Sensors, Volume 18, Issue 1 (January 2018) – 317 articles

In the fast-growing Internet of Things (IoT)[...] Full article

Peer review is an essential part in the publication process, ensuring that Sensors maintains high quality standards for its published papers.[...] Full article

Recently, implantable devices have become widely used in neural prostheses because they eliminate endemic drawbacks of conventional percutaneous neural interface systems. However, there are still several issues to be considered: low-efficiency wireless power transmission; wireless data communication over restricted operating distance with high power consumption; and limited functionality, working either as a neural signal recorder or as a stimulator. To overcome these issues, we suggest a novel implantable wireless neural interface system for simultaneous neural signal recording and stimulation using a single cuff electrode. By using widely available commercial off-the-shelf (COTS) components, an easily reconfigurable implantable wireless neural interface system was implemented into one compact module. The implantable device includes a wireless power consortium (WPC)-compliant power transmission circuit, a medical implant communication service (MICS)-band-based radio link and a cuff-electrode path controller for simultaneous neural signal recording and stimulation. During in vivo experiments with rabbit models, the implantable device successfully recorded and stimulated the tibial and peroneal nerves while communicating with the external device. The proposed system can be modified for various implantable medical devices, especially such as closed-loop control based implantable neural prostheses requiring neural signal recording and stimulation at the same time. Full article

A new technique for measuring liquid-level utilizing wedge wave is presented and demonstrated through FEM simulation and a corresponding experiment. The velocities of wedge waves in the air and the water, and the sensitivities for the measurement, are compared with the simulation and the results obtained in the experiments. Combining the simulation and the measurement theory, it is verified that the foundation framework for the methods is available. The liquid-level sensing is carried out using the aluminum waveguide with a 30° wedge in the water. The liquid-level is proportional to the traveling time of the mode 1 wedge wave. The standard deviations and the uncertainties of the measurement are 0.65 mm and 0.21 mm using interface echo, and 0.39 mm and 0.12 mm utilized by end echo, which are smaller than the industry standard of 1.5 mm. The measurement resolutions are 7.68 μm using the interface echo, which is the smallest among all the guided acoustic wave-based liquid-level sensing. Full article

Collecting and maintaining radio fingerprint for wireless indoor positioning systems involves considerable time and labor. We have proposed the quick radio fingerprint collection (QRFC) algorithm which employed the built-in accelerometer of Android smartphones to implement step detection in order to assist in collecting radio fingerprints. In the present study, we divided the algorithm into moving sampling (MS) and stepped MS (SMS), and describe the implementation of both algorithms and their comparison. Technical details and common errors concerning the use of Android smartphones to collect Wi-Fi radio beacons were surveyed and discussed. The results of signal sampling experiments performed in a hallway measuring 54 m in length showed that in terms of the amount of time required to complete collection of access point (AP) signals, static sampling (SS; a traditional procedure for collecting Wi-Fi signals) took at least 2 h, whereas MS and SMS took approximately 150 and 300 s, respectively. Notably, AP signals obtained through MS and SMS were comparable to those obtained through SS in terms of the distribution of received signal strength indicator (RSSI) and positioning accuracy. Therefore, MS and SMS are recommended instead of SS as signal sampling procedures for indoor positioning algorithms. Full article

This publication presents the realisation of a sensor concept, which is based on eddy current testing, to detect textile defects during preforming of semi-finished carbon fibre parts. The presented system has the potential for 100% control of manufactured carbon fibre based components, allowing the immediate exclusion of defective parts from further process steps. The core innovation of this system is given by the high degree of process integration, which has not been implemented in the state of the art. The publication presents the functional principle of the sensor that is based on half-transmission probes as well as the signals that can be gained by its application. Furthermore, a method to determine the optimum sensor resolution is presented as well as the sensor housing and its integration in the preforming process. Full article

The quality and safety of agricultural products are threatened by heavy metal ions in soil, which can be absorbed by the crops, and then accumulated in the human body through the food chain. In this paper, we report a low-cost and easy-to-use screen-printed electrode (SPE) for cadmium ion (Cd(II)) detection based on differential pulse voltammetry (DPV), which decorated with ionic liquid (IL), magnetite nanoparticle (Fe₃O₄), and deposited a bismuth film (Bi). The characteristics of Bi/Fe₃O₄/ILSPE were investigated using scanning electron microscopy, cyclic voltammetry, impedance spectroscopy, and linear sweep voltammetry. We found that the sensitivity of SPE was improved dramatically after functionalized with Bi/Fe₃O₄/IL. Under optimized conditions, the concentrations of Cd(II) are linear with current responses in a range from 0.5 to 40 µg/L with the lowest detection limit of 0.05 µg/L (S/N = 3). Additionally, the internal standard normalization (ISN) was used to process the response signals of Bi/Fe₃O₄/ILSPE and established a new linear equation. For detecting three different Cd(II) concentrations, the root-mean-square error using ISN (0.25) is lower than linear method (0.36). Finally, the proposed electrode was applied to trace Cd(II) in soil samples with the recovery in the range from 91.77 to 107.83%. Full article

Urban air pollution has caused public concern globally because it seriously affects human life. Modern monitoring systems providing pollution information with high spatio-temporal resolution have been developed to identify personal exposures. However, these systems’ hardware specifications and configurations are usually fixed according to the applications. They can be inconvenient to maintain, and difficult to reconfigure and expand with respect to sensing capabilities. This paper aims at tackling these issues by adopting the proposed Modular Sensor System (MSS) architecture and Universal Sensor Interface (USI), and modular design in a sensor node. A compact MSS sensor node is implemented and evaluated. It has expandable sensor modules with plug-and-play feature and supports multiple Wireless Sensor Networks (WSNs). Evaluation results show that MSS sensor nodes can easily fit in different scenarios, adapt to reconfigurations dynamically, and detect low concentration air pollution with high energy efficiency and good data accuracy. We anticipate that the efforts on system maintenance, adaptation, and evolution can be significantly reduced when deploying the system in the field. Full article

Target localization, which aims to estimate the location of an unknown target, is one of the key issues in applications of underwater acoustic sensor networks (UASNs). However, the constrained property of an underwater environment, such as restricted communication capacity of sensor nodes and sensing noises, makes target localization a challenging problem. This paper relies on fractional sensor nodes to formulate a support vector learning-based particle filter algorithm for the localization problem in communication-constrained underwater acoustic sensor networks. A node-selection strategy is exploited to pick fractional sensor nodes with short-distance pattern to participate in the sensing process at each time frame. Subsequently, we propose a least-square support vector regression (LSSVR)-based observation function, through which an iterative regression strategy is used to deal with the distorted data caused by sensing noises, to improve the observation accuracy. At the same time, we integrate the observation to formulate the likelihood function, which effectively update the weights of particles. Thus, the particle effectiveness is enhanced to avoid “particle degeneracy” problem and improve localization accuracy. In order to validate the performance of the proposed localization algorithm, two different noise scenarios are investigated. The simulation results show that the proposed localization algorithm can efficiently improve the localization accuracy. In addition, the node-selection strategy can effectively select the subset of sensor nodes to improve the communication efficiency of the sensor network. Full article

A soft sensor is presented that approximates certain health parameters of automotive rechargeable batteries from on-vehicle measurements of current and voltage. The sensor is based on a model of the open circuit voltage curve. This last model is implemented through monotonic neural networks and estimate over-potentials arising from the evolution in time of the Lithium concentration in the electrodes of the battery. The proposed soft sensor is able to exploit the information contained in operational records of the vehicle better than the alternatives, this being particularly true when the charge or discharge currents are between moderate and high. The accuracy of the neural model has been compared to different alternatives, including data-driven statistical models, first principle-based models, fuzzy observers and other recurrent neural networks with different topologies. It is concluded that monotonic echo state networks can outperform well established first-principle models. The algorithms have been validated with automotive Li-FePO₄ cells. Full article

The frequency of terahertz radar ranges from 0.1 THz to 10 THz, which is higher than that of microwaves. Multi-modal signals, including high-resolution range profile (HRRP) and Doppler signatures, can be acquired by the terahertz radar system. These two kinds of information are commonly used in automatic target recognition; however, dynamic gesture recognition is rarely discussed in the terahertz regime. In this paper, a dynamic gesture recognition system using a terahertz radar is proposed, based on multi-modal signals. The HRRP sequences and Doppler signatures were first achieved from the radar echoes. Considering the electromagnetic scattering characteristics, a feature extraction model is designed using location parameter estimation of scattering centers. Dynamic Time Warping (DTW) extended to multi-modal signals is used to accomplish the classifications. Ten types of gesture signals, collected from a terahertz radar, are applied to validate the analysis and the recognition system. The results of the experiment indicate that the recognition rate reaches more than 91%. This research verifies the potential applications of dynamic gesture recognition using a terahertz radar. Full article

Wireless sensor networks are widely used in environmental monitoring. The number of sensor nodes to be deployed will vary depending on the desired spatio-temporal resolution. Selecting an optimal number, position and sampling rate for an array of sensor nodes in environmental monitoring is a challenging question. Most of the current solutions are either theoretical or simulation-based where the problems are tackled using random field theory, computational geometry or computer simulations, limiting their specificity to a given sensor deployment. Using an empirical dataset from a mine rehabilitation monitoring sensor network, this work proposes a data-driven approach where co-integrated time series analysis is used to select the number of sensors from a short-term deployment of a larger set of potential node positions. Analyses conducted on temperature time series show 75% of sensors are co-integrated. Using only 25% of the original nodes can generate a complete dataset within a 0.5 °C average error bound. Our data-driven approach to sensor position selection is applicable for spatiotemporal monitoring of spatially correlated environmental parameters to minimize deployment cost without compromising data resolution. Full article

