Sensors — Open Access Journal

Device-free localization (DFL) is a promising technique which could provide localization information for a target without requiring an electronic device. With the development of the smart city and smart transportation, DFL could form part of a basic technique that could be used to track and localize roadside vehicles. In this paper, some algorithms for three-dimensional (3D) DFL for vehicle surveillance are developed, including statistical methods for data, a method for communication link selection, a novel method of communication link weight allocation and some other minor approaches to obtain the location and approximate size of a static vehicle, as a basic technique of moving vehicle detection. Then, an experimental system is designed. Through security monitoring wireless sensor networks (WSN), real-time vehicle characteristics (i.e., location and size) are calculated automatically and intuitively displayed through a heat map. Experiments are performed to validate the effect of the proposal and the accuracy of the localization and size estimation. Full article

Droplet-based microfluidics offers significant advantages, such as high throughput and scalability, making platforms based on this technology ideal candidates for point-of-care (POC) testing and clinical diagnosis. However, the efficiency of co-encapsulation in droplets is suboptimal, limiting the applicability of such platforms for the biosensing applications. The homogeneity of the bioanalytes in the droplets is an unsolved problem. While there is extensive literature on the experimental setups and active methods used to increase the efficiency of such platforms, passive techniques have received less attention, and their fundamentals have not been fully explored. Here, we develop a novel passive technique for investigating cell encapsulation using the finite element method (FEM). The level set method was used to track the interfaces of forming droplets. The effects of walls and the droplet interfaces on relatively large cells were calculated to track them more accurately during encapsulation. The static surface tension force was used to account for the effects of the interfaces on cells. The results revealed that the pairing efficiency is highly sensitive to the standard deviation (SD) of the distance between the cells in the entrance channel. The pairing efficiency prediction error of our model differed by less than 5% from previous experiments. The proposed model can be used to evaluate the performance of droplet-based microfluidic devices to ensure higher precision for co-encapsulation of cells. Full article

Due to the motion of the platform, the spectrum of first-order sea clutter will widen and mask low-velocity targets such as ships in shipborne high-frequency surface-wave radar (HFSWR). Limited by the quantity of qualified training samples, the performance of the generally used clutter-suppression method, space–time adaptive processing (STAP) degrades in shipborne HFSWR. To deal with this problem, an innovative training sample acquisition method is proposed, in the area of joint domain localized (JDL) reduced-rank STAP. In this clutter-suppression method, based on a single range of cell data, the unscented transformation is introduced as a preprocessing step to obtain adequate homogeneous secondary data and roughly estimated clutter covariance matrix (CCM). The accurate CCM is calculated by integrating the approximate CCM of different range of cells. Compared with existing clutter-suppression algorithms for shipborne HFSWR, the proposed approach has a better signal-to-clutter-plus-noise ratio (SCNR) improvement tested by real data. Full article

Selected optical fiber sensors offer extraordinary sensitivity to changes in external refractive (RI), which make them promising for label-free biosensing. In this work the most sensitive ones, namely long-period gratings working at (DTP-LPG) and micro-cavity in-line Mach-Zehnder interferometers (µIMZI) are discussed for application in bacteria sensing. We describe their working principles and RI sensitivity when operating in water environments, which is as high as 20,000 nm/RIU (Refractive index unit) for DTP-LPGs and 27,000 nm/RIU for µIMZIs. Special attention is paid to the methods to enhance the sensitivity by etching and nano-coatings. While the DTP-LPGs offer a greater interaction length and sensitivity to changes taking place at their surface, the µIMZIs are best suited for investigations of sub-nanoliter and picoliter volumes. The capabilities of both the platforms for bacteria sensing are presented and compared for strains of Escherichia coli, lipopolysaccharide E. coli, outer membrane proteins of E. coli, and Staphylococcus aureus. While DTP-LPGs have been more explored for bacteria detection in10²–10⁶ Colony Forming Unit (CFU)/mL for S. aureus and 10³–10⁹ CFU/mL for E. coli, the µIMZIs reached 10²–10⁸ CFU/mL for E. coli and have a potential for becoming picoliter bacteria sensors. Full article

