Sensors

We report the use of a novel technology based on optical photothermal infrared (O-PTIR) spectroscopy for obtaining simultaneous infrared and Raman spectra from the same location of the sample allowing us to study bacterial metabolism by monitoring the incorporation of ¹³C- and ¹⁵N-labeled compounds. Infrared data obtained from bulk populations and single cells via O-PTIR spectroscopy were compared to conventional Fourier transform infrared (FTIR) spectroscopy in order to evaluate the reproducibility of the results achieved by all three approaches. Raman spectra acquired were concomitant with infrared data from bulk populations as well as infrared spectra collected from single cells, and were subjected to principal component analysis in order to evaluate any specific separation resulting from the isotopic incorporation. Similar clustering patterns were observed in infrared data acquired from single cells via O-PTIR spectroscopy as well as from bulk populations via FTIR and O-PTIR spectroscopies, indicating full incorporation of heavy isotopes by the bacteria. Satisfactory discrimination between unlabeled (viz. ¹²C¹⁴N), ¹³C¹⁴N- and ¹³C¹⁵N-labeled bacteria was also obtained using Raman spectra from bulk populations. In this report, we also discuss the limitations of O-PTIR technology to acquire Raman data from single bacterial cells (with typical dimensions of 1 × 2 µm) as well as spectral artifacts induced by thermal damage when analyzing very small amounts of biomass (a bacterium tipically weighs ~ 1 pg). Full article

Automated inspection has proven to be the most effective approach to maintaining quality in industrial-scale manufacturing. This study employed the eye-in-hand architecture in conjunction with deep learning and convolutional neural networks to automate the detection of defects in forged aluminum rims for electric vehicles. RobotStudio software was used to simulate the environment and path trajectory for a camera installed on an ABB robot arm to capture 3D images of the rims. Four types of surface defects were examined: (1) dirt spots, (2) paint stains, (3) scratches, and (4) dents. Generative adversarial network (GAN) and deep convolutional generative adversarial networks (DCGAN) were used to generate additional images to expand the depth of the training dataset. We also developed a graphical user interface and software system to mark patterns associated with defects in the images. The defect detection algorithm based on YOLO algorithms made it possible to obtain results more quickly and with higher mean average precision (mAP) than that of existing methods. Experiment results demonstrated the accuracy and efficiency of the proposed system. Our developed system has been shown to be a helpful rim defective detection system for industrial applications. Full article

To stabilize the detection signal of palladium-based hydrogen sensors on paper substrates, a graphite intermediate layer was painted on the surface of paper. The graphite-on-paper (GOP) substrate offers advantages such as good thermo-electrical conductivity, low cost, and uncomplicated preparation technology. Quasi-1-dimensional palladium (Pd) thin films with 8 nm and 60 nm thicknesses were deposited on the GOP substrates using the vacuum evaporation technique. Thanks to the unique properties of the GOP substrate, a continuous Pd microfiber network structure appeared after deposition of the ultra-thin Pd film. Additionally, the sensing performance of the palladium-based hydrogen sensor was not affected, whether using GOP or paper substrate at 25 °C. Surprisingly, heating-induced loss of sensitivity was restrained due to the increased electrical conductivity of the GOP substrate at 50 °C. Full article

In Colombia, the second-largest exporter of cut flowers worldwide and one of the South American countries with the largest area of crops under cover, passive or naturally ventilated greenhouses predominate. Locally, there are several types of greenhouses that differ in architecture, size, height, shape of roof and ventilation surfaces, of which many characteristics of the microclimate generated in their interior environment are unknown. This generates productive limitations that in some way may be limiting the yield, quality and health of the final products harvested; in addition, Colombian producers do not have the ability to monitor the microclimate of their farms, much less to correlate microclimate data with data on crop production and yield. Therefore, there is a need for the Colombian grower to know the most relevant microclimate characteristics generated in the main greenhouses used locally. The objective of this work was to carry out a microclimatic characterization of the five most used types of greenhouses in Colombia. The main results allowed determining that in these structures, there are conditions of high humidity and low vapor pressure for several hours of the day, which affects the physiological processes of growth and development of the plants. It was also identified that for each type of greenhouse, depending on the level of radiation, there is a significant microclimatic heterogeneity that may be the cause of the heterogeneity in plant growth, which is a common characteristic observed by the technical cultivation personnel. Therefore, it can be concluded that it is urgent to propose microclimatic optimization strategies to help ensure the sustainability of the most important production systems in the country. Full article

