Sensors, Volume 18, Issue 9 (September 2018) – 431 articles

In a crowded harbor water area, it is a major concern to control ship traffic for assuring safety and maximizing the efficiency of port operations. Vessel Traffic Service (VTS) operators pay much attention to caution areas like ship route intersections or traffic congestion area in which there are some risks of ship collision. They want to control the traffic of the caution area at a proper level to lessen risk. Inertial ship movement makes swift changes in direction and speed difficult. It is hence important to predict future traffic of the caution area earlier on so as to get enough time for control actions on ship movements. In the harbor area, VTS stations collect a large volume of Automatic Identification Service (AIS) sensor data, which contain information about ship movement and ship attributes. This paper proposes a new deep neural network model called Ship Traffic Extraction Network (STENet) to predict the medium-term traffic and long-term traffic of the caution area. The STENet model is trained with AIS sensor data. The STENet model is organized into a hierarchical architecture in which the outputs of the movement and contextual feature extraction modules are concatenated and fed into a prediction module. The movement module extracts the features of overall ship movements with a convolutional neural network. The contextual modules consist of five separated fully-connected neural networks, each of which receives an associated attribute. The separation of feature extraction modules at the front phase helps extract the effective features by preventing unrelated attributes from crosstalking. To evaluate the performance of the proposed model, the developed model is applied to a real AIS sensor dataset, which has been collected over two years at a Korean port called Yeosu. In the experiments, four methods have been compared including two new methods: STENet and VGGNet-based models. For the real AIS sensor dataset, the proposed model has shown 50.65% relative performance improvement on average for the medium-term predictions and 57.65% improvement on average for the long-term predictions over the benchmark method, i.e., the SVR-based method. Full article

This paper focuses on the development of a new logistic approach based on reliability and maintenance assessment, with the final aim of establishing a more efficient interval for the maintenance activities for Unmanned Aerial Vehicles (UAV). In the first part, we develop an architectural philosophy to obtain a more detailed reliability evaluation; then, we study the intrinsic reliability at the design stage in order to avoid severe critical issues in the UAV. In the second part, we compare different maintenance philosophies for UAVs and develop the concepts of preventive and corrective maintenance that consider the system subjected (until real “hard failure”) to partial performance degradation (“soft failure”). Finally, by evaluation of the uncertainty through the confidence interval, we determine the new soft failure limits, taking into account the general knowledge of the systems and subsystems in order to guarantee the proper preventive maintenance interval. Full article

Robot navigation is an indispensable component of any mobile service robot. Many path planning algorithms generate a path which has many sharp or angular turns. Such paths are not fit for mobile robot as it has to slow down at these sharp turns. These robots could be carrying delicate, dangerous, or precious items and executing these sharp turns may not be feasible kinematically. On the contrary, smooth trajectories are often desired for robot motion and must be generated while considering the static and dynamic obstacles and other constraints like feasible curvature, robot and lane dimensions, and speed. The aim of this paper is to succinctly summarize and review the path smoothing techniques in robot navigation and discuss the challenges and future trends. Both autonomous mobile robots and autonomous vehicles (outdoor robots or self-driving cars) are discussed. The state-of-the-art algorithms are broadly classified into different categories and each approach is introduced briefly with necessary background, merits, and drawbacks. Finally, the paper discusses the current and future challenges in optimal trajectory generation and smoothing research. Full article

