Search Results (5,766)

Modern, large bridges and tunnels represent important nodes in transportation arteries and have a significant impact on the development of transportation. The health and safety monitoring of these structures has always been a significant concern and is reliant on various types of sensors. Distributed acoustic sensing (DAS) with telecommunication fibers is an emerging technology in the research areas of sensing and communication. DAS provides an effective and low-cost approach for the detection of various resources and seismic activities. In this study, field experiments are elucidated, using DAS for the Hong Kong–Zhuhai–Macao Bridge, and for studying vehicle trajectories, earthquakes, and other activities. The basic signal-processing methods of filtering and normalization are adopted for analyzing the data obtained with DAS. With the proposed DAS technology, the activities on shore, vehicle trajectories on bridges and in tunnels during both day and night, and microseisms within 200 km were successfully detected. Enabled by DAS technology and mass fiber networks, more studies on sensing and communication systems for the monitoring of bridge and tunnel engineering are expected to provide future insights. Full article

One of the critical challenges in mobile robotics is obstacle avoidance, ensuring safe navigation in dynamic environments. In this sense, this work presents a comparative study of two intelligent control approaches for mobile robot obstacle avoidance based on a fuzzy architecture. The first approach is a neuro-fuzzy interface that combines neural networks’ learning capabilities with fuzzy logic’s rule-based reasoning, offering a flexible and adaptable control strategy. The second is a classic Mamdani fuzzy system that relies on human-defined fuzzy rules, providing an intuitive approach to control. A key contribution of this work is the development of a fast comprehensive, model-based dataset for neural network training generated without the need for real sensor data. The results show the evaluation of these two systems’ performance, robustness, and computational efficiency using low-cost ultrasonic sensors on a Pioneer 3DX robot within the Coppelia Sim environment. Full article

The colorimetric method, due to its rapid and low-cost characteristics, demonstrates a wide range of application prospects in on-site water quality testing. Current research on colorimetric detection using deep learning algorithms predominantly focuses on single-target classification. To address this limitation, we propose a multi-task water quality colorimetric detection method based on YOLOv8n, leveraging deep learning techniques to achieve a fully automated process of “image input and result output”. Initially, we constructed a dataset that encompasses colorimetric sensor data under varying lighting conditions to enhance model generalization. Subsequently, to effectively improve detection accuracy while reducing model parameters and computational load, we implemented several improvements to the deep learning algorithm, including the MGFF (Multi-Scale Grouped Feature Fusion) module, the LSKA-SPPF (Large Separable Kernel Attention-Spatial Pyramid Pooling-Fast) module, and the GNDCDH (Group Norm Detail Convolution Detection Head). Experimental results demonstrate that the optimized deep learning algorithm excels in precision (96.4%), recall (96.2%), and mAP50 (98.3), significantly outperforming other mainstream models. Furthermore, compared to YOLOv8n, the parameter count and computational load were reduced by 25.8% and 25.6%, respectively. Additionally, precision improved by 2.8%, recall increased by 3.5%, mAP50 enhanced by 2%, and mAP95 rose by 1.9%. These results affirm the substantial potential of our proposed method for rapid on-site water quality detection, offering new technological insights for future water quality monitoring. Full article

In this paper, an inductively coupled capacitively loaded ring resonator (IC-CLRR)-based microwave resonant sensor has been proposed for the accurate identification of any unknown liquid sample and its permittivity estimation. The key element of this work is the sensor’s wide range capability towards the non-invasive testing of liquids covering a wide dielectric range of liquid samples, i.e., ε_r = 2 to 80. The proposed microwave sensor is etched over the FR-4 substrate and is excited by the microstrip line through inductive coupling. The placement of an unknown liquid sample in close proximity to the sensor alters its natural resonant frequency due to a change in effective inductance and capacitance as per the dielectric property of the liquid sample. Further, a mathematical formulation using curve fitting has also been derived. The measurement results show a good accuracy in estimating the permittivity and, thus, the unknown liquid identification capability of the designed sensor with a very low error (nearly 5%). This sensor design is simple to fabricate, cost-friendly, and small in size. Full article

