Sensors, Volume 16, Issue 2 (February 2016) – 128 articles

Diverse approaches to laser point segmentation have been proposed since the emergence of the laser scanning system. Most of these segmentation techniques, however, suffer from limitations such as sensitivity to the choice of seed points, lack of consideration of the spatial relationships among points, and inefficient performance. In an effort to overcome these drawbacks, this paper proposes a segmentation methodology that: (1) reduces the dimensions of the attribute space; (2) considers the attribute similarity and the proximity of the laser point simultaneously; and (3) works well with both airborne and terrestrial laser scanning data. A neighborhood definition based on the shape of the surface increases the homogeneity of the laser point attributes. The magnitude of the normal position vector is used as an attribute for reducing the dimension of the accumulator array. The experimental results demonstrate, through both qualitative and quantitative evaluations, the outcomes’ high level of reliability. The proposed segmentation algorithm provided 96.89% overall correctness, 95.84% completeness, a 0.25 m overall mean value of centroid difference, and less than 1° of angle difference. The performance of the proposed approach was also verified with a large dataset and compared with other approaches. Additionally, the evaluation of the sensitivity of the thresholds was carried out. In summary, this paper proposes a robust and efficient segmentation methodology for abstraction of an enormous number of laser points into plane information. Full article

Navigation Signal based on Compound Carrier (NSCC), is proposed as the potential future global navigation satellite system (GNSS) signal modulation scheme. NSCC, a kind of multi-carrier (MC) signal, is generated by superposition and multi-parameter adjustment of sub-carriers. Therefore, a judious choice of parameter configation is needed. The main objective of this paper is to investigate the performance of the NSCC which is influenced by these parameters and to demonstrate its structure characteristics and superiority, employing a comprehensive evaluation system. The results show that the proposed NSCC signal processes full spectral efficiency and limited out of band (OOB) emissions, satisfying the demands of crowed frequency resources. It also presents better performance in terms of spectral separation coefficients (SSCs), tracking accuracy, multipath mitigation capability and anti-jamming reduction compared with the legacy navigation signals. NSCC modulation represents a serious candidate for navigation satellite augmentation systems, especially for signals applied in challenging environments. Full article

In this paper, a new approach is adopted to update the user preference profile by seeking users with similar interests based on the context obtainable for a mobile network instead of from desktop networks. The trust degree between mobile users is calculated by analyzing their behavior based on the context, and then the approximate neighbors are chosen by combining the similarity of the mobile user preference and the trust degree. The approach first considers the communication behaviors between mobile users, the mobile network services they use as well as the corresponding context information. Then a similarity degree of the preference between users is calculated with the evaluation score of a certain mobile web service provided by a mobile user. Finally, based on the time attenuation function, the users with similar preference are found, through which we can dynamically update the target user’s preference profile. Experiments are then conducted to test the effect of the context on the credibility among mobile users, the effect of time decay factors and trust degree thresholds. Simulation shows that the proposed approach outperforms two other methods in terms of Recall Ratio, Precision Ratio and Mean Absolute Error, because neither of them consider the context mobile information. Full article

Bio-inspired imaging polarization navigation which can provide navigation information and is capable of sensing polarization information has advantages of high-precision and anti-interference over polarization navigation sensors that use photodiodes. Although all types of imaging polarimeters exist, they may not qualify for the research on the imaging polarization navigation algorithm. To verify the algorithm, a real-time imaging orientation determination system was designed and implemented. Essential calibration procedures for the type of system that contained camera parameter calibration and the inconsistency of complementary metal oxide semiconductor calibration were discussed, designed, and implemented. Calibration results were used to undistort and rectify the multi-camera system. An orientation determination experiment was conducted. The results indicated that the system could acquire and compute the polarized skylight images throughout the calibrations and resolve orientation by the algorithm to verify in real-time. An orientation determination algorithm based on image processing was tested on the system. The performance and properties of the algorithm were evaluated. The rate of the algorithm was over 1 Hz, the error was over 0.313°, and the population standard deviation was 0.148° without any data filter. Full article

Due to the recent explosive growth of location-aware services based on mobile devices, predicting the next places of a user is of increasing importance to enable proactive information services. In this paper, we introduce a data-driven framework that aims to predict the user’s next places using his/her past visiting patterns analyzed from mobile device logs. Specifically, the notion of the spatiotemporal-periodic (STP) pattern is proposed to capture the visits with spatiotemporal periodicity by focusing on a detail level of location for each individual. Subsequently, we present algorithms that extract the STP patterns from a user’s past visiting behaviors and predict the next places based on the patterns. The experiment results obtained by using a real-world dataset show that the proposed methods are more effective in predicting the user’s next places than the previous approaches considered in most cases. Full article

As the weak link in global navigation satellite system (GNSS) signal processing, the phase-locked loop (PLL) is easily influenced with frequent cycle slips and loss of lock as a result of higher vehicle dynamics and lower signal-to-noise ratios. With inertial navigation system (INS) aid, PLLs’ tracking performance can be improved. However, for harsh environments with high dynamics and signal attenuation, the traditional INS-aided PLL with fixed loop parameters has some limitations to improve the tracking adaptability. In this paper, an adaptive INS-aided PLL capable of adjusting its noise bandwidth and coherent integration time has been proposed. Through theoretical analysis, the relation between INS-aided PLL phase tracking error and carrier to noise density ratio (C/N₀), vehicle dynamics, aiding information update time, noise bandwidth, and coherent integration time has been built. The relation formulae are used to choose the optimal integration time and bandwidth for a given application under the minimum tracking error criterion. Software and hardware simulation results verify the correctness of the theoretical analysis, and demonstrate that the adaptive tracking method can effectively improve the PLL tracking ability and integrated GNSS/INS navigation performance. For harsh environments, the tracking sensitivity is increased by 3 to 5 dB, velocity errors are decreased by 36% to 50% and position errors are decreased by 6% to 24% when compared with other INS-aided PLL methods. Full article

Recently, the correct estimation of traffic flow has begun to be considered an essential component in intelligent transportation systems. In this paper, a new statistical method to predict traffic flows using time series analyses and geometric correlations is proposed. The novelty of the proposed method is two-fold: (1) a 3D heat map is designed to describe the traffic conditions between roads, which can effectively represent the correlations between spatially- and temporally-adjacent traffic states; and (2) the relationship between the adjacent roads on the spatiotemporal domain is represented by cliques in MRF and the clique parameters are obtained by example-based learning. In order to assess the validity of the proposed method, it is tested using data from expressway traffic that are provided by the Korean Expressway Corporation, and the performance of the proposed method is compared with existing approaches. The results demonstrate that the proposed method can predict traffic conditions with an accuracy of 85%, and this accuracy can be improved further. Full article

A well-defined condition-monitoring for power transformers is key to implementing a correct condition-based maintenance (CBM). In this regard, partial discharges (PD) measurement and its analysis allows to carry out on-line maintenance following the standards IEC-60270 and IEC-60076. However, new PD measurements techniques, such as acoustics or electromagnetic (EM) acquisitions using ultra-high-frequency (UHF) sensors are being taken into account, IEC-62478. PD measurements with antennas and the effect of their EM propagation in power transformer tanks is an open research topic that is considered in this paper. In this sense, an empty tank model is studied as a rectangular cavity and their resonances are calculated and compared with their measurement with a network analyser. Besides, two low cost improved monopole antennas deployed inside and outside of the tank model capture background noise and PD pulses in three different test objects (Nomex, twisted pair and insulator). The average spectrum of them are compared and can be found that mainly, the antenna frequency response, the frequency content distribution depending on the PD source and the enclosure resonances modes are the main factors to be considered in PD acquisitions with these sensors. Finally, with this set-up, it is possible to measure PD activity inside the tank from outside. Full article

One of the most suitable ways of distributing a resistive sensor array for reading is an array with M rows and N columns. This allows reduced wiring and a certain degree of parallelism in the implementation, although it also introduces crosstalk effects. Several types of circuits can carry out the analogue-digital conversion of this type of sensors. This article focuses on the use of operational amplifiers with capacitive feedback and FPGAs for this task. Specifically, modifications of a previously reported circuit are proposed to reduce the errors due to the non-idealities of the amplifiers and the I/O drivers of the FPGA. Moreover, calibration algorithms are derived from the analysis of the proposed circuitry to reduce the crosstalk error and improve the accuracy. Finally, the performances of the proposals is evaluated experimentally on an array of resistors and for different ranges. Full article

In this paper, multi-channel capacitive sensor arrays based on microstrip band-stop filters are studied. The sensor arrays can be used to detect the proximity of objects at different positions and directions. Each capacitive sensing structure in the array is connected to an inductive element to form resonance at different frequencies. The resonances are designed to be isolated in the frequency spectrum, such that the change in one channel does not affect resonances at other channels. The inductive element associated with each capacitive sensor can be surface-mounted inductors, integrated microstrip inductors or metamaterial-inspired structures. We show that by using metamaterial split-ring structures coupled to a microstrip line, the quality factor of each resonance can be greatly improved compared to conventional surface-mounted or microstrip meander inductors. With such a microstrip-coupled split-ring design, more sensing elements can be integrated in the same frequency spectrum, and the sensitivity can be greatly improved. Full article

To accurately, safely, and efficiently evaluate the bitterness of Traditional Chinese Medicines (TCMs), a robust predictor was developed using robust partial least squares (RPLS) regression method based on data obtained from an electronic tongue (e-tongue) system. The data quality was verified by the Grubb’s test. Moreover, potential outliers were detected based on both the standardized residual and score distance calculated for each sample. The performance of RPLS on the dataset before and after outlier detection was compared to other state-of-the-art methods including multivariate linear regression, least squares support vector machine, and the plain partial least squares regression. Both R² and root-mean-squares error (RMSE) of cross-validation (CV) were recorded for each model. With four latent variables, a robust RMSECV value of 0.3916 with bitterness values ranging from 0.63 to 4.78 were obtained for the RPLS model that was constructed based on the dataset including outliers. Meanwhile, the RMSECV, which was calculated using the models constructed by other methods, was larger than that of the RPLS model. After six outliers were excluded, the performance of all benchmark methods markedly improved, but the difference between the RPLS model constructed before and after outlier exclusion was negligible. In conclusion, the bitterness of TCM decoctions can be accurately evaluated with the RPLS model constructed using e-tongue data. Full article

