Search Results (57)

Division (DIV), reciprocal (REC), square root (SR), and reciprocal square root (RSR) are fundamental operations in digital signal processing (DSP), communication, and matrix decomposition applications. However, implementing these functions using dedicated hardware units increases area and resource utilization when multiple operations are required within the same system. This paper presents a multifunctional fixed-point architecture that supports DIV, REC, SR, and RSR operations within a unified Newton–Raphson-based framework. The proposed design employs scaling and de-scaling techniques to facilitate architectural parameterization across generic fixed-point formats, piecewise polynomial approximations for seed generation, and hardware sharing between the seed computation and Newton–Raphson stages to enhance overall computational efficiency. The architecture was described in Verilog–HDL and evaluated through FPGA and ASIC implementation flows. To demonstrate the feasibility of the design, the experimental validation and implementation scope were focused on a specific of 16 bits word-length. FPGA synthesis results show that the proposed multifunctional unit achieves operating frequencies comparable to dedicated implementations while reducing hardware cost by approximately 40% compared with separate arithmetic units. Exhaustive simulations using a 16-bits representation yield SQNR values ranging from 72.03 dB to 81.03 dB across the supported operations. Furthermore, ASIC implementation using an Intel 16 nm PDK confirms the feasibility of the proposed approach for advanced technology nodes under the verified format. These results demonstrate that the proposed architecture provides an effective trade-off among accuracy, latency, and hardware efficiency, making it well suited for high-performance fixed-point DSP accelerators. Full article

This study aimed to evaluate the surface changes depicted on Reciproc and Reciproc Blue R25 files after their immersion in different irrigants at different temperatures compared to the non-immersed controls, utilizing Atomic Force Microscopy (AFM). To measure the Root Mean Square (RMS) and Mean Roughness (Sa), eight R25 files (four/system) were divided into four groups (n = 2; one file/system): non-immersed (control), immersed in 17% ethylenediaminetetraacetic acid (EDTA) set at 37 °C, and immersed in 5.25% sodium hypochlorite (NaOCl) set at 37 °C or at 45 °C. Immersion time was 10 min after which AFM was conducted. There was no significant difference in mean RMS and Sa between the control and the 17% EDTA group (p > 0.05). Immersion in 5.25% NaOCl at 37 °C significantly increased surface irregularities on both files (p < 0.05). This increase was further accentuated by NaOCl’s temperature rise to 45 °C (p < 0.05). Reciproc exhibited significantly higher surface roughness compared to Reciproc Blue under all conditions (p < 0.05). Immersion in 5.25% NaOCl altered the surface topography of Reciproc and Reciproc Blue which was further accentuated by its temperature rise, while immersion in 17% EDTA had no significant effect on their surface changes. Reciproc demonstrated significantly higher surface roughness compared to Reciproc Blue under all tested conditions. Full article

Digital predistortion (DPD) is essential for mitigating nonlinear distortion in radio-frequency (RF) power amplifiers (PAs), particularly in modern multimode transmitters. Among the existing approaches, the neural-network-based DPD reference model adopted in this work is attractive due to its high modeling accuracy and effective predistortion capability. However, its practical implementation is hindered by the computational complexity of the preprocessing stage, which relies on magnitude extraction, phase normalization, and trigonometric operations. Motivated by this limitation, this work proposes a simplified hardware-efficient formulation, derived from an existing real-valued three-layer perceptron (TLP)-based DPD model, for multimode PA linearization. The proposed approach preserves the main characteristics of the reference model while replacing conventional magnitude and phase normalization with a simplified feature representation derived from complex-valued signal products, eliminating square-root, reciprocal, and trigonometric operations. Two configurations are investigated: a single-network formulation and an iterative cascaded structure composed of compact networks trained sequentially. Simulation results demonstrate accuracy comparable to the reference model while reducing computational complexity by up to 34% in multiplications, 25% in additions, and 73.9% in LUT usage, making the proposed approach suitable for FPGA and ASIC implementations. Full article

