Processing Efficiency Research Articles

Optimization of oak sawing parameters based on energy consumption and surface roughness

AbstractHigh energy consumption and poor processing quality are common problems in wood sawing. To address these issues, in this article, specific cutting energy and surface roughness were investigated with saw blade speed as control variables. Analysing the effect of parameters on specific cutting energy and surface roughness. The sawing parameters were optimised with the objectives of minimum specific cutting energy and minimum surface roughness. The findings indicate that specific cutting energy and surface roughness reduction with increasing rake angle; specific cutting energy and surface roughness decrease with increasing spindle speed; specific cutting energy decreases and surface roughness increases with increasing feed rate. ANOVA analysis reveals that sawing speed (n) has the most significant impact on specific cutting energy during oak cutting. The optimal solution derived from TOPSIS suggests a specific cutting energy of 2E7 J/m3 and a surface roughness of 1.758 μm. The innovation of this paper is the study of the specific cutting energy and the optimisation of parameters. These findings provide valuable theoretical and practical guidance for enhancing the efficiency and quality of oak processing while minimizing energy consumption.

Radar forward modeling and intelligent identification of shallow subgrade defects

Geological radar is the primary nondestructive testing method for evaluating shallow subgrade defects. However, the radar atlas contains a large amount of information, and the efficiency of manual data interpretation and processing is low. In this study, the characteristics of radar maps of different defects were analyzed via forward simulation using finite-difference time-domain technology. The instantaneous characteristic information of different defect maps was integrated using map post-processing technology to improve recognition and translation accuracy. Finally, the convolution neural network algorithm was used to conduct data recognition to achieve the intelligent recognition of subgrade defects with an average detection accuracy of 73.93 % based on the radar subgrade defect atlas dataset, and the results were practically verified. The results show that the developed approach can accurately distinguish subgrade shallow defect information in the radar atlas. This approach is useful for accurate and efficient identification of latent highway defects.

Data Mining Technology for Smart Campus in Behavior Association Analysis of College Students

The data in smart campuses is complex and massive, with insufficient utilization, and existing data processing methods have many limitations. Therefore, in order to improve the efficiency of data processing in universities and assist in student management, a data processing method integrating cluster analysis and association rule mining is proposed. The proposed method is divided into two parts. Firstly, an improved K-Means model based on information entropy and density optimization is constructed for clustering analysis of student consumption, learning, and other data; Secondly, use the improved Mapping Apriori to obtain the correlation between student grades, consumption records, and learning behavior. The clustering results on student consumption data show that the average accuracy of ED-K-Means clustering is 97.41%, which is 12.8%, 8.5% and 4.0% higher than the comparison algorithm. The result of the correlation between consumption level and achievement shows that when the amount of consumption is less than 1500 yuan, the student’s achievement is directly proportional to the amount of consumption. Therefore, the proposed method can effectively mine and analyze student behavior data, which has important practical significance for intelligent management in universities.

Accelerating DNA Sequence Alignment using Altera DE2-115

DNA sequence alignment is a technique for discovering information between two base sequences which the Smith-Waterman algorithm is the accurate method that provides a precise result for alignment compared to others. However, the performance was influence by size of dataset and a long DNA base sequence which resulted the time required for the alignment process is much longer in relation to the number of DNA sequence samples. There are many ways to accelerate DNA sequence alignment, and Field Programmable Gate Array (FPGA) is a good choice due to its parallel processing and cost efficiency. Although FPGA acceleration approaches are not new, this work investigates a purely software-based FPGA acceleration using the Altera Cyclone IV EP4CE115F29C7N FPGA as the target device. The SW algorithm was developed using the C language in Quartus II version 18.1 and the Nios II software build tools for Eclipse. The development starts with setting up the Qsys architecture before developing the code in Eclipse to determine the computational performance. The result shows the computational timing and speed of the implementation, with the highest speed achieved being 198.76 cells per millisecond. To summarise, the computational performance ultimately depends on the maximum matrix size of the FPGA, which is also influenced by the DNA-based pair length and able to complete using low-cost FPGA.