This study provides the first assessment of decadal changes in mangrove extents in Sierra Leone. While significant advances have been made in mangrove mapping using remote sensing, no study has documented long-term changes in mangrove extents in Sierra Leone—one of the most vulnerable countries in West Africa. Such understanding is critical for devising regional management strategies that can support local livelihoods. We utilize multi-date Landsat data and cloud computational techniques to quantify spatiotemporal changes in land cover, with focus on mangrove ecosystems, for 1990–2016 along the coast of Sierra Leone. We specifically focus on four estuaries—Scarcies, Sierra Leone, Yawri Bay, and Sherbro. We relied on the k-means approach for an unsupervised classification, and validated the classified map from 2016 using ground truth data collected from Sentinel-2 and high-resolution images and during field research (accuracy: 95%). Our findings indicate that the Scarcies river estuary witnessed the greatest mangrove loss since 1990 (45%), while the Sierra Leone river estuary experienced mangrove gain over the last 26 years (22%). Overall, the Sierra Leone coast lost 25% of its mangroves between 1990 and 2016, with the lowest coverage in 2000, during the period of civil war (1991–2002). However, natural mangrove dynamics, as supported by field observations, indicate the potential for regeneration and sustainability under carefully constructed management strategies. Full article

In the last decade, Time-of-Flight (ToF) range cameras have gained increasing popularity in robotics, automotive industry, and home entertainment. Despite technological developments, ToF cameras still suffer from error sources such as multipath interference or motion artifacts. Thus, simulation of ToF cameras, including these artifacts, is important to improve camera and algorithm development. This paper presents a physically-based, interactive simulation technique for amplitude modulated continuous wave (AMCW) ToF cameras, which, among other error sources, includes single bounce indirect multipath interference based on an enhanced image-space approach. The simulation accounts for physical units down to the charge level accumulated in sensor pixels. Furthermore, we present the first quantified comparison for ToF camera simulators. We present bidirectional reference distribution function (BRDF) measurements for selected, purchasable materials in the near-infrared (NIR) range, craft real and synthetic scenes out of these materials and quantitatively compare the range sensor data. Full article

Fall prevention is a human, economic and social issue. The Timed Up and Go (TUG) test is widely used to identify individuals with a high fall risk. However, this test has been criticized because its “diagnostic” is too dependent on the conditions in which it is performed and on the healthcare professionals running it. We used the Microsoft Kinect ambient sensor to automate this test in order to reduce the subjectivity of outcome measures and to provide additional information about patient performance. Each phase of the TUG test was automatically identified from the depth images of the Kinect. Our algorithms accurately measured and assessed the elements usually measured by healthcare professionals. Specifically, average TUG test durations provided by our system differed by only 0.001 s from those measured by clinicians. In addition, our system automatically extracted several additional parameters that allowed us to accurately discriminate low and high fall risk individuals. These additional parameters notably related to the gait and turn pattern, the sitting position and the duration of each phase. Coupling our algorithms to the Kinect ambient sensor can therefore reliably be used to automate the TUG test and perform a more objective, robust and detailed assessment of fall risk. Full article

In this work, a biosensing method based on in situ, fast, and sensitive measurements of ellipsometric parameters (Ψ, ∆) is proposed. Bare silicon wafer substrate is functionalized and used to bind biomolecules in the solution. Coupled with a 45° dual-drive symmetric photoelastic modulator-based ellipsometry, the parameters Ψ and ∆ of biolayer arising due to biomolecular interactions are determined directly, and the refractive index (RI) of the solution and the effective thickness and surface mass density of the biolayer for various interaction time can be further monitored simultaneously. To illustrate the performance of the biosensing method, immunosensing for immunoglobulin G (IgG) was taken as a case study. The experiment results show that the biosensor response of the limit of detection for IgG is 15 ng/mL, and the data collection time is in milliseconds. Moreover, the method demonstrates a good specificity. Such technique is a promising candidate in developing a novel sensor which can realize fast and sensitive, label-free, easy operation, and cost-effective biosensing. Full article

In this work we present the design and implementation of WARCProcessor, a novel multiplatform integrative tool aimed to build scientific datasets to facilitate experimentation in web spam research. The developed application allows the user to specify multiple criteria that change the way in which new corpora are generated whilst reducing the number of repetitive and error prone tasks related with existing corpus maintenance. For this goal, WARCProcessor supports up to six commonly used data sources for web spam research, being able to store output corpus in standard WARC format together with complementary metadata files. Additionally, the application facilitates the automatic and concurrent download of web sites from Internet, giving the possibility of configuring the deep of the links to be followed as well as the behaviour when redirected URLs appear. WARCProcessor supports both an interactive GUI interface and a command line utility for being executed in background. Full article

Heat stroke can be potentially damaging for people while exercising in hot environments. To prevent this dangerous situation, we designed a wearable heat-stroke-detection device (WHDD) with early notification ability. First, we used several physical sensors, such as galvanic skin response (GSR), heart beat, and body temperature, to acquire medical data from exercising people. In addition, we designed risk evaluation functional components that were based on fuzzy theory to detect the features of heat stroke for users. If a dangerous situation is detected, then the device will activate the alert function to remind the user to respond adequately to avoid heat stroke. Full article

In previous classification studies, three non-parametric classifiers, Random Forest (RF), k-Nearest Neighbor (kNN), and Support Vector Machine (SVM), were reported as the foremost classifiers at producing high accuracies. However, only a few studies have compared the performances of these classifiers with different training sample sizes for the same remote sensing images, particularly the Sentinel-2 Multispectral Imager (MSI). In this study, we examined and compared the performances of the RF, kNN, and SVM classifiers for land use/cover classification using Sentinel-2 image data. An area of 30 × 30 km² within the Red River Delta of Vietnam with six land use/cover types was classified using 14 different training sample sizes, including balanced and imbalanced, from 50 to over 1250 pixels/class. All classification results showed a high overall accuracy (OA) ranging from 90% to 95%. Among the three classifiers and 14 sub-datasets, SVM produced the highest OA with the least sensitivity to the training sample sizes, followed consecutively by RF and kNN. In relation to the sample size, all three classifiers showed a similar and high OA (over 93.85%) when the training sample size was large enough, i.e., greater than 750 pixels/class or representing an area of approximately 0.25% of the total study area. The high accuracy was achieved with both imbalanced and balanced datasets. Full article

This paper presents a novel experimental design for complex structural health monitoring (SHM) studies achieved by integrating 3D printing technologies, high-resolution laser displacement sensors, and multiscale entropy SHM theory. A seven-story structure with a variety of composite bracing systems was constructed using a dual-material 3D printer. A wireless Bluetooth vibration speaker was used to excite the ground floor of the structure, and high-resolution laser displacement sensors (1-μm resolution) were used to monitor the displacement history on different floors. Our results showed that the multiscale entropy SHM method could detect damage on the 3D-printed structures. The results of this study demonstrate that integrating 3D printing technologies and high-resolution laser displacement sensors enables the design of cheap, fast processing, complex, small-scale civil structures for future SHM studies. The novel experimental design proposed in this study provides a suitable platform for investigating the validity and sensitivity of SHM in different composite structures and damage conditions for real life applications in the future. Full article

The fixed-size non-overlapping sliding window (FNSW) and fixed-size overlapping sliding window (FOSW) approaches are the most commonly used data-segmentation techniques in machine learning-based fall detection using accelerometer sensors. However, these techniques do not segment by fall stages (pre-impact, impact, and post-impact) and thus useful information is lost, which may reduce the detection rate of the classifier. Aligning the segment with the fall stage is difficult, as the segment size varies. We propose an event-triggered machine learning (EvenT-ML) approach that aligns each fall stage so that the characteristic features of the fall stages are more easily recognized. To evaluate our approach, two publicly accessible datasets were used. Classification and regression tree (CART), k-nearest neighbor (k-NN), logistic regression (LR), and the support vector machine (SVM) were used to train the classifiers. EvenT-ML gives classifier F-scores of 98% for a chest-worn sensor and 92% for a waist-worn sensor, and significantly reduces the computational cost compared with the FNSW- and FOSW-based approaches, with reductions of up to 8-fold and 78-fold, respectively. EvenT-ML achieves a significantly better F-score than existing fall detection approaches. These results indicate that aligning feature segments with fall stages significantly increases the detection rate and reduces the computational cost. Full article

Herbicides may damage soybean in conventional production systems. Chlorophyll fluorescence imaging technology has been applied to identify herbicide stress in weed species a few days after application. In this study, greenhouse experiments followed by field experiments at five sites were conducted to investigate if the chlorophyll fluorescence imaging is capable of identifying herbicide stress in soybean shortly after application. Measurements were carried out from emergence until the three-to-four-leaf stage of the soybean plants. Results showed that maximal photosystem II (PS II) quantum yield and shoot dry biomass was significantly reduced in soybean by herbicides compared to the untreated control plants. The stress of PS II inhibiting herbicides occurred on the cotyledons of soybean and plants recovered after one week. The stress induced by DOXP synthase-, microtubule assembly-, or cell division-inhibitors was measured from the two-leaf stage until four-leaf stage of soybean. We could demonstrate that the chlorophyll fluorescence imaging technology is capable for detecting herbicide stress in soybean. The system can be applied under both greenhouse and field conditions. This helps farmers to select weed control strategies with less phytotoxicity in soybean and avoid yield losses due to herbicide stress. Full article

Internet of Things (IoT) technology is rapidly emerging in medical applications as it offers the possibility of lower-cost personalized healthcare monitoring. At the present time, the 2.45 GHz band is in widespread use for these applications but in this paper, the authors investigate the potential of the 915 MHz ISM band in implementing future, wearable IoT devices. The target sensor is a wrist-worn wireless heart rate and arterial oxygen saturation (SpO2) monitor with the goal of providing efficient wireless functionality and long battery lifetime using a commercial Sub-GHz low-power radio transceiver. A detailed analysis of current consumption for various wireless protocols is also presented and analyzed. A novel 915 MHz antenna design of compact size is reported that has good resilience to detuning by the human body. The antenna also incorporates a matching network to meet the challenging bandwidth requirements and is fabricated using standard, low-cost FR-4 material. Full-Wave EM simulations are presented for the antenna placed in both free-space and on-body cases. A prototype antenna is demonstrated and has dimensions of 44 mm × 28 mm × 1.6 mm. The measured results at 915 MHz show a 10 dB return loss bandwidth of 55 MHz, a peak realized gain of $-$ 2.37 dBi in free-space and $-$ 6.1 dBi on-body. The paper concludes by highlighting the potential benefits of 915 MHz operation for future IoT devices. Full article