The well-being of people with dementia (PWD) living in long-term care facilities is hindered due to disengagement and social isolation. Animal-like social robots are increasingly used in dementia care as they can provide companionship and engage PWD in meaningful activities. While most previous human–robot interaction (HRI) research studied engagement independent from the context, recent findings indicate that the context of HRI sessions has an impact on user engagement. This study aims to explore the effects of contextual interactions between PWD and a social robot embedded in the augmented responsive environment. Three experimental conditions were compared: reactive context-enhanced robot interaction; dynamic context-enhanced interaction with a static robot; a control condition with only the dynamic context presented. Effectiveness evaluations were performed with 16 participants using four observational rating scales on observed engagement, affective states, and apathy related behaviors. Findings suggested that the higher level of interactivity of a social robot and the interactive contextualized feedback helped capture and maintain users’ attention during engagement; however, it did not significantly improve their positive affective states. Additionally, the presence of either a static or a proactive robot reduced apathy-related behaviors by facilitating purposeful activities, thus, motivating behavioral engagement. Full article

Internet of Things (IoT) learning involves the acquisition of transversal skills ranging from the development based on IoT devices and sensors (edge computing) to the connection of the devices themselves to management environments that allow the storage and processing (cloud computing) of data generated by sensors. The usual development cycle for IoT applications consists of the following three stages: stage 1 corresponds to the description of the devices and basic interaction with sensors. In stage 2, data acquired by the devices/sensors are employed by communication models from the origin edge to the management middleware in the cloud. Finally, stage 3 focuses on processing and presentation models. These models present the most relevant indicators for IoT devices and sensors. Students must acquire all the necessary skills and abilities to understand and develop these types of applications, so lecturers need an infrastructure to enable the learning of development of full IoT applications. A Web of Things (WoT) platform named Labs of Things at UNED ([email protected]) has been used for this goal. This paper shows the fundamentals and features of this infrastructure, and how the different phases of the full development cycle of solutions in IoT environments are implemented using [email protected] The proposed system has been tested in several computer science subjects. Students can perform remote experimentation with a collaborative WoT learning environment in the cloud, including the possibility to analyze the generated data by IoT sensors. Full article

In the field of geo-hazards and geo-engineering, monitoring networks represent a key element for the geological risk assessment and the design and management of large infrastructures construction. In the last decade, we have observed a strong development on remote sensing techniques but just small changes in the subsoil observations. However, this type of measurement is very important to have a three-dimensional representation of the studied area, since the surface measurements often represent a sum of deformations that develop in a complex way in the subsoil. In this paper, we present a robotic inclinometer system developed to acquire deep-seated ground deformations in boreholes. This instrumentation combines advantages offered by manual inclinometer measurements with a robotized approach that improves the results in term of accuracy, revisiting time, and site accessibility. The Automated Inclinometer System (AIS) allows one to explore automatically all the length of the monitored borehole using just one inclinometer probe with a semi-wireless system. The paper presents the system and a detailed dataset of measurements acquired on three inclinometer tubes installed for the monitoring of the construction phase of the new Line C Metro of Rome. The dataset was acquired in real monitored site and undisturbed conditions and can represent a benchmark for modern inclinometer measurements. Full article

Deep neural networks (DNNs) have achieved significant advancements in speech processing, and numerous types of DNN architectures have been proposed in the field of sound localization. When a DNN model is deployed for sound localization, a fixed input size is required. This is generally determined by the number of microphones, the fast Fourier transform size, and the frame size. if the numbers or configurations of the microphones change, the DNN model should be retrained because the size of the input features changes. in this paper, we propose a configuration-invariant sound localization technique using the azimuth-frequency representation and convolutional neural networks (CNNs). the proposed CNN model receives the azimuth-frequency representation instead of time-frequency features as the input features. the proposed model was evaluated in different environments from the microphone configuration in which it was originally trained. for evaluation, single sound source is simulated using the image method. Through the evaluations, it was confirmed that the localization performance was superior to the conventional steered response power phase transform (SRP-PHAT) and multiple signal classification (MUSIC) methods. Full article

