Sensors — Open Access Journal

The goal of this paper is to improve our previous Dynamic Obstacle Mapping (DOMap) system by means of improving the perception stage. The new system must deal with robots and people as dynamic obstacles using LIght Detection And Range (LIDAR) sensor in order to collect the surrounding information. Although robot movement can be easily tracked by an Extended Kalman Filter (EKF), people’s movement is more unpredictable and it might not be correctly linearized by an EKF. Therefore, to deal with a better estimation of both types of dynamic objects in the local map it is recommended to improve our previous work. The DOMap has been extended in three key points: first the LIDAR reflectivity remission is used to make more robust the matching in the optical flow of the detection stage, secondly static and a dynamic occlusion detectors have been proposed, and finally a tracking stage based on Particle Filter (PF) has been used to deal with robots and people as dynamic obstacles. Therefore, our new improved-DOMap (iDOMap) provides maps with information about occupancy and velocities of the surrounding dynamic obstacles (robots, people, etc.) in a more robust way and they are available to improve the following planning stage. Full article

Composite spectrum (CS) is a data-fusion technique that reduces the number of spectra to be analyzed, simplifying the analysis process for machine monitoring and fault detection. In this work, vibration signals from five components of a combine harvester (thresher, chopper, straw walkers, sieve box, and engine) are obtained by placing four accelerometers along the combine-harvester chassis in non-optimal locations. Four individual spectra (one from each accelerometer) and three CS (non-coherent, coherent and poly-coherent spectra) from 18 cases are analyzed. The different cases result from the combination of three working conditions of the components—deactivated (off), balanced (healthy), and unbalanced (faulty)—and two speeds—idle and maximum revolutions per minute (RPM). The results showed that (i) the peaks can be identified in the four individual spectra that correspond to the rotational speeds of the five components in the analysis; (ii) the three formulations of the CS retain the relevant information from the individual spectra, thereby reducing the number of spectra required for monitoring and detecting rotating unbalances within a combine harvester; and, (iii) data noise reduction is observed in coherent and poly-coherent CS with respect to the non-coherent CS and the individual spectra. This study demonstrates that the rotating unbalances of various components within agricultural machines, can be detected with a reduced number of accelerometers located in non-optimal positions, and that it is feasible to simplify the monitoring with CS. Overall, the coherent CS may be the best composite spectra formulation in order to monitor and detect rotating unbalances in agricultural machines. Full article

Detailed information about temperature distribution can be important to understand structural behavior in fire. This study develops a method to image three-dimensional temperature distributions in steel–concrete composite slabs using distributed fiber optic sensors. The feasibility of the method is explored using six 1.2 m × 0.9 m steel–concrete composite slabs instrumented with distributed sensors and thermocouples subjected to fire for over 3 h. Dense point clouds of temperature in the slabs were measured using the distributed sensors. The results show that the distributed sensors operated at material temperatures up to 960 °C with acceptable accuracy for many structural fire applications. The measured non-uniform temperature distributions indicate a spatially distributed thermal response in steel–concrete composite slabs, which can only be adequately captured using approaches that provide a high density of through-depth data points. Full article

Atrial fibrillation (AF) is the most common arrhythmia and has a major impact on morbidity and mortality; however, detection of asymptomatic AF is challenging. This study sims to evaluate the sensitivity and specificity of non-invasive AF detection by a medical wearable. In this observational trial, patients with AF admitted to a hospital carried the wearable and an ECG Holter (control) in parallel over a period of 24 h, while not in a physically restricted condition. The wearable with a tight-fit upper armband employs a photoplethysmography technology to determine pulse rates and inter-beat intervals. Different algorithms (including a deep neural network) were applied to five-minute periods photoplethysmography datasets for the detection of AF. A total of 2306 h of parallel recording time could be obtained in 102 patients; 1781 h (77.2%) were automatically interpretable by an algorithm. Sensitivity to detect AF was 95.2% and specificity 92.5% (area under the receiver operating characteristics curve (AUC) 0.97). Usage of deep neural network improved the sensitivity of AF detection by 0.8% (96.0%) and specificity by 6.5% (99.0%) (AUC 0.98). Detection of AF by means of a wearable is feasible in hospitalized but physically active patients. Employing a deep neural network enables reliable and continuous monitoring of AF. Full article

