Search Results (159)

The display contrast and efficiency of power consumption for LCDs (Liquid Crystal Displays) continue to attract attention from both industry and academia. Local dimming approaches for direct-type backlight modules (BLMs, also referred to as backlight units, BLUs) are regarded as a potential solution. The purpose of this study is to explore how to optimize the local dimming method of LCD to achieve higher contrast and lower power consumption through deep learning techniques. In this paper, we propose a local dimming approach with dual modulation for LCD-LED displays based on VGG19 and UNet models. Experimental results have shown that this method not only reconstructs the input image into an HDR (High Dynamic Range) image but also automatically generates a control image for the backlight module and LCD panel. In addition, the proposed method can effectively improve the contrast and reduce the power consumption of the LCD in the absence of a public training dataset. Our method can achieve the best performance in MSE and HDR-VDP-2 among eight different combinations of mask and pre-training. Using deep learning techniques, this study has successfully optimized the local dimming approach of LCDs and demonstrated its benefits in improving contrast and reducing power consumption. Full article

The exposure parameters in stereolithography with liquid crystal display (SLA-LCD) influence the functional properties of photopolymers, which is particularly important for tribological applications. In this study, the influence of the exposure time of the layers (2–8 s) on the surface topography (ISO 25178), Brinell hardness (HB) and chemical structure (FTIR spectroscopy) of UV-cured resin samples is investigated. Both insufficient and excessive UV irradiation led to undesirable effects ranging from incomplete cross-linking and surface irregularities to excessive curing, micro-cracking and increased surface kurtosis (high Sku values). The most balanced mechanical and topographical performance was observed at a layer exposure time of 6 s, characterised by low Spk values, uniform surface texture and high cohesion between layers. FTIR analysis confirmed the progressive cross-linking with increasing exposure time. The results show that precise control of irradiation parameters enables optimisation of the interrelationships between microstructure, mechanical properties and surface functionality, which is critical for improving the durability and performance of components operating under boundary or mixed lubrication. Full article

Bio-based polymeric composites were prepared by dispersing different amounts of olive pit (OP) powder within an acrylate epoxidized soybean oil (AESO) photocurable resin using tetrahydrofurfuryl acrylate (THFA) as diluent and (2,4,6-trimethylbenzoyl), phosphine oxide (BAPO) as photo-initiator, and they were photocured by Vat Photopolymerization (VP) using a Liquid Crystal Display (LCD) 3D printer. Formulation viscosity was studied because of its important role in a VP process able to influence the printability of the final parts. Different 3D printed architectures were successfully realized with good resolution and accuracy, high level of detail, and flexibility. The effect of OP addition was investigated by thermal (TGA and DSC), morphological (SEM and PSD), viscoelastic (DMA), and mechanical (tensile testing) characterization. The filler led to an increase in the T_g, storage modulus, and tensile properties, underlining the stiffening effect induced by the OP particles onto the polymeric starting resin. This underlines the possibility to apply these bio-based composites in many application fields by valorizing agro-wastes, developing more sustainable materials, and taking advantages of VP 3D printing, such as low costs, minimal wastage, and customized geometry. Biocompatibility tests were also successfully carried out. The results clearly indicate that the AESO-based composites promote cell adhesion and viability. Full article

Characters on liquid crystal display (LCD) interfaces often appear densely arranged, with complex image backgrounds and significant variations in target appearance, posing considerable challenges for visual detection. To improve the accuracy and robustness of character detection, this paper proposes an enhanced character detection algorithm based on the DBNet framework, named EBiDNet (EfficientNetV2 and BiFPN Enhanced DBNet). This algorithm integrates machine vision with deep learning techniques and introduces the following architectural optimizations. It employs EfficientNetV2-S, a lightweight, high-performance backbone network, to enhance feature extraction capability. Meanwhile, a bidirectional feature pyramid network (BiFPN) is introduced. Its distinctive symmetric design ensures balanced feature propagation in both top-down and bottom-up directions, thereby enabling more efficient multiscale contextual information fusion. Experimental results demonstrate that, compared with the original DBNet, the proposed EBiDNet achieves a 9.13% increase in precision and a 14.17% improvement in F1-score, while reducing the number of parameters by 17.96%. In summary, the proposed framework maintains lightweight design while achieving high accuracy and strong robustness under complex conditions. Full article

