Sensors

Pipeline magnetic flux leakage (MFL) internal detection technology is the most widely used and effective method in the field of long-distance oil and gas pipeline online detection. With the improvement of data quantization precision, the influence of stress on MFL signal has been paid more and more attention. In this paper, the relationship between stress and saturation magnetization is introduced based on J-A theory. The analytical model of MFL detection signal for pipeline composite defects is established. The MFL signal characteristics of composite defects are quantitatively calculated. The effect of stress on MFL signal is studied. The theoretical analysis is verified by experimental data and excavation results. The researches show that the saturation magnetization of ferromagnets decreases exponentially with the increase of stress in strong magnetic field. The MFL signal of composite defect is weaker than that of volumetric defects of the same dimension. The axial amplitude and radial peak-to-peak value of MFL signal decrease with the increase of stress around the defect. The axial amplitude and radial peak-to-peak value of MFL signal increase non-linearly with the increase of width and depth of defects. When using MFL signal to judge the defect depth, it is necessary to make clear whether there is stress concentration phenomenon around the defect because the stress will lead to underestimation of the defect depth. Full article

The paper concerns multicast packet traffic throughput maximization in multi-hop wireless sensor networks with time division multiple access to radio channel. We assume that the modulation and coding schemes (MCSs) that are used by the (broadcasting) nodes as well as the transmission power of the nodes are adjustable. This leads to the main research question studied in this paper: to what extent traffic throughput can be increased by proper MCSs assignment and transmission power control (TPC) at the nodes? To answer this question, we introduce mixed-integer programming formulations for joint MCSs assignment and TPC optimization, together with a solution algorithm. Finally, we present a numerical study illustrating the considerations of the paper. The numerical results show a significant gain being achieved by proper MCSs assignment, which is further increased by applying TPC. Full article

High-resolution automotive radar sensors play an increasing role in detection, classification and tracking of moving objects in traffic scenes. Clustering is frequently used to group detection points in this context. However, this is a particularly challenging task due to variations in number and density of available data points across different scans. Modified versions of the density-based clustering method DBSCAN have mostly been used so far, while hierarchical approaches are rarely considered. In this article, we explore the applicability of HDBSCAN, a hierarchical DBSCAN variant, for clustering radar measurements. To improve results achieved by its unsupervised version, we propose the use of cluster-level constraints based on aggregated background information from cluster candidates. Further, we propose the application of a distance threshold to avoid selection of small clusters at low hierarchy levels. Based on exemplary traffic scenes from nuScenes, a publicly available autonomous driving data set, we test our constraint-based approach along with other methods, including label-based semi-supervised HDBSCAN. Our experiments demonstrate that cluster-level constraints help to adjust HDBSCAN to the given application context and can therefore achieve considerably better results than the unsupervised method. However, the approach requires carefully selected constraint criteria that can be difficult to choose in constantly changing environments. Full article

This study proposes a novel hybrid imitation learning (HIL) framework in which behavior cloning (BC) and state cloning (SC) methods are combined in a mutually complementary manner to enhance the efficiency of robotic manipulation task learning. The proposed HIL framework efficiently combines BC and SC losses using an adaptive loss mixing method. It uses pretrained dynamics networks to enhance SC efficiency and performs stochastic state recovery to ensure stable learning of policy networks by transforming the learner’s task state into a demo state on the demo task trajectory during SC. The training efficiency and policy flexibility of the proposed HIL framework are demonstrated in a series of experiments conducted to perform major robotic manipulation tasks (pick-up, pick-and-place, and stack tasks). In the experiments, the HIL framework showed about a 2.6 times higher performance improvement than the pure BC and about a four times faster training time than the pure SC imitation learning method. In addition, the HIL framework also showed about a 1.6 times higher performance improvement and about a 2.2 times faster training time than the other hybrid learning method combining BC and reinforcement learning (BC + RL) in the experiments. Full article

