Search Results (364)

This paper proposes a resource optimized cascaded quantum surface repetition code architecture integrated with a Union Find (UF) enhanced hybrid decoder, which suppresses biased noise and improves the scalability of quantum error correction through synergistic inner outer quantum code collaboration. The hybrid architecture employs inner quantum repetition codes for local error suppression and outer rotated quantum surface codes for topological robustness, reducing auxiliary quantum qubits by 12.5% via shared stabilizers and compact lattice embedding. An optimized UF decoder employing path compression and adaptive cluster merging achieves near-linear time complexity $\mathcal{O}\left(n\alpha \right(n\left)\right)$, outperforming minimum-weight perfect matching (MWPM) decoders $\mathcal{O}\left(n^{2.5}\right)$. Under Z-biased noise $\eta =10$, simulations demonstrate a 28.2% error threshold, 2.6% higher than standard quantum surface codes, and 15% lower logical error rates via dynamic boundary expansion. At code distance $d=7$, resource savings reach 9.3% with maximum relative error below 8.5%, fulfilling fault-tolerance criteria. The UF decoder exhibits 38% threshold advantage over MWPM at low bias $\eta \approx {10}^{-3}$ and 15% less degradation at high noise $p=0.5$, enabling scalable real-time decoding. This framework bridges theoretical thresholds with practical resource constraints, offering a noise-adaptive QEC solution for near-term quantum devices including photonic quantum systems referenced in the paper’s background on repetition cat qubits. Full article

This paper presents the development of an autonomous grasping mechanism for drone-based parcel delivery systems towards developing capabilities for in-flight package transfer. The approach integrates a mechanical gripper fitted with sensors and a pose estimation method for parcels, all coordinated through a hybrid Model Predictive Control (MPC) architecture. The gripper’s mechanical structure and prototype are developed using 3D printing technology for both the main framework and gear components. A hybrid dynamical model is formulated that integrates the gripper mechanics with simplified drone dynamics, capturing distinct operational phases including package acquisition, transport, and release. The hybrid MPC framework computes reference trajectories for both the gripper arm configuration and the drone’s spatial path toward designated target positions. Experimental validation is conducted using the operational gripper prototype and pose estimation system, while drone behavior is represented through simulation. Full article

The deployment of large remotely operated vehicles (ROVs) or autonomous underwater vehicles (AUVs) typically requires support vessels, crane systems, and specialized personnel, resulting in increased logistical complexity and operational costs. In this context, lightweight and modular underwater robots have emerged as a cost-effective alternative, capable of reaching significant depths and performing tasks traditionally associated with larger platforms. This article presents a system architecture for recovering a known object using a hybrid-controlled ROV, integrating autonomous perception, high-level interaction, and low-level control. The proposed architecture includes a perception module that estimates the object pose using a Perspective-n-Point (PnP) algorithm, combining object segmentation from a YOLOv11-seg network with 2D keypoints obtained from a YOLOv11-pose model. In addition, a Natural Language ROS Agent is incorporated to enable high-level command interaction between the operator and the robot. These modules interact with low-level controllers that regulate the vehicle degrees of freedom and with autonomous behaviors such as target approach and grasping. The proposed system is evaluated through simulation and experimental tank trials, including object recovery experiments conducted in a 12 × 8 × 5 m test tank at CIRTESU, as well as perception validation in simulated, tank, and harbor scenarios. The results demonstrate successful recovery of a black box using a BlueROV2 platform, showing that architectures of this type can effectively support operators in underwater intervention tasks, reducing operational risk, deployment complexity, and mission costs. Full article

This paper presents Aerokinesis, an IoT-based software–hardware system for intuitive gesture-driven control of quadcopter unmanned aerial vehicles (UAVs), developed within the Robot Operating System 2 (ROS2) framework. The proposed system addresses the challenge of providing an accessible human–drone interaction interface for operators in scenarios where traditional remote controllers are impractical or unavailable. The architecture comprises two hierarchical control levels: (1) high-level discrete command control utilizing a fully connected neural network classifier for static gesture recognition, and (2) low-level continuous flight control based on three-dimensional hand keypoint analysis from a depth camera. The gesture classification module achieves an accuracy exceeding 99% using a multi-layer perceptron trained on MediaPipe-extracted hand landmarks. For continuous control, we propose a novel approach that computes Euler angles (roll, pitch, yaw) and throttle from 3D hand pose estimation, enabling intuitive four-degree-of-freedom quadcopter manipulation. A hybrid signal filtering pipeline ensures robust control signal generation while maintaining real-time responsiveness. Comparative user studies demonstrate that gesture-based control reduces task completion time by 52.6% for beginners compared to conventional remote controllers. The results confirm the viability of vision-based gesture interfaces for IoT-enabled UAV applications. Full article