The absence of parental presence has a direct impact on the emotional stability and social routines of children, especially during extended periods of separation from their family environment, as in the case of daycare centers, hospitals, or when they remain alone at home. At the same time, the technology currently available to provide emotional support in these contexts remains limited. In response to the growing need for emotional support and companionship in child care, this project proposes the development of a multi-platform software architecture based on artificial intelligence (AI), designed to be integrated into humanoid robots that assist children between the ages of 6 and 14. The system enables daily verbal and non-verbal interactions intended to foster a sense of presence and personalized connection through conversations, games, and empathetic gestures. Built on the Robot Operating System (ROS), the software incorporates modular components for voice command processing, real-time facial expression generation, and joint movement control. These modules allow the robot to hold natural conversations, display dynamic facial expressions on its LCD (Liquid Crystal Display) screen, and synchronize gestures with spoken responses. Additionally, a graphical interface enhances the coherence between dialogue and movement, thereby improving the quality of human–robot interaction. Initial evaluations conducted in controlled environments assessed the system’s fluency, responsiveness, and expressive behavior. Subsequently, it was implemented in a pediatric hospital in Guayaquil, Ecuador, where it accompanied children during their recovery. It was observed that this type of artificial intelligence-based software, can significantly enhance the experience of children, opening promising opportunities for its application in clinical, educational, recreational, and other child-centered settings. Full article

This study aimed to develop dental resin composites containing ZnO/SiO₂ ceramic particles as an antimicrobial filler for producing provisional dental restorations using the lithography-based liquid crystal display (LCD) 3D printing technique. Three types of dental resin-ceramic composites with different filler contents (0 wt%, 5 wt%, and 10 wt%) were prepared to offer high antimicrobial efficacy. Printing parameters, particularly off-time, were optimized for each composition to achieve high-quality prints. Mechanical testing demonstrated increased hardness and elastic modulus. In addition, the 10 vol% composite exhibited a three-point flexural strength of 113.4 MPa, exceeding the 100 MPa requirement specified in ISO 4049:2019 for provisional dental materials. Antimicrobial testing showed a significant reduction in Streptococcus mutans colonies, with up to 84.4% decrease for the 10 vol% composite compared to the unfilled resin. A provisional 3-unit bridge was successfully printed using the 10 vol% composite, demonstrating practical applicability. Full article

Three-dimensional (3D) printing offers new opportunities in surgical planning and medical education, yet high costs and technological complexity often limit its widespread use, especially in low-resource settings. This study presents a personalized, cost-effective, and anatomically accurate liver model designed using open-source tools and affordable 3D-printing techniques. Segmentation of hepatic CT images was performed in 3D Slicer using a region-growing method, and the resulting models were optimized and exported as STL files. The external mold was printed with Fused Deposition Modeling (FDM) using PLA+, while internal structures such as vessels and tumors were fabricated via Liquid Crystal Display (LCD) printing using PLA Pro resin. The final assembly was cast in food-grade gelatin to mimic liver tissue texture. The complete model was produced for under USD 50, with an average total production time of under 128 h. An exploratory pedagogical evaluation with five medical trainees yielded high Likert scores for anatomical understanding (4.6), spatial awareness (4.4), planning confidence (4.2), and realism (4.4). This model demonstrated utility in preoperative discussions and training simulations. The proposed workflow enables the fabrication of low-cost, realistic hepatic phantoms suitable for education and surgical rehearsal, promoting the integration of 3D printing into everyday clinical practice. Full article

The dimensional accuracy of the intaglio surface of complete dentures fabricated using liquid crystal display (LCD) three-dimensional (3D) printing might be influenced by the build angle and post-processing storage conditions. This study evaluated the effect of build angle and natural light exposure duration on the intaglio surface trueness of maxillary complete denture bases. Standardized denture base designs (2 mm uniform thickness) were fabricated using an LCD 3D printer (Lilivis Print; Huvitz, Seoul, Republic of Korea) at build angles of 0°, 45°, and 90° (n = 7 per group). All specimens were printed using the same photopolymer resin (Tera Harz Denture; Graphy, Seoul, Republic of Korea) and identical printing parameters, followed by ultrasonic cleaning and ultraviolet post-curing. Specimens were stored under controlled light-emitting diode lighting and exposed to natural light (400–800 lux) for 0, 14, or 30 days. The intaglio surfaces were scanned and superimposed on the original design data, following the International Organization for Standardization 12836. Quantitative assessment included root mean square deviation, mean deviation, and tolerance percentage. Statistical analyses were performed using one-way analysis of variance and paired t-tests (α = 0.05). Build angle and light exposure duration significantly affected surface trueness (p < 0.05). The 90° build angle group exhibited the highest accuracy and dimensional stability, while the 0° group showed the greatest deviations (p < 0.05). These findings underscore the importance of optimizing build orientation and storage conditions in denture 3D printing. Full article

