ALBERT

Description: The long-lived pioneer species Pericopsis elata is one of the rare tropical timbers on the list of the Convention on International Trade of Endangered Species, supporting the need for accurate and reliable growth data. In one planted and one natural forest in the Democratic Republic of Congo, respectively four and 37 Pericopsis stem disks were collected. The tree-ring series of planted trees were used to confirm annual tree-ring formation. For the natural forest, a tree-ring chronology with 24 stem disks ranged from 1852 up to 2008. This chronology was compared with time-series of local precipitation, resulting in a significant correlation with the second half of the rainy season (September–November). This seasonal precipitation was related with sea surface temperatures of the West Indian Ocean. Higher precipitation during El Niño years corresponded with higher tree-ring indices but differences were not significant. In addition, pointer years were rare and did not have a consistent link with extreme climatic conditions. The existence of annual tree rings encourages further tree-ring analyses of P. elata and other flagship timber species in order to further document climate-growth responses and to provide the long-term framework that is needed for sustainable management planning.

Description: Three ideal point-based multi-criteria decision methods (MCDM), i.e., iterative ideal point thresholding (IIPT), compromise programming (CP) and a newly-proposed CP variant, called balanced compromise programming (BCP), were applied to the Tabacay catchment in Ecuador with the aim of finding a distribution of land use types (LUT) that optimizes regional land performance. This performance was expressed in terms of several conflicting on-site ecosystem services (ESS), namely water conservation, soil protection, carbon storage and monetary income. IIPT selects the best performing LUT on a per-land unit basis, that is the assignment of a LUT to a land unit is completely independent with respect to other land units. CP and BCP, on the other hand, aim at optimizing the integrated regional performance. These methods produce a LUT distribution that is as close as possible to the absolute optimal performance that would be achieved when conflict among ESS is not considered. In general, similar results were obtained with CP and BCP. This was not the case when the results produced by these two methods were contrasted with IIPT. For most ESS under consideration, CP and BCP produced balanced results that were closer to the absolute optimal values when compared to IIPT. We conclude from our results that, when optimization of land performance at a regional scale is at stake, CP-derived models emerge as the preferable option over IIPT, especially when balanced solutions are a requirement.

Description: Current understanding of aspen fire ecology in western North America includes the paradoxical characterization that aspen-dominated stands, although often regenerated following fire, are “fire-proof”. We tested this idea by predicting potential fire behavior across a gradient of aspen dominance in northern Utah using the Forest Vegetation Simulator and the Fire and Fuels Extension. The wind speeds necessary for crowning (crown-to-crown fire spread) and torching (surface to crown fire spread) were evaluated to test the hypothesis that predicted fire behavior is influenced by the proportion of aspen in the stand. Results showed a strong effect of species composition on crowning, but only under moderate fire weather, where aspen-dominated stands were unlikely to crown or torch. Although rarely observed in actual fires, conifer-dominated stands were likely to crown but not to torch, an example of “hysteresis” in crown fire behavior. Results support the hypothesis that potential crown fire behavior varies across a gradient of aspen dominance and fire weather, where it was likely under extreme and severe fire weather, and unlikely under moderate and high fire weather. Furthermore, the “fire-proof” nature of aspen stands broke down across the gradient of aspen dominance and fire weather.

Description: In this study we evaluate the capabilities of three satellite sensors for assessing water composition and bottom depth in Lake Garda, Italy. A consistent physics-based processing chain was applied to Moderate Resolution Imaging Spectroradiometer (MODIS), Landsat-8 Operational Land Imager (OLI) and RapidEye. Images gathered on 10 June 2014 were corrected for the atmospheric effects with the 6SV code. The computed remote sensing reflectance (Rrs) from MODIS and OLI were converted into water quality parameters by adopting a spectral inversion procedure based on a bio-optical model calibrated with optical properties of the lake. The same spectral inversion procedure was applied to RapidEye and to OLI data to map bottom depth. In situ measurements of Rrs and of concentrations of water quality parameters collected in five locations were used to evaluate the models. The bottom depth maps from OLI and RapidEye showed similar gradients up to 7 m (r = 0.72). The results indicate that: (1) the spatial and radiometric resolutions of OLI enabled mapping water constituents and bottom properties; (2) MODIS was appropriate for assessing water quality in the pelagic areas at a coarser spatial resolution; and (3) RapidEye had the capability to retrieve bottom depth at high spatial resolution. Future work should evaluate the performance of the three sensors in different bio-optical conditions.

Description: The sensorimotor system (SMS) plays an important role in sports and in every day movement. Several tools for assessment and training have been designed. Many of them are directed to specific populations, and have major shortcomings due to the training effect or safety. The aim of the present study was to design and assess a dynamic sensorimotor test and training device that can be adjusted for all levels of performance. The novel pneumatic-driven mechatronic device can guide the trainee, allow independent movements or disrupt the individual with unpredicted perturbations while standing on a platform. The test-reliability was evaluated using intraclass correlation coefficient (ICC). Subjects were required to balance their center of pressure (COP) in a target circle (TITC). The time in TITC and the COP error (COPe) were recorded for analysis. The results of 22 males and 14 females (23.7 ± 2.6 years) showed good to excellent test–retest reliability. The newly designed Active Balance System (ABS) was then compared with the Biodex Balance System SD® (BBS). The results of 15 females, 14 males (23.4 ± 1.6 years) showed modest correlation in static and acceptable correlation in dynamic conditions, suggesting that ABS could be a reliable and comparable tool for dynamic balance assessments.

Description: Land surface temperature (LST) retrieval is a key issue in infrared quantitative remote sensing. In this paper, a split window algorithm is proposed to estimate LST with daytime data in two mid-infrared channels (channel 66 (3.746~4.084 μm) and channel 68 (4.418~4.785 μm)) from Airborne Hyperspectral Scanner (AHS). The estimation is conducted after eliminating reflected direct solar radiance with the aid of water vapor content (WVC), the view zenith angle (VZA), and the solar zenith angle (SZA). The results demonstrate that the LST can be well estimated with a root mean square error (RMSE) less than 1.0 K. Furthermore, an error analysis for the proposed method is also performed in terms of the uncertainty of LSE and WVC, as well as the Noise Equivalent Difference Temperature (NEΔT). The results show that the LST errors caused by a LSE uncertainty of 0.01, a NEΔT of 0.33 K, and a WVC uncertainty of 10% are 0.4~2.8 K, 0.6 K, and 0.2 K, respectively. Finally, the proposed method is applied to the AHS data of 4 July 2008. The results show that the differences between the estimated and the ground measured LST for water, bare soil and vegetation areas are approximately 0.7 K, 0.9 K and 2.3K, respectively.

Description: A new method of depositing tin dioxide nanofibers in order to develop chemical sensors is presented. It involves an electrospinning process with in-plane electrostatic focusing over micromechanized substrates. It is a fast and reproducible method. After an annealing process, which can be performed by the substrate heaters, it is observed that the fibers are intertwined forming porous networks that are randomly distributed on the substrate. The fiber diameters oscillate from 100 nm to 200 nm and fiber lengths reach several tens of microns. Each fiber has a polycrystalline structure with multiple nano-grains. The sensors have been tested for the detection of acetone and hydrogen peroxide (precursors of the explosive triacetone triperoxide, TATP) in air in the ppm range. High and fast responses to these gases have been obtained.

Description: Light Detection and Ranging (LiDAR) is an active sensor that can effectively acquire a large number of three-dimensional (3-D) points. LiDAR systems can be equipped on different platforms for different applications, but to integrate the data, point cloud registration is needed to improve geometric consistency. The registration of airborne and terrestrial mobile LiDAR is a challenging task because the point densities and scanning directions differ. We proposed a scheme for the registration of airborne and terrestrial mobile LiDAR using the least squares 3-D surface registration technique to minimize the surfaces between two datasets. To analyze the effect of point density in registration, the simulation data simulated different conditions and estimated the theoretical errors. The test data were the point clouds of the airborne LiDAR system (ALS) and the mobile LiDAR system (MLS), which were acquired by Optech ALTM 3070 and Lynx, respectively. The resulting simulation analysis indicated that the accuracy of registration improved as the density increased. For the test dataset, the registration error of mobile LiDAR between different trajectories improved from 40 cm to 4 cm, and the registration error between ALS and MLS improved from 84 cm to 4 cm. These results indicate that the proposed methods can obtain 5 cm accuracy between ALS and MLS.

