Search Results (1,552)

A novel endoscopic palpation tool (EPT), designed for tactile and stiffness sensing using fiber Bragg gratings (FBGs) was evaluated in a surgical environment for intraoperative safety and effectiveness. The EPT consisted of four FBGs arranged in a cross pattern and embedded within an elastic, hollow, silicone hemispherical dome designed to deform upon contact with soft tissue. The EPT was employed to scan both in vivo and ex vivo tissue samples. Monitoring of porcine vital signs during minimally invasive and open surgical procedures showed no significant changes during use of the EPT. Perioperative blood tests including inflammatory markers and liver and renal function studies were unremarkable. Histopathological analyses of tissues involved (liver, spleen, bowel, and abdominal wall) showed no evidence of inflammation, necrosis, or tissue damage, confirming the device’s biocompatibility. To the best of our knowledge, this is the first study reporting in vivo stiffness measurements using an FBG-based EPT. The probe successfully distinguished between soft and hard tissue regions’ relative stiffness. Furthermore, successive measurements on liver samples demonstrated the device’s ability to generate stiffness maps, enabling clear visualization of spatial variation in tissue stiffness. Full article

High-resolution remote sensing images often suffer from inadequate fusion between global and local features, leading to the loss of long-range dependencies and blurred spatial details, while also exhibiting limited adaptability to multi-scale object segmentation. To overcome these limitations, this study proposes RST-Net, a semantic segmentation network featuring a dual-branch encoder structure. The encoder integrates a ResNeXt-50-based CNN branch for extracting local spatial features and a Shunted Transformer (ST) branch for capturing global contextual information. To further enhance multi-scale representation, the multi-scale feature enhancement module (MSFEM) is embedded in the CNN branch, leveraging atrous and depthwise separable convolutions to dynamically aggregate features. Additionally, the residual dynamic feature fusion (RDFF) module is incorporated into skip connections to improve interactions between encoder and decoder features. Experiments on the Vaihingen and Potsdam datasets show that RST-Net achieves promising performance, with MIoU scores of 77.04% and 79.56%, respectively, validating its effectiveness in semantic segmentation tasks. Full article

Object detection in synthetic aperture radar (SAR) imagery poses significant challenges due to low resolution, small objects, arbitrary orientations, and complex backgrounds. Standard object detectors often fail to capture sufficient semantic and geometric cues for such tiny targets. To address this issue, a new Convolutional Neural Network (CNN) framework called Deformable Vectorized Detection Network (DVDNet) has been proposed, specifically designed for detecting small, oriented, and densely packed objects in SAR images. The DVDNet consists of Grouped-Deformable Convolution for adaptive receptive field adjustment to diverse object scales, a Local Binary Pattern (LBP) Enhancement Module that enriches texture representations and enhances the visibility of small or camouflaged objects, and a Vector Decomposition Module that enables accurate regression of oriented bounding boxes via learnable geometric vectors. The DVDNet is embedded in a two-stage detection architecture and is particularly effective in preserving fine-grained features critical for mall object localization. The performance of DVDNet is validated on two SAR small target detection datasets, HRSID and SSDD, and it is experimentally demonstrated that it achieves 90.9% mAP on HRSID and 87.2% mAP on SSDD. The generalizability of DVDNet was also verified on the self-built SAR ship dataset and the remote sensing optical dataset HRSC2016. All these experiments show that DVDNet outperforms the standard detector. Notably, our framework shows substantial gains in precision and recall for small object subsets, validating the importance of combining deformable sampling, texture enhancement, and vector-based box representation for high-fidelity small object detection in complex SAR scenes. Full article

Building upon recent advances in tactile sensing platforms such as OptoSkin, this research introduces an enhanced multi-touch sensor design that integrates Frustrated Total Internal Reflection (FTIR) technology with embedded Time-of-Flight (ToF) sensors for superior performance. Utilizing a 2 mm thick poly(methyl methacrylate) (PMMA) acrylic light guide with an area of 200 × 300 mm², the system employs the AMS TMF8828 ToF sensor both as the illumination source and the receiver. The selected PMMA, with a refractive index of 1.49, achieves an optical field of view (FoV) of approximately 32 degrees for the ToF receiver and enables signal propagation with minimal optical loss. Remarkably, a single ToF sensor can cover an active area of 195 cm² with a linear resolution of approximately 1 cm and an angular resolution of up to 3.5 degrees. This configuration demonstrates not only the feasibility of direct FTIR–ToF integration without the need for external cameras or electrode arrays but also highlights the potential for precise, scalable, and cost-effective multi-touch sensing over large surfaces. The proposed system offers robust performance even under direct sunlight conditions, setting a new benchmark for advanced tactile interface development across consumer electronics, industrial control, and robotic skin applications. Full article