Soil spectra are often measured in the laboratory, and there is an increasing number of large-scale soil spectral libraries establishing across the world. However, calibration models developed from soil libraries are difficult to apply to spectral data acquired from the field or space. Transfer learning has the potential to bridge the gap and make the calibration model transferrable from one sensor to another. The objective of this study is to explore the potential of transfer learning for soil spectroscopy and its performance on soil clay content estimation using hyperspectral data. First, a one-dimensional convolutional neural network (1D-CNN) is used on Land Use/Land Cover Area Frame Survey (LUCAS) mineral soils. To evaluate whether the pre-trained 1D-CNN model was transferrable, LUCAS organic soils were used to fine-tune and validate the model. The fine-tuned model achieved a good accuracy (coefficient of determination (R²) = 0.756, root-mean-square error (RMSE) = 7.07 and ratio of percent deviation (RPD) = 2.26) for the estimation of clay content. Spectral index, as suggested as a simple transferrable feature, was also explored on LUCAS data, but did not performed well on the estimation of clay content. Then, the pre-trained 1D-CNN model was further fine-tuned by field samples collect in the study area with spectra extracted from HyMap imagery, achieved an accuracy of R² = 0.601, RMSE = 8.62 and RPD = 1.54. Finally, the soil clay map was generated with the fine-tuned 1D-CNN model and hyperspectral data. Full article

It is essential to provide reliable localization results to allow mobile robots to navigate autonomously. Even though many state-of-the-art localization schemes have so far shown satisfactory performance in various environments, localization has still been difficult under specific conditions, such as extreme environmental changes. Since many robots cannot diagnose for themselves whether the localization results are reliable, there can be serious autonomous navigation problems. To solve this problem, this study proposes a self-diagnosis scheme for the localization status. In this study, two indicators are empirically defined for the self-diagnosis of localization status. Each indicator shows significant changes when there are difficulties in light detection and ranging (LiDAR) sensor-based localization. In addition, the classification model of localization status is trained through machine learning using the two indicators. A robot can diagnose the localization status itself using the proposed classification model. To verify the usefulness of the proposed method, we carried out localization experiments in real environments. The proposed classification model successfully detected situations where the localization accuracy is significantly degraded due to extreme environmental changes. Full article

The detection of high frequency overvoltage and partial discharge is of great significance in evaluating the insulation condition of high-voltage power equipment. A wideband GHz electric field (E-field) sensor for electromagnetic pulse (EMP) signal measurement was proposed in this paper. An optical fiber transmission link was adopted in the design in order to implement high-voltage isolation and reduce electromagnetic interference during transmission. The designed sensor was mainly made up of a differential electric field (D-dot) antenna, transmitter, and receiver. The D-dot antenna was designed to detect high frequency E-field strength and generate a corresponding voltage signal, which was converted into an optical signal by the transmitter. The optical signal could be transmitted a large distance through an optical fiber without electromagnetic interference and changed back to a voltage signal again by a receiver. The design process of the sensor was introduced in detail, and experiments were performed using a gigahertz transverse electromagnetic (GTEM) cell and an EMP simulator to verify it. The results indicated that the designed sensor had a good performance besides an expectable delay due to the optional amplifier. Full article

A lead-free 0.94(Na_0.5Bi_0.5) TiO₃-0.06 BaTiO₃ (BNT-BT) thick film, with a thickness of 60 μm, has been fabricated using a tape-casting method. The longitudinal piezoelectric constant, clamped dielectric permittivity constant, remnant polarization and coercive field of the BNT-BT thick film were measured to be 150 pC/N, 1928, 13.6 μC/cm², and 33.6 kV/cm, respectively. The electromechanical coupling coefficient k_t was calculated to be 0.55 according to the measured electrical impedance spectrum. A high-frequency plane ultrasound transducer was successfully fabricated using a BNT-BT thick film. The performance of the transducer was characterized and evaluated by the pulse-echo testing and wire phantom imaging operations. The BNT-BT thick film transducer exhibits a center frequency of 34 MHz, a −6 dB bandwidth of 26%, an axial resolution of 77 μm and a lateral resolution of 484 μm. The results suggest that lead-free BNT-BT thick film fabricated by tape-casting method is a promising lead-free candidate for high-frequency ultrasonic transducer applications. Full article