In response to the growing interest in sustainable and material-efficient architectural solutions, this study focuses on innovative applications of engineered timber in lightweight structural systems. It investigates the material optimization and structural performance of engineered timber thin-shell structures through an integrated parametric design approach. The study compares three prefabricated, panelized building systems, gridshell, segmented full-plate shell, and ribbed shell, to evaluate their efficiency in terms of material intensity, stiffness, and geometric behavior. Using Rhinoceros and Grasshopper environments with Karamba3D, Kiwi3D, and Kangaroo plugins, a comprehensive parametric workflow was developed that integrates geometric modeling, structural analysis, and material evaluation. The results show that segmented ribbed shell and two segmented gridshell variants offer up to 70% reduction in material usage compared with full-plate segmented timber shells, with hybrid timber shells achieving the best balance between stiffness and mass, offering functional advantages (roofing without additional load). These findings highlight the potential of parametric and computational design methods to enhance both the environmental efficiency (LCA) and digital fabrication readiness of timber-based architecture. The study contributes to the ongoing development of computational timber architecture, emphasizing the role of design-to-fabrication strategies in sustainable construction and the digital transformation of architectural practice. Full article

To achieve low-cost and flexible wheel angles measurement, we propose a novel strategy that integrates wheel segmentation network with 3D vision. In this framework, a semantic segmentation network is first employed to extract the wheel rim, followed by angle estimation through ICP-based point cloud registration. Since wheel rim extraction is closely tied to angle computation accuracy, we introduce APCS-SwinUnet, a segmentation network built on the SwinUnet architecture and enhanced with ASPP, CBAM, and a hybrid loss function. Compared with traditional image processing methods in wheel alignment, APCS-SwinUnet delivers more accurate and refined segmentation, especially at wheel boundaries. Moreover, it demonstrates strong adaptability across diverse tire types and lighting conditions. Based on the segmented mask, the wheel rim point cloud is extracted, and an iterative closest point algorithm is then employed to register the target point cloud with a reference one. Taking the zero-angle condition as the reference, the rotation and translation matrices are obtained through point cloud registration. These matrices are subsequently converted into toe and camber angles via matrix-to-angle transformation. Experimental results verify that the proposed solution enables accurate angle measurement in a cost-effective, simple, and flexible manner. Furthermore, repeated experiments further validate its robustness and stability. Full article

Three-dimensional (3D) printing has transformed the development of microneedle arrays (MNAs) by enabling exceptional control over their geometry, distribution, materials, and functionality in a single-step, customizable process. This review represents a design-centric framework that organizes recent advancements in four interconnected levers: (i) individual microneedle (MN) geometry and size; (ii) patch-level MN distribution and multi-array architectures; (iii) computer-aided design (CAD), finite element analysis (FEA), computational fluid dynamics (CFD), and artificial intelligence/machine learning (AI/ML)-driven optimization; and (iv) manufacturing constraints and emerging solutions for scalability and reproducibility. Outcomes show that small changes in the radius of the MN’s tip, the MN’s aspect ratio, the MN’s internal lattice architecture, and the spacing of the array can dramatically influence their insertion force, mechanical reliability, payload capacity, and therapeutic coverage. Now, digital tools can bridge the design and experimental outcomes, while novel morphologies, hybrid materials, and theranostic integrations are expanding the clinical potential of MNs. The remaining challenges, resolution-versus-throughput trade-offs, biocompatibility, batch-to-batch consistency, and lack of testing standardization are examined alongside promising directions in high-throughput 3D printing, stimuli-responsive materials, and closed-loop systems. Finally, rational, model-guided design strategies are positioning 3D-printed MNAs as versatile platforms for painless, patient-specific drug delivery, diagnostics, and personalized medicine. Full article

Digital fringe projection profilometry (DFPP) is a widely used technique for full-field, non-contact 3D surface measurement, offering precision from the sub-micrometer-to-millimeter scale depending on system geometry and fringe design. This review provides a consolidated synthesis of advances reported between 2022 and 2025, covering projection and imaging architectures, phase formation and unwrapping strategies, calibration approaches, high-speed implementations, and learning-based reconstruction methods. A central contribution of this review is the integration of these developments within a metrological perspective, explicitly relating phase–height transformation, fringe parameters, system geometry, and calibration to dominant uncertainty sources and error propagation. Recent progress highlights trade-offs between sensitivity, robustness, computational complexity, and applicability to non-ideal surfaces, while learning-based and hybrid optical–computational approaches demonstrate substantial improvements in reconstruction reliability under challenging conditions. Remaining challenges include measurements on reflective or transparent surfaces, dynamic scenes, environmental instability, and real-time operation. The review outlines emerging research directions such as physics-informed learning, digital twins, programmable optics, and autonomous calibration, providing guidance for the development of next-generation DFPP systems for precision metrology. Full article

