Search Results (20)

The fetal electrocardiogram (FECG) is critical for assessing fetal cardiac electrophysiology and detecting fetal distress and arrhythmias. Single-channel abdominal electrocardiogram (AECG) enables home-based monitoring but faces challenges posed by weak fetal signals, maternal interference, and the lack of spatial information. Ensemble Empirical Mode Decomposition (EEMD) is suitable for nonstationary AECG signals but relies on accurate selection of intrinsic mode functions (IMFs). In this study, a deep learning-guided method was proposed: a one-dimensional convolutional neural network (1D CNN) scored and selected EEMD-derived IMFs, followed by maternal QRS template subtraction and secondary EEMD purification to achieve automatic FECG extraction. Leave-one-subject-out (LOSO) cross-validation was performed on 15 simulated cases and 5 ADFECGDB records, yielding a mean AUC of 0.9282 ± 0.0189 for the IMF classifier. On the independent DaISy and NIFEA arrhythmia datasets, the proposed CNN-2×EEMD method achieved correlation coefficients of 0.94–0.96, F1-scores of 0.8372–0.9565 for fetal R-peak detection, and SNR improvements of 13.39–15.88 dB. This method outperformed conventional automatic selection methods and matched the performance of manual selection. Ablation studies validated the optimal network design and IMF selection strategy, while complexity analysis (0.08 GFLOPs, 2.24 ms latency) confirmed its suitability for real-time wearable deployment. Full article

In Global Navigation Satellite System (GNSS)-denied environments, unmanned aerial vehicles (UAVs) relying on Vision-Based Navigation (VBN) in high-altitude, mountainous terrain face severe challenges due to geometric distortions in aerial imagery. This paper proposes a georeferenced localization framework that integrates orthorectified aerial imagery with Scene Matching (SM) to achieve robust positioning. The method employs a camera projection model combined with Digital Elevation Model (DEM) to orthorectify UAV images, thereby mitigating distortions from central projection and terrain relief. Pre-processing steps enhance consistency with reference orthophoto maps, after which template matching is performed using normalized cross-correlation (NCC). Sensor fusion is achieved through extended Kalman filters (EKFs) incorporating Inertial Navigation System (INS), GNSS (when available), barometric altimeter, and SM outputs. The framework was validated through flight tests with an aircraft over 45 km trajectories at altitudes of 2.5 km and 3.5 km in mountainous terrain. The results demonstrate that orthorectification improves image similarity and significantly reduces localization error, yielding lower 2D RMSE compared to conventional rectification. The proposed approach enhances VBN by mitigating terrain-induced distortions, providing a practical solution for UAV localization in GNSS-denied scenarios. Full article

The proliferation of biometric technology across various domains including user identification, financial services, healthcare, security, law enforcement, and border control introduces convenience in user identity verification while necessitating robust protection mechanisms for sensitive biometric data. While chaos-based encryption systems offer promising solutions, many existing chaos-based encryption schemes exhibit inherent shortcomings including deterministic randomness and constrained key spaces, often failing to balance security robustness with computational efficiency. To address this, we propose a novel dual-layer cryptographic framework leveraging a four-dimensional (4D) Qi hyperchaotic system for protecting biometric templates and facilitating secure feature matching operations. The framework implements a two-tier encryption mechanism where each layer independently utilizes a Qi hyperchaotic system to generate unique encryption parameters, ensuring template-specific encryption patterns that enhance resistance against chosen-plaintext attacks. The framework performs dimensional normalization of input biometric templates, followed by image pixel shuffling to permutate pixel positions before applying dual-key encryption using the Qi hyperchaotic system and XOR diffusion operations. Templates remain encrypted in storage, with decryption occurring only during authentication processes, ensuring continuous security while enabling biometric verification. The proposed system’s framework demonstrates exceptional randomness properties, validated through comprehensive NIST Statistical Test Suite analysis, achieving statistical significance across all 15 tests with p-values consistently above 0.01 threshold. Comprehensive security analysis reveals outstanding metrics: entropy values exceeding 7.99 bits, a key space of ${10}^{320}$, negligible correlation coefficients (<${10}^{-2}$), and robust differential attack resistance with an NPCR of 99.60% and a UACI of 33.45%. Empirical evaluation, on standard CASIA Face and Iris databases, demonstrates practical computational efficiency, achieving average encryption times of 0.50913s per user template for 256 × 256 images. Comparative analysis against other state-of-the-art encryption schemes verifies the effectiveness and reliability of the proposed scheme and demonstrates our framework’s superior performance in both security metrics and computational efficiency. Our findings contribute to the advancement of biometric template protection methodologies, offering a balanced performance between security robustness and operational efficiency required in real-world deployment scenarios. Full article