Description: Nighttime satellite imagery from the Defense Meteorological Satellite Programs’ Operational Linescan System (DMSP OLS) is being used for myriad applications including population mapping, characterizing economic activity, disaggregate estimation of CO2 emissions, wildfire monitoring, and more. Here we present a method for in situ radiance calibration of the DMSP OLS using a ground based light source as an active target. We found that the wattage of light used by our active target strongly correlates with the signal measured by the DMSP OLS. This approach can be used to enhance our ability to make intertemporal and intersatellite comparisons of DMSP OLS imagery. We recommend exploring the possibility of establishing a permanent active target for the calibration of nocturnal imaging systems.

Description: For near real-time water applications, the Moderate Resolution Imaging Spectroradiometers (MODIS) on Terra and Aqua are currently the only satellite instruments that can provide well-calibrated top-of-atmosphere (TOA) radiance data over the global aquatic environments. However, TOA radiance data in the MODIS ocean bands over turbid atmosphere in east China often saturate, leaving only four land bands to use. In this study, an approach based on Empirical Orthogonal Function (EOF) analysis has been developed and validated to estimate chlorophyll a concentrations (Chla, μg/L) in surface waters of Taihu Lake, the third largest freshwater lake in China. The EOF approach analyzed the spectral variance of normalized Rayleigh-corrected reflectance (Rrc) data at 469, 555, 645, and 859 nm, and subsequently related that variance to Chla using 28 concurrent MODIS and field measurements. This empirical algorithm was then validated using another 30 independent concurrent MODIS and field measurements. Image analysis and radiative transfer simulations indicated that the algorithm appeared to be tolerant to aerosol perturbations, with unbiased RMS uncertainties of 〈80% for Chla ranging between 3 and 100 μg/L. Application of the algorithm to a total of 853 MODIS images between 2000 and 2013 under cloud-free conditions revealed spatial distribution patterns and seasonal changes that are consistent to previous findings based on floating algae mats. The current study can provide additional quantitative estimates of Chla that can be assimilated in an existing forecast model, which showed improved performance over the use of a previous Chla algorithm. However, the empirical nature, relatively large uncertainties, and limited number of spectral bands all point to the need of further improvement in data availability and accuracy with future satellite sensors.

Description: MEMS Inertial Measurement Units are available at low cost and can replace expensive units in mobile mapping platforms which need direct georeferencing. This is done through the integration with GNSS measurements in order to achieve a continuous positioning solution and to obtain orientation angles. This paper presents the results of the assessment of the accuracy of a system that integrates GNSS and a MEMS IMU in a terrestrial platform. We describe the methodology used and the tests realized where the accuracy of the positions and orientation parameters were assessed using an independent photogrammetric technique employing cameras that integrate the mobile mapping system developed by the authors. Results for the accuracy of attitude angles and coordinates show that accuracies better than a decimeter in positions, and under a degree in angles, can be achieved even considering that the terrestrial platform is operating in less than favorable environments.

Description: This paper proposes an automated system for monitoring mobility patterns using a network of very low resolution visual sensors (30 30 pixels). The use of very low resolution sensors reduces privacy concern, cost, computation requirement and power consumption. The core of our proposed system is a robust people tracker that uses low resolution videos provided by the visual sensor network. The distributed processing architecture of our tracking system allows all image processing tasks to be done on the digital signal controller in each visual sensor. In this paper, we experimentally show that reliable tracking of people is possible using very low resolution imagery. We also compare the performance of our tracker against a state-of-the-art tracking method and show that our method outperforms. Moreover, the mobility statistics of tracks such as total distance traveled and average speed derived from trajectories are compared with those derived from ground truth given by Ultra-Wide Band sensors. The results of this comparison show that the trajectories from our system are accurate enough to obtain useful mobility statistics.

Description: Magnetic sensing technology offers an attractive alternative for in vivo tracking with much better performance than RF and ultrasound technologies. In this paper, an efficient in vivo magnetic tracking system is presented. The proposed system is intended to localize an endoscopic capsule which delivers biomarkers around specific locations of the gastrointestinal (GI) tract. For efficiently localizing a magnetic marker inside the capsule, a mathematical model has been developed for the magnetic field around a cylindrical magnet and used with a localization algorithm that provides minimum error and fast computation. The proposed tracking system has much reduced complexity compared to the ones reported in the literature to date. Laboratory tests and in vivo animal trials have demonstrated the suitability of the proposed system for tracking a magnetic marker with expected accuracy.

Description: This paper presents a sensor fusion system of cameras and a 2D laser sensorfor large-scale 3D reconstruction. The proposed system is designed to capture data on afast-moving ground vehicle. The system consists of six cameras and one 2D laser sensor,and they are synchronized by a hardware trigger. Reconstruction of 3D structures is doneby estimating frame-by-frame motion and accumulating vertical laser scans, as in previousworks. However, our approach does not assume near 2D motion, but estimates free motion(including absolute scale) in 3D space using both laser data and image features. In orderto avoid the degeneration associated with typical three-point algorithms, we present a newalgorithm that selects 3D points from two frames captured by multiple cameras. The problemof error accumulation is solved by loop closing, not by GPS. The experimental resultsshow that the estimated path is successfully overlaid on the satellite images, such that thereconstruction result is very accurate.

Description: The proliferation of mobile smart devices has led to a rapid increase of location-based services, many of which are amassing large datasets of user trajectory information. Unfortunately, current trajectory information is not yet sufficiently rich to support classification of user transportation modes. In this paper, we propose a method that employs both the Global Positioning System and accelerometer data from smart devices to classify user outdoor transportation modes. The classified modes include walking, bicycling, and motorized transport, in addition to the motionless (stationary) state, for which we provide new depth analysis. In our classification, stationary mode has two sub-modes: stay (remaining in the same place for a prolonged time period; e.g., in a parked vehicle) and wait (remaining at a location for a short period; e.g., waiting at a red traffic light). These two sub-modes present different semantics for data mining applications. We use support vector machines with parameters that are optimized for pattern recognition. In addition, we employ ant colony optimization to reduce the dimension of features and analyze their relative importance. The resulting classification system achieves an accuracy rate of 96.31% when applied to a dataset obtained from 18 mobile users.

Description: For many years, ultrasound has provided clinicians with an affordable and effective imaging tool for applications ranging from cardiology to obstetrics. Development of microbubble contrast agents over the past several decades has enabled ultrasound to distinguish between blood flow and surrounding tissue. Current clinical practices using microbubble contrast agents rely heavily on user training to evaluate degree of localized perfusion. Advances in separating the signals produced from contrast agents versus surrounding tissue backscatter provide unique opportunities for specialized sensors designed to image microbubbles with higher signal to noise and resolution than previously possible. In this review article, we describe the background principles and recent developments of ultrasound transducer technology for receiving signals produced by contrast agents while rejecting signals arising from soft tissue. This approach relies on transmitting at a low-frequency and receiving microbubble harmonic signals at frequencies many times higher than the transmitted frequency. Design and fabrication of dual-frequency transducers and the extension of recent developments in transducer technology for dual-frequency harmonic imaging are discussed.