Agriculture is facing increasing challenges due to labor shortages, rising productivity demands, and the need to operate in unstructured environments. Robotics, particularly soft robotics, offers promising solutions for automating delicate tasks such as fruit harvesting. While numerous soft grippers have been proposed, most focus on grasping and lack the capability to detach fruits with rigid peduncles, which require cutting. This paper presents a novel modular hexagonal soft gripper that integrates soft pneumatic actuators, embedded mechano-optical force sensors for real-time contact monitoring, and a self-centering iris-type cutting mechanism. The entire system is 3D-printed, enabling low-cost fabrication and rapid customization. Experimental validation demonstrates successful harvesting of bell peppers and identifies cutting limitations in tougher crops such as aubergine, primarily due to material constraints in the actuation system. This dual-capability design contributes to the development of multifunctional robotic harvesters capable of adapting to a wide range of fruit types with minimal requirements for perception and mechanical reconfiguration. Full article

Liquid metal (LM), which possesses unique material properties such as excellent flexibility, high thermal and electrical conductivities, and biocompatibility, has demonstrated broad application potential in the fields of intelligent manufacturing, flexible electronics, and biomedical engineering. This paper presents a systematic review of recent advances in multifunctional LM materials for biomimetic applications, with a focus on 3D printing, catalysis, sensing, and biomedical technologies. Through advanced 3D printing techniques—including direct writing, embedded printing, and extrusion/infiltration—LM has been effectively utilized in the fabrication of high-precision electronic components. In catalysis, LM-based catalysts exhibit superior performance in energy conversion and environmental remediation due to their high catalytic activity and selectivity. Moreover, LM has made notable progress in the development of high-performance sensors and biomedical devices, contributing significantly to the advancement of health monitoring and intelligent diagnostic and therapeutic technologies. This review aims to provide theoretical insights and technical references for further research and engineering applications of liquid metals. Full article

Wearable computing is evolving from a passive data collection paradigm into an active, precision-guided health orchestration system. This survey synthesizes developments across sensing modalities, wireless protocols, computational frameworks, and AI-driven analytics that collectively define the state of the art in mental and physical health monitoring. A narrative review methodology is used to map the landscape of hardware innovations—including microfluidic sweat sensing, smart textiles, and textile-embedded biosensing ecosystems—alongside advances in on-device AI acceleration, context-aware multimodal fusion, and privacy-preserving learning frameworks. The analysis highlights a shift toward multiplexed biochemical sensing for real-time metabolic profiling, neuromorphic and analog AI processors for ultra–low-power analytics, and closed-loop therapeutic systems capable of adapting interventions dynamically to both physiological and psychological states. These trends are examined in the context of emerging clinical and consumer use cases, with a focus on scalability, personalization, and data security. By grounding these insights in current research trajectories, this work positions wearable computing as a cornerstone of preventive, personalized, and participatory healthcare. Addressing identified technical and ethical challenges will be essential for the next generation of systems to become trusted, equitable, and clinically indispensable tools. Full article

