We investigated the distribution of air temperature (Ta) and the factors affecting it in low-rise areas surrounding an isolated high-rise building during the Japanese winter. The study site was the central part of a regional city in Japan (36°5′ N, 140°12′ E), lying north-east of the Tokyo metropolitan area. The daytime surface temperature (Ts) in the shade is generally considered to be comparable to Ta; however, according to airborne remote sensing conducted in December 2009 where a multi-spectral scanner was installed on a fixed-wing aircraft, Ts for pavements in the shade of a high-rise building was significantly lower than Ta of sub-urban areas, indicating an influence of cold storage on Ts. Then, we conducted mobile observations using instruments (thermocouple, four component radiometer, and so on) installed on a bicycle in January 2016 to investigate the detailed distribution of Ta and the factors affecting it. The results showed the Ta over the pavements in the shade of the high-rise building was lower than the Ta of sunlit areas in the same urban area by −2 °C and lower than the Ta of sub-urban areas by −1–1.5 °C, although the advection effect was large due to strong winds around the building. In conclusion, a locally lower Ta compared to the surrounding areas can develop during the day in winter, even in spaces that are open to areas beyond the canopy.

At low frequencies, in thin plates the phase velocity of the guided A0 mode can become slower than that of the ultrasound velocity in air. Such waves do not excite leaky waves in the surrounding air, and therefore, it is impossible to excite and receive them by conventional air-coupled methods. The objective of this research was the development of an air-coupled technique for the reception of slow A0 mode in thin plastic films. This study demonstrates the feasibility of picking up a subsonic A0 mode in plastic films by air-coupled ultrasonic arrays. The air-coupled reception was based on an evanescent wave in air accompanying the propagating A0 mode in a film. The efficiency of the reception was enhanced by using a virtual array which was arranged from the data collected by a single air-coupled receiver. The signals measured at the points corresponding to the positions of the phase-matched array were recorded and processed. The transmitting array excited not only the A0 mode in the film, but also a direct wave in air. This wave propagated at ultrasound velocity in air and was faster than the evanescent wave. For efficient reception of the A0 mode, the additional signal-processing procedure based on the application of the 2D Fourier transform in a spatial–temporal domain. The obtained results can be useful for the development of novel air-coupled ultrasonic non-destructive testing techniques.

Fleet mission planning for Unmanned Aerial Vehicles (UAVs) is the process of creating flight plans for a specific set of objectives and typically over a time period. Due to the increasing focus on the usage of large UAVs, a key challenge is to conduct mission planning addressing changing weather conditions, collision avoidance, and energy constraints specific to these types of UAVs. This paper presents a declarative approach for solving the complex mission planning resistant to weather uncertainty. The approach has been tested on several examples, analyzing how customer satisfaction is influenced by different values of the mission parameters, such as the fleet size, travel distance, wind direction, and wind speed. Computational experiments show the results that allow assessing alternative strategies of UAV mission planning.

Vehicular sensor networks (VSNs) have emerged as a paradigm for improving traffic safety in urban cities. However, there are still several issues with VSNs. Vehicles equipped with sensing devices usually upload large amounts of data reports to a remote cloud center for processing and analyzing, causing heavy computation and communication costs. Additionally, to choose an optimal route, it is required for vehicles to query the remote cloud center to obtain road conditions of the potential moving route, leading to an increased communication delay and leakage of location privacy. To solve these problems, this paper proposes an efficient privacy-preserving data sharing (EP 2 DS) scheme for fog-assisted vehicular sensor networks. Specifically, the proposed scheme utilizes fog computing to provide local data sharing with low latency; furthermore, it exploits a super-increasing sequence to format the sensing data of different road segments into one report, thus saving on the resources of communication and computation. In addition, using the modified oblivious transfer technology, the proposed scheme can query the road conditions of the potential moving route without disclosing the query location. Finally, an analysis of security suggests that the proposed scheme can satisfy all the requirements for security and privacy, with the evaluation results indicating that the proposed scheme leads to low costs in computation and communication.