Description: In this paper, a new low complexity and lossless image compression system for capsule endoscopy (CE) is presented. The compressor consists of a low-cost YEF color space converter and variable-length predictive with a combination of Golomb-Rice and unary encoding. All these components have been heavily optimized for low-power and low-cost and lossless in nature. As a result, the entire compression system does not incur any loss of image information. Unlike transform based algorithms, the compressor can be interfaced with commercial image sensors which send pixel data in raster-scan fashion that eliminates the need of having large buffer memory. The compression algorithm is capable to work with white light imaging (WLI) and narrow band imaging (NBI) with average compression ratio of 78% and 84% respectively. Finally, a complete capsule endoscopy system is developed on a single, low-power, 65-nm field programmable gate arrays (FPGA) chip. The prototype is developed using circular PCBs having a diameter of 16 mm. Several in-vivo and ex-vivo trials using pig's intestine have been conducted using the prototype to validate the performance of the proposed lossless compression algorithm. The results show that, compared with all other existing works, the proposed algorithm offers a solution to wireless capsule endoscopy with lossless and yet acceptable level of compression.

Description: A reflection fiber inclinometer is proposed and experimentally demonstrated based on two linearly polarized (LP) modes coupling. The configuration consists of a section of polarization-maintaining fiber (PMF) containing a fiber Bragg grating (FBG) splicing with single mode fiber (SMF). Bending the PMF in the upstream of FBG can induce an additional birefringence of PMF, which results in the intensity changes of two LP modes owing to orthogonal polarization coupling. The experimental results represent that the device shows different bending responses at the angle range from 0° to 40°and from 64° to 88°, respectively. Moreover, the temperature change just shifts the wavelengths of LP modes reflected and does not influence their intensities, which effectively avoid the temperature cross-sensitivity and make it a good candidate for measuring inclinometer and temperature simultaneously.

Description: Building change detection is useful for land management, disaster assessment, illegal building identification, urban growth monitoring, and geographic information database updating. This study proposes an automatic method that applies object-based analysis to multi-temporal point cloud data to detect building changes. The aim of this building change detection method is to identify areas that have changed and to obtain from-to information. In this method, the data are first preprocessed to generate two sets of digital surface models (DSMs), digital elevation models, and normalized DSMs from registered old and new point cloud data. Thereafter, on the basis of differential DSM, candidates for changed building objects are identified from the points in the smooth areas by using a connected component analysis technique. The random sample consensus fitting algorithm is then used to distinguish the true changed buildings from trees. The changed building objects are classified as “newly built”, “taller”, “demolished” or “lower” by using rule-based analysis. Finally, a test data set consisting of many buildings of different types in an 8.5 km2 area is selected for the experiment. In the test data set, the method correctly detects 97.8% of buildings larger than 50 m2. The accuracy of the method is 91.2%. Furthermore, to decrease the workload of subsequent manual checking of the result, the confidence index for each changed object is computed on the basis of object features.

Description: In this study, we test and demonstrate the utility of disturbance and recovery information derived from annual Landsat time series to predict current forest vertical structure (as compared to the more common approaches, that consider a sample of airborne Lidar and single-date Landsat derived variables). Mean Canopy Height (MCH) was estimated separately using single date, time series, and the combination of single date and time series variables in multiple regression and random forest (RF) models. The combination of single date and time series variables, which integrate disturbance history over the entire time series, overall provided better MCH prediction than using either of the two sets of variables separately. In general, the RF models resulted in improved performance in all estimates over those using multiple regression. The lowest validation error was obtained using Landsat time series variables in a RF model (R2 = 0.75 and RMSE = 2.81 m). Combining single date and time series data was more effective when the RF model was used (opposed to multiple regression). The RMSE for RF mean canopy height prediction was reduced by 13.5% when combining the two sets of variables as compared to the 3.6% RMSE decline presented by multiple regression. This study demonstrates the value of airborne Lidar and long term Landsat observations to generate estimates of forest canopy height using the random forest algorithm.

Description: Rice is the most important food security crop in Asia. Information on its seasonal extent forms part of the national accounting of many Asian countries. Synthetic Aperture Radar (SAR) imagery is highly suitable for detecting lowland rice, especially in tropical and subtropical regions, where pervasive cloud cover in the rainy seasons precludes the use of optical imagery. Here, we present a simple, robust, rule-based classification for mapping rice area with regularly acquired, multi-temporal, X-band, HH-polarized SAR imagery and site-specific parameters for classification. The rules for rice detection are based on the well-studied temporal signature of rice from SAR backscatter and its relationship with crop stages. We also present a procedure for estimating the parameters based on “temporal feature descriptors” that concisely characterize the key information in the rice signatures in monitored field locations within each site. We demonstrate the robustness of the approach on a very large dataset. A total of 127 images across 13 footprints in six countries in Asia were obtained between October 2012, and April 2014, covering 4.78 m ha. More than 1900 in-season site visits were conducted across 228 monitoring locations in the footprints for classification purposes, and more than 1300 field observations were made for accuracy assessment. Some 1.6 m ha of rice were mapped with classification accuracies from 85% to 95% based on the parameters that were closely related to the observed temporal feature descriptors derived for each site. The 13 sites capture much of the diversity in water management, crop establishment and maturity in South and Southeast Asia. The study demonstrates the feasibility of rice detection at the national scale using multi-temporal SAR imagery with robust classification methods and parameters that are based on the knowledge of the temporal dynamics of the rice crop. We highlight the need for the development of an open-access library of temporal signatures, further investigation into temporal feature descriptors and better ancillary data to reduce the risk of misclassification with surfaces that have temporal backscatter dynamics similar to those of rice. We conclude with observations on the need to define appropriate SAR acquisition plans to support policies and decisions related to food security.

Description: The amount of liquid water in snow characterizes the wetness of a snowpack. Its temporal evolution plays an important role for wet-snow avalanche prediction, as well as the onset of meltwater release and water availability estimations within a river basin. However, it is still a challenge and a not yet satisfyingly solved issue to measure the liquid water content (LWC) in snow with conventional in situ and remote sensing techniques. We propose a new approach based on the attenuation of microwave radiation in the L-band emitted by the satellites of the Global Positioning System (GPS). For this purpose, we performed a continuous low-cost GPS measurement experiment at the Weissfluhjoch test site in Switzerland, during the snow melt period in 2013. As a measure of signal strength, we analyzed the carrier-to-noise power density ratio (C/N0) and developed a procedure to normalize these data. The bulk volumetric LWC was determined based on assumptions for attenuation, reflection and refraction of radiation in wet snow. The onset of melt, as well as daily melt-freeze cycles were clearly detected. The temporal evolution of the LWC was closely related to the meteorological and snow-hydrological data. Due to its non-destructive setup, its cost-efficiency and global availability, this approach has the potential to be implemented in distributed sensor networks for avalanche prediction or basin-wide melt onset measurements.

Description: On-site analysis is an efficient approach to facilitate analysis at the location of the system under investigation as it can result in more accurate, more precise and quickly available analytical data. In our work, a novel self-made thermal desorption based interface was fabricated to couple solid-phase microextraction with ion mobility spectrometry for on-site water analysis. The portable interface can be connected with the front-end of an ion mobility spectrometer directly without other modifications. The analytical performance was evaluated via the extraction of chemical warfare agents and simulants in water samples. Several parameters including ionic strength and extraction time have been investigated in detail. The application of the developed method afforded satisfactory recoveries ranging from 72.9% to 114.4% when applied to the analysis of real water samples.

Description: We consider the problem of localising an unknown number of land mines usingconcentration information provided by a wireless sensor network. A number of vapoursensors/detectors, deployed in the region of interest, are able to detect the concentrationof the explosive vapours, emanating from buried land mines. The collected data iscommunicated to a fusion centre. Using a model for the transport of the explosive chemicalsin the air, we determine the unknown number of sources using a Principal ComponentAnalysis (PCA)-based technique. We also formulate the inverse problem of determiningthe positions and emission rates of the land mines using concentration measurementsprovided by the wireless sensor network. We present a solution for this problem basedon a probabilistic Bayesian technique using a Markov chain Monte Carlo sampling scheme,and we compare it to the least squares optimisation approach. Experiments conducted onsimulated data show the effectiveness of the proposed approach.

