Search Results (67,102)

This review examines recent advances in surface-enhanced Raman spectroscopy (SERS) for urinary metabolite analysis, focusing on the development and application of noble metal nanohybrids. We explore the diverse range of hybrid materials, including carbon-based, metal–organic-framework (MOF), silicon-based, semiconductor, and polymer-based systems, which have significantly improved SERS performance for detecting key urinary biomarkers. The principles underlying SERS enhancement in these nanohybrids are discussed, elucidating both electromagnetic and chemical enhancement mechanisms. We analyze various fabrication methods that enable precise control over nanostructure morphology, composition, and surface chemistry. The review critically evaluates the analytical performance of different hybrid systems for detecting specific urinary metabolites, considering factors such as sensitivity, selectivity, and stability. We address the analytical challenges associated with SERS-based urinary metabolite analysis, including sample preparation, matrix effects, and data interpretation. Innovative solutions, such as the integration of SERS with microfluidic devices and the application of machine learning algorithms for spectral analysis, are highlighted. The potential of these advanced SERS platforms for point-of-care diagnostics and personalized medicine is discussed, along with future perspectives on wearable SERS sensors and multi-modal analysis techniques. This comprehensive overview provides insights into the current state and future directions of SERS technology for urinary metabolite detection, emphasizing its potential to revolutionize non-invasive health monitoring and disease diagnosis. Full article

With the advancement of technology, the Single-Edge Nibble Transmission (SENT) protocol has become increasingly prevalent in automotive sensor applications, highlighting the need for a robust SENT signal simulation and acquisition system. This paper presents a real-time SENT signal acquisition system based on NI Field Programmable Gate Array (FPGA) technology. The system supports a range of message and data frame formats specified by the SAE J2716 SENT protocol, operates autonomously within a LabVIEW self-compiled environment, and is compatible with NI hardware-in-the-loop (HIL) systems for virtual electronic control units (ECU) calibration. This innovative, self-developed SENT system accommodates four message formats, seven data frame formats, and three pause pulse modes. Benchmarking tests were conducted by integrating this system with the dSPACE SCALEXIO HIL (located in Paderborn, Germany) system for SENT signal simulation and acquisition. The results confirm that the system effectively simulates and acquires SENT signals in accordance with SAE J2716 standards, establishing it as an invaluable asset in the electronification, intelligentization, informatization, and smart sensing of automotive and agricultural machinery. Full article

The combination of multispectral data and machine learning provides effective and flexible monitoring of the soil nutrient content, which consequently positively impacts plant productivity and food security, and ultimately promotes sustainable agricultural development overall. The aim of this study was to investigate the associations between spectral variables and soil physicochemical attributes, as well as to predict these attributes using spectral variables as inputs in machine learning models. One thousand soil samples were selected from agricultural areas 0–20 cm deep and collected from Northeast Mato Grosso do Sul state of Brazil. A total of 20 g of the dried and homogenized soil sample was added to the Petri dish to perform spectral measurements. Reflectance spectra were obtained by CROP CIRCLE ACS-470 using three spectral bands: green (532–550 nm), red (670–700 nm), and red-edge (730–760 nm). The models were developed with the aid of the Weka environment to predict the soil chemical attributes via the obtained dataset. The models tested were linear regression, random forest (RF), reptree M5P, multilayer preference neural network, and decision tree algorithms, with the correlation coefficient (r) and mean absolute error (MAE) used as accuracy parameters. According to our findings, sulfur exhibited a correlation greater than 0.6 and a reduced mean absolute error, with better performance for the M5P and RF algorithms. On the other hand, the macronutrients S, Ca, Mg, and K presented modest r values (approximately 0.3), indicating a moderate correlation with actual observations, which are not recommended for use in soil analysis. This soil analysis technique requires more refined correlation models for accurate prediction. Full article

The influence of potassium fertilizer on sugarcane (interspecific hybrids of Saccharum Spp.) yields and leaf and soil potassium levels was evaluated at six locations in Louisiana. The objective of this study was to determine if the sugarcane yields in Louisiana could be improved with potassium application. Different rates of potassium fertilizer (0–179 kg K₂O ha⁻¹) were applied to plant cane and ratoon sugarcane fields in Louisiana. Soil samples and sugarcane leaf samples were also collected from all experiments. Yield data were collected by harvesting plots with a single row, chopper harvester and a field transport wagon equipped with electronic load sensors. At all locations and soil types, potassium fertilizer did not increase cane or sugar yields. Soil properties data showed that significant increases in soil potassium levels did not occur until the second ratoon crop, where soil potassium increased by 30% for the high rate. Increases in plant potassium were also not observed until the second ratoon crop, where plant potassium increased by 10.5% for the high rate. The potential cause of the observed lack of response may be explained by interference from calcium and magnesium, combined with fixation by smectite and vermiculite clay minerals. Our soil and plant uptake data would suggest that repeated K applications at recommended rates, which currently vary from 90 to 157 kg ha⁻¹, may be required to achieve the potential benefits of K fertilizer in Louisiana sugarcane soils. However, this must be verified by additional on-farm trials. Full article