Monitoring trace toluene exposure is critical for early-stage lung cancer screening via breath analysis, yet conventional chemiresistive sensors face fundamental limitations, including compromised selectivity in complex VOC matrices and humidity-induced signal drift, with prevailing p–n heterojunction architectures suffering from inherent charge recombination and environmental instability. Herein, we pioneer a 2D core–shell n–n heterojunction strategy through rational design of TiO₂@MoS₂ heterostructures, where vertically aligned MoS₂ nanosheets are epitaxially grown on 2D TiO₂ derived from graphene-templated synthesis, creating built-in electric fields at the heterojunction interface that dramatically enhance charge carrier separation efficiency. At 240 °C, the TiO₂@MoS₂ sensor exhibits a superior response (R_a/R_g = 9.8 to 10 ppm toluene), outperforming MoS₂ (R_a/R_g = 2.8). Additionally, the sensor demonstrates rapid response/recovery kinetics (9 s/16 s), a low detection limit (50 ppb), and excellent selectivity against interfering gases and moisture. The enhanced performance is attributed to unidirectional electron transfer (TiO₂ → MoS₂) without hole recombination losses, methyl-specific adsorption through TiO₂ oxygen vacancy alignment, and steric exclusion of non-target VOCs via size-selective MoS₂ interlayers. This work establishes a transformative paradigm in gas sensor design by leveraging n–n heterojunction physics and 2D core–shell synergy, overcoming long-standing limitations of conventional architectures. Full article

Since the late 2000s, numerous studies have focused on the application of Heritage Building Information Modelling (HBIM) processes and technologies for the documentation of the historic built environment. Many of these studies have focused on the use of BIM software tools to generate intelligent 3D models using information gathered from a range of data capture techniques including laser scanning and photogrammetry. While this approach effectively preserves existing or partially extant heritage, it faces limitations in reconstructing lost or poorly documented structures. The aim of this study is to develop a novel approach to complement the existing tangible-based HBIM methods, towards an ‘Echo-based’ Heritage Digital Twin (EH-DT) an early-stage digital representation that leverages intangible, memory-based oral descriptions (or echoes) and AI text-to-image generation techniques. The overall methodology for the research presented in this paper proposes a three-phase framework. Phase 1: engineering a standardised heritage prompt template, Phase 2: creation of the Architectural Heritage Transformer, and Phase 3: implementing an AI text-to-image generation toolkit. Within these phases, intangible data, including collective memories (or oral histories) of people who had first-hand experience with the building, provide ‘echoes’ of past form. These can then be converted using a novel ‘Architectural Heritage Transformer’ (AHT), which converts plain language descriptions into architectural terminology through a generated taxonomy. The output of the AHT forms input for a pre-created standardised heritage prompt template for use in AI diffusion models. While the current EH-DT framework focuses on producing 2D visual representations, it lays the foundation for potential future integration with HBIM models or digital twin systems. However, the reliance on generative AI introduces potential risks of inaccuracies due to speculative outputs, necessitating rigorous validation and iterative refinement to ensure historical and architectural credibility. The findings indicate the potential of AI to extend the current HBIM paradigm by generating images of ‘lost’ heritage buildings, which can then be used to enhance and augment the more ‘traditional’ HBIM process. Full article

Core–shell micro/nanomotors have garnered significant interest in biomedicine owing to their versatile task-performing capabilities. However, their effectiveness for photothermal therapy (PTT) still faces challenges because of their poor tumor accumulation, lower light-to-heat conversion, and due to the limited penetration of near-infrared (NIR) light. In this study, we present a novel core–shell micromotor that combines magnetic and photothermal properties. It is synthesized via the template-assisted electrodeposition of iron (Fe) and reduced graphene oxide (rGO) on a microtubular pore-shaped membrane. The resulting Fe-rGO micromotor consists of a core of oval-shaped zero-valent iron nanoparticles with large magnetization. At the same time, the outer layer has a uniform reduced graphene oxide (rGO) topography. Combined, these Fe-rGO core–shell micromotors respond to magnetic forces and near-infrared (NIR) light (1064 nm), achieving a remarkable photothermal conversion efficiency of 78% at a concentration of 434 µg mL⁻¹. They can also carry doxorubicin (DOX) and rapidly release it upon NIR irradiation. Additionally, preliminary results regarding the biocompatibility of these micromotors through in vitro tests on a 3D breast cancer model demonstrate low cytotoxicity and strong accumulation. These promising results suggest that such Fe-rGO core–shell micromotors could hold great potential for combined photothermal therapy. Full article

