Single-lens-based optical range finding systems were developed as an efficient, compact alternative for conventional stereo camera systems. Among various single-lens-based approaches, a multiple color-filtered aperture (MCA) system can generate disparity information among color channels, as well as normal color information. In this paper, we consider a dual color-filtered aperture (DCA) system as the most minimal version of the MCA system and present a novel inter-color image registration algorithm for disparity estimation. This proposed registration algorithm consists of three steps: (i) color channel independent feature extraction; (ii) feature-based adaptive weight disparity estimation; and (iii) color mapping matrix (CMM)-based cross-channel image registration. Experimental results show that the proposed method can not only generate an accurate disparity map, but also realize high quality cross-channel registration with a disparity prior for DCA-based range finding and color image enhancement. Full article

Interest in extracellular vesicles and in particular microvesicles and exosomes, which are constitutively produced by cells, is on the rise for their huge potential as biomarkers in a high number of disorders and pathologies as they are considered as carriers of information among cells, as well as being responsible for the spreading of diseases. Current methods of analysis of microvesicles and exosomes do not fulfill the requirements for their in-depth investigation and the complete exploitation of their diagnostic and prognostic value. Lab-on-chip methods have the potential and capabilities to bridge this gap and the technology is mature enough to provide all the necessary steps for a completely automated analysis of extracellular vesicles in body fluids. In this paper we provide an overview of the biological role of extracellular vesicles, standard biochemical methods of analysis and their limits, and a survey of lab-on-chip methods that are able to meet the needs of a deeper exploitation of these biological entities to drive their use in common clinical practice. Full article

This paper proposes a microfluidic impedance tuner that is applied to a planar inverted-F antenna (PIFA). The proposed microfluidic impedance tuner is designed while using a simple double-stub and the impedance is changed by tuning the stub length. In this work, the stub length can be tuned by injecting a liquid metal alloy to the microfluidic channels. Initially, the PIFA operates at 900 MHz with impedance matching of 50 Ω. The impedance is mismatched when a hand is placed close to the antenna. The mismatched impedance is matched to 50 Ω by injecting the liquid metal alloy. The antenna is fabricated on the FR-4 substrate, and the impedance tuner is fabricated on polydimethylsiloxane (PDMS). In order to inject the liquid metal alloy, a piezoelectric micropump and microprocessor are used in the measurement. At 900 MHz, the return loss is successfully tuned from 4.69 dB to 18.4 dB when a hand is placed 1 mm above the antenna. Full article

In this paper, a self-adaption matched filter (SMF) and bi-directional difference techniques are proposed to detect a small moving target in urban environments. Firstly, the SMF technique is proposed to improve the signal-to-interference-noise ratio (SINR) by using the power factor. The properties of the transmitting signal, the target echoes and the interference and noise are considered during the power factor generation. The amplitude coherent accumulation technique that extracts the coherent amplitude information of echoes after being processed by the SMF, is used to improve the SINR based on multiple measurements. Finally, the bi-directional difference technique is proposed to distinguish the target echoes and the interference/noise. Simulations and experiments are conducted to validate and demonstrate that small moving targets can be detected with high probability using the proposed method in urban environments, even with just one measurement. Full article

We present an Arduino-based automation system to control the streetlights based on solar rays and object’s detection. We aim to design various systems to achieve the desired operations, which no longer require time-consuming manual switching of the streetlights. The proposed work is accomplished by using an Arduino microcontroller, a light dependent resistor (LDR) and infrared-sensors while, two main contributions are presented in this work. Firstly, we show that the streetlights can be controlled based on the night and object’s detection. In which the streetlights automatically turn to DIM state at night-time and turn to HIGH state on object’s detection, while during day-time the streetlights will remain OFF. Secondly, the proposed automated system is further extended to skip the DIM condition at night time, and streetlights turn ON based on the objects’ detection only. In addition, an automatic door system is introduced to improve the safety measurements, and most importantly, a counter is set that will count the number of objects passed through the road. The proposed systems are designed at lab-scale prototype to experimentally validate the efficiency, reliability, and low-cost of the systems. We remark that the proposed systems can be easily tested and implemented under real conditions at large-scale in the near future, that will be useful in the future applications for automation systems and smart homes. Full article

An Extended Line Map (ELM)-based precise vehicle localization method is proposed in this paper, and is implemented using 3D Light Detection and Ranging (LIDAR). A binary occupancy grid map in which grids for road marking or vertical structures have a value of 1 and the rest have a value of 0 was created using the reflectivity and distance data of the 3D LIDAR. From the map, lines were detected using a Hough transform. After the detected lines were converted into the node and link forms, they were stored as a map. This map is called an extended line map, of which data size is extremely small (134 KB/km). The ELM-based localization is performed through correlation matching. The ELM is converted back into an occupancy grid map and matched to the map generated using the current 3D LIDAR. In this instance, a Fast Fourier Transform (FFT) was applied as the correlation matching method, and the matching time was approximately 78 ms (based on MATLAB). The experiment was carried out in the Gangnam area of Seoul, South Korea. The traveling distance was approximately 4.2 km, and the maximum traveling speed was approximately 80 km/h. As a result of localization, the root mean square (RMS) position errors for the lateral and longitudinal directions were 0.136 m and 0.223 m, respectively. Full article

Visual homing is an attractive autonomous mobile robot navigation technique, which only uses vision sensors to guide the robot to the specified target location. Landmark is the only input form of the visual homing approaches, which is usually represented by scale-invariant features. However, the landmark distribution has a great impact on the homing performance of the robot, as irregularly distributed landmarks will significantly reduce the navigation precision. In this paper, we propose three strategies to solve this problem. We use scale-invariant feature transform (SIFT) features as natural landmarks, and the proposed strategies can optimize the landmark distribution without over-eliminating landmarks or increasing calculation amount. Experiments on both panoramic image databases and a real mobile robot have verified the effectiveness and feasibility of the proposed strategies. Full article

In this paper, multiple Fano resonances preferred in the refractive index sensing area are achieved based on sub-wavelength metal-insulator-metal (MIM) waveguides. Two slot cavities, which are placed between or above the MIM waveguides, can support the bright modes or the dark modes, respectively. Owing to the mode interferences, dual Fano resonances with obvious asymmetrical spectral responses are achieved. High sensitivity and high figure of merit are investigated by using the finite-difference time-domain (FDTD) method. In view of the development of chip-scale integrated photonics, two extra slot cavities are successively added to the structure, and consequently, three and four ultra-sharp Fano peaks with considerable performances are obtained, respectively. It is believed that this proposed structure can find important applications in the on-chip optical sensing and optical communication areas. Full article

In this paper, we present the fabrication of an efficient thin film temperature sensor utilizing chemical vapor deposited carbon nanotube (CNT) film as the sensing element on Si substrates, with diamond-like carbon (DLC):Ni as a catalyst in assisting CNT growth. The fabricated sensor showed good electrical response with change in temperature. Relative linear change in resistance of 18.4% for an increase in temperature from 22 °C to 200 °C was achieved. Various characterizing techniques, such as scanning electron microscopy (SEM) and Raman spectroscopy, were used to characterize the films. In an effort to study device performance, van der Pauw and Hall measurements were carried out to study the dependence of resistance on temperature and magnetic fields. Temperature coefficient of resistance of the sensor was calculated as 1.03 × 10⁻³/°C. All implications arising from the study are presented. The results establish the aptness of the as-grown CNT film to be used as an active sensing material in thin film temperature sensors. Full article

In this paper, a novel robust particle filter is proposed to address the measurement outliers occurring in the multiple autonomous underwater vehicles (AUVs) based cooperative navigation (CN). As compared with the classic unscented particle filter (UPF) based on Gaussian assumption of measurement noise, the proposed robust particle filter based on the maximum correntropy criterion (MCC) exhibits better robustness against heavy-tailed measurement noises that are often induced by measurement outliers in CN systems. Furthermore, the proposed robust particle filter is computationally much more efficient than existing robust UPF due to the use of a Kullback-Leibler distance-resampling to adjust the number of particles online. Experimental results based on actual lake trial show that the proposed maximum correntropy based unscented particle filter (MCUPF) has better estimation accuracy than existing state-of-the-art robust filters for CN systems with heavy-tailed measurement noises, and the proposed MCUPF has lower computational complexity than existing robust particle filters. Full article

Every application may have its specifically designed sensor. For studying the effects of short-term temperature variations on life in water, a high-resolution sensor has been designed with low noise level <0.1 mK. Pro and cons of the design include its adequacy for use in large heat-capacity environments like water but less in air. The sensor can be used under high static environmental pressure of >1000 Bar (>10⁸ N m⁻²) in the deepest ocean regions. Its response time of 0.5 s in water allows quantitative studies of internal wave turbulent mixing effects, e.g., on the redistribution of matter and on nearly completely submerged human bodies. In a chain of >100 sensors, clocks are synchronized to sample within 0.02 s and a verified range of 600 m. Full article

In this work, we have obtained a new constitutive matrix to calculate the induced Lorentz electric current of in a conductive disk in movement within a magnetic field using the cell method in 3D. This disk and a permanent magnet act as a magnetic brake. The results obtained are compared with those obtained with the finite element method (FEM) using the computer applications Getdp and femm. The error observed is less than 0.1173%. Likewise, a second verification has been made in the laboratory using Hall sensors to measure the magnetic field in the proximity of the magnetic brake. Full article

Accurate step counting is essential for indoor positioning, health monitoring systems, and other indoor positioning services. There are several publications and commercial applications in step counting. Nevertheless, over-counting, under-counting, and false walking problems are still encountered in these methods. In this paper, we propose to develop a highly accurate step counting method to solve these limitations by proposing four features: Minimal peak distance, minimal peak prominence, dynamic thresholding, and vibration elimination, and these features are adaptive with the user’s states. Our proposed features are combined with periodicity and similarity features to solve false walking problem. The proposed method shows a significant improvement of 99.42% and 96.47% of the average of accuracy in free walking and false walking problems, respectively, on our datasets. Furthermore, our proposed method also achieves the average accuracy of 97.04% on public datasets and better accuracy in comparison with three commercial step counting applications: Pedometer and Weight Loss Coach installed on Lenovo P780, Health apps in iPhone 5s (iOS 10.3.3), and S-health in Samsung Galaxy S5 (Android 6.01). Full article

The uncertainty evaluation of survey measurements is a daily and essential task in any surveying work. The result of a measurement is, in fact, only complete when accompanied by a statement of its uncertainty. Miscentring, or centring error, is one of the sources of uncertainty in every basic survey measurement which may have a great effect on horizontal angle measurement for short distances. In the literature, different terms and values are considered to refer to this source of uncertainty. Standard ISO 17123 provides different procedures for assessing the measurement uncertainty of geodetic and surveying instruments, with the aim of checking their suitability for the intending and immediate task in field conditions. ISO 17123 is aware of the importance of uncertainty in the instrument centring, but it does not propose any standardised procedure for its assessment. In this study, we propose such a procedure following a Type A evaluation (through the statistical analysis of series of observations), avoiding using values from Type B evaluations (from manufacturer’s specifications, handbooks, personal experiences, etc.) that could be unsuitable for the conditions of the task. Uncertainty can be individualised for a particular instrument (which includes the plummet type), ground mark, operator, and other factors on which the results could be dependent. The testing methodology includes a configuration of the test field, measurements, and calculation, following the structure of each part of the standard ISO 17123. An experimental application is included with two different total stations, which also includes a statistical analysis of the results. Full article

A new infrared thermometer, sensitive to wavelengths between 3 μm and 3.5 μm, has been developed. It is based on an Indium Arsenide Antimony (InAsSb) photodiode, a transimpedance amplifier, and a sapphire fiber optic cable. The thermometer used an uncooled photodiode sensor and received infrared radiation that did not undergo any form of optical chopping, thereby, minimizing the physical size of the device and affording its attachment to a milling machine tool holder. The thermometer is intended for applications requiring that the electronics are located remotely from high-temperature conditions incurred during machining but also affording the potential for use in other harsh conditions. Other example applications include: processes involving chemical reactions and abrasion or fluids that would otherwise present problems for invasive contact sensors to achieve reliable and accurate measurements. The prototype thermometer was capable of measuring temperatures between 200 °C and 1000 °C with sapphire fiber optic cable coupling to high temperature conditions. Future versions of the device will afford temperature measurements on a milling machine cutting tool and could substitute for the standard method of embedding thermocouple wires into the cutting tool inserts. Similarly, other objects within harsh conditions could be measured using these techniques and accelerate developments of the thermometer to suit particular applications. Full article

Hybrid optical gas sensors, based on different organic and inorganic materials, are proposed in this paper, with the aim of using them as optical artificial nose systems. Three types of organic and inorganic dyes, namely zinc-porphyrin, manganese-porphyrin, and zinc-phthalocyanine, were used as gas sensing materials to fabricate a thin-film coating on glass substrates. The performance of the gas sensor was enhanced by a thermal treatment process. The optical absorption spectra and morphological structure of the sensing films were confirmed by UV-Vis spectrophotometer and atomic force microscope, respectively. The optical gas sensors were tested with various volatile compounds, such as acetic acid, acetone, ammonia, ethanol, ethyl acetate, and formaldehyde, which are commonly found to be released during the growth of bacteria. These sensors were used to detect and discriminate between the bacterial odors of three pathogenic species (Staphylococcus aureus, Escherichia coli and Pseudomonas aeruginosa) grown in Luria-Bertani medium. Based on a pattern recognition (PARC) technique, we showed that the proposed hybrid optical gas sensors can discriminate among the three pathogenic bacterial odors and that the volatile organic compound (VOC) odor pattern of each bacterium was dependent on the phase of bacterial growth. Full article

In the aerospace field, compact optoelectronic platforms (COPs) are being increasingly equipped on unmanned aircraft systems (UAS). They assist UAS in a range of mission-specific tasks such as disaster relief, crop testing, and firefighting. However, the strict constraint of structure space makes COPs subject to multi-source disturbances. The application of a low-cost and low-precision sensor also affects the system control performance. A composite hierarchical anti-disturbance control (CHADC) scheme with multisensor fusion is explored herein to improve the motion performance of COPs in the presence of internal and external disturbances. Composite disturbance modelling combining the characteristic of wire-wound moment is presented in the inner layer. The adaptive mutation differential evolution algorithm is implemented to identify and optimise the model parameters of the system internal disturbance. Inverse model compensation and finite-time nonlinear disturbance observer are then constructed to compensate for multiple disturbances. A non-singular terminal sliding mode controller is constructed to attenuate disturbance in the outer layer. A stability analysis for both the composite disturbance compensator and the closed-loop system is provided using Lyapunov stability arguments. The phase lag-free low-pass filter is implemented to interfuse multiple sensors with different order information and achieve satisfactory noise suppression without phase lag. Experimental results demonstrate that the proposed CHADC strategy with a higher-quality signal has an improved performance for multi-source disturbance compensation. Full article

With wireless sensor networks (WSNs), a driver can access various useful information for convenient driving, such as traffic congestion, emergence, vehicle accidents, and speed. However, a driver and traffic manager can be vulnerable to various attacks because such information is transmitted through a public channel. Therefore, secure mutual authentication has become an important security issue, and many authentication schemes have been proposed. In 2017, Mohit et al. proposed an authentication protocol for WSNs in vehicular communications to ensure secure mutual authentication. However, their scheme cannot resist various attacks such as impersonation and trace attacks, and their scheme cannot provide secure mutual authentication, session key security, and anonymity. In this paper, we propose a secure authentication protocol for WSNs in vehicular communications to resolve the security weaknesses of Mohit et al.’s scheme. Our authentication protocol prevents various attacks and achieves secure mutual authentication and anonymity by using dynamic parameters that are changed every session. We prove that our protocol provides secure mutual authentication by using the Burrows–Abadi–Needham logic, which is a widely accepted formal security analysis. We perform a formal security verification by using the well-known Automated Validation of Internet Security Protocols and Applications tool, which shows that the proposed protocol is safe against replay and man-in-the-middle attacks. We compare the performance and security properties of our protocol with other related schemes. Overall, the proposed protocol provides better security features and a comparable computation cost. Therefore, the proposed protocol can be applied to practical WSNs-based vehicular communications. Full article

