Search Results (6,380)

Simultaneous Localization and Mapping (SLAM) is a critical technology for robot intelligence. Compared to cameras, Light Detection and Ranging (LiDAR) sensors achieve higher accuracy and stability in indoor environments. However, LiDAR can only capture the geometric structure of the environment, and LiDAR-based SLAM often fails in scenarios with insufficient geometric features or highly similar structures. Furthermore, low-cost mechanical LiDARs, constrained by sparse point cloud density, are particularly prone to odometry drift along the Z-axis, especially in environments such as tunnels or long corridors. To address the localization issues in such scenarios, we propose a forward-enhanced SLAM algorithm. Utilizing a 16-line LiDAR and a monocular camera, we construct a dense colored point cloud input and apply an efficient multi-modal feature extraction algorithm based on centroid distance to extract a set of feature points with significant geometric and color features. These points are then optimized in the back end based on constraints from points, lines, and planes. We compare our method with several classic SLAM algorithms in terms of feature extraction, localization, and elevation constraint. Experimental results demonstrate that our method achieves high-precision real-time operation and exhibits excellent adaptability to indoor environments with similar structures. Full article

In addressing the challenges posed by high costs, low accuracy, and cumbersome operations in mycotoxin detection, a novel aptamer-antibody sandwich chemiluminescent biosensor for detecting aflatoxin B₁ (AFB₁) was developed. The indirect competition between AFB₁, aflatoxin B₁-ovomucoid complete antigen (AFB₁-OVA), and rabbit anti-ovomucoid (OVA) antibody results in the formation of a sandwich complex. This sandwich assay is linked to a horseradish peroxidase-labeled antibody, which catalyzes luminol chemiluminescence for the indirect detection of AFB₁. The biosensor was designed to operate with high precision, low cost, and a low detection limit for AFB₁, which is contingent upon experimental conditions such as pH, reagent concentration, temperature, and incubation time. The optimization of pH, aptamer concentration, competitive incubation time, competitive incubation temperature, and HRP-labeled antibody concentration was instrumental in achieving these objectives. Experimental findings demonstrated that the sensor’s detection limit was 0.067 ng/mL, exhibiting excellent linearity (R² = 0.99679) within the concentration range of 0.25–10 ng/mL. The recovery rate of spiked samples ranged from 94.4% to 108.05%. This sensor boasts a low detection limit, straightforward operation, and minimal cost, thus offering a novel solution for developing cost-effective, high-precision mycotoxin detection methods. Full article

Accurate detection of water in organic solvents is essential for various industrial and analytical applications. In this study, we present a simple, rapid, and sensitive fluorescence-based method for water quantification using 1,5-diaminonaphthalene (1,5-DAN) as a solvatochromic probe. This method exploits the excited-state intramolecular charge transfer (ICT) behavior of 1,5-DAN, which undergoes a symmetry-breaking transition in the presence of protic solvents such as water, leading to a distinct redshift in its emission spectrum and a change from a structured double-band to a single ICT band. We demonstrate that, in solvents like acetonitrile and tetrahydrofuran, the emission maxima of 1,5-DAN correlate linearly with water content up to 100%, while ratiometric analysis of peak intensities allows for sensitive detection in low concentration ranges. This method achieved limits of detection as low as 0.08% (v/v) in MeCN, with high reproducibility and minimal sample preparation. Application to a real MeCN–water azeotrope confirms the method’s accuracy, matching classical refractometric measurements. Our findings highlight the potential of 1,5-DAN as a low-cost, efficient, and non-destructive fluorescent sensor for monitoring moisture in organic solvents, offering a practical alternative to conventional methods such as Karl Fischer titration for both bulk and trace water analysis. Full article

Non-nutritive sucking (NNS) is an instinctive behavior in newborns, consisting of two stages: sucking and expression. It plays a critical role in preparing the infant for oral feeding. In neonatal and pediatric units, NNS assessment is routinely performed to determine feeding readiness. However, these evaluations are often subjective and rely heavily on the clinician’s experience. While other medical devices that support the development of NNS skills exist, they are not specifically designed for the comprehensive assessment of NNS, and their high cost limits accessibility for many hospitals and tertiary care units globally. This paper presents the development and pilot testing of a low-cost, portable device and accompanying software for assessing NNS in newborns hospitalized in neonatal care units. Methods: The device uses force-sensitive resistors to capture expression pressure and a differential pressure sensor to measure NNS. Data were acquired through the analog–digital converter of a microcontroller and transmitted via Bluetooth for real-time graphical analysis. Pilot testing was conducted with six hospitalized preterm newborns, measuring intensity, number of bursts, and sucks per burst. Results demonstrated that the system reliably captures both stages of NNS. Significance: This device provides an affordable, portable solution to support clinical decision-making in clinical units, facilitating accurate, objective monitoring of feeding readiness and developmental progression. Full article