We investigated the design and characterization of a Lab-On-a-Chip (LoC) cell detection system primarily designed to support immunotherapy in cancer treatment. Immunotherapy uses Chimeric Antigen Receptors (CARs) and T Cell Receptors (TCRs) to fight cancer, engineering the response of the immune system. In recent years, it has emerged as a promising strategy for personalized cancer treatment. However, it requires bioreactor-based cell culture expansion and manual quality control (QC) of the modified cells, which is time-consuming, labour-intensive, and prone to errors. The miniaturized LoC device for automated QC demonstrated here is simple, has a low cost, and is reliable. Its final target is to become one of the building blocks of an LoC for immunotherapy, which would take the place of present labs and manual procedures to the benefit of throughput and affordability. The core of the system is a commercial, on-chip-integrated capacitive sensor managed by a microcontroller capable of sensing cells as accurately measured charge variations. The hardware is based on standardized components, which makes it suitable for mass manufacturing. Moreover, unlike in other cell detection solutions, no external AC source is required. The device has been characterized with a cell line model selectively labelled with gold nanoparticles to simulate its future use in bioreactors in which labelling can apply to successfully engineered CAR-T-cells. Experiments were run both in the air—free drop with no microfluidics—and in the channel, where the fluid volume was considerably lower than in the drop. The device showed good sensitivity even with a low number of cells—around 120, compared with the 10⁷ to 10⁸ needed per kilogram of body weight—which is desirable for a good outcome of the expansion process. Since cell detection is needed in several contexts other than immunotherapy, the usefulness of this LoC goes potentially beyond the scope considered here. Full article

This study proposes a portable and IoT-based electrochemical point-of-care sensing device for detecting zopiclone in cocktails. The system utilizes an electrochemical laccase biosensor and a potentiostat, offering a low-cost and portable device for detecting this sedative drug in cocktails. The sensor characterization experiments demonstrated the linear behavior of the oxidation and reduction currents for each of the targeted concentrations of zopiclone, enabling their detection and quantification even when mixed with an interfering substance. The proposed system could be used for the in situ analysis of cocktails, providing a valuable tool for monitoring the presence of hypnotic drugs in various social and clinical settings. The study utilized materials and reagents, including zopiclone, lab-made lemon juice, lab-made tequila, and lab-made triple sec, all prepared with reactants obtained in Bogotá, Colombia. The potentiostat used in the system was designed to manage cyclic voltammetry measurements. The electrochemical cells’ durability and longevity were also tested and characterized, with all electrodes undergoing 200 tests and their performance degradation varying according to the molecule used. The study concludes that the proposed system offers a valuable tool for detecting and monitoring pharmaceutical substances in various interfering ingredients that build up cocktails. Further research and application of this system can help address the global concern surrounding the administration of hypnotic substances to unknowing consumers through food or drinks to enable robbery and sexual assault. Full article

The efficient, economical, and sustainable production of biodiesel from waste cooking oils (WCOs) depends on the availability of simple, rapid, and low-cost methods to test the quality of potential feedstocks. The aim of this study was to establish the applicability of stochastic modeling of e-nose profiles in the evaluation of recycled WCO characteristics. Olfactory profiles of 10 WCOs were determined using a Sensigent Cyranose^® 320 chemical vapor-sensing device with a 32 sensor-array, and a stepwise multiple linear regression (MLR) analysis was performed to select stochastic parameters (explanatory variables) for inclusion in the final predictive models of the physicochemical properties of the WCOs. The most important model parameters for the characterization of WCOs were those relating to the time of inception of the e-nose signal “plateau” and to the concentration of volatile organic compounds (VOCs) in the sensor region. A comparison of acid values, peroxide values, water contents, and kinematic viscosities predicted by the MLR models with those determined by conventional laboratory methods revealed that goodness of fit and predictor accuracy varied from good to excellent, with all metric values >90%. Combining e-nose profiling with stochastic modeling was successful in predicting the physicochemical characteristics of WCOs and could be used to select suitable raw materials for efficient and sustainable biodiesel production. Full article

Gold nanoparticles (AuNPs) are highly attractive for applications in the field of biosensing, particularly for colorimetric nucleic acid detection. Their unique optical properties, which are highly sensitive to changes in their environment, make them ideal candidates for developing simple, rapid, and cost-effective assays. When functionalized with oligonucleotides (Au-nanoprobes), they can undergo aggregation or dispersion in the presence of complementary sequences, leading to distinct color changes that serve as a visual signal for detection. Aggregation-based assays offer significant advantages over other homogeneous assays, such as fluorescence-based methods, namely, label-free protocols, rapid interactions in homogeneous solutions, and detection by the naked eye or using low-cost instruments. Despite promising results, the application of Au-nanoprobe-based colorimetric assays in complex biological matrices faces several challenges. The most significant are related to the colloidal stability and oligonucleotide functionalization of the Au-nanoprobes but also to the mode of detection. The type of functionalization method, type of spacer, the oligo–AuNPs ratio, changes in pH, temperature, or ionic strength influence the Au-nanoprobe colloidal stability and thus the performance of the assay. This review elucidates characteristics of the Au-nanoprobes that are determined for colorimetric gold nanoparticles (AuNPs)-based nucleic acid detection, and how they influence the sensitivity and specificity of the colorimetric assay. These characteristics of the assay are fundamental to developing low-cost, robust biomedical sensors that perform effectively in biological fluids. Full article