Traffic sign detection and recognition plays an important role in expert systems, such as traffic assistance driving systems and automatic driving systems. It instantly assists drivers or automatic driving systems in detecting and recognizing traffic signs effectively. In this paper, a novel approach for real-time traffic sign detection and recognition in a real traffic situation was proposed. First, the images of the road scene were converted to grayscale images, and then we filtered the grayscale images with simplified Gabor wavelets (SGW), where the parameters were optimized. The edges of the traffic signs were strengthened, which was helpful for the next stage of the process. Second, we extracted the region of interest using the maximally stable extremal regions algorithm and classified the superclass of traffic signs using the support vector machine (SVM). Finally, we used convolution neural networks with input by simplified Gabor feature maps, where the parameters were the same as the detection stage, to classify the traffic signs into their subclasses. The experimental results based on Chinese and German traffic sign databases showed that the proposed method obtained a comparable performance with the state-of-the-art method, and furthermore, the processing efficiency of the whole process of detection and classification was improved and met the real-time processing demands. Full article

Multitarget tracking algorithms based on sonar usually run into detection uncertainty, complex channel and more clutters, which cause lower detection probability, single sonar sensors failing to measure when the target is in an acoustic shadow zone, and computational bottlenecks. This paper proposes a novel tracking algorithm based on multisensor data fusion to solve the above problems. Firstly, under more clutters and lower detection probability condition, a Gaussian Mixture Probability Hypothesis Density (GMPHD) filter with computational advantages was used to get local estimations. Secondly, this paper provided a maximum-detection capability multitarget track fusion algorithm to deal with the problems caused by low detection probability and the target being in acoustic shadow zones. Lastly, a novel feedback algorithm was proposed to improve the GMPHD filter tracking performance, which fed the global estimations as a random finite set (RFS). In the end, the statistical characteristics of OSPA were used as evaluation criteria in Monte Carlo simulations, which showed this algorithm’s performance against those sonar tracking problems. When the detection probability is 0.7, compared with the GMPHD filter, the OSPA mean of two sensor and three sensor fusion was decrease almost by 40% and 55%, respectively. Moreover, this algorithm successfully tracks targets in acoustic shadow zones. Full article

Owing to the rapid growth in wireless data traffic, millimeter-wave (mm-wave) communications have shown tremendous promise and are considered an attractive technique in fifth-generation (5G) wireless communication systems. However, to design robust communication systems, it is important to understand the channel dynamics with respect to space and time at these frequencies. Millimeter-wave signals are highly susceptible to blocking, and they have communication limitations owing to their poor signal attenuation compared with microwave signals. Therefore, by employing highly directional antennas, co-channel interference to or from other systems can be alleviated using line-of-sight (LOS) propagation. Because of the ability to shape, switch, or scan the propagating beam, phased arrays play an important role in advanced wireless communication systems. Beam-switching, beam-scanning, and multibeam arrays can be realized at mm-wave frequencies using analog or digital system architectures. This review article presents state-of-the-art phased arrays for mm-wave mobile terminals (MSs) and base stations (BSs), with an emphasis on beamforming arrays. We also discuss challenges and strategies used to address unfavorable path loss and blockage issues related to mm-wave applications, which sets future directions. Full article

Making use of the sol-gel technique, an optical pH sensor was prepared, which was made from an organic carrier with four indictors including congo red, bromophenol blue, cresol red, and chlorophenol red, cross-linked by tetraethyl orthosilicate (TEOS) and cellulose acetate. The actual detection range of the optical pH sensor is 2.5–11.0. The optimal ratio of ethyl orthosilicate, absolute ethanol, deionized water, and hydrochloric acid in glue precursor of the sensor-sensitive membrane was explored. The orthogonal experiment was designed to optimize the dosage of cellulose acetate, N,N-dimethylformamide (DMF), indicator, hydrochloric acid, and precursor glue in preparing the sensor-sensitive membrane. The linearity, measurement accuracy, repeatability, stability, and response time of the prepared pH sensor were tested. The measurement results were analyzed using a support vector machine and linear regression. The experimental results show that the optical pH sensor has a measurement accuracy of up to 0.2 pH and better stability and repeatability than the traditional pH glass electrode. Full article

In this work, we report the analysis of the electrochemical detection of electroactive species with band microelectrodes that operate under controlled convection. The study focuses on the determination of the collection efficiency of the analyte as a function of inlet flow velocity and microband geometry (inlaid, bumped and recessed), also providing a straightforward method for the theoretical determination of the lower detection limit. The analysis has been carried out by simulating the dimensionless mass transport with the finite element method, delivering the stationary limiting current density. Simulations have been performed on systems consisting of single and double band electrodes to investigate the trail effect on the electrochemical detection. We show that the obtained dimensionless results can be easily turned into dimensional data, providing a tool for the design of devices. The proposed method is general and can easily be extended to systems with different geometry. Full article

Intentional spoofing interference can cause damage to the navigation terminal and threaten the security of a global navigation satellite system (GNSS). For spoofing interference, an anti-spoofing algorithm based on pseudorange differences for a single receiver is proposed, which can be used to detect simplistic and intermediate spoofing attacks, as well as meaconing attacks. Double-difference models using the pseudorange of two adjacent epochs are established followed by the application of Taylor expansion to the position relationship between the satellite and the receiver (or the spoofer). The authenticity of the signal can be verified by comparing the results of the proposed spoofing detection algorithm with the traditional least squares method. The results will differ when spoofing is present. The parameter setting of the proposed algorithm is introduced. The algorithm has the advantage of both simplicity and efficiency and needs only a single receiver and pseudorange data. A NovAtel receiver is adopted for the actual experiments. The Texas spoofing test battery (TEXBAT), as well as two other simulation experiments are used to verify the performance of the algorithm. The simulation results validate the feasibility and effectiveness of the algorithm. Full article

Smart cities work with large volumes of data from sensor networks and other sources. To prevent data from being compromised by attacks or errors, smart city IT administrators need to apply attack detection techniques to evaluate possible incidents as quickly as possible. Machine learning has proven to be effective in many fields and, in the context of wireless sensor networks (WSNs), it has proven adequate to detect attacks. However, a smart city poses a much more complex scenario than a WSN, and it has to be evaluated whether these techniques are equally valid and effective. In this work, we evaluate two machine learning algorithms (support vector machines (SVM) and isolation forests) to detect anomalies in a laboratory that reproduces a real smart city use case with heterogeneous devices, algorithms, protocols, and network configurations. The experience has allowed us to show that, although these techniques are of great value for smart cities, additional considerations must be taken into account to effectively detect attacks. Thus, through this empiric analysis, we point out broader challenges and difficulties of using machine learning in this context, both for the technical complexity of the systems, and for the technical difficulty of configuring and implementing them in such environments. Full article

A velocity induced eddy current probe has been used to detect cracks in both non-ferromagnetic and ferromagnetic metals. The simulation and experimental results show that this probe can successfully detect cracks in both cases, but further investigation shows that the underlying principles for inspecting non-ferromagnetic and ferromagnetic metals are actually different. For an aluminum plate, the induced eddy current density and the signal amplitude both increase with probe speed, which means the signal is caused by velocity induced eddy currents. For a steel plate, probe speed changes the baselines of the testing signals; however, it has little influence on signal amplitudes. Simulation results show that the signal for cracks in a steel plate is mainly caused by direct magnetic field perturbation rather than velocity induced eddy currents. Full article

Remote Raman spectroscopy is widely used to detect minerals, explosives and air pollution, for example. One of its main problems, however, is background radiation that is caused by ambient light and sample fluorescence. We present here, to the best of our knowledge, the first time a distance-resolving Raman radar device that is based on an adjustable, time-correlated complementary metal-oxide-semiconductor (CMOS) single-photon avalanche diode line sensor which can measure the location of the target sample simultaneously with the normal stand-off spectrometer operation and suppress the background radiation dramatically by means of sub-nanosecond time gating. A distance resolution of 3.75 cm could be verified simultaneously during normal spectrometer operation and Raman spectra of titanium dioxide were distinguished by this system at distances of 250 cm and 100 cm with illumination intensities of the background of 250 lux and 7600 lux, respectively. In addition, the major Raman peaks of olive oil, which has a fluorescence-to-Raman signal ratio of 33 and a fluorescence lifetime of 2.5 ns, were distinguished at a distance of 30 cm with a 250 lux background illumination intensity. We believe that this kind of time-correlated CMOS single-photon avalanche diode sensor could pave the way for new compact distance-resolving Raman radars for application where distance information within a range of several metres is needed at the same time as a Raman spectrum. Full article

The automatic generation of music is an emergent field of research that has attracted the attention of countless researchers. As a result, there is a broad spectrum of state of the art research in this field. Many systems have been designed to facilitate collaboration between humans and machines in the generation of valuable music. This research proposes an intelligent system that generates melodies under the supervision of a user, who guides the process through a mechanical device. The mechanical device is able to capture the movements of the user and translate them into a melody. The system is based on a Case-Based Reasoning (CBR) architecture, enabling it to learn from previous compositions and to improve its performance over time. The user uses a device that allows them to adapt the composition to their preferences by adjusting the pace of a melody to a specific context or generating more serious or acute notes. Additionally, the device can automatically resist some of the user’s movements, this way the user learns how they can create a good melody. Several experiments were conducted to analyze the quality of the system and the melodies it generates. According to the users’ validation, the proposed system can generate music that follows a concrete style. Most of them also believed that the partial control of the device was essential for the quality of the generated music. Full article

Movement analysis of infants’ body parts is momentous for the early detection of various movement disorders such as cerebral palsy. Most existing techniques are either marker-based or use wearable sensors to analyze the movement disorders. Such techniques work well for adults, however they are not effective for infants as wearing such sensors or markers may cause discomfort to them, affecting their natural movements. This paper presents a method to help the clinicians for the early detection of movement disorders in infants. The proposed method is marker-less and does not use any wearable sensors which makes it ideal for the analysis of body parts movement in infants. The algorithm is based on the deformable part-based model to detect the body parts and track them in the subsequent frames of the video to encode the motion information. The proposed algorithm learns a model using a set of part filters and spatial relations between the body parts. In particular, it forms a mixture of part-filters for each body part to determine its orientation which is used to detect the parts and analyze their movements by tracking them in the temporal direction. The model is represented using a tree-structured graph and the learning process is carried out using the structured support vector machine. The proposed framework will assist the clinicians and the general practitioners in the early detection of infantile movement disorders. The performance evaluation of the proposed method is carried out on a large dataset and the results compared with the existing techniques demonstrate its effectiveness. Full article

This paper reviews the theories and applications of electromagnetic–acoustic (EMA) techniques (covering light-induced photoacoustic, microwave-induced thermoacoustic, magnetic-modulated thermoacoustic, and X-ray-induced thermoacoustic) belonging to the more general area of electromagnetic (EM) hybrid techniques. The theories cover excitation of high-power EM field (laser, microwave, magnetic field, and X-ray) and subsequent acoustic wave generation. The applications of EMA methods include structural imaging, blood flowmetry, thermometry, dosimetry for radiation therapy, hemoglobin oxygen saturation (SO₂) sensing, fingerprint imaging and sensing, glucose sensing, pH sensing, etc. Several other EM-related acoustic methods, including magnetoacoustic, magnetomotive ultrasound, and magnetomotive photoacoustic are also described. It is believed that EMA has great potential in both pre-clinical research and medical practice. Full article

Fiber-optic surface plasmon resonance (SPR) sensors possess the advantages of small size, flexible, allowing for a smaller sample volume, easy to be integrated, and high sensitivity. They have been intensively developed in recent decades. However, the polarizing nature of the surface plasmon waves (SPWs) always hinders the acquisition of SPR spectrum with high signal-noise ratio in wavelength modulation unless a polarizer is employed. The addition of polarizer complicates the system and reduces the degree of compactness. In this work, we propose and demonstrate a novel, polarization-independent fiber-optic SPR sensor based on a BK7 bi-prism with two incident planes orthogonal to each other. In the bi-prism, TM-polarized components of non-polarized incident lights excite SPWs on the first sensing channel, meanwhile the TE components and the remaining TM components are reflected, then the reflected TE components serve as TM components of incident lights for the second sensing channel to excite SPWs. Simulations show the proposed SPR structure permit us to completely eliminate the polarization dependence of the plasmon excitation. Experimental results agree well with the simulations. This kind of devices can be considered an excellent option for development of simple and compact SPR chemical sensors. Full article

Highly excited Rydberg atoms in a room-temperature vapor cell are promising for developing a radio-frequency (RF) electric field (E-field) sensor and relevant measurement standards with high accuracy and sensitivity. The all-optical sensing approach is based on electromagnetically-induced transparency and Autler-Townes splitting induced by the RF E-field. Systematic investigation of measurement uncertainty is of great importance for developing a national measurement standard. The presence of a dielectric vapor cell containing alkali atoms changes the magnitude, polarization, and spatial distribution of the incident RF field. In this paper, the field distortion of rubidium vapor cells is investigated, in terms of both field strength distortion and depolarization. Full-wave numerical simulation and analysis are employed to determine general optimization solutions for minimizing such distortion and validated by measuring the E-field vector distribution inside different vapor cells. This work can improve the accuracy of atom-based RF E-field measurements and contributes to the development of related RF quantum sensors. Full article

In this study, we applied a double-sided inductively coupled plasma (ICP) process to nanostructure long-period fiber grating (LPFG) in order to fabricate a double-notched LPFG (DNLPFG) sensor with a double-sided surface corrugated periodic grating. Using the sol-gel method, we also added thymol blue and ZnO to form a gas sensing layer, thus producing a DNLPFG CO₂ gas sensor. The resulting sensor is the first double-sided etching sensor used to measure CO₂. The experimental results showed that as the CO₂ concentration increased, the transmission loss increased, and that the smaller the fiber diameter, the greater the sensitivity and the greater the change in transmission loss. When the diameter of the fiber was 32 μm (and the period was 570 μm) and the perfusion rate of CO₂ gas was 15%, the maximum loss variation of up to 3.881 dB was achieved, while the sensitivity was 0.2146 dB/% and the linearity was 0.992. These results demonstrate that the DNLPG CO₂ gas sensor is highly sensitive. Full article

Demand Response (DR) aims to motivate end consumers to change their energy consumption patterns in response to changes in electricity prices or when the reliability of the electrical power system (EPS) is compromised. Most of the proposals found in the literature only aim at reducing the cost for end consumers. However, this article proposes a home energy management system (HEMS) that aims to schedule the use of each home appliance based on the price of electricity in real-time (RTP) and on the consumer satisfaction/comfort level in order to guarantee the stability and the safety of the EPS. Thus, this paper presents a multi-objective DR optimization model which was formulated as a multi-objective nonlinear programming problem subjected to a set of constraints and was solved using the Non-Dominated Sorted Genetic Algorithm (NSGA-II), in order to determine the scheduling of home appliances for the time horizon. The multi-objective DR optimization model not only to minimize the cost of electricity consumption but also to reduce the level of inconvenience for residential consumers. Moreover, a priori, it is expected to obtain a more uniform demand with fewer peaks in the system and, potentially, achieving a more reliable and safer EPS operation. Thus, the energy management controller (EMC) within the HEMS determines an optimized schedule for each home appliance through the multi-objective DR model presented in this article, and ensures a more economic scenario for end consumers. In this paper, a performance evaluation of HEMS in 15 Brazilian families between 1 January and 31 December 2016 is presented with different electric energy consumption patterns in the cities of Belém—PA, Teresina—PI, Cuiabá—MT, Florianópolis—SC and São Paulo—SP, with three families per city, located in the regions north, northeast, central west, south and the southeast of Brazil, respectively. In addition, a total of 425 home appliances were used in the simulations. The results show that the HEMS achieved reductions in the cost of electricity for all the Scenarios used while minimally affecting the satisfaction/comfort of the end consumers as well as taking into account all the restrictions. The largest reduction in the total cost of electricity occurred for the couple without children, resident in the city of Teresina—PI; with a drop from US$ 99.31 to US$ 90.72 totaling 8.65% savings in the electricity bill. Therefore, the results confirm that the proposed HEMS effectively improves the operating efficiency of home appliances and reduces electricity costs for end consumers. Full article