In this study, simultaneous electrochemical detection of ascorbic acid (AA), dopamine (DA), and uric acid (UA) was performed using a modified graphite paste electrode (MGPE) with epigallocatechin gallate (EGCG) and green tea (GT) powder. It was shown that the anodic peak current increased in comparison with that of the graphite paste electrode (GPE) in the cyclic voltammograms. The optimal pH for simultaneous determination of a quaternary mixture of AA–DA–UA was determined to be pH 2. The anodic peak potentials for a mixture containing AA–DA–UA were well separated from each other. The catalytic peak currents obtained at the surface of the MGPE/EGCG were linearly dependent on the AA, DA, and UA concentrations up to 23, 14, and 14 µM, respectively. The detection limits for AA, DA, and UA were 190, 90, and 70 nM, respectively. The analytical performance of this sensor has been evaluated for simultaneous detection of AA, DA, and UA in real samples. Finally, a modified electrode was prepared using GT and used for simultaneous determination of AA, DA, and UA. Based on the results, MPGE/GT showed two oxidation peaks at 0.43 and 0.6 V for DA and UA, respectively, without any oxidation peak for AA. The calibration curves at the surface of MGPE/GT were linear up to 14 µM with a detection limit of 0.18 and 0.33 µM for DA and UA, respectively. MGPEs provide a promising platform for the future development of sensors for multiplexed electrochemical detection of clinically important analytes. Full article

The dihedral corner reflectors are the basic geometric structure of many targets and are the main contributions of radar cross section (RCS) in the synthetic aperture radar (SAR) images. In stealth technologies, the elaborate design of the dihedral corners with different opening angles is a useful approach to reduce the high RCS generated by multiple reflections. As bistatic synthetic aperture sensors have flexible geometric configurations and are sensitive to the dihedral corners with different opening angles, they specially fit for the stealth target detections. In this paper, the scattering characteristic of dihedral corner reflectors is accurately analyzed in bistatic synthetic aperture images. The variation of RCS with the changing opening angle is formulated and the method to design a proper bistatic radar for maximizing the detection capability is provided. Both the results of the theoretical analysis and the experiments show the bistatic SAR could detect the dihedral corners, under a certain bistatic angle which is related to the geometry of target structures. Full article

Using arrays with digital MEMS (Micro-Electro-Mechanical System) microphones and FPGA-based (Field Programmable Gate Array) acquisition/processing systems allows building systems with hundreds of sensors at a reduced cost. The problem arises when systems with thousands of sensors are needed. This work analyzes the implementation and performance of a virtual array with 6400 (80 × 80) MEMS microphones. This virtual array is implemented by changing the position of a physical array of 64 (8 × 8) microphones in a grid with 10 × 10 positions, using a 2D positioning system. This virtual array obtains an array spatial aperture of 1 × 1 m². Based on the SODAR (SOund Detection And Ranging) principle, the measured beampattern and the focusing capacity of the virtual array have been analyzed, since beamforming algorithms assume to be working with spherical waves, due to the large dimensions of the array in comparison with the distance between the target (a mannequin) and the array. Finally, the acoustic images of the mannequin, obtained for different frequency and range values, have been obtained, showing high angular resolutions and the possibility to identify different parts of the body of the mannequin. Full article

The pressure- and temperature-sensitive paint (PSP/TSP) technique, for steady-state and unsteady-state measurements, is becoming widespread. However, unsteady quantitative measurement is still difficult because non-uniform distribution of the probes over a test model may cause errors in the results. We focus on the dipping method that applies two luminophores into a binding material to improve sensitivity uniformity over a model surface. A bullet-shaped axisymmetric test model with motion-capturing TSP was used to evaluate the sensitivity uniformity, and three dipping methods (static, convectional, and rotational) were examined. The average peak ratios in the longitudinal direction were 1.17–1.46 for static, 1.38–1.51 for convectional, and 1.42–1.45 for rotational dipping. The standard deviations in the transverse direction were the smallest for rotational (0.022–0.033), relative to static (0.086–0.104), and convectional (0.044–0.065) dipping. Full article

This paper presents a novel full-depletion Si X-ray detector based on silicon-on-insulator pixel (SOIPIX) technology using a pinned depleted diode structure, named the SOIPIX-PDD. The SOIPIX-PDD greatly reduces stray capacitance at the charge sensing node, the dark current of the detector, and capacitive coupling between the sensing node and SOI circuits. These features of the SOIPIX-PDD lead to low read noise, resulting high X-ray energy resolution and stable operation of the pixel. The back-gate surface pinning structure using neutralized p-well at the back-gate surface and depleted n-well underneath the p-well for all the pixel area other than the charge sensing node is also essential for preventing hole injection from the p-well by making the potential barrier to hole, reducing dark current from the Si-SiO₂ interface and creating lateral drift field to gather signal electrons in the pixel area into the small charge sensing node. A prototype chip using 0.2 μm SOI technology shows very low readout noise of 11.0 e⁻_rms, low dark current density of 56 pA/cm² at −35 °C and the energy resolution of 200 eV(FWHM) at 5.9 keV and 280 eV (FWHM) at 13.95 keV. Full article

Monitoring is one of the best ways to evaluate the behavior of computer systems. When the monitored system is a distributed system—such as a wireless sensor network (WSN)—the monitoring operation must also be distributed, providing a distributed trace for further analysis. The temporal sequence of occurrence of the events registered by the distributed monitoring platform (DMP) must be correctly established to provide cause-effect relationships between them, so the logs obtained in different monitor nodes must be synchronized. Many of synchronization mechanisms applied to DMPs consist in adjusting the internal clocks of the nodes to the same value as a reference time. However, these mechanisms can create an incoherent event sequence. This article presents a new method to achieve global synchronization of the traces obtained in a DMP. It is based on periodic synchronization signals that are received by the monitor nodes and logged along with the recorded events. This mechanism processes all traces and generates a global post-synchronized trace by scaling all times registered proportionally according with the synchronization signals. It is intended to be a simple but efficient offline mechanism. Its application in a WSN-DMP demonstrates that it guarantees a correct ordering of the events, avoiding the aforementioned issues. Full article

A wearable electroencephalogram (EEG) device for continuous monitoring of patients suffering from epilepsy would provide valuable information for the management of the disease. Currently no EEG setup is small and unobtrusive enough to be used in daily life. Recording behind the ear could prove to be a solution to a wearable EEG setup. This article examines the feasibility of recording epileptic EEG from behind the ear. It is achieved by comparison with scalp EEG recordings. Traditional scalp EEG and behind-the-ear EEG were simultaneously acquired from 12 patients with temporal, parietal, or occipital lobe epilepsy. Behind-the-ear EEG consisted of cross-head channels and unilateral channels. The analysis on Electrooculography (EOG) artifacts resulting from eye blinking showed that EOG artifacts were absent on cross-head channels and had significantly small amplitudes on unilateral channels. Temporal waveform and frequency content during seizures from behind-the-ear EEG visually resembled that from scalp EEG. Further, coherence analysis confirmed that behind-the-ear EEG acquired meaningful epileptic discharges similarly to scalp EEG. Moreover, automatic seizure detection based on support vector machine (SVM) showed that comparable seizure detection performance can be achieved using these two recordings. With scalp EEG, detection had a median sensitivity of 100% and a false detection rate of 1.14 per hour, while, with behind-the-ear EEG, it had a median sensitivity of 94.5% and a false detection rate of 0.52 per hour. These findings demonstrate the feasibility of detecting seizures from EEG recordings behind the ear for patients with focal epilepsy. Full article

Crop monitoring is an essential practice within the field of precision agriculture since it is based on observing, measuring and properly responding to inter- and intra-field variability. In particular, “on ground crop inspection” potentially allows early detection of certain crop problems or precision treatment to be carried out simultaneously with pest detection. “On ground monitoring” is also of great interest for woody crops. This paper explores the development of a low-cost crop monitoring system that can automatically create accurate 3D models (clouds of coloured points) of woody crop rows. The system consists of a mobile platform that allows the easy acquisition of information in the field at an average speed of 3 km/h. The platform, among others, integrates an RGB-D sensor that provides RGB information as well as an array with the distances to the objects closest to the sensor. The RGB-D information plus the geographical positions of relevant points, such as the starting and the ending points of the row, allow the generation of a 3D reconstruction of a woody crop row in which all the points of the cloud have a geographical location as well as the RGB colour values. The proposed approach for the automatic 3D reconstruction is not limited by the size of the sampled space and includes a method for the removal of the drift that appears in the reconstruction of large crop rows. Full article

A coplanar electrode array sensor is established for the imaging of composite-material adhesive-layer defect detection. The sensor is based on the capacitive edge effect, which leads to capacitance data being considerably weak and susceptible to environmental noise. The inverse problem of coplanar array electrical capacitance tomography (C-ECT) is ill-conditioning, in which a small error of capacitance data can seriously affect the quality of reconstructed images. In order to achieve a stable image reconstruction process, a redundancy analysis method for capacitance data is proposed. The proposed method is based on contribution rate and anti-interference capability. According to the redundancy analysis, the capacitance data are divided into valid and invalid data. When the image is reconstructed by valid data, the sensitivity matrix needs to be changed accordingly. In order to evaluate the effectiveness of the sensitivity map, singular value decomposition (SVD) is used. Finally, the two-dimensional (2D) and three-dimensional (3D) images are reconstructed by the Tikhonov regularization method. Through comparison of the reconstructed images of raw capacitance data, the stability of the image reconstruction process can be improved, and the quality of reconstructed images is not degraded. As a result, much invalid data are not collected, and the data acquisition time can also be reduced. Full article