Evaluating the response of maize crops to different nitrogen fertilization rates is essential to ensure their agronomic, environmental, and economic efficiency. In this study, the spectral information of maize plants subjected to five distinct nitrogen fertilization strategies was analyzed. The fertilization strategies were based on the practices commonly used in maize fields in the study area, with the aim of ensuring the research findings’ applicability. The spectral reflectance was measured using a spectroradiometer covering the 350–2500 nm range, and the results enabled the identification of optimal spectral regions for monitoring plants’ nitrogen status, particularly in the visible and infrared ranges. A Principal Component Analysis (PCA) of the reflectance data revealed the key wavelengths most sensitive to the nitrogen availability: 555 nm and 720 nm during the vegetative stage and 680 nm during the reproductive stage. This information will support the development of drone-mounted multispectral sensor systems for large-scale monitoring, as well as the design of low-cost sensors for early nitrogen deficiency detection. Furthermore, the study demonstrated the feasibility of estimating the cornstalk nitrate content based on direct reflectance measurements of maize stems. The prediction model showed satisfactory performance, with a coefficient of determination (R²) of 0.845 and a root mean square error of prediction (RMSECV) of 2035.3 ppm, indicating its strong potential for predicting the NO₃-N concentrations in maize stems. Full article

We present a comprehensive study of LED-based optical sensing systems for the classification of waste materials, analyzing recent developments in the field. Accurate identification of materials such as plastics, glass, aluminum, and paper is a crucial yet challenging task in waste management for recycling. The first approach uses short-wave infrared reflectance spectroscopy with commercial Germanium photodetectors and selected LEDs to keep data complexity and cost at a minimum while achieving classification accuracies up to 98% with machine learning algorithms. The second system employes a voltage-tunable Germanium-on-Silicon photodetector that operates across a broader spectral range (400–1600 nm), in combination with three LEDs in both the visible and short-wave infrared bands. This configuration enables an adaptive spectral response and simplifies the optical setup, supporting energy-efficient and scalable integration. Accuracies up to 99% were obtained with the aid of machine learning algorithms. Across all systems, the strategic use of low-cost LEDs as light sources and compact optical sensors demonstrates the potential of light-emitting devices in the implementation of compact, intelligent, and sustainable solutions for real-time material recognition. This article explores the design, characterization, and performance of such systems, providing insights into the way light-emitting and optoelectronic components can be leveraged for advanced sensing in waste classification applications. Full article

Students and educators spend considerable time in indoor learning spaces on university campuses, where indoor air quality (IAQ), of which particulate matter (PM) is an important component, is a critical concern that architecture students must address. However, IAQ is seldom monitored and very rarely, if at all, reported in these spaces. We used a novel living lab approach to provide third-year students of building services with a hands-on learning activity. During a two-week monitoring period, students designed, assembled, and operated low-cost PM sensors using Arduino platforms. The data analysis showed hotspots where the IAQ was consistently compromised and showed repetitive patterns in time. Workshop and laboratory areas repeatedly recorded the highest PM levels in 15 min sampling events distributed over daily two-hour segments, averaging 43.3 and 47.9 μg/m³ PM₁₀, respectively, with maxima of 118.6 and 119.9 μg/m³ PM₁₀. These measurements would have qualified as ‘moderate’ IAQ if sustained over a full day. A distinct weekly pattern was discovered, with Mondays being worse. The results demonstrated a new practical approach to monitoring the building’s IAQ at minimal cost while obtaining reproducible data. This tool provided educators with a valuable teaching tool that provided students with a deeper understanding of indoor air pollution. Full article

Sixth-generation (6G) wireless technology has facilitated the rapid development of the Internet of Things (IoT), enabling various end devices to be deployed in applications such as wireless multimedia sensor networks. However, most end devices encounter difficulties when dealing a large amount of IoT video data due to their lack of computational resources for visual object tracking. Discriminative correlation filter (DCF)-based tracking approaches possess favorable properties for resource-constrained end devices, such as low computational costs and robustness to motion blur and illumination variations. Most current DCF trackers employ multiple features and the spatial–temporal scale space to estimate the target state, both of which may be suboptimal due to their fixed feature dimensions and dense scale intervals. In this paper, we present an adaptive mapped-feature and scale-interval method based on DCF to alleviate the problem of suboptimality. Specifically, we propose an adaptive mapped-feature response based on dimensionality reduction and histogram score maps to integrate multiple features and boost tracking effectiveness. Moreover, an adaptive temporal scale estimation method with sparse intervals is proposed to further improve tracking efficiency. Extensive experiments on the DTB70, UAV112, UAV123@10fps and UAVDT datasets demonstrate the superiority of our method, with a running speed of 41.3 FPS on a cheap CPU, compared to state-of-the-art trackers. Full article