Combinative model compression approach for enhancing 1D CNN efficiency for EIT-based Hand Gesture Recognition on IoT edge devices

Tiny Machine Learning is rapidly evolving in edge computing and intelligent Internet of Things (IoT) devices. This paper investigates model compression techniques with the aim of determining their compatibility when combined, and identifying an effective approach to improve inference speed and energy efficiency within IoT edge devices. The study is carried out on the application scenario of Hand Gesture Recognition (HGR) based on Electrical Impedance Tomography (EIT), which involves complex signal processing and needs real-time processing and energy efficiency. Therefore, a customized 1-Dimensional Convolutional Neural Network (1D CNN) HGR classification model has been designed. An approach based on strategically combining model compression techniques was then implemented resulting in a model customized for faster inference and improved energy efficiency for IoT embedded devices. The model size became compact at 10.42 kB, resulting in a substantial size reduction of 98.8%, and an inference gain of 94.73% on a personal computer with approximately 8.56% decrease in accuracy. The approach of combinative model compression techniques was applied to a wide range of edge-computing IoT devices with limited processing power, resulting in a significant improvement in model execution speed and energy efficiency for these devices. Specifically, there was an average power consumption gain of 52% for Arduino Nano BLE and 34.05% for Raspberry Pi 4. Inference time was halved for Arduino Nano BLE Sense, Nicla Sense, and Raspberry Pi 4, with a remarkable gain of 94% for ESP32.

Acoustic black hole immersed sonoreactor for high-efficiency cavitation treatment

Developing innovative sonoreactors to enhance acoustic processing efficiency holds immense importance in the field of sonochemistry. Traditional immersed sonoreactors (TISs) mainly produce cavitation at the probe tip, with a relatively weak cavitation around the probe, resulting in posing challenges for high-efficiency cavitation treatment. Here we propose an acoustic black hole immersed sonoreactor (ABHIS) in longitudinal-flexural coupled vibration, enabling high-efficiency cavitation treatment by unleashing the cavitation potential of the probe. The symmetrical structure of the probe is altered to introduce a coupling of flexural vibration mode, and an acoustic black hole (ABH) profile is integrated to further enhance both flexural wave number and amplitude. In this paper, we present a systematic theoretical design method for ABHIS and compare its performance with TIS using finite element method (FEM). An ABHIS prototype is fabricated and subjected to experimental tests and cavitation observation. The results demonstrate that our theoretical analysis model accurately predicts the frequency characteristics of ABHIS. The proposed ABHIS exhibits satisfactory dynamic characteristics, with significantly increased vibration displacement and acoustic radiation ability compared to TIS. Importantly, the ABH design significantly expands ultrasonic cavitation regions and enhances acoustic radiation intensity of ABHIS resulting in a substantial improvement in acoustic processing efficiency.

Drying Strawberry Slices: A Comparative Study of Electrohydrodynamic, Hot Air, and Electrohydrodynamic‐Hot Air Techniques

ABSTRACTThe investigation encompassed an examination of the drying durations, modeling, and quality attributes (color, rehydration capacity, microstructural features, total soluble solid [TSS], and pH values) of strawberry slices subjected to diverse drying methodologies, namely electrohydrodynamic (EHD), EHD‐hot air, and hot air processes. Furthermore, 10 distinct thin‐layer drying models were applied, and their goodness‐of‐fit was assessed to identify the most suitable model for the drying process. This analysis encompassed applying two distinct temperatures (50°C and 55°C), and voltage levels (20 and 30 kV). The EHD method yielded the lengthiest drying durations for the strawberry samples, with hot air and EHD‐hot air drying techniques subsequent in descending order of drying time. The outcomes of the modeling analyses demonstrated that the thin‐layer drying behaviors of used drying methods were best described by the Midilli et al. and Wang and Singh models in terms of their goodness‐of‐fit. A decline in the L* values was noted with the elevation of temperature in the hot air drying method and with the escalation of voltage in the EHD drying. The minimum a* value was detected in the hot air drying method conducted at 55°C. The rehydration capacity of strawberry samples subjected to the EHD and EHD‐hot air combination drying methods (except 20 kV‐55°C) did not exhibit any statistically significant variation in response to different voltage settings. Although the structure of strawberry samples dried with 20 kV application was observed as smoother, cracks occurred on the product surface in other drying applications. In hot air and EHD methods, varying temperature and volt applications did not show a significant effect on TSS and pH values. As a result, it has been seen that EHD technology, which is a promising drying method used in this study, is a suitable method in terms of processing efficiency and consumer acceptability of dried strawberry products.

