Search Results (3,232)

This study presents the development and characterization of a novel electronic nose system based on customized surface acoustic wave (SAW) sensors. The system includes four sensors, customized with different custom polymer coatings, in order to detect volatile organic compounds (VOCs). The main innovation lies in the design of a robust and versatile switching electronics system that allows for the integration of the SAW sensors into portable systems, as well as interoperability with other gas sensor technologies. The system includes a modular architecture that allows multiple sensor arrays to be combined to improve the selectivity and discrimination of complex gas mixtures. To verify the proper performance of the system and the detection capability of the manufactured sensors, experimental laboratory tests have been carried out. Specifically, ethanol and acetone measurements up to a 2000 ppm concentration have been performed. These preliminary experimental results demonstrate the capability of the SAW sensors with different response patterns across the sensor array. In particular, the sensor made with the polyvinyl acetate polymer exhibits high sensitivity to both VOCs. Full article

Treating ankle fractures in athletes, commonly resulting from training injuries, remains a significant challenge. Current approaches to managing both non-surgical and postoperative foot and ankle disorders have focused on integrating sensory systems into orthotic devices. Recent analyses have identified several gaps in rehabilitation strategies, especially regarding gait pattern reformation during recovery. This work aims to enhance rehabilitation effectiveness for patients with ankle injuries by controlling load distribution and monitoring joint flexion/extension angles, as well as the reactive forces during therapeutic exercises and walking. We developed an exoskeleton device model using SolidWorks 2024 software, based on data from two patients: one healthy and one with an ankle fracture. Pressure measurements in the posterior limb region were taken using the F-Socket system and a custom electromechanical sensor designed by the authors. The collected data were analyzed using the butterfly parameterization method. This research led to the development of an adaptive exoskeleton device that provided pressure distribution data, gait cycle graphs, and a diagram correlating foot angles with the duration of exoskeleton use. The device demonstrated improvement in the patients’ conditions, facilitating a more normalized gait pattern. A reduction in the load applied to the ankle joint was also observed, with the butterfly parameter confirming the device’s correct operation. Full article

To address the issue of reduced sowing depth detection accuracy caused by varying soil topography during the operation of wheat row drills, an indoor bench test device suitable for wheat row drills was developed. The device integrates a laser sensor and an array sensor for terrain and sowing depth detection. The laser sensor provides the detected sowing depth values, while the array sensor captures different terrain features. The actual sowing depth values are obtained through the indoor experimental setup. The experiment includes three types of terrain: convex, concave, and flat. The terrain slope matrix is obtained using the array sensor, and terrain feature values are extracted. The laser sensor is then used to obtain the detected sowing depth, and the actual sowing depth is manually measured. PCA analysis is conducted to correlate terrain feature values with sowing depth deviations. Results indicate that under different terrain conditions, the slope mean and slope standard deviation are the main components affecting sowing depth deviations. Compared to using a single sensor, this system enables more accurate sowing depth measurement by analyzing terrain features. The device provides valuable data support for controlling sowing depth under varying terrain conditions in subsequent operations. Full article

To date, there is little publicly available scientific data on unidentified aerial phenomena (UAP) whose properties and kinematics purportedly reside outside the performance envelope of known phenomena. To address this deficiency, the Galileo Project is designing, building, and commissioning a multi-modal, multi-spectral ground-based observatory to continuously monitor the sky and collect data for UAP studies via a rigorous long-term aerial census of all aerial phenomena, including natural and human-made. One of the key instruments is an all-sky infrared camera array using eight uncooled long-wave-infrared FLIR Boson 640 cameras. In addition to performing intrinsic and thermal calibrations, we implement a novel extrinsic calibration method using airplane positions from Automatic Dependent Surveillance–Broadcast (ADS-B) data that we collect synchronously on site. Using a You Only Look Once (YOLO) machine learning model for object detection and the Simple Online and Realtime Tracking (SORT) algorithm for trajectory reconstruction, we establish a first baseline for the performance of the system over five months of field operation. Using an automatically generated real-world dataset derived from ADS-B data, a dataset of synthetic 3D trajectories, and a hand-labeled real-world dataset, we find an acceptance rate (fraction of in-range airplanes passing through the effective field of view of at least one camera that are recorded) of 41% for ADS-B-equipped aircraft, and a mean frame-by-frame aircraft detection efficiency (fraction of recorded airplanes in individual frames which are successfully detected) of 36%. The detection efficiency is heavily dependent on weather conditions, range, and aircraft size. Approximately 500,000 trajectories of various aerial objects are reconstructed from this five-month commissioning period. These trajectories are analyzed with a toy outlier search focused on the large sinuosity of apparent 2D reconstructed object trajectories. About 16% of the trajectories are flagged as outliers and manually examined in the IR images. From these ∼80,000 outliers and 144 trajectories remain ambiguous, which are likely mundane objects but cannot be further elucidated at this stage of development without information about distance and kinematics or other sensor modalities. We demonstrate the application of a likelihood-based statistical test to evaluate the significance of this toy outlier analysis. Our observed count of ambiguous outliers combined with systematic uncertainties yields an upper limit of 18,271 outliers for the five-month interval at a 95% confidence level. This test is applicable to all of our future outlier searches. Full article