Indoor positioning technologies are of great use in GPS-denied areas. They can be partitioned into two types of systems—infrastructure-free based and infrastructure-dependent based. WiFi based indoor positioning system is somewhere between the infrastructure-free and infrastructure-dependent systems. The reason is that in WiFi based systems, Access Points (APs) as pre-installed infrastructures are necessary. However, the APs do not need to be specially installed, because WiFi APs are already widely deployed in many indoor areas, for example, offices, malls and airports. This feature makes WiFi based indoor positioning suitable for many practical applications. In this paper, a seq2seq model based, deep learning method is proposed for WiFi based fingerprinting. The model can learn from different length of training sequences, and thus can exploit the context information for positioning. The context information denotes the information contained in the sequence, which can help finding the correspondences between RSS fingerprints and the coordinate positions. A simple example piece of context information is human walking routine (such as no sharp turns). The proposed method shows an improvement with an open source dataset, when compared against deep learning based counterpart methods. Full article

Geological objects are characterized by a high complexity inherent to a strong compositional variability at all scales and usually unclear class boundaries. Therefore, dedicated processing schemes are required for the analysis of such data for mineralogical mapping. On the other hand, the variety of optical sensing technology reveals different data attributes and therefore multi-sensor approaches are adapted to solve such complicated mapping problems. In this paper, we devise an adapted multi-optical sensor fusion (MOSFus) workflow which takes the geological characteristics into account. The proposed processing chain exhaustively covers all relevant stages, including data acquisition, preprocessing, feature fusion, and mineralogical mapping. The concept includes (i) a spatial feature extraction based on morphological profiles on RGB data with high spatial resolution, (ii) a specific noise reduction applied on the hyperspectral data that assumes mixed sparse and Gaussian contamination, and (iii) a subsequent dimensionality reduction using a sparse and smooth low rank analysis. The feature extraction approach allows one to fuse heterogeneous data at variable resolutions, scales, and spectral ranges and improve classification substantially. The last step of the approach, an SVM classifier, is robust to unbalanced and sparse training sets and is particularly efficient with complex imaging data. We evaluate the performance of the procedure with two different multi-optical sensor datasets. The results demonstrate the superiority of this dedicated approach over common strategies. Full article

Emotional awareness perception is a largely growing field that allows for more natural interactions between people and machines. Electroencephalography (EEG) has emerged as a convenient way to measure and track a user’s emotional state. The non-linear characteristic of the EEG signal produces a high-dimensional feature vector resulting in high computational cost. In this paper, characteristics of multiple neural networks are combined using Deep Feature Clustering (DFC) to select high-quality attributes as opposed to traditional feature selection methods. The DFC method shortens the training time on the network by omitting unusable attributes. First, Empirical Mode Decomposition (EMD) is applied as a series of frequencies to decompose the raw EEG signal. The spatiotemporal component of the decomposed EEG signal is expressed as a two-dimensional spectrogram before the feature extraction process using Analytic Wavelet Transform (AWT). Four pre-trained Deep Neural Networks (DNN) are used to extract deep features. Dimensional reduction and feature selection are achieved utilising the differential entropy-based EEG channel selection and the DFC technique, which calculates a range of vocabularies using k-means clustering. The histogram characteristic is then determined from a series of visual vocabulary items. The classification performance of the SEED, DEAP and MAHNOB datasets combined with the capabilities of DFC show that the proposed method improves the performance of emotion recognition in short processing time and is more competitive than the latest emotion recognition methods. Full article