Understanding the variations in the bidirectional reflectance distribution function (BRDF) and albedo over snow surface under various conditions is important for interpreting the surface–atmosphere processes of the cryosphere, and the kernel-driven model is among the most popular methods to obtain this information for a comprehensive analysis. Recently, the RossThick-LiSparseReciprocal-Snow (RTLSRS) model was developed to better characterize the anisotropic reflectance of snow and shows strong potential for integration into operational remote sensing algorithms for snow BRDF/albedo retrieval. To comprehensively test the ability of the RTLSRS model to reproduce snow reflectance, the fitting accuracy to different multi-angular data derived from ground, tower, aircraft, and satellite platforms across the full optical wavelength range were demonstrated in this study. Special attention in this study was directed to analyzing the model performance under extreme illumination observation geometries, particularly with respect to the retrieval accuracy and stability under large Solar Zenith Angles (SZAs) and different Relative Azimuth Angles (RAAs). The model performance for silt-polluted snow surface with different concentrations is also assessed to provide necessary supplementation, relative to “pure” snow surface in the previous study. The main findings of this study are summarized as follows: (1) The RTLSRS model exhibits strong robustness under various SZAs; even when the SZA exceeds 80°, the model maintains high accuracy in BRDF reconstruction, with root mean square error (RMSE) values below 0.05. (2) The model also demonstrates satisfactory inversion capability when observations deviate from the principal plane (PP); the model can achieve fitting accuracy with R² approaching 0.5 and RMSE below 0.05 for MODIS data. (3) In the spectral range below 1300 nm, the RTLSRS model effectively reconstructs the scattering characteristics of snow surfaces with light impurity levels (<20 g/0.5 m²). (4) The spectral shape of snow reflectance remains consistent across different view zenith angles (VZAs) in general. However, the variations caused by different SZAs can be as high as 38.49% and such SZA-induced difference can result in WSA estimation discrepancy of up to 63.43%. This comprehensive assessment further affirms and demonstrates the applicability of the RTLSRS model for the first time in fitting observations across different platforms with various optical wavelengths and geometries, and provides an improved understanding to analyze BRDF variations for the user community. Full article

This study proposes a novel transformation method for analyzing block minima using the generalized extreme value distribution (GEVD). The negative power transformation (NPT), which includes a tunable hyperparameter and reduces to the reciprocal transformation (RT) when set to 1, improves the accuracy and robustness in estimating long-term return levels (RL). Compared to traditional methods, the NPT-GEVD demonstrates lower bias, standard errors, and root mean square errors in Monte Carlo simulations. Furthermore, the NPT-GEVD provides consistent RL estimates with improved robustness across varying parameterizations and sample sizes, mainly when using L-moments for small datasets. The application of the NPT-GEVD to rainfall data from South Korea revealed that the RLs for detecting hourly cumulative rainfall threshold levels varied from 30 min to over 4 h, depending on the location and threshold. This research underscores the value of advanced transformation techniques in environmental risk management, offering critical insights for flood prediction and mitigation strategies in climate change. Full article

Calcium silicate-based sealers are widely used in contemporary endodontics, but their strong interaction with dentinal substrates may complicate their removal during nonsurgical retreatment and potentially hinder canal disinfection. This ex vivo study evaluated the effectiveness of different irrigation activation techniques in removing a calcium silicate-based sealer from oval-shaped root canals. Sixty extracted single-rooted teeth were instrumented and obturated using the single-cone technique with NeoSealer Flo, followed by retreatment using a reciprocating system. Specimens were randomly assigned to four final irrigation protocols: conventional needle irrigation (CNI) with NaOCl/EDTA, ultrasonic activation (US), diode laser activation (LI), and Er:YAG laser activation using the SWEEPS mode (SW) (n = 15). Residual filling material was quantified before and after final irrigation using stereomicroscopic imaging and ImageJ (version 1.54) analysis. Dentinal surface morphology and residual sealer were further evaluated using SEM–EDS. Statistical analysis included one-way ANOVA and chi-square tests (p < 0.05). All protocols significantly reduced residual filling material compared with mechanical retreatment alone (US 15.08%, CNI 7.89%, LI 8.01%, SW 7.20%) (p < 0.01). US resulted in significantly greater sealer removal compared with CNI, LI, and SW, with mean differences ranging from 7.08% to 7.88% (p < 0.05). These findings indicate that irrigation activation enhances the removal of NeoSealer Flo calcium silicate-based sealer, with ultrasonic activation demonstrating greater effectiveness among the evaluated techniques, under the conditions of this experimental setup. Full article

With the rapid advancement of Transformer-based large language models (LLMs), these models have found widespread applications in industrial domains such as code generation and non-functional requirement (NFR) classification in software engineering. However, recent research has primarily focused on optimizing linear matrix operations, while nonlinear operators remain relatively underexplored. This paper proposes hardware-efficient approximation and acceleration methods for the Softmax and RMSNorm operators to reduce resource cost and accelerate Transformer inference while maintaining model accuracy. For the Softmax operator, an additional range reduction based on the SafeSoftmax technique enables the adoption of a bipartite lookup table (LUT) approximation and acceleration. The bit-width configuration is optimized through Pareto frontier analysis to balance precision and hardware cost, and an error compensation mechanism is further applied to preserve numerical accuracy. The division is reformulated as a logarithmic subtraction implemented with a small LOD-driven lookup table, eliminating expensive dividers. For RMSNorm, LOD is further leveraged to decompose the reciprocal square root into mantissa and exponent parts, enabling parallel table lookup and a single multiplication. Based on these optimizations, an FPGA-based pipelined accelerator is implemented, achieving low operator-level latency and power consumption with significantly reduced hardware resource usage while preserving model accuracy. Full article