The urban setting is a challenging environment for GNSS receivers. Multipath and other anomalies typically increase the positioning error of the receiver. Moreover, the error estimate of the position is often unreliable. In this study, we detect GNSS trajectory anomalies by using similarity comparison methods between a pedestrian dead reckoning trajectory, recorded using a foot-mounted inertial measurement unit, and the corresponding GNSS trajectory. During a normal walk, the foot-mounted inertial dead reckoning setup is trustworthy up to a few tens of meters. Thus, the differing GNSS trajectory can be detected using form similarity comparison methods. Of the eight tested methods, the Hausdorff distance (HD) and the accumulated distance difference (ADD) give slightly more consistent detection results compared to the rest. Full article

Visual landmarks are important navigational aids for research into and design of applications for last mile pedestrian navigation, e.g., business card route of pedestrian navigation. The business card route is a route between a fixed origin (e.g., campus entrance) to a fixed destination (e.g., office). The changing characteristics and combinations of various sensors’ data in smartphones or navigation devices can be viewed as invisible salient landmarks for business card route of pedestrian navigation. However, the advantages of these invisible landmarks have not been fully utilized, despite the prevalence of GPS and digital maps. This paper presents an improvement to the Dempster–Shafer theory of evidence to find invisible landmarks along predesigned pedestrian routes, which can guide pedestrians by locating them without using digital maps. This approach is suitable for use as a “business card” route for newcomers to find their last mile destinations smoothly by following precollected sensor data along a target route. Experiments in real pedestrian navigation environments show that our proposed approach can sense the location of pedestrians automatically, both indoors and outdoors, and has smaller positioning errors than purely GPS and Wi-Fi positioning approaches in the study area. Consequently, the proposed methodology is appropriate to guide pedestrians to unfamiliar destinations, such as a room in a building or an exit from a park, with little dependency on geographical information. Full article

Microcontact printing (µCP) is a practical and versatile approach to create nanostructured patterns of biomolecular probes, but it involves conformational changes on the patterned bioreceptors that often lead to a loss on the biological activity of the resulting structures. Herein we introduce indirect µCP to create functional patterns of bioreceptors on solid substrates. This is a simple strategy that relies on physisorbing biomolecular probes of interest in the nanostructured gaps that result after patterning backfilling agents by standard µCP. This study presents the approach, assesses bovine serum albumin as backfilling agent for indirect µCP on different materials, reports the limitations of standard µCP on the functionality of patterned antibodies, and demonstrates the capabilities of indirect µCP to solve this issue. Bioreceptors were herein structured as diffractive gratings and used to measure biorecognition events in label-free conditions. Besides, as a preliminary approach towards sensing biomarkers, this work also reports the implementation of indirect µCP in an immunoassay to detect human immunoglobulin E. Full article

We demonstrate the fabrication of novel reduced graphene oxide (rGO)-based double network (DN) hydrogels through the polymerization of poly(N-isopropylacrylamide) (PNIPAm) and carboxymethyl chitosan (CMC). The facile synthesis of DN hydrogels includes the reduction of graphene oxide (GO) by CMC, and the subsequent polymerization of PNIPAm. The presence of rGO in the fabricated PNIPAm/CMC/rGO DN hydrogels enhances the compressibility and flexibility of hydrogels with respect to pure PNIPAm hydrogels, and they exhibit favorable thermoresponsivity, compressibility, and conductivity. The created hydrogels can be continuously cyclically compressed and have excellent bending properties. Furthermore, it was found that the hydrogels are pressure- and temperature-sensitive, and can be applied to the design of both pressure and temperature sensors to detect mechanical deformation and to measure temperature. Our preliminary results suggest that these rGO-based DN hydrogels exhibit a high potential for the fabrication of soft robotics and artificially intelligent skin-like devices. Full article