The utilization of lattice-type cellular architectures has seen a significant increase, owing to their predictable shape and the ability to fabricate templated porous materials through low-cost 3D-printing methods. Frames based on atomic lattice structures such as face-centered cubic (FCC), body-centered cubic (BCC), or simple cubic (SC) have been utilized. In FDM, the mechanical performance has been impeded by stress concentration at the nodes and melt-solidification interfaces arising from layer-by-layer deposition. Adding plates to the frames has resulted in improvements with a concurrent increase in weight and hot-pocket-induced dimensional impact in the closed cells formed. In this paper, we explore compressive performance from the partial addition of plates to the frames of a SC-BCC lattice. Compression testing of both single unit cells and 4 × 4 × 4 lattices in all three axial directions is conducted to examine stress transfer to the nearest neighbor and assess scale-up stress transfer. Our findings reveal that hybrid lattice structure unit cells exhibit significantly improved modulus in the range of 125% to 393%, specific modulus in the range of 13% to 120%, and energy absorption in the range of 17% to 395% over the open lattice. The scaled-up lattice modulus increased by 8% to 400%, specific modulus by 2% to 107%, and energy absorption by 37% to 553% over the lattice frame. Parameters that emerged as key to improved lightweighting. Full article

(1) Background: In digital-technology-assisted nasal defect reconstruction methods, a crucial step involves utilizing computer-aided design to virtually reconstruct the nasal defect’s complete morphology. However, current digital methods for virtual nasal defect reconstruction have yet to achieve efficient, precise, and personalized outcomes. In this research paper, we propose a novel approach for reconstructing external nasal defects based on the Facial Mesh Generation Network (FMGen-Net), aiming to enhance the levels of automation and personalization in virtual reconstruction. (2) Methods: We collected data from 400 3D scans of faces with normal morphology and combined the structured 3D face template and the Meshmonk non-rigid registration algorithm to construct a structured 3D facial dataset for training FMGen-Net. Guided by defective facial data, the trained FMGen-Net automatically generated an intact 3D face that was similar to the defective face, and maintained a consistent spatial position. This intact 3D face served as the 3D target reference face (3D-TRF) for nasal defect reconstruction. The reconstructed nasal data were extracted from the 3D-TRF based on the defective area using reverse engineering software. The ‘3D surface deviation’ between the reconstructed nose and the original nose was calculated to evaluate the effect of 3D morphological restoration of the nasal defects. (3) Results: In the simulation experiment of 20 cases involving full nasal defect reconstruction, the ‘3D surface deviation’ between the reconstructed nasal data and the original nasal data was 1.45 ± 0.24 mm. The reconstructed nasal data, constructed from the personalized 3D-TRF, accurately reconstructed the anatomical morphology of nasal defects. (4) Conclusions: This paper proposes a novel method for the virtual reconstruction of external nasal defects based on the FMGen-Net model, achieving the automated and personalized construction of the 3D-TRF and preliminarily demonstrating promising clinical application potential. Full article

The demand for object pose estimation is steadily increasing, and deep learning has propelled the advancement of this field. However, the majority of research endeavors face challenges in their applicability to industrial production. This is primarily due to the high cost of annotating 3D data, which places higher demands on the generalization capabilities of neural network models. Additionally, existing methods struggle to handle the abundance of textureless objects commonly found in industrial settings. Finally, there is a strong demand for real-time processing capabilities in industrial production processes. Therefore, in this study, we introduced a dual-channel Siamese framework to address these challenges in industrial applications. The architecture employs a Siamese structure for template matching, enabling it to learn the matching capability between the templates constructed from high-fidelity simulated data and real-world scenes. This capacity satisfies the requirements for generalization to unseen objects. Building upon this, we utilized two feature extraction channels to separately process RGB and depth information, addressing the limited feature issue associated with textureless objects. Through our experiments, we demonstrated that this architecture effectively estimates the three-dimensional pose of objects, achieving a 6.0% to 10.9% improvement compared to the state-of-the-art methods, while exhibiting robust generalization and real-time processing capabilities. Full article