Application and practice of industrial IoT cloud platform in oilfield intelligent transformation

Abstract Under the context of global energy transformation and low-carbon development, the traditional petroleum industry is facing a series of challenges such as technological innovation, market competition, environment protection, etc. Therefore, there is an urgent demand to realize green, safe, economical, and efficient development of oil and gas through oilfield intelligent transformation. This study develops a novel cloud platform covering the whole business based on the technical architecture of the industrial Internet of Things (IoT) for assisting the intelligent transformation of oilfields. Since the application of the IoT-based cloud platform in Qingcheng Oilfield, functions such as automatic collection of whole-domain data, remote monitoring, early warning, as well as intelligent analysis and decision-making have been realized. Furthermore, compared with the traditional platform, the new platform has improved eight indicators, such as data processing efficiency and sharing capacity, by more than 20%. Consequently, the developed IoT-based cloud platform can significantly improve the production and management level of the oilfield, and efficiently support oilfield intelligent transformation.

A revolutionary approach to automate labelling of CAD files

Abstract This innovation represents a significant advancement in enhancing processing efficiency when dealing with engineering drawings. Traditionally, the review of customer drawings needs to be executed by experienced experts before manufacture. The identification and labelling of key features were reliant on human intervention for subsequent Design for Manufacturability (DFM) or G-code programming. However, the previous method not only consumed valuable time but also introduced the potential for human errors. In this context, we introduce a high-efficiency technology for recognizing engineering drawings, automatically labelling key features, and converting unstructured data into structured data. It is noteworthy that this technology distinguishes itself from traditional methods, such as R-CNN or similar neural network approaches and differs from pre-labeled engineering drawings reliant on experts manually inputting keywords for dimensions or layers. This work is a vital step towards the realization of intelligent manufacturing. For instance, our customers can now review engineering drawings online and receive immediate feedback. This online interaction is facilitated by processing structured data, a direct output of this presented technology, with other specific modules. Furthermore, the structured data seamlessly integrates into subsequent manufacturing procedures, including notifying the supply chain, generating 3D drawings, and CNC programming. In conclusion, this technology showcases its significance in advancing our vision of intelligent manufacturing at ZY Corp.

Developmental dynamics of brain network modularity and temporal co-occurrence diversity in childhood

ObjectiveBrain development during childhood involves significant structural, functional, and connectivity changes, reflecting the interplay between modularity, information interaction, and functional segregation. This study aims to understand the dynamic properties of brain connectivity and their impact on cognitive development, focusing on temporal co-occurrence diversity patterns. MethodsWe recruited 481 children aged 6 to 12 years from the Healthy Brain Network database. Functional MRI data were used to construct dynamic functional connectivity matrices with a sliding window approach. Modular structures were identified using multilayer network community detection, and the Dagum Gini coefficient decomposition technique, which uniquely allows for multi-faceted exploration of modular temporal co-occurrence diversities, quantified these diversities. Mediation analysis assessed the impact on small-world properties. ResultsTemporal co-occurrence diversity in brain networks increased with age, especially in the default mode, frontoparietal, and salience networks. These changes were driven by disparities within and between communities. The small-world coefficient increased with age, indicating improved information processing efficiency. To validate the impact of changes in spatiotemporal interaction disparities during childhood on information transmission within brain networks, we used mediation analysis to verify its effect on alterations in small-world properties. ConclusionThis study highlights the critical developmental changes in brain modularity and spatiotemporal interaction patterns during childhood, emphasizing their role in cognitive maturation. These insights into neural mechanisms can inform the diagnosis and intervention of developmental disorders.

A Novel Mechanism to Accelerate Stress Relaxation of Toughened Blends: Stress-Induced Core-Shell Morphological Reconstruction and Its Application in Thermoforming and Dimensional Stabilization

Improving thermoforming efficiency and dimensional stability in thermoplastic products is a common challenge. This study investigates toughened polyamide 612 (PA612) blends made by adding maleic anhydride-functionalized SEBS (mSEBS) elastomers, along with high-density polyethylene (HDPE), polyphenylene Oxide (PPO), and polyphenylene Sulfide (PPS). Analyses showed that mSEBS forms a core-shell structure with HDPE or PPO and a sea-island structure with PPS in the PA612 matrix. The study uniquely examines how these structures affect stress relaxation. The results, modeled by a steady-state creep model, revealed that the core-shell structures reduced the characteristic relaxation time (λ) by over three orders of magnitude compared to PA612/mSEBS blends. Additionally, PA612/mSEBS/HDPE blends were more temperature-sensitive, reducing λ by six orders of magnitude compared to PA612/mSEBS/PPO blends. Further analysis showed that stress-induced core-shell morphological reconstruction (SCMR) significantly improved stress relaxation by promoting extensive plastic deformation and energy dissipation. These toughened PA612 blends exhibited excellent thermoforming efficiency and dimensional stability. A 3D finite element model confirmed SCMR as an effective strategy for stress relaxation, providing valuable insights for designing toughened blends with superior processing efficiency and stability.