Small object detection in UAV-based remote sensing imagery is crucial for applications such as traffic monitoring, emergency response, and urban management. However, aerial images often suffer from low object resolution, complex backgrounds, and varying lighting conditions, leading to missed or false detections. To address these challenges, we propose DCS-YOLOv8, an enhanced object detection framework tailored for small target detection in UAV scenarios. The proposed model integrates a Dynamic Convolution Attention Mixture (DCAM) module to improve global feature representation and combines it with the C2f module to form the C2f-DCAM block. The C2f-DCAM block, together with a lightweight SCDown module for efficient downsampling, constitutes the backbone DCS-Net. In addition, a dedicated P2 detection layer is introduced to better capture high-resolution spatial features of small objects. To further enhance detection accuracy and robustness, we replace the conventional CIoU loss with a novel Scale-based Dynamic Balanced IoU (SDBIoU) loss, which dynamically adjusts loss weights based on object scale. Extensive experiments on the VisDrone2019 dataset demonstrate that the proposed DCS-YOLOv8 significantly improves small object detection performance while maintaining efficiency. Compared to the baseline YOLOv8s, our model increases precision from 51.8% to 54.2%, recall from 39.4% to 42.1%, $mA{P}_{0.5}$ from 40.6% to 44.5%, and $mA{P}_{0.5:0.95}$ from 24.3% to 26.9%, while reducing parameters from 11.1 M to 9.9 M. Moreover, real-time inference on RK3588 embedded hardware validates the model’s suitability for onboard UAV deployment in remote sensing applications. Full article

With the growing use of digital technologies and interactive games, there is rising interest in how people respond to challenges, stress, and decision-making in virtual environments. Studying human behavior in such settings helps to improve design, training, and user experience. Instead of relying on complex devices, Virtual Reality (VR) creates new ways to observe and understand these responses in a simple and engaging format. This study introduces a lightweight method for monitoring stress levels that uses VR as the primary sensing platform. Detection relies on behavioral signals from VR. A minimal sensor such as Galvanic Skin Response (GSR), which measures skin conductance as a sign of physiological body response, supports the Sensor-Assisted Unity Architecture. The proposed Sensor-Assisted Unity Architecture focuses on analyzing the user’s behavior inside the virtual environment along with physical sensory measurements. Most existing systems rely on physiological wearables, which add both cost and complexity. The Sensor-Assisted Unity Architecture shifts the focus to behavioral analysis in VR supplemented by minimal physiological input. Behavioral cues captured within the VR environment are analyzed in real time by an embedded processor, which then triggers simple physical feedback. Results show that combining VR behavioral data with a minimal sensor can improve detection in cases where behavioral or physiological signals alone may be insufficient. While this study does not quantitatively compare the Sensor-Assisted Unity Architecture to multi-sensor setups, it highlights VR as the main platform, with sensor input offering targeted enhancements without significantly increasing system complexity. Full article

This work reports the synthesis and characterization of Carbon Quantum Dots (CQDs) embedded in an organic–inorganic hybrid SiO₂: PMMA matrix, designed as a novel plastic scintillator material. The CQDs were synthesized through a solvo-hydrothermal method and incorporated using a sol–gel polymerization process, resulting in a mechanically durable and optically active hybrid. Structural analysis with X-ray diffraction and TEM confirmed crystalline quantum dots approximately 10 nm in size. Extensive optical characterization, including band gap measurement, photoluminescence under 325 nm UV excitation, lifetime evaluations, and quantum yield measurement, revealed a blue emission centered at 426 nm with a decay time of 3–3.6 ns. The hybrid scintillator was integrated into a compact cosmic ray detector using a photomultiplier tube optimized for 420 nm detection. The system effectively detected secondary atmospheric muons produced by low-energy cosmic rays, validated through the vertical equivalent muon (VEM) technique. These findings highlight the potential of CQD-based hybrid materials for advanced optical sensing and scintillation applications in complex environments, supporting the development of compact and sensitive detection systems. Full article

The primary distinction between technical design and engineering design lies in the role of analysis and optimization. From its inception, descriptive geometry has supported military and engineering applications, and its graphical rules inherently reflect principles of optimization—similar to the core ideas of sparse representation and compressed sensing. This paper explores the geometric and mathematical significance of the center line in symmetrical objects and the axis of rotation in solids of revolution, framing these elements within the theory of sparse representation. It further establishes rigorous correspondences between geometric primitives—points, lines, planes, and symmetric solids—and their sparse representations in descriptive geometry. By re-examining traditional engineering drawing techniques from the perspective of optimization analysis, this study reveals the hidden mathematical logic embedded in geometric constructions. The findings not only support the deeper integration of mathematical reasoning in engineering education but also provide an intuitive framework for teaching abstract concepts such as sparsity and signal reconstruction. This work contributes to interdisciplinary understanding between descriptive geometry, mathematical modeling, and engineering pedagogy. Full article