Although saline tidal wetlands cover less than a fraction of one percent of the earth’s surface (~0.01%), they efficiently sequester organic carbon due to high rates of primary production coupled with surfaces that aggrade in response to sea level rise. Here, we report on multi-decadal changes (1972–2008) in the extent of tidal marshes and mangroves, and characterize soil carbon density and source, for five regions of tidal wetlands located on Baja California’s Pacific coast. Land-cover change analysis indicates the stability of tidal wetlands relative to anthropogenic and climate change impacts over the past four decades, with most changes resulting from natural coastal processes that are unique to arid environments. The disturbance of wetland soils in this region (to a depth of 50 cm) would liberate 2.55 Tg of organic carbon (C) or 9.36 Tg CO₂eq. Based on stoichiometry and carbon stable isotope ratios, the source of organic carbon in these wetland sediments is derived from a combination of wetland macrophyte, algal, and phytoplankton sources. The reconstruction of natural wetland dynamics in Baja California provides a counterpoint to the history of wetland destruction elsewhere in North America, and measurements provide new insights on the control of carbon sequestration in arid wetlands. Full article

In this work we show an optical fiber evanescent wave absorption probe for glucose detection in different physiological media. High selectivity is achieved by functionalizing the surface of an only-core poly(methyl methacrylate) (PMMA) polymer optical fiber with phenilboronic groups, and enhanced sensitivity by using a U-shaped geometry. Employing a supercontinuum light source and a high-resolution spectrometer, absorption measurements are performed in the broadband visible light spectrum. Experimental results suggest the feasibility of such a fiber probe as a low-cost and selective glucose detector. Full article

In this study, a comparative theoretical and experimental analysis of two immuno-chromatographic serodiagnostics schemes, which differ in the immobilization of immunoreagents and the order of the formation of immune complexes, is performed. Based on the theoretical models, the assays are characterized to determine which scheme has a higher quantity of the detected complex and thus ensures the sensitivity of the analysis. The results show that for the effective detection of low-affinity antibodies, the scheme involving the immobilization of the antigen on gold nanoparticles and the antibody-binding protein on the test strip was more sensitive than the predominantly used scheme, which inverts the immunoreagents’ locations. The theoretical predictions were confirmed by the experimental testing of sera collected from tuberculosis patients. Full article

In this study, an organic-inorganic semiconductor gas sensor was fabricated to detect ammonia gas. An inorganic semiconductor was a zinc oxide (ZnO) nanowire array produced by atomic layer deposition (ALD) while an organic material was a p-type semiconductor, poly(3-hexylthiophene) (P3HT). P3HT was suitable for the gas sensing application due to its high hole mobility, good stability, and good electrical conductivity. In this work, P3HT was coated on the zinc oxide nanowires by the spin coating to form an organic-inorganic heterogeneous interface of the gas sensor for detecting ammonia gas. The thicknesses of the P3HT were around 462 nm, 397 nm, and 277 nm when the speeds of the spin coating were 4000 rpm, 5000 rpm, and 6000 rpm, respectively. The electrical properties and sensing characteristics of the gas sensing device at room temperature were evaluated by Hall effect measurement and the sensitivity of detecting ammonia gas. The results of Hall effect measurement for the P3HT-ZnO nanowires semiconductor with 462 nm P3HT film showed that the carrier concentration and the mobility were 2.7 × 10¹⁹ cm⁻³ and 24.7 cm²∙V⁻¹∙s⁻¹ respectively. The gas sensing device prepared by the P3HT-ZnO nanowires semiconductor had better sensitivity than the device composed of the ZnO film and P3HT film. Additionally, this gas sensing device could reach a maximum sensitivity around 11.58 per ppm. Full article

Sensors integrated into objects of everyday life potentially allow unobtrusive health monitoring at home. However, since the coupling of sensors and subject is not as well-defined as compared to a clinical setting, the signal quality is much more variable and can be disturbed significantly by motion artifacts. One way of tackling this challenge is the combined evaluation of multiple channels via sensor fusion. For robust and accurate sensor fusion, analyzing the influence of motion on different modalities is crucial. In this work, a multimodal sensor setup integrated into an armchair is presented that combines capacitively coupled electrocardiography, reflective photoplethysmography, two high-frequency impedance sensors and two types of ballistocardiography sensors. To quantify motion artifacts, a motion protocol performed by healthy volunteers is recorded with a motion capture system, and reference sensors perform cardiorespiratory monitoring. The shape-based signal-to-noise ratio SNR ${}_{\mathrm{S}}$ is introduced and used to quantify the effect on motion on different sensing modalities. Based on this analysis, an optimal combination of sensors and fusion methodology is developed and evaluated. Using the proposed approach, beat-to-beat heart-rate is estimated with a coverage of 99.5% and a mean absolute error of 7.9 ms on 425 min of data from seven volunteers in a proof-of-concept measurement scenario. Full article

By simulating the geomagnetic fields and analyzing thevariation of intensities, this paper presents a model for calculating the objective function ofan Autonomous Underwater Vehicle (AUV)geomagnetic navigation task. By investigating the biologically inspired strategies, the AUV successfullyreachesthe destination duringgeomagnetic navigation without using the priori geomagnetic map. Similar to the pattern of a flatworm, the proposed algorithm relies on a motion pattern to trigger a local searching strategy by detecting the real-time geomagnetic intensity. An adapted strategy is then implemented, which is biased on the specific target. The results show thereliabilityandeffectivenessofthe proposed algorithm. Full article

The use of a voltammetric electronic tongue for the quantitative analysis of quality parameters in spring water is proposed here. The electronic voltammetric tongue consisted of a set of four noble electrodes (iridium, rhodium, platinum, and gold) housed inside a stainless steel cylinder. These noble metals have a high durability and are not demanding for maintenance, features required for the development of future automated equipment. A pulse voltammetry study was conducted in 83 spring water samples to determine concentrations of nitrate (range: 6.9–115 mg/L), sulfate (32–472 mg/L), fluoride (0.08–0.26 mg/L), chloride (17–190 mg/L), and sodium (11–94 mg/L) as well as pH (7.3–7.8). These parameters were also determined by routine analytical methods in spring water samples. A partial least squares (PLS) analysis was run to obtain a model to predict these parameter. Orthogonal signal correction (OSC) was applied in the preprocessing step. Calibration (67%) and validation (33%) sets were selected randomly. The electronic tongue showed good predictive power to determine the concentrations of nitrate, sulfate, chloride, and sodium as well as pH and displayed a lower R² and slope in the validation set for fluoride. Nitrate and fluoride concentrations were estimated with errors lower than 15%, whereas chloride, sulfate, and sodium concentrations as well as pH were estimated with errors below 10%. Full article

A growing number of applications needs GIS mapping information and commercial 3-D roadmaps especially. This paper presents a solution of accessing freely to 3-D map information and updating in the context of transport applications. The method relies on the OSM road networks that is 2-D modeled intrinsically. The objective is to estimate the road elevation and inclination parameters by fusing GPS, OSM and DEM data through a nonlinear filter. An experimental framework, using ASTER GDEM2 data, shows some results of the improvement of the roads modeling that includes their slopes also. The map database can be enriched with the estimated inclinations. The accuracy depends on the GPS and DEM elevation errors (typically a few meters with the GNSS sensors used and the DEM under consideration). Full article

We proposed a new method for gas detection in photoacoustic spectroscopy based on acousto-optic Q-switched fiber laser by merging a transmission PAS cell (resonant frequency f₀ = 5.3 kHz) inside the fiber laser cavity. The Q-switching was achieved by an acousto-optic modulator, achieving a peak pulse power of ~679 mW in the case of the acousto-optic modulation signal with an optimized duty ratio of 10%. We used a custom-made fiber Bragg grating with a central wavelength of 1530.37 nm (the absorption peak of C₂H₂) to select the laser wavelength. The system achieved a linear response (R² = 0.9941) in a concentration range from 400 to 7000 ppmv, and the minimum detection limit compared to that of a conventional intensity modulation system was enhanced by 94.2 times. Full article

Synthetic aperture radar (SAR) sliding spotlight work mode can achieve high resolutions and wide swath (HRWS) simultaneously by steering the radar antenna beam. This paper aims to obtain well focused images using sliding spotlight mode with the Chinese Gaofen-3 SAR sensor. We proposed an integrated imaging scheme with sliding spotlight echoes. In the imaging scheme, the two-step approach is applied to the spaceborne sliding spotlight SAR imaging algorithm, followed by the Doppler parameter estimation algorithm. The azimuth spectral folding phenomenon is overcome by the two-step approach. The results demonstrate a high Doppler parameter estimation accuracy. The proposed imaging process is accurate and highly efficient for sliding spotlight SAR mode. Full article

The evaluation of the structural efficiency of airport infrastructures is a complex task. Faulting is one of the most important indicators of rigid pavement performance. The aim of our study is to provide a new method for faulting detection and computation on jointed concrete pavements. Nowadays, the assessment of faulting is performed with the use of laborious and time-consuming measurements that strongly hinder aircraft traffic. We proposed a field procedure for Terrestrial Laser Scanner data acquisition and a computation flow chart in order to identify and quantify the fault size at each joint of apron slabs. The total point cloud has been used to compute the least square plane fitting those points. The best-fit plane for each slab has been computed too. The attitude of each slab plane with respect to both the adjacent ones and the apron reference plane has been determined by the normal vectors to the surfaces. Faulting has been evaluated as the difference in elevation between the slab planes along chosen sections. For a more accurate evaluation of the faulting value, we have then considered a few strips of data covering rectangular areas of different sizes across the joints. The accuracy of the estimated quantities has been computed too. Full article