The integrated circuit (IC) manufacturing process involves many players, from chip/board design and fabrication to firmware design and installation. In today’s global supply chain, any of these steps are prone to interference from rogue players, creating a security risk. Therefore, manufactured devices need to be verified to perform only their intended operations since it is not economically feasible to control the supply chain and use only trusted facilities. This paper presents a detection technique for malicious activity that can stem from hardware or firmware Trojans. The proposed technique relies on (i) repetitious side-channel sample collection of the active device, (ii) time-domain stitching, and (iii) frequency domain analysis. Since finding a trusted sample is generally impractical, the proposed technique is based on self-referencing to remove the effects of environmental or device-to-device variation in the frequency domain. We first observe that the power spectrum of the Trojan activity is confined to a low-frequency band. Then, we exploit this fact to achieve self-referencing using signal detection theory. The proposed technique’s effectiveness is demonstrated through experiments on a wearable electronics prototype and system-on-chip (SoC) under a variety of practical scenarios. Experimental results show the proposed detection technique enables a high overall detection coverage for malicious activities of varying types with 0.8 s monitoring time overhead, which is negligible. Full article

Lightweight UAVs equipped with deep learning models have become a trend, which can be deployed for automatic navigation in a wide range of civilian and military missions. However, real-time applications usually need to process a large amount of image data, which leads to a very large computational complexity and storage consumption, and restricts its deployment on resource-constrained embedded edge devices. To reduce the computing requirements and storage occupancy of the neural network model, we proposed the ensemble binarized DroNet (EBDN) model, which implemented the reconstructed DroNet with the binarized and ensemble learning method, so that the model size of DroNet was effectively compressed, and ensemble learning method was used to overcome the defect of the poor performance of the low-precision network. Compared to the original DroNet, EBDN saves more than 7 times of memory footprint with similar model accuracy. Meanwhile, we also proposed a novel and high-efficiency hardware architecture to realize the EBDN on the chip (EBDNoC) system, which perfectly realizes the mapping of an algorithm model to hardware architecture. Compared to other solutions, the proposed architecture achieves about 10.21 GOP/s/kLUTs resource efficiency and 208.1 GOP/s/W energy efficiency, while also providing a good trade-off between model performance and resource utilization. Full article

Precise localization and pose estimation in indoor environments are commonly employed in a wide range of applications, including robotics, augmented reality, and navigation and positioning services. Such applications can be solved via visual-based localization using a pre-built 3D model. The increase in searching space associated with large scenes can be overcome by retrieving images in advance and subsequently estimating the pose. The majority of current deep learning-based image retrieval methods require labeled data, which increase data annotation costs and complicate the acquisition of data. In this paper, we propose an unsupervised hierarchical indoor localization framework that integrates an unsupervised network variational autoencoder (VAE) with a visual-based Structure-from-Motion (SfM) approach in order to extract global and local features. During the localization process, global features are applied for the image retrieval at the level of the scene map in order to obtain candidate images, and are subsequently used to estimate the pose from 2D-3D matches between query and candidate images. RGB images only are used as the input of the proposed localization system, which is both convenient and challenging. Experimental results reveal that the proposed method can localize images within 0.16 m and 4° in the 7-Scenes data sets and 32.8% within 5 m and 20° in the Baidu data set. Furthermore, our proposed method achieves a higher precision compared to advanced methods. Full article

In this paper, we propose a new algorithm, called FOCUSED (FOrecast Correction Using Successive prEDictions), for forecast correction of short-term wind speed predictions. We developed FOCUSED with the aim of improving the forecast of bora gusts, which frequently result in high-speed wind situations dangerous for traffic. The motivation arises from occasionally ambiguous results of the currently deployed decision support system, which aids traffic management in strong and gusty wind conditions at the coast of Croatia. The proposed correction algorithm uses characteristics of numerical weather prediction models to iteratively forecast the wind speed multiple times for the same future window. We use these iterative predictions as input features of the FOCUSED algorithm and get the corrected predictions as the output. We compared the proposed algorithm with artificial neural networks, random forests, support vector machines, and linear regression to demonstrate the superiority of the algorithm’s performance on a data set comprising five years of real data measurements at the Croatian bridge “Krk” and complementary historical forecasts by ALADIN (Aire Limitée Adaptation dynamique Développement InterNational) numerical weather prediction model. Full article