Description: A smart-card-based user authentication scheme for wireless sensor networks (in short, a SUA-WSN scheme) is designed to restrict access to the sensor data only to users who are in possession of both a smart card and the corresponding password. While a significant number of SUA-WSN schemes have been suggested in recent years, their intended security properties lack formal definitions and proofs in a widely-accepted model. One consequence is that SUA-WSN schemes insecure against various attacks have proliferated. In this paper, we devise a security model for the analysis of SUA-WSN schemes by extending the widely-accepted model of Bellare, Pointcheval and Rogaway (2000). Our model provides formal definitions of authenticated key exchange and user anonymity while capturing side-channel attacks, as well as other common attacks. We also propose a new SUA-WSN scheme based on elliptic curve cryptography (ECC), and prove its security properties in our extended model. To the best of our knowledge, our proposed scheme is the first SUA-WSN scheme that provably achieves both authenticated key exchange and user anonymity. Our scheme is also computationally competitive with other ECC-based (non-provably secure) schemes.

Description: Optical mouse chips—equipped with adequate lenses—can serve as small, light, precise, fast, and cheap motion sensors monitoring optic flow induced by self motion of an agent in a contrasted environment. We present a device that extracts self motion parameters exclusively from flow in eight mouse sensors. Four pairs of sensors with opposite azimuth are mounted on a sensor head, each individual sensor looking down with \(-\)45\(^{\circ}\) elevation. The head is mounted on a carriage and is moved at constant height above a textured planar ground. The calibration procedure and tests on the precision of self motion estimates are reported.

Description: Future wireless sensor networks are expected to provide various sensing services and energy efficiency is one of the most important criterions. The node scheduling strategy aims to increase network lifetime by selecting a set of sensor nodes to provide the required sensing services in a periodic manner. In this paper, we are concerned with the service-oriented node scheduling problem to provide multiple sensing services while maximizing the network lifetime. We firstly introduce how to model the data correlation for different services by using Markov Random Field (MRF) model. Secondly, we formulate the service-oriented node scheduling issue into three different problems, namely, the multi-service data denoising problem which aims at minimizing the noise level of sensed data, the representative node selection problem concerning with selecting a number of active nodes while determining the services they provide, and the multi-service node scheduling problem which aims at maximizing the network lifetime. Thirdly, we propose a Multi-service Data Denoising (MDD) algorithm, a novel multi-service Representative node Selection and service Determination (RSD) algorithm, and a novel MRF-based Multi-service Node Scheduling (MMNS) scheme to solve the above three problems respectively. Finally, extensive experiments demonstrate that the proposed scheme efficiently extends the network lifetime.

Description: Global insurance markets are vast and diverse, and may offer many opportunities for remote sensing. To date, however, few operational applications of remote sensing for insurance exist. Papers claiming potential application of remote sensing typically stress the technical possibilities, without considering its contribution to customer value for the insured or to the profitability of the insurance industry. Based on a systematic search of available literature, this review investigates the potential and actual support of remote sensing to the insurance industry. The review reveals that research on remote sensing in classical claim-based insurance described in the literature revolve around crop damage and flood and fire risk assessment. Surprisingly, the use of remote sensing in claim-based insurance appears to be instigated by government rather than the insurance industry. In contrast, insurance companies are offering various index insurance products that are based on remote sensing. For example, remotely sensed index insurance for rangelands and livestock are operational, while various applications in crop index insurance are being considered or under development. The paper discusses these differences and concludes that there is particular scope for application of remote sensing by the insurance industry in index insurance because (1) indices can be constructed that correlate well with what is insured; (2) these indices can be delivered at low cost; and (3) it opens up new markets that are not served by claim-based insurance. The paper finally suggests that limited adoption of remote sensing in insurance results from a lack of mutual understanding and calls for greater cooperation between the insurance industry and the remote sensing community.

Description: Vegetation cover can affect the lithological mapping capability of space- and airborne instruments because it obscures the spectral signatures of the underlying geological substrate. Despite being widely accepted as a hindrance, few studies have explicitly demonstrated the impact vegetation can have on remote lithological mapping. Accordingly, this study comprehensively elucidates the impact of vegetation on the lithological mapping capability of airborne multispectral data in the Troodos region, Cyprus. Synthetic spectral mixtures were first used to quantify the potential impact vegetation cover might have on spectral recognition and remote mapping of different rock types. The modeled effects of green grass were apparent in the spectra of low albedo lithologies for 30%–40% fractional cover, compared to just 20% for dry grass cover. Lichen was found to obscure the spectra for 30%–50% cover, depending on the spectral contrast between bare rock and lichen cover. The subsequent impact of vegetation on the remote mapping capability is elucidated by considering the outcomes of three airborne multispectral lithological classifications alongside the spectral mixing analysis and field observations. Vegetation abundance was found to be the primary control on the inability to classify large proportions of pixels in the imagery. Matched Filtering outperformed direct spectral matching algorithms owing to its ability to partially unmix pixel spectra with vegetation abundance above the modeled limits. This study highlights that despite the limited spectral sampling and resolution of the sensor and dense, ubiquitous vegetation cover, useful lithological information can be extracted using an appropriate algorithm. Furthermore, the findings of this case study provide a useful insight to the potential capabilities and challenges faced when utilizing comparable sensors (e.g., Landsat 8, Sentinel-2, WorldView-3) to map similar types of terrain.

Description: The origin of large areas dominated by pristine open vegetation that is in sharp contrast with surrounding dense forest within the Amazonian lowland has generally been related to past arid climates, but this is still an issue open for debate. In this paper, we characterize a large open vegetation patch over a residual megafan located in the northern Amazonia. The main goal was to investigate the relationship between this paleolandform and vegetation classes mapped based on the integration of optical and SAR data using the decision tree. Our remote sensing dataset includes PALSAR and TM/Landsat images. Five classes were identified: rainforest; flooded forest; wooded open vegetation; grassy-shrubby open vegetation; and water body. The output map resulting from the integration of PALSAR and TM/Landsat images showed an overall accuracy of 94%. Narrow, elongated and sinuous belts of forest within the open vegetation areas progressively bifurcate into others revealing paleochannels arranged into distributary pattern. Such characteristics, integrated with pre-existing geological information, led us to propose that the distribution of vegetation classes highlight a morphology attributed to a Quaternary megafan developed previous to the modern fluvial tributary system. The characterization of such megafan is important for reconstructing landscape changes associated with the evolution of the Amazon drainage basin.

Description: Flash Flood Guidance consists of indices that estimate the amount of rain of a certain duration that is needed over a given small basin in order to cause minor flooding. Backwater catchment inundation from swollen rivers or regional groundwater inputs are not significant over the spatial and temporal scales for the majority of upland flash flood prone basins, as such, these effects are not considered. However, some lowland areas and flat terrain near large rivers experience standing water long after local precipitation has ceased. NASA is producing an experimental product from the MODIS that detects standing water. These observations were assimilated into the hydrologic model in order to more accurately represent soil moisture conditions within basins, from sources of water from outside of the basin. Based on the upper soil water content, relations are used to derive an error estimate for the modeled soil saturation fraction; whereby, the soil saturation fraction model state can be updated given the availability of satellite observed inundation. Model error estimates were used in a Monte Carlo ensemble forecast of soil water and flash flood potential. Numerical experiments with six months of data (July 2011–December 2011) showed that MODIS inundation data, when assimilated to correct soil moisture estimates, increased the likelihood that bankfull flow would occur, over non-assimilated modeling, at catchment outlets for approximately 44% of basin-days during the study time period. While this is a much more realistic representation of conditions, no actual events occurred allowing for validation during the time period.