Although communication quality is widely recognized as a catalyst for workplace innovation, existing research seldom integrates communication quality, trust in leadership, and organizational commitment within a single explanatory framework, particularly in team-based enterprises operating in emerging economies. This study examines how communication quality fosters employee innovation through the sequential mediating roles of trust in leadership and organizational commitment, emphasizing its contribution to sustainable enterprise performance. Rooted in Social Exchange Theory (SET), the study illustrates how transparent, reciprocal, and supportive communication enhances relational trust, strengthens employees’ emotional attachment to their organizations, and creates a climate conducive to creativity and collaborative problem-solving. A quantitative design was employed using data from employees engaged in innovation-driven projects within medium- and large-sized software firms in Turkey’s ICT sector. A total of 339 valid responses were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) to test the hypothesized relationships. The findings demonstrate that communication quality directly promotes employee innovation and indirectly strengthens innovation through trust in leadership and organizational commitment as sequential mediators. Additionally, organizational commitment amplifies the influence of communication quality on innovation, indicating that committed employees more effectively translate constructive communication into innovative behaviors. These results underscore the strategic importance of communicative clarity, relational leadership, and commitment-building practices in shaping resilient, innovation-oriented teams. The study advances SET by identifying trust and commitment as key relational mechanisms through which communication quality drives innovation, offering theoretical enrichment and practical guidance for sustainable human resource management and team-based organizational development. Full article

This study quantifies how pressurized water immersion alters the reciprocating sliding behavior of glass and Kevlar woven fabric-reinforced polymer hybrid composite laminates. Specimens were immersed in deionized water at 10 bar and 25 °C for 0, 7, 14, and 21 days, then tested against a 6 mm 100Cr6 steel ball at 20 N under four regimes that combine 1 or 2 Hz with 10 m or 20 m total sliding. Water uptake rose from 0 to 8.54% by day 21 and followed a short-time Fickian square root of time trend, indicating diffusion-controlled sorption. The coefficient of friction exhibited a robust nonmonotonic response with a pronounced minimum at 14 days that was typically 20 to 40% lower than the unaged reference across frequencies and distances, while 7 days produced a partial decrease and 21 days trended upward. Three-dimensional profilometry showed progressive widening and deepening of wear tracks with immersion, for example, at 1 Hz and 10 m width increased from about 1596 to about 2050 to 2101 μm and depth from about 128 to about 184 to 185 μm, with a transient narrowing at 2 Hz after 7 days. Scanning electron microscopy corroborated a transition from mild plowing to matrix plasticization with fiber–matrix debonding and debris compaction. Beyond geometric wear metrics, this study re-processed the existing profilometry and COF records to derive a moisture-dependent mechanistic approach. Moisture uptake up to 8.54% reorganizes the third body at the interface so that friction drops markedly at 14 days (typically 20–40% below the unaged state), while concurrent matrix plasticization and interface weakening enlarge the wear cross-section extracted from the same 3D maps, decoupling friction from damage width/depth under wet conditioning. Factorial analysis ranked immersion time as the dominant driver of damage for width and depth with frequency as a secondary factor and sliding distance as a minor factor, highlighting immersion-controlled tribological design windows for marine and humid service. Full article