Diabetes has become a chronic metabolic disorder, and the growing diabetes population makes medical care more important. We investigated using a portable and noninvasive contact lens as an ideal sensor for diabetes patients whose tear fluid contains glucose. The key feature is the reversible covalent interaction between boronic acid and glucose, which can provide a noninvasive glucose sensor for diabetes patients. We present a phenylboronic acid (PBA)-based HEMA contact lens that exhibits a reversible swelling/shrinking effect to change its thickness. The difference in thickness can be detected in a picture taken with a smartphone and analyzed using software. Our novel technique offers the following capabilities: (i) non-enzymatic and continuous glucose detection with the contact lens; (ii) no need for an embedded circuit and power source for the glucose sensor; and (iii) the use of a smartphone to detect the change in thickness of the contact lens with no need for additional photo-sensors. This technique is promising for a noninvasive measurement of the glucose level and simple implementation of glucose sensing with a smartphone. Full article

This paper proposes a way for drift compensation in electronic noses (e-nose) that often suffers from uncertain and unpredictable sensor drift. Traditional machine learning methods for odor recognition require consistent data distribution, which makes the model trained with previous data less generalized. In the actual application scenario, the data collected previously and the data collected later may have different data distributions due to the sensor drift. If the dataset without sensor drift is treated as a source domain and the dataset with sensor drift as a target domain, a domain correction based on kernel transformation (DCKT) method is proposed to compensate the sensor drift. The proposed method makes the distribution consistency of two domains greatly improved through mapping to a high-dimensional reproducing kernel space and reducing the domain distance. A public benchmark sensor drift dataset is used to verify the effectiveness and efficiency of the proposed DCKT method. The experimental result shows that the proposed method yields the highest average accuracies compared to other considered methods. Full article

Micro-displacement measurements play a crucial role in many industrial applications. Aiming to address the defects of existing optical-fiber displacement sensors, such as low sensitivity and temperature interference, we propose and demonstrate a novel surface plasmon resonance (SPR)-based optical-fiber micro-displacement sensor with temperature compensation. The sensor consists of a displacement-sensing region (DSR) and a temperature-sensing region (TSR). We employed a graded-index multimode fiber (GI-MMF) to fabricate the DSR and a hetero-core structure fiber to fabricate the TSR. For the DSR, we employed a single-mode fiber (SMF) to change the radial position of the incident beam as displacement. The resonance angle in the DSR is highly sensitive to displacement; thus, the resonance wavelength of the DSR shifts. For the TSR, we employed polydimethylsiloxane (PDMS) as a temperature-sensitive medium, whose refractive index is highly sensitive to temperature; thus, the resonance wavelength of the TSR shifts. The displacement and temperature detection ranges are 0–25 μm and 20–60 °C; the displacement and temperature sensitivities of the DSR are 4.24 nm/μm and −0.19 nm/°C, and those of the TSR are 0.46 nm/μm and −2.485 nm/°C, respectively. Finally, by means of a sensing matrix, the temperature compensation was realized. Full article

Current mobile devices need to run applications with high computational demands and critical response times. The mobile edge computing (MEC) paradigm was developed to improve the performance of these devices. This new computation architecture allows for the mobile devices to execute applications on fog nodes at the network edge; this process is called data processing offloading. This article presents a security model for the externalization of application execution in multi-tier MEC environments. The principal novelty of this study is that the model is able to modify the required security level in each tier of the distributed architecture as a function of the degree of trust associated with that tier. The basic idea is that a higher degree of trust requires a lower level of security, and vice versa. A formal framework is introduced that represents the general environment of application execution in distributed MEC architectures. An architecture is proposed that allows for deployment of the model in production environments and is implemented for evaluation purposes. The results show that the security model can be applied in multi-tier MEC architectures and that the model produces a minimal overhead, especially for computationally intensive applications. Full article

Optofluidics, a research discipline combining optics with microfluidics, currently aspires to revolutionize the analysis of biological and chemical samples, e.g., for medicine, pharmacology, or molecular biology. In order to detect low concentrations of analytes in water, we have developed an optofluidic device containing a nanostructured substrate for surface enhanced Raman spectroscopy (SERS). The geometry of the gold surface allows localized plasmon oscillations to give rise to the SERS effect, in which the Raman spectral lines are intensified by the interaction of the plasmonic field with the electrons in the molecular bonds. The SERS substrate was enclosed in a microfluidic system, which allowed transport and precise mixing of the analyzed fluids, while preventing contamination or abrasion of the highly sensitive substrate. To illustrate its practical use, we employed the device for quantitative detection of persistent environmental pollutant 1,2,3-trichloropropane in water in submillimolar concentrations. The developed sensor allows fast and simple quantification of halogenated compounds and it will contribute towards the environmental monitoring and enzymology experiments with engineered haloalkane dehalogenase enzymes. Full article

Remote sensing imagery has become an operative and applicable tool for the preparation of geological maps by reducing the costs and increasing the precision. In this study, ASTER satellite remote sensing data were used to extract lithological information of Deh-Molla sedimentary succession, which is located in the southwest of Shahrood city, Semnan Province, North Iran. A robust and effective approach named Band Ratio Matrix Transformation (BRMT) was developed to characterize and discriminate the boundary of sedimentary rock formations in Deh-Molla region. The analysis was based on the forward and continuous division of the visible-near infrared (VNIR) and the shortwave infrared (SWIR) spectral bands of ASTER with subsequent application of principal component analysis (PCA) for producing new transform datasets. The approach was implemented to ASTER spectral band ratios for mapping dominated mineral assemblages in the study area. Quartz, carbonate, and Al, Fe, Mg –OH bearing-altered minerals such as kaolinite, alunite, chlorite and mica were appropriately mapped using the BRMT approach. The results match well with geology map of the study area, fieldwork data and laboratory analysis. Accuracy assessment of the mapping result represents a reasonable kappa coefficient (0.70%) and appropriate overall accuracy (74.64%), which verified the robustness of the BRMT approach. This approach has great potential and capability for mapping sedimentary succession with diverse local–geological–physical characteristics around the world. Full article

Extracting horizontal planes in heavily cluttered three-dimensional (3D) scenes is an essential procedure for many robotic applications. Aiming at the limitations of general plane segmentation methods on this subject, we present HoPE, a Horizontal Plane Extractor that is able to extract multiple horizontal planes in cluttered scenes with both organized and unorganized 3D point clouds. It transforms the source point cloud in the first stage to the reference coordinate frame using the sensor orientation acquired either by pre-calibration or an inertial measurement unit, thereby leveraging the inner structure of the transformed point cloud to ease the subsequent processes that use two concise thresholds for producing the results. A revised region growing algorithm named Z clustering and a principal component analysis (PCA)-based approach are presented for point clustering and refinement, respectively. Furthermore, we provide a nearest neighbor plane matching (NNPM) strategy to preserve the identities of extracted planes across successive sequences. Qualitative and quantitative evaluations of both real and synthetic scenes demonstrate that our approach outperforms several state-of-the-art methods under challenging circumstances, in terms of robustness to clutter, accuracy, and efficiency. We make our algorithm an off-the-shelf toolbox which is publicly available. Full article

In recent times, Wireless Sensor Networks (WSNs) are broadly applied in the Industrial Internet of Things (IIoT) in order to enhance the productivity and efficiency of existing and prospective manufacturing industries. In particular, an area of interest that concerns the use of WSNs in IIoT is the concept of sensor network virtualization and overlay networks. Both network virtualization and overlay networks are considered contemporary because they provide the capacity to create services and applications at the edge of existing virtual networks without changing the underlying infrastructure. This capability makes both network virtualization and overlay network services highly beneficial, particularly for the dynamic needs of IIoT based applications such as in smart industry applications, smart city, and smart home applications. Consequently, the study of both WSN virtualization and overlay networks has become highly patronized in the literature, leading to the growth and maturity of the research area. In line with this growth, this paper provides a review of the development made thus far concerning virtualized sensor networks, with emphasis on the application of overlay networks in IIoT. Principally, the process of virtualization in WSN is discussed along with its importance in IIoT applications. Different challenges in WSN are also presented along with possible solutions given by the use of virtualized WSNs. Further details are also presented concerning the use of overlay networks as the next step to supporting virtualization in shared sensor networks. Our discussion closes with an exposition of the existing challenges in the use of virtualized WSN for IIoT applications. In general, because overlay networks will be contributory to the future development and advancement of smart industrial and smart city applications, this review may be considered by researchers as a reference point for those particularly interested in the study of this growing field. Full article

This paper presents a mid-infrared dimethyl sulfide (CH₃SCH₃, DMS) sensor based on tunable laser absorption spectroscopy with a distributed feedback interband cascade laser to measure DMS in the atmosphere. Different from previous work, in which only DMS was tested and under pure nitrogen conditions, we measured DMS mixed by common air to establish the actual atmospheric measurement environment. Moreover, we used tunable laser absorption spectroscopy with spectral fitting to enable multi-species (i.e., DMS, CH₄, and H₂O) measurement simultaneously. Meanwhile, we used empirical mode decomposition and greatly reduced the interference of optical fringes and noise. The sensor performances were evaluated with atmospheric mixture in laboratory conditions. The sensor’s measurement uncertainties of DMS, CH₄, and H₂O were as low as 80 ppb, 20 ppb, and 0.01% with an integration time 1 s, respectively. The sensor possessed a very low detection limit of 9.6 ppb with an integration time of 164 s for DMS, corresponding to an absorbance of 7.4 × 10⁻⁶, which showed a good anti-interference ability and stable performance after optical interference removal. We demonstrated that the sensor can be used for DMS measurement, as well as multi-species atmospheric measurements of DMS, H₂O, and CH₄ simultaneously. Full article

A non-linear filtering algorithm based on the alpha-divergence is proposed, which uses the exponential family distribution to approximate the actual state distribution and the alpha-divergence to measure the approximation degree between the two distributions; thus, it provides more choices for similarity measurement by adjusting the value of $\alpha$ during the updating process of the equation of state and the measurement equation in the non-linear dynamic systems. Firstly, an $\alpha$ -mixed probability density function that satisfies the normalization condition is defined, and the properties of the mean and variance are analyzed when the probability density functions $p(x)$ and $q(x)$ are one-dimensional normal distributions. Secondly, the sufficient condition of the alpha-divergence taking the minimum value is proven, that is when $\alpha \ge 1$ , the natural statistical vector’s expectations of the exponential family distribution are equal to the natural statistical vector’s expectations of the $\alpha$ -mixed probability state density function. Finally, the conclusion is applied to non-linear filtering, and the non-linear filtering algorithm based on alpha-divergence minimization is proposed, providing more non-linear processing strategies for non-linear filtering. Furthermore, the algorithm’s validity is verified by the experimental results, and a better filtering effect is achieved for non-linear filtering by adjusting the value of $\alpha$ . Full article

This article discusses the issue of Automatic Target Recognition (ATR) on Synthetic Aperture Radar (SAR) images. Through learning the hierarchy of features automatically from a massive amount of training data, learning networks such as Convolutional Neural Networks (CNN) has recently achieved state-of-the-art results in many tasks. To extract better features about SAR targets, and to obtain better accuracies, a new framework is proposed: First, three CNN models based on different convolution and pooling kernel sizes are proposed. Second, they are applied simultaneously on the SAR images to generate image features via extracting CNN features from different layers in two scenarios. In the first scenario, the activation vectors obtained from fully connected layers are considered as the final image features; in the second scenario, dense features are extracted from the last convolutional layer and then encoded into global image features through one of the commonly used feature coding approaches, which is Fisher Vectors (FVs). Finally, different combination and fusion approaches between the two sets of experiments are considered to construct the final representation of the SAR images for final classification. Extensive experiments on the Moving and Stationary Target Acquisition and Recognition (MSTAR) dataset are conducted. Experimental results prove the capability of the proposed method, as compared to several state-of-the-art methods. Full article

With the recent advancement in wearable computing, sensor technologies, and data processing approaches, it is possible to develop smart clothing that integrates sensors into garments. The main objective of this study was to develop the method of automatic recognition of sedentary behavior related to cardiovascular risk based on quantitative measurement of physical activity. The solution is based on the designed prototype of the smart shirt equipped with a processor, wearable sensors, power supply and telemedical interface. The data derived from wearable sensors were used to create feature vector that consisted of the estimation of the user-specific relative intensity and the variance of filtered accelerometer data. The method was validated using an experimental protocol which was designed to be safe for the elderly and was based on clinically validated short physical performance battery (SPPB) test tasks. To obtain the recognition model six classifiers were examined and compared including Linear Discriminant Analysis, Support Vector Machines, K-Nearest Neighbors, Naive Bayes, Binary Decision Trees and Artificial Neural Networks. The classification models were able to identify the sedentary behavior with an accuracy of 95.00% ± 2.11%. Experimental results suggested that high accuracy can be obtained by estimating sedentary behavior pattern using the smart shirt and machine learning approach. The main advantage of the developed method to continuously monitor patient activities in a free-living environment and could potentially be used for early detection of increased cardiovascular risk. Full article

This paper focuses on the exploitation of fifth generation (5G) centimetre-wave (cmWave) and millimetre-wave (mmWave) transmissions for high-accuracy positioning, in order to complement the availability of Global Navigation Satellite Systems (GNSS) in harsh environments, such as urban canyons. Our goal is to present a representative methodology to simulate and assess their hybrid positioning capabilities over outdoor urban, suburban and rural scenarios. A novel scenario definition is proposed to integrate the network density of 5G deployments with the visibility masks of GNSS satellites, which helps to generate correlated scenarios of both technologies. Then, a generic and representative modeling of the 5G and GNSS observables is presented for snapshot positioning, which is suitable for standard protocols. The simulations results indicate that GNSS drives the achievable accuracy of its hybridisation with 5G cmWave, because non-line-of-sight (NLoS) conditions can limit the cmWave localization accuracy to around 20 m. The 5G performance is significantly improved with the use of mmWave positioning with dominant line-of-sight (LoS) conditions, which can even achieve sub-meter localization with one or more base stations. Therefore, these results show that NLoS conditions need to be weighted in 5G localization, in order to complement and outperform GNSS positioning over urban environments. Full article

Reliability and energy efficiency are two key considerations when designing a compressive sensing (CS)-based data-gathering scheme. Most researchers assume there is no packets loss, thus, they focus only on reducing the energy consumption in wireless sensor networks (WSNs) while setting reliability concerns aside. To balance the performance–energy trade-off in lossy WSNs, a distributed data storage (DDS) and gathering scheme based on CS (CS-DDSG) is introduced, which combines CS and DDS. CS-DDSG utilizes broadcast properties to resist the impact of packet loss rates. Neighboring nodes receive packets with process constraints imposed to decrease the volume of both transmissions and receptions. The mobile sink randomly queries nodes and constructs a measurement matrix based on received data with the purpose of avoiding measuring the lossy nodes. Additionally, we demonstrate how this measurement matrix satisfies the restricted isometry property. To analyze the efficiency of the proposed scheme, an expression that reflects the total number of transmissions and receptions is formulated via random geometric graph theory. Simulation results indicate that our scheme achieves high precision for unreliable links and reduces the number of transmissions, receptions and fusions. Thus, our proposed CS-DDSG approach effectively balances energy consumption and reconstruction accuracy. Full article

