Search Results (13)

This paper presents a low-cost Internet-of-Things (IoT) telemetry and alerting system for monitoring and protecting sea turtle nests. The proposed platform integrates temperature, humidity, vibration, ultrasonic proximity, and ambient light sensors into an autonomous sensing node based on the ESP8266 microcontroller. Measurements are transmitted wirelessly to a cloud backend for real-time visualization and rule-based alert generation. The system is designed to support continuous nest-level monitoring and rapid response to environmental and anthropogenic threats such as overheating, artificial light exposure during hatching, and physical disturbance. In contrast to approaches that require extensive historical datasets or machine-learning models, the proposed solution employs transparent threshold-based rules that provide reliable operation without training data. The platform emphasizes low cost, ease of deployment, and scalability, making it suitable for large-scale conservation deployments across multiple nesting sites. It provides conservation practitioners with actionable situational awareness that complements existing field-based monitoring and protection strategies. Full article

The study of wide-bandgap nanomaterials has gained considerable attention in recent years, especially in the case of semiconductor oxides that exhibit full or partial optical transparency in fundamental research and technological applications. These include optoelectronic devices, gas sensors and photovoltaic cells, among others. The activation or adjustment of optical and structural properties, especially the bandgap and the parameters of unit cell lattice, can be achieved by varying the dopant concentration during the synthesis of semiconductor thin films in these applications. In this context, copper oxide has emerged as a valuable material, owing to its thoroughly analyzed structural behavior and its broad potential across multiple technological fields. The present work focuses on the synthesis of zinc-doped copper oxide (Zn_xCu_1−xO) thin films on silicon and quartz substrates through ultrasonic spray pyrolysis. The effects of varying the zinc doping concentration (0.0, 5.0, 10.0 and 20.0 at. %) on the morphological, structural, and optical characteristics of the Zn_xCu_1−xO films were analyzed. Scanning electron microscopy (SEM) analysis indicated a gradual increase in nanoparticle size, rising from 221 nm for CuO to approximately 322 nm for the Zn_0.2Cu_0.8O samples as the zinc content increased. Structural characterization via X-ray diffraction (XRD) confirmed a monoclinic crystal arrangement belonging to the $C_{2h}^6$ (c2/c) space group. As the percentage of zinc increased, the XRD peaks shifted to lower angles, consequently increasing the volume and crystal lattice parameters of the Zn_xCu_1−xO structure; this finding was additionally supported by a redshift observed in the Raman analysis. The transmittance spectra of the films showed low transmittance between 40 and 44%. The optical bandgap of the Zn_xCu_1−xO thin films was estimated from the transmittance data by applying the Tauc plot method. A decrease in the band gap was observed at higher doping concentrations. It can be confirmed that no secondary phases are observed at a doping level of 20.0 at. % of zinc, indicating good solubility of zinc in CuO. The analysis and discussion of these findings are included throughout this work to elucidate the controversies noted in the literature. Full article

The Fourth Industrial Revolution and artificial intelligence (AI) technology are driving the transformation of the meat industry from mechanization and automation to intelligence and digitization. This paper provides a systematic review of key technological innovations in this field, including physical technologies (such as smart cutting precision improved to the millimeter level, pulse electric field sterilization efficiency exceeding 90%, ultrasonic-assisted marinating time reduced by 12 h, and ultra-high-pressure processing extending shelf life) and digital technologies (IoT real-time monitoring, blockchain-enhanced traceability transparency, and AI-optimized production decision-making). Additionally, it explores the potential of alternative meat production technologies (cell-cultured meat and 3D bioprinting) to disrupt traditional models. In application scenarios such as central kitchen efficiency improvements (e.g., food companies leveraging the “S2B2C” model to apply AI agents, supply chain management, and intelligent control systems, resulting in a 26.98% increase in overall profits), end-to-end temperature control in cold chain logistics (e.g., using multi-array sensors for real-time monitoring of meat spoilage), intelligent freshness recognition of products (based on deep learning or sensors), and personalized customization (e.g., 3D-printed customized nutritional meat products), these technologies have significantly improved production efficiency, product quality, and safety. However, large-scale application still faces key challenges, including high costs (such as the high investment in cell-cultured meat bioreactors), lack of standardization (such as the absence of unified standards for non-thermal technology parameters), and consumer acceptance (surveys indicate that approximately 41% of consumers are concerned about contracting illnesses from consuming cultured meat, and only 25% are willing to try it). These challenges constrain the economic viability and market promotion of the aforementioned technologies. Future efforts should focus on collaborative innovation to establish a truly intelligent and sustainable meat production system. Full article