Hyperspectral images possess properties such as rich spectral information, narrow bandwidth, and large numbers of bands. Finding effective methods to retrieve land features from an image by using similarity assessment indices with specific spectral characteristics is an important research question. This paper reports a novel hyperspectral image similarity assessment index based on spectral curve patterns and a reflection-absorption index. First, some spectral reflection-absorption features are extracted to restrict the subsequent curve simplification. Then, the improved Douglas-Peucker algorithm is employed to simplify all spectral curves without setting the thresholds. Finally, the simplified curves with the feature points are matched, and the similarities among the spectral curves are calculated using the matched points. The Airborne Visible Infrared Imaging Spectrometer (AVIRIS) and Reflective Optics System Imaging Spectrometer (ROSIS) hyperspectral image datasets are then selected to test the effect of the proposed index. The practical experiments indicate that the proposed index can achieve higher precision and fewer points than the traditional spectral information divergence and spectral angle match. Full article

Information from complementary and redundant sensors are often combined within sensor fusion algorithms to obtain a single accurate observation of the system at hand. However, measurements from each sensor are characterized by uncertainties. When multiple data are fused, it is often unclear how all these uncertainties interact and influence the overall performance of the sensor fusion algorithm. To address this issue, a benchmarking procedure is presented, where simulated and real data are combined in different scenarios in order to quantify how each sensor’s uncertainties influence the accuracy of the final result. The proposed procedure was applied to the estimation of the pelvis orientation using a waist-worn magnetic-inertial measurement unit. Ground-truth data were obtained from a stereophotogrammetric system and used to obtain simulated data. Two Kalman-based sensor fusion algorithms were submitted to the proposed benchmarking procedure. For the considered application, gyroscope uncertainties proved to be the main error source in orientation estimation accuracy for both tested algorithms. Moreover, although different performances were obtained using simulated data, these differences became negligible when real data were considered. The outcome of this evaluation may be useful both to improve the design of new sensor fusion methods and to drive the algorithm tuning process. Full article

A novel cooperative integrity monitoring concept, called “local integrity”, suitable to automotive applications in urban scenarios, is discussed in this paper. The idea is to take advantage of a collaborative Vehicular Ad hoc NETwork (VANET) architecture in order to perform a spatial/temporal characterization of possible degradations of Global Navigation Satellite System (GNSS) signals. Such characterization enables the computation of the so-called “Local Protection Levels”, taking into account local impairments to the received signals. Starting from theoretical concepts, this paper describes the experimental validation by means of a measurement campaign and the real-time implementation of the algorithm on a vehicular prototype. A live demonstration in a real scenario has been successfully carried out, highlighting effectiveness and performance of the proposed approach. Full article

The zero-potential scanning circuit is widely used as read-out circuit for resistive sensor arrays because it removes a well known problem: crosstalk current. The zero-potential scanning circuit can be divided into two groups based on type of row drivers. One type is a row driver using digital buffers. It can be easily implemented because of its simple structure, but we found that it can cause a large read-out error which originates from on-resistance of the digital buffers used in the row driver. The other type is a row driver composed of operational amplifiers. It, very accurately, reads the sensor resistance, but it uses a large number of operational amplifiers to drive rows of the sensor array; therefore, it severely increases the power consumption, cost, and system complexity. To resolve the inaccuracy or high complexity problems founded in those previous circuits, we propose a new row driver which uses only one operational amplifier to drive all rows of a sensor array with high accuracy. The measurement results with the proposed circuit to drive a 4 × 4 resistor array show that the maximum error is only 0.1% which is remarkably reduced from 30.7% of the previous counterpart. Full article

Gender information has many useful applications in computer vision systems, such as surveillance systems, counting the number of males and females in a shopping mall, accessing control systems in restricted areas, or any human-computer interaction system. In most previous studies, researchers attempted to recognize gender by using visible light images of the human face or body. However, shadow, illumination, and time of day greatly affect the performance of these methods. To overcome this problem, we propose a new gender recognition method based on the combination of visible light and thermal camera images of the human body. Experimental results, through various kinds of feature extraction and fusion methods, show that our approach is efficient for gender recognition through a comparison of recognition rates with conventional systems. Full article

A novel structure of the resonant pressure sensor is presented in this paper, which tactfully employs intercoupling between dual pressure-sensing diaphragms and a laterally driven resonant strain gauge. After the resonant pressure sensor principle is introduced, the coupling mechanism of the diaphragms and resonator is analyzed and the frequency equation of the resonator based on the triangle geometry theory is developed for this new coupling structure. The finite element (FE) simulation results match the theoretical analysis over the full scale of the device. This pressure sensor was first fabricated by dry/wet etching and thermal silicon bonding, followed by vacuum-packaging using anodic bonding technology. The test maximum error of the fabricated sensor is 0.0310%F.S. (full scale) in the range of 30 to 190 kPa, its pressure sensitivity is negative and exceeding 8 Hz/kPa, and its Q-factor reaches 20,000 after wafer vacuum-packaging. A novel resonant pressure sensor with high accuracy is presented in this paper. Full article

Based on a laser diode, a 12 × 6 photodiode array sensor, and machine learning techniques, a new void fraction measurement method for gas-liquid two-phase flow in small channels is proposed. To overcome the influence of flow pattern on the void fraction measurement, the flow pattern of the two-phase flow is firstly identified by Fisher Discriminant Analysis (FDA). Then, according to the identification result, a relevant void fraction measurement model which is developed by Support Vector Machine (SVM) is selected to implement the void fraction measurement. A void fraction measurement system for the two-phase flow is developed and experiments are carried out in four different small channels. Four typical flow patterns (including bubble flow, slug flow, stratified flow and annular flow) are investigated. The experimental results show that the development of the measurement system is successful. The proposed void fraction measurement method is effective and the void fraction measurement accuracy is satisfactory. Compared with the conventional laser measurement systems using standard laser sources, the developed measurement system has the advantages of low cost and simple structure. Compared with the conventional void fraction measurement methods, the proposed method overcomes the influence of flow pattern on the void fraction measurement. This work also provides a good example of using low-cost laser diode as a competent replacement of the expensive standard laser source and hence implementing the parameter measurement of gas-liquid two-phase flow. The research results can be a useful reference for other researchers’ works. Full article

This paper presents a full parallel event driven readout method which is implemented in an area array single-photon avalanche diode (SPAD) image sensor for high-speed fluorescence lifetime imaging microscopy (FLIM). The sensor only records and reads out effective time and position information by adopting full parallel event driven readout method, aiming at reducing the amount of data. The image sensor includes four 8 × 8 pixel arrays. In each array, four time-to-digital converters (TDCs) are used to quantize the time of photons’ arrival, and two address record modules are used to record the column and row information. In this work, Monte Carlo simulations were performed in Matlab in terms of the pile-up effect induced by the readout method. The sensor’s resolution is 16 × 16. The time resolution of TDCs is 97.6 ps and the quantization range is 100 ns. The readout frame rate is 10 Mfps, and the maximum imaging frame rate is 100 fps. The chip’s output bandwidth is 720 MHz with an average power of 15 mW. The lifetime resolvability range is 5–20 ns, and the average error of estimated fluorescence lifetimes is below 1% by employing CMM to estimate lifetimes. Full article

Continuous human action recognition (CHAR) is more practical in human-robot interactions. In this paper, an online CHAR algorithm is proposed based on skeletal data extracted from RGB-D images captured by Kinect sensors. Each human action is modeled by a sequence of key poses and atomic motions in a particular order. In order to extract key poses and atomic motions, feature sequences are divided into pose feature segments and motion feature segments, by use of the online segmentation method based on potential differences of features. Likelihood probabilities that each feature segment can be labeled as the extracted key poses or atomic motions, are computed in the online model matching process. An online classification method with variable-length maximal entropy Markov model (MEMM) is performed based on the likelihood probabilities, for recognizing continuous human actions. The variable-length MEMM method ensures the effectiveness and efficiency of the proposed CHAR method. Compared with the published CHAR methods, the proposed algorithm does not need to detect the start and end points of each human action in advance. The experimental results on public datasets show that the proposed algorithm is effective and highly-efficient for recognizing continuous human actions. Full article

A near-IR CO trace gas sensor based on quartz-enhanced photoacoustic spectroscopy (QEPAS) is evaluated using humidified nitrogen samples. Relaxation processes in the CO-N₂-H₂O system are investigated. A simple kinetic model is used to predict the sensor performance at different gas pressures. The results show that CO has a ~3 and ~5 times slower relaxation time constant than CH₄ and HCN, respectively, under dry conditions. However, with the presence of water, its relaxation time constant can be improved by three orders of magnitude. The experimentally determined normalized detection sensitivity for CO in humid gas is 1.556 × 10 − 8 W ⋅ cm − 1 / Hz 1 / 2 . Full article

Indoor positioning remains an open problem, because it is difficult to achieve satisfactory accuracy within an indoor environment using current radio-based localization technology. In this study, we investigate the use of Indoor Messaging System (IMES) radio for high-accuracy indoor positioning. A hybrid positioning method combining IMES radio strength information and pedestrian dead reckoning information is proposed in order to improve IMES localization accuracy. For understanding the carrier noise ratio versus distance relation for IMES radio, the signal propagation of IMES radio is modeled and identified. Then, trilateration and extended Kalman filtering methods using the radio propagation model are developed for position estimation. These methods are evaluated through robot localization and pedestrian localization experiments. The experimental results show that the proposed hybrid positioning method achieved average estimation errors of 217 and 1846 mm in robot localization and pedestrian localization, respectively. In addition, in order to examine the reason for the positioning accuracy of pedestrian localization being much lower than that of robot localization, the influence of the human body on the radio propagation is experimentally evaluated. The result suggests that the influence of the human body can be modeled. Full article

Underwater photogrammetry and in particular systematic visual surveys of the deep sea are by far less developed than similar techniques on land or in space. The main challenges are the rough conditions with extremely high pressure, the accessibility of target areas (container and ship deployment of robust sensors, then diving for hours to the ocean floor), and the limitations of localization technologies (no GPS). The absence of natural light complicates energy budget considerations for deep diving flash-equipped drones. Refraction effects influence geometric image formation considerations with respect to field of view and focus, while attenuation and scattering degrade the radiometric image quality and limit the effective visibility. As an improvement on the stated issues, we present an AUV-based optical system intended for autonomous visual mapping of large areas of the seafloor (square kilometers) in up to 6000 m water depth. We compare it to existing systems and discuss tradeoffs such as resolution vs. mapped area and show results from a recent deployment with 90,000 mapped square meters of deep ocean floor. Full article