One hundred years of piñon nuts, a largely forgotten wild food crop from the American Southwest (1850–1950)

This is the first paper to intensively examine the history of piñon nuts, the seeds of the piñon pine (Pinus edulis), which Indigenous Americans have harvested in the Four Corners region of the American Southwest for millennia. The history of the piñon nut industry is reconstructed from its inception in the mid-19th century in the New Mexico Territory to its heyday in the early 20th century until consumption began to decline in the mid-20th century. Additionally, this paper provides some suggestions for reviving this iconic Southwestern American food. The commercial harvest of piñon nuts primarily involved Native and Mexican Americans. While the economic benefits of piñon nut harvesting were significant, the work was often dangerous, with reports of deaths due to exposure, accidents, and violence. Piñon nut production varied yearly due to the trees’ masting behavior, where trees produce large seed crops intermittently. Although there was considerable interest in cultivation during the early 20th century, slow growth and delayed nut-bearing limited cultivation efforts. Piñon nuts were popular throughout the United States in the late 19th and early 20th centuries, gaining popularity, especially among immigrant communities in New York. In 1918, Fannie Spitz patented a piñon nut-shelling machine, significantly improving processing efficiency and impacting the industry by enabling quicker and more effective removal of kernels. Shelled piñon nuts were now sold for consumption out of hand and also for use in candy production. Farmers experimented with feeding hogs and chickens piñon nuts. However, this practice did not become widespread due to the availability of cheaper corn. Piñon wood was also valued for its use as firewood and in mining as props, though it had limited application as lumber for railroad ties. At the same time, smaller piñon trees gained popularity as Christmas trees. Piñon resin was chewed and also used medicinally in salves. By the mid-20th century, piñon nuts lost popularity to peanuts (Arachis hypogaea), which were marketed more aggressively and produced in higher quantities at lower prices. The piñon industry has struggled with crop variability, low supply, and high prices. Still, it might be revived with improved marketing, working cooperatively and through the construction of a strategic reserve to ensure a steady supply of piñon nuts every year, through both good and bad harvests.

Force rheological polishing of polycrystalline magnesium aluminate spinel using agglomerated diamond abrasive

The high hardness and polycrystalline structure of polycrystalline magnesium aluminate spinel (PMAS) present significant challenges in achieving high-precision processing, particularly during polishing. Issues such as low processing efficiency and grain effects can compromise processing accuracy, surface quality, and optical performance. To address these challenges and enhance both polishing efficiency and surface quality, this study employed agglomerated diamond (AD) abrasives and single crystal diamond (SCD) abrasives in force rheological polishing (FRP). The experimental results revealed that the lowest surface roughness (Sa) and optimal selective material removal were achieved using 25 μm AD abrasives, particularly those agglomerated with 0.2 μm SCD abrasives. This approach reduced Sa from 146.1 nm to 4.6 nm, peak-to-valley height difference (Sz) from 1329 nm to 69.3 nm and achieved a material removal rate (MRR) of 228.8 nm/min. Conversely, polishing with 1 μm SCD abrasives resulted in poorer selective material removal, with Sz decreasing from 1265.1 nm to 280.9 nm and Sa decreasing from 138.3 nm to 27.6 nm, although it exhibited the highest MRR of 323.64 nm/min. The superior polishing results obtained with AD abrasives, compared to SCD abrasives of the same size, can be attributed to the polycrystalline-like structure of AD abrasives, which exhibit more micro cutting edges. Additionally, the surface and subsurface damage after polishing with AD abrasives was analyzed. The results indicated that material removal of PMAS using AD abrasives was effective in FRP, resulting in minimal lattice distortion. This study provides both theoretical and experimental foundations for polishing PMAS using larger-sized AD abrasives in FRP.