This study focuses on a comprehensive analysis of the common methods for road infrastructure monitoring, as well as the perspective of various fiber-optic sensor (FOS) realization solutions in road monitoring applications. Fiber-optic sensors are a topical technology that ensures multiple advantages such as passive nature, immunity to electromagnetic interference, multiplexing capabilities, high sensitivity, and spatial resolution, as well as remote operation and multiple physical parameter monitoring, hence offering embedment potential within the road pavement structure for needed smart road solutions. The main key factors that affect FOS-based road monitoring scenarios and configurations are analyzed within this review. One such factor is technology used for optical sensing—fiber Bragg grating (FBG), Brillouin, Rayleigh, or Raman-based sensing. A descriptive comparison is made comparing typical sensitivity, spatial resolution, measurement distance, and applications. Technological approaches for monitoring physical parameters, such as strain, temperature, vibration, humidity, and pressure, as a means of assessing road infrastructure integrity and smart application integration, are also evaluated. Another critical aspect concerns spatial positioning, focusing on the point, quasi-distributed, and distributed methodologies. Lastly, the main topical FOS-based application areas are discussed, analyzed, and evaluated. Full article

The construction and enhancement of ecological niches are essential for small and medium-sized enterprises (SMEs), with digital platforms serving as key carriers for achieving niche improvement. However, SMEs encounter a “distinctiveness–conformity” paradox when leveraging digital platforms: they are expected to sustain differentiation to attract resource tilt while simultaneously integrating into the platform ecosystem to obtain a sense of belonging and complementary resources. Grounded in optimal distinctiveness theory, this study analyzes questionnaire data from 383 Chinese SMEs embedded in digital platforms. Results show that digital platform capabilities (integration and reconfiguration) enhance SMEs ecological niches through organizational agility and platform eco-embeddedness. Polynomial regression and response surface analyses reveal that balanced improvement in organizational agility and eco-embeddedness significantly strengthens niche enhancement, whereas imbalance between the two weakens it. This research clarifies how SMEs leverage digital platform capabilities to advance their ecological niches, offering theoretical and practical insights for achieving strategic balance between distinctiveness and conformity in digital platform ecosystems. Full article

In the context of secure and efficient data transmission over the future Internet, particularly for remote sensing and geospatial applications, reversible data hiding (RDH) in encrypted hyperspectral images (HSIs) has emerged as a critical technology. This paper proposes a novel RDH scheme specifically designed for encrypted HSIs, offering enhanced embedding capacity without compromising data security or reversibility. The approach introduces a multi-layer block labeling mechanism that leverages the similarity of most significant bits (MSBs) to accurately locate embeddable regions. To minimize auxiliary information overhead, we incorporate an Extended Run-Length Encoding (ERLE) algorithm for effective label map compression. The proposed method achieves embedding rates of up to 3.79 bits per pixel per band (bpppb), while ensuring high-fidelity reconstruction, as validated by strong PSNR metrics. Comprehensive security evaluations using NPCR, UACI, and entropy confirm the robustness of the encryption. Extensive experiments across six standard hyperspectral datasets demonstrate the superiority of our method over existing RDH techniques in terms of capacity, embedding rate, and reconstruction quality. These results underline the method’s potential for secure data embedding in next-generation Internet-based geospatial and remote sensing systems. Full article

This paper presents a physics-informed digital twin designed for real-time thermal monitoring and visualization of a metallic plate. The system comprises a physical layer consisting of an aluminum plate equipped with thermistors to capture boundary conditions, a computational layer that implements the steady-state Laplace equation using the finite difference method, and an embedded execution framework deployed on a microcontroller that utilizes Direct Memory Access-driven ADC for efficient concurrent acquisition. The computed thermal field is transmitted through a serial interface and displayed in real time using a Python-based visualization interface. The Steinhart–Hart model was used to experimentally characterize the sensors, ensuring accuracy in the boundary condition acquisition. While the current formulation is restricted to steady-state conditions, it enables accurate spatial reconstructions with acceptable error margins and demonstrates operational concurrency with the physical system. The compact and modular architecture allows adaptation to other physical domains governed by elliptic PDEs, making it suitable for educational applications, diagnostic prototyping, and embedded edge deployments. Full article