Although log processing of network equipment is a common technique in cellular network management, several factors make said task challenging, especially during mass attendance events. The present paper assesses classic methods for cellular network measurement and acquisition, showing how the use of on-the-field user probes can provide relevant capabilities to the analysis of cellular network performance. Therefore, a framework for the deployment of this kind of system is proposed here based on the development of a new hardware virtualization platform with radio frequency capabilities. Accordingly, an analysis of the characteristics and requirements for the use of virtual probes was performed. Moreover, the impact that social events (e.g., sports matches) may have on the service provision was evaluated through a real cellular scenario. For this purpose, a long-term measurement study during crowded events (i.e., football matches) in a stadium has been conducted, and the performances of different services and operators under realistic settings has been evaluated. As a result, several considerations are presented that can be used for better management of future networks. Full article

Drift compensation is an important issue for metal oxide semiconductor (MOS) gas sensor arrays. General machine learning methods require constant calibration and a large amount of label gas data. At the same time, recalibration will cause a lot of costs, and label gas is difficult to obtain in practice. In this paper, a novel drift compensation method based on balanced distribution adaptation (BDA) is proposed. First, the BDA drift compensation method can adjust the conditional distribution and marginal distribution between the two domains through the weight balance factor, thereby more effectively reducing the mismatch between the two domains. When the BDA method performs classification tasks through machine learning, no labeled data is required in the target domain. Then, the particle swarm optimization algorithm is used to improve the accuracy of drift compensation. Individuals in the population are initialized randomly, and their fitness values are calculated. Iterative optimization of the population individuals is conducted until the optimal weight balance factor parameters are calculated. Finally, the BDA method is experimentally verified on the public gas sensor drift data set. Experimental results showed that the BDA method was significantly better than the existing joint distribution adaptation (JDA) method and other standard drift compensation methods such as K-Nearest Neighbor (KNN). In the two setting groups, the recognition accuracy was 4.54% and 1.62% ahead of the JDA method, and 12.23% and 15.83% ahead of the KNN method. Full article

The popularity of the Power Line Communication (PLC) system has decreased due to significant deficiencies in the technology itself, even though new wire installation is not required. In particular, regarding the request for high-speed throughput to fulfill smart-grid requirements, Broadband Power Line (BPLC) can be considered. This paper approaches PLC technology as an object of simulation experimentation in the Broadband Power Line Communication (BPLC) area. Several experimental measurements in a real environment are also given. This paper demonstrates these experimental simulation results as potential mechanisms for creating a complex simulation tool for various PLC technologies focusing on communication with end devices such as sensors and meters. The aim is to demonstrate the potential and limits of BPLC technology for implementation in Smart Grids or Smart Metering applications. Full article

We address the accuracy of wideband direct position estimation of a radio transmitter via a distributed antenna array in 5G cellular systems. Our derivations are based only on the presence of spatially coherent line-of-sight (LoS) signal components, which is a realistic assumption in small cells, especially in the mmWave range. The system model considers collocated time and phase synchronized receiving front-ends with antennas distributed in 3D space at known locations and connected to the front-ends via calibrated coaxial cables or analog radio-frequency-over-fiber links. Furthermore, the signal model assumes spherical wavefronts. We derive the Cramér-Rao bounds (CRBs) for two implementations of the system: with (a) known signals and (b) random Gaussian signals. The results show how the bounds depend on the carrier frequency, number of samples used for estimation, and signal-to-noise ratios. They also show that increasing the number of antennas (such as in massive MIMO systems) considerably improves the accuracy and lowers the signal-to-noise threshold for localization even for non-cooperative transmitters. Finally, our derivations show that the square roots of the bounds are two to three orders of magnitude below the carrier wavelength for realistic system parameters. Full article

