of poor detection accuracy caused by complex shape of defect features and imbalance between object scale and feature scale, this paper proposes a multi-scale sensing and multi-dimensional feature enhancement algorithm. Firstly, a multi-branch residual aggregation module (MRAM) is proposed, which uses the interactive features of different receptive fields between layers to capture global and local texture features, improving the feature extraction capability for defects of different scales. Then, the multi-dimensional attention enhancement head (MAEH) is proposed, which integrates feature information from different prediction branches and realises feature information enhancement in scale, space and channel dimensions for the aggregated information to improve the accuracy and robustness of steel surface defect detection. The results show that the proposed algorithm achieves 82.7% mAP and 89 FPS on the NEU-DET dataset, and 73.3% mAP and 94 FPS on the GC10-DET dataset. Compared with state-of-the-art algorithms, the proposed algorithm has the highest mAP, strong generalisation ability, and good real-time performance, which proves its effectiveness in detecting hot-rolled steel strip surface defects.

Cascaded Cross-modal Alignment for Visible-Infrared Person Re-Identification

Visible-Infrared Person Re-Identification faces significant challenges due to cross-modal and intra-modal variations. Although existing methods explore semantic alignment from various angles, severe distribution shifts in heterogeneous data limit the effectiveness of single-level alignment approaches. To address this issue, we propose a Cascaded Cross-modal Alignment (CCA) framework that gradually eliminates distribution discrepancies and aligns semantic features from three complementary perspectives in a cascaded manner. First, at the input-level, we propose a Channel-Spatial Recombination (CSR) strategy that strategically reorganizes and preserves crucial details from channel and spatial dimensions to diminish visual discrepancies between modalities, thereby narrowing the modality gap in input images. Second, at the frequency-level, we introduce a Low Frequency Masking (LFM) module to emphasize global details that CSR might overlook by randomly masking low-frequency information, thus driving comprehensive alignment of identity semantics. Third, at the part-level, we design a Prototype-based Semantic Refinement (PSR) module to refine fine-grained features and mitigate the impact of irrelevant areas in LFM. It accurately aligns body parts and enhances semantic consistency guided by global discriminative clues from LFM and flipped views with pose variations. Comprehensive experimental results on the SYSU-MM01 and RegDB datasets demonstrate the superiority of our proposed CCA.

Improved YOLOv8 for Gas-Flame State Recognition under Low-Pressure Conditions

This paper introduces a lightweight flame detection algorithm, enhancing the accuracy and speed of gas-flame state recognition in low-pressure environments using an improved YOLOv8n model. This method effectively resolves the aforementioned problems. Firstly, GhostNet is integrated into the backbone to form the GhostConv module, reducing the model’s computational parameters. Secondly, the C2f module is improved by integrating RepGhost, forming the C2f_RepGhost module, which performs deep convolution, extends feature dimensions, and simplifies the inference structure. Additionally, the CBAM attention mechanism is added to enhance the model’s ability to capture fine-grained features of flames in both channel and spatial dimensions. The replacement of CIoU with WIoU improves the sensitivity and accuracy of the model’s regression loss. Experimental results on a simulated dataset of the theoretical testbed indicate that compared to the original model, the proposed improvements achieve good performance in low-pressure flame state detection. The model’s parameter count is reduced by 12.64%, the total floating-point operations are reduced by 12.2%, and the detection accuracy is improved by 21.2%. Although the detection frame rate slightly decreases, it still meets real-time detection requirements. The experimental results demonstrate that the feasibility and effectiveness of the proposed algorithm have been significantly improved.

Referring Image Segmentation With Fine-Grained Semantic Funneling Infusion.

Recently, referring image segmentation has attracted wide attention given its huge potential in human-robot interaction. Networks to identify the referred region must have a deep understanding of both the image and language semantics. To do so, existing works tend to design various mechanisms to achieve cross-modality fusion, for example, tile and concatenation and vanilla nonlocal manipulation. However, the plain fusion usually is either coarse or constrained by the exorbitant computation overhead, finally causing not enough understanding of the referent. In this work, we propose a fine-grained semantic funneling infusion (FSFI) mechanism to solve the problem. The FSFI introduces a constant spatial constraint on the querying entities from different encoding stages and dynamically infuses the gleaned language semantic into the vision branch. Moreover, it decomposes the features from different modalities into more delicate components, allowing the fusion to happen in multiple low-dimensional spaces. The fusion is more effective than the one only happening in one high-dimensional space, given its ability to sink more representative information along the channel dimension. Another problem haunting the task is that the instilling of high-abstract semantic will blur the details of the referent. Targetedly, we propose a multiscale attention-enhanced decoder (MAED) to alleviate the problem. We design a detail enhancement operator (DeEh) and apply it in a multiscale and progressive way. Features from the higher level are used to generate attention guidance to enlighten the lower-level features to more attend to the detail regions. Extensive results on the challenging benchmarks show that our network performs favorably against the state-of-the-arts (SOTAs).