A frequency-shift keying (FSK) radar in the 2.45-GHz band is proposed for highly accurate vital-signs detection. The measurement accuracy of the proposed detector for the heartbeat is increased by using the cross-correlation between the phase differences of signals at two frequencies used by the FSK radar, which alternately transmits and receives the signals with different frequencies. Two frequencies—2.45 and 2.5 GHz—are effectively discriminated by using the envelope detection with the frequency control signal of the signal generator in the output waveform of the FSK radar. The phase difference between transmitted and received signals at each frequency is determined after calibrating the I / Q imbalance and direct-current offset using a data-based imbalance compensation algorithm, the Gram–Schmidt procedure, and the Pratt method. The absolute-distance measurement results for a human being show that the vital signs obtained at each frequency using the proposed FSK radar have a cross-correlation. The heartbeat detection results for the proposed FSK radar at a distance of < 2.4 m indicate a reduction in the error rate and an increase in the signal-to-noise ratio compared with those obtained using a single operating frequency. Full article

The uneven surface of the weld seam makes eddy current testing more susceptible to the lift-off effect of the probe. Therefore, the defect of carbon steel plate welds has always been a difficult problem in eddy current testing. This study aimed to design a new type of eddy current orthogonal axial probe and establish the finite element simulation model of the probe. The effect of the probe structure, coil turns, and coil size on the detection sensitivity was simulated. Further, a designed orthogonal axial probe was used to conduct a systematic experiment on the weld of carbon steel specimens, and the 0.2 mm width and 1 mm depth of weld defects of carbon steel plates were effectively detected. The experimental results showed that the new orthogonal axial eddy current probe effectively suppressed the unevenness effect of the weld surface on the lift-off effect during the detection process. Full article

A personal glucose meter (PGM)-based method for quantitative detection of a urinary nucleic acid biomarker in prostate cancer screening, the so-called PCA3, is reported herein. A sandwich-type genoassay is conducted on magnetic beads to collect the target from the sample by specific hybridization, making the assay appropriate for PCA3 detection in biological fluids. The success of the method hinges on the use of alkaline phosphatase (ALP) to link the amount of nucleic acid biomarker to the generation of glucose. In particular, specifically attached ALP molecules hydrolyze D-glucose-1-phosphate into D-glucose, thus enabling the amplification of the recorded signal on the personal glucose meter. The developed genoassay exhibits good sensitivity (3.3 ± 0.2 mg glucose dL⁻¹ pM⁻¹) for PCA3, with a dynamic range of 5 to 100 pM and a quantification limit of 5 pM. Likewise, it facilitates point-of-care testing of nucleic acid biomarkers by using off-the-shelf PGM instead of complex instrumentation involved in traditional laboratory-based tests. Full article

Background: Available methods for studying muscle dynamics, including electromyography (EMG), mechanomyography (MMG) and M-mode ultrasound, have limitations in terms of spatial resolution. Methods: This study developed a novel method/protocol of two-dimensional mapping of muscle motion onset using ultrafast ultrasound imaging, i.e., sono-mechano-myo-graphy (SMMG). The developed method was compared with the EMG, MMG and force outputs of tibialis anterior (TA) muscle during ankle dorsiflexion at different percentages of maximum voluntary contraction (MVC) force in healthy young adults. Results: Significant differences between all pairwise comparisons of onsets were identified, except between SMMG and MMG. The EMG onset significantly led SMMG, MMG and force onsets by 40.0 ± 1.7 ms (p < 0.001), 43.1 ± 5.2 ms (p < 0.005) and 73.0 ± 4.5 ms (p < 0.001), respectively. Muscle motion also started earlier at the middle aponeurosis than skin surface and deeper regions when viewed longitudinally (p < 0.001). No significant effect of force level on onset delay was found. Conclusions: This study introduced and evaluated a new method/protocol, SMMG, for studying muscle dynamics and demonstrated its feasibility for muscle contraction onset research. This novel technology can potentially provide new insights for future studies of neuromuscular diseases, such as multiple sclerosis and muscular dystrophy. Full article

