Search Results (49)

This study presents the optimization of a laser ablation process designed to achieve the precise removal of polyamide coatings from ultra-thin platinum wires. Removing polymer coatings is a critical challenge in high-reliability manufacturing processes such as aerospace thermocouple fabrication. The ablation process must not only ensure the complete removal of the polyamide insulation but also maintain the tensile strength of the wire to withstand mechanical handling in subsequent manufacturing stages. Additionally, the exposed platinum surface must exhibit low surface roughness to enable effective soldering and be free of thermal damage or residual debris to pass strict visual inspections. The wires have a total diameter of 65 µm, consisting of a 50 µm platinum core encased in a 15 µm polyamide coating. By utilizing a UV laser with a wavelength of 355 nm, average power of 3 W, a repetition rate range of 20 to 200 kHz, and a high-speed marking system, the process parameters were systematically refined. Initial attempts to perform the ablation in an air medium were unsuccessful due to inadequate thermal control and incomplete removal of the polyamide coating. Hence, a water-assisted ablation technique was explored to address these limitations. Experimental results demonstrated that a scanning speed of 1200 mm/s, coupled with a line spacing of 1 µm and a single ablation pass, resulted in complete coating removal while ensuring the integrity of the platinum substrate. The incorporation of a water layer above the ablation region was considered crucial for effective heat dissipation, preventing substrate overheating and ensuring uniform ablation. The laser’s spot diameter of 20 µm in air and a focal length of 130 mm introduced challenges related to overlap control between successive passes, requiring precise calibration to maintain consistency in coating removal. This research demonstrates the feasibility and reliability of water-assisted laser ablation as a method for a high-precision, non-contact coating material. Full article

Ultrafast laser welding technology for transparent materials has developed rapidly in recent years; however, high-strength non-optical contact transparent material welding has been a challenge. This work presents a welding method for silica glass using a double-pulse femtosecond (fs) laser and optimizes the laser processing parameters to enhance the welding performance. The welding characteristics of silica glass are analyzed under different time delays by controlling the pulse delay of double pulses. In addition to comprehensively study the influence of various experimental conditions on double-pulse fs laser welding, multi-level tests are designed for five factors, including average laser power, pulse delay, scanning interval, scanning speed, and repetition rate. Finally, by optimizing the parameters, a welding strength of 57.15 MPa is achieved at an average power of 3500 mW, repetition rate of 615 kHz, pulse delay of 66.7 ps, scanning interval of 10 µm, and scanning speed of 1000 µm/s. This work introduces a new approach to glass welding and presents optimal parameters for achieving higher welding strength, which can be widely used in aerospace, microelectronic packaging, microfluidics, and other fields. Full article

The popularity of mobile laser scanning systems as a surveying tool is growing among construction contractors, architects, land surveyors, and urban planners. The user-friendliness and rapid capture of precise and complete data on places and objects make them serious competitors for traditional surveying approaches. Considering the low cost and constantly improving availability of Mobile Laser Scanning (MLS), mainly handheld surveying tools, the measurement possibilities seem unlimited. We conducted a comprehensive investigation into the quality and accuracy of a point cloud generated by a recently marketed low-cost mobile surveying system, the MandEye MLS. The purpose of the study is to conduct exhaustive laboratory tests to determine the actual metrological characteristics of the device. The test facility was the surveying laboratory of the University of Agriculture in Kraków. The results of the MLS measurements (dynamic and static) were juxtaposed with a reference base, a geometric system of reference points in the laboratory, and in relation to a reference point cloud from a higher-class laser scanner: Leica ScanStation P40 TLS. The Authors verified the geometry of the point cloud, technical parameters, and data structure, as well as whether it can be used for surveying and mapping objects by assessing the point cloud density, noise and measurement errors, and detectability of objects in the cloud. Full article

To improve the laser cleaning surface quality of rust layers in Q390 steel, a method of determining the optimal cleaning parameters is proposed that is based on response surface methodology and the second-generation non-dominated sorting genetic algorithm (NSGA-II). It involves constructing a mathematical model of the input variables (laser power, cleaning speed, scanning speed, and repetition frequency) and the objective values (surface oxygen content, rust layer removal rate, and surface roughness). The effects of the laser cleaning process parameters on the cleaning surface quality were analyzed in our study, and accordingly, NSGA-II was used to determine the optimal process parameters. The results indicate that the optimal process parameters are as follows: a laser power of 44.99 W, cleaning speed of 174.01 mm/min, scanning speed of 3852.03 mm/s, and repetition frequency of 116 kHz. With these parameters, the surface corrosion is effectively removed, revealing a distinct metal luster and meeting the standard for surface treatment before welding. Full article