Doppler Radar Tomography (DRT) relies on spatial diversity from rotational motion of a target rather than spectral diversity from wide bandwidth signals. The slow-time k-space is a novel form of the spatial frequency space generated by the relative rotational motion of a target at a single radar frequency, which can be exploited for high-resolution target imaging by a narrowband radar with Doppler tomographic signal processing. This paper builds on a previously published work and demonstrates, with real experimental data, a unique and interesting characteristic of the slow-time k-space: it can be augmented and significantly enhance imaging resolution by signal processing. High resolution can reveal finer details in the image, providing more information to identify unknown targets detected by the radar.

The optimization-based alignment (OBA) methods, which are implemented by the optimal attitude estimation using vector observations—also called double-vectors—have proven to be effective at solving the in-flight alignment (IFA) problem. However, the traditional OBA methods are not applicable for the low-cost strap-down inertial navigation system (SINS) since the error of double-vectors will be accumulated over time due to the substantial drift of micro-electronic- mechanical system (MEMS) gyroscope. Moreover, the existing optimal estimation method is subject to a large computation burden, which results in a low alignment speed. To address these issues, in this article we propose a new fast IFA method based on modified double-vectors construction and the gradient descent method. To be specific, the modified construction method is implemented by reducing the integration interval and identifying the gyroscope bias during the construction procedure, which improves the accuracy of double-vectors and IFA; the gradient descent scheme is adopted to estimate the optimal attitude of alignment without complex matrix operation, which results in the improvement of alignment speed. The effect of different sizes of mini-batch on the performance of the gradient descent method is also discussed. Extensive simulations and vehicle experiments demonstrate that the proposed method has better accuracy and faster alignment speed than the related traditional methods for the low-cost SINS/global positioning system (GPS) integrated navigation system

The development of sensitive methods for in situ detection of biomarkers is a real challenge to bring medical diagnosis a step forward. The proof-of-concept of a remote multiplexed biomolecular interaction detection through a plasmonic optical fiber bundle is demonstrated here. The strategy relies on a fiber optic biosensor designed from a 300 µm diameter bundle composed of 6000 individual optical fibers. When appropriately etched and metallized, each optical fiber exhibits specific plasmonic properties. The surface plasmon resonance phenomenon occurring at the surface of each fiber enables to measure biomolecular interactions, through the changes of the retro-reflected light intensity due to light/plasmon coupling variations. The functionalization of the microstructured bundle by multiple protein probes was performed using new polymeric 3D-printed microcantilevers. Such soft cantilevers allow for immobilizing the probes in micro spots, without damaging the optical microstructures nor the gold layer. We show here the potential of this device to perform the multiplexed detection of two different antibodies with limits of detection down to a few tenths of nanomoles per liter. This tool, adapted for multiparametric, real-time, and label free monitoring is minimally invasive and could then provide a useful platform for in vivo targeted molecular analysis.

For a structural health monitoring (SHM) system, the operational functionality of sensors is critical for successful implementation of a damage identification process. This study presents experimental and analytical investigations on sensor fault diagnosis for impedance-based SHM using the piezoelectric interface technique. Firstly, the piezoelectric interface-based impedance monitoring is experimentally conducted on a steel bolted connection to investigate the effect of structural damage and sensor defect on electromechanical (EM) impedance responses. Based on the experimental analysis, sensor diagnostic approaches using EM impedance features are designed to distinguish the sensor defect from the structural damage. Next, a novel impedance model of the piezoelectric interface-driven system is proposed for the analytical investigation of sensor fault diagnosis. Various parameters are introduced into the EM impedance formulation to model the effect of shear-lag phenomenon, sensor breakage, sensor debonding, and structural damage. Finally, the proposed impedance model is used to analytically estimate the change in EM impedance responses induced by the structural damage and the sensor defect. The analytical results are found to be consistent with experimental observations, thus evidencing the feasibility of the novel impedance model for sensor diagnosis and structural integrity assessment. The study is expected to provide theoretical and experimental foundations for impedance monitoring practices, using the piezoelectric interface technique, with the existence of sensor faults.