This work assesses the frictional wear of lubricated transmission chains, correlating the coefficient of friction, root mean square (RMS) acoustic emissions, and vibrations induced by friction, incorporating Industrial Internet of Things (IIoT) components. The work is divided into two phases: understanding the frictional interactions between the steel pins of commercial transmission chain and high chrome steel plate (mimicking the interaction between the pin and roller of the chain) using a reciprocating tribometer (20 N, 2.5 Hz, 15.1 stroke length) in the presence of three commercial lubricant aerosols (Grade A, Grade B, and Grade C) and analyzing the frictional wear, sound, and vibration signals generated during the tribo-tests. In the second phase, the findings from the laboratory scale are validated using a commercial transmission chain under aerosol lubrication. Results indicated that the coefficient of friction in the case of dry conditions was 41% higher than that of Grade A aerosol and Grade C aerosol and 28% higher than that of Grade B aerosol. However, the average wear scar diameter on the pin with Grade C (0.401 ± 0.129 mm) was higher than that on the pins with Grades A (0.209 ± 0.159 mm) and B (0.204 ± 0.165 mm). Grade A and Grade B aerosols exhibited similar frictional conditions, while the wear-scar diameter in Grade C was the highest among Grades A and B but still less than in dry conditions. Analyzing the sound and vibrations generated during the friction test, it can be seen that the dry condition produced approximately 60% more sound level than the Grade A and Grade B conditions, and 41% more sound than the Grade C condition. The laboratory results were validated with a real-time transmission chain using an in-house chain wear test rig. Results from the chain wear test rig indicated that the elongation of the chain with Grade B is the least amongst the aerosols and dry conditions. The surface characterizations of the steel pins also indicated intense deep grooves and surface damage in dry conditions, with Grade A exhibiting the most severe damage, followed by Grade C, and the least severe in Grade B. Additionally, dark patches were visually observed on the rollers of the lubricated commercial chains, indicating stressed areas on the rollers, while polished wear was observed on the rollers under dry conditions. Full article

Objective: This study investigated the shaping efficiency of four reciprocating systems—WaveOne Gold, Reciproc Blue, Excalibur, and One Reci—in three dimensional (3D) resin models of natural mandibular premolar teeth with two canals. Methods: Forty 3D-printed mandibular premolars (Vertucci configuration type V) were divided into four groups, each of which was assigned one of the reciprocating systems. According to the manufacturer’s protocols, each canal was prepared, with pre- and post-instrumentation micro computed tomography (micro-CT) scans evaluating canal volume, surface area, percentage of unprepared canal walls, and resin reduction in the pericervical area. Instrumentation time and screw-in sensation were recorded as qualitative performance indicators. Statistical analysis was performed using one-way ANOVA and chi-square tests with a significance of (p < 0.05). Results: All systems increased canal volume and surface area, primarily in the apical third, with Reciproc Blue and One Reci achieving the largest volume. WaveOne Gold had the highest percentage of unprepared walls (27.03%) and Reciproc Blue the lowest (19.65%), though these differences were not statistically significant (p > 0.05). Reciproc Blue caused the highest pericervical resin loss (22.24%), significantly higher than Excalibur (15.09%) and One Reci (15.17%) (p = 0.035). Reciproc Blue exhibited the highest incidence of screw-in sensation (70%), while WaveOne Gold achieved the shortest instrumentation time (86.7 s), although neither variable showed statistical significance. Conclusions: All systems effectively shaped complex canal anatomies, with Reciproc Blue demonstrating the highest dentin removal and WaveOne Gold proving the most time efficient. Clinically, these findings suggest that instrument selection should balance shaping efficiency with dentin preservation. Minimizing unprepared areas and preserving pericervical dentin are essential for enhancing disinfection and reducing the risk of root fractures, ultimately contributing to the long-term success of endodontic treatment. Full article

Monitoring dust on plant canopies around open-pit coal mines is crucial to assessing environmental pollution and developing effective dust suppression strategies. This research focuses on the Ha’erwusu open-pit coal mine in Inner Mongolia, China, using measured dust content on plant canopies and UAV-borne VNIR hyperspectral data as the data sources. The study employed five spectral transformation forms—first derivative (FD), second derivative (SD), logarithm transformation (LT), reciprocal transformation (RT), and square root (SR)—alongside the competitive adaptive reweighted sampling (CARS) method to extract characteristic bands associated with canopy dust. Various regression models, including extreme learning machine (ELM), random forest (RF), partial least squares regression (PLSR), and support vector machine (SVM), were utilized to establish dust inversion models. The spatial distribution of canopy dust was then analyzed. The results demonstrate that the geometric and radiometric correction of the UAV-borne VNIR hyperspectral images successfully restored the true spatial information and spectral features. The spectral transformations significantly enhance the feature information for canopy dust. The CARS algorithm extracted characteristic bands representing 20 to 30% of the total spectral bands, evenly spread across the entire range, thereby reducing the estimation model’s computational complexity. Both feature extraction and model selection influence the inversion accuracy, with the LT-CARS and RF combination offering the best predictive performance. Canopy dust content decreases with increasing distance from the dust source. These findings offer valuable insights for canopy dust retention monitoring and offer a solid foundation for dust pollution management and the development of suppression strategies. Full article