Near-infrared (NIR) spectral sensors deliver the spectral response of the light absorbed by materials for quantification, qualification or identification. Spectral analysis technology based on the NIR sensor has been a useful tool for complex information processing and high precision identification in the tobacco industry. In this paper, a novel method based on the support vector machine (SVM) is proposed to discriminate the tobacco cultivation region using the near-infrared (NIR) sensors, where the genetic algorithm (GA) is employed for input subset selection to identify the effective principal components (PCs) for the SVM model. With the same number of PCs as the inputs to the SVM model, a number of comparative experiments were conducted between the effective PCs selected by GA and the PCs orderly starting from the first one. The model performance was evaluated in terms of prediction accuracy and four parameters of assessment criteria (true positive rate, true negative rate, positive predictive value and F1 score). From the results, it is interesting to find that some PCs with less information may contribute more to the cultivation regions and are considered as more effective PCs, and the SVM model with the effective PCs selected by GA has a superior discrimination capacity. The proposed GA-SVM model can effectively learn the relationship between tobacco cultivation regions and tobacco NIR sensor data. Full article

Air pollution is a global problem and can be perceived as a modern-day curse. One way of dealing with it is by finding economical ways to monitor and forecast air quality. Accurately monitoring and forecasting fine particulate matter (PM2.5) concentrations is a challenging prediction task but Internet of Things (IoT) can help in developing economical and agile ways to design such systems. In this paper, we use a historical data-based approach to perform PM2.5 forecasting. A forecasting method is developed which uses exponential smoothing with drift. Experiments and evaluation were performed using the real-time PM2.5 data obtained from large scale deployment of IoT devices in Taichung region in Taiwan. We used the data from 132 monitoring stations to evaluate our model’s performance. A comparison of prediction accuracy and computation time between the proposed model and three widely used forecasting models was done. The results suggest that our method can perform PM2.5 forecast for 132 monitoring stations with error as low as 0.16 $\mathsf{\mu }$ g/ ${\mathrm{m}}^3$ and also with an acceptable computation time of 30 s. Further evaluation was done by forecasting PM2.5 for next 3 h. The results show that 90 % of the monitoring stations have error under 1.5 $\mathsf{\mu }$ g/ ${\mathrm{m}}^3$ which is significantly low. Full article

In this paper, a new approach to enhance the performance of the interdigital electrode-piezoelectric quartz crystal (IDE-PQC) salt solution concentration sensor by modifying the electrode area of PQC was proposed. Equivalent circuit analysis showed that the static capacitor (C₀) which is related to the electrode area of PQC directly affected the response sensitivity of the IDE-PQC sensor. Further, the sensing responses of IDE-PQC sensors to various concentrations of salt solution were measured. Three kinds of salt solution, including NaCl, KCl, and Na₂SO₄, were adpoted to evaluate the sensing performances of the IDE-PQC sensors. The experimental results also indicated that increasing the electrode area of PQC can enhance the sensitivity response of the IDE-PQC sensors to the change of salt solution concentration. For example, the detection sensitivity of the IDE-PQC sensor with an electrode diameter of 5 mm was about three times larger than that of the sensor with an electrode diameter of 3 mm. Meanwhile, we found that the frequency stability of the IDE-PQC sensor was also improved by increasing the electrode area of PQC. In addition, the influence of the electrode area of PQC on the repeatability and the transient response of IDE-PQC salt solution concentration sensor were also studied. This work demonstrates simple and cost-effective method to achieve the performance enhancement of IDE-PQC salt solution concentration sensor by modifying the electrode area of PQC. Full article

This paper investigates the design of overlapped chunked codes (OCC) for multi-source multi-relay networks where a physical-layer network coding approach, compute-and-forward (CF) based on nested lattice codes (NLC), is applied for the simultaneous transmissions from the sources to the relays. This code is called OCC/CF. In this paper, OCC is applied before NLC before transmitting for each source. Random linear network coding is applied within each chunk. A decodability condition to design OCC/CF is provided. In addition, an OCC with a contiguously overlapping, but non-rounded-end fashion is employed for the design, which is done by using the probability distributions of the number of innovative codeword combinations and the probability distribution of the participation factor of each source to the codeword combinations received for a chunk transmission. An estimation is done to select an allocation, i.e., the number of innovative blocks per chunk and the number of blocks taken from the previous chunk for all sources, that is expected to provide the desired performance. From the numerical results, the design overhead of OCC/CF is low when the probability distribution of the participation factor of each source is dense at the chunk size for each source. Full article

To find out the feasibility of different neural networks in sEMG-based force estimation, in this paper, three types of networks, namely convolutional neural network (CNN), long short-term memory (LSTM) network and their combination (C-LSTM) were applied to predict muscle force generated in static isometric elbow flexion across three different circumstances (multi-subject, subject-dependent and subject-independent). Eight healthy men were recruited for the experiments, and the results demonstrated that all the three models were applicable for force estimation, and LSTM and C-LSTM achieved better performances. Even under subject-independent situation, they maintained mean RMSE% of as low as 9.07 ± 1.29 and 8.67 ± 1.14. CNN turned out to be a worse choice, yielding a mean RMSE% of 12.13 ± 1.98. To our knowledge, this work was the first to employ CNN, LSTM and C-LSTM in sEMG-based force estimation, and the results not only prove the strength of the proposed networks, but also pointed out a potential way of achieving high accuracy in real-time, subject-independent force estimation. Full article

Melamine, a nitrogen-rich compound, has been used as a food and milk additive to falsely increase the protein content. However, melamine is toxic, and high melamine levels in food or in milk can cause kidney and urinary problems, or even death. Hence, the detection of melamine in food and milk is desirable, for which numerous detection methods have been developed. Several methods have successfully detected melamine in raw milk; however, they require a sample preparation before the analyses. This study aimed to develop an aptamer-DNAzyme conjugated biosensor for label-free detection of melamine, in raw milk, without any sample preparation. An aptamer-DNAzyme conjugated biosensor was developed via screening using microarray analysis to identify the candidate aptamers followed by an optimization, to reduce the background noise and improve the aptamer properties, thereby, enhancing the signal-to-noise (S/N) ratio of the screened biosensor. The developed biosensor was evaluated via colorimetric detection and tested with raw milk without any sample preparation, using N-methylmesoporphyrin IX for fluorescence detection. The biosensor displayed significantly higher signal intensity at 2 mM melamine (S/N ratio, 20.2), which was sufficient to detect melamine at high concentrations, in raw milk. Full article

The growing interest and the market for indoor Location Based Service (LBS) have been drivers for a huge demand for building data and reconstructing and updating of indoor maps in recent years. The traditional static surveying and mapping methods can’t meet the requirements for accuracy, efficiency and productivity in a complicated indoor environment. Utilizing a Simultaneous Localization and Mapping (SLAM)-based mapping system with ranging and/or camera sensors providing point cloud data for the maps is an auspicious alternative to solve such challenges. There are various kinds of implementations with different sensors, for instance LiDAR, depth cameras, event cameras, etc. Due to the different budgets, the hardware investments and the accuracy requirements of indoor maps are diverse. However, limited studies on evaluation of these mapping systems are available to offer a guideline of appropriate hardware selection. In this paper we try to characterize them and provide some extensive references for SLAM or mapping system selection for different applications. Two different indoor scenes (a L shaped corridor and an open style library) were selected to review and compare three different mapping systems, namely: (1) a commercial Matterport system equipped with depth cameras; (2) SLAMMER: a high accuracy small footprint LiDAR with a fusion of hector-slam and graph-slam approaches; and (3) NAVIS: a low-cost large footprint LiDAR with Improved Maximum Likelihood Estimation (IMLE) algorithm developed by the Finnish Geospatial Research Institute (FGI). Firstly, an L shaped corridor (2nd floor of FGI) with approximately 80 m length was selected as the testing field for Matterport testing. Due to the lack of quantitative evaluation of Matterport indoor mapping performance, we attempted to characterize the pros and cons of the system by carrying out six field tests with different settings. The results showed that the mapping trajectory would influence the final mapping results and therefore, there was optimal Matterport configuration for better indoor mapping results. Secondly, a medium-size indoor environment (the FGI open library) was selected for evaluation of the mapping accuracy of these three indoor mapping technologies: SLAMMER, NAVIS and Matterport. Indoor referenced maps were collected with a small footprint Terrestrial Laser Scanner (TLS) and using spherical registration targets. The 2D indoor maps generated by these three mapping technologies were assessed by comparing them with the reference 2D map for accuracy evaluation; two feature selection methods were also utilized for the evaluation: interactive selection and minimum bounding rectangles (MBRs) selection. The mapping RMS errors of SLAMMER, NAVIS and Matterport were 2.0 cm, 3.9 cm and 4.4 cm, respectively, for the interactively selected features, and the corresponding values using MBR features were 1.7 cm, 3.2 cm and 4.7 cm. The corresponding detection rates for the feature points were 100%, 98.9%, 92.3% for the interactive selected features and 100%, 97.3% and 94.7% for the automated processing. The results indicated that the accuracy of all the evaluated systems could generate indoor map at centimeter-level, but also variation of the density and quality of collected point clouds determined the applicability of a system into a specific LBS. Full article

In this study, individual nanofabricated SnO micro-disks, previously shown to exhibit exceptional sensitivity to NO_x, are investigated to further our understanding of gas sensing mechanisms. The SnO disks presenting different areas and thickness were isolated and electrically connected to metallic electrodes aided by a Dual Beam Microscope (SEM/FIB). While single micro-disk devices were found to exhibit short response and recovery times and low power consumption, large interconnected arrays of micro-disks exhibit much higher sensitivity and selectivity. The source of these differences is discussed based on the gas/solid interaction and transport mechanisms, which showed that thickness plays a major role during the gas sensing of single-devices. The calculated Debye length of the SnO disk in presence of NO₂ is reported for the first time. Full article

The human hand is important for the performance of activities of daily living which are directly related to quality of life. Various conditions, such as Duchenne muscular dystrophy (DMD) can affect the function of the human hand and wrist. The ability to assess the impairment in the hand and the wrist by measuring the range of motion (ROM), is essential for the development of effective rehabilitation protocols. Currently the clinical standard is the goniometer. In this study we explore the feasibility and reliability of an optical sensor (Leap motion sensor) in measuring active hand/wrist ROM. We measured the hand/wrist ROM of 20 healthy adults with the goniometer and the Leap motion sensor, in order to check the agreement between the two methods and additionally, we performed a test-retest of the Leap motion sensor with 12 of them, to assess its reliability. The results suggest low agreement between the goniometer and the leap motion sensor, yet showing a large decrease in measurement time and high reliability when using the later. Despite the low agreement between the two methods, we believe that the Leap motion sensor shows potential to contribute to the development of hand rehabilitation protocols and be used with patients in a clinical setting. Full article

The conventional initial alignment algorithms are invalid in the polar region. This is caused by the rapid convergence of the Earth meridians in the high-latitude areas. However, the initial alignment algorithms are important for the accurate navigation of Unmanned Underwater Vehicles. The polar transversal initial alignment algorithm is proposed to overcome this problem. In the polar transversal initial alignment algorithm, the transversal geographic frame is chosen as the navigation frame. The polar region in the conventional frames is equivalent to the equatorial region in the transversal frames. Therefore, the polar transversal initial can be effectively applied in the polar region. According to the complex environment in the polar region, a large misalignment angle is considered in this paper. Based on the large misalignment angle condition, the non-linear dynamics models are established. In addition, the simplified unscented Kalman filter (UKF) is chosen to realize the data fusion. Two comparison simulations and an experiment are performed to verify the performance of the proposed algorithm. The simulation and experiment results indicate the validity of the proposed algorithm, especially when large misalignment angles occur. Full article

Efficient and accurate semantic segmentation is the key technique for automatic remote sensing image analysis. While there have been many segmentation methods based on traditional hand-craft feature extractors, it is still challenging to process high-resolution and large-scale remote sensing images. In this work, a novel patch-wise semantic segmentation method with a new training strategy based on fully convolutional networks is presented to segment common land resources. First, to handle the high-resolution image, the images are split as local patches and then a patch-wise network is built. Second, training data is preprocessed in several ways to meet the specific characteristics of remote sensing images, i.e., color imbalance, object rotation variations and lens distortion. Third, a multi-scale training strategy is developed to solve the severe scale variation problem. In addition, the impact of conditional random field (CRF) is studied to improve the precision. The proposed method was evaluated on a dataset collected from a capital city in West China with the Gaofen-2 satellite. The dataset contains ten common land resources (Grassland, Road, etc.). The experimental results show that the proposed algorithm achieves 54.96% in terms of mean intersection over union (MIoU) and outperforms other state-of-the-art methods in remote sensing image segmentation. Full article

Inspired by the exceptional flight ability of birds and insects, a bio-inspired neural adaptive flight control structure of a small unmanned aerial vehicle was presented. Eight pressure sensors were elaborately installed in the leading-edge area of the forward wing. A back propagation neural network was trained to predict the aerodynamic moment based on pressure measurements. The network model was trained, validated, and tested. An adaptive controller was designed based on a radial basis function neural network. The new adaptive laws guaranteed the boundedness of the adaptive parameters. The closed-loop stability was analyzed via Lyapunov theory. The simulation results demonstrated the robustness of the bio-inspired flight control system when subjected to measurement noise, parametric uncertainties, and external disturbance. Full article

Electronic products and systems are widely used in industrial network systems, control devices, and data acquisition devices across many industry sectors. Failures of such electronic systems might lead to unexpected downtime, loss of productivity, additional work for repairs, and delay in product and service development. Thus, developing an appropriate sensing technique is necessary, because it is the first step in system fault diagnosis and prognosis. Many sensing techniques often require external and additional sensing devices, which might disturb system operation and consequently increase operating costs. In this study, we present an on-chip health sensing method for non-destructive and non-invasive interconnect degradation detection. Bit error rate (BER), which represents data integrity during digital signal transmission, was selected to sense interconnect health without connecting external sensing devices. To verify the health sensing performance, corrosion tests were conducted with in situ monitoring of the BER and direct current (DC) resistance. The eye size, extracted from the BER measurement, showed the highest separation between the intact and failed interconnect, as well as a gradual transition, compared with abrupt changes in the DC resistance, during interconnect degradation. These experimental results demonstrate the potential of the proposed sensing method for on-chip interconnect health monitoring applications without disturbing system operation. Full article

On-line vibration monitoring is significant for high-speed rotating blades, and blade tip-timing (BTT) is generally regarded as a promising solution. BTT methods must assume that rotating speeds are constant. This assumption is impractical, and blade damages are always formed and accumulated during variable operational conditions. Thus, how to carry out BTT vibration monitoring under variable rotation speed (VRS) is a big challenge. Angular sampling-based order analyses have been widely used for vibration signals in rotating machinery with variable speeds. However, BTT vibration signals are well under-sampled, and Shannon’s sampling theorem is not satisfied so that existing order analysis methods will not work well. To overcome this problem, a reconstructed order analysis-based BTT vibration monitoring method is proposed in this paper. First, the effects of VRS on BTT vibration monitoring are analyzed, and the basic structure of angular sampling-based BTT vibration monitoring under VRS is presented. Then a band-pass sampling-based engine order (EO) reconstruction algorithm is proposed for uniform BTT sensor configuration so that few BTT sensors can be used to extract high EOs. In addition, a periodically non-uniform sampling-based EO reconstruction algorithm is proposed for non-uniform BTT sensor configuration. Next, numerical simulations are done to validate the two reconstruction algorithms. In the end, an experimental set-up is built. Both uniform and non-uniform BTT vibration signals are collected, and reconstructed order analysis are carried out. Simulation and experimental results testify that the proposed algorithms can accurately capture characteristic high EOs of synchronous and asynchronous vibrations under VRS by using few BTT sensors. The significance of this paper is to overcome the limitation of conventional BTT methods of dealing with variable blade rotating speeds. Full article