On-satellite information processing enables all-weather target tracking. The background of videos from satellite sensors exhibits an affine transformation due to their motion relative to the Earth. In complex moving backgrounds, moving vehicles have a small number of pixels and weak texture features. At the same time, the resources and performance of on-satellite equipment are limited. To address these issues, we propose a multi-object tracking (MOT) algorithm with a detection–association framework for moving vehicles in complex moving backgrounds and implement the algorithm on a satellite to achieve real-time MOT. We use feature matching to effectively eliminate the effects of background motion and use the neighborhood pixel difference method to extract moving vehicle targets in the detection stage. The accurate extraction of motion targets ensures the effectiveness of target association to achieve MOT of moving vehicles in complex moving backgrounds. Additionally, we use a Field-Programmable Gate Array (FPGA) to implement the algorithm completely on a satellite. We propose a pixel-level stream processing mode and a cache access processing mode, given the characteristics of on-satellite equipment and sensors. According to the experimental results, the prototype on-satellite implementation method proposed in this paper can achieve real-time processing at 1024 × 1024 px@47 fps. Full article

The pH value of maize silage can accurately reflect its quality. In this study, a colorimetric sensor array (CSA) combined with hyperspectral imaging (HSI) was used to predict the pH value of maize silage during secondary fermentation. Seventeen color-sensitive dyes were used to construct the CSA, which was subsequently applied to capture the volatile odor profiles of maize silage samples. Hyperspectral images of the color-sensitive dyes on the CSA were acquired using the HSI technique. Different algorithms were used to preprocess the raw spectral data of each dye, and a partial least squares regression (PLSR) model was built for each dye separately. Subsequently, the adaptive bacterial foraging optimization (ABFO) algorithm was employed to identify three color-sensitive dyes that demonstrated heightened sensitivity to pH variations in maize silage. This study further compared the capabilities of individual dyes, as well as their combinations, in predicting the pH value of maize silage. Additionally, a novel feature wavelength extraction method based on the ABFO algorithm was proposed, which was then compared with two traditional feature extraction algorithms. These methods were combined with PLSR and backpropagation neural network (BPNN) algorithms to construct a quantitative prediction model for the pH value of maize silage. The results show that the quantitative prediction model constructed based on three dyes was more accurate than that constructed based on an individual dye. Among them, the ABFO-BPNN model constructed on the basis of combined dyes had the best prediction performance, with prediction correlation coefficient ($R_P^2$), root mean square error of the prediction set (RMSEP), and ratio of performance deviation (RPD) values of 0.9348, 0.3976, and 3.9695, respectively. The aim of this study was to develop a reliable evaluation model to achieve fast and accurate predictions of silage pH. Full article

Recent advances in spectral imaging have enabled snapshot acquisition, as a means to mitigate the impracticalities of spectral imaging, e.g., expert operators and cumbersome hardware. Snapshot spectral imaging, e.g., in technologies like spectral filter arrays, has also enabled higher temporal resolution at the expense of the spatio-spectral resolution, allowing for the observation of temporal events. Designing, realising, and deploying such technologies is yet challenging, particularly due to the lack of clear, user-meaningful quality criteria across diverse applications, sensor types, and workflows. Key research gaps include optimising raw image processing from snapshot spectral imagers and assessing spectral image and video quality in ways valuable to end-users, manufacturers, and developers. This paper identifies several challenges and current opportunities. It proposes considering them jointly and suggests creating a new unified snapshot spectral imaging paradigm that would combine new systems and standards, new algorithms, new cost functions, and quality indices. Full article