The core of saline–alkali soil improvement lies in salt leaching by water and reducing alkalinity by improved materials such as acid material or desulphurized gypsum. This study conducted simulation experiments to clarify the impact of furfural residue combined with desulfurization gypsum on saline–alkali soil water–salt and infiltration characteristics in Ningxia. Based on a consistent leaching water volume of 4500 m³/hm² and a furfural residue application amount of 7.5 t/hm², the experiment established three desulfurization gypsum application amounts of 15 t/hm², 22.5 t/hm², and 30 t/hm², with a control group that received no improved materials. The effects of different application amounts of desulfurization gypsum on water and salt distributions, alkalinity, infiltration rate, cumulative infiltration volume, and wetting front of saline–alkali soil were elucidated, and the Philip infiltration model was employed to fit the variations in cumulative infiltration volume. The results indicated the following: (1) Compared to the control group, the application of furfural residue and desulfurization gypsum resulted in an average reduction of 36.7% in soil alkalinity. The enhanced hydraulic conductivity of saline–alkali soil promoted the infiltration of water into deeper soil layers. The desalination effect in the 0–60 cm soil layer was significant; however, excessive application of desulfurization gypsum could lead to the accumulation of salts in soil layers below 80 cm. (2) The downward movement depth of the wetting front, cumulative infiltration volume, and infiltration rate all demonstrated a power function relationship with the infiltration time, with a coefficient of determination (R²) greater than 0.97. Additionally, the infiltration rate exhibited a linear correlation with the square root of the reciprocal of infiltration time, achieving an R² exceeding 0.99. (3) The Philip infiltration model is suitable for describing the relationship between cumulative infiltration volume and infiltration time. Therefore, the application of 7.5 t/hm² of furfural residue and 22.5 t/hm² of desulfurization gypsum can effectively improve the saline–alkali soils in Ningxia. Full article

Recent advancements in hyperspectral imaging have significantly increased the acquired data volume, creating a need for more efficient compression methods for handling the growing storage and transmission demands. These challenges are particularly critical for onboard satellite systems, where power and computational resources are limited, and real-time processing is essential. In this article, we present a novel FPGA-based hardware acceleration of a near-lossless compression technique for hyperspectral images by leveraging a division-free quadrature-based square rooting method. In this regard, the two division operations inherent in the original approach were replaced with pre-computed reciprocals, multiplications, and a geometric series expansion. Optimized for real-time applications, the synthesis results show that our approach achieves a high throughput of 1611.77 Mega Samples per second (MSps) and a low power requirement of 0.886 Watts on the economical Cyclone V FPGA. This results in an efficiency of 1819.15 MSps/Watt, which, to the best of our knowledge, surpasses recent state-of-the-art hardware implementations in the context of near-lossless compression of hyperspectral images. Full article

Rapidly obtaining information on the content and spatial distribution of soil organic carbon (SOC) in farmland is crucial for evaluating regional soil quality, land degradation, and crop yield. This study focuses on mountain soils in various crop cultivation areas in Shangzhou District, Shangluo City, Southern Shaanxi, utilizing ZY1-02D hyperspectral satellite imagery, field-measured hyperspectral data, and field sampling data to achieve precise inversion and spatial mapping of the SOC content. First, to address spectral bias caused by environmental factors, the Spectral Space Transformation (SST) algorithm was employed to establish a transfer relationship between measured and satellite image spectra, enabling systematic correction of the image spectra. Subsequently, multiple spectral transformation methods, including continuous wavelet transform (CWT), reciprocal, first-order derivative, second-order derivative, and continuum removal, were applied to the corrected spectral data to enhance their spectral response characteristics. For feature band selection, three methods were utilized: Variable Importance Projection (VIP), Competitive Adaptive Reweighted Sampling (CARS), and Stepwise Projection Algorithm (SPA). SOC content prediction was conducted using three models: partial least squares regression (PLSR), stepwise multiple linear regression (Step-MLR), and random forest (RF). Finally, leave-one-out cross-validation was employed to optimize the L4-CARS-RF model, which was selected for SOC spatial distribution mapping. The model achieved a coefficient of determination (R²) of 0.81, a root mean square error of prediction (RMSEP) of 1.54 g kg⁻¹, and a mean absolute error (MAE) of 1.37 g kg⁻¹. The results indicate that (1) the Spectral Space Transformation (SST) algorithm effectively eliminates environmental interference on image spectra, enhancing SOC prediction accuracy; (2) continuous wavelet transform significantly reduces data noise compared to other spectral processing methods, further improving SOC prediction accuracy; and (3) among feature band selection methods, the CARS algorithm demonstrated the best performance, achieving the highest SOC prediction accuracy when combined with the random forest model. These findings provide scientific methods and technical support for SOC monitoring and management in mountainous areas and offer valuable insights for assessing the long-term impacts of different crops on soil ecosystems. Full article