Popular photoluminescent (PL) nanomaterials, such as carbon dots, have attracted substantial attention from scientists due to their photophysical properties, biocompatibility, low cost, and diverse applicability. Carbon dots have been used in sensors, cell imaging, and cancer therapy. Leek seeds with anticancer, antimicrobial, and antioxidant functions serve as traditional Chinese medicine. However, leek seeds have not been studied as a precursor of carbon dots. In this study, leek seeds underwent a supercritical fluid extraction process. Leek seed extract was obtained and then carbonized using a dry heating method, followed by hydrolysis to form carbon dot micelles (CD-micelles). CD-micelles exhibited analyte-induced PL quenching against Co²⁺ through the static quenching mechanism, with the formation of self-assembled Co²⁺-CD-micelle sphere particles. In addition, CD-micelles extracted metal ion through liquid–liquid extraction, with removal efficiencies of >90% for Pb²⁺, Al³⁺, Fe³⁺, Cr³⁺, Pd²⁺, and Au³⁺. Moreover, CD-micelles exhibited ABTS•⁺ radical scavenging ability and cytotoxicity for cisplatin-resistant lung cancer cells. CD-micelles killed cisplatin-resistant small-cell lung cancer cells in a dose-dependent manner with a cancer cell survival rate down to 12.8 ± 4.2%, with a similar treatment function to that of cisplatin. Consequently, CD-micelles functionalized as novel antioxidants show great potential as anticancer nanodrugs in cancer treatment. Full article

With escalating global environmental challenges and worsening air quality, there is an urgent need for enhanced environmental monitoring capabilities. Low-cost sensor networks are emerging as a vital solution, enabling widespread and affordable deployment at fine spatial resolutions. In this context, machine learning for the calibration of low-cost sensors is particularly valuable. However, traditional machine learning models often lack interpretability and generalizability when applied to complex, dynamic environmental data. To address this, we propose a causal feature selection approach based on convergent cross mapping within the machine learning pipeline to build more robustly calibrated sensor networks. This approach is applied in the calibration of a low-cost optical particle counter OPC-N3, effectively reproducing the measurements of PM1 and PM2.5 as recorded by research-grade spectrometers. We evaluated the predictive performance and generalizability of these causally optimized models, observing improvements in both while reducing the number of input features, thus adhering to the Occam’s razor principle. For the PM1 calibration model, the proposed feature selection reduced the mean squared error on the test set by 43.2% compared to the model with all input features, while the SHAP value-based selection only achieved a reduction of 29.6%. Similarly, for the PM2.5 model, the proposed feature selection led to a 33.2% reduction in the mean squared error, outperforming the 30.2% reduction achieved by the SHAP value-based selection. By integrating sensors with advanced machine learning techniques, this approach advances urban air quality monitoring, fostering a deeper scientific understanding of microenvironments. Beyond the current test cases, this feature selection method holds potential for broader applications in other environmental monitoring applications, contributing to the development of interpretable and robust environmental models. Full article

Due to the growth of renewable energies, which requires cost reduction and efficiency in terms of structural health assessment, failure prevention, effective maintenance scheduling, and equipment lifespan optimization, in this paper, we propose an Ultra Wideband (UWB)-based accelerometer Sensor Node for low-power applications in offshore platforms. The proposed Sensor Node integrates a high-resolution accelerometer together with an Impulse Radio Ultra-Wideband (IR-UWB) transceiver. This approach enables effective remote monitoring of structural vibrations. This provides an easy-to-install, scalable, and flexible wireless solution without sacrificing robustness and low power consumption in marine environments. Additionally, due to the diverse and highly demanding applications of condition monitoring systems, we propose two modes of operation for the Sensor Node. It can be remotely configured to either transmit raw data for further analysis or process data at the edge. A hardware (HW) description of the proposed Sensor Node is provided. Moreover, we describe the power management strategies implemented in our system at the firmware (FW) level. We show detailed power consumption measurements, including power profiles and the battery-powered autonomy of the proposed Sensor Node. We compare data from a wired acquisition system and the proposed wireless Sensor Node in a laboratory environment.The wired sensor integrated into this acquisition system, fully characterized and tested, is our golden reference. Thus, we validate our proposal. Furthermore, this research work is within the scope of the SUREWAVE Project and is conducted in collaboration with the MARIN Institute, where wave basin tests are carried out to evaluate the behavior of a Floating Photovoltaic (FPV) system. These tests have provided a valuable opportunity to assess the effectiveness of the proposed Sensor Node for offshore platforms and to compare its performance with a wired system. Full article