Impervious surface as an evaluation indicator of urbanization is crucial for urban planning and management. It is necessary to obtain impervious surface information with high accuracy and resolution to meet dynamic monitoring under rapid urban development. At present, the methods of impervious surface extraction are primarily based on medium-low-resolution images. Therefore, it is of theoretical and application value to construct an impervious surface extraction method that applies to high-resolution satellite images and can solve the shadow misclassification problem. This paper builds an impervious surface extraction model by Bayes discriminant analysis (BDA). The Gaussian prior model is incorporated into the Bayes discriminant analysis to establish a new impervious surface extraction model (GBDA) applicable to high-resolution remote sensing images. Using GF-2 and Sentinel-2 remote sensing images as experimental data, we discuss and analyze the applicability of BDA and GBDA in impervious surface extraction of high-resolution remote sensing images. The results showed that the four methods, SVM, RF, BDA and GBDA, had OA values of 91.26%, 94.91%, 94.64% and 97.84% and Kappa values of 0.825, 0.898, 0.893 and 0.957, respectively, in the extraction results of GF-2. In the results of effective Sentinel-2 extraction, the OA values of the four methods were 87.94%, 91.79%, 92.19% and 93.51% and the Kappa values were 0.759, 0.836, 0.844 and 0.870, respectively. Compared with the support vector machine (SVM), random forest (RF) and BDA methods, GBDA has significantly improved the extraction accuracy. GBDA enhances the robustness and generalization ability of the model and can improve the shadow misclassification phenomenon of high-resolution images. The model constructed in this paper is highly reliable for extracting impervious surfaces from high-resolution remote sensing images, exploring the application value of Bayes discriminant analysis in impervious surface extraction and providing technical support for impervious surface information of high spatial resolution and high quality. Full article

Radio communication systems are very widely present in our current smart lifestyles. It consists of two ends, which can carry the transmitter’s information to the receptor. Before installing any radio communication system, it is necessary to analyze the link resources. Hence, this analysis allows the determination of the received radio communication strength to prove if it is sufficient for the link to work correctly and assure a high quality of service. For this reason, new services and technologies are integrated. The objective of the present work is to improve the performance of the radio communication link of 4G systems. The study is based on real measurements using the drive test. The data collected by the drive test are analyzed to increase the performance of the radio communication. Based on this data analysis, recommendations and suggestions are issued for improving the radio communication link. The obtained results indicate a significant amelioration in the performance of the radio communication link. Full article

A robust visual understanding of complex urban environments using passive optical sensors is an onerous and essential task for autonomous navigation. The problem is heavily characterized by the quality of the available dataset and the number of instances it includes. Regardless of the benchmark results of perception algorithms, a model would only be reliable and capable of enhanced decision making if the dataset covers the exact domain of the end-use case. For this purpose, in order to improve the level of instances in datasets used for the training and validation of Autonomous Vehicles (AV), Advanced Driver Assistance Systems (ADAS), and autonomous driving, and to reduce the void due to the no-existence of any datasets in the context of railway smart mobility, we introduce our multimodal hybrid dataset called ESRORAD. ESRORAD is comprised of 34 videos, 2.7 k virtual images, and 100 k real images for both road and railway scenes collected in two Normandy towns, Rouen and Le Havre. All the images are annotated with 3D bounding boxes showing at least three different classes of persons, cars, and bicycles. Crucially, our dataset is the first of its kind with uncompromised efforts on being the best in terms of large volume, abundance in annotation, and diversity in scenes. Our escorting study provides an in-depth analysis of the dataset’s characteristics as well as a performance evaluation with various state-of-the-art models trained under other popular datasets, namely, KITTI and NUScenes. Some examples of image annotations and the prediction results of our 3D object detection lightweight algorithms are available in ESRORAD dataset. Finally, the dataset is available online. This repository consists of 52 datasets with their respective annotations performed. Full article