Characteristics and evaluation of recycled waste PVCs as a filler in composite structures: Validation through simulation and experimental methods

Solar Cell as a renewable energy utilization in today's era is considered a suitable choice due to encompass sustainability, environmental preservation, and energy processing efficiency. Solar cells have a finite lifespan that need replacement to maintain energy absorption efficiency. Unfortunately, discarded materials are often underutilized or improperly disposed of. In this study, used photovoltaic solar cell materials are explored as reinforcements in composites. The results showed that 4 % cell filler specimen exhibited highest ultimate tensile strength (UTS) with 51.43 MPa. Followed by Compression strength with 35.38 MPa and flexural strength with 45.54 MPa. SEM/EDS analysis of PV filler specimens revealed the dominance of Carbon (C) and Silica (Si) materials, comprising over 60 %. FT-IR analysis indicated varying compound bond intensities affecting polymerization and material strength under applied forces. Simulation results showed a difference of <2 % when compared to experimental testing outcomes. The current study benefited in environmental conservation efforts through waste reduction and the reuse of recycled materials and are listed in several applications such as in wind turbine, structures, lightweight laminates, automotive structures, and sport equipment.

Melt flow analysis in rotational nozzle fused filament fabrication process

Fused filament fabrication (FFF) is a widely used processing method; however, heat transfer limitations within a conventional nozzle result in relatively low flow rates, leading to lengthy production times, compared to traditional processing methods, ultimately restricting its industrial application. Recently, a novel rotational nozzle FFF three-dimensional (3 D) printer has been patented and developed to enhance processing efficiency. Despite this achievement, the fundamental mechanisms behind this novel process remain unclear. In this study, both analytical analysis and numerical simulations were conducted based on a force-controlled scaled-down experimental setup. This setup, designed according to the pressure-induced melt removal theory, provided melt throughput data under varying heater temperatures, extrusion forces, and rotational speeds. Agreement between the modeling and experimental results confirms the generalizability of the models. Modeling predictions of temperature and velocity distributions indicate that viscous dissipation affects the average temperature and filament velocity. To simulate the real-world working conditions of FFF 3 D printing, a velocity-controlled simulation was introduced. It was observed that the average melt film thickness increases with nozzle rotational speed due to viscous dissipation. Additionally, the extrusion force required for the same printing speed decreases with increasing nozzle rotational speed, primarily due to the higher shear rate reducing melt viscosity.

Enhancing Coconut Processing Efficiency: Design and Evaluation of A Cost-Effective Coconut De-Husking Machine

Coconut (Cocos nucifera) plays a crucial role in agricultural economies, particularly in coconut-producing regions, where traditional de-husking methods are labor-intensive and time-consuming. This study focuses on the design, fabrication, and evaluation of a coconut de-husking machine aimed at improving processing efficiency, reducing labor demands, and increasing throughput. The machine was designed to address key performance factors such as frame stability, blade strength, and spring tension. Testing demonstrated that the machine consistently outperformed manual methods, reducing de-husking time to an average of 96.4 seconds compared to 164.8 seconds for traditional techniques. The machine achieved an efficiency rate of 80%, with a throughput capacity of 0.0086 kg/s, significantly higher than the manual method's 0.005 kg/s. Additionally, the machine's cost, approximately N32,000, makes it accessible to smallholder farmers, offering a practical solution for improving productivity and sustainability in coconut farming. The study highlights the potential for mechanized de-husking to transform traditional practices, reduce labor costs, and contribute to economic growth in coconut-rich regions. Future research should explore further optimization of the machine design and its adaptability to different coconut varieties and moisture levels, ensuring broad applicability in diverse farming contexts.

Partial chemogenetic inhibition of the locus coeruleus due to heterogeneous transduction of noradrenergic neurons preserved auditory salience processing in wild-type rats.