UAT:Unsupervised object tracking based on graph attention information embedding

An excellent unsupervised tracker includes a powerful base tracker and an effective unsupervised tracking strategy. However, most base trackers lack internal feature representations for information embedding processes. Most unsupervised trackers are not robust enough in complex environments and lack an effective template update strategy. We propose an unsupervised object tracking based on graph attention information embedding (UAT) to solve the above problems. UAT combines graph attention mechanism with multi-scale features to construct a multi-scale graph attention module (MGA). MGA module dynamically and efficiently completes the information embedding between the template branch and the search area branch. The response map obtained by fusing the feature maps of the two branches is more informative about the location of the target. An attention based information reinforcement update module (RUM) improves the robustness of the tracker. RUM enhances the representation of the feature map in both the spatial dimension and the channel dimension. Template features are also updated indirectly through information transfer between the two branches. RUM suppresses background interference and improves network perception during tracking. Experiments on challenging benchmarks such as VOT2018, VOT2019, TrackingNet, OTB100, LaSOT and UAV123 demonstrate that the proposed UAT achieves state-of-the-art performance in unsupervised trackers.

A speech separation model improved based on Conv-TasNet network

Abstract In the field of single-channel speech separation, the extraction and separation of features from mixed audio have always been the focus and difficulty of research. Currently, mainstream methods mainly suffer from poor generalization ability and issues such as inadequate feature extraction, which leads to the models’ inferior separation capability. This paper proposes an improved DConv-TasNet network model, focusing on the optimization of the encoder/decoder modules and separation modules and utilizing deep dilated encoders/decoders to extract features from mixed speech signals. It enhances feature extraction capability and generalization compared to conventional encoders/decoders. In terms of the separation module, improvements were made to the convolutional blocks within the module by enhancing feature extraction in the channel dimension, leading to improved performance of the separation network. Validation of the model’s performance was conducted using the WSJ0-Mix2 dataset, demonstrating superior performance compared to the Conv-TasNet network.

The longer arc of channel recovery post-dam removal

Dam removals are opportunistic experiments to address fundamental questions about river recovery to disturbance. Previous studies have shown that gravel-bedded rivers are resilient with covariate adjustments to channel dimensions occurring rapidly in the wake of disturbance. Yet, beyond the cross section, at the reach or watershed-scale, adjustment appears to take much longer. Understanding of the longer arc of reach and watershed recovery is limited by the relatively few dam removals studies that include long-term monitoring. Here, we present results from a five-year dam removal study punctuated by an extreme flood and show that the initial, rapid response of a channel is driven by the prevailing hydrology whereas longer-term adjustments to morphology at the reach scale are driven by external forces imposed on the channel. We summarize these results by classifying various channel features as either ‘intrinsic channel properties’ that are rapidly adjustable by the prevailing hydrology or ‘extrinsic channel properties’ that respond over various time scales to external boundary conditions imposed on the channel by climate, vegetation, geology, and valley dimensions (extrinsic controls). We show that this framework applies to channel recovery beyond the former reservoir and thus may prove applicable to channel disturbances beyond dam removal, such as extreme floods.

Integrated Seismic Attributes as a Tool to Delineate the Oligocene Channels Architecture, Baltim Field, Offshore Central Nile Delta, Egypt

The hydrocarbons in the Nile Delta area are produced from both clastic and non-clastic reservoirs, with ages ranging from the Early Cretaceous to the Plio-Pleistocene. The study area is situated in Baltim field offshore, Central Nile Delta, approximately 10 kilometers off the Egyptian coast (Fig. 1). The main producing reservoir in Baltim field is Abu Madi Formation (Miocene age) as there were many discoveries in the last decades resulting in most of the area had been considered as a brown field. Nowadays, the Oligocene section is considered as the hope for new hydrocarbon potentiality in the deep section (below Miocene). Among the drilled sedimentary sequence in the Nile Delta, Tineh Formation (Oligocene age) contains multiple levels of reservoir rocks with high-quality reservoirs including: Satis-1, Satis-3, Notus-1, Tineh-1, Atoll-1 & Salamat-1 (Fig. 2). This work's objective is to delineate the different channels of the Oligocene section in trial to add more reserves to the field. One of the main challenges for the exploration of these deep reservoirs is to delineate the reservoir geometry itself. A strong tool for improving bed thickness imaging and mapping, geologic discontinuities and channel delineation is the spectral decomposition technique providing more details than the conventional amplitude extractions. So, in this work, we applied the spectral decomposition tool to track the geometric dimensions of the Oligocene channels. RGB color blending performed from 10, 16, and 20 Hz represents the most significant geological features to get a better picture of the channel’s geometry. After determining the channel’s boundary by using the RGB blending, the channel geo-body was extracted. This technique helps in reducing the uncertainties of hydrocarbon exploration. Consequently to the integration between RMS amplitude maps and the channel geo-body that was extracted from the spectral decomposition, Oligocene channels became clearer and the boundaries well defined.