Aging infrastructure is a growing concern worldwide, with many bridges exceeding 50 years of service, prompting questions about their structural integrity. Over the past decade, the deterioration of bridges has driven extensive research into Structural Health Monitoring (SHM), a tool for early detection of structural deterioration, with particular emphasis on remote-sensing technologies. This review combines a scientometric analysis and a state-of-the-art review to assess recent advancements in the field. From a dataset of 702 publications (2014–2024), 171 relevant papers were analyzed, covering key SHM aspects including sensing devices, data acquisition, processing, damage detection, and reporting. Results show a 433% increase in publications, with the United States leading in output (28.65%), and Glisic, B., with collaborators forming the largest research cluster (11.7%). Accelerometers are the most commonly used sensors (50.88%), and data processing dominates the research focus (50.29%). Key challenges identified include cost (noted in 17.5% of studies), data corruption, and WSN limitations, particularly energy supply. Trends show a notable growth in AI applications (400%), and increasing interest in low-cost, crowdsource-based SHM using smartphones, MEMS, and cameras. These findings highlight both progress and future opportunities in SHM of footbridges. Full article

A facile and cost-effective sol–gel method for the synthesis of uniformly porous alumina (Al₂O₃) was developed using stable CTAB/hexanol/water microemulsions as soft templates. The phase behavior of the ternary system was investigated to identify compositions that form kinetically stable microemulsions, with an optimal ratio of 7.5 wt.% CTAB, 5 wt.% hexanol, and 87.5 wt.% water exhibiting minimal droplet size variation over one week. Gelation was induced by partial neutralization to pH 4.2 with ammonium carbonate, promoting the formation of polynuclear Al species and enabling the uniform entrapment of hexanol droplets. Lyophilization preserved the porous network, and calcination at 500 °C yielded η-Al₂O₃ with a large specific surface area (~225 m²·g⁻¹) and a narrow mesopore size distribution centered around 100 nm, consistent with the original droplet size. Mercury porosimetry and SEM analyses confirmed a highly porous, low-density material (0.75 g·cm⁻³) with an interconnected pore morphology. This scalable synthesis method, supported by the high kinetic stability of the microemulsion, provides sufficient processing time and eliminates the need for post-synthesis purification. It shows strong potential for producing advanced alumina materials for use in energy storage, catalysis, and sensor applications. Full article

The livestock sector significantly contributes to atmospheric emissions of various pollutants, such as ammonia (NH₃) and particulate matter of diameter under 2.5 µm (PM2.5) from activity and barn management. The objective of this study was to evaluate the reliability of low-cost sensors integrated with an IoT system for monitoring PM2.5 concentrations in a dairy barn. To this end, data acquired by a PM2.5 measurement device has been validated by using a high-precision one. Results demonstrated that the performances of low-cost sensors were highly correlated with temperature and humidity parameters recorded in its own IoT platform. Therefore, a parameter-based adjustment methodology is proposed. As a result of the statistical assessments conducted on this data, it has been demonstrated that the analysed sensor, when corrected using the proposed correction model, is an effective device for the purpose of monitoring the mean daily levels of PM2.5 within the barn. Although the model was developed and validated by using data collected from a dairy barn, the proposed methodology can be applied to these sensors in similar environments. Implementing reliable and affordable monitoring systems for key pollutants is crucial to enable effective mitigation strategies. Due to their low cost, ease of transport, and straightforward installation, these sensors can be used in multiple locations within a barn or moved between different barns for flexible and widespread air quality monitoring applications in livestock barns. Full article