Description: For a foam-covered sea surface, it is difficult to retrieve sea surface salinity (SSS) with L-band brightness temperature (1.4 GHz) because of the effect of a foam layer with wind speeds stronger than 7 m/s, especially at low sea surface temperature (SST). With foam-controlled experiments, emissivities of a foam-covered water surface at low SST (−1.4 °C to 1.7 °C) are measured for varying SSS, foam thickness, incidence angle, and polarization. Furthermore, a theoretical model of emissivity is introduced by combining wave approach theory with the effective medium approximation method. Good agreement is obtained upon comparing theoretical emissivities with those of experiments. The results indicate that foam parameters have a strong influence on increasing emissivity of a foam-covered water surface. Increments of experimental emissivities caused by foam thickness of 1 cm increase from about 0.014 to 0.131 for horizontal polarization and 0.022 to 0.150 for vertical polarization with SSS increase and SST decrease. Contributions of the interface between the foam layer and water surface to the foam layer emissivity increments are discussed for frequencies between 1 and 37 GHz.

Description: Maintaining a healthy ecosystem is essential for maximizing sustainable ecological services of the best quality to human beings. Ecological and conservation research has provided a strong scientific background on identifying ecological health indicators and correspondingly making effective conservation plans. At the same time, ecologists have asserted a strong need for spatially explicit and temporally effective ecosystem health assessments based on remote sensing data. Currently, remote sensing of ecosystem health is only based on one ecosystem attribute: vigor, organization, or resilience. However, an effective ecosystem health assessment should be a comprehensive and dynamic measurement of the three attributes. This paper reviews opportunities of remote sensing, including optical, radar, and LiDAR, for directly estimating indicators of the three ecosystem attributes, discusses the main challenges to develop a remote sensing-based spatially-explicit comprehensive ecosystem health system, and provides some future perspectives. The main challenges to develop a remote sensing-based spatially-explicit comprehensive ecosystem health system are: (1) scale issue; (2) transportability issue; (3) data availability; and (4) uncertainties in health indicators estimated from remote sensing data. However, the Radarsat-2 constellation, upcoming new optical sensors on Worldview-3 and Sentinel-2 satellites, and improved technologies for the acquisition and processing of hyperspectral, multi-angle optical, radar, and LiDAR data and multi-sensoral data fusion may partly address the current challenges.

Description: This paper reviews the most dependable heat flux sensors, which can be used with InfraRed (IR) thermography to measure convective heat transfer coefficient distributions, and some of their applications performed by the authors’ research group at the University of Naples Federico II. After recalling the basic principles that make IR thermography work, the various heat flux sensors to be used with it are presented and discussed, describing their capability to investigate complex thermo-fluid-dynamic flows. Several applications to streams, which range from natural convection to hypersonic flows, are also described.

Description: In contrast to ground-based solar-induced chlorophyll fluorescence (Fs) detection, the influence of atmospheric radiation transfer is the major difficulty in Fs retrieval from space. In this study, we first simulated top-of-atmosphere (TOA) radiance using FluorMODgui3.1 and MODTRAN5 code. Based on the simulated dataset, we analyzed the sensitivities of five potential Fs retrieval bands (Hα, K I, Fe, O2-A, and O2-B) to different atmospheric transfer parameters, including atmosphere profile, aerosol optical depth (AOD550), vertical water vapor column (H2O), vertical ozone column (O3), solar zenith angle (SZA), view zenith angle (VZA), relative azimuth angle (RAA) and elevation. The results demonstrate that the Hα, O2-A and O2-B bands are the most sensitive to these atmospheric parameters. However, only the O2-A and O2-B bands were found to be sensitive to the imaging geometric parameters. When the spectral resolution was sufficient, the K I and Fe bands proved to have the best potential for space-based Fs retrieval given the current available accuracies of atmospheric products, while the O2-A band was shown to perform better at lower spectral resolutions. The band sensitivity analysis presented here will be useful for band selection and atmospheric correction for space-based Fs retrieval.

Description: Mean-shift tracking has gained more interests, nowadays, aided by its feasibility of real-time and reliable tracker implementation. In order to reduce background clutter interference to mean-shift object tracking, this paper proposes a novel indicator function generation method. The proposed method takes advantage of two ‘a priori’ knowledge elements, which are inherent to a kernel support for initializing a target model. Based on the assured background labels, a gradient-based label propagation is performed, resulting in a number of objects differentiated from the background. Then the proposed region growing scheme picks up one largest target object near the center of the kernel support. The grown object region constitutes the proposed indicator function and this allows an exact target model construction for robust mean-shift tracking. Simulation results demonstrate the proposed exact target model could significantly enhance the robustness as well as the accuracy of mean-shift object tracking.

Description: In this paper, an adaptive activity and environment recognition algorithm running on a mobile phone is presented. The algorithm makes inferences based on sensor and radio receiver data provided by the phone. A wide set of features that can be extracted from these data sources were investigated, and a Bayesian maximum a posteriori classifier was used for classifying between several user activities and environments. The accuracy of the method was evaluated on a dataset collected in a real-life trial. In addition, comparison to other state-of-the-art classifiers, namely support vector machines and decision trees, was performed. To make the system adaptive for individual user characteristics, an adaptation algorithm for context model parameters was designed. Moreover, a confidence measure for the classification correctness was designed. The proposed adaptation algorithm and confidence measure were evaluated on a second dataset obtained from another real-life trial, where the users were requested to provide binary feedback on the classification correctness. The results show that the proposed adaptation algorithm is effective at improving the classification accuracy.

Description: A novel wireless and passive surface acoustic wave (SAW) based temperature-compensated vibration sensor utilizing a flexible Y-cut quartz cantilever beam with a relatively substantial proof mass and two one-port resonators is developed. One resonator acts as the sensing device adjacent to the clamped end for maximum strain sensitivity, and the other one is used as the reference located on clamped end for temperature compensation for vibration sensor through the differential approach. Vibration directed to the proof mass flex the cantilever, inducing relative changes in the acoustic propagation characteristics of the SAW travelling along the sensing device, and generated output signal varies in frequency as a function of vibration.  A theoretical mode using the Rayleigh method was established to determine the optimal dimensions of the cantilever beam. Coupling of Modes (COM) model was used to extract the optimal design parameters of the SAW devices prior to fabrication. The performance of the developed SAW sensor attached to an antenna towards applied vibration was evaluated wirelessly by using the precise vibration table, programmable incubator chamber, and reader unit.  High vibration sensitivity of ~10.4 kHz/g, good temperature stability, and excellent linearity were observed in the wireless measurements.

Description: The constantly growing amount ofWeb content and the success of the SocialWeb lead to increasing needs for Web archiving. These needs go beyond the pure preservationo of Web pages. Web archives are turning into “community memories” that aim at building a better understanding of the public view on, e.g., celebrities, court decisions and other events. Due to the size of the Web, the traditional “collect-all” strategy is in many cases not the best method to build Web archives. In this paper, we present the ARCOMEM (From Future Internet 2014, 6 689 Collect-All Archives to Community Memories) architecture and implementation that uses semantic information, such as entities, topics and events, complemented with information from the Social Web to guide a novel Web crawler. The resulting archives are automatically enriched with semantic meta-information to ease the access and allow retrieval based on conditions that involve high-level concepts..

Description: Recharging implantable electronics from the outside of the human body is very important for applications such as implantable biosensors and other implantable electronics. In this paper, a recharging method for implantable biosensors based on a wearable incoherent light source has been proposed and simulated. Firstly, we develop a model of the incoherent light source and a multi-layer model of skin tissue. Secondly, the recharging processes of the proposed method have been simulated and tested experimentally, whereby some important conclusions have been reached. Our results indicate that the proposed method will offer a convenient, safe and low-cost recharging method for implantable biosensors, which should promote the application of implantable electronics.

Description: In this paper we explore the performances and the opportunities provided by the European satellite Sentinel-1 for water resource management applications in low-income countries. The analysis is supported by a synthetic aperture radar (SAR) simulator, which allowed the quantification of the expected characteristics of Sentinel-1 products in three applications: interferometric digital elevation models (DEMs) generation, land cover mapping and estimation of water volumes retained by small reservoirs. The obtained results quantitatively show that Sentinel-1 data characteristics are fully suitable for most of the application already explored in the recent SAR literature.