In this work, a highly sensitive, selective, and industrially compatible gas sensor prototype is presented. The sensor utilizes three distributed-feedback quantum cascade lasers (DFB-QCLs), employing wavelength modulation spectroscopy (WMS) for the detection of hydrogen sulfide (H₂S), methane (CH₄), methyl mercaptan (CH₃SH), and carbonyl sulfide (COS) in the spectral regions of 8.0 µm, 7.5 µm, and 4.9 µm, respectively. In addition, field-programmable gate array (FPGA) hardware is used for real-time signal generation, laser driving, signal processing, and handling industrial communication protocols. To comply with on-site safety standards, the QCL sensor prototype is housed in an industrial-grade enclosure and equipped with the necessary safety features to ensure certified operation under ATEX/IECEx regulations for hazardous and explosive environments. The system integrates an automated gas sampling and conditioning module, alongside a purge and pressurization system, with intrinsic safety electronic components, thereby enabling reliable explosion prevention and malfunction protection. Detection limits of approximately 0.3 ppmv for H₂S, 60 ppbv for CH₃SH, and 5 ppbv for COS are demonstrated. Noise-equivalent absorption sensitivity (NEAS) levels for H₂S, CH₃SH, and COS were determined to be 5.93 × 10⁻⁹, 4.65 × 10⁻⁹, and 5.24 × 10⁻¹⁰ cm⁻¹ Hz^−1/2. The suitability of the sensor prototype for simultaneous sulfur species monitoring is demonstrated in process streams of a hydrodesulphurization (HDS) and fluid catalytic cracking (FCC) unit at the project’s industrial partner, OMV AG. Full article

A prototype of a mobile electric charging station was developed to simulate the energy supply to a rural medical post. A 20 m² medical post module was built, divided into two rooms (medical staff room and patient room) and a heater, a freezer, a refrigerator, lights and a personal computer were added inside. The mobile electric charging station was made up of an array of 2.88 kW flexible photovoltaic panels, a 48 V and 19.2 kW·h LiFePO₄ battery bank, a charger inverter with a total capacity of 5 kW and a 4 kW electric generator. All of this equipment was placed in an all-terrain pickup truck. Temperature sensors and electrical sensors were installed to evaluate the performance of the prototype in charging and discharging scenarios. Results were obtained according to the operation over 10 months in the city of Arequipa, Peru. The results indicate an indefinite autonomy on clear days, the autonomy varying between 7 and 10 days for a climate with medium cloudiness, and with very cloudy conditions (i.e., with rain), the autonomy is 2 to 3 days. In circumstances of low solar irradiance, the generator had to supply the energy, thereby improving energy autonomy. Full article

Radio frequency (RF) spectrum sensing is critical for applications requiring precise object and posture detection and classification. This survey aims to provide a focused review of context-aware RF-based sensing, emphasizing its principles, advancements, and challenges. It specifically examines state-of-the-art techniques such as phased array radar, synthetic aperture radar, and passive RF sensing, highlighting their methodologies, data input domains, and spatial diversity strategies. The paper evaluates feature extraction methods and machine learning approaches used for detection and classification, presenting their accuracy metrics across various applications. Additionally, it investigates the integration of RF sensing with other modalities, such as inertial sensors, to enhance context awareness and improve performance. Challenges like environmental interference, scalability, and regulatory constraints are addressed, with insights into real-world mitigation strategies. The survey concludes by identifying emerging trends, practical applications, and future directions for advancing RF sensing technologies. Full article

The key equipment for performing aerodynamic testing of objects, such as road and railway vehicles, aircraft, and wind turbines, as well as stationary objects such as bridges and buildings, are multichannel pressure measurement instruments (pressure scanners). These instruments are typically based on arrays of separate pressure sensors built in an enclosure that also contains temperature sensors used for temperature compensation. However, there are significant limitations to such a construction, especially when increasing requirements in terms of miniaturization, the number of pressure channels, and high measurement performance must be met at the same time. In this paper, we present the development and realization of an innovative MEMS multisensor chip, which is designed with the intention of overcoming these limitations. The chip has four MEMS piezoresistive pressure-sensing elements and two resistive temperature-sensing elements, which are all monolithically integrated, enabling better sensor matching and thermal coupling while providing a high number of pressure channels per unit area. The main steps of chip development are preliminary chip design, numerical simulations of the chip’s mechanical behavior when exposed to the measured pressure, final chip design, fabrication processes (photolithography, thermal oxidation, diffusion, layer deposition, micromachining, anodic bonding, and wafer dicing), and electrical testing. Full article