Forecasts about the aging trend of the world population agree on identifying increased life expectancy as a serious risk factor for the financial sustainability of social healthcare systems if not properly supported by innovative care management policies. Such policies should include the integration within traditional healthcare services of assistive technologies as tools for prolonging healthy and independent living at home, but also for introducing innovations in clinical practice such as long-term and remote health monitoring. For their part, solutions for active and assisted living have now reached a high degree of technological maturity, thanks to the considerable amount of research work carried out in recent years to develop highly reliable and energy-efficient wearable sensors capable of enabling the development of systems to monitor activity and physiological parameters over time, and in a minimally invasive manner. This work reviews the role of wearable sensors in the design and development of assisted living solutions, focusing on human activity recognition by joint use of onboard electromyography sensors and inertial measurement units and on the acquisition of parameters related to overall physical and psychological conditions, such as heart activity and skin conductance. Full article

As a crucial component of CRH (China Railway High-speed) trains, the safety and stability of the suspension system are of paramount importance to the overall vehicle system. Based on the framework of probabilistic relevant principal component analysis (PRPCA), this paper proposes a novel method for incipient fault diagnosis in the CRH suspension system using PRPCA and support vector machine (SVM). Firstly, simulation data containing multiple types of fault information are obtained from the Simpack2018.1-Matlab2016a/Simulink co-simulation platform. Secondly, the nonlinear PRPCA approach, based on the Wasserstein distance, is employed for fault detection and data preprocessing in the suspension system. Furthermore, SVM is used for fault recognition, and the F1-Measure index is utilized for a comprehensive evaluation to assess the fault diagnosis performance more intuitively. Finally, based on the comparison results with traditional principal component analysis (PCA) and SVM-based methods, the proposed incipient fault diagnosis method demonstrates superior efficiency in fault detection and recognition. However, the proposed method is not very sensitive to sensor faults, and the performance of sensor fault diagnosis needs to be further improved in subsequent research. Full article

Many medical conditions impair proprioception but there are few easy-to-deploy technologies for assessing proprioceptive deficits. Here, we developed a method—called “OpenPoint”—to quantify upper extremity (UE) proprioception using only a webcam as the sensor. OpenPoint automates a classic neurological test: the ability of a person to use one hand to point to a finger on their other hand with vision obscured. Proprioception ability is quantified with pointing error in the frontal plane measured by a deep-learning-based, computer vision library (MediaPipe). In a first experiment with 40 unimpaired adults, pointing error significantly increased when we replaced the target hand with a fake hand, verifying that this task depends on the availability of proprioceptive information from the target hand, and that we can reliably detect this dependence with computer vision. In a second experiment, we quantified UE proprioceptive ability in 16 post-stroke participants. Individuals post stroke exhibited increased pointing error (p < 0.001) that was correlated with finger proprioceptive error measured with an independent, robotic assessment (r = 0.62, p = 0.02). These results validate a novel method to assess UE proprioception ability using affordable computer technology, which provides a potential means to democratize quantitative proprioception testing in clinical and telemedicine environments. Full article

The health, safety, and well-being of household pets such as cats has become a challenging task in previous years. To estimate a cat’s behavior, objective observations of both the frequency and variability of specific behavior traits are required, which might be difficult to come by in a cat’s ordinary life. There is very little research on cat activity and cat disease analysis based on real-time data. Although previous studies have made progress, several key questions still need addressing: What types of data are best suited for accurately detecting activity patterns? Where should sensors be strategically placed to ensure precise data collection, and how can the system be effectively automated for seamless operation? This study addresses these questions by pointing out whether the cat should be equipped with a sensor, and how the activity detection system can be automated. Magnetic, motion, vision, audio, and location sensors are among the sensors used in the machine learning experiment. In this study, we collect data using three types of differentiable and realistic wearable sensors, namely, an accelerometer, a gyroscope, and a magnetometer. Therefore, this study aims to employ cat activity detection techniques to combine data from acceleration, motion, and magnetic sensors, such as accelerometers, gyroscopes, and magnetometers, respectively, to recognize routine cat activity. Data collecting, data processing, data fusion, and artificial intelligence approaches are all part of the system established in this study. We focus on One-Dimensional Convolutional Neural Networks (1D-CNNs) in our research, to recognize cat activity modeling for detection and classification. Such 1D-CNNs have recently emerged as a cutting-edge approach for signal processing-based systems such as sensor-based pet and human health monitoring systems, anomaly identification in manufacturing, and in other areas. Our study culminates in the development of an automated system for robust pet (cat) activity analysis using artificial intelligence techniques, featuring a 1D-CNN-based approach. In this experimental research, the 1D-CNN approach is evaluated using training and validation sets. The approach achieved a satisfactory accuracy of 98.9% while detecting the activity useful for cat well-being. Full article