A new capacitively coupled contactless conductivity detection (C⁴D) sensor with an improved simulated inductor is developed in this work. The improved simulated inductor is designed on the basis of the Riordan-type floating simulated inductor. With the improved simulated inductor, the negative influence of the coupling capacitances is overcome and the conductivity measurement is implemented by the series resonance principle. The conductivity measurement experiments are carried out in three pipes with different inner diameters of 3.0 mm, 4.6 mm and 6.4 mm, respectively. The experimental results show that the designs of the new C⁴D sensor and the improved simulated inductor are successful. The maximum relative error of the conductivity measurement is less than 5%. Compared with the C⁴D sensors using practical inductors, the measurement accuracy of the new C⁴D sensor is comparable. The research results also indicate that the adjustability of a simulated inductor can reduce the requirement for the AC source and guarantee the interchangeableness. Meanwhile, it is recommended that making the potential of one terminal of a simulated inductor stable is beneficial to the running stability. Furthermore, this work indirectly verifies the possibility and feasibility of the miniaturization of the C⁴D sensor by using the simulated inductor technique and lays a good foundation for future research work. Full article

In determining position and attitude, vision navigation via real-time image processing of data collected from imaging sensors is advanced without a high-performance global positioning system (GPS) and an inertial measurement unit (IMU). Vision navigation is widely used in indoor navigation, far space navigation, and multiple sensor-integrated mobile mapping. This paper proposes a novel vision navigation approach aided by imaging sensors and that uses a high-accuracy geo-referenced image database (GRID) for high-precision navigation of multiple sensor platforms in environments with poor GPS. First, the framework of GRID-aided vision navigation is developed with sequence images from land-based mobile mapping systems that integrate multiple sensors. Second, a highly efficient GRID storage management model is established based on the linear index of a road segment for fast image searches and retrieval. Third, a robust image matching algorithm is presented to search and match a real-time image with the GRID. Subsequently, the image matched with the real-time scene is considered to calculate the 3D navigation parameter of multiple sensor platforms. Experimental results show that the proposed approach retrieves images efficiently and has navigation accuracies of 1.2 m in a plane and 1.8 m in height under GPS loss in 5 min and within 1500 m. Full article

The error bound is a typical measure of the limiting performance of all filters for the given sensor measurement setting. This is of practical importance in guiding the design and management of sensors to improve target tracking performance. Within the random finite set (RFS) framework, an error bound for joint detection and estimation (JDE) of multiple targets using a single sensor with clutter and missed detection is developed by using multi-Bernoulli or Poisson approximation to multi-target Bayes recursion. Here, JDE refers to jointly estimating the number and states of targets from a sequence of sensor measurements. In order to obtain the results of this paper, all detectors and estimators are restricted to maximum a posteriori (MAP) detectors and unbiased estimators, and the second-order optimal sub-pattern assignment (OSPA) distance is used to measure the error metric between the true and estimated state sets. The simulation results show that clutter density and detection probability have significant impact on the error bound, and the effectiveness of the proposed bound is verified by indicating the performance limitations of the single-sensor probability hypothesis density (PHD) and cardinalized PHD (CPHD) filters for various clutter densities and detection probabilities. Full article

In many wireless sensor network application scenarios the key management scheme with a Mobile Sink (MS) should be fully investigated. This paper proposes a key management scheme based on dynamic clustering and optimal-routing choice of MS. The concept of Traveling Salesman Problem with Neighbor areas (TSPN) in dynamic clustering for data exchange is proposed, and the selection probability is used in MS route planning. The proposed scheme extends static key management to dynamic key management by considering the dynamic clustering and mobility of MSs, which can effectively balance the total energy consumption during the activities. Considering the different resources available to the member nodes and sink node, the session key between cluster head and MS is established by modified an ECC encryption with Diffie-Hellman key exchange (ECDH) algorithm and the session key between member node and cluster head is built with a binary symmetric polynomial. By analyzing the security of data storage, data transfer and the mechanism of dynamic key management, the proposed scheme has more advantages to help improve the resilience of the key management system of the network on the premise of satisfying higher connectivity and storage efficiency. Full article

Wireless sensor networks deployed in coal mines could help companies provide workers working in coal mines with more qualified working conditions. With the underground information collected by sensor nodes at hand, the underground working conditions could be evaluated more precisely. However, sensor nodes may tend to malfunction due to their limited energy supply. In this paper, we study the cross-layer optimization problem for wireless rechargeable sensor networks implemented in coal mines, of which the energy could be replenished through the newly-brewed wireless energy transfer technique. The main results of this article are two-fold: firstly, we obtain the optimal relay nodes’ placement according to the minimum overall energy consumption criterion through the Lagrange dual problem and KKT conditions; secondly, the optimal strategies for recharging locomotives and wireless sensor networks are acquired by solving a cross-layer optimization problem. The cyclic nature of these strategies is also manifested through simulations in this paper. Full article

The emergence of the Internet of Things and the proliferation of mobile wireless devices has brought the area of mobile cognitive radio sensor networks (MCRSN) to the research spot light. Notwithstanding the potentials of CRSNs in terms of opportunistic channel usage for bursty traffic, the effect of the mobility of resource-constrained nodes to route stability, mobility-induced spatio-temporal spectral opportunities and primary user (PU) protection still remain open issues that need to be jointly addressed. To this effect, this paper proposes a mobile reliable geographical forwarding routing (MROR) protocol. MROR provides a robust mobile framework for geographical forwarding that is based on a mobility-induced channel availability model. It presents a comprehensive routing strategy that considers PU activity (to take care of routes that have to be built through PU coverage), PU signal protection (by the introduction of a mobility-induced guard (mguard) distance) and the random mobility-induced spatio-temporal spectrum opportunities (for enhancement of throughput). It also addresses the issue of frequent route maintenance that arises when speeds of the mobile nodes are considered as a routing metric. As a result, simulation has shown the ability of MROR to reduce the route failure rate by about 65% as against other schemes. In addition, further results show that MROR can improve both the throughput and goodput at the sink in an energy-efficient manner that is required in CRSNs as against compared works. Full article

Automatic recognition of mature fruits in a complex agricultural environment is still a challenge for an autonomous harvesting robot due to various disturbances existing in the background of the image. The bottleneck to robust fruit recognition is reducing influence from two main disturbances: illumination and overlapping. In order to recognize the tomato in the tree canopy using a low-cost camera, a robust tomato recognition algorithm based on multiple feature images and image fusion was studied in this paper. Firstly, two novel feature images, the a*-component image and the I-component image, were extracted from the L*a*b* color space and luminance, in-phase, quadrature-phase (YIQ) color space, respectively. Secondly, wavelet transformation was adopted to fuse the two feature images at the pixel level, which combined the feature information of the two source images. Thirdly, in order to segment the target tomato from the background, an adaptive threshold algorithm was used to get the optimal threshold. The final segmentation result was processed by morphology operation to reduce a small amount of noise. In the detection tests, 93% target tomatoes were recognized out of 200 overall samples. It indicates that the proposed tomato recognition method is available for robotic tomato harvesting in the uncontrolled environment with low cost. Full article

Rockfall protection barriers are connected to the ground using steel cables fixed with anchors and foundations for the steel posts. It is common practice to measure the forces in the cables, while to date measurements of forces in the foundations have been inadequately resolved. An overview is presented of existing methods to measure the loads on the post foundations of rockfall protection barriers. Addressing some of the inadequacies of existing approaches, a novel sensor unit is presented that is able to capture the forces acting on post foundations in all six degrees of freedom. The sensor unit consists of four triaxial force sensors placed between two steel plates. To correctly convert the measurements into the directional forces acting on the foundation a special in-situ calibration procedure is proposed that delivers a corresponding conversion matrix. Full article

Errors compensation of micromachined-inertial-measurement-units (MIMU) is essential in practical applications. This paper presents a new compensation method using a neural-network-based identification for MIMU, which capably solves the universal problems of cross-coupling, misalignment, eccentricity, and other deterministic errors existing in a three-dimensional integrated system. Using a neural network to model a complex multivariate and nonlinear coupling system, the errors could be readily compensated through a comprehensive calibration. In this paper, we also present a thermal-gas MIMU based on thermal expansion, which measures three-axis angular rates and three-axis accelerations using only three thermal-gas inertial sensors, each of which capably measures one-axis angular rate and one-axis acceleration simultaneously in one chip. The developed MIMU (100 × 100 × 100 mm³) possesses the advantages of simple structure, high shock resistance, and large measuring ranges (three-axes angular rates of ±4000°/s and three-axes accelerations of ±10 g) compared with conventional MIMU, due to using gas medium instead of mechanical proof mass as the key moving and sensing elements. However, the gas MIMU suffers from cross-coupling effects, which corrupt the system accuracy. The proposed compensation method is, therefore, applied to compensate the system errors of the MIMU. Experiments validate the effectiveness of the compensation, and the measurement errors of three-axis angular rates and three-axis accelerations are reduced to less than 1% and 3% of uncompensated errors in the rotation range of ±600°/s and the acceleration range of ±1 g, respectively. Full article

This paper proposes the first all-digital on-chip linearity enhancement technique for improving the accuracy of the time-domain complementary metal-oxide semiconductor (CMOS) smart temperature sensor. To facilitate on-chip application and intellectual property reuse, an all-digital time-domain smart temperature sensor was implemented using 90 nm Field Programmable Gate Arrays (FPGAs). Although the inverter-based temperature sensor has a smaller circuit area and lower complexity, two-point calibration must be used to achieve an acceptable inaccuracy. With the help of a calibration circuit, the influence of process variations was reduced greatly for one-point calibration support, reducing the test costs and time. However, the sensor response still exhibited a large curvature, which substantially affected the accuracy of the sensor. Thus, an on-chip linearity-enhanced circuit is proposed to linearize the curve and achieve a new linearity-enhanced output. The sensor was implemented on eight different Xilinx FPGA using 118 slices per sensor in each FPGA to demonstrate the benefits of the linearization. Compared with the unlinearized version, the maximal inaccuracy of the linearized version decreased from 5 °C to 2.5 °C after one-point calibration in a range of −20 °C to 100 °C. The sensor consumed 95 μW using 1 kSa/s. The proposed linearity enhancement technique significantly improves temperature sensing accuracy, avoiding costly curvature compensation while it is fully synthesizable for future Very Large Scale Integration (VLSI) system. Full article