With increasingly prevalent wireless sensors and devices, low power and lossy networks (LLNs) play an essential role in the realization of ubiquitous computing and communication infrastructure, which, in turn, leads to enhanced data accessibility and availability. A multicast protocol for LLNs (MPL), has been standardized to provide both efficient and reliable multicast communication. Due to the shared wireless medium, lack of tamper resistance, and inherent resource constraints, MPL-based LLNs are undoubtedly vulnerable to various Denial-of-Service (DoS) attacks. In this paper, we propose a heuristic-based detection scheme, called HED, against the suppression attack in MPL-based LLNs, where a malicious node multicasts a series of spoof data messages with continuous sequence numbers to prevent normal nodes from accepting valid data messages and cause them to delete cached data messages. In the HED, each node maintains an increment rate of the minimum sequence number in the Seed Set to detect the potential malicious node by comparing the recent increment of sequence numbers with the heuristically calculated increment threshold of sequence numbers. We evaluate the proposed scheme through extensive simulation experiments using OMNeT++ and compare its performance with original MPL with and without adversary, respectively. The simulation results show high detection rate and packet reception rate but low false detection rate, and indicate that the proposed scheme is a potentially viable approach against the suppression attack in MPL-based LLNs. Full article

Recent technological advancement in wireless communication has led to the invention of wireless body area networks (WBANs), a cutting-edge technology in healthcare applications. WBANs interconnect with intelligent and miniaturized biomedical sensor nodes placed on human body to an unattended monitoring of physiological parameters of the patient. These sensors are equipped with limited resources in terms of computation, storage, and battery power. The data communication in WBANs is a resource hungry process, especially in terms of energy. One of the most significant challenges in this network is to design energy efficient next-hop node selection framework. Therefore, this paper presents a green communication framework focusing on an energy aware link efficient routing approach for WBANs (ELR-W). Firstly, a link efficiency-oriented network model is presented considering beaconing information and network initialization process. Secondly, a path cost calculation model is derived focusing on energy aware link efficiency. A complete operational framework ELR-W is developed considering energy aware next-hop link selection by utilizing the network and path cost model. The comparative performance evaluation attests the energy-oriented benefit of the proposed framework as compared to the state-of-the-art techniques. It reveals a significant enhancement in body area networking in terms of various energy-oriented metrics under medical environments. Full article

Acute hypoxia changes the redox-state of pulmonary arterial smooth muscle cells (PASMCs). This might influence the activity of redox-sensitive voltage-gated K⁺-channels (Kv-channels) whose inhibition initiates hypoxic pulmonary vasoconstriction (HPV). However, the molecular mechanism of how hypoxia—or the subsequent change in the cellular redox-state—inhibits Kv-channels remains elusive. For this purpose, a new multifunctional gas-tight microfluidic system was developed enabling simultaneous single-cell Raman spectroscopic studies (to sense the redox-state under normoxic/hypoxic conditions) and patch-clamp experiments (to study the Kv-channel activity). The performance of the system was tested by optically recording the O₂-content and taking Raman spectra on murine PASMCs under normoxic/hypoxic conditions or in the presence of H₂O₂. Oxygen sensing showed that hypoxic levels in the gas-tight microfluidic system were achieved faster, more stable and significantly lower compared to a conventional open system (1.6 ± 0.2%, respectively 6.7 ± 0.7%, n = 6, p < 0.001). Raman spectra revealed that the redistribution of biomarkers (cytochromes, FeS, myoglobin and NADH) under hypoxic/normoxic conditions were improved in the gas-tight microfluidic system (p-values from 0.00% to 16.30%) compared to the open system (p-value from 0.01% to 98.42%). In conclusion, the new redox sensor holds promise for future experiments that may elucidate the role of Kv-channels during HPV. Full article

Photoplethysmography (PPG) of the foot sole could provide additional health-related information compared with traditional PPG of the finger or wrist. Previously, foot PPG required the procedural binding of a light-emitting diode (LED)-photodetector (PD) pair. We achieved PPG of the foot sole without binding any sensors to the foot while the participant stood in a natural standing position on the testing device. Foot PPG was performed using multiple LED-PD pairs to overcome motion artefacts caused by stabilization. We identified regions of the sole suitable for reliable sensor positioning with optimal LED-PD pairs on the basis of the estimated heart rate (HR) and signal quality index derived by dynamic time warping (wSQI). The first experiment included four participants with direct skin-to-sensor contact, and the results showed a mean HR estimation error of 0.01 beats/min and a wSQI of 0.909. The extended experiment with 53 participants, which involved including a gap between the skin and sensors to consider real-life applications, yielded a mean HR estimation error of 0.638 beats/min and a wSQI of 0.751. Based on the selection ratio of optimal LED-PD pairs, the best region of the sole for PPG was the midfoot, except the medial longitudinal arch. In conclusion, we confirmed that foot PPG using multiple LED-PD pairs is appropriate for HR evaluation and further applications. Full article

Hydrogel materials offer many advantages for chemical and biological sensoring due to their response to a small change in their environment with a related change in volume. Several designs have been outlined in the literature in the specific field of hydrogel-based optical sensors, reporting a large number of steps for their fabrication. In this work we present a three-dimensional, hydrogel-based sensor the structure of which is fabricated in a single step using thermal nanoimprint lithography. The sensor is based on a waveguide with a grating readout section. A specific hydrogel formulation, based on a combination of PEGDMA (Poly(Ethylene Glycol DiMethAcrylate)), NIPAAm (N-IsoPropylAcrylAmide), and AA (Acrylic Acid), was developed. This stimulus-responsive hydrogel is sensitive to pH and to water. Moreover, the hydrogel has been modified to be suitable for fabrication by thermal nanoimprint lithography. Once stimulated, the hydrogel-based sensor changes its topography, which is characterised physically by AFM and SEM, and optically using a specific optical set-up. Full article

Standard Bayesian filtering algorithms only work well when the statistical properties of system noises are exactly known. However, this assumption is not always plausible in real target tracking applications. In this paper, we present a new estimation approach named adaptive fifth-degree cubature information filter (AFCIF) for multi-sensor bearings-only tracking (BOT) under the condition that the process noise follows zero-mean Gaussian distribution with unknown covariance. The novel algorithm is based on the fifth-degree cubature Kalman filter and it is constructed within the information filtering framework. With a sensor selection strategy developed using observability theory and a recursive process noise covariance estimation procedure derived using the covariance matching principle, the proposed filtering algorithm demonstrates better estimation accuracy and filtering stability. Simulation results validate the superiority of the AFCIF. Full article

The multi-sensor information fusion particle filter (PF) has been put forward for nonlinear systems with correlated noises. The proposed algorithm uses the Taylor series expansion method, which makes the nonlinear measurement functions have a linear relationship by the intermediary function. A weighted measurement fusion PF (WMF-PF) was put forward for systems with correlated noises by applying the full rank decomposition and the weighted least square theory. Compared with the augmented optimal centralized fusion particle filter (CF-PF), it could greatly reduce the amount of calculation. Moreover, it showed asymptotic optimality as the Taylor series expansion increased. The simulation examples illustrate the effectiveness and correctness of the proposed algorithm. Full article

This paper reviews low-cost vehicle and pedestrian detection methods and compares their accuracy. The main goal of this survey is to summarize the progress achieved to date and to help identify the sensing technologies that provide high detection accuracy and meet requirements related to cost and ease of installation. Special attention is paid to wireless battery-powered detectors of small dimensions that can be quickly and effortlessly installed alongside traffic lanes (on the side of a road or on a curb) without any additional supporting structures. The comparison of detection methods presented in this paper is based on results of experiments that were conducted with a variety of sensors in a wide range of configurations. During experiments various sensor sets were analyzed. It was shown that the detection accuracy can be significantly improved by fusing data from appropriately selected set of sensors. The experimental results reveal that accurate vehicle detection can be achieved by using sets of passive sensors. Application of active sensors was necessary to obtain satisfactory results in case of pedestrian detection. Full article

During January 2014, Norway experienced unusually cold and dry weather conditions leading to very low indoor relative humidity (RH) in inhabited (heated) wooden homes. The resulting dry wood played an important role in the two most severe accidental fires in Norway recorded since 1923. The present work describes testing of low cost consumer grade weather stations for recording temperature and relative humidity as a proxy for dry wood structural fire risk assessment. Calibration of the weather stations relative humidity (RH) sensors was done in an atmosphere stabilized by water saturated LiCl, MgCl₂ and NaCl solutions, i.e., in the range 11% RH to 75% RH. When calibrated, the weather station results were well within ±3% RH. During the winter 2015/2016 weather stations were placed in the living room in eight wooden buildings. A period of significantly increased fire risk was identified in January 2016. The results from the outdoor sensors compared favorably with the readings from a local meteorological station, and showed some interesting details, such as higher ambient relative humidity for a home close to a large and comparably warmer sea surface. It was also revealed that a forecast predicting low humidity content gave results close to the observed outdoor weather station data, at least for the first 48 h forecast. Full article

We demonstrate a high-resolution temperature sensor based on optical heterodyne spectroscopy technology by virtue of the narrow linewidth characteristic of a single-frequency fiber laser. When the single-frequency ring fiber laser has a Lorentzian-linewidth <1 kHz and the temperature sensor operates in the range of 3−85 °C, an average sensitivity of 14.74 pm/°C is obtained by an optical spectrum analyzer. Furthermore, a resolution as high as ~5 × 10⁻³ °C is demonstrated through optical heterodyne spectroscopy technology by an electrical spectrum analyzer in the range of 18.26–18.71 °C with the figure of merit up to 3.1 × 10⁵ in the experiment. Full article

This study is focused on the development of water-based ITO nanoparticle dispersions and ink-jet fabrication methodology of an indium tin oxide (ITO) sensor for room temperature operations. Dimensionless correlations of material-tool-process variables were used to map the printing process and several interpretational frameworks were re-examined. A reduction of the problem to the Newtonian fluid approach was applied for the sake of simplicity. The ink properties as well as the properties of the deposited layers were tested for various nanoparticles loading. High-quality films were prepared and annealed at different temperatures. The best performing material composition, process parameters and post-print treatment conditions were used for preparing the testing sensor devices. Printed specimens were exposed to toluene vapours at room temperature. Good sensitivity, fast responses and recoveries were observed in ambient air although the n-type response mechanism to toluene is influenced by moisture in air and baseline drift was observed. Sensing response inversion was observed in an oxygen and moisture-free N₂ atmosphere which is explained by the charge-transfer mechanism between the adsorbent and adsorbate molecules. The sensitivity of the device was slightly better and the response was stable showing no drifts in the protective atmosphere. Full article

A Love wave-based sensing chip incorporating a supramolecular cryptophane A (CrypA) thin film was proposed for methane gas sensing in this work. The waveguide effect in the structure of SiO₂/36° YX LiTaO₃ will confine the acoustic wave energy in SiO₂ thin-film, which contributes well to improvement of the mass loading sensitivity. The CrypA synthesized from vanillyl alcohol by a double trimerisation method was dropped onto the wave propagation path of the sensing device, and the adsorption to methane gas molecules by supramolecular interactions in CrypA modulates the acoustic wave propagation, and the corresponding frequency shifts were connected as the sensing signal. A theoretical analysis was performed to extract the coupling of modes for sensing devices simulation. Also, the temperature self-compensation of the Love wave devices was also achieved by using reverse polarity of the temperature coefficient in each media in the waveguide structure. The developed CrypA coated Love wave sensing device was connected into the differential oscillation loop, and the corresponding gas sensitive characterization was investigated. High sensitivity, fast response, and excellent temperature stability were successfully achieved. Full article

Odour perception has been the object of fast growing research interest in the last three decades. Parallel to the study of the corresponding biological systems, attempts are being made to model the olfactory system with electronic devices. Such projects range from the fabrication of individual sensors, tuned to specific chemicals of interest, to the design of multipurpose smell detectors using arrays of sensors assembled in a sort of artificial nose. Recently, proteins have attracted increasing interest as sensing elements. In particular, soluble olfaction proteins, including odorant-binding proteins (OBPs) of vertebrates and insects, chemosensory proteins (CSPs) and Niemann-Pick type C2 (NPC2) proteins possess interesting characteristics for their use in sensing devices for odours. In fact, thanks to their compact structure, their soluble nature and small size, they are extremely stable to high temperature, refractory to proteolysis and resistant to organic solvents. Moreover, thanks to the availability of many structures solved both as apo-proteins and in complexes with some ligands, it is feasible to design mutants by replacing residues in the binding sites with the aim of synthesising proteins with better selectivity and improved physical properties, as demonstrated in a number of cases. Full article

As one of the most widely investigated matrix metalloproteinases (MMPs), MMP-9 is a significant protease which plays vital roles in many biological processes. MMP-9 can cleave many extracellular matrix (ECM) proteins to regulate ECM remodeling. It can also cleave many plasma surface proteins to release them from the cell surface. MMP-9 has been widely found to relate to the pathology of cancers, including but not limited to invasion, metastasis and angiogenesis. Some recent research evaluated the value of MMP-9 as biomarkers to various specific cancers. Besides, recent research of MMP-9 biosensors discovered various novel MMP-9 biosensors to detect this enzyme. In this review, some recent advances in exploring MMP-9 as a biomarker in different cancers are summarized, and recent discoveries of novel MMP-9 biosensors are also presented. Full article

Velocity measurement by an incremental encoder is an important issue for advanced motion control applications such as robotics. In this paper, we deal with a kind of MT-type velocity estimation method. Though the conventional MT method is well known and has been well proven in practice, it requires execution of an arithmetic division operation that prevents an efficient implementation on low-cost FPGA-based control platforms. Thus, we propose a divisionless MT-type algorithm, which can provide a similar performance in velocity estimation accuracy as the conventional method, but requiring significantly less FPGA resources, since it implements only simple arithmetic operations such as addition, subtraction, and multiplication, that can be implemented more easily on the processing hardware. Furthermore, the algorithm is fast in execution, thus, it provides the output in only a few clock cycles. Though the proposed algorithm can be described in a recursive form, the stability of the estimation process is not jeopardized, although it is an important issue in this case. Hence, the algorithm is introduced in a form which assures stability in a wide speed range. We show the implementation of the algorithm on the experimental FPGA platform. The experimental results validated the proposed divisionless MT-type algorithm fully for accurate velocity estimation. Full article

This paper presents a linear Kalman filter for yaw estimation of land vehicles using magnetic angular rate and gravity (MARG) sensors. A gyroscope measurement update depending on the vehicle status and constraining yaw estimation is introduced. To determine the vehicle status, the correlations between outputs from different sensors are analyzed based on the vehicle kinematic model and Coriolis theorem, and a vehicle status marker is constructed. In addition, a two-step measurement update method is designed. The method treats the magnetometer measurement update separately after the other updates and eliminates its impact on attitude estimation. The performances of the proposed algorithm are tested in experiments and the results show that: the introduced measurement update is an effective supplement to the magnetometer measurement update in magnetically disturbed environments; the two-step measurement update method makes attitude estimation immune to errors induced by magnetometer measurement update, and the proposed algorithm provides more reliable yaw estimation for land vehicles than the conventional algorithm. Full article