Ear recognition has made good progress as an emerging biometric technology. However, the recognition performance, generalization ability, and feature robustness of ear recognition systems based on hand-crafted features are relatively poor. With the development of deep learning, these problems have been partly overcome. However, the recognition performance of existing ear recognition systems still needs to be improved when facing unconstrained ear databases in realistic scenarios. Another critical problem is that most systems with ear feature template databases are vulnerable to software attacks that disclose users’ privacy and even bring down the system. This paper proposes a software-attack-proof ear recognition system using deep feature learning and blockchain protection to address the problem that the recognition performance of existing systems is generally poor in the face of unconstrained ear databases in realistic scenarios. First, we propose an accommodative DropBlock (AccDrop) to generate drop masks with adaptive shapes. It has an advantage over DropBlock in coping with unconstrained ear databases. Second, we introduce a simple and parameterless attention module that uses 3D weights to refine the ear features output from the convolutional layer. To protect the security of the ear feature template database and the user’s privacy, we use Merkle tree nodes to store the ear feature templates, ensuring the determinism of the root node in the smart contract. We achieve Rank-1 (R1) recognition accuracies of 83.87% and 96.52% on the AWE and EARVN1.0 ear databases, which outperform most advanced ear recognition systems. Full article

In the context of energy efficiency and resource scarcity, lightweight construction has gained significant importance. Composite materials, particularly sandwich structures, have emerged as a key area within this field, finding numerous applications in various industries. The exceptional strength-to-weight ratio and the stiffness-to-weight ratio of sandwich structures allow the reduction in mass in components and structures without compromising strength. Among the widely used core designs, the honeycomb pattern, inspired by bee nests, has been extensively employed in the aviation and aerospace industry due to its lightweight and high resistance. The hexagonal cells of the honeycomb structure provide a dense arrangement, enhancing stiffness while reducing weight. However, nature offers a multitude of other structures that have evolved over time and hold great potential for lightweight construction. This paper focuses on the development, modeling, simulation, and testing of lightweight sandwich composites inspired by biological models, following the principles of biomimetics. Initially, natural and resilient design templates are researched and abstracted to create finished core structures. Numerical analysis is then employed to evaluate the structural and mechanical performance of these structures. The most promising designs are subsequently fabricated using 3D printing technology and subjected to three-point bending tests. Carbon-fiber-reinforced nylon filament was used for printing the face sheets, while polylactic acid (PLA+) was used as the core material. A honeycomb-core composite is also simulated and tested for comparative purposes, as it represents an established design in the market. Key properties such as stiffness, load-bearing capacity, and flexibility are assessed to determine the potential of the new core geometries. Several designs demonstrated improved characteristics compared to the honeycomb design, with the developed structures exhibiting a 38% increase in stiffness and an 18% enhancement in maximum load-bearing capacity. Full article

Au nanoparticles were synthesized in a soft template of pseudo-polyanions composed of polyvinylpyrrolidone (PVP) and sodium dodecyl sulfate (SDS) by the in situ reduction of chloroauric acid (HAuCl₄) with PVP. The particle sizes and morphologies of the Au nanoparticles were regulated with concentrations of PVP or SDS at room temperature. Distinguished from the Au nanoparticles with various shapes, Au nanoflowers (AuNFs) with rich protrusion on the surface were obtained at the low final concentration of SDS and PVP. The typical AuNF synthesized in the PVP (50 g·L⁻¹)–SDS (5 mmol·L⁻¹)–HAuCl₄ (0.25 mmol·L⁻¹) solution exhibited a face-centered cubic structure dominated by a {111} crystal plane with an average equivalent particle size of 197 nm and an average protrusion height of 19 nm. Au nanoparticles with four different shapes, nanodendritic, nanoflower, 2D nanoflower, and nanoplate, were synthesized and used to modify the bare glassy carbon electrode (GCE) to obtain Au/GCEs, which were assigned as AuND/GCE, AuNF/GCE, 2D-AuNF/GCE, and AuNP/GCE, respectively. Electrochemical sensing platforms for nitrite detection were constructed by these Au/GCEs, which presented different detection sensitivity for nitrites. The results of cyclic voltammetry (CV) demonstrated that the AuNF/GCE exhibited the best detection sensitivity for nitrites, and the surface area of the AuNF/GCE was 1.838 times of the bare GCE, providing a linear c(NO₂⁻) detection range of 0.01–5.00 µmol·L⁻¹ with a limit of detection of 0.01 µmol·L⁻¹. In addition, the AuNF/GCE exhibited good reproducibility, stability, and high anti-interference, providing potential for application in electrochemical sensing platforms. Full article