Hydrogen peroxide (H₂O₂) is an important signaling molecule and plays key roles in multiple plant physiological processes. The rapid and direct monitoring of H₂O₂ could improve our understanding of its regulatory mechanisms in plants. In this study, we developed a paper-based analytical device consisting of a disposable nano-gold modified indium tin oxide working electrode to provide a platform for the rapid and direct detection of H₂O₂. The total analytical time was dramatically shortened to be approximate 3 min due to the avoidance of the time-consuming and complex treatment of plant samples. In addition, the amount of plant samples required was less than 3 mg in our approach. We used this system to monitor the concentrations of H₂O₂ in tomato leaves infected by Botrytiscinerea within 24 h. Our results showed that the concentration of H₂O₂ in tomato leaves was increased in the initial phase, peaked at 1.5 μmol gFW⁻¹ at 6 h, and then decreased. The production trend of H₂O₂ in tomato leaves inoculated with Botrytiscinerea detected with our approach is similar to the 3,3-diaminobenzidine staining method. Taken together, our study offers a rapid and direct approach for the detection of H₂O₂, which will not only pave the way for the further investigation of the regulation mechanisms of H₂O₂ in plants, but also promote the development of precision agriculture technology. Full article

Methods for autonomous navigation systems using sonars in air traditionally use the time-of-flight technique for obstacle detection and environment mapping. However, this technique suffers from constructive and destructive interference of ultrasonic reflections from multiple obstacles in the environment, requiring several acquisitions for proper mapping. This paper presents a novel approach for obstacle detection and localisation using inverse problems and compressed sensing concepts. Experiments were conducted with multiple obstacles present in a controlled environment using a hardware platform with four transducers, which was specially designed for sending, receiving and acquiring raw ultrasonic signals. A comparison between the performance of compressed sensing using Orthogonal Matching Pursuit and two traditional image reconstruction methods was conducted. The reconstructed 2D images representing the cross-section of the sensed environment were quantitatively assessed, showing promising results for robotic mapping tasks using compressed sensing. Full article

Rehabilitative mobility aids are being used extensively for physically impaired people. Efforts are being made to develop human machine interfaces (HMIs), manipulating the biosignals to better control the electromechanical mobility aids, especially the wheelchairs. Creating precise control commands such as move forward, left, right, backward and stop, via biosignals, in an appropriate HMI is the actual challenge, as the people with a high level of disability (quadriplegia and paralysis, etc.) are unable to drive conventional wheelchairs. Therefore, a novel system driven by optical signals addressing the needs of such a physically impaired population is introduced in this paper. The present system is divided into two parts: the first part comprises of detection of eyeball movements together with the processing of the optical signal, and the second part encompasses the mechanical assembly module, i.e., control of the wheelchair through motor driving circuitry. A web camera is used to capture real-time images. The processor used is Raspberry-Pi with Linux operating system. In order to make the system more congenial and reliable, the voice-controlled mode is incorporated in the wheelchair. To appraise the system’s performance, a basic wheelchair skill test (WST) is carried out. Basic skills like movement on plain and rough surfaces in forward, reverse direction and turning capability were analyzed for easier comparison with other existing wheelchair setups on the bases of controlling mechanisms, compatibility, design models, and usability in diverse conditions. System successfully operates with average response time of 3 s for eye and 3.4 s for voice control mode. Full article

Next generation cellular systems need efficient content-distribution schemes. Content-sharing via Device-to-Device (D2D) clustered networks has emerged as a popular approach for alleviating the burden on the cellular network. In this article, we utilize Content-Centric Networking and Network Virtualization to propose a distributed architecture, that supports efficient content delivery. We propose to use clustering at the user level for content-distribution. A weighted multifactor clustering algorithm is proposed for grouping the D2D User Equipment (DUEs) sharing a common interest. The proposed algorithm is evaluated in terms of energy efficiency, area spectral efficiency, and throughput. The effect of the number of clusters on these performance parameters is also discussed. The proposed algorithm has been further modified to allow for a tradeoff between fairness and other performance parameters. A comprehensive simulation study demonstrates that the proposed clustering algorithm is more flexible and outperforms several classical and state-of-the-art algorithms. Full article