Infrastructure supporting vehicular network (V2X) capability is the key factor to the success of smart city because it enables many smart transportation services. In order to reduce the traffic congestion and improve the public transport efficiency, many intelligent transportation systems (ITS) need to be developed. In this paper, a smart traffic signal control (STSC) system is designed and implemented, it supports several smart city transportation applications including emergency vehicle signal preemption (EVSP), public transport signal priority (TSP), adaptive traffic signal control (ATSC), eco-driving supporting, and message broadcasting. The roadside unit (RSU) controller is the core of the proposed STSC system, where the system architecture, middleware, control algorithms, and peripheral modules are detailed discussed in this paper. It is compatible with existed traffic signal controller so that it can be fast and cost−effectively deployed. A new traffic signal scheme is specially designed for the EVSP scenario, it can inform all the drivers near the intersection regarding which direction the emergency vehicle (EV) is approaching, smoothing the traffic flow, and enhancing the safety. EVSP scenario and the related control algorithms are implemented in this work; integration test and field test are performed to demonstrate the STSC system.

Optical sensors can be used to assess crop N status to assist with N fertilizer management. Differences between cultivars may affect optical sensor measurement. Cultivar effects on measurements made with the SPAD-502 (Soil Plant Analysis Development) meter and the MC-100 (Chlorophyll Concentration Meter), and of several vegetation indices measured with the Crop Circle ACS470 canopy reflectance sensor, were assessed. A cucumber (Cucumis sativus L.) crop was grown in a greenhouse, with three cultivars. Each cultivar received three N treatments, of increasing N concentration, being deficient (N1), sufficient (N2) and excessive (N3). There were significant differences between cultivars in the measurements made with both chlorophyll meters, particularly when N supply was sufficient and excessive (N2 and N3 treatments, respectively). There were no consistent differences between cultivars in vegetation indices. Optical sensor measurements were strongly linearly related to leaf N content in each of the three cultivars. The lack of a consistent effect of cultivar on the relationship with leaf N content suggests that a unique equation to estimate leaf N content from vegetation indices can be applied to all three cultivars. Results of chlorophyll meter measurements suggest that care should be taken when using sufficiency values, determined for a particular cultivar

In this paper, a kind of green triboelectric nano-generator based on natural degradable cellulose is proposed. Different kinds of regenerated cellulose composite layers are prepared by a blending doping method, and then assembled with poly(tetrafluoroethylene) (PTFE) thin films to form tribioelectric nanogenerator (TENG). The results show that the open circuit output voltage and the short circuit output current using a pure cellulose membrane is 7.925 V and 1.095 μA. After adding a certain amount of polyamide (PA6)/polyvinylidene fluoride (PVDF)/barium titanate (BaTiO3), the open circuit output voltage peak and the peak short circuit output current increases by 254.43% (to 20.155 V) and 548.04% (to 6.001 μA). The surface morphology, elemental composition and functional group of different cellulose layers are characterized by Scanning Electronic Microscopy (SEM), Fourier transform infrared spectroscopy (FT-IR), X-ray diffraction (XRD), and tested by the electrochemical analyze. Moreover, after multiple assembly and rectification processing, the electrical output performance shows that the peak value of open-circuit output voltage and the peak value of short circuit output current increases by 132.06% and 116.13%. Within 500 s of the charge-discharge test, the single peak charge reached 3.114 V, and the two peak charges reached 3.840 V. The results demonstrate that the nano-generator based on cellulose showed good stability and reliability, and the application and development of natural biomaterials represented by cellulose are greatly promoted in miniature electronic sensing area.

Out of control proliferation of toxic phytoplankton, called harmful algal blooms (HABs), have a significant economic impact on bivalve aquaculture and harvesting in coastal waters. Some phytotoxins, such as paralytic shellfish toxins (PSTs), are of concern due to the life-threatening symptoms they can cause. Development of rapid and low-cost screening tools would be a welcome addition to the laboratory methodologies employed in routine monitoring programs. However, most of the assays and biosensors for the screening of PSTs, are restricted to a single target, saxitoxin (STX), which is the most potent PST. The present study aimed at developing an assay for the detection of N-sulfocarbamoyl PST—GTX5, which is one of the most abundant toxins in bivalves during G. catenatum blooms as found on the Portuguese coast. Enzymatic assay employing PSTs’ transforming enzyme—carbamoylase—was proposed. Carbamoylase was extracted and purified from the surf clam S. solida. Carbamoylase displayed similar specificity to both carbamate (STX) and N-sulfocarbamate toxins (GTX5 and C1+2) converting them into decarbamoyl saxitoxin (dcSTX) and decarbamoyl gonyautoxins 2+3 (dcGTX2+3), respectively. The enzymatic assay involved hydrolysis of GTX5 by carbamoylase and quantification of the product of enzymatic reaction, dcSTX, using a potentiometric chemical sensor. A potentiometric sensor with plasticized PVC membrane that displayed sensitivity to dcSTX and selectivity in the presence of GTX5 was employed. Enzymatic assay allowed determination of GTX5 in the concentration range from 0.43 to 3.30 µmolL−1, which encompasses levels of GTX5 in contaminated bivalve extracts with toxicities above PSTs regulatory limits. The feasibility of the carbamoylase-based potentiometric assay for detection of GTX5 was demonstrated.