In this paper, an infrared pre-processing modality is presented. Different from a signal smoothing modality which only uses a polynomial fitting as the pre-processing method, the presented modality instead takes into account the low-order derivatives to pre-process the raw thermal data prior to applying the advanced post-processing techniques such as principal component thermography and pulsed phase thermography. Different cases were studied involving several defects in CFRPs and GFRPs for pulsed thermography and vibrothermography. Ultrasonic testing and signal-to-noise ratio analysis are used for the validation of the thermographic results. Finally, a verification that the presented modality can enhance the thermal image performance effectively is provided. Full article

eHealth systems have adopted recent advances on sensing technologies together with advances in information and communication technologies (ICT) in order to provide people-centered services that improve the quality of life of an increasingly elderly population. As these eHealth services are founded on the acquisition and processing of sensitive data (e.g., personal details, diagnosis, treatments and medical history), any security threat would damage the public’s confidence in them. This paper proposes a solution for the design and runtime management of indoor eHealth applications with security requirements. The proposal allows applications definition customized to patient particularities, including the early detection of health deterioration and suitable reaction (events) as well as security needs. At runtime, security support is twofold. A secured component-based platform supervises applications execution and provides events management, whilst the security of the communications among application components is also guaranteed. Additionally, the proposed event management scheme adopts the fog computing paradigm to enable local event related data storage and processing, thus saving communication bandwidth when communicating with the cloud. As a proof of concept, this proposal has been validated through the monitoring of the health status in diabetic patients at a nursing home. Full article

Due to the non-contact nature, using Doppler radar sensors to detect vital signs such as heart and respiration rates of a human subject is getting more and more attention. However, the related detection-method research meets lots of challenges due to electromagnetic interferences, clutter and random motion interferences. In this paper, a novel third-order cyclic cummulant (TOCC) detection method, which is insensitive to Gaussian interference and non-cyclic signals, is proposed to investigate the heart and respiration rate based on continuous wave Doppler radars. The k-th order cyclostationary properties of the radar signal with hidden periodicities and random motions are analyzed. The third-order cyclostationary detection theory of the heart and respiration rate is studied. Experimental results show that the third-order cyclostationary approach has better estimation accuracy for detecting the vital signs from the received radar signal under low SNR, strong clutter noise and random motion interferences. Full article

As the sound signal of ships obtained by sensors contains other many significant characteristics of ships and called ship-radiated noise (SN), research into a denoising algorithm and its application has obtained great significance. Using the advantage of variational mode decomposition (VMD) combined with the correlation coefficient for denoising, a hybrid secondary denoising algorithm is proposed using secondary VMD combined with a correlation coefficient (CC). First, different kinds of simulation signals are decomposed into several bandwidth-limited intrinsic mode functions (IMFs) using VMD, where the decomposition number by VMD is equal to the number by empirical mode decomposition (EMD); then, the CCs between the IMFs and the simulation signal are calculated respectively. The noise IMFs are identified by the CC threshold and the rest of the IMFs are reconstructed in order to realize the first denoising process. Finally, secondary denoising of the simulation signal can be accomplished by repeating the above steps of decomposition, screening and reconstruction. The final denoising result is determined according to the CC threshold. The denoising effect is compared under the different signal-to-noise ratio and the time of decomposition by VMD. Experimental results show the validity of the proposed denoising algorithm using secondary VMD (2VMD) combined with CC compared to EMD denoising, ensemble EMD (EEMD) denoising, VMD denoising and cubic VMD (3VMD) denoising, as well as two denoising algorithms presented recently. The proposed denoising algorithm is applied to feature extraction and classification for SN signals, which can effectively improve the recognition rate of different kinds of ships. Full article

The electrochemical detection of interactions between aptamers and low-molecular-weight targets often lacks sensitivity. Signal amplification improves the detection of the aptamer-analyte complex; Bsm DNA polymerase was used to amplify the signal from the interaction of vanillin and its aptamer named Van_74 on an ion-sensitive field-effect transistor (ISFET)-based biosensor. The aptamer was immobilized on the ISFET sensitive surface. A short DNA probe was hybridized with the aptamer and dissociated from it upon vanillin addition. A free probe interacted with a special DNA molecular beacon initiated the Bsm DNA polymerase reaction that was detected by ISFET. A buffer solution suitable for both aptamer action and Bsm DNA polymerase activity was determined. The ISFET was shown to detect the Bsm DNA polymerase reaction under the selected conditions. Vanillin at different concentrations (1 × 10⁻⁶–1 × 10⁻⁸ M) was detected using the biosensor with signal amplification. The developed detection system allowed for the determination of vanillin, starting at a 10⁻⁸ M concentration. Application of the Bsm DNA polymerase resulted in a 15.5 times lower LoD when compared to the biosensor without signal amplification (10.1007/s00604-017-2586-4). Full article

ZnO film was deposited by the magnetron sputtering method. The thickness of ZnO film is approximately 2 μm. The influence of UV light illumination on C₂H₅OH sensing properties of ZnO film was investigated. Gas sensing results revealed that the UV-illuminated ZnO film displays excellent C₂H₅OH characteristics in terms of high sensitivity, excellent selectivity, rapid response/recovery, and low detection limit down to 0.1 ppm. The excellent sensing performance of the sensor with UV activation could be attributed to the photocatalytic oxidation of ethanol on the surface of the ZnO film, the planar film structure with high utilizing efficiency of UV light, high electron mobility, and a good surface/volume ratio of of ZnO film with a relatively rough and porous surface. Full article

Underwater wireless technologies demand to transmit at higher data rate for ocean exploration. Currently, large coverage is achieved by acoustic sensor networks with low data rate, high cost, high latency, high power consumption, and negative impact on marine mammals. Meanwhile, optical communication for underwater networks has the advantage of the higher data rate albeit for limited communication distances. Moreover, energy consumption is another major problem for underwater sensor networks, due to limited battery power and difficulty in replacing or recharging the battery of a sensor node. The ultimate solution to this problem is to add energy harvesting capability to the acoustic-optical sensor nodes. Localization of underwater sensor networks is of utmost importance because the data collected from underwater sensor nodes is useful only if the location of the nodes is known. Therefore, a novel localization technique for energy harvesting hybrid acoustic-optical underwater wireless sensor networks (AO-UWSNs) is proposed. AO-UWSN employs optical communication for higher data rate at a short transmission distance and employs acoustic communication for low data rate and long transmission distance. A hybrid received signal strength (RSS) based localization technique is proposed to localize the nodes in AO-UWSNs. The proposed technique combines the noisy RSS based measurements from acoustic communication and optical communication and estimates the final locations of acoustic-optical sensor nodes. A weighted multiple observations paradigm is proposed for hybrid estimated distances to suppress the noisy observations and give more importance to the accurate observations. Furthermore, the closed form solution for Cramer-Rao lower bound (CRLB) is derived for localization accuracy of the proposed technique. Full article

In this paper, we propose an adaptive single differential coherent detection (SDCD) scheme for the binary phase shift keying (BPSK) signals in IEEE 802.15.4 Wireless Sensor Networks (WSNs). In particular, the residual carrier frequency offset effect (CFOE) for differential detection is adaptively estimated, with only linear operation, according to the changing channel conditions. It was found that the carrier frequency offset (CFO) and chip signal-to-noise ratio (SNR) conditions do not need a priori knowledge. This partly benefits from that the combination of the trigonometric approximation ${\sin }^{-1}\left(x\right)\approx x$ and a useful assumption, namely, the asymptotic or high chip SNR, is considered for simplification of the full estimation scheme. Simulation results demonstrate that the proposed algorithm can achieve an accurate estimation and the detection performance can completely meet the requirement of the IEEE 802.15.4 standard, although with a little loss of reliability and robustness as compared with the conventional optimal single-symbol detector. Full article

Multisensor capacitance probes (MCPs) have traditionally been used for soil moisture monitoring and irrigation scheduling. This paper presents a new application of these probes, namely the simultaneous monitoring of ponded water level, soil moisture, and temperature profile, conditions which are particularly important for rice crops in temperate growing regions and for rice grown with prolonged periods of drying. WiFi-based loggers are used to concurrently collect the data from the MCPs and ultrasonic distance sensors (giving an independent reading of water depth). Models are fit to MCP water depth vs volumetric water content (VWC) characteristics from laboratory measurements, variability from probe-to-probe is assessed, and the methodology is verified using measurements from a rice field throughout a growing season. The root-mean-squared error of the water depth calculated from MCP VWC over the rice growing season was 6.6 mm. MCPs are used to simultaneously monitor ponded water depth, soil moisture content when ponded water is drained, and temperatures in root, water, crop and ambient zones. The insulation effect of ponded water against cold-temperature effects is demonstrated with low and high water levels. The developed approach offers advantages in gaining the full soil-plant-atmosphere continuum in a single robust sensor. Full article

A general, approximate perturbation method, able to provide closed-form expressions of scattering from a layered structure with an arbitrary number of rough interfaces, has been recently developed. Such a method provides a unique tool for the characterization of radar response patterns of natural rough multilayers. In order to show that, here, for the first time in a journal paper, we describe the application of the developed perturbation theory to fractal interfaces; we then employ the perturbative method solution to analyze the scattering from real-world layered structures of practical interest in remote sensing applications. We focus on the dependence of normalized radar cross section on geometrical and physical properties of the considered scenarios, and we choose two classes of natural stratifications: wet paleosoil covered by a low-loss dry sand layer and a sea-ice layer above water with dry snow cover. Results are in accordance with the experimental evidence available in the literature for the low-loss dry sand layer, and they may provide useful indications about the actual ability of remote sensing instruments to perform sub-surface sensing for different sensor and scene parameters. Full article

Using gold nanoparticles (AuNPs) on “capillary enzyme-linked immunosorbent assay (ELISA)”, we produced highly sensitive and rapid assays, which are the major attributes for point-of-care applications. First, in order to understand the size effect of AuNPs, AuNPs of varying diameters (5 nm, 10 nm, 15 nm, 20 nm, 30 nm, and 50 nm) conjugated with Horseradish Peroxidase (HRP)-labeled anti-C reactive protein (antiCRP) (AuNP•antiCRP-HRP) were used for well-plate ELISA. AuNP of 10 nm produced the largest optical density, enabling detection of 0.1 ng/mL of CRP with only 30 s of incubation, in contrast to 10 ng/mL for the ELISA run in the absence of AuNP. Then, AuNP of 10 nm conjugated with antiCRP-HRP (AuNP•antiCRP-HRP) was used for “capillary ELISA” to detect as low as 0.1 ng/mL of CRP. Also, kinetic study on both 96-well plates and in a capillary tube using antiCRP-HRP or AuNP•antiCRP-HRP showed a synergistic effect between AuNP and the capillary system, in which the fastest assay was observed from the “AuNP capillary ELISA”, with its maximum absorbance reaching 2.5 min, while the slowest was the typical well-plate ELISA with its maximum absorbance reaching in 13.5 min. Full article