The acoustic startle reflex (ASR) and prepulse inhibition of the ASR (PPI) assess the efficiency of salience processing, a fundamental brain function that is impaired in many psychiatric conditions. Both ASR and PPI depend on noradrenergic transmission, yet the modulatory role of the locus coeruleus (LC) remains controversial. Clonidine (0.05 mg/kg, i.p.), an alpha2-adrenoreceptor agonist, strongly reduced the ASR amplitude. In contrast, chemogenetic LC inhibition only mildly suppressed the ASR and did affect the PPI in virus-transduced rats. The canine adenovirus type 2 (CAV2)-based vector carrying a gene cassette for the expression of inhibitory receptors (hM4Di) and noradrenergic cell-specific promoter (PRSx8) had high cell-type specificity (94.4±3.1%) but resulted in heterogeneous virus transduction of DbH-positive LC neurons (range: 9.2-94.4%). Clozapine-N-oxide (CNO; 1mg/kg, i.p.), a hM4Di actuator, caused the firing cessation of hM4Di-expressing LC neurons, yet complete inhibition of the entire population of LC neurons was not achieved. Case-based immunohistochemistry revealed that virus injections distal (> 150μm) to the LC core resulted in partial LC transduction, while proximal (< 50 μm) injections caused neuronal loss due to virus neurotoxicity. Neither the ASR nor PPI differed between the intact and virus-transduced rats. Our results suggest that a residual activity of virus-non-transduced LC neurons might have been sufficient for mediating an unaltered ASR and PPI. Our study highlights the importance of a case-based assessment of the virus efficiency, specificity, and neurotoxicity for targeted cell populations and of considering these factors when interpreting behavioral effects in experiments employing chemogenetic modulation.

NEUROBIOLOGICAL PROPERTIES OF THE STRUCTURE OF THE PARALLEL-HIERARCHICAL NETWORK AND ITS USAGE FOR PATTERN RECOGNITION

The paper presents the analysis of neurobiological data on the existence of the structure of a parallel-hierarchical network. Discussed method of parallel-hierarchical transformation based on population coding and its application for the pattern recognition task. Based on the analysis, we can conclude that using the methods proposed, it is possible to measure the geometric parameters and properties of images, which can significantly increase the efficiency of processing, in particular estimating the center of mass based on moment characteristics. Experimental results demonstrate that due to various destabilizing factors, accurately measuring the energy center coordinates of laser beam spot images is challenging. However, training the PI network and classifying the fragments into "good" and "bad" can considerably enhance the accuracy of these measurements.

Promoting Synergies to Improve Manufacturing Efficiency in Industrial Material Processing: A Systematic Review of Industry 4.0 and AI

The manufacturing industry continues to suffer from inefficiency, excessively high prices, and uncertainty over product quality. This statement remains accurate despite the increasing use of automation and the significant influence of Industry 4.0 and AI on industrial operations. This review details an extensive analysis of a substantial body of literature on artificial intelligence (AI) and Industry 4.0 to improve the efficiency of material processing in manufacturing. This document includes a summary of key information (i.e., various input tools, contributions, and application domains) on the current production system, as well as an in-depth study of relevant achievements made thus far. The major areas of attention were adaptive manufacturing, predictive maintenance, AI-driven process optimization, and quality control. This paper summarizes how Industry 4.0 technologies like Cyber-Physical Systems (CPS), the Internet of Things (IoT), and big data analytics have been utilized to enhance, supervise, and monitor industrial activities in real-time. These techniques help to increase the efficiency of material processing in the manufacturing process, based on empirical research conducted across different industrial sectors. The results indicate that Industry 4.0 and AI both significantly help to raise manufacturing sector efficiency and productivity. The fourth industrial revolution was formed by AI, technology, industry, and convergence across different engineering domains. Based on the systematic study, this article critically explores the primary limitations and identifies potential prospects that are promising for greatly expanding the efficiency of smart factories of the future by merging Industry 4.0 and AI technology.

The method of forming ensembles of complex signals based on multi-scale decomposition of time intervals at different levels of detail

This article addresses the challenges of forming signal ensembles in dynamic cognitive radio environments, focusing on the limitations of traditional methods. The study proposes a new approach based on multiscale time interval decomposition, which allows for the creation of signal ensembles at varying levels of temporal detail. The key innovation of this method is its ability to improve signal reproduction accuracy, reduce inter-symbol and inter-channel interference, and enhance the overall efficiency of data processing. The method's adaptability to changing environmental conditions is also a core advantage, enabling better use of bandwidth and reduced transmission delay. Through the decomposition of time intervals into coarse, intermediate, and fine levels, this method allows for the detailed analysis of short-term, medium-term, and long-term signal components. Experimental results demonstrate the superiority of this approach in terms of signal recovery accuracy, noise resilience, and processing speed. These findings highlight the potential for optimizing signal processing in cognitive radio networks, particularly in environments with high levels of noise and interference. Future research aims to integrate machine learning algorithms to further enhance adaptability in real-time scenarios.