With the rise of smart manufacturing, defect detection in small-size liquid crystal display (LCD) screens has become essential for ensuring product quality. Traditional manual inspection is inefficient and labor-intensive, making it unsuitable for modern automated production. Although machine vision techniques offer improved efficiency, the lack of high-quality defect datasets limits their performance. To overcome this, we propose a symmetry-aware generative framework, the Squeeze-and-Excitation Wasserstein GAN with Gradient Penalty and Visual Geometry Group(VGG)-based perceptual loss (SWG-VGG), for realistic defect image synthesis.By leveraging the symmetry of feature channels through attention mechanisms and perceptual consistency, the model generates high-fidelity defect images that align with real-world structural patterns. Evaluation using the You Only Look Once version 8(YOLOv8) detection model shows that the synthetic dataset improves mAP@0.5 to 0.976—an increase of 10.5% over real-data-only training. Further assessment using Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index Measure (SSIM), Root Mean Square Error (RMSE), and Content Similarity (CS) confirms the visual and structural quality of the generated images.This symmetry-guided method provides an effective solution for defect data augmentation and aligns closely with Symmetry’s emphasis on structured pattern generation in intelligent vision systems. Full article

This study evaluates the manufacturing accuracy of Merlon fracture models produced using two vat-photopolymerization-based three-dimensional (3D) printers: digital light processing (DLP) and liquid-crystal display (LCD). The Merlon fracture model is used to assess dimensional precision and machining accuracy. The root mean square (RMS) values, wall and bottom thicknesses, and field-emission scanning electron microscopy images are analyzed. The DLP-based printers exhibit lower RMS values and superior accuracy compared with LCD-based printing and subtractive milling. Polymer-based slurries for permanent dental applications exhibit better dimensional stability than those for temporary restorations. This study also highlights the significant impact of postprocessing and cleaning procedures on the final model accuracy. These findings suggest that optimizing the postprocessing parameters is crucial for enhancing the precision of 3D-printed dental restorations. The Merlon fracture model is a viable method for evaluating additive manufacturing accuracy, contributing to the improved clinical application of vat photopolymerization in dental prosthetics. Full article

Conventional etchants for multi-metal/alloy stacked structures often suffer from nonuniform etching, residual layers, or undercutting, failing to meet high-generation production standards. This study presents a stable copper-molybdenum (Cu-Mo) etchant with extended bath life for thin film transistor liquid crystal display (TFT-LCD) applications, achieved through compositional optimization. Systematic investigations have been conducted on the effects of etching time, copper ion (Cu²⁺) loading (bath life) and storage time on the etch performance, alongside evaluations of sudden-eruption point and material compatibility. Results demonstrate that over-etching beyond the “detected endpoint” by 10% to 90% maintains critical dimension (CD) bias and taper angle of MoNiTi(MTD)/Cu/MTD three-layer and Cu/MTD two-layer within process specifications, as well as the difference between the CD bias of the three-layer and two-layer structures at the same over-etch time. The optimized formulation exhibits a 20% broader process window and 20% longer bath life compared to the process-of-record (POR) etchant. Shelf stability exceeds 15 days with minimal performance degradation, while maintaining compatibility with industrial equipment materials. These advancements address key challenges in high-precision etching for advanced TFT-LCD manufacturing, providing a scalable solution for next-generation display production. Full article

Pixel circuits are key components of flat panel displays, including liquid crystal displays (LCDs), organic light-emitting diode displays (OLEDs), and micro light-emitting diode displays (micro-LEDs). Depending on the active layer material of the thin film transistor (TFT), pixel circuits are categorised into amorphous silicon (a-Si) technology, low-temperature polycrystalline silicon (LTPS) technology, metal oxide (MO) technology, and low-temperature polycrystalline silicon and oxide (LTPO) technology. In this review, we outline the fundamental display principles and four major TFT technologies, covering conventional single-gated TFTs to novel two-gated TFTs. We focus on novel pixel circuits for three glass-based display technologies with additional mention of pixel circuits for silicon-based OLED and silicon-based micro-LED. Full article