Noise pollution in urban environments is becoming increasingly common and it has potential to negatively impact people’s health and decrease overall productivity. In order to alleviate these effects, it is important to better quantify noise patterns and levels through data collection and analysis. Wireless sensor networks offer a method for achieving this with a higher level of granularity than traditional handheld devices. In this study, a wireless sensing unit (WSU) was developed that possesses the same functionality as a handheld sound level meter. The WSU is comprised of a microcontroller unit that enables on-board computations, a wireless transceiver that uses Zigbee protocol for data transmission, and an external peripheral board that houses the microphone transducer. The WSU utilizes on-board data processing techniques to monitor noise by computing equivalent continuous sound levels, L_eqT, which effectively minimizes data transmission and increases the overall longevity of the node. Strategies are also employed to ensure real-time functionality is maintained on the sensing unit, with a focus on preventing bottlenecks between data acquisition, data processing, and wireless transmission. Four units were deployed in two weeks field validation test and were shown to be capable of monitoring noise for extended periods of time. Full article

We integrate, for the first time in the literature, the following ingredients to deal with emerging dynamic spectrum management (DSM) problem in heterogeneous wireless sensors and actuators networks (WSANs), Internet of things (IoT) and Wi-Fi: (i) named-based routing to provide provenance and location-independent access to control plane; (ii) temporary storage of control data for efficient and cohesive control dissemination, as well as asynchronous communication between software-controllers and devices; (iii) contract-based control to improve trust-ability of actions; (iv) service-defined configuration of wireless devices, approximating their configurations to real services needs. The work is implemented using NovaGenesis architecture and a proof-of-concept is evaluated in a real scenario, demonstrating our approach to automate radio frequency channel optimization in Wi-Fi and IEEE 802.15.4 networks in the 2.4 GHz bands. An integrated cognitive radio system provides the dual-mode best channel indications for novel DSM services in NovaGenesis. By reconfiguring Wi-Fi/IoT devices to best channels, the proposed solution more than doubles the network throughput, when compared to the case of mutual interference. Therefore, environments equipped with the proposal provide enhanced performance to their users. Full article

In this article, a study of characteristic vibrations of marine oils separation system is presented. Vibrations analysis allows for the extraction of representative features that could be related to the lifetime of their pieces. Actual measurements were carried out on these systems on Ro-Pax vessels to transport passengers and freight. The vibrations obtained were processed in the frequency domain and following this, they were used in a Genetic Neuro-Fuzzy System in order to design new predictive maintenance strategies. The obtained results show that these techniques as a promising strategy can be utilized to determine incipient faults. Full article

Since the increment of the threat to public health caused by foodborne pathogens, researches have been widely studied on developing the miniaturized detection system for the on-site pathogen detection. In the study, we focused on the development of portable, robust, and disposable film-based polymerase chain reaction (PCR) chip containing a multiplex chamber for simultaneous gene amplification. In order to simply fabricate and operate a film-based PCR chip, different kinds of PCR chambers were designed and fabricated using polyethylene terephthalate (PET) and polyvinyl chloride (PVC) adhesive film, in comparison with commercial PCR, which employs a stereotyped system at a bench-top scale. No reagent leakage was confirmed during the PCR thermal cycling using the film PCR chip, which indicates that the film PCR chip is structurally stable for rapid heat cycling for DNA amplification. Owing to use of the thin film to fabricate the PCR chip, we are able to realize fast thermal transfer from the heat block that leads to short PCR amplification time. Moreover, using the film PCR chip, we could even amplify the target pathogen with 10 CFU mL⁻¹. The artificially infected milk with various concentration of Bacillus cereus was successfully amplified on a single film PCR chip. On the basis of the reliable results, the developed film PCR chip could be a useful tool as a POCT device to detect foodborne pathogens via genetic analysis. Full article