Increasing global plastic usage has raised critical concerns regarding marine pollution. This study addresses the pressing issue of floating marine macro-litter (FMML) by developing a novel monitoring system using a multi-spectral sensor and drones along the southern coast of South Korea. Subsequently, a convolutional neural network (CNN) model was utilized to classify four distinct marine litter materials: film, fiber, fragment, and foam. Automatic atmospheric correction with the drone data atmospheric correction (DROACOR) method, which is specifically designed for currently available drone-based sensors, ensured consistent reflectance across altitudes in the FMML dataset. The CNN models exhibited promising performance, with precision, recall, and F1 score values of 0.9, 0.88, and 0.89, respectively. Furthermore, gradient-weighted class activation mapping (Grad-CAM), an object recognition technique, allowed us to interpret the classification performance. Overall, this study will shed light on successful FMML identification using multi-spectral observations for broader applications in diverse marine environments. Full article

The article presents accelerations occurring in the human body when falling onto a safety net. An attitude and heading reference system (AHRS) consists of sensors on three axes that provide attitude information for objects, including pitch, roll, and yaw. These sensors are made of microelectromechanical systems (MEMS) gyroscopes, accelerometers, and magnetometers. Usually, they are used in aircraft flight instruments due to their high precision. In the present article, these sensors were used to test safety nets, protecting people or objects falling from heights. The measurement was made for two heights: 6 m and 3.5 m. During the research, a type of mannequin that is a representative model of the human body for the largest segment of the adult population was used. The measurement was carried out using two independent measurement systems. One recorded the accelerations at the chest of the tested object, while the sensors of the second system were placed at the head, arms, and legs. The compiled measurement results were related to the permissible acceleration values that do not threaten human health and life. Full article

In this paper, a novel topology and method for designing a multi-band rectenna is proposed to improve its RF-DC efficiency. The rectifier achieves simultaneous rectification using both series and parallel configurations by connecting two branches to the respective terminals of the diode, directing the energy input from two ports to the anode and cathode of the diode. Six desired operating frequency bands are evenly distributed across these two branches, each of which is connected to antennas corresponding to their specific operating frequencies, serving as the receiving end of the system. To optimize the design process, a low-pass filter is incorporated into the rectifier design. This filter works in conjunction with a matching network that includes filtering capabilities to isolate the two ports of the rectifier. The addition of the filter ensures that each structure within the rectifier can be designed independently without adversely affecting the performance of the already completed structures. Based on the proposed design methodology, a dual-port rectenna operating at six frequency bands—1.85 GHz, 2.25 GHz, 2.6 GHz, 3.52 GHz, 5.01 GHz, and 5.89 GHz—was designed, covering the 4G, 5G, and Wi-Fi/WLAN frequency bands. The measured results indicate that high-power conversion efficiency was achieved at an input power of −10 dBm: 43.01% @ 1.85 GHz, 41.00% @ 2.25 GHz, 41.33% @ 2.6 GHz, 35.88% @ 3.52 GHz, 22.36% @ 5.01 GHz, and 19.27% @ 5.89 GHz. When the input power is −20 dBm, the conversion efficiency of the rectenna can be improved from 5.2% for single-tone input to 27.7% for six-tone input, representing a 22.5 percentage point improvement. The proposed rectenna demonstrates significant potential for applications in powering low-power sensors and other devices within the Internet of Everything context. Full article

Chronic and non-communicable diseases (NCDs) account for a large proportion of global disorders and mortality, posing significant burdens on healthcare systems. Early diagnosis and timely interference are critical for effective management and disease prevention. However, the traditional methods of diagnosis still suffer from high costs, time delays in processing, and infrastructure requirements that are usually unaffordable in resource-constrained settings. Aptamer-based biosensors have emerged as promising alternatives to offer enhanced specificity, stability, and cost-effectiveness for disease biomarker detection. The SELEX (Systematic Evolution of Ligands by Exponential Enrichment) methodology allows developing aptamers with high-affinity binding capabilities to a variety of targets, for instance proteins, cells, or even small molecules, hence rendering them suitable for NCD diagnosis. Aptasensors—recent developments in the electrochemical and optical dominion—offer much enhanced sensitivity, selectivity, and stability of detection across a diverse range of diseases from lung cancer and leukemia to diabetes and chronic respiratory disorders. This study provides a comprehensive review of progress in aptamer-based sensors, focusing on their role in point-of-care diagnostics and adaptability in a real-world environment with future directions in overcoming current limitations. Full article