Description: In this paper, we study mobile element (ME)-based data-gathering schemes in wireless sensor networks. Due to the physical speed limits of mobile elements, the existing data-gathering schemes that usemobile elements can suffer from high data-gathering latency. In order to address this problem, this paper proposes a new hierarchical and cooperative data-gathering (HiCoDG) scheme that enables multiple mobile elements to cooperate with each other to collect and relay data. In HiCoDG, two types of mobile elements are used: the mobile collector (MC) and the mobile relay (MR). MCs collect data from sensors and forward them to the MR, which will deliver them to the sink. In this work, we also formulated an integer linear programming (ILP) optimization problem to find the optimal trajectories for MCs and the MR, such that the traveling distance of MEs is minimized. Two variants of HiCoDG, intermediate station (IS)-based and cooperative movement scheduling (CMS)-based, are proposed to facilitate cooperative data forwarding from MCs to theMR. An analytical model for estimating the average data-gathering latency in HiCoDG was also designed. Simulations were performed to compare the performance of the IS and CMS variants, as well as a multiple traveling salesman problem (mTSP)-based approach. The simulation results show that HiCoDG outperformsmTSP in terms of latency. The results also show that CMS can achieve the lowest latency with low energy consumption.

Description: Wireless Sensor Network (WSN) is leading to a new paradigm of Internet of Everything (IoE). WSNs have a wide range of applications but are usually deployed in a particular application. However, the future of WSNs lies in the aggregation and allocation of resources, serving diverse applications. WSN virtualization by the middleware is an emerging concept that enables aggregation of multiple independent heterogeneous devices, networks, radios and software platforms; and enhancing application development. WSN virtualization, middleware can further be categorized into sensor virtualization and network virtualization. Middleware for WSN virtualization poses several challenges like efficient decoupling of networks, devices and software. In this paper efforts have been put forward to bring an overview of the previous and current middleware designs for WSN virtualization, the design goals, software architectures, abstracted services, testbeds and programming techniques. Furthermore, the paper also presents the proposed model, challenges and future opportunities for further research in the middleware designs for WSN virtualization.

Description: We processed X-band COSMO-SkyMed 3-m resolution StripMap HIMAGE time series (March 2011–June 2013) with the Stanford Method for Persistent Scatterers (StaMPS), to retrieve an updated picture of the condition and structural health of the historic centre of Rome, Italy, and neighbouring quarters. Taking advantage of an average target density of over 2800 PS/km2, we analysed the spatial distribution of more than 310,000 radar targets against: (1) land cover; (2) the location of archaeological ruins and restoration activities; and (3) the size, orientation and morphology of historical buildings. Radar interpretation was addressed from the perspective of conservators, and the deformation estimates were correlated to local geohazards and triggering factors of structural collapse. In the context of overall stability, deformation was identified at the single-monument scale, e.g., for the Roman cistern and exedra in the Oppian Hill. Comparative assessment against InSAR processing of C-band imagery (1992–2010) published in the literature confirms the persistence of ground motions affecting monuments and subsidence in southern residential quarters adjacent to the Tiber River, due to the consolidation of compressible deposits. Vertical velocity estimated from COSMO-SkyMed PS exceeds −7.0 mm/y in areas of recent urbanization.

Description: The purpose of this study was to examine how different polarimetric parameters and an object-based approach influence the classification results of various land use/land cover types using fully polarimetric ALOS PALSAR data over coastal wetlands in Yancheng, China. To verify the efficiency of the proposed method, five other classifications (the Wishart supervised classification, the proposed method without polarimetric parameters, the proposed method without an object-based analysis, the proposed method without textural and geometric information and the proposed method using the nearest-neighbor classifier) were applied for comparison. The results indicated that some polarimetric parameters, such as Shannon entropy, Krogager_Kd, Alpha, HAAlpha_T11, VanZyl3_Vol, Derd, Barnes2_T33, polarization fraction, Barnes1_T33, Neuman_delta_mod and entropy, greatly improved the classification results. The shape index was a useful feature in distinguishing fish ponds and rivers. The distance to the sea can be regarded as an important factor in reducing the confusion between herbaceous wetland vegetation and grasslands. Furthermore, the decision tree algorithm increased the overall accuracy by 6.8% compared with the nearest neighbor classifier. This research demonstrated that different polarimetric parameters and the object-based approach significantly improved the performance of land cover classification in coastal wetlands using ALOS PALSAR data.

Description: A new fluorescent probe P derived from naphthalimide bearing a pyridine group has been synthesized and characterized. The proposed probe P shows high selectivity and sensitivity to Cu2+ in aqueous media. Under optimized conditions, the linear response of P (2 μM) toward Cu2+ was 0.05–0.9 μM in ethanol-water solution (3:2, v:v, 50 mM HEPES, pH 7.4), and the detection limit was 0.03 μM.

Description: Due to an error on our part, the pagination and the doi number of this manuscript [1] were missing during updating. The correct pagination is 9600–9652, and the doi number is 10.3390/rs6109600. Here is the correct version:[...]

Description: This paper presents a performance analysis of two open-source, laser scanner-based Simultaneous Localization and Mapping (SLAM) techniques (i.e., Gmapping and Hector SLAM) using a Microsoft Kinect to replace the laser sensor. Furthermore, the paper proposes a new system integration approach whereby a Linux virtual machine is used to run the open source SLAM algorithms. The experiments were conducted in two different environments; a small room with no features and a typical office corridor with desks and chairs. Using the data logged from real-time experiments, each SLAM technique was simulated and tested with different parameter settings. The results show that the system is able to achieve real time SLAM operation. The system implementation offers a simple and reliable way to compare the performance of Windows-based SLAM algorithm with the algorithms typically implemented in a Robot Operating System (ROS). The results also indicate that certain modifications to the default laser scanner-based parameters are able to improve the map accuracy. However, the limited field of view and range of Kinect’s depth sensor often causes the map to be inaccurate, especially in featureless areas, therefore the Kinect sensor is not a direct replacement for a laser scanner, but rather offers a feasible alternative for 2D SLAM tasks.

Description: Winemaking is a dynamic process, where microbiological and chemical effects may strongly differentiate products from the same vineyard and even between wine vats. This high variability means an increase in work in terms of control and process management. The winemaking process therefore requires a site-specific approach in order to optimize cellar practices and quality management, suggesting a new concept of winemaking, identified as Precision Enology. The Institute of Biometeorology of the Italian National Research Council has developed a wireless monitoring system, consisting of a series of nodes integrated in barrel bungs with sensors for the measurement of wine physical and chemical parameters in the barrel. This paper describes an open-source evolution of the preliminary prototype, using Arduino-based technology. Results have shown good performance in terms of data transmission and accuracy, minimal size and power consumption. The system has been designed to create a low-cost product, which allows a remote and real-time control of wine evolution in each barrel, minimizing costs and time for sampling and laboratory analysis. The possibility of integrating any kind of sensors makes the system a flexible tool that can satisfy various monitoring needs.

Description: Due to instability and poor identification ability of single pyroelectric infrared (PIR) detector for human target identification, this paper proposes a new approach to fuse the information collected from multiple PIR sensors for human identification. Firstly, Fast Fourier Transform (FFT), Short Time Fourier Transform (STFT), Wavelet Transform (WT) and Wavelet Packet Transform (WPT) are adopted to extract features of the human body, which can be achieved by single PIR sensor. Then, we apply Principal Component Analysis (PCA) and Support Vector Machine (SVM) to reduce the characteristic dimensions and to classify the human targets, respectively. Finally, Fuzzy Comprehensive Evaluation (FCE) is utilized to fuse recognition results from multiple PIR sensors to finalize human identification. The pyroelectric characteristics under scenarios with different people and/or different paths are analyzed by various experiments, and the recognition results with/without fusion procedure are also shown and compared. The experimental results demonstrate our scheme has improved efficiency for human identification.