Concern about falling is prevalent and increases the risk of falling in people with peripheral neuropathy (PN). However, the assessment of concern about falling relies on self-report surveys, and thus continuous monitoring has not been possible. We investigated the influence of concern about falling on sensor-based daily physical activity among people with PN. Forty-nine people with PN and various levels of concern about falling participated in this study. Physical activity outcomes were measured over a period of 48 hours using a validated chest-worn sensor. The level of concern about falling was assessed using the falls efficacy scale-international (FES-I). The low concern group spent approximately 80 min more in walking and approximately 100 min less in sitting/lying compared to the high concern group. In addition, the low concern group had approximately 50% more walking bouts and step counts compared to the high concern group. Across all participants, the duration of walking bouts and total step counts was significantly correlated with FES-I scores. The duration of walking bouts and total step counts may serve as eHealth targets and strategies for fall risk assessment among people with PN.

To achieve an automatic unloading of a reactor during the sherardizing process, it is necessary to calculate the pose and position of the reactors in an industrial environment with various amounts of luminance and floating dust. In this study, the defects of classic image processing methods and deep learning methods used for locating the reactors are first analyzed. Next, an improved You Only Look Once(YOLO) model is employed to find the region of interest of the handling hole and a handling hole corner detection method based on the image morphology and a Hough transform is presented. Finally, the position and pose of the reactors will be obtained by establishing a 3D handling hole model according to the principle of a binocular stereo system. To test the performance of the proposed method, a set of experimental systems was set up and experiments were conducted. The results indicate that the proposed location method is effective and the precision of the position recognition can be controlled to within 4.64 mm and 1.68 ° when the cameras are approximately 5 m away from the reactor, meeting the requirements.

Advances in research in Cultural Heritage see increasing application of a multidisciplinary approach and the combined use of physical and chemical characterization of artefacts that can be used to define their structure and their state of conservation, also providing valuable information in selecting the most suitable microclimatic conditions for the exhibition environment. This approach provides a platform for a synergic collaboration amongst researchers, restorers, conservators, and archaeologists. Existing state-of-the-art technologies for neutron-based methods are currently being applied to the study of objects of historical and cultural interest in several neutron-beam facilities around the world. Such techniques are non-invasive and non-destructive and are, therefore, ideal to provide structural information about artefacts, such as their composition, presence of alterations due to the environmental conditions, inclusions, structure of the bulk, manufacturing techniques, and elemental composition, which provide an overall fingerprint of the object’s characteristics, thanks to the nature of the interaction of neutrons with matter. Here, we present an overview of the main neutron methods for the characterization of materials of interest in Cultural Heritage and we provide a brief introduction to the sensors and detectors that are used in this framework. We conclude with some case studies underlining the impact of these applications in different archaeological and historical contexts.

The Industrial Internet of Things (IIoT) consists of sensors, networks, and services to connect and control production systems. Its benefits include supply chain monitoring and machine failure detection. However, it has many vulnerabilities, such as industrial espionage and sabotage. Furthermore, many IIoT devices are resource-constrained, which impedes the use of traditional security services for them. Authentication allows devices to be confident of each other’s identity, preventing some security attacks. Many authentication protocols have been proposed for IIoT; however, they have high computing requirements not viable to resource-constrained devices, or they have been found insecure. In this paper, an authentication protocol for resource-constrained IIoT devices is proposed. It is based on the lightweight operations xor, addition, and subtraction, and a hash function. Also, only four messages are exchanged between the principals to authenticate. It has a low execution-time and communication-cost. Its security was successfully assessed with the formal methods Automated Validation of Internet Security Protocols and Applications (AVISPA) tool and Burrows–Abadi–Needham (BAN) logic, together with an informal analysis of its resistance to known attacks. Its performance and security were compared with state-of-the-art protocols, resulting in a good performance for resource-constrained IIoT devices, and higher security similar to computational expensive schemes.