This study presents fibers based on methacrylic acid–methyl methacrylate (Eudragit L100) as Cu(II) adsorbents, resulting in antimicrobial complexes. Eudragit L100, an anionic copolymer synthesized by radical polymerization, was electrospun in dimethylformamide (DMF) and ethanol (EtOH). The electrospinning process was optimized through a 2²-factorial design, with independent variables (copolymer concentration and EtOH/DMF volume ratio) and three repetitions at the central point. The smallest average fiber diameter (259 ± 53 nm) was obtained at 14% w/v Eudragit L100 and 80/20 EtOH/DMF volume ratio. The fibers were characterized using scanning electron microscopy (SEM), infrared spectroscopy in attenuated total reflectance mode (FTIR-ATR), and differential scanning calorimetry (DSC). The pseudo-second-order mechanism explained the kinetic adsorption toward Cu(II). The fibers exhibited a maximum adsorption capacity (q_e) of 43.70 mg/g. The DSC analysis confirmed the Cu(II) absorption, indicating complexation between metallic ions and copolymer networks. The complexed fibers showed a lower degree of swelling than the non-complexed fibers. The complexed fibers exhibited bacteriostatic activity against Gram-negative (Pseudomonas aeruginosa) and Gram-positive (Staphylococcus aureus) bacteria. This study successfully optimized the electrospinning process to produce thin fibers based on Eudragit L100 for potential applications as adsorbents for Cu(II) ions in aqueous media and for controlling bacterial growth. Full article

In this paper, we propose a novel, vision-transformer-based end-to-end pose estimation method, LidPose, for real-time human skeleton estimation in non-repetitive circular scanning (NRCS) lidar point clouds. Building on the ViTPose architecture, we introduce novel adaptations to address the unique properties of NRCS lidars, namely, the sparsity and unusual rosetta-like scanning pattern. The proposed method addresses a common issue of NRCS lidar-based perception, namely, the sparsity of the measurement, which needs balancing between the spatial and temporal resolution of the recorded data for efficient analysis of various phenomena. LidPose utilizes foreground and background segmentation techniques for the NRCS lidar sensor to select a region of interest (RoI), making LidPose a complete end-to-end approach to moving pedestrian detection and skeleton fitting from raw NRCS lidar measurement sequences captured by a static sensor for surveillance scenarios. To evaluate the method, we have created a novel, real-world, multi-modal dataset, containing camera images and lidar point clouds from a Livox Avia sensor, with annotated 2D and 3D human skeleton ground truth. Full article

Photodynamic therapy (PDT) is increasingly used in modern medicine. It has found application in the treatment of breast cancer. The most common cancer among women is breast cancer. We collected cancer cells from the breast from the material received after surgery. We focused on tumors that were larger than 10 mm in size. Breast cancer tissues for this quantitative non-contrast magnetic resonance imaging (MRI) study could be seen macroscopically. The current study aimed to present findings on quantitative non-contrast MRI of breast cancer cells post-PDT through the evaluation of relaxation times. The aim of this work was to use and optimize a 1.5 T MRI system. MRI tests were performed using a clinical scanner, namely the OPTIMA MR360 manufactured by General Electric HealthCare. The work included analysis of T1 and T2 relaxation times. This analysis was performed using the MATLAB package (produced by MathWorks). The created application is based on medical MRI images saved in the DICOM3.0 standard. T1 and T2 measurements were subjected to the Shapiro–Wilk test, which showed that both samples belonged to a normal distribution, so a parametric t-test for dependent samples was used to test for between-sample variability. The study included 30 sections tested in 2 stages, with consistent technical parameters. For T1 measurements, 12 scans were performed with varying repetition times (TR) and a constant echo time (TE) of 3 ms. For T2 measurements, 12 scans were performed with a fixed repetition time of 10,000 ms and varying echo times. After treating samples with PpIX disodium salt and bubbling with pure oxygen, PDT irradiation was applied. The cell relaxation time after therapy was significantly shorter than the cell relaxation time before PDT. The cells were exposed to PpIX disodium salt as the administered pharmacological substance. The study showed that the therapy significantly affected tumor cells, which was confirmed by a significant reduction in tumor cell relaxation time on the MRI results. Full article