As the need for wildfire detection increases, research on wildfire smoke detection combining low-cost cameras and deep learning technology is increasing. Camera-based wildfire smoke detection is inexpensive, allowing for a quick detection, and allows a smoke to be checked by the naked eye. However, because a surveillance system must rely only on visual characteristics, it often erroneously detects fog and clouds as smoke. In this study, a combination of a You-Only-Look-Once detector and a long short-term memory (LSTM) classifier is applied to improve the performance of wildfire smoke detection by reflecting on the spatial and temporal characteristics of wildfire smoke. However, because it is necessary to lighten the heavy LSTM model for real-time smoke detection, in this paper, we propose a new method for applying the teacher–student framework to deep LSTM. Through this method, a shallow student LSTM is designed to reduce the number of layers and cells constituting the LSTM model while maintaining the original deep LSTM performance. As the experimental results indicate, our proposed method achieves up to an 8.4-fold decrease in the number of parameters and a faster processing time than the teacher LSTM while maintaining a similar detection performance as deep LSTM using several state-of-the-art methods on a wildfire benchmark dataset. Full article

User behaviour and choice is a significant parameter in the consumption patterns of energy in the built environment. This paper introduces a behavior-based approach for developing smart energy applications. With the rapid development of wireless sensor networks and the Internet of Things (IoT), human-computer interfaces can be created through the mapping of user experiences. These applications can provide users with dynamic feedback on their energy consumption patterns in their built environment. The paper describes a “Sensible Energy System” (SENS) that is based on user experience design methods with sensor network technology. Through SENS, solar energy simulation is combined with device consumption data in order to achieve an IoT network to facilitate the interaction between user behaviors and electricity consumption. The interaction between users and devices through SENS can not only optimize power consumption, but also provide consumers with additional choice and dynamic decision making regarding their consumption. This article provides an (1) understanding and analysis of users’ spatial interaction, explains the (2) planning of the new smart environment design and user experiences, discusses (3) designing a suitable Wireless sensor network (WSN) agent and energy connection, describes (4) the information that has been collected, and (5) incorporates a rooftop solar potential simulation for predicting energy outputs into the sensor network model. Full article

Tiny changes in the mass of the sensor in a quartz crystal microbalance with dissipation monitoring (QCM-D) can be observed. However, the lack of specificity for target species has hindered the use of QCM-D. Here, molecularly imprinted polymers (MIPs) were used to modify a QCM-D sensor to provide specificity. The MIPs were formed in the presence of sodium dodecyl benzene sulfonate. Imprinted layers on Fe₃O₄ nanoparticles were formed using pyrrole as the functional monomer and cross-linker and methylene blue (MB) as a template. The MIPs produced were then attached to the surface of a QCM-D sensor. The MIPs-coated QCM-D sensor could recognize MB and gave a linear response in the concentration range 25 to 1.5 × 10² µg/L and a detection limit of 1.4 µg/L. The QCM-D sensor was selective for MB over structural analogs. The MIPs-coated QCM-D sensor was successfully used to detect MB in river water and seawater samples, and the recoveries were good. This is the first time MB has been detected using a QCM-D sensor. Mass is an intrinsic property of matter, so this method could easily be extended to other target species by using different MIPs. Full article

In manufacturing, traditional task pre-programming methods limit the efficiency of human–robot skill transfer. This paper proposes a novel task-learning strategy, enabling robots to learn skills from human demonstrations flexibly and generalize skills under new task situations. Specifically, we establish a markerless vision capture system to acquire continuous human hand movements and develop a threshold-based heuristic segmentation algorithm to segment the complete movements into different movement primitives (MPs) which encode human hand movements with task-oriented models. For movement primitive learning, we adopt a Gaussian mixture model and Gaussian mixture regression (GMM-GMR) to extract the optimal trajectory encapsulating sufficient human features and utilize dynamical movement primitives (DMPs) to learn for trajectory generalization. In addition, we propose an improved visuo-spatial skill learning (VSL) algorithm to learn goal configurations concerning spatial relationships between task-relevant objects. Only one multioperation demonstration is required for learning, and robots can generalize goal configurations under new task situations following the task execution order from demonstration. A series of peg-in-hole experiments demonstrate that the proposed task-learning strategy can obtain exact pick-and-place points and generate smooth human-like trajectories, verifying the effectiveness of the proposed strategy. Full article