A framework for optimal sensor placement (OSP) for virtual sensing using the modal expansion technique and taking into account uncertainties is presented based on information and utility theory. The framework is developed to handle virtual sensing under output-only vibration measurements. The OSP maximizes a utility function that quantifies the expected information gained from the data for reducing the uncertainty of quantities of interest (QoI) predicted at the virtual sensing locations. The utility function is extended to make the OSP design robust to uncertainties in structural model and modeling error parameters, resulting in a multidimensional integral of the expected information gain over all possible values of the uncertain parameters and weighted by their assigned probability distributions. Approximate methods are used to compute the multidimensional integral and solve the optimization problem that arises. The Gaussian nature of the response QoI is exploited to derive useful and informative analytical expressions for the utility function. A thorough study of the effect of model, prediction and measurement errors and their uncertainties, as well as the prior uncertainties in the modal coordinates on the selection of the optimal sensor configuration is presented, highlighting the importance of accounting for robustness to errors and other uncertainties. Full article

Tropical peatlands such as Ghana’s Greater Amanzule peatland are highly valuable ecosystems and under great pressure from anthropogenic land use activities. Accurate measurement of their occurrence and extent is required to facilitate sustainable management. A key challenge, however, is the high cloud cover in the tropics that limits optical remote sensing data acquisition. In this work we combine optical imagery with radar and elevation data to optimise land cover classification for the Greater Amanzule tropical peatland. Sentinel-2, Sentinel-1 and Shuttle Radar Topography Mission (SRTM) imagery were acquired and integrated to drive a machine learning land cover classification using a random forest classifier. Recursive feature elimination was used to optimize high-dimensional and correlated feature space and determine the optimal features for the classification. Six datasets were compared, comprising different combinations of optical, radar and elevation features. Results showed that the best overall accuracy (OA) was found for the integrated Sentinel-2, Sentinel-1 and SRTM dataset (S2+S1+DEM), significantly outperforming all the other classifications with an OA of 94%. Assessment of the sensitivity of land cover classes to image features indicated that elevation and the original Sentinel-1 bands contributed the most to separating tropical peatlands from other land cover types. The integration of more features and the removal of redundant features systematically increased classification accuracy. We estimate Ghana’s Greater Amanzule peatland covers 60,187 ha. Our proposed methodological framework contributes a robust workflow for accurate and detailed landscape-scale monitoring of tropical peatlands, while our findings provide timely information critical for the sustainable management of the Greater Amanzule peatland. Full article

While the Player Load is a widely-used parameter for physical demand quantification using wearable accelerometers, its calculation is subjected to potential errors related to rotational changes of the reference frame. The aims of this study were (i) to assess the concurrent validity of accelerometry-based Player Load against force plates; (ii) to validate a novel metric, the Accel’Rate overcoming this theoretical issue. Twenty-one recreational athlete males instrumented with two triaxial accelerometers positioned at the upper and lower back performed running-based locomotor movements at low and high intensity over six in-series force plates. We examined the validity of the Player Load and the Accel’Rate by using force plates. Standard error of the estimate was small to moderate for all tested conditions (Player Load: 0.45 to 0.87; Accel’Rate: 0.25 to 0.95). Accel’Rate displayed trivial to small mean biases (−1.0 to 6.1 a.u.) while the Player Load displayed systematic very large to extremely large mean biases (17.1 to 226.0 a.u.). These findings demonstrate a better concurrent validity of the Accel’Rate compared to the Player Load. This metric could be used to improve the estimation of whole-body mechanical load, easily accessible in sport training and competition settings. Full article

In 2019, a new virus, SARS-CoV-2, responsible for the COVID-19 disease, was discovered. Asymptomatic and mildly symptomatic patients were forced to quarantine and closely monitor their symptoms and vital signs, most of the time at home. This paper describes e-CoVig, a novel mHealth application, developed as an alternative to the current monitoring paradigm, where the patients are followed up by direct phone contact. The e-CoVig provides a set of functionalities for remote reporting of symptoms, vital signs, and other clinical information to the health services taking care of these patients. The application is designed to register and transmit the heart rate, blood oxygen saturation (${\mathrm{SpO}}_2$), body temperature, respiration, and cough. The system features a mobile application, a web/cloud platform, and a low-cost specific device to acquire the temperature and ${\mathrm{SpO}}_2$. The architecture of the system is flexible and can be configured for different operation conditions. Current commercial devices, such as oximeters and thermometers, can also be used and read using the optical character recognition (OCR) functionality of the system. The data acquired at the mobile application are sent automatically to the web/cloud application and made available in real-time to the medical staff, enabling the follow-up of several users simultaneously without the need for time consuming phone call interactions. The system was already tested for its feasibility and a preliminary deployment was performed on a nursing home showing promising results. Full article