Many industries, such as manufacturing, aviation, and power generation, employ sensitive measurement devices to be calibrated by certified experts. The diversity and sophistication of measurement devices and their calibration needs require networked and automated solutions. Internet of Measurement Things (IoMT) is an architectural framework that is based on the Industrial Internet of Things for the calibration industry. This architecture involves a layered model with a cloud-centric middle layer. In this article, the realization of this conceptual architecture is described. The applicability of the IoMT architecture in the calibration industry is shown through an editor application for Scope of Accreditation. The cloud side of the implementation is deployed to Microsoft Azure. The editor itself is created as a cloud service, and IoT Hub is used to collect data from calibration laboratories. By adapting the IoMT architecture to a commonly used cloud platform, considerable progress is achieved to encompass Metrology data and serve the majority of the stakeholders.

One of the modern trends in the design of human–machine interfaces (HMI) is to involve the so called spiking neuron networks (SNNs) in signal processing. The SNNs can be trained by simple and efficient biologically inspired algorithms. In particular, we have shown that sensory neurons in the input layer of SNNs can simultaneously encode the input signal based both on the spiking frequency rate and on varying the latency in generating spikes. In the case of such mixed temporal-rate coding, the SNN should implement learning working properly for both types of coding. Based on this, we investigate how a single neuron can be trained with pure rate and temporal patterns, and then build a universal SNN that is trained using mixed coding. In particular, we study Hebbian and competitive learning in SNN in the context of temporal and rate coding problems. We show that the use of Hebbian learning through pair-based and triplet-based spike timing-dependent plasticity (STDP) rule is accomplishable for temporal coding, but not for rate coding. Synaptic competition inducing depression of poorly used synapses is required to ensure a neural selectivity in the rate coding. This kind of competition can be implemented by the so-called forgetting function that is dependent on neuron activity. We show that coherent use of the triplet-based STDP and synaptic competition with the forgetting function is sufficient for the rate coding. Next, we propose a SNN capable of classifying electromyographical (EMG) patterns using an unsupervised learning procedure. The neuron competition achieved via lateral inhibition ensures the “winner takes all” principle among classifier neurons. The SNN also provides gradual output response dependent on muscular contraction strength. Furthermore, we modify the SNN to implement a supervised learning method based on stimulation of the target classifier neuron synchronously with the network input. In a problem of discrimination of three EMG patterns, the SNN with supervised learning shows median accuracy 99.5% that is close to the result demonstrated by multi-layer perceptron learned by back propagation of an error algorithm.

In this paper, the authors used an acoustic wave acting as a disturbance (acoustic vibration), which travelled in all directions on the whole surface of a dried strawberry fruit in its specified area. The area of space in which the acoustic wave occurs is defined as the acoustic field. When the vibrating surface—for example, the surface of the belt—becomes the source, then one can observe the travelling of surface waves. For any shape of the surface of the dried strawberry fruit, the signal of travelling waves takes the form that is imposed by this irregular surface. The aim of this work was to research the effectiveness of recognizing the two trials in the process of convection drying on the basis of the acoustic signal backed up by neural networks. The input variables determined descriptors such as frequency (Hz) and the level of luminosity (dB). During the research, the degree of crispiness relative to the degree of maturity was compared. The results showed that the optimal neural model in respect of the lowest value of the root mean square turned out to be the Multi-Layer Perceptron network with the technique of dropping single fruits into water (data included in the learning data set Z2). The results confirm that the choice of method can have an influence on the effectives of recognizing dried strawberry fruits, and also this can be a basis for creating an effective and fast analysis tool which is capable of analyzing the degree of ripeness of fruits including their crispness in the industrial process of drying fruits.