Fatty acids (FAs) are of interest to the areas of food science and medicine because they are important dietary sources of fuel for animals and play important roles in many biological processes. The health effects of FAs are different due to the diversity of olefinic bonds in the alkyl chains including number, position and configuration. However, the discrimination of FAs is difficult from a chemical sensing perspective due to the lack of diversity in terms of functional groups. Until now, only a few chemosensors have been developed for selective sensing of FAs based on their overall shape, however they are still limited in discrimination of FAs with subtle structural differences, moreover, they cannot be used for rapid and in situ inspections. Herein, for the first time, we designed a test paper for in situ colorimetric inspection for FAs based on the combination of the highly selective binding of Ag⁺ to olefinic bonds and Ag⁺ mediated color variation of 3,3′,5,5′,-tetramethylbenzidine. As a result, the sensor exhibited high sensitivity and good selectivity for five FAs with subtle structural differences. Furthermore, our method described herein was successfully applied to monitor the structural variations of FAs and quality changes in mixture edible hot pot oils with heat treatment in time course. Hence, the test paper presented herein holds great potential in the inspection of fats and edible oils in food industries. Full article

The new standard oneM2M (one machine-to-machine) aims to standardize the architecture and protocols of Internet of Things (IoT) middleware for better interoperability. Although the standard seems promising, it lacks several features for efficiently searching and retrieving IoT data which satisfy users’ intentions. In this paper, we design and develop a oneM2M-based query engine, called OMQ, that provides a real-time processing over IoT data streams. For this purpose, we define a query language which enables users to retrieve IoT data from data sources using JavaScript Object Notation (JSON). We also propose efficient query processing algorithms which utilizes the oneM2M architecture consisting of two nodes: (1) the IoT node and (2) the infrastructure node. IoT nodes of OMQ are mainly sensor devices execute user queries the aggregate, transform and filter operators, whereas the infrastructure node handles the join operator of user queries. Since the query processing algorithms are implemented as the hybrid infrastructure-edge processing, user queries can be executed efficiently in each IoT node rather than only in the infrastructure node. Thus, our OMQ system reduces the query processing time and the network bandwidth. We conducted a comprehensive evaluation of OMQ using a real and a synthetic data set. Experimental results demonstrate the feasibility and efficiency of OMQ system for executing queries and transferring data from each IoT node. Full article

Amidst the rapid development of the fifth generation (5G) networks, Internet of Things (IoT) is considered as one of the most important part of 5G next generation networks as it can support massive object communications. These massive object communications in the context of IoT is expected to consume a huge power. Furthermore, IoT sensors or devices are rather power constrained and are mostly battery operated. Therefore, energy efficiency of such network of IoT devices is a major concern. On the other hand, energy harvesting (EH) is an emerging paradigm that allows the wireless nodes to recharge themselves through radio frequency (RF) signals directed to them from the source node and then relaying or transmitting the information. Although a myriad of works have been carried out in the literature for EH, the vast majority of those works only consider RF EH at the relay node and successfully transmitting the source node data. Those approaches do not consider the data transmission of the relay node that may be an energy deprived IoT node which needs to transmit its own data along with the source node data to their respective destination nodes. Therefore, in this paper, we envisioned a RF EH and information transmission system based on time switching (TS) relaying, power splitting (PS) relaying and non-orthogonal multiple access (NOMA) which is suitable for wireless powered IoT relay systems. A source node information data is relayed through power constrained IoT relay node $Io{T}_R$ that first harvests the energy from source node RF signal using either TS and PS relaying protocol and then transmits the source node information along with its information using NOMA protocol to the respective destination nodes. Considering NOMA as a transmission protocol, we have mathematically derived analytical expressions for TS and PS relaying protocol for our proposed system. We have also formulated an algorithm to find out optimal TS and PS factor that maximizes the sum-throughput for our proposed system. Our proposed system analytical results for TS and PS protocol are validated by the simulation results. Full article

Fibre-reinforced polymer (FRP)-reinforced concrete members exhibit low ductility due to the linear-elastic behaviour of FRP materials. Concrete members reinforced by hybrid FRP–steel bars can improve strength and ductility simultaneously. In this study, the plastic hinge problem of hybrid FRP–steel reinforced concrete beams was numerically assessed through finite element analysis (FEA). Firstly, a finite element model was proposed to validate the numerical method by comparing the simulation results with the test results. Then, three plastic hinge regions—the rebar yielding zone, concrete crushing zone, and curvature localisation zone—of the hybrid reinforced concrete beams were analysed in detail. Finally, the effects of the main parameters, including the beam aspect ratio, concrete grade, steel yield strength, steel reinforcement ratio, steel hardening modulus, and FRP elastic modulus on the lengths of the three plastic zones, were systematically evaluated through parametric studies. It is determined that the hybrid reinforcement ratio exerts a significant effect on the plastic hinge lengths. The larger the hybrid reinforcement ratio, the larger is the extent of the rebar yielding zone and curvature localisation zone. It is also determined that the beam aspect ratio, concrete compressive strength, and steel hardening ratio exert significant positive effects on the length of the rebar yielding zone. Full article

Electronic nose (E-nose) systems have become popular in food and fruit quality evaluation because of their rapid and repeatable availability and robustness. In this paper, we propose an E-nose system that has potential as a non-destructive system for monitoring variation in the volatile organic compounds produced by fruit during the maturing process. In addition to the E-nose system, we also propose a camera system to monitor the peel color of fruit as another feature for identification. By incorporating E-nose and camera systems together, we propose a non-destructive solution for fruit maturity monitoring. The dual E-nose/camera system presents the best Fisher class separability measure and shows a perfect classification of the four maturity stages of a banana: Unripe, half-ripe, fully ripe, and overripe. Full article

As a possible implementation of a low-power wide-area network (LPWAN), Long Range (LoRa) technology is considered to be the future wireless communication standard for the Internet of Things (IoT) as it offers competitive features, such as a long communication range, low cost, and reduced power consumption, which make it an optimum alternative to the current wireless sensor networks and conventional cellular technologies. However, the limited bandwidth available for physical layer modulation in LoRa makes it unsuitable for high bit rate data transfer from devices like image sensors. In this paper, we propose a new method for mangrove forest monitoring in Malaysia, wherein we transfer image sensor data over the LoRa physical layer (PHY) in a node-to-node network model. In implementing this method, we produce a novel scheme for overcoming the bandwidth limitation of LoRa. With this scheme the images, which requires high data rate to transfer, collected by the sensor are encrypted as hexadecimal data and then split into packets for transfer via the LoRa physical layer (PHY). To assess the quality of images transferred using this scheme, we measured the packet loss rate, peak signal-to-noise ratio (PSNR), and structural similarity (SSIM) index of each image. These measurements verify the proposed scheme for image transmission, and support the industrial and academic trend which promotes LoRa as the future solution for IoT infrastructure. Full article

The paper considers the construction of a piezoelectric accelerometer capable of measuring constant linear acceleration. A number of designs are proposed that make it possible to achieve high sensitivity with small dimensions and a wide frequency band (from 10⁻⁵ Hz). The finite element model of the proposed design was investigated, and its output characteristic and scale factor (36 mV/g) were obtained. Full article

A portable surface plasmon resonance (SPR) instrument was tested for the first time for the detection of oligonucleotide sequences derived from the 16S rRNA gene of Oleispira antarctica RB-8, a bioindicator species of marine oil contamination, using morpholino-functionalized sensor surfaces. We evaluated the stability and specificity of morpholino coated sensor surfaces and tested two signal amplification regimes: (1) sequential injection of sample followed by magnetic bead amplifier and (2) a single injection of magnetic bead captured oligo. We found that the sensor surfaces could be regenerated for at least 85 consecutive sample injections without significant loss of signal intensity. Regarding specificity, the assay clearly differentiated analytes with only one or two mismatches. Signal intensities of mismatch oligos were lower than the exact match target at identical concentrations down to 200 nM, in standard phosphate buffered saline with 0.1 % Tween-20 added. Signal amplification was achieved with both strategies; however, significantly higher response was observed with the sequential approach (up to 16-fold), where first the binding of biotin-probe-labeled target oligo took place on the sensor surface, followed by the binding of the streptavidin magnetic beads onto the immobilized targets. Our experiments so far indicate that a simple coating procedure in combination with a relatively cost-efficient magnetic-bead-based signal amplification will provide robust SPR based nucleic acid sensing down to 0.5 nM of a 45-nucleotide long oligo target (7.2 ng/mL). Full article

Smart structures mimic biological systems by using thousands of sensors serving as a nervous system analog. One approach to give structures this sensing ability is to develop a multifunctional sensor network. Previous work has demonstrated stretchable sensor networks consisting of temperature sensors and impact detectors for monitoring external environments and interacting with other objects. The objective of this work is to develop distributed, robust and reliable strain gauges for obtaining the strain distribution of a designated region on the target structure. Here, we report a stretchable network that has 27 rosette strain gauges, 6 resistive temperature devices and 8 piezoelectric transducers symmetrically distributed over an area of 150 × 150 mm to map and quantify multiple physical stimuli with a spatial resolution of 2.5 × 2.5 mm. We performed computational modeling of the network stretching process to improve measurement accuracy and conducted experimental characterizations of the microfabricated strain gauges to verify their gauge factor and temperature coefficient. Collectively, the results represent a robust and reliable sensing system that is able to generate a distributed strain profile of a common structure. The reported strain gauge network may find a wide range of applications in morphing wings, smart buildings, autonomous cars and intelligent robots. Full article

The zero velocity update (ZUPT) algorithm is an effective way to suppress the error growth for a foot-mounted pedestrian navigation system. To make ZUPT work properly, it is necessary to detect zero velocity intervals correctly. Existing zero velocity detection methods cannot provide good performance at high gait speeds or stair climbing. An adaptive zero velocity detection approach based on multi-sensor fusion is proposed in this paper. The measurements of an accelerometer, gyroscope and pressure sensor were employed to construct a zero-velocity detector. Then, the adaptive threshold was proposed to improve the accuracy of the detector under various motion modes. In addition, to eliminate the height drift, a stairs recognition method was developed to distinguish staircase movement from level walking. Detection performance was examined with experimental data collected at varying motion modes in real scenarios. The experimental results indicate that the proposed method can correctly detect zero velocity intervals under various motion modes. Full article

A polarimetric-phase-enhanced intensity interrogation scheme leveraging the polarization-dependent sharp phase change induced by the surface wave excitation at a low-optical-loss sensor’s surface is proposed and experimentally demonstrated. Based on a simple setup with no moving parts during interrogation, a polarimetric-phase-enhanced intensity can be obtained by subtracting the reflected intensities of two beam polarization states. Our results show a ~4-fold sensitivity increase compared to traditional intensity detection schemes for similar sensors. As novel surface wave optical sensors are designed and engineered with optimized phase responses, this scheme offers a low-complexity solution for such devices instead of traditional phase interrogation schemes. Full article

Wireless Sensor Networks (WSN) are enabler technologies for the implementation of the Internet of Things (IoT) concept. WSNs provide an adequate infrastructure for the last-link communication with smart objects. Nevertheless, the wireless communication medium being inherently unreliable, there is the need to increase its communication reliability. Techniques based on the use of cooperative communication concepts are one of the ways to achieve this target. Within cooperative communication techniques, nodes selected as relays transmit not only their own data, but also cooperate by retransmitting data from other nodes. A fundamental step to improve the communication reliability of WSNs is related to the use of efficient relay selection techniques. This paper proposes a relay selection technique based on multiple criteria to select the smallest number of relay nodes and, at the same time, to ensure an adequate operation of the network. Additionally, two relay updating schemes are also investigated, defining periodic and adaptive updating policies. The simulation results show that both proposed schemes, named Periodic Relay Selection and Adaptive Relay Selection, significantly improve the communication reliability of the network, when compared to other state-of-the-art relay selection schemes. Full article

As a typical machine olfactory system index, the accuracy of hybrid gas identification and concentration detection is low. This paper proposes a novel hybrid gas identification and concentration detection method. In this method, Kernel Principal Component Analysis (KPCA) is employed to extract the nonlinear mixed gas characteristics of different components, and then K-nearest neighbour algorithm (KNN) classification modelling is utilized to realize the recognition of the target gas. In addition, this method adopts a multivariable relevance vector machine (MVRVM) to regress the multi-input nonlinear signal to realize the detection of the concentration of the hybrid gas. The proposed method is validated by using CO and CH₄ as the experimental system samples. The experimental results illustrate that the accuracy of the proposed method reaches 98.33%, which is 5.83% and 14.16% higher than that of principal component analysis (PCA) and independent component analysis (ICA), respectively. For the hybrid gas concentration detection method, the CO and CH₄ concentration detection average relative errors are reduced to 5.58% and 5.38%, respectively. Full article

Biometric authentication is popular in authentication systems, and gesture as a carrier of behavior characteristics has the advantages of being difficult to imitate and containing abundant information. This research aims to use three-dimensional (3D) depth information of gesture movement to perform authentication with less user effort. We propose an approach based on depth cameras, which satisfies three requirements: Can authenticate from a single, customized gesture; achieves high accuracy without an excessive number of gestures for training; and continues learning the gesture during use of the system. To satisfy these requirements respectively: We use a sparse autoencoder to memorize the single gesture; we employ data augmentation technology to solve the problem of insufficient data; and we use incremental learning technology for allowing the system to memorize the gesture incrementally over time. An experiment has been performed on different gestures in different user situations that demonstrates the accuracy of one-class classification (OCC), and proves the effectiveness and reliability of the approach. Gesture authentication based on 3D depth cameras could be achieved with reduced user effort. Full article

Concrete-filled square steel tube column (CFSSTC) joints are the most important parts of concrete-filled steel tube frame structures. It is of great significance to study the damage of CFSSTC joints under the seismic loads. In this paper, embedded piezoceramic transducers are used to monitor the damage of core concrete of CFSSTC joints under cyclic loading and surface-bonded piezoceramic disks are used to monitor the debonding damage of the steel tube and core concrete of two specimens. The damages of the joints under different loading levels and different loading cycles are evaluated by the received signal of the piezoceramic transducers. The experimental results show that the amplitude of the signal attenuates obviously with the appearance of damage in the joints, and the degree of attenuation increases with the development of the damage. The monitoring results from piezoceramic transducers are basically consistent with the hysteresis loops and skeleton curves of the CFSSTC joints during the cyclic loading. The effectiveness of the piezoceramic transducers are verified by the experimental results in structural health monitoring of the CFSSTC joint under cyclic loading. Full article

Fog computing, which places computing resources close to IoT devices, can offer low latency data processing for IoT applications. With software-defined networking (SDN), fog computing can enable network control logics to become programmable and run on a decoupled control plane, rather than on a physical switch. Therefore, network switches are controlled via the control plane. However, existing control planes have limitations in providing isolation and high performance, which are crucial to support multi-tenancy and scalability in fog computing. In this paper, we present optimization techniques for Linux to provide isolation and high performance for the control plane of SDN. The new techniques are (1) separate execution environment (SE2), which separates the execution environments between multiple control planes, and (2) separate packet processing (SP2), which reduces the complexity of the existing network stack in Linux. We evaluate the proposed techniques on commodity hardware and show that the maximum performance of a control plane increases by four times compared to the native Linux while providing strong isolation. Full article