In wireless sensor networks, sensor nodes collect plenty of data for each time period. If all of data are transmitted to a Fusion Center (FC), the power of sensor node would run out rapidly. On the other hand, the data also needs a filter to remove the noise. Therefore, an efficient fusion estimation model, which can save the energy of the sensor nodes while maintaining higher accuracy, is needed. This paper proposes a novel mixed H₂/H_∞-based energy-efficient fusion estimation model (MHEEFE) for energy-limited Wearable Body Networks. In the proposed model, the communication cost is firstly reduced efficiently while keeping the estimation accuracy. Then, the parameters in quantization method are discussed, and we confirm them by an optimization method with some prior knowledge. Besides, some calculation methods of important parameters are researched which make the final estimates more stable. Finally, an iteration-based weight calculation algorithm is presented, which can improve the fault tolerance of the final estimate. In the simulation, the impacts of some pivotal parameters are discussed. Meanwhile, compared with the other related models, the MHEEFE shows a better performance in accuracy, energy-efficiency and fault tolerance. Full article

The Internet of Things (IoT) involves a wide variety of heterogeneous technologies and resource-constrained devices that interact with each other. Due to such constraints, IoT devices usually require lightweight protocols that optimize the use of resources and energy consumption. Among the different commercial IoT devices, Bluetooth and Bluetooth Low Energy (BLE)-based beacons, which broadcast periodically certain data packets to notify their presence, have experienced a remarkable growth, specially due to their application in indoor positioning systems. This article proposes a family of protocols named Lightweight Protocol for Sensors (LP4S) that provides fast responses and enables plug-and-play mechanisms that allow IoT telemetry systems to discover new nodes and to describe and auto-register the sensors and actuators connected to a beacon. Thus, three protocols are defined depending on the beacon hardware characteristics: LP4S-6 (for resource-constraint beacons), LP4S-X (for more powerful beacons) and LP4S-J (for beacons able to run complex firmware). In order to demonstrate the capabilities of the designed protocols, the most restrictive (LP4S-6) is tested after implementing it for a telemetry application in a beacon based on Eddystone (Google’s open beacon format). Thus, the beacon specification is extended in order to increase its ability to manage unlimited sensors in a telemetry system without interfering in its normal operation with Eddystone frames. The performed experiments show the feasibility of the proposed solution and its superiority, in terms of latency and energy consumption, with respect to approaches based on Generic Attribute Profile (GATT) when multiple users connect to a mote or in scenarios where latency is not a restriction, but where low-energy consumption is essential. Full article

The influence of graphene oxide (GO) over the features of an optical fiber ethanol sensor based on lossy mode resonances (LMR) has been studied in this work. Four different sensors were built with this aim, each comprising a multimode optical fiber core fragment coated with a SnO₂ thin film. Layer by layer (LbL) coatings made of 1, 2 and 4 bilayers of polyethyleneimine (PEI) and graphene oxide were deposited onto three of these devices and their behavior as aqueous ethanol sensors was characterized and compared with the sensor without GO. The sensors with GO showed much better performance with a maximum sensitivity enhancement of 176% with respect to the sensor without GO. To our knowledge, this is the first time that GO has been used to make an optical fiber sensor based on LMR. Full article

Unmanned aerial vehicles (UAVs) are equipped with optical systems including an infrared (IR) camera such as electro-optical IR (EO/IR), target acquisition and designation sights (TADS), or forward looking IR (FLIR). However, images obtained from IR cameras are subject to noise such as dead pixels, lines, and fixed pattern noise. Nonuniformity correction (NUC) is a widely employed method to reduce noise in IR images, but it has limitations in removing noise that occurs during operation. Methods have been proposed to overcome the limitations of the NUC method, such as two-point correction (TPC) and scene-based NUC (SBNUC). However, these methods still suffer from unfixed pattern noise. In this paper, a background registration-based adaptive noise filtering (BRANF) method is proposed to overcome the limitations of conventional methods. The proposed BRANF method utilizes background registration processing and robust principle component analysis (RPCA). In addition, image quality verification methods are proposed that can measure the noise filtering performance quantitatively without ground truth images. Experiments were performed for performance verification with middle wave infrared (MWIR) and long wave infrared (LWIR) images obtained from practical military optical systems. As a result, it is found that the image quality improvement rate of BRANF is 30% higher than that of conventional NUC. Full article

Heart disease is the leading cause of mortality in the U.S. with approximately 610,000 people dying every year. Effective therapies for many cardiac diseases are lacking, largely due to an incomplete understanding of their genetic basis and underlying molecular mechanisms. Zebrafish (Danio rerio) are an excellent model system for studying heart disease as they enable a forward genetic approach to tackle this unmet medical need. In recent years, our team has been employing electrocardiogram (ECG) as an efficient tool to study the zebrafish heart along with conventional approaches, such as immunohistochemistry, DNA and protein analyses. We have overcome various challenges in the small size and aquatic environment of zebrafish in order to obtain ECG signals with favorable signal-to-noise ratio (SNR), and high spatial and temporal resolution. In this paper, we highlight our recent efforts in zebrafish ECG acquisition with a cost-effective simplified microelectrode array (MEA) membrane providing multi-channel recording, a novel multi-chamber apparatus for simultaneous screening, and a LabVIEW program to facilitate recording and processing. We also demonstrate the use of machine learning-based programs to recognize specific ECG patterns, yielding promising results with our current limited amount of zebrafish data. Our solutions hold promise to carry out numerous studies of heart diseases, drug screening, stem cell-based therapy validation, and regenerative medicine. Full article

The acetabular cup (AC) implant stability is determinant for the success of cementless hip arthroplasty. A method based on the analysis of the impact force applied during the press-fit insertion of the AC implant using a hammer instrumented with a force sensor was developed to assess the AC implant stability. The aim of the present study was to investigate the performance of a method using a hammer equipped with strain sensors to retrieve the AC implant stability. Different AC implants were inserted in five bovine samples with different stability conditions leading to 57 configurations. The AC implant was impacted 16 times by the two hammers consecutively. For each impact; an indicator I_S (respectively I_F) determined by analyzing the time variation of the signal corresponding to the averaged strain (respectively force) obtained with the stress (respectively strain) hammer was calculated. The pull-out force F was measured for each configuration. F was significantly correlated with I_S (R² = 0.79) and I_F (R² = 0.80). The present method has the advantage of not modifying the shape of the hammer that can be sterilized easily. This study opens new paths towards the development of a decision support system to assess the AC implant stability. Full article

This paper describes a flexible camera calibration method using refined vanishing points without prior information. Vanishing points are estimated from human-made features like parallel lines and repeated patterns. With the vanishing points extracted from the three mutually orthogonal directions, the interior and exterior orientation parameters can be further calculated using collinearity condition equations. A vanishing point refinement process is proposed to reduce the uncertainty caused by vanishing point localization errors. The fine-tuning algorithm is based on the divergence of grouped feature points projected onto the reference plane, minimizing the standard deviation of each of the grouped collinear points with an O(1) computational complexity. This paper also presents an automated vanishing point estimation approach based on the cascade Hough transform. The experiment results indicate that the vanishing point refinement process can significantly improve camera calibration parameters and the root mean square error (RMSE) of the constructed 3D model can be reduced by about 30%. Full article

Sustainability is at the heart of many application fields where the use of Unmanned Aerial Systems (UAS) is becoming more and more important (e.g., agriculture, fire detection and prediction, environmental surveillance, mapping, etc.). However, their usage and evolution are highly conditioned by the specific application field they are designed for, and thus, they cannot be easily reused among different application fields. From this point of view, being that they are not multipurpose, we can say that they are not fully sustainable. Bearing this in mind, the objective of this paper is two-fold: on the one hand, to identify the whole set of features that must be provided by a UAS to be considered sustainable and to show that there is no UAS satisfying all these features; on the other hand, to present an open and sustainable UAS architecture that may be used to build UAS on demand to provide the features needed in each application field. Since this architecture is mainly based on software and hardware adaptability, it contributes to the technical sustainability of cities. Full article

Tropospheric delay is one of the major factors affecting the accuracy of electromagnetic distance measurements. To provide wide-area real-time high precision zenith tropospheric delay (ZTD), the temporal and spatial variations of ZTD with altitude were analyzed on the bases of the latest meteorological reanalysis product (ERA-Interim) provided by the European Center for Medium-Range Weather Forecasts (ECMWF). An inverse scale height model at given locations taking latitude, longitude and day of year as inputs was then developed and used to convert real-time ZTD at GPS stations in Crustal Movement Observation Network of China (CMONOC) from station height to mean sea level (MSL). The real-time ZTD grid product (RtZTD) over China was then generated with a time interval of 5 min. Compared with ZTD estimated in post-processing mode, the bias and error RMS of ZTD at test GPS stations derived from RtZTD are 0.39 and 1.56 cm, which is significantly more accurate than commonly used empirical models. In addition, simulated real-time kinematic Precise Point Positioning (PPP) tests show that using RtZTD could accelerate the BDS-PPP convergence time by up to 32% and 65% in the horizontal and vertical components (set coordinate error thresholds to 0.4 m), respectively. For GPS-PPP, the convergence time using RtZTD can be accelerated by up to 29% in the vertical component (0.2 m). Full article