Autism spectrum disorder (ASD) is a developmental disorder that encompasses difficulties in communication (both verbal and non-verbal), social skills, and repetitive behaviors. The diagnosis of autism spectrum disorder typically involves specialized procedures and techniques, which can be time-consuming and expensive. The accuracy and efficiency of the diagnosis depend on the expertise of the specialists and the diagnostic methods employed. To address the growing need for early, rapid, cost-effective, and accurate diagnosis of autism spectrum disorder, there has been a search for advanced smart methods that can automatically classify the disorder. Machine learning offers sophisticated techniques for building automated classifiers that can be utilized by users and clinicians to enhance accuracy and efficiency in diagnosis. Eye-tracking scan paths have emerged as a tool increasingly used in autism spectrum disorder clinics. This methodology examines attentional processes by quantitatively measuring eye movements. Its precision, ease of use, and cost-effectiveness make it a promising platform for developing biomarkers for use in clinical trials for autism spectrum disorder. The detection of autism spectrum disorder can be achieved by observing the atypical visual attention patterns of children with the disorder compared to typically developing children. This study proposes a deep learning model, known as T-CNN-Autism Spectrum Disorder (T-CNN-ASD), that utilizes eye-tracking scans to classify participants into ASD and typical development (TD) groups. The proposed model consists of two hidden layers with 300 and 150 neurons, respectively, and underwent 10 rounds of cross-validation with a dropout rate of 20%. In the testing phase, the model achieved an accuracy of 95.59%, surpassing the accuracy of other machine learning algorithms such as random forest (RF), decision tree (DT), K-Nearest Neighbors (KNN), and multi-layer perceptron (MLP). Furthermore, the proposed model demonstrated superior performance when compared to the findings reported in previous studies. The results demonstrate that the proposed model can accurately classify children with ASD from those with TD without human intervention. Full article

This study is a post-hoc examination of baseline MRI data from a clinical trial investigating the efficacy of repetitive transcranial magnetic stimulation (rTMS) as a treatment for patients with mild–moderate Alzheimer’s disease (AD). Herein, we investigated whether the analysis of baseline MRI data could predict the response of patients to rTMS treatment. Whole-brain T1-weighted MRI scans of 75 participants collected at baseline were analyzed. The analyses were run on the gray matter (GM) and white matter (WM) of the left and right dorsolateral prefrontal cortex (DLPFC), as that was the rTMS application site. The primary outcome measure was the Alzheimer’s disease assessment scale—cognitive subscale (ADAS-Cog). The response to treatment was determined based on ADAS-Cog scores and secondary outcome measures. The analysis of covariance showed that responders to active treatment had a significantly lower baseline GM volume in the right DLPFC and a higher GM asymmetry index in the DLPFC region compared to those in non-responders. Logistic regression with a repeated five-fold cross-validated analysis using the MRI-driven features of the initial 75 participants provided a mean accuracy of 0.69 and an area under the receiver operating characteristic curve of 0.74 for separating responders and non-responders. The results suggest that GM volume or asymmetry in the target area of active rTMS treatment (DLPFC region in this study) may be a weak predictor of rTMS treatment efficacy. These results need more data to draw more robust conclusions. Full article