At present, sensor-based E-Healthcare systems are attracting more and more attention from academia and industry. E-Healthcare systems are usually a Wireless Body Area Network (WBANs), which can monitor or diagnose human health by placing miniaturized, low-power sensor nodes in or on patient’s bodies to measure various physiological parameters. However, in this process, WBAN nodes usually use batteries, and especially for implantable flexible nodes, it is difficult to accomplish the battery replacement, so the energy that the node can carry is very limited, making the efficient use of energy the most important problem to consider when designing WBAN routing algorithms. By considering factors such as residual energy of node, the importance level of nodes, path cost and path energy difference ratios, this paper gives a definition of Optimal Path of Energy Consumption (OPEC) in WBANs, and designs the Optimal Energy Consumption routing based on Artificial Bee Colony (ABC) for WBANs (OEABC). A performance simulation is carried out to verify the effectiveness of the OEABC. Simulation results demonstrate that compared with the genetic algorithm and ant colony algorithm, the proposed OEABC has a better energy efficiency and faster convergence rate. Full article

Excessive pressure and shear stress while walking cause a risk of callus formation, which eventually causes foot ulcers in patients with diabetes mellitus. Callus under the second metatarsal head (MTH) has been associated with increased shear stress/pressure ratios (SPR). Callus under the fifth MTH has been associated with increased peak shear stress (PSS). The purpose of this study is to examine whether the effect of the suitable size and width of shoes prevents diabetic foot ulcers under the second and fifth MTH. We measured the pressure and shear stress by testing three kinds of sizes and two types of width of shoes. Significant difference was not observed in the SPR under the second MTH among different sizes of shoes. However, the pressure and shear stress were significantly lower when putting on shoes of fit size compared with larger sizes. The PSS under the fifth MTH was significantly smaller when putting on shoes of fit width compared with those of narrow width. Wearing shoes of fit size and width has the potential to prevent callus formation by reducing the pressure and shear stress constituting SPR under the second MTH and PSS under the fifth MTH. Full article

Self-localization is a crucial task for intelligent vehicles. Existing localization methods usually require high-cost IMU (Inertial Measurement Unit) or expensive LiDAR sensors (e.g., Velodyne HDL-64E). In this paper, we propose a low-cost yet accurate localization solution by using a custom-level GPS receiver and a low-cost camera with the support of HD map. Unlike existing HD map-based methods, which usually requires unique landmarks within the sensed range, the proposed method utilizes common lane lines for vehicle localization by using Kalman filter to fuse the GPS, monocular vision, and HD map for more accurate vehicle localization. In the Kalman filter framework, the observations consist of two parts. One is the raw GPS coordinate. The other is the lateral distance between the vehicle and the lane, which is computed from the monocular camera. The HD map plays the role of providing reference position information and correlating the local lateral distance from the vision and the GPS coordinates so as to formulate a linear Kalman filter. In the prediction step, we propose using a data-driven motion model rather than a Kinematic model, which is more adaptive and flexible. The proposed method has been tested with both simulation data and real data collected in the field. The results demonstrate that the localization errors from the proposed method are less than half or even one-third of the original GPS positioning errors by using low cost sensors with HD map support. Experimental results also demonstrate that the integration of the proposed method into existing ones can greatly enhance the localization results. Full article

Due to the limited availability of battery power of the acoustic node, an efficient utilization is desired. Additionally, the aquatic environment is harsh; therefore, the battery cannot be replaced, which leaves the network prone to sudden failures. Thus, an efficient node battery dissipation is required to prolong the network lifespan and optimize the available resources. In this paper, we propose four schemes: Adaptive transmission range in WDFAD-Depth-Based Routing (DBR) (A-DBR), Cluster-based WDFAD-DBR (C-DBR), Backward transmission-based WDFAD-DBR (B-DBR) and Collision Avoidance-based WDFAD-DBR (CA-DBR) for Internet of Things-enabled Underwater Wireless Sensor Networks (IoT, UWSNs). A-DBR adaptively adjusts its transmission range to avoid the void node for forwarding data packets at the sink, while C-DBR minimizes end-to-end delay along with energy consumption by making small clusters of nodes gather data. In continuous transmission range adjustment, energy consumption increases exponentially; thus, in B-DBR, a fall back recovery mechanism is used to find an alternative route to deliver the data packet at the destination node with minimal energy dissipation; whereas, CA-DBR uses a fall back mechanism along with the selection of the potential node that has the minimum number of neighbors to minimize collision on the acoustic channel. Simulation results show that our schemes outperform the baseline solution in terms of average packet delivery ratio, energy tax, end-to-end delay and accumulated propagation distance. Full article

Mobilization, verticalization and position change are mandatory for severely affected neurological patients in early neurorehabilitation in order to improve neurological status and prevent complications. However, with the exception of hospitals and rehabilitation facilities, this activity is not usually monitored and so far the automated monitoring of position changes in immobile patients has not been investigated. Therefore, we investigated whether accelerometers on the upper trunk could reliably detect body position changes in immobile patients. Thirty immobile patients in early neurorehabilitation (Barthel Index ≤ 30) were enrolled. Two tri-axial accelerometers were placed on the upper trunk and on the thigh. Information on the position and position changes of the subject were derived from accelerometer data and compared to standard written documentation in the hospital over 24 h. Frequency and duration of different body positions (supine, sidelying, sitting) were measured. Data are presented as mean ± SEM. Groups were compared using one-way ANOVA or Kruskal-Wallis-test. Differences were considered significant if p < 0.05. Trunk sensors detected 100% and thigh sensors 66% of position changes (p = 0.0004) compared to standard care documentation. Furthermore, trunk recording also detected additional spontaneous body position changes that were not documented in standard care (81.8 ± 4.4% of all position changes were documented in standard care documentation) (p < 0.0001). We found that accelerometric trunk sensors are suitable for recording position changes and mobilization of severely affected patients. Our findings suggest that using accelerometers for care documentation is useful for monitoring position changes and mobilization frequencies in and outside of hospital for severely affected neurological patients. Accelerometric sensors may be valuable in monitoring continuation of care plans after intensive neurorehabilitation. Full article

In recent years, Thorpe analysis has been used to retrieve the characteristics of turbulence in free atmosphere from balloon-borne sensor data. However, previous studies have mainly focused on the mid-high latitude region, and this method is still rarely applied at heights above 30 km, especially above 35 km. Therefore, seven sets of upper air (>35 km) sounding data from the Changsha Sounding Station (28°12′ N, 113°05′ E), China are analyzed with Thorpe analysis in this article. It is noted that, in the troposphere, Thorpe analysis can better retrieve the turbulence distribution and the corresponding turbulence parameters. Also, because of the thicker troposphere at low latitudes, the values of the Thorpe scale $L_T$ and turbulent energy dissipation rate $\epsilon$ remain greater in a larger height range. In the stratosphere below the height of 35 km, the obtained $\epsilon$ is higher, and Thorpe analysis can only be used to analyze the characteristics of large-scale turbulence. In the stratosphere at a height of 35–40 km, because of the interference of sensor noise, Thorpe analysis can only help to retrieve the rough distribution position of large-scale turbulence, while it can hardly help with the calculation of the turbulence parameters. Full article

As a dedicated communication protocol for Internet-of-Things, narrowband internet of things (NB-IoT) needs to establish the communication link rapidly and reduce retransmissions as much as possible to achieve low power consumption and stable performance. To achieve these targets, the low-power scheme of the initial cell search and frequency tracking is investigated in this paper. The cell search process can be subdivided into narrowband primary synchronization signal (NPSS) detection and narrowband secondary synchronization signal (NSSS) detection. We present an NPSS detection method whose timing metric is composed of symbol-wise autocorrelation and a dedicated normalization factor. After the detection of NPSS, the symbol timing and fractional frequency offset estimation is implemented in a resource-efficient way. NSSS detection is conducted in the frequency domain with a calculation-reduced algorithm based on the features of NSSS sequences. To compensate the accumulated frequency offset during uplink transmission, a pilot-aided rapid frequency tracking algorithm is proposed. The simulation results of the proposed cell search scheme are outstanding in both normal coverage and extended coverage NB-IoT scenarios, and the accumulated frequency offset can be estimated with high efficiency. Full article

Global navigation satellite systems (GNSS) have become a primary navigation means for aircraft. However, the signal power of GNSS is very weak, and its service can be disrupted at any time when there is interference or jamming. For this reason, the Federal Aviation Administration (FAA) in the United States has recently chosen a distance measuring equipment (DME)-based aircraft navigation technique, called DME/DME, as an alternative aircraft navigation means for use by around 2030. The reason that the FAA plans to use DME/DME in such a short duration, by around 2030, is presumed to be because the ranging accuracy of DMEs is between 70 to 300 m, which is about 7 to 30 times worse than that of GNSS. Thus, a significant loss of positioning performance is unavoidable with current DMEs. To make DME/DME a more competent alternative positioning source, this paper proposes an advanced DME that could provide a ranging accuracy of around 30 m by employing a recently developed Stretched-Front-Leg (SFOL) pulse. The paper introduces optimal ground station augmentation algorithms that help to efficiently transform the current DME ground network to enable a DME/DME positioning accuracy of up to 0.3 nm or 92.6 m with a minimal number of new ground DME sites. The positioning performance and augmented ground network using the proposed SFOL pulse-based DME are evaluated in two regions which have distinct terrain conditions. Full article

Target tracking technologies in wireless sensor network (WSNs) environments fall into two categories: active and passive schemes. Unlike with the active positioning schemes, in which the targets are required to hold cooperative devices, the research on passive tracking, i.e., tracking device-free targets, has recently showed promise. In the WSN, device-free targets can be tracked by sensing radio frequency tomography (RFT) on the line-of-sight links (LOSLs). In this paper, we propose a passive tracking scheme exploiting both adaptive-networking LOSL webs and geometric constraint methodology for tracking single targets, as well as multiple targets. Regarding fundamental knowledge, we firstly explore the spatial diversity technique for RFT detection in realistic situations. Then, we analyze the power consumption of the WSN and propose an adaptive networking scheme for the purpose of energy conservation. Instead of maintaining a fixed LOSL density, the proposed scheme can adaptively adjust the networking level to save energy while guaranteeing tracking accuracy. The effectiveness of the proposed scheme is evaluated with computer simulations. According to the results, it is observed that the proposed scheme can sufficiently reduce power consumption, while providing qualified tracking performance. Full article

The purpose of this work was to study bias error in acceleration-based transfer alignment, which is probably caused by cross-correlation between the dynamic lever-arm and the linear motion of a ship. A new prediction model for the cross-correlation-caused error is proposed in this paper. We adopt the Bernoulli-Euler Beam as a simplified ship hull-girder model to verify the existence of the cross-correlation. Then, the mathematical mechanism and the prediction model of the bias error are deduced via the ordinary least squares theory. Three factors influence the bias error in the prediction model: the amplitude of the dynamic lever arm acceleration, the amplitude of the ship motion acceleration, and the cross-correlation between them. Simulation experiments are then conducted to test the influence of the factors. The results show that the mechanism analysis is reasonable, and the bias error prediction model is in agreement with the experimental results. Thus, the proposed prediction model has the potential to deduce the bias error in high-accuracy transfer alignment. Full article

The pine processionary moth (Thaumetopoea pityocampa Dennis and Schiff.), one of the major defoliating insects in Mediterranean forests, has become an increasing threat to the forest health of the region over the past two decades. After a recent outbreak of T. pityocampa in Catalonia, Spain, we attempted to estimate the damage severity by capturing the maximum defoliation period over winter between pre-outbreak and post-outbreak images. The difference in vegetation index (dVI) derived from Landsat 8 was used as the change detection indicator and was further calibrated with Unmanned Aerial Vehicle (UAV) imagery. Regression models between predicted dVIs and observed defoliation degrees by UAV were compared among five selected dVIs for the coefficient of determination. Our results found the highest R-squared value (0.815) using Moisture Stress Index (MSI), with an overall accuracy of 72%, as a promising approach for estimating the severity of defoliation in affected areas where ground-truth data is limited. We concluded with the high potential of using UAVs as an alternative method to obtain ground-truth data for cost-effectively monitoring forest health. In future studies, combining UAV images with satellite data may be considered to validate model predictions of the forest condition for developing ecosystem service tools. Full article

Due to the significant multipath and Doppler effects in the underwater acoustic (UWA) channel, the quality of the received signal is degraded, which seriously affects the performance of UWA communication. The paper proposes a time reversal UWA communication method combined with a symbol-based Doppler compensation (SBDC) technique to solve those problems. A single element time reversal mirror (TRM) is used to realize channel equalization and mitigate the inter-symbol interference (ISI) resulting from multipath propagation. The SBDC technique is subsequently used to compensate Doppler effects in the received signal, thereby reducing the bit error rate (BER) and improving the communication performance. In order to verify the performance of the proposed communication method, some simulations with real sounding channels were performed. Moreover, a field UWA communication experiment was conducted in the Songhua River (Harbin, China). The UWA communication experiment achieves nearly error-free performance with a communication rate of 100 bit/s in the bandwidth of 2 kHz. The results of the experiment demonstrate the feasibility and robustness of the proposed UWA communication method. Full article

In this paper, a multifunctional biosensing platform for sensitively detecting Hg²⁺ and Ag⁺, based on ion-mediated base mismatch, fluorescent labeling, and strand displacement, is introduced. The sensor can also be used as an OR logic gate, the multifunctional design of sensors is realized. Firstly, orthogonal experiments with three factors and three levels were carried out on the designed sensor, and preliminary optimization of conditions was performed for subsequent experiments. Next, the designed sensor was tested the specificity and target selectivity under the optimized conditions, and the application to actual environmental samples further verified the feasibility. Generally, this is a convenient, fast, stable, and low-cost method that provides a variety of ideas and an experimental basis for subsequent research. Full article

In this study, a label-free fluorescent, enzyme-free, simple, highly sensitive AND logic gate aptasensor was developed for the detection of adenosine triphosphate (ATP). Double-stranded deoxyribonucleic acid (DNA) with cohesive ends was attached to graphene oxide (GO) to form an aptasensor probe. ATP and single-stranded DNA were used as input signals. Fluorescence intensity of PicoGreen dye was used as an output signal. The biosensor-related performances, including the logic gate construction, reaction time, linearity, sensitivity, and specificity, were investigated and the results showed that an AND logic gate was successfully constructed. The ATP detection range was found to be 20 to 400 nM (R² = 0.9943) with limit of detection (LOD) of 142.6 pM, and the sensitivity range was 1.846 × 10⁶ to 2.988 × 10⁶ M⁻¹. This method for the detection of ATP has the characteristics of being simple, low cost, and highly sensitive. Full article

This work presents a numerical simulation of a Hartmann-Shack wavefront sensor (WFS) that assesses the impact of integrated electronic circuitry on the sensor performance, by evaluating a full detection chain encompassing wavefront sampling, photodetection, electronic circuitry and wavefront reconstruction. This platform links dedicated C algorithms for WFS to a SPICE circuit simulator for integrated electronics. The complete codes can be easily replaced in order to represent different detection or reconstruction methods, while the circuit simulator employs reliable models of either off-the-shelf circuit components or custom integrated circuit modules. The most relevant role of this platform is to enable the evaluation of the applicability and constraints of the focal plane of a given wavefront sensor prior to the actual fabrication of the detector chip. In this paper, we will present the simulation results for a Hartmann-Shack wavefront sensor with an orthogonal array of quad-cells (QC) integrated along with active-pixel (active-pixel sensor (APS)) circuitry and analog-to-digital converters (ADC) on a “complementary metal oxide semiconductor” (CMOS) process and deploying a modal wavefront reconstructor. This extended simulation capability for wavefront sensors enables the test and verification of different photosensitive and circuitry topologies for position-sensitive detectors combined with the simulation of sampling microlenses and reconstruction algorithms, with the goal of enhancing the accuracy in the prediction of the wavefront-sensor performance before a detector CMOS chip is actually fabricated. Full article