Spatio-temporal measurements of turbulent free surface flows remain challenging with in situ point methods. This study explores the application of an inexpensive depth-sensing RGB-D camera, the Intel^® RealSense™ D455, to capture detailed water surface measurements of a highly turbulent, self-aerated flow in the case of a stepped spillway. Ambient lighting conditions and various sensor settings, including configurations and parameters affecting data capture and quality, were assessed. A free surface profile was extracted from the 3D measurements and compared against phase detection conductivity probe (PDCP) and ultrasonic sensor (USS) measurements. Measurements in the non-aerated region were influenced by water transparency and a lack of detectable surface features, with flow depths consistently smaller than USS measurements (up to 32.5% less). Measurements in the clear water region also resulted in a “no data” region with holes in the depth map due to shiny reflections. In the aerated flow region, the camera effectively detected the dynamic water surface, with mean surface profiles close to characteristic depths measured with PDCP and within one standard deviation of the mean USS flow depths. The flow depths were within 10% of the USS depths and corresponded to depths with 80–90% air concentration levels obtained with the PDCP. Additionally, the depth camera successfully captured temporal fluctuations, allowing for the calculation of time-averaged entrapped air concentration profiles and dimensionless interface frequency distributions. This facilitated a direct comparison with PDCP and USS sensors, demonstrating that this camera sensor is a practical and cost-effective option for detecting free surfaces of high velocity, aerated, and dynamic flows in a stepped chute. Full article

The sustainable development and preservation of natural resources have highlighted the critical need for the effective maintenance of civil engineering infrastructures. Recent advancements in technology and data digitization enable the acquisition of data from sensors on structures like bridges, tunnels, and energy production facilities. This paper explores “smart” uses of these data to optimize maintenance actions through interdisciplinary approaches, integrating artificial intelligence in civil engineering. Corrosion, a key factor affecting infrastructure health, underscores the need for robust predictive maintenance models. Supervised Machine Learning regression methods, particularly Random Forest (RF) and Artificial Neural Networks (ANNs), are investigated for predicting structural properties based on Non-Destructive Testing (NDT) data. The dataset includes various measurements such as ultrasonic, electromagnetic, and electrical on concrete samples. This study compares the performances of RF and ANN in predicting concrete characteristics, like compressive strength, elastic modulus, porosity, density, and saturation rate. The results show that, while both models exhibit strong predictive capabilities, RF generally outperforms ANN in most metrics. Additionally, SHapley Additive exPlanation (SHAP) provides insights into model decisions, ensuring transparency and interpretability. This research emphasizes the potential of integrating Machine Learning with empirical and mechanical methods to enhance infrastructure maintenance, providing a comprehensive framework for future applications. Full article

Drainage pipelines face significant threats to underground safety due to external defects. Ground Penetrating Radar (GPR) is a primary tool for detecting such defects from within the pipeline. However, existing methods are limited to single or multiple axial scan lines, which cannot provide the precise spatial coordinates of the defects. To address this limitation, this study introduces a novel GPR-based drainage pipeline inspection robot system integrated with multiple sensors. The system incorporates MEMS-IMU, encoder modules, and ultrasonic ranging modules to control the GPR antenna for axial and circumferential scanning. A novel Cross-Line Fusion of GPR (CLF-GPR) method is introduced to integrate axial and circumferential scan data for the precise localization of external pipeline defects. Laboratory simulations were performed to assess the effectiveness of the proposed technology and method, while its practical applicability was further validated through real-world drainage pipeline inspections. The results demonstrate that the proposed approach achieves axial positioning errors of less than 2.0 cm, spatial angular positioning errors below 2°, and depth coordinate errors within 2.3 cm. These findings indicate that the proposed approach is reliable and has the potential to support the transparency and digitalization of urban underground drainage networks. Full article