The integration of 3D printing into the development of potentiometric sensors has revolutionized sensor fabrication by enabling customizable, low-cost, and rapid prototyping of analytical devices. Techniques like fused deposition modeling (FDM) and stereolithography (SLA) allow researchers to produce different sensor parts, such as electrode housings, solid contacts, reference electrodes, and even microfluidic systems. This review explains the basic principles of potentiometric sensors and shows how 3D printing helps solve problems faced in traditional sensor manufacturing. Benefits include smaller size, flexible shapes, the use of different materials in one print, and quick production of working prototypes. However, some challenges still exist—like differences between prints, limited chemical resistance of some materials, and the long-term stability of sensors in real-world conditions. This paper overviews recent examples of 3D-printed ion-selective electrodes and related components and discusses new ideas to improve their performance. It also points to future directions, such as better materials and combining different manufacturing methods. Overall, 3D printing is a powerful and growing tool for developing the next generation of potentiometric sensors for use in healthcare, environmental monitoring, and industry. Full article

This study presents a portable gas sensor system that achieves high performance while minimizing power consumption and production costs for biomedical applications. The proposed system integrates a low-power readout integrated circuit (ROIC) capable of processing large-amplitude sensor signals using a 1.2 V ADC, significantly reducing the power consumption compared with conventional high-voltage solutions. To address the inherent limitations of single-core/single-thread microcontrollers, an optimized Wi-Fi communication algorithm is implemented, enabling real-time data transmission and accurate signal reconstruction without data loss. Experimental validation using a hydrogen gas detection setup demonstrated that the system achieves less than 0.15% error in reconstructed signals, while substantially reducing overall power consumption and component cost. Comparative analysis confirms that the proposed approach achieves a performance comparable to conventional systems while offering significant reductions in energy usage and hardware expense. These results demonstrate the feasibility of a scalable, low-cost solution for portable gas sensing, particularly in biomedical applications requiring precise and reliable monitoring. Full article

For fruit harvesting robots, rapid and accurate detection of fruits and picking points is one of the main challenges for their practical deployment. Several fruits typically grow in clusters or bunches, such as grapes, cherry tomatoes, and blueberries. For such clustered fruits, it is desired for them to be picked by bunches instead of individually. This study proposes utilizing a low-cost off-the-shelf RGB-D sensor mounted on the end effector and a lightweight improved YOLOv8-Pose neural network to detect cherry tomato bunches and picking points for robotic harvesting. The problem of occlusion and overlap is alleviated by merging RGB and depth images from the RGB-D sensor. To enhance detection robustness in complex backgrounds and reduce the complexity of the model, the Starblock module from StarNet and the coordinate attention mechanism are incorporated into the YOLOv8-Pose network, termed StarBL-YOLO, to improve the efficiency of feature extraction and reinforce spatial information. Additionally, we replaced the original OKS loss function with the L1 loss function for keypoint loss calculation, which improves the accuracy in picking points localization. The proposed method has been evaluated on a dataset with 843 cherry tomato RGB-D image pairs acquired by a harvesting robot at a commercial greenhouse farm. Experimental results demonstrate that the proposed StarBL-YOLO model achieves a 12% reduction in model parameters compared to the original YOLOv8-Pose while improving detection accuracy for cherry tomato bunches and picking points. Specifically, the model shows significant improvements across all metrics: for computational efficiency, model size (−11.60%) and GFLOPs (−7.23%); for pickable bunch detection, mAP50 (+4.4%) and mAP50-95 (+4.7%); for non-pickable bunch detection, mAP50 (+8.0%) and mAP50-95 (+6.2%); and for picking point detection, mAP50 (+4.3%), mAP50-95 (+4.6%), and RMSE (−23.98%). These results validate that StarBL-YOLO substantially enhances detection accuracy for cherry tomato bunches and picking points while improving computational efficiency, which is valuable for resource-constrained edge-computing deployment for harvesting robots. Full article

Tactile sensing is increasingly vital in robotics, especially for tasks like object manipulation and texture classification. Among tactile technologies, optical and electrical sensors are widely used, yet no rigorous direct comparison of their performance has been conducted. This paper addresses that gap by presenting a comparative study between a high-resolution optical tactile sensor (a modified TacTip) and a low-resolution electrical sensor combining accelerometers and piezoelectric elements. We evaluate both sensor types on two tasks: texture classification and coefficient of dynamic friction prediction. Various configurations and resolutions were explored, along with multiple machine learning classifiers to determine optimal performance. The optical sensor achieved 99.9% accuracy on a challenging texture dataset, significantly outperforming the electrical sensor, which reached 82%. However, for dynamic friction prediction, both sensors performed comparably, with only a $\tilde{5}$% accuracy difference. We also found that the optical sensor retained high classification accuracy even when image resolution was reduced to 25% of its original size, suggesting that ultra-high resolution is not essential. In conclusion, the optical sensor is the better choice when high accuracy is required. However, for low-cost or computationally efficient systems, the electrical sensor provides a practical alternative with competitive performance in some tasks. Full article