Poly(N-isopropylacrylamide) (PNIPAM) nanoparticles formed in water-methanol binary solvent were successfully deposited on a resonator surface at room temperature by exploiting the cononsolvency effect on the phase transition of PNIPAM aqueous solutions. Scanning electron microscopic observation revealed that the nanoparticles were secondary and made up of agglomerated primary spherical particles of about 10-nm diameter, buried in the film. The magnitude of the sensor response toward HCl gas was larger than that of the nanoparticle sensor prepared from pure water solvent, and the sensitivity to 1 ppm of HCl of sensor-coated nanoparticles based on the present method was 3.3 Hz/ppm. The recovery of the sensors was less than 90% at first cycle measurement, but had improved to almost 100% at the third cycle. Full article

The geomagnetic field is as fundamental a constituent of passive navigation as Earth’s gravity. In cases where no other external attitude reference is available, for the direct heading angle estimation by a typical magnetic compass, a two-step optimized correction algorithm is proposed to correct the model coefficients caused by hard and soft iron nearby. Specifically, in Step 1, a Levenberg-Marquardt (L-M) fitting estimator with an ellipsoid constraint is applied to solve the hard magnetic coefficients. In Step 2, a Lagrange multiplier estimator is used to deal with the soft magnetic iron circumstance. The essential attribute of “the two-step” lies in its eliminating the coupling effects of hard and soft magnetic fields, and their mutual interferences on the pure geomagnetic field. Under the conditions of non-deterministic magnetic interference sources with noise, the numerical simulation by referring to International Geomagnetic Reference Field (IGRF), and the laboratory tests based upon the turntable experiments with Honeywell HMR3000 compass (Honeywell, Morristown, NJ, USA) conducted, the experimental results indicate that, in the presence of the variation of multi-magnetic interferences, the RMSE (Root Mean Square Error) value of the estimated total magnetic flux density by the proposed two-step estimator falls to 0.125 μT from its initial 2.503 μT, and the mean values of the heading angle error estimates are less than 1°. The proposed solution therefore, exhibits ideal convergent properties, fairly meeting the accuracy requirements of non-tactical level navigation applications. Full article

We demonstrate a simple-to-fabricate refractometer based on the inscription of fiber Bragg gratings in a special C-shaped optical fiber. The C-shaped fiber was drawn into shape using a quarter cladding removed preform of a commercial standard single-mode fiber by simple machining. The sensor did not suffer from cross-sensitivity of the refractive index with ambient temperature fluctuations, commonly occurring with many optical fiber refractometers. A refractive index sensitivity of 1300 pm per refractive index unit (RIU) was achieved without employing any additional sensitization techniques such as tapering or etching. Full article

Foggy days pose many difficulties for outdoor camera surveillance systems. On foggy days, the optical attenuation and scattering effects of the medium significantly distort and degenerate the scene radiation, making it noisy and indistinguishable. Aiming to solve this problem, in this paper we propose a novel object detection method that has the ability to exploit the information in the color and depth domains. To prevent the error propagation problem, we clean the depth information before the training process and remove false samples from the database. A domain adaptation strategy is employed to adaptively fuse the decisions obtained in the color and depth domains. In the experiments, we evaluate the contribution of the depth information for object detection on foggy days. Moreover, the advantages of the multiple-domain adaptation strategy are experimentally demonstrated via comparison with other methods. Full article

One of the most common symptoms observed among most of the Parkinson’s disease patients that affects movement pattern and is also related to the risk of fall, is usually termed as “freezing of gait (FoG)”. To allow systematic assessment of FoG, objective quantification of gait parameters and automatic detection of FoG are needed. This will help in personalizing the treatment. In this paper, the objectives of the study are (1) quantification of gait parameters in an objective manner by using the data collected from wearable accelerometers; (2) comparison of five estimated gait parameters from the proposed algorithm with their counterparts obtained from the 3D motion capture system in terms of mean error rate and Pearson’s correlation coefficient (PCC); (3) automatic discrimination of FoG patients from no FoG patients using machine learning techniques. It was found that the five gait parameters have a high level of agreement with PCC ranging from 0.961 to 0.984. The mean error rate between the estimated gait parameters from accelerometer-based approach and 3D motion capture system was found to be less than 10%. The performances of the classifiers are compared on the basis of accuracy. The best result was accomplished with the SVM classifier with an accuracy of approximately 88%. The proposed approach shows enough evidence that makes it applicable in a real-life scenario where the wearable accelerometer-based system would be recommended to assess and monitor the FoG. Full article

In this paper, a quantum-based method for measuring the microwave magnetic field in free space is presented by exploring atomic Rabi resonance in the clock transition of ¹³³Cs. A compact cesium glass cell serving as the microwave magnetic field sensing head was used to measure the spatial distribution of microwave radiation from an open-ended waveguide antenna. The measured microwave magnetic field was not restricted by other microwave devices. The longitudinal distribution of the magnetic field was measured. The experimental results measured by the sensor were in agreement with the simulation. In addition, a slightly electromagnetic perturbation caused by the glass cell was investigated through simulation calculations. Full article

In order to improve the performance of storage and transmission of massive hyperspectral data, a prediction-based spatial-spectral adaptive hyperspectral compressive sensing (PSSAHCS) algorithm is proposed. Firstly, the spatial block size of hyperspectral images is adaptively obtained according to the spatial self-correlation coefficient. Secondly, a k-means clustering algorithm is used to group the hyperspectral images. Thirdly, we use a local means and local standard deviations (LMLSD) algorithm to find the optimal image in the group as the key band, and the non-key bands in the group can be smoothed by linear prediction. Fourthly, the random Gaussian measurement matrix is used as the sampling matrix, and the discrete cosine transform (DCT) matrix serves as the sparse basis. Finally, the stagewise orthogonal matching pursuit (StOMP) is used to reconstruct the hyperspectral images. The experimental results show that the proposed PSSAHCS algorithm can achieve better evaluation results—the subjective evaluation, the peak signal-to-noise ratio, and the spatial autocorrelation coefficient in the spatial domain, and spectral curve comparison and correlation between spectra-reconstructed performance in the spectral domain—than those of single spectral compression sensing (SSCS), block hyperspectral compressive sensing (BHCS), and adaptive grouping distributed compressive sensing (AGDCS). PSSAHCS can not only compress and reconstruct hyperspectral images effectively, but also has strong denoise performance. Full article

Powdery mildew is one of the dominant diseases in winter wheat. The accurate monitoring of powdery mildew is important for crop management and production. Satellite-based remote sensing monitoring has been proven as an efficient tool for regional disease detection and monitoring. However, the information provided by single-date satellite scene is hard to achieve acceptable accuracy for powdery mildew disease, and incorporation of early period contextual information of winter wheat can improve this situation. In this study, a multi-temporal satellite data based powdery mildew detecting approach had been developed for regional disease mapping. Firstly, the Lansat-8 scenes that covered six winter wheat growth periods (expressed in chronological order as periods 1 to 6) were collected to calculate typical vegetation indices (VIs), which include disease water stress index (DSWI), optimized soil adjusted vegetation index (OSAVI), shortwave infrared water stress index (SIWSI), and triangular vegetation index (TVI). A multi-temporal VIs-based k-nearest neighbors (KNN) approach was then developed to produce the regional disease distribution. Meanwhile, a backward stepwise elimination method was used to confirm the optimal multi-temporal combination for KNN monitoring model. A classification and regression tree (CART) and back propagation neural networks (BPNN) approaches were used for comparison and validation of initial results. VIs of all periods except 1 and 3 provided the best multi-temporal data set for winter wheat powdery mildew monitoring. Compared with the traditional single-date (period 6) image, the multi-temporal images based KNN approach provided more disease information during the disease development, and had an accuracy of 84.6%. Meanwhile, the accuracy of the proposed approach had 11.5% and 3.8% higher than the multi-temporal images-based CART and BPNN models’, respectively. These results suggest that the use of satellite images for early critical disease infection periods is essential for improving the accuracy of monitoring models. Additionally, satellite imagery also assists in monitoring powdery mildew in late wheat growth periods. Full article

This paper studies a simultaneous wireless information and power transfer (SWIPT)-aware fog computing by using a simple model, where a sensor harvests energy and receives information from a hybrid access point (HAP) through power splitting (PS) receiver architecture. Two information processing modes, local computing and fog offloading modes are investigated. For such a system, two optimization problems are formulated to minimize the sensor’s required power for the two modes under the information rate and energy harvesting constraints by jointly optimizing the time assignment and the transmit power, as well as the PS ratio. The closed-form and semi-closed-form solutions to the proposed optimization problems are derived based on convex optimization theory. Simulation results show that neither mode is always superior to the other one. It also shows that when the number of logic operations per bit associated with local computing is less than a certain value, the local computing mode is a better choice; otherwise, the fog offloading mode should be selected. In addition, the mode selection associated with the positions of the user for fixed HAP and fog server (FS) is also discussed. Full article

This paper investigates the joint impact of channel estimation errors (CEEs) and hardware impairments (HIs) on the performance of a cognitive satellite-terrestrial relay network (CSTRN), where the terrestrial and satellite links are considered following Rayleigh fading and shadowed Rician (SR) fading distributions, respectively. Besides, the terrestrial relay is working in half-duplex decode-and-forward (DF) mode. By employing a general and practical model to account for both the CEEs and HIs at each link, the end-to-end signal-to-noise-plus-distortion-and-error ratio (SNDER) is first obtained for the CSTRN. Then, closed-form expressions for the outage probability (OP) and throughput of the CSTRN are obtained, which allows us to demonstrate the aggregate impact of CEEs and HIs. In order to gain insightful findings, we further elaborate on the asymptotic OP and throughput at the high signal-to-noise-ratio (SNR) condition and quantitatively determine the fundamental performance ceiling. Finally, Monte Carlo (MC) computer simulations are provided to verify the correctness of the analytical results. Besides, with representative numerical analysis’s help, interesting findings are presented. Full article

In this paper, we present an adaptive ship detection method for single-look complex synthetic aperture radar (SAR) images. First, noncircularity is analyzed and adopted in ship detection task; besides, similarity variance weighted information entropy (SVWIE) is proposed for clutter reduction and target enhancement. According to the analysis of scattering of SVWIE and noncircularity, SVWIE-noncircularity (SN) decomposition is developed. Based on the decomposition, two components, the high-noncircularity SVWIE amplitude (h) and the low-noncircularity SVWIE amplitude (l), are obtained. We demonstrate that ships and clutter in SAR images are different for h detector and h detector can be effectively used for ship detection. Finally, to extract ships from the background, the generalized Gamma distribution (G $\mathrm{\Gamma }$ D) is used to fit h statistics of clutter and the constant false alarm rate (CFAR) is utilized to choose an adaptive threshold. The performance of the proposed method is demonstrated on HH polarization of Alos-2 images. Experimental results show that the proposed method can accurately detect ships in complex background, i.e., ships are close to small islands or with strong noise. Full article

Vehicle to everything (V2X) has been deemed a promising technology due to its potential to achieve traffic safety and efficiency. This paper considers a V2X downlink system with a simultaneous wireless information and power transfer (SWIPT) system where the base station not only conveys data and energy to two types of wireless vehicular receivers, such as one hybrid power-splitting vehicular receiver, and multiple energy vehicular receivers, but also prevents information from being intercepted by the potential eavesdroppers (idle energy vehicular receivers). Both the base station and the energy vehicular receivers are equipped with multiple antennas, whereas the information vehicular receiver is equipped with a single antenna. In particular, the imperfect channel state information (CSI) and the practical nonlinear energy harvesting (EH) model are taken into account. The non-convex optimization problem is formulated to maximize the minimum harvested energy power among the energy vehicular receivers satisfying the lowest harvested energy power threshold at the information vehicular receiver and secure vehicular communication requirements. In light of the intractability of the optimization problem, the semidefinite relaxation (SDR) technique and variable substitutions are applied, and the optimal solution is proven to be tight. A number of results demonstrate that the proposed robust secure beamforming scheme has better performance than other schemes. Full article

Integration of functional nanomaterials with optical micro/nanofibers (OMNFs) can bring about novel optical properties and provide a versatile platform for various sensing applications. OMNFs as the key element, however, have seldom been investigated. Here, we focus on the optimization of fiber diameter by taking micro/nanofiber-based localized surface plasmon resonance sensors as a model. We systematically study the dependence of fiber diameter on the sensing performance of such sensors. Both theoretical and experimental results show that, by reducing fiber diameter, the refractive index sensitivity can be significantly increased. Then, we demonstrate the biosensing capability of the optimized sensor for streptavidin detection and achieve a detection limit of 1 pg/mL. Furthermore, the proposed theoretical model is applicable to other nanomaterials and OMNF-based sensing schemes for performance optimization. Full article

How to reconstruct drawing and handwriting traces from surface electromyography (sEMG) signals accurately has attracted a number of researchers recently. An effective algorithm is crucial to reliable reconstruction. Previously, nonlinear regression methods have been utilized successfully to some extent. In the quest to improve the accuracy of transient myoelectric signal decoding, a novel hybrid algorithm KF-GEP fusing Gene Expression Programming (GEP) into Kalman Filter (KF) framework is proposed for sEMG-based drawing trace reconstruction. In this work, the KF-GEP was applied to reconstruct fourteen drawn shapes and ten numeric characters from sEMG signals across five participants. Then the reconstruction performance of KF-GEP, KF and GEP were compared. The experimental results show that the KF-GEP algorithm performs best because it combines the advantages of KF and GEP. The findings add to the literature on the muscle-computer interface and can be introduced to many practical fields. Full article

Along with the development of computer technology and informatization, the unmanned vehicle has become an important equipment in military, civil and some other fields. The navigation system is the basis and core of realizing the autonomous control and completing the task for unmanned vehicles, and the Strapdown Inertial Navigation System (SINS) is the preferred due to its autonomy and independence. The initial alignment technique is the premise and the foundation of the SINS, whose performance is susceptible to system nonlinearity and uncertainty. To improving system performance for SINS, an improved initial alignment algorithm is proposed in this manuscript. In the procedure of this presented initial alignment algorithm, the original signal of inertial sensors is denoised by utilizing the improved signal denoising method based on the Empirical Mode Decomposition (EMD) and the Extreme Learning Machine (ELM) firstly to suppress the high-frequency noise on coarse alignment. Afterwards, the accuracy and reliability of initial alignment is further enhanced by utilizing an improved Robust Huber Cubarure Kalman Filer (RHCKF) method to minimize the influence of system nonlinearity and uncertainty on the fine alignment. In addition, real tests are used to verify the availability and superiority of this proposed initial alignment algorithm. Full article

In the last decade, the growth of the automotive market with the aid of technologies has been notable for the economic, automotive and technological sectors. Alongside this growing recognition, the so called Internet of Intelligent Vehicles (IoIV) emerges as an evolution of the Internet of Things (IoT) applied to the automotive sector. Closely related to IoIV, emerges the concept of Industrial Internet of Things (IIoT), which is the current revolution seen in industrial automation. IIoT, in its turn, relates to the concept of Industry 4.0, that is used to represent the current Industrial Revolution. This revolution, however, involves different areas: from manufacturing to healthcare. The Industry 4.0 can create value during the entire product lifecycle, promoting customer feedback, that is, having information about the product history throughout it is life. In this way, the automatic communication between vehicle and factory was facilitated, allowing the accomplishment of different analysis regarding vehicles, such as the identification of a behavioral pattern through historical driver usage, fuel consumption, maintenance indicators, so on. Thus, allowing the prevention of critical issues and undesired behaviors, since the automakers lose contact with the vehicle after the purchase. Therefore, this paper aims to propose a customer feedback platform for vehicle manufacturing in Industry 4.0 context, capable of collecting and analyzing, through an OBD-II (On-Board Diagnostics) scanner, the sensors available by vehicles, with the purpose of assisting in the management, prevention, and mitigation of different vehicular problems. An intercontinental evaluation conducted between Brazil and Italy locations shown the feasibility of platform and the potential to use in order to improve the vehicle manufacturing process. Full article