In this paper, we propose an intelligent segment-fitting algorithm to increase the measurement accuracy of quadrant detector (QD) in straightness measuring system. Add-subtract algorithm and polynomial fitting are usually adopted to acquire the estimate of the spot projected on QD. The accuracy of QD is limited by the nonlinear relationship between the spot position and its estimate. In order to obtain higher accuracy, the polynomial fitting order must be higher with more calculation amounts, so the new algorithm is designed to optimize the nonlinearity. Through the specific method mentioned in the new algorithm, the whole measurement range of QD can be divided into several segments intelligently. And the data in each segment and between adjacent segments can be simply expressed by the third order polynomial fitting and linear interpolation. The experiment results verified that the algorithm could increase the measurement accuracy with good real-time performance. Within the measurement range of ±250 μm, the residual RMSE of the QD using the proposed algorithm, compared with that using the third order polynomial fitting, was reduced more than 80%, and the calculation amount was reduced to about 1/4 of that using the seventh order polynomial fitting.

It is crucial for sparse aperture systems to preserve imaging quality, which can be addressed when fast corrections of pistons within a fraction of a wavelength are available. In this paper, we demonstrate that only a single deep convolutional neural network is sufficient to extract pistons from wide-band extended images once being appropriately trained. To eliminate the object characters, the feature vector is calculated as the input by a pair of focused and defocused images. This method possesses the capability of fine phasing with high sensing accuracy, and a large-scale capture range without the use of combined wavelengths. Simple and fast, the proposed technique might find wide applications in phasing telescope arrays or segmented mirrors.

Haze limits visibility and degrades image contrast, and images lose color fidelity owing to the scattering and absorption of light caused by particles. Consequently, removing haze is critical for enhancing the image visibility and quality, with estimation of airlight and transmission map being critical steps. We propose a simple and convenient improved scheme, which is polarization-based method for dehazing. The method uses four polarization orientations (0º, 45º, 90º, 135º) to measure the Stokes vector, and obtained the degree of polarization (DOP) of the airlight by angle of polarization (AOP) in three channels. Subsequently, estimating the airlight at infinity by average total intensity and re-constructing a matrix to estimate the transmission maps. Experimental results demonstrate that a high-quality transmission map can be obtained, and this improved method can restore outdoor scene haze images with high quality and contrast. In addition, this method also proved to be useful for enhancing color saturation and restoring color fidelity.

This research explores the optical properties of a large group of silicon/silica nanofibrous thin films. A picosecond pulse laser was employed for indirect deposition of ablated silicon on glass substrates. Prominent parameters such as laser power, repetition rate, pulse duration and scanning speed were changed to vary the structural and compositional properties of synthesized nanofibrous thin films. Transmission and specular reflection measurements along with material characterization techniques, Raman and FTIR, were employed for better interpretation of the results. By and large, an increase in the values for repetition rate and scanning speed produced a corresponding increase in optical data intensity, while an increase in power and pulse duration produced a drop in the same data. The results show that degree of oxidation and inherent porous structure are driving the light interaction in thin samples, as indicated by the changes in intensity or spectrum shape. Observation of these trends enabled us to apply tunable fabrication procedures to obtain desired groups of nanofibrous thin films. Electron and optical microscopy as well as background knowledge certify tangled nano-wired morphology in most cases. Structures with highly desirable usages, such as in sensing technology, can be optimized by their porosity, density and thickness.

Phosphor layers are of vital importance for the development of advanced phosphor-converted light-emitting diodes (LEDs). However, owing to the fixed ratio of red-green-blue (RGB) phosphors, it has been difficult for the RGB phosphor layers with conventional structure to tune light emission colours. Herein, we have experimentally fabricated nine types of phosphor layers with patterned RGB pixel array, which consist of tuneable RGB ratio in a planar configuration. Moreover, we carried out optical simulation based on Monte-Carlo theory to assist in adjusting the light-emission colours and the corresponding chromaticity coordinates. The simulations were further verified by the experimental results via samples fabricated by the stencil printing technique. In accordance with the nine types of phosphor layers with patterned pixel arrays in various RGB ratios, we have finally obtained corresponding nine light-emission colours for the applications of LED light emission decoration. These designed advanced pixel-array phosphor layers demonstrate great potential for applications in decorated light emission and display devices with significant implications for industrial improvement in these research areas.