The profile of Volatile Organic Compounds (VOCs) may help prioritise at-risk groups for early cancer detection. Urine sampling has been shown to provide good disease accuracy whilst being patient acceptable compared to faecal analysis. Thus, in this study, urine samples were examined using an electronic nose with metal oxide gas sensors and a solid-phase microextraction sampling system. A calibration dataset (derived from a previous study) with CRC-positive patients and healthy controls was used to train a radial basis function neural network. However, a blinded analysis failed to detect CRC accurately, necessitating an enhanced data-processing strategy. This new approach categorised samples by significant bowel diseases, including CRC and high-risk polyps. Retraining the neural network showed an area under the ROC curve of 0.88 for distinguishing CRC versus non-significant bowel disease (without CRC, polyps or inflammation). These findings suggest that, with appropriate training sets, urine VOC analysis could be a rapid, low-cost method for early detection of precancerous colorectal polyps and CRC. Full article

Lock-in amplifiers (LIAs) are critical tools in precision measurement, particularly for applications involving weak signals obscured by noise. Advances in signal processing algorithms and hardware synthesis have enabled accurate signal extraction, even in extremely noisy environments, making LIAs indispensable in sensor applications for healthcare, industry, and other services. For instance, the electrical impedance measurement of the human body, organs, tissues, and cells, known as bioelectrical impedance, is commonly used in biomedical and healthcare applications because it is non-invasive and relatively inexpensive. Also, due to its portability and miniaturization capabilities, it has great potential for the development of new point-of-care and portable testing devices. In this document, we highlight existing techniques for high-frequency resolution and precise phase detection in LIA reference signals from field-programmable gate array (FPGA) designs. A comprehensive review is presented under the key requirements and techniques for single- and dual-phase digital LIA architectures, where relevant insights are provided to address the LIAs’ digital precision in measurement system configurations. Furthermore, the document highlights a novel method to enhance the spurious-free dynamic range (SFDR), thereby advancing the precision and effectiveness of LIAs in complex measurement environments. Finally, we summarize the diverse applications of impedance measurement, highlighting the wide range of fields that can benefit from the design of high performance in modern measurement technologies. Full article

We use an array of nine elliptical Planar Hall Effect (PHE) sensors and machine learning algorithms to map the magnetic signal generated by a magnetic source. Based on the obtained mapping, the location and nature of the magnetic source can be determined. The sensors are positioned at the vertices of a symmetrical and evenly spaced 3 × 3 grid. The main electronic card orchestrates their measurement by supplying the required driving current and amplifying and sampling their output in a synchronized manner. A two-dimensional interpolation of the data collected from the nine sensors fails to yield a satisfactory mapping. To address this, we employed the Levenberg–Marquardt Algorithm (LMA) as a deterministic optimization method to estimate the magnetic source’s position and parameters, as well as machine earning (ML) algorithms, which consist of a Fully Connected Neural Network (FCNN). While LMA provided reasonable results, its reliance on a sparse sensor network and initial guesses for variables limited its accuracy. We show that the mapping is significantly improved if the data are processed with an FCNN that undergoes training and testing. Using simulations, we demonstrate that achieving similar improvement without ML would require increasing the number of sensors to more than 50. Full article

DNA damage response (DDR) contributes to seed quality by guarding genome integrity in the delicate phases of pre- and post-germination. As a key determinant of stress tolerance and resilience, DDR has notable implications on the wider scale of the agroecosystems challenged by harsh climatic events. The present review focuses on the existing and documented links that interconnect DDR efficiency with an array of molecular hallmarks with biochemical, molecular, and physiological valence within the seed metabolic networks. The expression of genes encoding DDR sensors, transducers, mediators, and effectors is interpreted as a source of conserved hallmarks, along with markers of oxidative damage reflecting the seed’s ability to germinate. Similarly, the accumulation patterns of proteins and metabolites that contribute to DNA stability are predictive of seed quality traits. While a list of candidates is presented from multiple models and crop species, their interaction with chromatin dynamics, cell cycle progression, and hormonal regulation provides further levels of analysis to investigate the seed stress response holistically. The identification of novel hallmarks of DDR in seeds constitutes a framework to prompt validation with different experimental systems, to refine the current models of pre-germinative metabolism, and to promote targeted approaches for seed quality evaluation. Full article