In this paper, an ultrasonic target detection system based on Piezoelectric Micromachined Ultrasonic Transducers (PMUTs) is proposed, which consists of the PMUTs based ultrasonic sensor and the sensor system. Two pieces of 3 × 3 PMUTs arrays with the resonant frequency of 115 kHz are used as transmitter and receiver of the PMUTs-based ultrasonic sensor. Then, the sensor system can calculate the target’s position through the signal received by the above receiver. The static and dynamic performance of the proposed prototype system are characterized on black, white, and transparent targets. The experiment results demonstrated that the proposed system can detect targets of different colors, transparencies, and motion states. In the static experiments, the static location errors of the proposed system in the range of 200 mm to 320 mm are 0.51 mm, 0.50 mm and 0.53 mm, whereas the errors of a commercial laser sensor are 2.89 mm, 0.62 mm, and N\A. In the dynamic experiments, the experimental materials are the targets with thicknesses of 1 mm, 1.5 mm, 2 mm and 2.5 mm, respectively. The proposed system can detect the above targets with a maximum detection error of 4.00%. Meanwhile, the minimum resolution of the proposed system is about 0.5 mm. Finally, in the comprehensive experiments, the proposed system successfully guides a robotic manipulator to realize the detecting, grasping, and moving of a transparent target with 1 mm. This ultrasonic target detection system has demonstrated a cost-effective method to detect targets, especially transparent targets, which can be widely used in the detection and transfer of glass substrates in automated production lines. Full article

This study synthesized pristine and aluminum (Al)-doped zinc oxide (Al:ZnO) nanostructures through a simplistic low-temperature ultrasonicated solution immersion method. Al:ZnO nanostructures were synthesized as a sensing material using different immersion times varying from two to five hours. The Al:ZnO nanostructured-based flexible humidity sensor was fabricated by employing cellulose filter paper as a substrate and transparent paper glue as a binder through a simplistic brush printing technique. XRD, FESEM, HRTEM, EDS, XPS, a two-probe I–V measurement system, and a humidity measurement system were employed to investigate the structural, morphological, chemical, electrical, and humidity-sensing properties of the pristine ZnO and Al:ZnO nanostructures. The structural and morphological analysis confirmed that Al cations successfully occupied the Zn lattice or integrated into interstitial sites of the ZnO lattice matrix. Humidity-sensing performance analysis indicated that the resistance of the Al:ZnO nanostructure samples decreased almost linearly as the humidity level increased, leading to better sensitivity and sensing response. The Al:ZnO-4 h nanostructured-based flexible humidity sensor had a maximum sensing response and demonstrated the highest sensitivity towards humidity changes, which was noticeably superior to the other tested samples. Finally, this study explained the Al:ZnO nanostructures-based flexible humidity sensor sensing mechanism in terms of chemical adsorption, physical adsorption, and capillary condensation mechanisms. Full article

Smart wearable technologies such as fitness trackers are creating many new opportunities to improve the quality of life for everyone. It is usually impossible for visually impaired people to orientate themselves in large spaces and navigate an unfamiliar area without external assistance. The design space for assistive technologies for the visually impaired is complex, involving many design parameters including reliability, transparent object detection, handsfree operations, high-speed real-time operations, low battery usage, low computation and memory requirements, ensuring that it is lightweight, and price affordability. State-of-the-art visually impaired devices lack maturity, and they do not fully meet user satisfaction, thus more effort is required to bring innovation to this field. In this work, we develop a pair of smart glasses called LidSonic that uses machine learning, LiDAR, and ultrasonic sensors to identify obstacles. The LidSonic system comprises an Arduino Uno device located in the smart glasses and a smartphone app that communicates data using Bluetooth. Arduino collects data, manages the sensors on smart glasses, detects objects using simple data processing, and provides buzzer warnings to visually impaired users. The smartphone app receives data from Arduino, detects and identifies objects in the spatial environment, and provides verbal feedback about the object to the user. Compared to image processing-based glasses, LidSonic requires much less processing time and energy to classify objects using simple LiDAR data containing 45-integer readings. We provide a detailed description of the system hardware and software design, and its evaluation using nine machine learning algorithms. The data for the training and validation of machine learning models are collected from real spatial environments. We developed the complete LidSonic system using off-the-shelf inexpensive sensors and a microcontroller board costing less than USD 80. The intention is to provide a design of an inexpensive, miniature, green device that can be built into, or mounted on, any pair of glasses or even a wheelchair to help the visually impaired. This work is expected to open new directions for smart glasses design using open software tools and off-the-shelf hardware. Full article