Fungal infections pose a significant global health problem, affecting 20–25% of the population and contributing to over 3.75 million deaths annually. Clotrimazole (CLO) is a widely used topical antifungal drug, but its efficacy is limited by poor penetration through the stratum corneum. Microneedle (MN) systems, composed of micron-scale structures arranged on a patch, offer a promising strategy to overcome the outermost skin barrier and enhance drug penetration into deeper layers. However, optimizing MN design, particularly in terms of size, shape, and fabrication technology, is essential for efficient drug delivery. This study aimed to develop CLO-coated MN systems using an Liquid Crystal Display (LCD)-based 3D printing technique and a thin-film dip-coating method. A comprehensive optimization of printing parameters, including anti-aliasing, layer thickness, curing time, and printing angle, was conducted to ensure the desired mechanical properties. The optimized MNs were coated with either suspension or ethanol-based CLO-hydrogels, with ethanol hydrogel demonstrating superior characteristics. Additionally, the study investigated how microneedle geometry and coating formulation influenced drug release. Antifungal activity against reference and clinical origin Candida albicans strains varied significantly depending on the coating formulation. Finally, the acute toxicity test confirmed no significant toxic effects on Aliivibrio fischeri, indicating the potential biocompatibility and safety of the developed MN-based drug delivery system. Full article

In brain–computer interface (BCI) systems, steady-state visual-evoked potentials (SSVEP) and P300 responses have achieved widespread implementation owing to their superior information transfer rates (ITR) and minimal training requirements. These neurophysiological signals have exhibited robust efficacy and versatility in external device control, demonstrating enhanced precision and scalability. However, conventional implementations predominantly utilise liquid crystal display (LCD)-based visual stimulation paradigms, which present limitations in practical deployment scenarios. This investigation presents the development and evaluation of a novel light-emitting diode (LED)-based dual stimulation apparatus designed to enhance SSVEP classification accuracy through the integration of both SSVEP and P300 paradigms. The system employs four distinct frequencies—7 Hz, 8 Hz, 9 Hz, and 10 Hz—corresponding to forward, backward, right, and left directional controls, respectively. Oscilloscopic verification confirmed the precision of these stimulation frequencies. Real-time feature extraction was accomplished through the concurrent analysis of maximum Fast Fourier Transform (FFT) amplitude and P300 peak detection to ascertain user intent. Directional control was determined by the frequency exhibiting maximal amplitude characteristics. The visual stimulation hardware demonstrated minimal frequency deviation, with error differentials ranging from $0.15$% to $0.20$% across all frequencies. The implemented signal processing algorithm successfully discriminated between all four stimulus frequencies whilst correlating them with their respective P300 event markers. Classification accuracy was evaluated based on correct task intention recognition. The proposed hybrid system achieved a mean classification accuracy of $86.25$%, coupled with an average ITR of $42.08$ bits per minute (bpm). These performance metrics notably exceed the conventional 70% accuracy threshold typically employed in BCI system evaluation protocols. Full article

Pillar-to-pillar dashboards have become common in modern electric vehicles. These dashboards are made of liquid crystal displays (LCDs), of which backlight units (BLUs) are an integral part. Particulate contamination inside BLUs can lead to either an aesthetic or functional failure and is in consequence a part of quality control. Automatic optical inspection (AOI) was used to detect particulate matter to enable a process chain analysis to be carried out. The investigation showed that a high percentage of all contaminants originated from the assembly of the edge/side lightguide. The implementation of an additional cleaning process was the favored countermeasure to reduce the contaminants. The objective (cleanliness requirement) was to remove all contaminants larger than 100 µm from the lightguide with contactless (non-destructive) cleaning methods. The preferred cleaning methods of choice were compressed air and CO₂ snow jet cleaning. This work investigates the cleaning efficacy of both cleaning methods under consideration of the following impact factors: distance, orientation (inclination) and speed. The central question of this paper was as follows: would cleaning with compressed air be sufficient to meet the cleanliness requirements? In order to answer this question, a cleaning validation was carried out, based on a Box–Behnken design of experiments (DoE). To do so, representative test contaminants had to be selected in step one, followed by the selection of an appropriate measurement technology to be able to count the contaminants on the lightguide. In the third step, a test rig had to be designed and built to finally carry out the experiments. The data revealed that CO₂ was able to achieve a cleaning efficacy of 100% in five of the experiments, while the best cleaning efficacy of compressed air was 89.87%. The cleaning efficacy of compressed air could be improved by a parameter optimization to 94.19%. In contrast, a 100% cleaning efficacy is achievable with CO₂ after parameter optimization, which is what is needed to meet the cleanliness requirements. Full article