Description: In volcanic areas, where it can be difficult to perform direct surveys, digital photogrammetry techniques are rarely adopted for routine volcano monitoring. Nevertheless, they have remarkable potentialities for observing active volcanic features (e.g., fissures, lava flows) and the connected deformation processes. The ability to obtain accurate quantitative data of definite accuracy in short time spans makes digital photogrammetry a suitable method for controlling the evolution of rapidly changing large-area volcanic phenomena. The systematic acquisition of airborne photogrammetric datasets can be adopted for implementing a more effective procedure aimed at long-term volcano monitoring and hazard assessment. In addition, during the volcanic crisis, the frequent acquisition of oblique digital images from helicopter allows for quasi-real-time monitoring to support mitigation actions by civil protection. These images are commonly used to update existing maps through a photo-interpretation approach that provide data of unknown accuracy. This work presents a scientific tool (Orthoview) that implements a straightforward photogrammetric approach to generate digital orthophotos from single-view oblique images provided that at least four Ground Control Points (GCP) and current Digital Elevation Models (DEM) are available. The influence of the view geometry, of sparse and not-signalized GCP and DEM inaccuracies is analyzed for evaluating the performance of the developed tool in comparison with other remote sensing techniques. Results obtained with datasets from Etna and Stromboli volcanoes demonstrate that 2D features measured on the produced orthophotos can reach sub-meter-level accuracy.

Description: When object-based analysis is applied to very high-resolution imagery, pixels within the segments reveal large spectral inhomogeneity; their distribution can be considered complex rather than normal. When normality is violated, the classification methods that rely on the assumption of normally distributed data are not as successful or accurate. It is hard to detect normality violations in small samples. The segmentation process produces segments that vary highly in size; samples can be very big or very small. This paper investigates whether the complexity within the segment can be addressed using multiple random sampling of segment pixels and multiple calculations of similarity measures. In order to analyze the effect sampling has on classification results, statistics and probability value equations of non-parametric two-sample Kolmogorov-Smirnov test and parametric Student’s t-test are selected as similarity measures in the classification process. The performance of both classifiers was assessed on a WorldView-2 image for four land cover classes (roads, buildings, grass and trees) and compared to two commonly used object-based classifiers—k-Nearest Neighbor (k-NN) and Support Vector Machine (SVM). Both proposed classifiers showed a slight improvement in the overall classification accuracies and produced more accurate classification maps when compared to the ground truth image.

Description: An LC resonant pressure sensor with improved performance is presented in this paper. The sensor is designed with a buried structure, which protects the electrical components from contact with harsh environments and reduces the resonant-frequency drift of the sensor in high-temperature environments. The pressure-sensitive membrane of the sensor is optimized according to small-deflection-plate theory, which allows the sensor to operate in high-pressure environments. The sensor is fabricated using low-temperature co-fired ceramic (LTCC) technology, and a fugitive film is used to create a completed sealed embedded cavity without an evacuation channel. The experimental results show that the frequency drift of the sensor versus the temperature is approximately 0.75 kHz/°C, and the responsivity of the sensor can be up to 31 kHz/bar within the pressure range from atmospheric pressure to 60 bar.

Description: The explosive triacetone triperoxide (TATP), which can be prepared from commercially readily available reagents following an easy synthetic procedure, is one of the most common components of improvised explosive devices (IEDs). Molecularly-imprinted polymer (MIP) electrochemical sensors have proved useful for the determination of different compounds in different matrices with the required sensitivity and selectivity. In this work, a highly sensitive and selective molecularly imprinted polymer with electrochemical capabilities for the determination of TATP has been developed. The molecular imprinting has been performed via electropolymerisation onto a glassy carbon electrode surface by cyclic voltammetry from a solution of pyrrole functional monomer, TATP template and LiClO4. Differential Pulse Voltammetry of TATP, with LiClO4 as supporting electrolyte, was performed in a potential range of −2.0 V to +1.0 V (vs. Ag/AgCl). Three-factor two-level factorial design was used to optimise the monomer concentration at 0.1 mol∙L−1, template concentration at 100 mmol∙L−1 and the number of cyclic voltammetry scan cycles to 10. The molecularly imprinted polymer-modified glassy carbon electrode demonstrated good performance at low concentrations for a linear range of 82–44,300 µg∙L−1 and a correlation coefficient of r2 = 0.996. The limits of detection (LoD) and quantification (LoQ) achieved were 26.9 μg∙L−1 and 81.6 μg∙L−1, respectively. The sensor demonstrated very good repeatability with precision values (n = 6, expressed as %RSD) of 1.098% and 0.55% for 1108 and 2216 µg∙L−1, respectively. It also proved selective for TATP in the presence of other explosive substances such as PETN, RDX, HMX, and TNT.

Description: Delay tracking of spread-spectrum signals is widely used for ranging in radio frequency based navigation. Its use in non-coherent optical ranging, however, has not been extensively studied since optical channels are less subject to narrowband interference situations where these techniques become more useful. In this work, an early-late delay-locked loop adapted to indoor optical ranging is presented and analyzed. The specific constraints of free-space infrared channels in this context substantially differ from those typically considered in radio frequency applications. The tracking stage is part of an infrared differential range measuring system with application to mobile target indoor localization. Spread-spectrum signals are used in this context to provide accurate ranging while reducing the effect of multipath interferences. The performance of the stage regarding noise and dynamic errors is analyzed and validated, providing expressions that allow an adequate selection of the design parameters depending on the expected input signal characteristics. The behavior of the stage in a general multipath scenario is also addressed to estimate the multipath error bounds. The results, evaluated under realistic conditions corresponding to an 870 nm link with 25 MHz chip-rate, built with low-cost up-to-date devices, show that an overall error below 6% of a chip time can be achieved.

Description: Many intelligent transportation system applications require accurate, reliable, and continuous vehicle positioning. How to achieve such positioning performance in extended GPS-denied environments such as tunnels is the main challenge for land vehicles. This paper proposes a hybrid multi-sensor fusion strategy for vehicle positioning in tunnels. First, the preliminary positioning algorithm is developed. The Radio Frequency Identification (RFID) technology is introduced to achieve preliminary positioning in the tunnel. The received signal strength (RSS) is used as an indicator to calculate the distances between the RFID tags and reader, and then a Least Mean Square (LMS) federated filter is designed to provide the preliminary position information for subsequent global fusion. Further, to improve the positioning performance in the tunnel, an interactive multiple model (IMM)-based global fusion algorithm is developed to fuse the data from preliminary positioning results and low-cost in-vehicle sensors, such as electronic compasses and wheel speed sensors. In the actual implementation of IMM, the strong tracking extended Kalman filter (STEKF) algorithm is designed to replace the conventional extended Kalman filter (EKF) to achieve model individual filtering. Finally, the proposed strategy is evaluated through experiments. The results validate the feasibility and effectiveness of the proposed strategy.

Description: A high accuracy localization technique using Long Term Evolution (LTE) based on a new and accurate multiple carrier noise model has been developed. In the noise consideration, the LTE multiple carriers phase noise has been incorporated so that a new and accurate noise model is achieved. An experiment was performed to characterize the phase noise of carriers at 2 GHz. The developed noise model was incorporated into LTE localization analysis in a high traffic area in Hong Kong to evaluate the accuracy of localization. The evaluation and analysis reveals that the new localization method achieves an improvement of about 10% accuracy comparing to existing widely adopted schemes.

Description: In biomimetics, living systems are imitated to develop receptors for ions, molecules and bioparticles. The most pertinent idea is self-organization in analogy to evolution in nature, which created the key-lock principle. Today, modern science has been developing host-guest chemistry, a strategy of supramolecular chemistry for designing interactions of analytes with synthetic receptors. This can be realized, e.g., by self-assembled monolayers (SAMs) or molecular imprinting. The strategies are used for solid phase extraction (SPE), but preferably in developing recognition layers of chemical sensors.