Visible light communications are considered as a promising solution for inter-vehicle communications, which in turn can significantly enhance the traffic safety and efficiency. However, the vehicular visible light communications (VLC) channel is highly dynamic, very unpredictable, and subject to many noise sources. Enhancing VLC systems with self-aware capabilities would maximize the communication performances and efficiency, whatever the environmental conditions. Within this context, this letter proposes a novel signal to noise ratio (SNR)-adaptive visible light communication receiver architecture aimed for automotive applications. The novelty of this letter comes from an open loop signal processing technique in which the signal treatment complexity is established based on a real-time SNR analysis. So, the receiver evaluates the SNR, and based on this assessment, it reconfigures its structural design in order to ensure a proper signal treatment, while providing an optimal tradeoff between communication performances and computational resources usage. This approach based on software reconfiguration has the potential to provide the system with enhanced flexibility and enables its usage in resource sharing application. As far as we know, this approach has not been considered in vehicular VLC systems. The performances of the proposed architecture are demonstrated by simulations, which confirm the SNR-adaptive capacity and the optimized performances. Full article

In this paper, we propose and experimentally demonstrate a simple technique to enhance the curvature sensitivity of a bending fiber optic sensor based on anti-resonant reflecting optical waveguide (ARROW) guidance. The sensing structure is assembled by splicing a segment of capillary hollow-core fiber (CHCF) between two single-mode fibers (SMF), and the device is set on a steel sheet for measuring different curvatures. Without any surface treatment, the ARROW sensor exhibits a curvature sensitivity of 1.6 dB/m⁻¹ in a curvature range from 0 to 2.14 m⁻¹. By carefully coating half of the CHCF length with polydimethylsiloxane (PDMS), the curvature sensitivity of the ARROW sensor is enhanced to −5.62 dB/m⁻¹, as well as an increment in the curvature range (from 0 to 2.68 m⁻¹). Moreover, the covered device exhibits a low-temperature sensitivity (0.038 dB/°C), meaning that temperature fluctuations do not compromise the bending fiber optic sensor operation. The ARROW sensor fabricated with this technique has high sensitivity and a wide range for curvature measurements, with the advantage that the technique is cost-effective and easy to implement. All these features make this technique appealing for real sensing applications, such as structural health monitoring. Full article

In this paper, a novel optical approach to double the sensitivity to angular rate of interferometric fiber optic gyroscope (IFOG) is proposed. Two fiber polarization combiner/splitters (FPCSs), as the key components, are added in the traditional IFOG light path. The FPCSs are able to either combine two orthogonal polarizations transmitting at two different polarization-maintaining fibers (PMFs) into the two orthogonal axes of one PMF, respectively, or split two polarizations transmitting at the two orthogonal axes of one PMF into two polarizations to transmit at two different PMFs, respectively. Through the specific placement and coupling of these two FPCSs, the incident light can transmit twice along the polarization-maintaining fiber coil (PMFC). The novel approach is verified experimentally and the experimental results show consistency with the theoretical analysis. The proposed approach is able to double the sensitivity of IFOGs and can increase the signal-to-noise ratio (SNR) without increasing the length of PMFC, which is very susceptible to environmental influences and is of great significance in the technical improvement of IFOGs, as well as the miniaturization of IFOGs. Full article

This paper compares two positioning systems, namely ultra-wideband (UWB) based micro-location technology and dead reckoning and a RPLidar based simultaneous localization and mapping (SLAM) solution. This new approach can be used to improve the quality of the positioning system and increase the functionality of advanced driver assistance systems (ADAS). This is achieved by using stationary nodes and UWB tags on the vehicles. Thus, the redundancy of localization can be achieved by this approach, e.g., as a backup to onboard sensors like RPlidar or radar. Additionally, UWB based micro-location allows additional data channels to be used for communication purposes. Furthermore, it is shown that the regular use of correction data increases UWB and dead reckoning accuracy. These correction data can be based on onboard sensors. This shows that it is promising to develop a system that fuses onboard sensors and micro-localization for safety-critical tasks like the platooning of commercial vehicles. Full article