Event cameras, as bio-inspired visual sensors, offer significant advantages in their high dynamic range and high temporal resolution for visual tasks. These capabilities enable efficient and reliable motion estimation even in the most complex scenes. However, these advantages come with certain trade-offs. For instance, current event-based vision sensors have low spatial resolution, and the process of event representation can result in varying degrees of data redundancy and incompleteness. Additionally, due to the inherent characteristics of event stream data, they cannot be utilized directly; pre-processing steps such as slicing and frame compression are required. Currently, various pre-processing algorithms exist for slicing and compressing event streams. However, these methods fall short when dealing with multiple subjects moving at different and varying speeds within the event stream, potentially exacerbating the inherent deficiencies of the event information flow. To address this longstanding issue, we propose a novel and efficient Asynchronous Spike Dynamic Metric and Slicing algorithm (ASDMS). ASDMS adaptively segments the event stream into fragments of varying lengths based on the spatiotemporal structure and polarity attributes of the events. Moreover, we introduce a new Adaptive Spatiotemporal Subject Surface Compensation algorithm (ASSSC). ASSSC compensates for missing motion information in the event stream and removes redundant information, thereby achieving better performance and effectiveness in event stream segmentation compared to existing event representation algorithms. Additionally, after compressing the processed results into frame images, the imaging quality is significantly improved. Finally, we propose a new evaluation metric, the Actual Performance Efficiency Discrepancy (APED), which combines actual distortion rate and event information entropy to quantify and compare the effectiveness of our method against other existing event representation methods. The final experimental results demonstrate that our event representation method outperforms existing approaches and addresses the shortcomings of current methods in handling event streams with multiple entities moving at varying speeds simultaneously. Full article

The accurate and timely acquisition of high-frequency three-dimensional (3D) displacement responses of large structures is crucial for evaluating their condition during seismic excitation on shaking tables. This paper presents a distributed high-speed videogrammetric method designed to rapidly measure the 3D displacement of large shaking table structures at high sampling frequencies. The method uses non-coded circular targets affixed to key points on the structure and an automatic correspondence approach to efficiently estimate the extrinsic parameters of multiple cameras with large fields of view. This process eliminates the need for large calibration boards or manual visual adjustments. A distributed computation and reconstruction strategy, employing the alternating direction method of multipliers, enables the global reconstruction of time-sequenced 3D coordinates for all points of interest across multiple devices simultaneously. The accuracy and efficiency of this method were validated through comparisons with total stations, contact sensors, and conventional approaches in shaking table tests involving large structures with RCBs. Additionally, the proposed method demonstrated a speed increase of at least six times compared to the advanced commercial photogrammetric software. It could acquire 3D displacement responses of large structures at high sampling frequencies in real time without requiring a high-performance computing cluster. Full article

With the introduction of advanced Fiber Bragg Grating (FBG) technology, Fabry–Pérot (FP) interferometers have become widely used in fiber-optic ultrasound detection. In these applications, the slope of the reflectance is a critical factor influencing detection results. Due to the intensity limitations of the laser source in fiber-optic ultrasound detection, the reflectance of the FBG is generally increased to enhance the signal-to-noise ratio (SNR). However, increasing reflectance can cause the reflectance curve to deviate from a sinusoidal shape, which in turn affects the slope of the reflectance and introduces greater errors. This paper first investigates the relationship between the transmission curve of the FP interferometer and reflectance, with a focus on the errors introduced by simplified assumptions. Further research shows that in sensors with asymmetric reflectance slopes, their transmittance curves deviate significantly from sinusoidal signals. This discrepancy highlights the importance of achieving symmetrical slopes to ensure consistent and accurate detection. To address this issue, this paper proposes an innovative method to adjust the rear-end reflectance of the FP interferometer by combining stress modulation, UV adhesive, and a high-reflectivity metal disk. Additionally, by adjusting the rear-end reflectance to ensure that the transmittance curve approximates a sinusoidal signal, the symmetry of the slope is maintained. Finally, through practical ultrasound testing, by adjusting the incident wavelength to the positions of slope extrema (or zero) at equal intervals, the expected ultrasound signals at extrema (or zero) can be detected. This method converts the problem of approximating a sinusoidal signal into a problem of the slope adjustment of the transmittance curve, making it easier and more direct to determine its impact on detection results. The proposed method not only improves the performance of fiber-optic ultrasound sensors but also reduces costs, paving the way for broader applications in medical diagnostics and structural health monitoring. Full article