The transition toward Industry 4.0 requires advanced sensing platforms capable of delivering real-time, high-fidelity data under extreme industrial conditions. Thin-film sensors, leveraging both photonic and functional approaches, are emerging as key enablers of this transformation. By exploiting optical phenomena such as Fabry–Pérot interference, guided-mode resonance, plasmonics, and photonic crystal effects, thin-film photonic devices provide highly sensitive, electromagnetic interference-immune, and remotely interrogated solutions for monitoring temperature, strain, and chemical environments. Complementarily, functional thin films including oxide-based chemiresistors, nanoparticle coatings, and flexible electronic skins extend sensing capabilities to diverse industrial contexts, from hazardous gas detection to structural health monitoring. This review surveys the fundamental optical principles, material platforms, and deposition strategies that underpin thin-film sensors, emphasizing advances in nanostructured oxides, 2D materials, hybrid perovskites, and additive manufacturing methods. Application-focused sections highlight their deployment in temperature and stress monitoring, chemical leakage detection, and industrial safety. Integration into Internet of Things (IoT) networks, cyber-physical systems, and photonic integrated circuits is examined, alongside challenges related to durability, reproducibility, and packaging. Future directions point to AI-driven signal processing, flexible and printable architectures, and autonomous self-calibration. Together, these developments position thin-film sensors as foundational technologies for intelligent, resilient, and adaptive manufacturing in Industry 4.0. Full article

The proliferation of Internet of Things (IoT) devices has significantly expanded the threat landscape for malicious software (malware), rendering traditional signature-based detection methods increasingly ineffective in coping with the volume and evolving nature of modern threats. In response, researchers are utilising artificial intelligence (AI) for a more dynamic and robust malware detection solution. An innovative approach utilising AI is focusing on image classification techniques to detect malware on resource-constrained Single-Board Computers (SBCs) such as the Raspberry Pi. In this method the conversion of malware binaries into 2D images is examined, which can be analysed by deep learning models such as convolutional neural networks (CNNs) to classify them as benign or malicious. The results show that the image-based approach demonstrates high efficacy, with many studies reporting detection accuracy rates exceeding 98%. That said, there is a significant challenge in deploying these demanding models on devices with limited processing power and memory, in particular those involving of both calculation and time complexity. Overcoming this issue requires critical model optimisation strategies. Successful approaches include the use of a lightweight CNN architecture and federated learning, which may be used to preserve privacy while training models with decentralised data are processed. This hybrid workflow in which models are trained on powerful servers before the learnt algorithms are deployed on SBCs is an emerging field attacting significant interest in the field of cybersecurity. This paper synthesises the current state of the art, performance compromises, and optimisation techniques contributing to the understanding of how AI and image representation can enable effective low-cost malware detection on resource-constrained systems. Full article

Images captured underwater frequently suffer from color casts, blurring, and distortion, which are mainly attributable to the unique optical characteristics of water. Although conventional UIE methods rooted in physics are available, their effectiveness is often constrained, particularly in challenging aquatic and illumination conditions. More recently, deep learning has become a leading paradigm for UIE, recognized for its superior performance and operational efficiency. This paper proposes UCA-Net, a lightweight CNN-Transformer hybrid network. It incorporates multiple attention mechanisms and utilizes composite attention to effectively enhance textures, reduce blur, and correct color. A novel adaptive sparse self-attention module is introduced to jointly restore global color consistency and fine local details. The model employs a U-shaped encoder–decoder architecture with three-stage up- and down-sampling, facilitating multi-scale feature extraction and global context fusion for high-quality enhancement. Experimental results on multiple public datasets demonstrate UCA-Net’s superior performance, achieving a PSNR of 24.75 dB and an SSIM of 0.89 on the UIEB dataset, while maintaining an extremely low computational cost with only 1.44M parameters. Its effectiveness is further validated by improvements in various downstream image tasks. UCA-Net achieves an optimal balance between performance and efficiency, offering a robust and practical solution for underwater vision applications. Full article