(1) Background: Three-dimensional (3D) facial anatomical landmarks are the premise and foundation of facial morphology analysis. At present, there is no ideal automatic determination method for 3D facial anatomical landmarks. This research aims to realize the automatic determination of 3D facial anatomical landmarks based on the non-rigid registration algorithm developed by our research team and to evaluate its landmark localization accuracy. (2) Methods: A 3D facial scanner, Face Scan, was used to collect 3D facial data of 20 adult males without significant facial deformities. Using the radial basis function optimized non-rigid registration algorithm, TH-OCR, developed by our research team (experimental group: TH group) and the non-rigid registration algorithm, MeshMonk (control group: MM group), a 3D face template constructed in our previous research was deformed and registered to each participant’s data. The automatic determination of 3D facial anatomical landmarks was realized according to the index of 32 facial anatomical landmarks determined on the 3D face template. Considering these 32 facial anatomical landmarks manually selected by experts on the 3D facial data as the gold standard, the distance between the automatically determined and the corresponding manually selected facial anatomical landmarks was calculated as the “landmark localization error” to evaluate the effect and feasibility of the automatic determination method (template method). (3) Results: The mean landmark localization error of all facial anatomical landmarks in the TH and MM groups was 2.34 ± 1.76 mm and 2.16 ± 1.97 mm, respectively. The automatic determination of the anatomical landmarks in the middle face was better than that in the upper and lower face in both groups. Further, the automatic determination of anatomical landmarks in the center of the face was better than in the marginal part. (4) Conclusions: In this study, the automatic determination of 3D facial anatomical landmarks was realized based on non-rigid registration algorithms. There is no significant difference in the automatic landmark localization accuracy between the TH-OCR algorithm and the MeshMonk algorithm, and both can meet the needs of oral clinical applications to a certain extent. Full article

Bladder cancer and prostate cancer are the most common malignant tumors of the genitourinary system. Conventional strategies still face great challenges of high recurrence rate and severe trauma. Therefore, minimally invasive photothermal therapy (PTT) has been extensively explored to address these challenges. Herein, fluorescent Au nanoparticles (NPs) were first prepared using glutathione as template, which were then capped with SiO₂ shell to improve the biocompatibility. Next, Au nanoclusters were deposited on the NPs surface to obtain Au@SiO₂@Au NPs for photothermal conversion. The gaps between Au nanoparticles on their surface could enhance their photothermal conversion efficiency. Finally, hyaluronic acid (HA), which targets cancer cells overexpressing CD44 receptors, was attached on the NPs surface via 1-(3-dimethylaminopropyl)-3-ethylcarbodiimide hydrochloride (EDC) chemistry to improve the accumulation of NPs in tumor tissues. Photothermal experiments showed that NPs with an average size of 37.5 nm have a high photothermal conversion efficiency (47.6%) and excellent photostability, thus exhibiting potential application as a PTT agent. The temperature of the NPs (100 μg·mL⁻¹) could rapidly increase to 38.5 °C within 200 s and reach the peak of 57.6 °C with the laser power density of 1.5 W·cm⁻² and irradiation time of 600 s. In vivo and in vitro PTT experiments showed that the NPs have high biocompatibility and excellent targeted photothermal ablation capability of cancer cells. Both bladder and prostate tumors disappeared at 15 and 18 d post-treatment with HA-Au@SiO₂@Au NPs, respectively, and did not recur. In summary, HA-Au@SiO₂@Au NPs can be used a powerful PTT agent for minimally invasive treatment of genitourinary tumors. Full article

Hollow mesoporous silica nanoparticles have been widely applied as a carrier material in the molecular imprinting process because of their excellent properties, with high specific surface area and well-defined active centers. However, these kinds of materials face the inevitable problem that they have low mass transfer efficiency and cannot be conveniently recycled. In order to solve this problem, this work has developed a composite hydrogel microsphere (MMHSG) encapsulated with hollow mesoporous imprinted nanoparticles for the selective extraction of 2’-deoxyadenosine (dA). Subsequently, the hollow mesoporous imprinted polymers using dA as template molecule and synthesized 5-(2-carbomethoxyvinyl)-2′-deoxyuridine (AcrU) as functional monomer were encapsulated in hydrogel. MMHSG displayed good performance in specifically recognizing and quickly separating dA, whereas no imprinting effect was observed among 2′-deoxyguanosine (dG), deoxycytidine (dC), or 5′-monophosphate disodium salt (AMP). Moreover, the adsorption of dA by MMHSG followed chemisorption and could reach adsorption equilibrium within 60 min; the saturation adsorption capacity was 20.22 μmol·g⁻¹. The introduction of AcrU could improve selectivity through base complementary pairing to greatly increase the imprinting factor to 3.79. Therefore, this was a successful attempt to combine a hydrogel with hollow mesoporous silica nanoparticles and molecularly imprinted material. Full article