Bystromite (MgSb₂O₆) nanorods were prepared using a colloidal method in the presence of ethylenediamine, after a calcination step at 800 °C in static air. From X-ray powder diffraction analyses, a trirutile-type structure with lattice parameters a = 4.64 Å and c = 9.25 Å and space group P4₂/mnm was identified. Using scanning electron microscopy (SEM), microrods with sizes from 0.2 to 1.6 μm were observed. Transmission electron microscopy (TEM) analyses revealed that the nanorods had a length of ~86 nm and a diameter ~23.8 nm. The gas-sensing properties of these nanostructures were tested using pellets elaborated with powders of the MgSb₂O₆ oxide (calcined at 800 °C) at temperatures 23, 150, 200, 250 and 300 °C. The pellets were exposed to different concentrations of carbon monoxide (CO) and propane (C₃H₈) at these temperatures. The results showed that the MgSb₂O₆ nanorods possess excellent stability and high sensitivity in these atmospheres. Full article

This paper deals with a current sensor fault reconstruction algorithm for the torque closed-loop drive system of an interior PMSM. First, sensor faults are equated to actuator ones by a new introduced state variable. Then, in αβ coordinates, based on the motor model with active flux linkage, a current observer is constructed with a specific sliding mode equivalent control methodology to eliminate the effects of unknown disturbances, and the phase current sensor faults are reconstructed by means of an adaptive method. Finally, an αβ axis current fault processing module is designed based on the reconstructed value. The feasibility and effectiveness of the proposed method are verified by simulation and experimental tests on the RT-LAB platform. Full article

With the development of body sensor networks and the pervasiveness of smart phones, different types of personal data can be collected in real time by body sensors, and the potential value of massive personal data has attracted considerable interest recently. However, the privacy issues of sensitive personal data are still challenging today. Aiming at these challenges, in this paper, we focus on the threats from telemetry interface and present a secure and privacy-preserving body sensor data collection and query scheme, named SPCQ, for outsourced computing. In the proposed SPCQ scheme, users’ personal information is collected by body sensors in different types and converted into multi-dimension data, and each dimension is converted into the form of a number and uploaded to the cloud server, which provides a secure, efficient and accurate data query service, while the privacy of sensitive personal information and users’ query data is guaranteed. Specifically, based on an improved homomorphic encryption technology over composite order group, we propose a special weighted Euclidean distance contrast algorithm (WEDC) for multi-dimension vectors over encrypted data. With the SPCQ scheme, the confidentiality of sensitive personal data, the privacy of data users’ queries and accurate query service can be achieved in the cloud server. Detailed analysis shows that SPCQ can resist various security threats from telemetry interface. In addition, we also implement SPCQ on an embedded device, smart phone and laptop with a real medical database, and extensive simulation results demonstrate that our proposed SPCQ scheme is highly efficient in terms of computation and communication costs. Full article

This paper describes, for the first time, the procedure for the full design, calibration, uncertainty analysis, and practical application of a personal, distributed exposimeter (PDE) for the detection of personal exposure in the Global System for Mobile Communications (GSM) downlink (DL) band around 900 MHz (GSM 900 DL). The PDE is a sensor that consists of several body-worn antennas. The on-body location of these antennas is investigated using numerical simulations and calibration measurements in an anechoic chamber. The calibration measurements and the simulations result in a design (or on-body setup) of the PDE. This is used for validation measurements and indoor radio frequency (RF) exposure measurements in Ghent, Belgium. The main achievements of this paper are: first, the demonstration, using both measurements and simulations, that a PDE consisting of multiple on-body textile antennas will have a lower measurement uncertainty for personal RF exposure than existing on-body sensors; second, a validation of the PDE, which proves that the device correctly estimates the incident power densities; and third, a demonstration of the usability of the PDE for real exposure assessment measurements. To this aim, the validated PDE is used for indoor measurements in a residential building in Ghent, Belgium, which yield an average incident power density of 0.018 mW/m². Full article

Resistive sensor arrays are formed by a large number of individual sensors which are distributed in different ways. This paper proposes a direct connection between an FPGA and a resistive array distributed in M rows and N columns, without the need of analog-to-digital converters to obtain resistance values in the sensor and where the conditioning circuit is reduced to the use of a capacitor in each of the columns of the matrix. The circuit allows parallel measurements of the N resistors which form each of the rows of the array, eliminating the resistive crosstalk which is typical of these circuits. This is achieved by an addressing technique which does not require external elements to the FPGA. Although the typical resistive crosstalk between resistors which are measured simultaneously is eliminated, other elements that have an impact on the measurement of discharge times appear in the proposed architecture and, therefore, affect the uncertainty in resistance value measurements; these elements need to be studied. Finally, the performance of different calibration techniques is assessed experimentally on a discrete resistor array, obtaining for a new model of calibration, a maximum relative error of 0.066% in a range of resistor values which correspond to a tactile sensor. Full article

During cutting and processing of meat, the loss of water is critical in determining both product quality and value. From the point of slaughter until packaging, water is lost due to the hanging, movement, handling, and cutting of the carcass, with every 1% of lost water having the potential to cost a large meat processing plant somewhere in the region of €50,000 per day. Currently the options for monitoring the loss of water from meat, or determining its drip loss, are limited to destructive tests which take 24–72 h to complete. This paper presents results from work which has led to the development of a novel microwave cavity sensor capable of providing an indication of drip loss within 6 min, while demonstrating good correlation with the well-known EZ-Driploss method (R² = 0.896). Full article

An integrated cellulose polymer concentrator/single-walled carbon nanotube (SWCNT) sensing system is demonstrated to detect benzene, toluene, and xylenes (BTX) vapors. The sensing system consists of functionalized cellulose as a selective concentrator disposed directly on top of a conductive SWCNT sensing layer. Functionalized cellulose concentrator (top layer) selectively adsorbs the target analyte and delivers the concentrated analyte as near as possible to the SWCNT sensing layer (bottom layer), which enables the simultaneous concentrating and sensing within a few seconds. The selectivity can be achieved by functionalizing cellulose acetate with a pentafluorophenylacetyl selector that interacts strongly with the target BTX analytes. A new design of the integrated cellulose concentrator/SWCNT sensing system allows high sensitivity with limits of detection for benzene, toluene, and m-xylene vapors of 55 ppm, 19 ppm, and 14 ppm, respectively, selectivity, and fast responses (<10 s to reach equilibrium), exhibiting the potential ability for on-site, real-time sensing applications. The sensing mechanism involves the selective adsorption of analytes in the concentrator film, which in turn mediates changes in the electronic potentials at the polymer-SWCNT interface and potentially changes in the tunneling barriers between nanotubes. Full article

This paper focuses on the research on the state of the art for sensor fusion techniques, applied to the sensors embedded in mobile devices, as a means to help identify the mobile device user’s daily activities. Sensor data fusion techniques are used to consolidate the data collected from several sensors, increasing the reliability of the algorithms for the identification of the different activities. However, mobile devices have several constraints, e.g., low memory, low battery life and low processing power, and some data fusion techniques are not suited to this scenario. The main purpose of this paper is to present an overview of the state of the art to identify examples of sensor data fusion techniques that can be applied to the sensors available in mobile devices aiming to identify activities of daily living (ADLs). Full article

This study combines signal de-noising, feature extraction, two pairwise-coupled relevance vector machines (PCRVMs) and particle swarm optimization (PSO) for parameter optimization to form an intelligent diagnostic framework for gearbox fault detection. Firstly, the noises of sensor signals are de-noised by using the wavelet threshold method to lower the noise level. Then, the Hilbert-Huang transform (HHT) and energy pattern calculation are applied to extract the fault features from de-noised signals. After that, an eleven-dimension vector, which consists of the energies of nine intrinsic mode functions (IMFs), maximum value of HHT marginal spectrum and its corresponding frequency component, is obtained to represent the features of each gearbox fault. The two PCRVMs serve as two different fault detection committee members, and they are trained by using vibration and sound signals, respectively. The individual diagnostic result from each committee member is then combined by applying a new probabilistic ensemble method, which can improve the overall diagnostic accuracy and increase the number of detectable faults as compared to individual classifiers acting alone. The effectiveness of the proposed framework is experimentally verified by using test cases. The experimental results show the proposed framework is superior to existing single classifiers in terms of diagnostic accuracies for both single- and simultaneous-faults in the gearbox. Full article

An empirical tropospheric delay model, together with a mapping function, is commonly used to correct the tropospheric errors in global navigation satellite system (GNSS) processing. As is well-known, the accuracy of tropospheric delay models relies mainly on the correction efficiency for tropospheric wet delays. In this paper, we evaluate the accuracy of three tropospheric delay models, together with five mapping functions in wet delays calculation. The evaluations are conducted by comparing their slant wet delays with those measured by water vapor radiometer based on its satellite-tracking function (collected data with large liquid water path is removed). For all 15 combinations of three tropospheric models and five mapping functions, their accuracies as a function of elevation are statistically analyzed by using nine-day data in two scenarios, with and without meteorological data. The results show that (1) no matter with or without meteorological data, there is no practical difference between mapping functions, i.e., Chao, Ifadis, Vienna Mapping Function 1 (VMF1), Niell Mapping Function (NMF), and MTT Mapping Function (MTT); (2) without meteorological data, the UNB3 is much better than Saastamoinen and Hopfield models, while the Saastamoinen model performed slightly better than the Hopfield model; (3) with meteorological data, the accuracies of all three tropospheric delay models are improved to be comparable, especially for lower elevations. In addition, the kinematic precise point positioning where no parameter is set up for tropospheric delay modification is conducted to further evaluate the performance of tropospheric delay models in positioning accuracy. It is shown that the UNB3 model is best and can achieve about 10 cm accuracy for the N and E coordinate component while 20 cm accuracy for the U coordinate component no matter the meteorological data is available or not. This accuracy can be obtained by the Saastamoinen model only when meteorological data is available, and degraded to 46 cm for the U component if the meteorological data is not available. Full article