Recent developments in the area of condition monitoring research have been targeted towards predicting machinery health condition for the purpose of preventative maintenance. Typically, published research uses data collected from rotating components (bearings, cutting tools, etc.) working in an idealized lab environment as the case study for prognosis algorithm validations. However, the operational implementation in industry is still very sporadic, mainly owing to the lack of proper data allowing sufficiently mature development of comprehensive methodologies. The prognosis methodology presented herein bridges the gap between academic research and industrial implementations by employing a novel time period for prognosis and implementing random coefficients regression models. The definition of the remaining maintenance-free operating period (RMFOP) is proposed first, which helps to transform the usefulness of the degradation data that is readily available from data short of failure. Degradation patterns are subsequently extracted from the original degradation data, before fitting into either of two regression models (linear or exponential). The system residual life distributions are then computed and updated by estimating the parameter statistics within the model. This RMFOP-based methodology is validated using real-world degradation data collected from multiple operational railway switch systems across Great Britain. The results indicate that both the linear model and the exponential model can produce residual life distributions with a sufficient prediction accuracy for this specific application. The exponential model gives better predictions, the accuracy of which also improves as more of system life percentage has elapsed. By using the RMFOP methodology, switch system health condition affected by an incipient overdriving fault is recognized and predicted. Full article

Water-soluble chemicals, involving a wide range of toxic chemicals in aqueous solutions, remain essential in both daily living or industrial uses. However, most toxicants are evaporated with water through their use and thus cause deleterious effects on the domestic environment and health in humans. Unfortunately, most current low-dose chemical vapor detection technologies are restricted by the use of sophisticated instruments and unable to promptly detect the quantity of diverse toxicants in a single analysis. To address these issues, this study reports the development of simple and fast chemical vapor detection using doctor-blade-coated macroporous poly(2-hydroxyethyl methacrylate)/poly(ethoxylated trimethylolpropane triacrylate) photonic crystals, in which the poly(2-hydroxyethyl methacrylate) has strong affinity to insecticide vapor owing to a favorable Gibbs free energy change for their mixing. The condensation of water-soluble chemical vapor therefore results in a significant reflection peak shift and an obvious color change. The visual colorimetric readout can be further improved by increasing the lattice spacing of the macroporous photonic crystals. Furthermore, the dependence of the reflection peak position on vapor pressure under actual conditions and the reproducibility of vapor detecting are also evaluated in this study. Full article

In the last decades, the increase in global industrialization and the consequent technological progress have damaged the quality of the environment. As a consequence, the high levels of hazardous compounds such as metals and gases released in the atmosphere and water, have raised several concerns about the health of living organisms. Today, many analytical techniques are available with the aim to detect pollutant chemical species. However, a lot of them are not affordable due to the expensive instrumentations, time-consuming processes and high reagents volumes. Last but not least, their use is exclusive to trained operators. Contrarily, colorimetric sensing devices, including paper-based devices, are easy to use, providing results in a short time, without particular specializations to interpret the results. In addition, the colorimetric response is suitable for fast detection, especially in resource-limited environments or underdeveloped countries. Among different chemical species, transition and heavy metals such as iron Fe(II) and copper Cu(II) as well as volatile compounds, such as ammonia (NH₃) and acetaldehyde (C₂H₄O) are widespread mainly in industrialized geographical areas. In this work, we developed a colorimetric paper-based analytical device (PAD) to detect different contaminants, including Fe²⁺ and Cu²⁺ ions in water, and NH₃ and C₂H₄O in air at low concentrations. This study is a “proof of concept” of a new paper sensor in which the intensity of the colorimetric response is proportional to the concentration of a detected pollutant species. The sensor model could be further implemented in other technologies, such as drones, individual protection devices or wearable apparatus to monitor the exposure to toxic species in both indoor and outdoor environments. Full article