This paper explores simulation application functions for the computer monitoring system of a hydro–wind–solar integrated control center, focusing on five core areas: platform management, operational training, performance optimization, exception handling, and emergency drills. Against the “dual carbon” backdrop, multi-energy complementary system simulation faces key challenges including multi-energy coupling, real-time response, and cybersecurity protection. Research shows that integrating digital twin, heterogeneous computing, and artificial intelligence technologies markedly improve simulation accuracy and intelligent decision-making. Dispatch strategies have shifted from single-energy optimization to system-level coordination, while cybersecurity frameworks now provide comprehensive safeguards covering algorithms, data, systems, user behavior, and architecture. Intelligent operation and maintenance with fault diagnosis—powered by big data and deep learning—enables equipment condition prediction, and emergency drill platforms boost response capacity via 3D visualization and scriptless modeling. Current hurdles include absent multi-energy modeling standards, poor extreme-condition adaptability, and inadequate knowledge transfer mechanisms. Future research should prioritize hybrid physical–data-driven approaches, multi-dimensional robust scheduling, federated learning-based diagnostics, and integrated digital twin, edge computing, and decentralized ledger technologies. These advances will drive simulation platforms toward greater intelligence, interoperability, and reliability, laying the technical foundation for unified hydro–wind–solar control centers. Full article

Accurately estimating the absolute position of robots under external loads is challenging due to nonlinear dynamics, posture-dependent manipulability, and structural sensitivities. This study investigates a data-driven approach for absolute position prediction of a 2-DOF planar delta robot by learning time-series force signals generated during manipulability-driven free motion. Constant torques of opposite directions were applied to the robot without any position or trajectory control, allowing the mechanism to move naturally according to its configuration-dependent manipulability. Reaction forces measured at the end-effector and relative encoder variations were collected across a grid of workspace locations and used to construct a 12-channel time-series input. A hybrid deep learning architecture combining 1D convolutional layers and a bidirectional LSTM network was trained to regress the robot’s absolute X–Y position. Experimental results demonstrate that the predicted trajectories closely match the measured paths in the workspace, yielding overall RMSE values of 3.81 mm(X) and 2.94 mm(Y). Statistical evaluation using RMSE shows that approximately 83.73% of all test sequences achieve an error below 5 mm. The findings confirm that LSTM models can effectively learn posture-dependent dynamic behavior and force-manipulability relationships. Full article

Low work function (LWF) materials are essential for enabling efficient systems’ behavior in applications ranging from vacuum electronics to energy conversion devices and next-generation opto-electronic interfaces. Recent advances in theory, characterization, and materials engineering have dramatically expanded the candidates for LWF systems, including alkali-based compounds, perovskites, borides, nitrides, barium and scandium oxides, 2D materials, MXenes, functional polymers, carbon materials, and hybrid architectures. This review provides a comprehensive overview of the fundamental mechanisms governing the work function (WF) and discusses the state-of-the-art measurement techniques, as well as the most used computational approaches for predicting and validating WF values. The recent breakthroughs in engineering LWF surfaces through different methods are discussed. Special emphasis is placed on the relationship between predicted and experimentally measured WF values, highlighting the role of surface contamination, reconstruction, and environmental stability. Performance, advantages, and limitations of major LWF material families are fully analyzed, identifying emerging opportunities for next applications. Finally, current and fundamental challenges in achieving scalable, stable, and reproducible LWF surfaces are considered, presenting promising research directions such as high-throughput computational discovery and in situ surface engineering with protective coatings. This review aims to provide a unified framework for understanding, achieving, and advancing LWF materials toward practical and industrially relevant technologies. Full article

The growing deployment of connected and autonomous vehicles (CAVs) requires coordinated control strategies that jointly address safety, mobility, and energy efficiency. This paper presents a novel two-stage cooperative control framework for autonomous hybrid electric vehicle (HEV) platoons based on machine learning. In the first stage, a metric learning-based distributed model predictive control (ML-DMPC) strategy is proposed to enable cooperative longitudinal control among heterogeneous vehicles, explicitly incorporating inter-vehicle interactions to improve speed tracking, ride comfort, and platoon-level energy efficiency. In the second stage, a multi-agent twin-delayed deep deterministic policy gradient (MATD3) algorithm is developed for real-time energy management, achieving an optimal power split between the engine and battery while reducing Q-value overestimation and accelerating learning convergence. Simulation results across multiple standard driving cycles demonstrate that the proposed framework outperforms conventional distributed model predictive control (DMPC) and multi-agent deep deterministic policy gradient (MADDPG)-based methods in fuel economy, stability, and convergence speed, while maintaining battery state of charge (SOC) within safe limits. To facilitate future experimental validation, a dSPACE-based hardware-in-the-loop (HIL) architecture is designed to enable real-time deployment and testing of the proposed control framework. Full article