Electrochemical impedance spectroscopy (EIS) has been used for monitoring the enzymatic pineapple waste hydrolysis process. The system employed consists of a device called Advanced Voltammetry, Impedance Spectroscopy & Potentiometry Analyzer (AVISPA) equipped with a specific software application and a stainless steel double needle electrode. EIS measurements were conducted at different saccharification time intervals: 0, 0.75, 1.5, 6, 12 and 24 h. Partial least squares (PLS) were used to model the relationship between the EIS measurements and the sugar determination by HPAEC-PAD. On the other hand, artificial neural networks: (multilayer feed forward architecture with quick propagation training algorithm and logistic-type transfer functions) gave the best results as predictive models for glucose, fructose, sucrose and total sugars. Coefficients of determination (R²) and root mean square errors of prediction (RMSEP) were determined as R² > 0.944 and RMSEP < 1.782 for PLS and R² > 0.973 and RMSEP < 0.486 for artificial neural networks (ANNs), respectively. Therefore, a combination of both an EIS-based technique and ANN models is suggested as a promising alternative to the traditional laboratory techniques for monitoring the pineapple waste saccharification step. Full article

This paper provides an approach for recognizing human activities with wearable sensors. The continuous autoencoder (CAE) as a novel stochastic neural network model is proposed which improves the ability of model continuous data. CAE adds Gaussian random units into the improved sigmoid activation function to extract the features of nonlinear data. In order to shorten the training time, we propose a new fast stochastic gradient descent (FSGD) algorithm to update the gradients of CAE. The reconstruction of a swiss-roll dataset experiment demonstrates that the CAE can fit continuous data better than the basic autoencoder, and the training time can be reduced by an FSGD algorithm. In the experiment of human activities’ recognition, time and frequency domain feature extract (TFFE) method is raised to extract features from the original sensors’ data. Then, the principal component analysis (PCA) method is applied to feature reduction. It can be noticed that the dimension of each data segment is reduced from 5625 to 42. The feature vectors extracted from original signals are used for the input of deep belief network (DBN), which is composed of multiple CAEs. The training results show that the correct differentiation rate of 99.3% has been achieved. Some contrast experiments like different sensors combinations, sensor units at different positions, and training time with different epochs are designed to validate our approach. Full article

Time-domain reflectometry (TDR) methods used for measuring the dielectric properties of materials mostly utilize step or needle electrical pulses of constant amplitudes and shapes. Our novel approach enables determining the dielectric relaxation time of a sample using the analysis of the amplitudes of reflected pulses of two widths, in addition to bulk dielectric permittivity and electrical conductivity commonly obtained by the TDR technique. The method was developed for various values of electrical conductivity and relaxation time using numerical simulations of a five-rod probe placed in a material with complex dielectric permittivity described by the Debye model with an added electrical conductivity term. The characterization of amplitudes of two pulses of selected widths was done with regard to the dielectric parameters of simulated materials. The required probe parameters were obtained solely from numerical simulations. Verification was performed for the probe placed in aqueous KCl solutions with 14 different electrical conductivity values. The determined relaxation time remained roughly constant and independent of electrical conductivity. The obtained electrical conductivity agreed with the reference values. Our results indicate that the relaxation time, dielectric permittivity and electrical conductivity of the tested solutions can be simultaneously determined using a simple analysis of the amplitude and reflection time of two needle pulses of different widths. Full article

In this study, a numerical simulation method was employed to investigate and analyze superstructure fiber Bragg gratings (SFBGs) with five duty cycles (50%, 33.33%, 14.28%, 12.5%, and 10%). This study focuses on demonstrating the relevance between design period and spectral characteristics of SFBGs (in the form of graphics) for SFBGs of all duty cycles. Compared with complicated and hard-to-learn conventional coupled-mode theory, the result of the present study may assist beginner and expert designers in understanding the basic application aspects, optical characteristics, and design techniques of SFBGs, thereby indirectly lowering the physical concepts and mathematical skills required for entering the design field. To effectively improve the accuracy of overall computational performance and numerical calculations and to shorten the gap between simulation results and actual production, this study integrated a perfectly matched layer (PML), perfectly reflecting boundary (PRB), object meshing method (OMM), and boundary meshing method (BMM) into the finite element method (FEM) and eigenmode expansion method (EEM). The integrated method enables designers to easily and flexibly design optical fiber communication systems that conform to the specific spectral characteristic by using the simulation data in this paper, which includes bandwidth, number of channels, and band gap size. Full article

Vehicle positioning is a key factor for numerous information and assistance applications that are included in vehicles and for which satellite positioning is mainly used. However, this positioning process can result in errors and lead to measurement uncertainties. These errors come mainly from two sources: errors and simplifications of digital maps and errors in locating the vehicle. From that inaccurate data, the task of assigning the vehicle’s location to a link on the digital map at every instant is carried out by map-matching algorithms. These algorithms have been developed to fulfil that need and attempt to amend these errors to offer the user a suitable positioning. In this research; an algorithm is developed that attempts to solve the errors in positioning when the Global Navigation Satellite System (GNSS) signal reception is frequently lost. The algorithm has been tested with satisfactory results in a complex urban environment of narrow streets and tall buildings where errors and signal reception losses of the GPS receiver are frequent. Full article

Localization, which is a technique required by service robots to operate indoors, has been studied in various ways. Most localization techniques have the robot measure environmental information to obtain location information; however, this is a high-cost option because it uses extensive equipment and complicates robot development. If an external device is used to determine a robot’s location and transmit this information to the robot, the cost of internal equipment required for location recognition can be reduced. This will simplify robot development. Thus, this study presents an effective method to control robots by obtaining their location information using a map constructed by visual information from surveillance cameras installed indoors. With only a single image of an object, it is difficult to gauge its size due to occlusion. Therefore, we propose a localization method using several neighboring surveillance cameras. A two-dimensional map containing robot and object position information is constructed using images of the cameras. The concept of this technique is based on modeling the four edges of the projected image of the field of coverage of the camera and an image processing algorithm of the finding object’s center for enhancing the location estimation of objects of interest. We experimentally demonstrate the effectiveness of the proposed method by analyzing the resulting movement of a robot in response to the location information obtained from the two-dimensional map. The accuracy of the multi-camera setup was measured in advance. Full article

An optical sensor platform based on surface plasmon resonance (SPR) in a plastic optical fiber (POF) integrated into a thermo-stabilized flow cell for biochemical sensing applications is proposed. This device has been realized and experimentally tested by using a classic receptor-analyte assay. For this purpose, the gold surface of the POF was chemically modified through the formation of a self-assembling monolayer. The surface robustness of the POF-SPR platform has been tested for the first time thanks to the flow cell. The experimental results show that the proposed device can be successfully used for label-free biochemical sensing. The final goal of this work is to achieve a complete, small-size, simple to use and low cost optical sensor system. The whole system with the flow cell and the optical sensor are extensively described, together with the experimental results obtained with an immunoglobulin G (IgG)/anti-IgG assay. Full article

The integration of semiconductor quantum dots (QDs) into homogeneous Förster resonance energy transfer (FRET) immunoassay kits for clinical diagnostics can provide significant advantages concerning multiplexing and sensitivity. Here we present a facile and functional QD-antibody conjugation method using three commercially available QDs with different photoluminescence (PL) maxima (605 nm, 655 nm, and 705 nm). The QD-antibody conjugates were successfully applied for FRET immunoassays against prostate specific antigen (PSA) in 50 µL serum samples using Lumi4-Tb (Tb) antibody conjugates as FRET donors and time-gated PL detection on a KRYPTOR clinical plate reader. Förster distance and Tb donor background PL were directly related to the analytical sensitivity for PSA, ...which resulted in the lowest limits of detection for Tb-QD705 (2 ng/mL), followed by Tb-QD655 (4 ng/mL), and Tb-QD605 (23 ng/mL). Duplexed PSA detection using the Tb-QD655 and Tb-QD705 FRET-pairs demonstrated the multiplexing ability of our immunoassays. Our results show that FRET based on QD acceptors is suitable for multiplexed and sensitive biomarker detection in clinical diagnostics. Full article

Extracellular neural recording, with multi-electrode arrays (MEAs), is a powerful method used to study neural function at the network level. However, in a high density array, it can be costly and time consuming to integrate the active circuit with the expensive electrodes. In this paper, we present a 4 mm × 4 mm neural recording integrated circuit (IC) chip, utilizing IBM C4 bumps as recording electrodes, which enable a seamless active chip and electrode integration. The IC chip was designed and fabricated in a 0.13 μm BiCMOS process for both in vitro and in vivo applications. It has an input-referred noise of 4.6 μV rms for the bandwidth of 10 Hz to 10 kHz and a power dissipation of 11.25 mW at 2.5 V, or 43.9 μW per input channel. This prototype is scalable for implementing larger number and higher density electrode arrays. To validate the functionality of the chip, electrical testing results and acute in vivo recordings from a rat barrel cortex are presented. Full article

The reader-to-reader collision in an RFID system is a challenging problem for communications technology. In order to model the interference between RFID readers, different interference models have been proposed, mainly based on two approaches: single and additive interference. The former only considers the interference from one reader within a certain range, whereas the latter takes into account the sum of all of the simultaneous interferences in order to emulate a more realistic behavior. Although the difference between the two approaches has been theoretically analyzed in previous research, their effects on the estimated performance of the reader-to-reader anti-collision protocols have not yet been investigated. In this paper, the influence of the interference model on the anti-collision protocols is studied by simulating a representative state-of-the-art protocol. The results presented in this paper highlight that the use of additive models, although more computationally intensive, is mandatory to improve the performance of anti-collision protocols. Full article

We demonstrate mechanical modulation of phonon-assisted field emission in a free-standing silicon nanomembrane detector for time-of-flight mass spectrometry of proteins. The impacts of ion bombardment on the silicon nanomembrane have been explored in both mechanical and electrical points of view. Locally elevated lattice temperature in the silicon nanomembrane, resulting from the transduction of ion kinetic energy into thermal energy through the ion bombardment, induces not only phonon-assisted field emission but also a mechanical vibration in the silicon nanomembrane. The coupling of these mechanical and electrical phenomenon leads to mechanical modulation of phonon-assisted field emission. The thermal energy relaxation through mechanical vibration in addition to the lateral heat conduction and field emission in the silicon nanomembrane offers effective cooling of the nanomembrane, thereby allowing high resolution mass analysis. Full article