Mobile phones are increasingly used for purposes that have nothing to do with phone calls or simple data transfers, and one such use is indoor inertial navigation. Nevertheless, the development of a standalone application able to detect the displacement of the user starting only from the data provided by the most common inertial sensors in the mobile phones (accelerometer, gyroscope and magnetometer), is a complex task. This complexity lies in the hardware disparity, noise on data, and mostly the many movements that the mobile phone can experience and which have nothing to do with the physical displacement of the owner. In our case, we describe a proposal, which, after using quaternions and a Kalman filter to project the sensors readings into an Earth Centered inertial reference system, combines a classic Peak-valley detector with an ensemble of SVMs (Support Vector Machines) and a standard deviation based classifier. Our proposal is able to identify and filter out those segments of signal that do not correspond to the behavior of “walking”, and thus achieve a robust detection of the physical displacement and counting of steps. We have performed an extensive experimental validation of our proposal using a dataset with 140 records obtained from 75 different people who were not connected to this research. Full article

Ultrasonic non-destructive testing techniques (NDT) based on the application of guided waves are already used for inspection of plate-type structures made of various materials, including composite materials. Air-coupled ultrasonic techniques are used to test such structures by means of guided waves. The objective of this research was development and investigation of air-coupled excitation of a slow A₀ Lamb wave mode in thin plastic films by a PMN-32%PT ultrasonic array. It is known that when the velocity of the A₀ mode in the film is less than the ultrasound velocity in air no leaky wave is observed in a surrounding air. It opens new possibilities for NDT of composite structures. The influence of the airborne wave may be eliminated by 3D filtering in a wavenumbers-frequency domain. A special filter and corresponding signals processing technique were developed in order to obtain directivity patterns and velocity maps of the waves propagating in all directions. The measured ultrasound velocity values prove that, with the proposed method, it is possible to excite a slow A₀ Lamb wave mode and to separate it from other parasitic waves propagating in air. Measurements of the parameters of the slow A₀ mode, such as the propagation velocity in the plastic film, may be applied for the material characterization. Full article

This paper presents the bandwidth enhancement and frequency scanning for fan beam array antenna utilizing novel technique of band-pass filter integration for wireless vital signs monitoring and vehicle navigation sensors. First, a fan beam array antenna comprising of a grounded coplanar waveguide (GCPW) radiating element, CPW fed line, and the grounded reflector is introduced which operate at a frequency band of 3.30 GHz and 3.50 GHz for WiMAX (World-wide Interoperability for Microwave Access) applications. An advantageous beam pattern is generated by the combination of a CPW feed network, non-parasitic grounded reflector, and non-planar GCPW array monopole antenna. Secondly, a miniaturized wide-band bandpass filter is developed using SCSRR (Semi-Complementary Split Ring Resonator) and DGS (Defective Ground Structures) operating at 3–8 GHz frequency band. Finally, the designed filter is integrated within the frequency scanning beam array antenna in a novel way to increase the impedance bandwidth as well as frequency scanning. The new frequency beam array antenna with integrated band-pass filter operate at 2.8 GHz to 6 GHz with a wide frequency scanning from the 50 to 125-degree range. Full article

Imaging past the diffraction limit is of significance to an optical system. Fourier ptychography (FP) is a novel coherent imaging technique that can achieve this goal and it is widely used in microscopic imaging. Most phase retrieval algorithms for FP reconstruction are based on Gaussian measurements which cannot extend straightforwardly to long range, sub-diffraction imaging setup because of laser speckle noise corruption. In this work, a new FP reconstruction framework is proposed for macroscopic visible imaging. When compared with existing research, the reweighted amplitude flow algorithm is adopted for better signal modeling, and the Regularization by Denoising (RED) scheme is introduced to reduce the effects of speckle. Experiments demonstrate that the proposed method can obtain state-of-the-art recovered results on both visual and quantitative metrics without increasing computation cost, and it is flexible for real imaging applications. Full article