Frequency combs (FCs)—spectra containing equidistant coherent peaks—have enabled researchers and engineers to measure the frequencies of complex signals with high precision, thereby revolutionising the areas of sensing, metrology and communications and also benefiting the fundamental science. Although mostly optical FCs have found widespread applications thus far, in general FCs can be generated using waves other than light. Here, we review and summarise recent achievements in the emergent field of acoustic frequency combs (AFCs), including phononic FCs and relevant acousto-optical, Brillouin light scattering and Faraday wave-based techniques that have enabled the development of phonon lasers, quantum computers and advanced vibration sensors. In particular, our discussion is centred around potential applications of AFCs in precision measurements in various physical, chemical and biological systems in conditions where using light, and hence optical FCs, faces technical and fundamental limitations, which is, for example, the case in underwater distance measurements and biomedical imaging applications. This review article will also be of interest to readers seeking a discussion of specific theoretical aspects of different classes of AFCs. To that end, we support the mainstream discussion by the results of our original analysis and numerical simulations that can be used to design the spectra of AFCs generated using oscillations of gas bubbles in liquids, vibrations of liquid drops and plasmonic enhancement of Brillouin light scattering in metal nanostructures. We also discuss the application of non-toxic room-temperature liquid–metal alloys in the field of AFC generation. Full article

In selective serial disassembly sequence planning, when the target node (component) is reached, the selective disassembly task is completed and the refurbished component is repaired, reused or remanufactured. Since the efficient utilization of existing resources is necessary, it is crucial to predict disassembly operation times and the condition of joints for recycling, reusing or remanufacturing. The method of estimating the disassembly times of a joint if it is intended for remanufacturing, recycling and reuse is an important and urgent requirement for research development and results. The aim of the paper is to investigate the disassembly system with predicted operation times and the quality of product connections (joints) in order to balance the line smoothness index, to minimize a line time factor, line efficiency and profit and minimize an ex post error. Disassembly times for remanufacturing, recycling and reuse are estimated separately based on the historical data of disassembly times and the quality of joints. The presented estimation method of disassembly operation times increases the reliability and efficiency of elaborated balances of tasks in lines. Underestimated disassembly operation times can be compensated for during the idle points in the successive cycles, provided that the transport operations are performed manually and that travel time determines the cycle time. Full article

This study presents a real-time monitoring technique for radioactive substances that meets safety management needs. We studied the accumulation characteristics of radiation response signals of monolithic active pixel sensors (MAPSs) based on their response and discrimination ability to gamma (γ) photon or neutron radiation. The radiation status of the radioactive substances was determined by monitoring the accumulation data of radiation responses. As per the results, Am-Be and ²⁵²Cf radiation response signals are primarily concentrated in the range of 0–70 pixels. Response signals of ⁶⁰Co and ¹³⁷Cs γ-ray were concentrated in two regions; there was a peak in the region with a pixel value of less than 50, and a plateau in the region with a pixel value of more than 75. Therefore, the results are able to discriminate between spectra. Furthermore, we designed a radioactivity monitoring system that is able to examine multiple radioactive materials. Its working principle is that a change in the accumulation of radioactivity monitoring data indicates a radiation change during the last accumulation cycle. This study provides vital technical support for the long-term supervision of radioactive substances. Full article

Far infrared radiation (FIR) is emitted by every body at a given temperature, including the human body. FIR ranging between 4–14 μm is considered useful for cell growth, and the human body emits a maximum of infrared (IR) radiation at the wavelength of approximately 9.3 µm. In the present study, fabrics based on five different raw textiles having the same yarn count as well as the same weaving patterns were designed and created. Some of them were subjected to a coating process. The fabrics to be tested were as follows: coated with TiO₂ nanoparticles, coated with SiO₂ nanoparticles, coated fabric that does not contain bioceramic nanoparticle (BNFC), and non-coated fabrics (NCF). The structural characterization of the resulting samples was performed using scanning electron microscopy (SEM), abrasion tests, and air permeability. Following the structural characterization, the infrared emissivity properties were investigated using infrared thermography as well as attenuated total reflectance Fourier-transform infrared spectroscopy in the 8–14 IR range. According to the experimental findings, the fabrics coated with TiO₂ and SiO₂ displayed increased infrared emissivity values compared to the uncoated ones. In addition, it was observed that the use of bioceramic powders had no effect on air permeability and abrasion properties. Full article