This paper proposes a compressive sensing (CS) method for multi-channel electroencephalogram (EEG) signals in Wireless Body Area Network (WBAN) applications, where the battery life of sensors is limited. For the single EEG channel case, known as the single measurement vector (SMV) problem, the Block Sparse Bayesian Learning-BO (BSBL-BO) method has been shown to yield good results. This method exploits the block sparsity and the intra-correlation (i.e., the linear dependency) within the measurement vector of a single channel. For the multichannel case, known as the multi-measurement vector (MMV) problem, the Spatio-Temporal Sparse Bayesian Learning (STSBL-EM) method has been proposed. This method learns the joint correlation structure in the multichannel signals by whitening the model in the temporal and the spatial domains. Our proposed method represents the multi-channels signal data as a vector that is constructed in a specific way, so that it has a better block sparsity structure than the conventional representation obtained by stacking the measurement vectors of the different channels. To reconstruct the multichannel EEG signals, we modify the parameters of the BSBL-BO algorithm, so that it can exploit not only the linear but also the non-linear dependency structures in a vector. The modified BSBL-BO is then applied on the vector with the better sparsity structure. The proposed method is shown to significantly outperform existing SMV and also MMV methods. It also shows significant lower compression errors even at high compression ratios such as 10:1 on three different datasets. Full article

Clinical rehabilitation assessment is an important part of the therapy process because it is the premise for prescribing suitable rehabilitation interventions. However, the commonly used assessment scales have the following two drawbacks: (1) they are susceptible to subjective factors; (2) they only have several rating levels and are influenced by a ceiling effect, making it impossible to exactly detect any further improvement in the movement. Meanwhile, energy constraints are a primary design consideration in wearable sensor network systems since they are often battery-operated. Traditionally, for wearable sensor network systems that follow the Shannon/Nyquist sampling theorem, there are many data that need to be sampled and transmitted. This paper proposes a novel wearable sensor network system to monitor and quantitatively assess the upper limb motion function, based on compressed sensing technology. With the sparse representation model, less data is transmitted to the computer than with traditional systems. The experimental results show that the accelerometer signals of Bobath handshake and shoulder touch exercises can be compressed, and the length of the compressed signal is less than 1/3 of the raw signal length. More importantly, the reconstruction errors have no influence on the predictive accuracy of the Brunnstrom stage classification model. It also indicated that the proposed system can not only reduce the amount of data during the sampling and transmission processes, but also, the reconstructed accelerometer signals can be used for quantitative assessment without any loss of useful information. Full article

Precise time-of-arrival (TOA) estimation is one of the most important techniques in RF-based positioning systems that use wireless sensor networks (WSNs). Because the accuracy of TOA estimation is proportional to the RF signal bandwidth, using broad bandwidth is the most fundamental approach for achieving higher accuracy. Hence, ultra-wide-band (UWB) systems with a bandwidth of 500 MHz are commonly used. However, wireless systems with broad bandwidth suffer from the disadvantages of high complexity and high power consumption. Therefore, it is difficult to employ such systems in various WSN applications. In this paper, we present a precise time-of-arrival (TOA) estimation algorithm using an IEEE 802.15.4 ZigBee system with a narrow bandwidth of 2 MHz. In order to overcome the lack of bandwidth, the proposed algorithm estimates the fractional TOA within the sampling interval. Simulation results show that the proposed TOA estimation algorithm provides an accuracy of 0.5 m at a signal-to-noise ratio (SNR) of 8 dB and achieves an SNR gain of 5 dB as compared with the existing algorithm. In addition, experimental results indicate that the proposed algorithm provides accurate TOA estimation in a real indoor environment. Full article

Single crystal silicon (SCS) diaphragms are widely used as pressure sensitive elements in micromachined pressure sensors. However, for harsh environments applications, pure silicon diaphragms are hardly used because of the deterioration of SCS in both electrical and mechanical properties. To survive at the elevated temperature, the silicon structures must work in combination with other advanced materials, such as silicon carbide (SiC) or silicon on insulator (SOI), for improved performance and reduced cost. Hence, in order to extend the operating temperatures of existing SCS microstructures, this work investigates the mechanical behavior of pressurized SCS diaphragms at high temperatures. A model was developed to predict the plastic deformation of SCS diaphragms and was verified by the experiments. The evolution of the deformation was obtained by studying the surface profiles at different anneal stages. The slow continuous deformation was considered as creep for the diaphragms with a radius of 2.5 mm at 600 °C. The occurrence of plastic deformation was successfully predicted by the model and was observed at the operating temperature of 800 °C and 900 °C, respectively. Full article

A novel generalized play operator-based (GPO-based) nonlinear adaptive filter is proposed to model rate-dependent hysteresis nonlinearity for smart actuators. In the proposed filter, the input signal vector consists of the output of a tapped delay line. GPOs with various thresholds are used to construct a nonlinear network and connected with the input signals. The output signal of the filter is composed of a linear combination of signals from the output of GPOs. The least-mean-square (LMS) algorithm is used to adjust the weights of the nonlinear filter. The modeling results of four adaptive filter methods are compared: GPO-based adaptive filter, Volterra filter, backlash filter and linear adaptive filter. Moreover, a phenomenological operator-based model, the rate-dependent generalized Prandtl-Ishlinskii (RDGPI) model, is compared to the proposed adaptive filter. The various rate-dependent modeling methods are applied to model the rate-dependent hysteresis of a giant magnetostrictive actuator (GMA). It is shown from the modeling results that the GPO-based adaptive filter can describe the rate-dependent hysteresis nonlinear of the GMA more accurately and effectively. Full article

On 28 August 2009, one thrust-faulting Mw 6.3 earthquake struck the northern Qaidam basin, China. Due to the lack of ground observations in this remote region, this study presents high-precision and high spatio-temporal resolution post-seismic deformation series with a small baseline subset InSAR technique. At the temporal scale, this changes from fast to slow with time, with a maximum uplift up to 7.4 cm along the line of sight 334 days after the event. At the spatial scale, this is more obvious at the hanging wall than that at the footwall, and decreases from the middle to both sides at the hanging wall. We then propose a method to calculate the correlation coefficient between co-seismic and post-seismic deformation by normalizing them. The correlation coefficient is found to be 0.73, indicating a similar subsurface process occurring during both phases. The results indicate that afterslip may dominate the post-seismic deformation during 19–334 days after the event, which mainly occurs with the fault geometry and depth similar to those of the c-seismic rupturing, and partly extends to the shallower and deeper depths. Full article

Remote sensing technology plays an important role in monitoring rapid changes of the Earth's surface. However, sensors that can simultaneously provide satellite images with both high temporal and spatial resolution haven’t been designed yet. This paper proposes an improved spatial and temporal adaptive reflectance fusion model (STARFM) with the help of an Unmixing-based method (USTARFM) to generate the high spatial and temporal data needed for the study of heterogeneous areas. The results showed that the USTARFM had higher accuracy than STARFM methods in two aspects of analysis: individual bands and of heterogeneity analysis. Taking the predicted NIR band as an example, the correlation coefficients (r) for the USTARFM, STARFM and unmixing methods were 0.96, 0.95, 0.90, respectively (p-value < 0.001); Root Mean Square Error (RMSE) values were 0.0245, 0.0300, 0.0401, respectively; and ERGAS values were 0.5416, 0.6507, 0.8737, respectively. The USTARM showed consistently higher performance than STARM when the degree of heterogeneity ranged from 2 to 10, highlighting that the use of this method provides the capacity to solve the data fusion problems faced when using STARFM. Additionally, the USTARFM method could help researchers achieve better performance than STARFM at a smaller window size from its heterogeneous land surface quantitative representation. Full article

Vision-based Pose Estimation (VPE) represents a non-invasive solution to allow a smooth and natural interaction between a human user and a robotic system, without requiring complex calibration procedures. Moreover, VPE interfaces are gaining momentum as they are highly intuitive, such that they can be used from untrained personnel (e.g., a generic caregiver) even in delicate tasks as rehabilitation exercises. In this paper, we present a novel master–slave setup for hand telerehabilitation with an intuitive and simple interface for remote control of a wearable hand exoskeleton, named HX. While performing rehabilitative exercises, the master unit evaluates the 3D position of a human operator’s hand joints in real-time using only a RGB-D camera, and commands remotely the slave exoskeleton. Within the slave unit, the exoskeleton replicates hand movements and an external grip sensor records interaction forces, that are fed back to the operator-therapist, allowing a direct real-time assessment of the rehabilitative task. Experimental data collected with an operator and six volunteers are provided to show the feasibility of the proposed system and its performances. The results demonstrate that, leveraging on our system, the operator was able to directly control volunteers’ hands movements. Full article

Wireless sensor networks (WSNs) can gather in situ real data measurements and work unattended for long periods, even in remote, rough places. A critical aspect of WSN design is node placement, as this determines sensing capacities, network connectivity, network lifetime and, in short, the whole operational capabilities of the WSN. This paper proposes and studies a new node placement algorithm that focus on these aspects. As a motivating example, we consider a network designed to describe the distribution of helium-3 (³He), a potential enabling element for fusion reactors, on the Moon. ³He is abundant on the Moon’s surface, and knowledge of its distribution is essential for future harvesting purposes. Previous data are inconclusive, and there is general agreement that on-site measurements, obtained over a long time period, are necessary to better understand the mechanisms involved in the distribution of this element on the Moon. Although a mission of this type is extremely complex, it allows us to illustrate the main challenges involved in a multi-objective WSN placement problem, i.e., selection of optimal observation sites and maximization of the lifetime of the network. To tackle optimization, we use a recent adaptation of the ant colony optimization (ACO_R) metaheuristic, extended to continuous domains. Solutions are provided in the form of a Pareto frontier that shows the optimal equilibria. Moreover, we compared our scheme with the four-directional placement (FDP) heuristic, which was outperformed in all cases. Full article