Photoacoustic imaging is a new type of noninvasive, nonradiation imaging modality that combines the deep penetration of ultrasonic imaging and high specificity of optical imaging. Photoacoustic imaging systems employing conventional ultrasonic sensors impose certain constraints such as obstructions in the optical path, bulky sensor size, complex system configurations, difficult optical and acoustic alignment, and degradation of signal-to-noise ratio. To overcome these drawbacks, an ultrasonic sensor in the optically transparent form has been introduced, as it enables direct delivery of excitation light through the sensors. In recent years, various types of optically transparent ultrasonic sensors have been developed for photoacoustic imaging applications, including optics-based ultrasonic sensors, piezoelectric-based ultrasonic sensors, and microelectromechanical system-based capacitive micromachined ultrasonic transducers. In this paper, the authors review representative transparent sensors for photoacoustic imaging applications. In addition, the potential challenges and future directions of the development of transparent sensors are discussed. Full article

Cellulose and its derivatives have evoked much attention in sensor technology as host-matrices for conducting materials because of their versatility, renewability, and biocompatibility. However, only a few studies have dealt with the potential utilization of cellulose as a sensing material without a composite structure. In this study, cellulose nanofibers (CNF) and 2,2,6,6-tetramethylpiperidine-1-oxyl (TEMPO)-oxidized cellulose nanofibers (TOCNF) extracted from rice husks by using ultrasonic-assisted methods are introduced as a potential gas sensing material with highly sensitive performance. To fabricate nanocellulose-based films, CNF, TOCNF, and TOCNF with glycerol (TOCNF/G) were dispersed in water and applied on polyimide substrate with digital electrodes to form self-standing thin films by a drop-casting method. A transparent coating layer on the surface of the plate after drying is used for the detection of water-soluble gases such as acetone, ammonia, methane, and hydrogen sulfide gases at room temperature at 52% relative humidity. The sensor prototypes exhibited high sensitivity, and the detection limit was between 1 ppm and 5 ppm, with less than 10 min response and recovery time. The results indicate that both the CNF- and the TOCNF-coated sensors show good sensitivity toward ammonia and acetone, compared to other gases. A TOCNF/G-coated sensor exhibited minimum time in regard to response/recovery time, compared to a CNF-coated sensor. In this study, nanocellulose-based sensors were successfully fabricated using a low-cost process and a bio-based platform. They showed good sensitivity for the detection of various gases under ambient conditions. Therefore, our study results should further propel in-depth research regarding various applications of cellulose-based sensors in the future. Full article

In this paper, we propose a theoretical framework to explain how the transparent elastic grating structure can be employed to enhance the mechanical and optical properties for ultrasonic detection. Incident ultrasonic waves can compress the flexible material, where the change in thickness of the elastic film can be measured through an optical interferometer. Herein, the polydimethylsiloxane (PDMS) was employed in the design of a thin film grating pattern. The PDMS grating with the grating period shorter than the ultrasound wavelength allowed the ultrasound to be coupled into surface acoustic wave (SAW) mode. The grating gaps provided spaces for the PDMS grating to be compressed when the ultrasound illuminated on it. This grating pattern can provide an embedded thin film based optical interferometer through Fabry–Perot resonant modes. Several optical thin film-based technologies for ultrasonic detection were compared. The proposed elastic grating gave rise to higher sensitivity to ultrasonic detection than a surface plasmon resonance-based sensor, a uniform PDMS thin film, a PDMS sensor with shearing interference, and a conventional Fabry–Perot-based sensor. The PDMS grating achieved the enhancement of sensitivity up to 1.3 × 10⁻⁵ Pa⁻¹ and figure of merit of 1.4 × 10⁻⁵ Pa⁻¹ which were higher than those of conventional Fabry–Perot structure by 7 times and 4 times, respectively. Full article

Environment maps must first be generated to drive mobile robots automatically. Path planning is performed based on the information given in an environment map. Various types of sensors, such as ultrasonic and laser sensors, are used by mobile robots to acquire data on its surrounding environment. Among these, the laser sensor, which has the property of being able to go straight and high accuracy, is used most often. However, the beams from laser sensors are refracted and reflected when it meets a transparent obstacle, thus generating noise. Therefore, in this paper, a state-of-the-art algorithm was proposed to detect transparent obstacles by analyzing the pattern of the reflected noise generated when a laser meets a transparent obstacle. The experiment was carried out using the environment map generated by the aforementioned method and gave results demonstrating that the robot could avoid transparent obstacles while it was moving towards the destination. Full article