Response of submarine braided channels to varying inflow hydrographs: Geomorphic experiments and stratigraphic implications

AbstractSubmarine channels on the seafloor experience a range of turbidity current‐flood hydrographs. The characteristics of the flood hydrograph influence channel dimensions, the number of channel threads (i.e. braiding intensity) and the depositional properties of sandbars within the channels. However, the morphodynamic response of submarine braided channels to different hydrograph patterns is poorly understood. In this study, six geomorphic experiments are used to test how varied flood hydrographs affect the braiding intensity and sandbar geometry within submarine channels. Three experiments represent the control group with constant density‐current inflow, and three experiments use different time‐varying hydrograph patterns. The experiments show that, as the inflow‐to‐sediment discharge ratio increases, multiple small channels coalesce into a few main channels, causing a decrease in active braiding intensity defined by the channel threads with flow. When the flow returns to the initial low flow rate, active braiding intensity increases again. These observations show that the inflow‐to‐sediment discharge ratio determines the value of active braiding intensity. Additionally, active braiding intensity is directly proportional to dimensionless sediment‐stream power and dimensionless stream power, in agreement with previously established trends for both submarine and fluvial braided channels. In contrast to active braiding intensity changes, the measured sandbar aspect ratio and compactness ratio (i.e. ratio of planform area to perimeter) of submarine braided channels is insensitive to changes in the hydrograph. However, when inflows reach peak discharge, the higher transport capacity erodes pre‐existing deposits, forming noticeable erosional surfaces, and subsequently depositing thicker sandbars during that stage. These observations imply that sandbar planform morphology is largely insensitive to hydrograph, but hydrograph variability induces a greater variability in the resultant stratigraphic packages. The experimental results predict that field‐scale stratigraphy should be dominated by strata deposited during intervals of high flow, but that these strata will also exhibit reduced lateral continuity compared to formation scenarios with constant discharge.

High current density operation of proton exchange membrane fuel cells at a constant relative humidity

To further reduce the size and cost of proton exchange membrane fuel cells, it is desired to operate at high current densities exceeding 2 A/cm2, and consequently achieve power densities above 1 W/cm2. In a prior one-dimensional modeling study that employed the Engineering Equation Solver (EES), it was suggested that proton exchange membrane fuel cells might operate at current densities as high as 10 A/cm2 and even more when appropriate operating and channel dimensions are chosen, and perforated metal plates are used as porous transport layers instead of carbon fiber papers. The current study verifies the general findings of the previous 1-D model by a 3-D multiphase computational fluid dynamics model in Ansys CFX to obtain additional insights. Operating a proton exchange membrane fuel cell at a constant humidity (100%, 80%, 60%) from the inlet to the outlet at the cathode side appears feasible, and results for a current density above 6 A/cm2 were obtained. This mode of operation is promising also for automotive applications.

High current density operation of proton exchange membrane fuel cells at a constant relative humidity

To further reduce the size and cost of proton exchange membrane fuel cells, it is desired to operate at high current densities exceeding 2 A/cm2, and consequently achieve power densities above 1 W/cm2. In a prior one-dimensional modeling study that employed the Engineering Equation Solver (EES), it was suggested that proton exchange membrane fuel cells might operate at current densities as high as 10 A/cm2 and even more when appropriate operating and channel dimensions are chosen, and perforated metal plates are used as porous transport layers instead of carbon fiber papers. The current study verifies the general findings of the previous 1-D model by a 3-D multiphase computational fluid dynamics model in Ansys CFX to obtain additional insights. Operating a proton exchange membrane fuel cell at a constant humidity (100%, 80%, 60%) from the inlet to the outlet at the cathode side appears feasible, and results for a current density above 6 A/cm2 were obtained. This mode of operation is promising also for automotive applications.