Deep learning techniques have boosted the performance of hyperspectral image (HSI) classification. In particular, convolutional neural networks (CNNs) have shown superior performance to that of the conventional machine learning algorithms. Recently, a novel type of neural networks called capsule networks (CapsNets) was presented to improve the most advanced CNNs. In this paper, we present a modified two-layer CapsNet with limited training samples for HSI classification, which is inspired by the comparability and simplicity of the shallower deep learning models. The presented CapsNet is trained using two real HSI datasets, i.e., the PaviaU (PU) and SalinasA datasets, representing complex and simple datasets, respectively, and which are used to investigate the robustness or representation of every model or classifier. In addition, a comparable paradigm of network architecture design has been proposed for the comparison of CNN and CapsNet. Experiments demonstrate that CapsNet shows better accuracy and convergence behavior for the complex data than the state-of-the-art CNN. For CapsNet using the PU dataset, the Kappa coefficient, overall accuracy, and average accuracy are 0.9456, 95.90%, and 96.27%, respectively, compared to the corresponding values yielded by CNN of 0.9345, 95.11%, and 95.63%. Moreover, we observed that CapsNet has much higher confidence for the predicted probabilities. Subsequently, this finding was analyzed and discussed with probability maps and uncertainty analysis. In terms of the existing literature, CapsNet provides promising results and explicit merits in comparison with CNN and two baseline classifiers, i.e., random forests (RFs) and support vector machines (SVMs). Full article

Interactive textiles are reaching maturity. First technology augmented textiles in form of clothes and furnitures are becoming commercially available. In contrast to the close link between technological development and innovations, future users’ acceptance and usage of such interactive textiles has not been integrated sufficiently, yet. The current study investigates future users’ consumer behavior and acceptance of interactive textiles using a scenario-based conjoint analysis study, which was presented in an online questionnaire ( $n=324$ ). Two prototypical interactive textiles were focused on: a smart jacket and a smart armchair. To assess the textile products, the participants had to choose the preferred product alternative consisting each of the acceptance-relevant factors “connectivity”, “input modality”, “feature range”, “usability”, and “ease of cleaning”and their respective levels. The results revealed that the “ease of cleaning” is the most important decision criterion for both textile devices (even more important for the smart jacket), followed by “feature range”, “connectivity”, and “usability”. In contrast, the “input modality” is perceived as least important. The study also identified user profiles based on the projected consumer behavior (“adopters”, “rejecters”, and “undecided”) for both products. Besides the differences in product evaluation and projected consumer behavior, the user groups are significantly influenced by the individual affinity to textiles (both products) and gender (smart jacket). The findings are used to derive design and communication guidelines referring to interactive textiles in order to incorporate users’ needs, wishes, and requirements into future products. Full article

An eye tracker’s accuracy and system behavior play critical roles in determining the reliability and usability of eye gaze data obtained from them. However, in contemporary eye gaze research, there exists a lot of ambiguity in the definitions of gaze estimation accuracy parameters and lack of well-defined methods for evaluating the performance of eye tracking systems. In this paper, a set of fully defined evaluation metrics are therefore developed and presented for complete performance characterization of generic commercial eye trackers, when they operate under varying conditions on desktop or mobile platforms. In addition, some useful visualization methods are implemented, which will help in studying the performance and data quality of eye trackers irrespective of their design principles and application areas. Also the concept of a graphical user interface software named GazeVisual v1.1 is proposed that would integrate all these methods and enable general users to effortlessly access the described metrics, generate visualizations and extract valuable information from their own gaze datasets. We intend to present these tools as open resources in future to the eye gaze research community for use and further advancement, as a contribution towards standardization of gaze research outputs and analysis. Full article

With the increasing number of ubiquitous terminals and the continuous expansion of network scale, the problem of unbalanced energy consumption in sensor networks has become increasingly prominent in recent years. However, a node scheduling strategy or an energy consumption optimization algorithm may be not enough to meet the requirements of large-scale application. To address this problem a type of Annulus-based Energy Balanced Data Collection (AEBDC) method is proposed in this paper. The circular network is divided into several annular sectors of different sizes. Nodes in the same annulus-sector form a cluster. Based on this model, a multi-hop data forwarding strategy with the help of the candidate cluster headers is proposed to balance energy consumption during transmission and to avoid buffer overflow. Meanwhile, in each annulus, there is a Wireless Charging Vehicle (WCV) that is responsible for periodically recharging the cluster headers as well as the candidate cluster headers. By minimizing the recharging cost, the energy efficiency is enhanced. Simulation results show that AEBDC can not only alleviate the “energy hole problem” in sensor networks, but also effectively prolong the network lifetime. Full article