A rehabilitation robot plays an important role in relieving the therapists’ burden and helping patients with ankle injuries to perform more accurate and effective rehabilitation training. However, a majority of current ankle rehabilitation robots are rigid and have drawbacks in terms of complex structure, poor flexibility and lack of safety. Taking advantages of pneumatic muscles’ good flexibility and light weight, we developed a novel two degrees of freedom (2-DOF) parallel compliant ankle rehabilitation robot actuated by pneumatic muscles (PMs). To solve the PM’s nonlinear characteristics during operation and to tackle the human-robot uncertainties in rehabilitation, an adaptive backstepping sliding mode control (ABS-SMC) method is proposed in this paper. The human-robot external disturbance can be estimated by an observer, who is then used to adjust the robot output to accommodate external changes. The system stability is guaranteed by the Lyapunov stability theorem. Experimental results on the compliant ankle rehabilitation robot show that the proposed ABS-SMC is able to estimate the external disturbance online and adjust the control output in real time during operation, resulting in a higher trajectory tracking accuracy and better response performance especially in dynamic conditions. Full article

Nowadays, the complexity of global video products has substantially increased. They are composed of several associated services whose functionalities need to adapt across heterogeneous networks with different technologies and administrative domains. Each of these domains has different operational procedures; therefore, the comprehensive management of multi-domain services presents serious challenges. This paper discusses an approach to service management linking fault diagnosis system and Business Processes for Telefónica’s global video service. The main contribution of this paper is the proposal of an extended service management architecture based on Multi Agent Systems able to integrate the fault diagnosis with other different service management functionalities. This architecture includes a distributed set of agents able to coordinate their actions under the umbrella of a Shared Knowledge Plane, inferring and sharing their knowledge with semantic techniques and three types of automatic reasoning: heterogeneous, ontology-based and Bayesian reasoning. This proposal has been deployed and validated in a real scenario in the video service offered by Telefónica Latam. Full article

The importance of detection of parking space availability is still growing, particularly in major cities. This paper deals with the design of a distributed wireless camera system for the management of parking spaces, which can determine occupancy of the parking space based on the information from multiple cameras. The proposed system uses small camera modules based on Raspberry Pi Zero and computationally efficient algorithm for the occupancy detection based on the histogram of oriented gradients (HOG) feature descriptor and support vector machine (SVM) classifier. We have included information about the orientation of the vehicle as a supporting feature, which has enabled us to achieve better accuracy. The described solution can deliver occupancy information at the rate of 10 parking spaces per second with more than 90% accuracy in a wide range of conditions. Reliability of the implemented algorithm is evaluated with three different test sets which altogether contain over 700,000 samples of parking spaces. Full article

The routine surveillance of oil spills in major ports is important. However, existing techniques and sensors are unable to trace oil and micron-thin oil films on the surface of seawater. Therefore, we designed and studied a coastal-mounted sensor, using ultraviolet-induced fluorescence and fluorescence-filter systems (FFSs), to monitor oil spills and overcome the disadvantages of traditional surveillance systems. Using seawater from the port of Lingshui (Yellow Sea, China) and six oil samples of different types, we found that diesel oil’s relative fluorescence intensity (RFI) was significantly higher than those of heavy fuel and crude oils in the 180–300 nm range—in the 300–400 nm range, the RFI value of diesel is far lower. The heavy fuel and crude oils exhibited an opposite trend in their fluorescence spectra. A photomultiplier tube, employed as the fluorescence detection unit, efficiently monitored different oils on seawater in field experiments. On-site tests indicated that this sensor system could be used as a coastal-mounted early-warning detection system for oil spills. Full article

Green roofs have many benefits, but in countries with semiarid climates the amount of water needed for irrigation is a limiting factor for their maintenance. The use of drought-tolerant plants such as Sedum species, reduces the water requirements in the dry season, but, even so, in semiarid environments these can reach up to 60 L m⁻² per day. Continuous substrate/soil water content monitoring would facilitate the efficient use of this critical resource. In this context, the use of plant microbial fuel cells (PMFCs) emerges as a suitable and more sustainable alternative for monitoring water content in green roofs in semiarid climates. In this study, bench and pilot-scale experiments using seven Sedum species showed a positive relationship between current generation and water content in the substrate. PMFC reactors with higher water content (around 27% vs. 17.5% v/v) showed larger power density (114.6 and 82.3 μW m⁻² vs. 32.5 μW m⁻²). Moreover, a correlation coefficient of 0.95 (±0.01) between current density and water content was observed. The results of this research represent the first effort of using PMFCs as low-cost water content biosensors for green roofs. Full article

Packing density and moisture content are important factors in investigating the ensiling quality. Low packing density is a major cause of loss of sugar content. The moisture content also plays a determinant role in biomass degradation. To comprehensively evaluate the ensiling quality, this study focused on developing a compound sensor. In it, moisture electrodes and strain gauges were embedded into an ASABE Standard small cone for the simultaneous measurements of the penetration resistance (PR) and moisture content (MC) of silage. In order to evaluate the performance of the designed sensor and the theoretical analysis being used, relevant calibration and validation tests were conducted. The determination coefficients are 0.996 and 0.992 for PR calibration and 0.934 for MC calibration. The validation indicated that this measurement technique could determine the packing density and moisture content of the silage simultaneously and eliminate the influence of the friction between the penetration shaft and silage. In this study, we not only design a compound sensor but also provide an alternative way to investigate the ensiling quality which would be useful for further silage research. Full article

The automatic classification of sub-techniques in classical cross-country skiing provides unique possibilities for analyzing the biomechanical aspects of outdoor skiing. This is currently possible due to the miniaturization and flexibility of wearable inertial measurement units (IMUs) that allow researchers to bring the laboratory to the field. In this study, we aimed to optimize the accuracy of the automatic classification of classical cross-country skiing sub-techniques by using two IMUs attached to the skier’s arm and chest together with a machine learning algorithm. The novelty of our approach is the reliable detection of individual cycles using a gyroscope on the skier’s arm, while a neural network machine learning algorithm robustly classifies each cycle to a sub-technique using sensor data from an accelerometer on the chest. In this study, 24 datasets from 10 different participants were separated into the categories training-, validation- and test-data. Overall, we achieved a classification accuracy of 93.9% on the test-data. Furthermore, we illustrate how an accurate classification of sub-techniques can be combined with data from standard sports equipment including position, altitude, speed and heart rate measuring systems. Combining this information has the potential to provide novel insight into physiological and biomechanical aspects valuable to coaches, athletes and researchers. Full article

With the advancements in micro-electromechanical systems (MEMS) technologies, magnetic and inertial sensors are becoming more and more accurate, lightweight, smaller in size as well as low-cost, which in turn boosts their applications in human movement analysis. However, challenges still exist in the field of sensor orientation estimation, where magnetic disturbance represents one of the obstacles limiting their practical application. The objective of this paper is to systematically analyze exactly how magnetic disturbances affects the attitude and heading estimation for a magnetic and inertial sensor. First, we reviewed four major components dealing with magnetic disturbance, namely decoupling attitude estimation from magnetic reading, gyro bias estimation, adaptive strategies of compensating magnetic disturbance and sensor fusion algorithms. We review and analyze the features of existing methods of each component. Second, to understand each component in magnetic disturbance rejection, four representative sensor fusion methods were implemented, including gradient descent algorithms, improved explicit complementary filter, dual-linear Kalman filter and extended Kalman filter. Finally, a new standardized testing procedure has been developed to objectively assess the performance of each method against magnetic disturbance. Based upon the testing results, the strength and weakness of the existing sensor fusion methods were easily examined, and suggestions were presented for selecting a proper sensor fusion algorithm or developing new sensor fusion method. Full article

The automatic observation of the night sky through wide-angle video systems with the aim of detecting meteor and fireballs is currently among routine astronomical observations. The observation is usually done in multi-station or network mode, so it is possible to estimate the direction and the speed of the body flight. The high velocity of the meteorite flying through the atmosphere determines the important features of the camera systems, namely the high frame rate. Thanks to high frame rates, such imaging systems produce a large amount of data, of which only a small fragment has scientific potential. This paper focuses on methods for the real-time detection of fast moving objects in the video sequences recorded by intensified TV systems with frame rates of about 60 frames per second. The goal of our effort is to remove all unnecessary data during the daytime and make free hard-drive capacity for the next observation. The processing of data from the MAIA (Meteor Automatic Imager and Analyzer) system is demonstrated in the paper. Full article

This study presents a flexible wireless electronic skin (e-skin) sensor system that includes a multi-functional sensor device, a triple-mode reconfigurable readout integrated circuit (ROIC), and a mobile monitoring interface. The e-skin device’s multi-functionality is achieved by an interlocked micro-dome array structure that uses a polyvinylidene fluoride and reduced graphene oxide (PVDF/RGO) composite material that is inspired by the structure and functions of the human fingertip. For multi-functional implementation, the proposed triple-mode ROIC is reconfigured to support piezoelectric, piezoresistance, and pyroelectric interfaces through single-type e-skin sensor devices. A flexible system prototype was developed and experimentally verified to provide various wireless wearable sensing functions—including pulse wave, voice, chewing/swallowing, breathing, knee movements, and temperature—while their real-time sensed data are displayed on a smartphone. Full article

Life expectancy is increasing and, so, the years that patients have to live with chronic diseases and co-morbidities. Type 2 diabetes is one of the most prevalent chronic diseases, specifically linked to being overweight and ages over sixty. Recent studies have demonstrated the effectiveness of new strategies to delay and even prevent the onset of type 2 diabetes by a combination of active and healthy lifestyle on cohorts of mid to high risk subjects. Prospective research has been driven on large groups of the population to build risk scores that aim to obtain a rule for the classification of patients according to the odds for developing the disease. Currently, there are more than two hundred models and risk scores for doing this, but a few have been properly evaluated in external groups and integrated into a clinical application for decision support. In this paper, we present a novel system architecture based on service choreography and hybrid modeling, which enables a distributed integration of clinical databases, statistical and mathematical engines and web interfaces to be deployed in a clinical setting. The system was assessed during an eight-week continuous period with eight endocrinologists of a hospital who evaluated up to 8080 patients with seven different type 2 diabetes risk models implemented in two mathematical engines. Throughput was assessed as a matter of technical key performance indicators, confirming the reliability and efficiency of the proposed architecture to integrate hybrid artificial intelligence tools into daily clinical routine to identify high risk subjects. Full article