Preprocessing for Multi-Dimensional Enhancement and Reconstruction in Neural Video Compression

The surge in ultra-high-definition video content has intensified the demand for advanced video compression techniques. Video encoding preprocessing can improve coding efficiency while ensuring a high degree of compatibility with existing codecs. Existing video encoding preprocessing methods are limited in their ability to fully exploit redundant features in video data and recover high-frequency details, and their network architectures often lack compatibility with neural video encoders. To addressing these challenges, we propose a Multi-Dimensional Enhancement and Reconstruction (MDER) preprocessing method to improve the efficiency of deep learning-based neural video encoders. Firstly, our approach integrates a degradation compensation module to mitigate encoding noise and boost feature extraction efficiency. Secondly, a lightweight fully convolutional neural network is employed, which utilizes residual learning and knowledge distillation to refine and suppress irrelevant features across spatial and channel dimensions. Furthermore, to maximize the use of redundant information, we incorporate Dense Blocks, which can enhance and reconstruct important features in the video data during preprocessing. Finally, the preprocessed frames are then mapped from pixel space to feature space through the Dense Feature-Enhanced Video Compression (DFVC) module, which improves motion estimation and compensation accuracy. The experimental results demonstrate that, compared to neural video encoders, the MDER method can reduce bits per pixel (Bpp) by 0.0714 and 0.0536 under equivalent PSNR and MS-SSIM conditions, respectively. These results demonstrate significant improvements in compression efficiency and reconstruction quality, highlighting the effectiveness of the MDER preprocessing method and its compatibility with neural video codec workflows.

Novel deep‐learning model for chemical process fault detection based on DCW transformer

AbstractIn this study, a double‐channel convolutional neural network and weighted (DCW) transformer model is proposed to address the problem of insufficient extraction of local information and no attention to channel‐step information in the traditional transformer model. First, a double‐channel information extraction method is proposed, so both the channel‐step and time‐step information achieve attention; second, the local information in the time and channel dimension of the data is extracted from deep and multiple scales, improving the feature extraction capability for local information; third, the long distance dependency relationship of the data is preserved by the attention mechanism, hence, the global correlation of the data is extracted effectively; finally, using the Gumbel‐SoftMax function, the weights of the time‐step and channel‐step feature information are assigned, so the extracted feature information has been optimized. The proposed method was applied for the penicillin fermentation process to verify its efficacy. Experimental results show that the proposed method achieved a better fault detection accuracy, outperforming the existing models. Further ablation experiments were conducted to demonstrate the effectiveness of each component of the proposed model.

Performance of channel shear connectors in steel frame with reinforced concrete infill walls

To examine the mechanical properties of channel shear connectors employed in steel frame with reinforced concrete infill wall (SRCW) structures, the improved push-out tests were performed on three specimens of channel shear connectors subjected to the monotonic and cyclic loading. This study investigated the influences of loading protocol and concrete strength on the mechanical behavior of channel shear connectors. After the refined finite element (EF) models of channel shear connectors were validated based on the push-out tests, a systematical parametric analysis was conducted to evaluate the effects of RC infill wall thickness, concrete strength, and channel dimensions on its shear strength of the channel shear connectors. In addition, this study also compared the accuracy of the CSA S16, AISC 360 and GB50017 design code in predicting the shear capacity of channel shear connectors in SRCW structures. The experimental results and parametric studies indicated that the current design equations inaccurately evaluate the shear capacity of channel shear connectors used in SRCW structures. Consequently, an alternative equation was developed to estimate the shear-resistant capacity of channel shear connectors in SRCW structures, which provided the desirable calculation precision for engineering design.

High-resolution multicomponent LFM parameter estimation based on deep learning

This paper addresses the complex challenge of parameter estimation in multi-component Linear Frequency Modulation (LFM) signals by introducing an innovative approach to high-resolution Fractional Fourier Transform (FrFT) parameter estimation, facilitated by convolutional neural networks. Initially, it analyzes the issues of peak shifts and the masking of weaker components due to spectral overlap in the FrFT domain of multi-component LFM signals. Convolutional neural networks are then employed to train and achieve high-resolution representations of FrFT parameters. Specifically, convolutional modules with residual structures are utilized to learn coarse features, while a weighted attention mechanism refines independent features across both channel and spatial dimensions. This approach effectively addresses the challenges posed by spectral peak overlap and frequency shifts in multi-component LFM signals, thereby enhancing the quality of high-resolution parameter estimation. Experimental results demonstrate that the proposed method significantly outperforms traditional methods in processing multi-component LFM signals. Moreover, it exhibits robust detection capabilities for both weak and compact components, thereby underscoring its potential applicability in the field of complex signal processing.