We introduce a novel method for indoor localization with the user’s own smartphone by learning personalized walking patterns outdoors. Most smartphone and pedestrian dead reckoning (PDR)-based indoor localization studies have used an operation between step count and stride length to estimate the distance traveled via generalized formulas based on the manually designed features of the measured sensory signal. In contrast, we have applied a different approach to learn the velocity of the pedestrian by using a segmented signal frame with our proposed hybrid multiscale convolutional and recurrent neural network model, and we estimate the distance traveled by computing the velocity and the moved time. We measured the inertial sensor and global position service (GPS) position at a synchronized time while walking outdoors with a reliable GPS fix, and we assigned the velocity as a label obtained from the displacement between the current position and a prior position to the corresponding signal frame. Our proposed real-time and automatic dataset construction method dramatically reduces the cost and significantly increases the efficiency of constructing a dataset. Moreover, our proposed deep learning model can be naturally applied to all kinds of time-series sensory signal processing. The performance was evaluated on an Android application (app) that exported the trained model and parameters. Our proposed method achieved a distance error of <2.4% and >1.5% on indoor experiments. Full article

Recently, with the increasing demand for virtual reality (VR), experiencing immersive contents with VR has become easier. However, a tremendous amount of calculation and bandwidth is required when processing 360 videos. Moreover, additional information such as the depth of the video is required to enjoy stereoscopic 360 contents. Therefore, this paper proposes an efficient method of streaming high-quality 360 videos. To reduce the bandwidth when streaming and synthesizing the 3DoF+ 360 videos, which supports limited movements of the user, a proper down-sampling ratio and quantization parameter are offered from the analysis of the graph between bitrate and peak signal-to-noise ratio. High-efficiency video coding (HEVC) is used to encode and decode the 360 videos, and the view synthesizer produces the video of intermediate view, providing the user with an immersive experience. Full article

A hybrid structure gas sensor of reduced graphene oxide (RGO) decorated graphene (RGO-Gr) is designed for ultra-low concentration ammonia detection. The resistance value of the RGO-Gr hybrid is the indicator of the ammonia concentration and controlled by effective charge transport from RGO to graphene after ammonia molecule adsorption. In this hybrid material, RGO is the adsorbing layer to catch ammonia molecules and graphene is the conductive layer to effectively enhance charge/electron transport. Compared to a RGO gas sensor, the signal-to-noise ratio (SNR) of the RGO-Gr is increased from 22 to 1008. Meanwhile, the response of the RGO-Gr gas sensor is better than that of either a pristine graphene or RGO gas sensor. It is found that the RGO reduction time is related to the content of functional groups that directly reflect on the gas sensing properties of the sensor. The RGO-Gr gas sensor with 10 min reduction time has the best gas sensing properties in this type of sensor. The highest sensitivity is 2.88% towards 0.5 ppm, and the ammonia gas detection limit is calculated to be 36 ppb. Full article

The normal operation of a power grid largely depends on the effective monitoring and maintenance of transmission lines, which is a process that has many challenges. The traditional method of the manual or remote inspection of transmission lines is time-consuming, laborious, and inefficient. To address this problem, a novel method has been proposed for the Multi-Robot Cyber Physical System (MRCPS) of a power grid based on inspection robots, a wireless sensor network (WSN), and multi-agent theory to achieve a low-cost, efficient, fault-tolerant, and remote monitoring of power grids. For the sake of an effective monitoring system for smart grids, the very research is conducted focusing on designing a methodology that will realize the efficient, fault-tolerant, and financial balance of a multi-robot team for monitoring transmission lines. Multiple testing scenarios are performed, in which various aspects are explored so as to determine the optimal parameters balancing team performance and financial cost. Furthermore, multi-robot team communication and navigation control in smart grid environments are introduced. Full article