The recognition of the user’s context with wearable sensing systems is a common problem in ubiquitous computing. However, the typically small battery of such systems often makes continuous recognition impractical. The strain on the battery can be reduced if the sensor setting is adapted to each context. We propose a method that efficiently finds near-optimal sensor settings for each context. It uses Markov chains to simulate the behavior of the system in different configurations and the multi-objective genetic algorithm to find a set of good non-dominated configurations. The method was evaluated on three real-life datasets and found good trade-offs between the system’s energy expenditure and the system’s accuracy. One of the solutions, for example, consumed five-times less energy than the default one, while sacrificing only two percentage points of accuracy. Full article

The research described in this paper deals with the documentation of hiking trails in alpine areas. The study presents a novel research topic, applying up-to-date survey techniques and top quality equipment with practical applications in nature conservation. The research presents the initial part of the process—capturing imagery, photogrammetric processing, quality checking, and a discussion on possibilities of the further data analysis. The research described in this article was conducted in the Tatra National Park (TNP) in Poland, which is considered as one of the most-visited national parks in Europe. The exceptional popularity of this place is responsible for intensification of morphogenetic processes, resulting in the development of numerous forms of erosion. This article presents the outcomes of research, whose purpose was to verify the usability of UAVs to check the condition of hiking trails in alpine areas. An octocopter equipped with a non-metric camera was used for measurements. Unlike traditional methods of measuring landscape features, such a solution facilitates acquisition of quasi-continuous data that has uniform resolution throughout the study area and high spatial accuracy. It is also a relatively cheap technology, which is its main advantage over equally popular laser scanning. The paper presents the complete methodology of data acquisition in harsh conditions and demanding locations of hiking trails on steep Tatra slopes. The paper also describes stages that lead to the elaboration of basic photogrammetric products relying on structure from motion (SfM) technology and evaluates the accuracy of the materials obtained. Finally, it shows the applicability of the prepared products to the evaluation of the spatial reach and intensity of erosion along hiking trails, and to the study of plant succession or tree stand condition in the area located next to hiking trails. Full article

Vehicular nodes are equipped with more and more sensing units, and a large amount of sensing data is generated. Recently, more and more research considers cooperative urban sensing as the heart of intelligent and green city traffic management. The key components of the platform will be a combination of a pervasive vehicular sensing system, as well as a central control and analysis system, where data-gathering is a fundamental component. However, the data-gathering and monitoring are also challenging issues in vehicular sensor networks because of the large amount of data and the dynamic nature of the network. In this paper, we propose an efficient continuous event-monitoring and data-gathering framework based on fog nodes in vehicular sensor networks. A fog-based two-level threshold strategy is adopted to suppress unnecessary data upload and transmissions. In the monitoring phase, nodes sense the environment in low cost sensing mode and generate sensed data. When the probability of the event is high and exceeds some threshold, nodes transfer to the event-checking phase, and some nodes would be selected to transfer to the deep sensing mode to generate more accurate data of the environment. Furthermore, it adaptively adjusts the threshold to upload a suitable amount of data for decision making, while at the same time suppressing unnecessary message transmissions. Simulation results showed that the proposed scheme could reduce more than 84 percent of the data transmissions compared with other existing algorithms, while it detects the events and gathers the event data. Full article

This study reports a cost-effective method of replicating glass microfluidic chips using a vitreous carbon (VC) stamp. A glass replica with the required microfluidic microstructures was synthesized without etching. The replication method uses a VC stamp fabricated by combining thermal replication using a furan-based, thermally-curable polymer with carbonization. To test the feasibility of this method, a flow focusing droplet generator with flow-focusing and channel widths of 50 µm and 100 µm, respectively, was successfully fabricated in a soda-lime glass substrate. Deviation between the geometries of the initial shape and the vitreous carbon mold occurred because of shrinkage during the carbonization process, however this effect could be predicted and compensated for. Finally, the monodispersity of the droplets generated by the fabricated microfluidic device was evaluated. Full article

Massive multiple-input multiple-output (MIMO) systems can be applied to support numerous internet of things (IoT) devices using its excessive amount of transmitter (TX) antennas. However, one of the big obstacles for the realization of the massive MIMO system is the overhead of reference signal (RS), because the number of RS is proportional to the number of TX antennas and/or related user equipments (UEs). It has been already reported that antenna group-based RS overhead reduction can be very effective to the efficient operation of massive MIMO, but the method of deciding the number of antennas needed in each group is at question. In this paper, we propose a simplified determination scheme of the number of antennas needed in each group for RS overhead reduced massive MIMO to support many IoT devices. Supporting many distributed IoT devices is a framework to configure wireless sensor networks. Our contribution can be divided into two parts. First, we derive simple closed-form approximations of the achievable spectral efficiency (SE) by using zero-forcing (ZF) and matched filtering (MF) precoding for the RS overhead reduced massive MIMO systems with channel estimation error. The closed-form approximations include a channel error factor that can be adjusted according to the method of the channel estimation. Second, based on the closed-form approximation, we present an efficient algorithm determining the number of antennas needed in each group for the group-based RS overhead reduction scheme. The algorithm depends on the exact inverse functions of the derived closed-form approximations of SE. It is verified with theoretical analysis and simulation that the proposed algorithm works well, and thus can be used as an important tool for massive MIMO systems to support many distributed IoT devices. Full article

This paper demonstrates a commercial-level field-portable lens-free cell analyzer called the NaviCell (No-stain and Automated Versatile Innovative cell analyzer) capable of automatically analyzing cell count and viability without employing an optical microscope and reagents. Based on the lens-free shadow imaging technique, the NaviCell (162 × 135 × 138 mm³ and 1.02 kg) has the advantage of providing analysis results with improved standard deviation between measurement results, owing to its large field of view. Importantly, the cell counting and viability testing can be analyzed without the use of any reagent, thereby simplifying the measurement procedure and reducing potential errors during sample preparation. In this study, the performance of the NaviCell for cell counting and viability testing was demonstrated using 13 and six cell lines, respectively. Based on the results of the hemocytometer (de facto standard), the error rate (ER) and coefficient of variation (CV) of the NaviCell are approximately 3.27 and 2.16 times better than the commercial cell counter, respectively. The cell viability testing of the NaviCell also showed an ER and CV performance improvement of 5.09 and 1.8 times, respectively, demonstrating sufficient potential in the field of cell analysis. Full article

A novel wake-up receiver for wireless sensor networks is introduced. It operates with a modified medium access protocol (MAC), allowing low-energy consumption and practical latency. The ultra-low-power wake-up receiver operates with enhanced duty-cycled listening. The analysis of energy models of the duty-cycle-based communication is presented. All the WuRx blocks are studied to obey the duty-cycle operation. For a mean interval time for the data exchange cycle between a transmitter and a receiver over $1.7$ $\mathrm{s}$ and a 64-bit wake-up packet detection latency of 32 ms, the average power consumption of the wake-up receiver (WuRx) reaches down to 3 μ $\mathrm{W}$ . It also features scalable addressing of more than 512 bit at a data rate of 128 $\mathrm{k}$ $\mathrm{bit}$ / $\mathrm{s}$ ⁻¹. At a wake-up packet error rate of ${10}^{-2}$ , the detection sensitivity reaches a minimum of $-90$ $\mathrm{dBm}$ . The combination of the MAC protocol and the WuRx eases the adoption of different kinds of wireless sensor networks. In low traffic communication, the WuRx dramatically saves more energy than that of a network that is implementing conventional duty-cycling. In this work, a prototype was realized to evaluate the intended performance. Full article

Transportation planning and solutions have an enormous impact on city life. To minimize the transport duration, urban planners should understand and elaborate the mobility of a city. Thus, researchers look toward monitoring people’s daily activities including transportation types and duration by taking advantage of individual’s smartphones. This paper introduces a novel segment-based transport mode detection architecture in order to improve the results of traditional classification algorithms in the literature. The proposed post-processing algorithm, namely the Healing algorithm, aims to correct the misclassification results of machine learning-based solutions. Our real-life test results show that the Healing algorithm could achieve up to 40% improvement of the classification results. As a result, the implemented mobile application could predict eight classes including stationary, walking, car, bus, tram, train, metro and ferry with a success rate of 95% thanks to the proposed multi-tier architecture and Healing algorithm. Full article

The complexity reduction of receivers in ultrawideband (UWB) communication when time reversal (TR) technique is applied makes it suitable for low-cost and low-power sensor systems. Larger antenna dispersion can generally lead to a less stable phase center and will increase the interference in UWB communications based on pulse radio, whereas a higher antenna gain will result in higher channel gain and further larger channel capacity. To find out the trade-off between antenna gain and dispersion, we performed the channel measurements using different antennas in a dense multipath environment and established the distribution of channel capacities based on the measured channel responses. The results show that the capacity loss caused by antenna dispersion cannot be compensated by antenna gain with line-of-sight transmission to some extent, the effect of phase center on the communication system is negligible, and antennas with smaller time dispersion will have a better energy focusing property and anti-interference performance in TR systems. Full article

This work proposes the use of a hybrid acoustic and optical indoor positioning system for the accurate 3D positioning of Unmanned Aerial Vehicles (UAVs). The acoustic module of this system is based on a Time-Code Division Multiple Access (T-CDMA) scheme, where the sequential emission of five spread spectrum ultrasonic codes is performed to compute the horizontal vehicle position following a 2D multilateration procedure. The optical module is based on a Time-Of-Flight (TOF) camera that provides an initial estimation for the vehicle height. A recursive algorithm programmed on an external computer is then proposed to refine the estimated position. Experimental results show that the proposed system can increase the accuracy of a solely acoustic system by 70–80% in terms of positioning mean square error. Full article