Information on a crop’s three-dimensional (3D) structure is important for plant phenotyping and precision agriculture (PA). Currently, light detection and ranging (LiDAR) has been proven to be the most effective tool for crop 3D characterization in constrained, e.g., indoor environments, using terrestrial laser scanners (TLSs). In recent years, affordable laser scanners onboard unmanned aerial systems (UASs) have been available for commercial applications. UAS laser scanners (ULSs) have recently been introduced, and their operational procedures are not well investigated particularly in an agricultural context for multi-temporal point clouds. To acquire seamless quality point clouds, ULS operational parameter assessment, e.g., flight altitude, pulse repetition rate (PRR), and the number of return laser echoes, becomes a non-trivial concern. This article therefore aims to investigate DJI Zenmuse L1 operational practices in an agricultural context using traditional point density, and multi-temporal canopy height modeling (CHM) techniques, in comparison with more advanced simulated full waveform (WF) analysis. Several pre-designed ULS flights were conducted over an experimental research site in Fargo, North Dakota, USA, on three dates. The flight altitudes varied from 50 m to 60 m above ground level (AGL) along with scanning modes, e.g., repetitive/non-repetitive, frequency modes 160/250 kHz, return echo modes (1n), (2n), and (3n), were assessed over diverse crop environments, e.g., dry corn, green corn, sunflower, soybean, and sugar beet, near to harvest yet with changing phenological stages. Our results showed that the return echo mode (2n) captures the canopy height better than the (1n) and (3n) modes, whereas (1n) provides the highest canopy penetration at 250 kHz compared with 160 kHz. Overall, the multi-temporal CHM heights were well correlated with the in situ height measurements with an R² (0.99–1.00) and root mean square error (RMSE) of (0.04–0.09) m. Among all the crops, the multi-temporal CHM of the soybeans showed the lowest height correlation with the R² (0.59–0.75) and RMSE (0.05–0.07) m. We showed that the weaker height correlation for the soybeans occurred due to the selective height underestimation of short crops influenced by crop phonologies. The results explained that the return echo mode, PRR, flight altitude, and multi-temporal CHM analysis were unable to completely decipher the ULS operational practices and phenological impact on acquired point clouds. For the first time in an agricultural context, we investigated and showed that crop phenology has a meaningful impact on acquired multi-temporal ULS point clouds compared with ULS operational practices revealed by WF analyses. Nonetheless, the present study established a state-of-the-art benchmark framework for ULS operational parameter optimization and 3D crop characterization using ULS multi-temporal simulated WF datasets. Full article

Allometric equations are the most common way of assessing Aboveground biomass (AGB) but few exist for savanna ecosystems. The need for the accurate estimation of AGB has triggered an increase in the amount of research towards the 3D quantification of tree architecture through Terrestrial Laser Scanning (TLS). Quantitative Structure Models (QSMs) of trees have been described as the most accurate way. However, the accuracy of using QSMs has yet to be established for the savanna. We implemented a non-destructive method based on TLS and QSMs. Leaf-off multi scan TLS point clouds were acquired in 2015 in Kruger National Park, South Africa using a Riegl VZ1000. The 3D data covered 80.8 ha with an average point density of 315.3 points/m². Individual tree segmentation was applied using the comparative shortest-path algorithm, resulting in 1000 trees. As 31 trees failed to be reconstructed, we reconstructed optimized QSMs for 969 trees and the computed tree volume was converted to AGB using a wood density of 0.9. The TLS-derived AGB was compared with AGB from three allometric equations. The best modelling results had an RMSE of 348.75 kg (mean = 416.4 kg) and a Concordance Correlation Coefficient (CCC) of 0.91. Optimized QSMs and model repetition gave robust estimates as given by the low coefficient of variation (CoV = 19.9% to 27.5%). The limitations of allometric equations can be addressed by the application of QSMs on high-density TLS data. Our study shows that the AGB of savanna vegetation can be modelled using QSMs and TLS point clouds. The results of this study are key in understanding savanna ecology, given its complex and dynamic nature. Full article

Three-dimensional point cloud registration is a fundamental task for localization and mapping in autonomous navigation applications. Over the years, registration algorithms have evolved; nevertheless, several challenges still remain. Recently, non-repetitive scanning LiDAR sensors have emerged as a promising 3D data acquisition tool. However, the feasibility of this type of sensor to leverage robust point cloud registration still needs to be ascertained. In this paper, we explore the feasibility of one such LiDAR sensor with a Spirograph-type non-repetitive scanning pattern for robust 3D point cloud registration. We first characterize the data of this unique sensor; then, utilizing these results, we propose a new 3D point cloud registration method that exploits the unique scanning pattern of the sensor to register successive 3D scans. The characteristic equations of the unique scanning pattern, determined during the characterization phase, are used to reconstruct a perfect scan at the target distance. The real scan is then compared with this reconstructed scan to extract objects in the scene. The displacement of these extracted objects with respect to the center of the unique scanning pattern is compared in successive scans to determine the transformations that are then used to register these scans. The proposed method is evaluated on two real and different datasets and compared with other state-of-the-art registration methods. After analysis, the performance (localization and mapping results) of the proposed method is further improved by adding constraints like loop closure and employing a Curve Fitting Derivative Filter (CFDT) to better estimate the trajectory. The results clearly demonstrate the suitability of the sensor for such applications. The proposed method is found to be comparable with other methods in terms of accuracy but surpasses them in performance in terms of processing time. Full article