The objective of this research was to develop new precise point positioning (PPP) processing models using triple-frequency GPS/Galileo observations. Different triple-frequency PPP models were developed including undifferenced, between-satellite single-difference (BSSD) and semi-decoupled PPP models. Additionally, a dual-frequency ionosphere-free undifferenced PPP model was developed. The performance of our developed PPP models was evaluated for both static and kinematic applications. To validate the proposed PPP models for static applications, triple-frequency GPS/Galileo observations spanning three successive days from eight globally distributed reference stations were acquired. Then, the observations were processed using the four static PPP solutions. It is found that the 3D positioning accuracy of the triple-frequency semi-decoupled, BSSD and undifferenced PPP models is enhanced after 10 min by about 50, 41 and 29%, respectively, compared with the dual-frequency undifferenced PPP model. After 20 min of processing, improvements in the 3D positioning accuracy by 40, 31 and 21% are obtained for the triple-frequency semi-decoupled, BSSD and undifferenced PPP models, respectively, with respect to the dual-frequency PPP model. The 3D positioning accuracy is also improved after 60 min, compared with the dual-frequency solution, by 40, 40 and 35% for the triple-frequency semi-decoupled, BSSD and undifferenced PPP solutions, respectively. For kinematic application validation, a vehicle trajectory was carried out. The collected triple-frequency GPS/Galileo observations were processed using the four kinematic PPP solutions. It is shown that the triple-frequency semi-decupled, BSSD and undifferenced PPP solutions enhance the 3D positioning accuracy by 31, 23 and 10%, respectively, in comparison with the dual-frequency undifferenced PPP solutions. Full article

When the use of optical images is not practical due to cloud cover, Synthetic Aperture Radar (SAR) imagery is a preferred alternative for monitoring coastal wetlands because it is unaffected by weather conditions. Polarimetric SAR (PolSAR) enables the detection of different backscattering mechanisms and thus has potential applications in land cover classification. Gaofen-3 (GF-3) is the first Chinese civilian satellite with multi-polarized C-band SAR imaging capability. Coastal wetland classification with GF-3 polarimetric SAR imagery has attracted increased attention in recent years, but it remains challenging. The aim of this study was to classify land cover in coastal wetlands using an object-oriented random forest algorithm on the basis of GF-3 polarimetric SAR imagery. First, a set of 16 commonly used SAR features was extracted. Second, the importance of each SAR feature was calculated, and the optimal polarimetric features were selected for wetland classification by combining random forest (RF) with sequential backward selection (SBS). Finally, the proposed algorithm was utilized to classify different land cover types in the Yancheng Coastal Wetlands. The results show that the most important parameters for wetland classification in this study were Shannon entropy, Span and orientation randomness, combined with features derived from Yamaguchi decomposition, namely, volume scattering, double scattering, surface scattering and helix scattering. When the object-oriented RF classification approach was used with the optimal feature combination, different land cover types in the study area were classified, with an overall accuracy of up to 92%. Full article

With the extensive application of robots, such as unmanned aerial vehicle (UAV) in exploring unknown environments, visual odometry (VO) algorithms have played an increasingly important role. The environments are diverse, not always textured, or low-textured with insufficient features, making them challenging for mainstream VO. However, for low-texture environment, due to the structural characteristics of man-made scene, the lines are usually abundant. In this paper, we propose a virtual-real hybrid map based monocular visual odometry algorithm. The core idea is that we reprocess line segment features to generate the virtual intersection matching points, which can be used to build the virtual map. Introducing virtual map can improve the stability of the visual odometry algorithm in low-texture environment. Specifically, we first combine unparallel matched line segments to generate virtual intersection matching points, then, based on the virtual intersection matching points, we triangulate to get a virtual map, combined with the real map built upon the ordinary point features to form a virtual-real hybrid 3D map. Finally, using the hybrid map, the continuous camera pose estimation can be solved. Extensive experimental results have demonstrated the robustness and effectiveness of the proposed method in various low-texture scenes. Full article