For improving the tradeoff between the sensitivity and the resonant frequency of piezoresistive accelerometers, the dependency between the stress of the piezoresistor and the displacement of the structure is taken into consideration in this paper. In order to weaken the dependency, a novel structure with suspended piezoresistive beams (SPBs) is designed, and a theoretical model is established for calculating the location of SPBs, the stress of SPBs and the resonant frequency of the whole structure. Finite element method (FEM) simulations, comparative simulations and experiments are carried out to verify the good agreement with the theoretical model. It is demonstrated that increasing the sensitivity greatly without sacrificing the resonant frequency is possible in the piezoresistive accelerometer design. Therefore, the proposed structure with SPBs is potentially a novel option for improving the tradeoff between the sensitivity and the resonant frequency of piezoresistive accelerometers. Full article

This study presents the development and evaluation of a next generation air monitoring system with both laboratory and field tests. A multi-parameter algorithm was used to correct for the impact of environmental conditions on the electrochemical sensors for carbon monoxide (CO) and nitrogen dioxide (NO₂) pollutants. The field evaluation in an urban roadside environment in comparison to designated monitors showed good agreement with measurement error within 5% of the pollutant concentrations. Multiple sets of the developed system were then deployed in the Hong Kong Marathon 2015 forming a sensor-based network along the marathon route. Real-time air pollution concentration data were wirelessly transmitted and the Air Quality Health Index (AQHI) for the Green Marathon was calculated, which were broadcast to the public on an hourly basis. The route-specific sensor network showed somewhat different pollutant patterns than routine air monitoring, indicating the immediate impact of traffic control during the marathon on the roadside air quality. The study is one of the first applications of a next generation sensor network in international sport events, and it demonstrated the usefulness of the emerging sensor-based air monitoring technology in rapid network deployment to supplement existing air monitoring. Full article

Due to their special environment, Underwater Wireless Sensor Networks (UWSNs) are usually deployed over a large sea area and the nodes are usually floating. This results in a lower beacon node distribution density, a longer time for localization, and more energy consumption. Currently most of the localization algorithms in this field do not pay enough consideration on the mobility of the nodes. In this paper, by analyzing the mobility patterns of water near the seashore, a localization method for UWSNs based on a Mobility Prediction and a Particle Swarm Optimization algorithm (MP-PSO) is proposed. In this method, the range-based PSO algorithm is used to locate the beacon nodes, and their velocities can be calculated. The velocity of an unknown node is calculated by using the spatial correlation of underwater object’s mobility, and then their locations can be predicted. The range-based PSO algorithm may cause considerable energy consumption and its computation complexity is a little bit high, nevertheless the number of beacon nodes is relatively smaller, so the calculation for the large number of unknown nodes is succinct, and this method can obviously decrease the energy consumption and time cost of localizing these mobile nodes. The simulation results indicate that this method has higher localization accuracy and better localization coverage rate compared with some other widely used localization methods in this field. Full article

Motor imagery-based brain-computer interface (BCI) is a communication interface between an external machine and the brain. Many kinds of spatial filters are used in BCIs to enhance the electroencephalography (EEG) features related to motor imagery. The approach of channel selection, developed to reserve meaningful EEG channels, is also an important technique for the development of BCIs. However, current BCI systems require a conventional EEG machine and EEG electrodes with conductive gel to acquire multi-channel EEG signals and then transmit these EEG signals to the back-end computer to perform the approach of channel selection. This reduces the convenience of use in daily life and increases the limitations of BCI applications. In order to improve the above issues, a novel wearable channel selection-based brain-computer interface is proposed. Here, retractable comb-shaped active dry electrodes are designed to measure the EEG signals on a hairy site, without conductive gel. By the design of analog CAR spatial filters and the firmware of EEG acquisition module, the function of spatial filters could be performed without any calculation, and channel selection could be performed in the front-end device to improve the practicability of detecting motor imagery in the wearable EEG device directly or in commercial mobile phones or tablets, which may have relatively low system specifications. Finally, the performance of the proposed BCI is investigated, and the experimental results show that the proposed system is a good wearable BCI system prototype. Full article

A right-angle prism was used to enhance the acoustic signal of a quartz-enhanced photoacoustic spectroscopy (QEPAS) system. The incident laser beam was parallelly inverted by the right-angle prism and passed through the gap between two tuning fork prongs again to produce another acoustic excitation. Correspondingly, two pairs of rigid metal tubes were used as acoustic resonators with resonance enhancement factors of 16 and 12, respectively. The QEPAS signal was enhanced by a factor of 22.4 compared with the original signal, which was acquired without resonators or a prism. In addition, the system noise was reduced a little with double resonators due to the Q factor decrease. The signal-to-noise ratio (SNR) was greatly improved. Additionally, a normalized noise equivalent absorption coefficient (NNEA) of 5.8 × 10⁻⁸ W·cm⁻¹·Hz^−1/2 was achieved for water vapor detection in the atmosphere. Full article

In the last few years, the increasing use of LEDs in illumination systems has been conducted due to the emergence of Visible Light Communication (VLC) technologies, in which data communication is performed by transmitting through the visible band of the electromagnetic spectrum. In 2011, the Institute of Electrical and Electronics Engineers (IEEE) published the IEEE 802.15.7 standard for Wireless Personal Area Networks based on VLC. Due to limitations in the coverage of the transmitted signal, wireless networks can suffer from the hidden node problems, when there are nodes in the network whose transmissions are not detected by other nodes. This problem can cause an important degradation in communications when they are made by means of the Carrier Sense Multiple Access with Collision Avoidance (CSMA/CA) access control method, which is used in IEEE 802.15.7 This research work evaluates the effects of the hidden node problem in the performance of the IEEE 802.15.7 standard We implement a simulator and analyze VLC performance in terms of parameters like end-to-end goodput and message loss rate. As part of this research work, a solution to the hidden node problem is proposed, based on the use of idle patterns defined in the standard. Idle patterns are sent by the network coordinator node to communicate to the other nodes that there is an ongoing transmission. The validity of the proposed solution is demonstrated with simulation results. Full article

We describe the design and 3D sensing performance of an omnidirectional stereo (omnistereo) vision system applied to Micro Aerial Vehicles (MAVs). The proposed omnistereo sensor employs a monocular camera that is co-axially aligned with a pair of hyperboloidal mirrors (a vertically-folded catadioptric configuration). We show that this arrangement provides a compact solution for omnidirectional 3D perception while mounted on top of propeller-based MAVs (not capable of large payloads). The theoretical single viewpoint (SVP) constraint helps us derive analytical solutions for the sensor’s projective geometry and generate SVP-compliant panoramic images to compute 3D information from stereo correspondences (in a truly synchronous fashion). We perform an extensive analysis on various system characteristics such as its size, catadioptric spatial resolution, field-of-view. In addition, we pose a probabilistic model for the uncertainty estimation of 3D information from triangulation of back-projected rays. We validate the projection error of the design using both synthetic and real-life images against ground-truth data. Qualitatively, we show 3D point clouds (dense and sparse) resulting out of a single image captured from a real-life experiment. We expect the reproducibility of our sensor as its model parameters can be optimized to satisfy other catadioptric-based omnistereo vision under different circumstances. Full article

Accurate spray characterization helps to better understand the pesticide spray application process. The goal of this research was to present the proof of principle of a droplet size and velocity measuring technique for different types of hydraulic spray nozzles using a high speed backlight image acquisition and analysis system. As only part of the drops of an agricultural spray can be in focus at any given moment, an in-focus criterion based on the gray level gradient was proposed to decide whether a given droplet is in focus or not. In a first experiment, differently sized droplets were generated with a piezoelectric generator and studied to establish the relationship between size and in-focus characteristics. In a second experiment, it was demonstrated that droplet sizes and velocities from a real sprayer could be measured reliably in a non-intrusive way using the newly developed image acquisition set-up and image processing. Measured droplet sizes ranged from 24 μm to 543 μm, depending on the nozzle type and size. Droplet velocities ranged from around 0.5 m/s to 12 m/s. The droplet size and velocity results were compared and related well with the results obtained with a Phase Doppler Particle Analyzer (PDPA). Full article

Herein we describe poly(ionic liquid) hydrogel actuators that are capable of responding to multiple stimuli, namely temperature, ionic strength and white light irradiation. Using two starting materials, a crosslinked poly ionic liquid (PIL) and a linear poly(N-isopropylacrylamide-co-spiropyran-co-acrylic acid), several semi-interpenetrating (sIPN) hydrogels were synthesised. The dimensions of hydrogels discs were measured before and after applying the stimuli, to quantify their response. Samples composed of 100% crosslinked PIL alone showed an average area reduction value of ~53% when the temperature was raised from 20 °C to 70 °C, ~24% when immersed in 1% w/w NaF salt solution and no observable photo-response. In comparison, sIPNs containing 300% w/w linear polymer showed an average area reduction of ~45% when the temperature was raised from 20 °C to 70 °C, ~36% when immersed in 1% NaF w/w salt solution and ~10% after 30 min exposure to white light irradiation, respectively. Moreover, by varying the content of the linear component, fine-control over the photo-, thermo- and salt response, swelling-deswelling rate and mechanical properties of the resulting sIPN was achieved. Full article

Nowadays, researchers are paying increasing attention to embedding systems. Cost reduction has lead to an increase in the number of platforms supporting the operating system Linux, jointly with the Raspberry Pi motherboard. Thus, embedding devices on Raspberry-Linux systems is a goal in order to make competitive commercial products. This paper presents a low-cost fingerprint recognition system embedded into a Raspberry Pi with Linux. Full article

In terahertz (THz) materials science, imaging by scanning prevails when low power THz sources are used. However, the application of array detectors operating with high power THz sources is increasingly reported. We compare the imaging properties of four different array detectors that are able to record THz radiation directly. Two micro-bolometer arrays are designed for infrared imaging in the 8–14 μm wavelength range, but are based on different absorber materials (i) vanadium oxide; (ii) amorphous silicon; (iii) a micro-bolometer array optimized for recording THz radiation based on silicon nitride; and (iv) a pyroelectric array detector for THz beam profile measurements. THz wavelengths of 96.5 μm, 118.8 μm, and 393.6 μm from a powerful far infrared laser were used to assess the technical performance in terms of signal to noise ratio, detector response and detectivity. The usefulness of the detectors for beam profiling and digital holography is assessed. Finally, the potential and limitation for real-time digital holography are discussed. Full article