The rise of mobile communication, low-power chips, and the Internet of Things has made smartwatches increasingly popular. Equipped with inertial measurement units (IMUs), these devices can recognize user activities through artificial intelligence (AI) analysis of sensor data. However, most existing AI-based activity recognition algorithms require significant computational power and storage, making them unsuitable for low-power devices like smartwatches. Additionally, discrepancies between training data and real-world data often hinder model generalization and performance. To address these challenges, we propose LIMUNet and its smaller variant LIMUNet-Tiny—lightweight neural networks designed for human activity recognition on smartwatches. LIMUNet utilizes depthwise separable convolutions and residual blocks to reduce computational complexity and parameter count. It also incorporates a dual attention mechanism specifically tailored to smartwatch sensor data, improving feature extraction without sacrificing efficiency. Experiments on the PAMAP2 and LIMU datasets show that the LIMUNet improves recognition accuracy by 2.9% over leading lightweight models while reducing parameters by 88.3% and computational load by 58.4%. Compared to other state-of-the-art models, LIMUNet achieves a 9.6% increase in accuracy, with a 60% reduction in parameters and a 57.8% reduction in computational cost. LIMUNet-Tiny further reduces parameters by 75% and computational load by 80%, making it even more suitable for resource-constrained devices. Full article

Breathing is the process of exchanging gases between the human body and the surrounding environment. It plays a vital role in maintaining human health, sustaining life, and supporting various bodily functions. Unfortunately, current methods for monitoring respiration are impractical for medical applications because of their high costs and need for bulky equipment. When measuring changes in moisture during respiration, we observed a slow response time for 2D nanomaterial-based resistance measurement methods used in respiration sensors. Through thermal annealing, the crystal structure of MoS₂ is transformed from 1T@2H to 2H, allowing the measurement of respiration at more than 30 cycles per minute and enabling analysis of the response. This study highlights the potential of two-dimensional nanomaterials for the development of low-cost and highly sensitive humidity and respiration sensors for various applications. Full article

Heat velocity (V_h) is a key metric to estimate sap flow which is linked to transpiration rate and is commonly measured using thermocouples implanted in plant stems or tree trunks. However, measuring transpiration rates in the Gramineae family, characterized by thin and hollow stems, is challenging. Commercially available sensors based on the measurement of heat velocity can be unaffordable, especially in developing countries. In this work, a real-time heat pulse flux monitoring system based on the heat ratio approach was configured to estimate heat velocity in wheat (Triticum durum L.). The heat velocity sensors were designed to achieve optimal performance for a stem diameter smaller than 5 mm. Sensor parameterization included the determination of casing thermal properties, stabilization time, and time to achieve maximum heat velocity which occurred 30 s after applying a heat pulse. Heat velocity sensors were able to track plant water transport dynamics during phenological stages with high crop water demand (milk development, dough development, and end of grain filling) reporting maximum V_h values in the order of 0.004 cm s⁻¹ which scale to sap flow rates in the order of 3.0 g h⁻¹ comparing to reports from other methods to assess sap flow in wheat. Full article

Underwater wireless sensor networks (UWSNs) have been applied in lots of fields. However, coverage holes are usually caused by complex underwater environment. Coverage holes seriously affect UWSNs’ performance and quality of service; thus, their recovery is crucial for 3D UWSNs. Although most of the current research recovery algorithms demand hole detection, the number of additional mobile nodes is too large, the communication and computing costs are high, and the coverage and energy balance are poor. Therefore, these methods are not suitable for UWSN hole repairing. In order to enhance the performance of hole recovery, a coverage hole recovery method for 3D UWSNs in complex underwater environments based on virtual force guidance and energy balance is proposed. The proposed method closely combines the node energy and considers complex environmental factors. A series of multi-dimensional virtual force models are established based on energy between nodes, area boundaries, zero-energy holes, low-energy coverage holes, underwater terrain, and obstacle forces. Then, a coverage hole recovery method for 3D UWSNs based on virtual force guidance and energy balance (CHRVE) is proposed. In this method, the direction and step size of mobile repairing node movement is guided by distributed computation of virtual forces, and the nodes are driven towards the target location by means of AUV or other carrier devices. The optimal position to improve coverage rate and node force balance is obtained. Simulation experiments show good adaptability and robustness to complex underwater terrain and different environments. The algorithm does not require precise coverage hole boundary detection. Furthermore, it balances network energy distribution significantly. Therefore, this method reduces the frequency of coverage hole emergence and network maintenance costs. Full article