Infrared cameras allow non-invasive and 24 h continuous monitoring. Thus, they are widely used in automatic pig monitoring, which is essential to maintain the profitability and sustainability of intensive pig farms. However, in practice, impurities such as insect secretions continuously pollute camera lenses. This causes problems with IR reflections, which can seriously affect pig detection performance. In this study, we propose a noise-robust, real-time pig detection system that can improve accuracy in pig farms where infrared cameras suffer from the IR reflection problem. The system consists of a data collector to collect infrared images, a preprocessor to transform noisy images into clean images, and a detector to detect pigs. The preprocessor embeds a multi-scale spatial attention module in U-net and generative adversarial network (GAN) models, enabling the model to pay more attention to the noisy area. The GAN model was trained on paired sets of clean data and data with simulated noise. It can operate in a real-time and end-to-end manner. Experimental results show that the proposed preprocessor was able to significantly improve the average precision of pig detection from 0.766 to 0.906, with an additional execution time of only 4.8 ms on a PC environment. Full article

Recently, vibration-based monitoring technologies have become extremely popular, providing effective tools to assess the health condition and evaluate the structural integrity of civil structures and infrastructures in real-time. In this context, battery-operated wireless sensors allow us to stop using wired sensor networks, providing easy installation processes and low maintenance costs. Nevertheless, wireless transmission of high-rate data such as structural vibration consumes considerable power. Consequently, these wireless networks demand frequent battery replacement, which is problematic for large structures with poor accessibility, such as long-span bridges. This work proposes a low-power multi-hop wireless sensor network suitable for monitoring large-sized civil infrastructures to handle this problem. The proposed network employs low-power wireless devices that act in the sub-GHz band, permitting long-distance data transmission and communication surpassing 1 km. Data collection over vast areas is accomplished via multi-hop communication, in which the sensor data are acquired and re-transmitted by neighboring sensors. The communication and transmission times are synchronized, and time-division communication is executed, which depends on the wireless devices to sleep when the connection is not necessary to consume less power. An experimental field test is performed to evaluate the reliability and accuracy of the designed wireless sensor network to collect and capture the acceleration response of the long-span Manhattan Bridge. Thanks to the high-quality monitoring data collected with the developed low-power wireless sensor network, the natural frequencies and mode shapes were robustly recognized. The monitoring tests also showed the benefits of the presented wireless sensor system concerning the installation and measuring operations. Full article

Detecting railroad station anomalies is a critical task prior to segmentation and making optimization decisions for each cluster. Three types of anomalies (local clustered, axis paralleled, and surrounded by normal instances) caused by the specialty of railroad operations bring the existing methods non-trivial challenges in detecting them accurately and efficiently. To tackle this limitation of existing methods, this paper proposes a novel anomaly detection method named Huffman Anomaly Detection Forest (HuffForest) to detect station anomalies, which leverages Huffman encoding to measure abnormalities in certain railroad scenarios with high accuracy. The proposed method establishes a Huffman forest by constructing trees from the perspective of data points and subsequently computes anomaly scores of instances considering both local and global information. A sampling-based version is also developed to improve scalability for large datasets. Taking advantage of the encoding mechanism, the proposed method can effectively recognize the underlying patterns of railroad stations and detect outliers in various complicated scenarios where the conventional methods are not reliable. Experiment results on both synthesized and public benchmarks are demonstrated to show the advances of the proposed method compared to the state-of-the-art isolation forest (iForest) and local outlier factor (LOF) methods on detection accuracy with an acceptable computational complexity. Full article

This paper considers locating an underwater target, where many sonobuoys are positioned to measure the bearing of the target’s sound. A sonobuoy has very low bearing accuracy, such as 10 degrees. In practice, we can use multiple heterogeneous sonobuoys, such that the variance of a sensor noise may be different from that of another sensor. In addition, the maximum sensing range of a sensor may be different from that of another sensor. The true target must exist within the sensing range of a sensor if the sensor detects the bearing of the target. In order to estimate the target position based on bearings-only measurements with low accuracy, this paper introduces a novel target localization approach based on multiple Virtual Measurement Sets (VMS). Here, each VMS is derived considering the bearing measurement noise of each sonar sensor. As far as we know, this paper is novel in locating a target’s 2D position based on heterogeneous sonobuoy sensors with low accuracy, considering the maximum sensing range of a sensor. The superiority (considering both time efficiency and location accuracy) of the proposed localization is verified by comparing it with other state-of-the-art localization methods using computer simulations. Full article