In this paper, we show that the possibility of using polyethylene glycol (EG) as a hydrogen source and it is used to assist the hydrothermal synthesis of ZnO nanorods (ZNRs). EG doping in ZNRs has been found to significantly improve their optical and chemical sensing characteristics toward glutamate. The EG was found to have no role on the structural properties of the ZNRs. However, the x-ray photoelectron spectroscopy (XPS) suggests that the EG could induce donor impurities effect in ZnO. Photoluminescence (PL) and UV-Vis. spectra demonstrated this doping effect. Mott-Schottky analysis at the ZNRs/electrolyte interface was used to investigate the charge density for the doped ZNRs and showed comparable dependence on the used amount of EG. Moreover, the doped ZNRs were used in potentiometric measurements for glutamate for a range from 10⁻⁶ M to 10⁻³ M and the potential response of the sensor electrode was linear with a slope of 91.15 mV/decade. The wide range and high sensitivity of the modified ZNRs based glutamate biosensor is attributed to the doping effect on the ZNRs that is dictated by the EG along with the high surface area-to-volume ratio. The findings in the present study suggest new avenues to control the growth of n-ZnO nanostructures and enhance the performance of their sensing devices. Full article

A room-temperature continuous-wave (CW) quantum cascade laser (QCL)-based methane (CH₄) sensor operating in the mid-infrared near 8 μm was developed for continuous measurement of CH₄ concentrations in ambient air. The well-isolated absorption line (7F_2,4 ← 8F_1,2) of the ν₄ fundamental band of CH₄ located at 1255.0004 cm⁻¹ was used for optical measurement of CH₄ concentration by direct absorption in a White-type multipass cell with an effective path-length of 175 m. A 1σ (SNR = 1) detection limit of 33.3 ppb in 218 s was achieved with a measurement precision of 1.13%. The developed sensor was deployed in a campaign of measurements of time series CH₄ concentration on a site near a suburban traffic road in Dunkirk (France) from 9th to 22nd January 2013. An episode of high CH₄ concentration of up to ~3 ppm has been observed and analyzed with the help of meteorological parameters combined with back trajectory calculation using the Hybrid Single Particle Lagrangian Integrated Trajectory (HYSPLIT) model of NOAA. Full article

Crustal movement is one of the main factors influencing the change of the Earth system, especially in its vertical direction, which affects people’s daily life through the frequent occurrence of earthquakes, geological disasters, and so on. In order to get a better study and application of the vertical crustal movement,as well as its changes, the foundation and prerequisite areto devise and establish its reference frame; especially, a unified global reference frame is required. Since SLR (satellite laser ranging) is one of the most accurate space techniques for monitoring geocentric motion and can directly measure the ground station’s geocentric coordinates and velocities relative to the centre of the Earth’s mass, we proposed to take the vertical velocity of the SLR technique in the ITRF2008 framework as the reference frame of vertical crustal motion, which we defined as the SLR vertical reference frame (SVRF). The systematic bias between other velocity fields and the SVRF was resolved by using the GPS (Global Positioning System) and VLBI (very long baseline interferometry) velocity observations, and the unity of other velocity fields and SVRF was realized,as well. The results show that it is feasible and suitable to take the SVRF as a reference frame, which has both geophysical meanings and geodetic observations, so we recommend taking the SLR vertical velocity under ITRF2008 as the global reference frame of vertical crustal movement. Full article

Vehicle analysis involves license-plate recognition (LPR), vehicle-type classification (VTC), and vehicle make and model recognition (MMR). Among these tasks, MMR plays an important complementary role in respect to LPR. In this paper, we propose a novel framework for MMR using local tiled deep networks. The frontal views of vehicle images are first extracted and fed into the local tiled deep networks for training and testing. A local tiled convolutional neural network (LTCNN) is proposed to alter the weight sharing scheme of CNN with local tiled structure. The LTCNN unties the weights of adjacent units and then ties the units k steps from each other within a local map. This architecture provides the translational, rotational, and scale invariance as well as locality. In addition, to further deal with the colour and illumination variation, we applied the histogram oriented gradient (HOG) to the frontal view of images prior to the LTCNN. The experimental results show that our LTCNN framework achieved a 98% accuracy rate in terms of vehicle MMR. Full article

This paper introduces a method to remove the unwanted interdependency between vertical and horizontal eye-movement components in electrooculograms (EOGs). EOGs have been widely used to estimate eye movements without a camera in a variety of human-computer interaction (HCI) applications using pairs of electrodes generally attached either above and below the eye (vertical EOG) or to the left and right of the eyes (horizontal EOG). It has been well documented that the vertical EOG component has less stability than the horizontal EOG one, making accurate estimation of the vertical location of the eyes difficult. To address this issue, an experiment was designed in which ten subjects participated. Visual inspection of the recorded EOG signals showed that the vertical EOG component is highly influenced by horizontal eye movements, whereas the horizontal EOG is rarely affected by vertical eye movements. Moreover, the results showed that this interdependency could be effectively removed by introducing an individual constant value. It is therefore expected that the proposed method can enhance the overall performance of practical EOG-based eye-tracking systems. Full article

One of the main applications of mobile robots is the large-scale perception of the outdoor environment. One of the main challenges of this application is fusing environmental data obtained by multiple robots, especially heterogeneous robots. This paper proposes an enhanced iterative closest point (ICP) method for the fast and accurate registration of 3D environmental models. First, a hierarchical searching scheme is combined with the octree-based ICP algorithm. Second, an early-warning mechanism is used to perceive the local minimum problem. Third, a heuristic escape scheme based on sampled potential transformation vectors is used to avoid local minima and achieve optimal registration. Experiments involving one unmanned aerial vehicle and one unmanned surface vehicle were conducted to verify the proposed technique. The experimental results were compared with those of normal ICP registration algorithms to demonstrate the superior performance of the proposed method. Full article

Localization is one of the hottest research topics in Underwater Wireless Sensor Networks (UWSNs), since many important applications of UWSNs, e.g., event sensing, target tracking and monitoring, require location information of sensor nodes. Nowadays, a large number of localization algorithms have been proposed for UWSNs. How to improve location accuracy are well studied. However, few of them take location reliability or security into consideration. In this paper, we propose a Collaborative Secure Localization algorithm based on Trust model (CSLT) for UWSNs to ensure location security. Based on the trust model, the secure localization process can be divided into the following five sub-processes: trust evaluation of anchor nodes, initial localization of unknown nodes, trust evaluation of reference nodes, selection of reference node, and secondary localization of unknown node. Simulation results demonstrate that the proposed CSLT algorithm performs better than the compared related works in terms of location security, average localization accuracy and localization ratio. Full article

A taste sensor that uses lipid/polymer membranes can evaluate aftertastes felt by humans using Change in membrane Potential caused by Adsorption (CPA) measurements. The sensor membrane for evaluating bitterness, which is caused by acidic bitter substances such as iso-alpha acid contained in beer, needs an immersion process in monosodium glutamate (MSG) solution, called “MSG preconditioning”. However, what happens to the lipid/polymer membrane during MSG preconditioning is not clear. Therefore, we carried out three experiments to investigate the changes in the lipid/polymer membrane caused by the MSG preconditioning, i.e., measurements of the taste sensor, measurements of the amount of the bitterness substance adsorbed onto the membrane and measurements of the contact angle of the membrane surface. The CPA values increased as the preconditioning process progressed, and became stable after 3 d of preconditioning. The response potentials to the reference solution showed the same tendency of the CPA value change during the preconditioning period. The contact angle of the lipid/polymer membrane surface decreased after 7 d of MSG preconditioning; in short, the surface of the lipid/polymer membrane became hydrophilic during MSG preconditioning. The amount of adsorbed iso-alpha acid was increased until 5 d preconditioning, and then it decreased. In this study, we revealed that the CPA values increased with the progress of MSG preconditioning in spite of the decrease of the amount of iso-alpha acid adsorbed onto the lipid/polymer membrane, and it was indicated that the CPA values increase because the sensor sensitivity was improved by the MSG preconditioning. Full article

Food waste is one of the main problems in our society. This is mainly caused by people’s behaviors and attitudes, which influence the whole food chain, from production to final consumption. In fact, food is generally perceived as a commodity by adults, who transmit this behavior to children, who in turn do not develop any consciousness about food’s source. One way to reduce the problem seems to be by changing consumers’ attitudes, which develop during the early years of childhood. Research has shown that after attending school garden classes, children’s food-related behavior changes. Growing crops is not always easy—it can’t be done in the domestic space, and this lead to a loss of the long term positive effects. This paper presents a project that tries to teach children how to grow their own food indoors and outdoors, mixing real and virtual reality, connecting something natural like a plant to the Internet of Things (or IOT, a network of physical objects virtually connected to each other and to the web). The use of sensors related to an app makes this process more fun and useful for educational purposes. The aim of the project is to change children’s attitude towards food, increasing their knowledge about production and consumption, in order to reduce waste on a long term basis. The research has been developed in collaboration with Cisco NL and MediaLAB Amsterdam. The user testing has been executed with Dutch children in Amsterdam. Full article

The feature extraction technique for an electronic nose (e-nose) applied in tobacco smell detection in an open country/outdoor environment with periodic background strong interference is studied in this paper. Principal component analysis (PCA), Independent component analysis (ICA), re-filtering and a priori knowledge are combined to separate and suppress background interference on the e-nose. By the coefficient of multiple correlation (CMC), it can be verified that a better separation of environmental temperature, humidity, and atmospheric pressure variation related background interference factors can be obtained with ICA. By re-filtering according to the on-site interference characteristics a composite smell curve was obtained which is more related to true smell information based on the tobacco curer’s experience. Full article

The purpose of this paper is to present a multi-sensor approach to the detection and inspection of steel bars in reinforced concrete structures. In connection with our past experience related to non-destructive testing of different materials, we propose using two potentially effective methods: active infrared thermography with microwave excitation and the eddy current technique. In this article active infrared thermography with microwave excitation is analyzed both by numerical modeling and experiments. This method, based on thermal imaging, due to its characteriatics should be considered as a preliminary method for the assessment of relatively shallowly located steel bar reinforcements. The eddy current technique, on the other hand, allows for more detailed evaluation and detection of deeply located rebars. In this paper a series of measurement results, together with the initial identification of certain features of steel reinforcement bars will be presented. Full article