High Definition (HD) maps are becoming key elements of the autonomous driving because they can provide information about the surrounding environment of the autonomous car without being affected by the real-time perception limit. To provide the most recent environmental information to the autonomous driving system, the HD map must maintain up-to-date data by updating changes in the real world. This paper presents a simultaneous localization and map change update (SLAMCU) algorithm to detect and update the HD map changes. A Dempster–Shafer evidence theory is applied to infer the HD map changes based on the evaluation of the HD map feature existence. A Rao–Blackwellized particle filter (RBPF) approach is used to concurrently estimate the vehicle position and update the new map state. The detected and updated map changes by the SLAMCU are reported to the HD map database in order to reflect the changes to the HD map and share the changing information with the other autonomous cars. The SLAMCU was evaluated through experiments using the HD map of traffic signs in the real traffic conditions. Full article

Electrochemical detection of amino acids is important due to their correlation with certain diseases; however, most amino acids require a catalyst to electrochemically activate. One common catalyst for electrochemical detection of amino acids are metal oxides. Metal oxide nanoparticles were electrodeposited onto glassy carbon and platinum working electrodes. Cyclic voltammetry (CV) experiments in a flow cell were performed to evaluate the sensors’ ability to detect arginine, alanine, serine, and valine at micromolar and nanomolar concentrations as high as 4 mM. Solutions were prepared in phosphate buffer saline (PBS) and then 100 mM NaOH. Specifically, NiO surfaces were responsive to amino acids but variable, especially when exposed to arginine. Polarization resistance experiments and scanning electron microscopy (SEM) and energy-dispersive X-ray spectroscopy (EDS) data indicated that arginine accelerated the corrosion of the NiO catalyst through the formation of a Schiff base complex. Full article

Over two-thirds of a century ago, Hodgkin and Huxley proposed the existence of voltage gated ion channels (VGICs) to carry Na⁺ and K⁺ ions across the cell membrane to create the nerve impulse, in response to depolarization of the membrane. The channels have multiple physiological roles, and play a central role in a wide variety of diseases when they malfunction. The first channel structure was found by MacKinnon and coworkers in 1998. Subsequently, the structure of a number of VGICs was determined in the open (ion conducting) state. This type of channel consists of four voltage sensing domains (VSDs), each formed from four transmembrane (TM) segments, plus a pore domain through which ions move. Understanding the gating mechanism (how the channel opens and closes) requires structures. One TM segment (S4) has an arginine in every third position, with one such segment per domain. It is usually assumed that these arginines are all ionized, and in the resting state are held toward the intracellular side of the membrane by voltage across the membrane. They are assumed to move outward (extracellular direction) when released by depolarization of this voltage, producing a capacitive gating current and opening the channel. We suggest alternate interpretations of the evidence that led to these models. Measured gating current is the total charge displacement of all atoms in the VSD; we propose that the prime, but not sole, contributor is proton motion, not displacement of the charges on the arginines of S4. It is known that the VSD can conduct protons. Quantum calculations on the K_v1.2 potassium channel VSD show how; the key is the amphoteric nature of the arginine side chain, which allows it to transfer a proton. This appears to be the first time the arginine side chain has had its amphoteric character considered. We have calculated one such proton transfer in detail: this proton starts from a tyrosine that can ionize, transferring to the NE of the third arginine on S4; that arginine’s NH then transfers a proton to a glutamate. The backbone remains static. A mutation predicted to affect the proton transfer has been qualitatively confirmed experimentally, from the change in the gating current-voltage curve. The total charge displacement in going from a normal closed potential of −70 mV across the membrane to 0 mV (open), is calculated to be approximately consistent with measured values, although the error limits on the calculation require caution in interpretation. Full article