Wireless Underground Sensor Networks (WUSNs) that collect geospatial in situ sensor data are a backbone of internet-of-things (IoT) applications for agriculture and terrestrial ecology. In this paper, we first show how WUSNs can operate reliably under field conditions year-round and at the same time be used for determining and mapping soil conditions from the buried sensor nodes. We demonstrate the design and deployment of a 23-node WUSN installed at an agricultural field site that covers an area with a 530 m radius. The WUSN has continuously operated since September 2019, enabling real-time monitoring of soil volumetric water content (VWC), soil temperature (ST), and soil electrical conductivity. Secondly, we present data collected over a nine-month period across three seasons. We evaluate the performance of a deep learning algorithm in predicting soil VWC using various combinations of the received signal strength (RSSI) from each buried wireless node, above-ground pathloss, the distance between wireless node and receive antenna (D), ST, air temperature (AT), relative humidity (RH), and precipitation as input parameters to the model. The AT, RH, and precipitation were obtained from a nearby weather station. We find that a model with RSSI, D, AT, ST, and RH as inputs was able to predict soil VWC with an R² of 0.82 for test datasets, with a Root Mean Square Error of ±0.012 (m³/m³). Hence, a combination of deep learning and other easily available soil and climatic parameters can be a viable candidate for replacing expensive soil VWC sensors in WUSNs. Full article

The accurate measurement of aircraft mass properties, such as the mass, centroid, and moment of inertia (MOI), plays a key role in the precise control of aircraft. In order to obtain high-precision information on the parameters of the mass, centroid, and MOI of an aircraft using a single instrument, an integrated mass property measurement system was developed in this study by analyzing and comparing the latest technologies, especially the function-switching device, which switches the measurement states between the center of mass and the MOI. The purpose of mass property measurement was achieved through single clamping. In addition, the system has strong versatility and expansion and can be used with different tooling or adapter rings to measure the mass properties of aircraft with different shapes. In this paper, the main mechanical structure of the measurement system, the measurement method of relevant mass parameters, and the solution method of the transformation matrix are introduced, and the standard parts and the aircraft were verified experimentally. The test results showed that the mass measurement accuracy was 0.03%, the centroid measurement error was within ±0.2 mm, and the measurement accuracy of the MOI was within 0.2%, all of which meet the high-precision measurement requirements for the mass properties. Full article

Recent industrial robotics covers a broad part of the manufacturing spectrum and other human everyday life applications; the performance of these devices has become increasingly important. Positioning accuracy and repeatability, as well as operating speed, are essential in any industrial robotics application. Robot positioning errors are complex due to the extensive combination of their sources and cannot be compensated for using conventional methods. Some robot positioning errors can be compensated for only using machine learning (ML) procedures. Reinforced machine learning increases the robot’s positioning accuracy and expands its implementation capabilities. The provided methodology presents an easy and focused approach for industrial in situ robot position adjustment in real-time during production setup or readjustment cases. The scientific value of this approach is a methodology using an ML procedure without huge external datasets for the procedure and extensive computing facilities. This paper presents a deep q-learning algorithm applied to improve the positioning accuracy of an articulated KUKA youBot robot during operation. A significant improvement of the positioning accuracy was achieved approximately after 260 iterations in the online mode and initial simulation of the ML procedure. Full article

Clustering is a promising technique for optimizing energy consumption in sensor-enabled Internet of Things (IoT) networks. Uneven distribution of cluster heads (CHs) across the network, repeatedly choosing the same IoT nodes as CHs and identifying cluster heads in the communication range of other CHs are the major problems leading to higher energy consumption in IoT networks. In this paper, using fuzzy logic, bio-inspired chicken swarm optimization (CSO) and a genetic algorithm, an optimal cluster formation is presented as a Hybrid Intelligent Optimization Algorithm (HIOA) to minimize overall energy consumption in an IoT network. In HIOA, the key idea for formation of IoT nodes as clusters depends on finding chromosomes having a minimum value fitness function with relevant network parameters. The fitness function includes minimization of inter- and intra-cluster distance to reduce the interface and minimum energy consumption over communication per round. The hierarchical order classification of CSO utilizes the crossover and mutation operation of the genetic approach to increase the population diversity that ultimately solves the uneven distribution of CHs and turnout to be balanced network load. The proposed HIOA algorithm is simulated over MATLAB2019A and its performance over CSO parameters is analyzed, and it is found that the best fitness value of the proposed algorithm HIOA is obtained though setting up the parameters$po{p}_{size}=60$, number of rooster$N_r=0.3$, number of hen’s $N_h=0.6$ and swarm updating frequency $\theta =10$. Further, comparative results proved that HIOA is more effective than traditional bio-inspired algorithms in terms of node death percentage, average residual energy and network lifetime by 12%, 19% and 23%. Full article