Nuclear power components contain radioactivity on their surfaces after long-term service, which can be harmful to personnel and the environment during maintenance, dismantling, and decommissioning. In this experiment, laser decontamination technology is utilized to remove radioactivity from their surfaces. In order to meet the actual needs, a laser decontamination process without spot overlapping has been studied. Under the same equipment conditions, the decontamination efficiency of the non-spot overlapping process is 10 times higher than that of the spot overlapping process. Alloy 690 is used as the test substrate, and non-radioactive specimens are prepared by simulating primary-circuit hydrochemical conditions. The surface morphology, elemental composition, and phase composition of the specimens before and after laser decontamination are investigated with SEM and XRD using the single-pulse experiment and power single-factor experiment methods, and the laser decontamination effect was evaluated. The results show that the decontamination efficiency reached 10.8 m²/h under the conditions of a pulse width of 500 ns, a laser repetition frequency of 40 kHz, a scanning speed of 15,000 mm/s, and a line spacing of 0.2 mm, according to which the removal effect was achieved when the laser power was 160 W and the oxygen content on the surface was 6.29%; additionally, there were no oxide phases in the XRD spectra after decontamination. Therefore, the laser cleaning process without spot overlap can provide reference for future practical operations to achieve efficient removal of radioactivity from nuclear power components. Full article

Large-area nanostructuring of glasses using intense laser beams is a challenging task due to the material’s extreme non-linear absorption of laser energy. Precise optimization of the process parameters is essential for fabricating nanostructures with large-area coverage. In this study, we report the findings on creating high-spatial-frequency LIPSS (HSFL) on borosilicate glass through direct laser writing, using a femtosecond laser with a wavelength λ = 800 nm, pulse duration τ = 35 fs, and repetition frequency f_rep = 1 kHz. We measured the single-pulse ablation threshold and incubation factor of Borosilicate glasses to achieve high-precision control of the large-area surface structuring. Single-spot experiments indicated that, when there was higher fluence and a larger number of irradiated laser pulses, a melt formation inside the irradiated area limited the uniformity of LIPSS formation. Additionally, the orientation of the scan axis with the laser beam polarization was found to significantly influence the uniformity of LIPSS generated along the scan line, with more redeposition and melt formation when the scan axis was perpendicular to the laser beam polarization. For large-area processing, the borosilicate glass surface was scanned line-by-line by the laser beam, with a scan orientation parallel to the polarization of the laser. The optical characterization revealed that the transmittance and reflectance of the borosilicate glass decreased significantly after processing. Additionally, the surface’s wettability changed from hydrophilic to super-hydrophilic after processing. These chemical contamination-free and uniformly distributed structures have potential applications in optics, microfluidics, photovoltaics, and biomaterials. Full article

To tackle the problems of Doppler spectrum, aliasing caused by azimuth beam scanning and azimuthal serious non-uniform sampling in squint sliding spotlight synthetic aperture radar (SAR) with varying repetition frequency technology, the azimuth sampling method of sub-aperture block-varying pulse repetition frequency (SBV-PRF) is proposed, where the sub-aperture division judgement makes the azimuth acquisition time of each sub-block small enough so that the Doppler bandwidth caused by the Doppler center change can be ignored. Based on the echo signal characteristics of a SBV-PRF transmission scheme, an azimuth pre-processing method combining SBV-PRF transmission scheme with sub-aperture division is proposed. Using this method, de-skewing is first performed on each set of sub-aperture data to eliminate the additional Doppler bandwidth introduced by the squint angle, and then the azimuth signal resampling is performed to ensure different sub-aperture data have the same sampling rate. The SBV-PRF technology reduces the difficulty of azimuth signal pre-processing while ensuring the complete acquisition of the complete echo data of the squint sliding spotlight mode. The effectiveness of the SBV-PRF system design and the signal processing method is verified by the point target echo simulation and imaging simulation results. Full article