Undoubtedly, the supply chain management (SCM) system is an important part of many organizations worldwide; over time, the technologies used to manage a supply chain ecosystem have, therefore, a great impact on businesses’ effectiveness. Among others, numerous developments have been made that targeted to have robust supply chain systems to efficiently manage the growing demands of various supplies, considering the underlying requirements and main challenges such as scalability, specifically privacy and security, of various business networks. Internet of things (IoT) comes with a solution to manage a complex, scalable supply chain system, but to provide and attain enough security during information exchange, along with keeping the privacy of its users, is the great inherent challenge of IoT. To fulfill these limitations, this study designs and models a scaled IoT-based supply chain (IoT-SC) system, comprising several operations and participants, and deploys mechanisms to leverage the security, mainly confidentially, integrity, authentication (CIA), and a digital signature scheme to leverage potentially secured non-repudiation security service for the worst-case scenario, and to leverage privacy to keep users sensitive personal and location information protected against adversarial entities to the IoT-SC system. Indeed, a scaled IoT-SC system certainly opens new challenges to manage privacy and security while communicating. Therefore, in the IoT-SC system, each transaction writes from edge computing nodes to the IoT-SC controller is thoroughly examined to ensure the proposed solutions in bi-directional communication, and their robustness against adversarial behaviors. Future research works, employing blockchain and its integrations, are detailed as paces to accelerate the privacy and security of the IoT-SC system, for example, migrating IoT-centric computing to an immutable, decentralized platform. Full article

The autonomous underwater glider has attracted enormous interest for underwater activities, especially in long-term and large-scale underwater data collection. In this paper, we focus on the application of gliders gathering data from underwater sensor networks over underwater acoustic channels. However, this application suffers from a rapidly time-varying environment and limited energy. To optimize the performance of data collection and maximize the network lifetime, we propose a distributed, energy-efficient sensor scheduling algorithm based on the multi-armed bandit formulation. Besides, we design an indexable threshold policy to tradeoff between the data quality and the collection delay. Moreover, to reduce the computational complexity, we divide the proposed algorithm into off-line computation and on-line scheduling parts. Simulation results indicate that the proposed policy significantly improves the performance of the data collection and reduces the energy consumption. They prove the effectiveness of the threshold, which could reduce the collection delay by at least 10% while guaranteeing the data quality. Full article

The invasive method of fetal electrocardiogram (fECG) monitoring is widely used with electrodes directly attached to the fetal scalp. There are potential risks such as infection and, thus, it is usually carried out during labor in rare cases. Recent advances in electronics and technologies have enabled fECG monitoring from the early stages of pregnancy through fECG extraction from the combined fetal/maternal ECG (f/mECG) signal recorded non-invasively in the abdominal area of the mother. However, cumbersome algorithms that require the reference maternal ECG as well as heavy feature crafting makes out-of-clinics fECG monitoring in daily life not yet feasible. To address these challenges, we proposed a pure end-to-end deep learning model to detect fetal QRS complexes (i.e., the main spikes observed on a fetal ECG waveform). Additionally, the model has the residual network (ResNet) architecture that adopts the novel 1-D octave convolution (OctConv) for learning multiple temporal frequency features, which in turn reduce memory and computational cost. Importantly, the model is capable of highlighting the contribution of regions that are more prominent for the detection. To evaluate our approach, data from the PhysioNet 2013 Challenge with labeled QRS complex annotations were used in the original form, and the data were then modified with Gaussian and motion noise, mimicking real-world scenarios. The model can achieve a F₁ score of 91.1% while being able to save more than 50% computing cost with less than 2% performance degradation, demonstrating the effectiveness of our method. Full article