Description: Estimating forest canopy height from large-footprint satellite LiDAR waveforms is challenging given the complex interaction between LiDAR waveforms, terrain, and vegetation, especially in dense tropical and equatorial forests. In this study, canopy height in French Guiana was estimated using multiple linear regression models and the Random Forest technique (RF). This analysis was either based on LiDAR waveform metrics extracted from the GLAS (Geoscience Laser Altimeter System) spaceborne LiDAR data and terrain information derived from the SRTM (Shuttle Radar Topography Mission) DEM (Digital Elevation Model) or on Principal Component Analysis (PCA) of GLAS waveforms. Results show that the best statistical model for estimating forest height based on waveform metrics and digital elevation data is a linear regression of waveform extent, trailing edge extent, and terrain index (RMSE of 3.7 m). For the PCA based models, better canopy height estimation results were observed using a regression model that incorporated both the first 13 principal components (PCs) and the waveform extent (RMSE = 3.8 m). Random Forest regressions revealed that the best configuration for canopy height estimation used all the following metrics: waveform extent, leading edge, trailing edge, and terrain index (RMSE = 3.4 m). Waveform extent was the variable that best explained canopy height, with an importance factor almost three times higher than those for the other three metrics (leading edge, trailing edge, and terrain index). Furthermore, the Random Forest regression incorporating the first 13 PCs and the waveform extent had a slightly-improved canopy height estimation in comparison to the linear model, with an RMSE of 3.6 m. In conclusion, multiple linear regressions and RF regressions provided canopy height estimations with similar precision using either LiDAR metrics or PCs. However, a regression model (linear regression or RF) based on the PCA of waveform samples with waveform extent information is an interesting alternative for canopy height estimation as it does not require several metrics that are difficult to derive from GLAS waveforms in dense forests, such as those in French Guiana.

Description: Quality segment generation is a well-known challenge and research objective within Geographic Object-based Image Analysis (GEOBIA). Although methodological avenues within GEOBIA are diverse, segmentation commonly plays a central role in most approaches, influencing and being influenced by surrounding processes. A general approach using supervised quality measures, specifically user provided reference segments, suggest casting the parameters of a given segmentation algorithm as a multidimensional search problem. In such a sample supervised segment generation approach, spatial metrics observing the user provided reference segments may drive the search process. The search is commonly performed by metaheuristics. A novel sample supervised segment generation approach is presented in this work, where the spectral content of provided reference segments is queried. A one-class classification process using spectral information from inside the provided reference segments is used to generate a probability image, which in turn is employed to direct a hybridization of the original input imagery. Segmentation is performed on such a hybrid image. These processes are adjustable, interdependent and form a part of the search problem. Results are presented detailing the performances of four method variants compared to the generic sample supervised segment generation approach, under various conditions in terms of resultant segment quality, required computing time and search process characteristics. Multiple metrics, metaheuristics and segmentation algorithms are tested with this approach. Using the spectral data contained within user provided reference segments to tailor the output generally improves the results in the investigated problem contexts, but at the expense of additional required computing time.

Description: Using HyMap data, mineral identification and mineral mapping were conducted on the basis of the spectral absorption index (SAI) and other spectral absorption features in a study area in Tudun, eastern Tien Shan. Alteration minerals, such as calcite, alumina-rich (Al-rich) muscovite, epidote, and antigorite, were explored, and their relative abundance was depicted. A cross-validation was performed, and it showed a high degree of consistency between the imagery results and the results of previous literature. To further validate the mineral mapping from HyMap data, a field survey was carried out and rock samples were collected for quantitative analysis using a Por Infrared Mineral Analyzer (PIMA) and the software affiliated with it. Minerals were discriminated, and their relative abundance was calculated from the spectra. Although we found that the absorption band-depth and SAI agreed well with each other and with the relative abundance of mineral alterations, the spectral absorption band-depth provided a better representation. Finally, ore prospecting of the study area was presented, and we found the distribution and close spatial relationships among the minerals extracted using the HyMap data. In the northern and northwestern part of the Gold-mine area, there was a mineralized muscovite alteration showing a sheet or block distribution. In the Copper-mine area, Al-poor muscovite with a sheet distribution was distributed in the north and northeast region, and Al-rich muscovite showed a block distribution enclosed by the distribution area of Al-poor muscovite. These all showed good ore prospects for the study area.

Description: Many driver assistance systems require knowledge of the vehicle environment. As these systems are increasing in complexity and performance, this knowledge of the environment needs to be more complete and reliable, so sensor fusion combining long, medium and short range sensors is now being used. This paper analyzes the feasibility of using ultrasonic sensors for low cost vehicle-positioning and tracking in the lane adjacent to the host vehicle in order to identify free areas around the vehicle and provide information to an automatic avoidance collision system that can perform autonomous braking and lane change manoeuvres. A laser scanner is used for the early detection of obstacles in the direction of travel while two ultrasonic sensors monitor the blind spot of the host vehicle. The results of tests on a test track demonstrate the ability of these sensors to accurately determine the kinematic variables of the obstacles encountered, despite a clear limitation in range.

Description: Detailed information on tree cover structure is critical for research and monitoring programs targeting African woodlands, including agroforestry parklands. High spatial resolution satellite imagery represents a potentially effective alternative to field-based surveys, but requires the development of accurate methods to automate information extraction. This study presents a method for tree crown mapping based on Geographic Object Based Image Analysis (GEOBIA) that use spectral and geometric information to detect and delineate individual tree crowns and crown clusters. The method was implemented on a WorldView-2 image acquired over the parklands of Saponé, Burkina Faso, and rigorously evaluated against field reference data. The overall detection rate was 85.4% for individual tree crowns and crown clusters, with lower accuracies in areas with high tree density and dense understory vegetation. The overall delineation error (expressed as the difference between area of delineated object and crown area measured in the field) was 45.6% for individual tree crowns and 61.5% for crown clusters. Delineation accuracies were higher for medium (35–100 m2) and large (≥100 m2) trees compared to small (〈35 m2) trees. The results indicate potential of GEOBIA and WorldView-2 imagery for tree crown mapping in parkland landscapes and similar woodland areas.

Description: Total suspended solid (TSS) is an important water quality parameter. This study was conducted to test the feasibility of the band combination of hyperspectral sensing for inland turbid water monitoring in Taiwan. The field spectral reflectance in the Wu river basin of Taiwan was measured with a spectroradiometer; the water samples were collected from the different sites of the Wu river basin and some water quality parameters were analyzed on the sites (in situ) as well as brought to the laboratory for further analysis. To obtain the data set for this study, 160 in situ sample observations were carried out during campaigns from August to December, 2005. The water quality results were correlated with the reflectivity to determine the spectral characteristics and their relationship with turbidity and TSS. Furthermore, multiple-regression (MR) and artificial neural network (ANN) were used to model the transformation function between TSS concentration and turbidity levels of stream water, and the radiance measured by the spectroradiometer. The value of the turbidity and TSS correlation coefficient was 0.766, which implies that turbidity is significantly related to TSS in the Wu river basin. The results indicated that TSS and turbidity are positively correlated in a significant way across the entire spectrum, when TSS concentration and turbidity levels were under 800 mg·L−1 and 600 NTU, respectively. Optimal wavelengths for the measurements of TSS and turbidity are found in the 700 and 900 nm range, respectively. Based on the results, better accuracy was obtained only when the ranges of turbidity and TSS concentration were less than 800 mg·L−1 and less than 600 NTU, respectively and used rather than using whole dataset (R2 = 0.93 versus 0.88 for turbidity and R2 = 0.83 versus 0.58 for TSS). On the other hand, the ANN approach can improve the TSS retrieval using MR. The accuracy of TSS estimation applying ANN (R2 = 0.66) was better than with the MR approach (R2 = 0.58), as expected due to the nonlinear nature of the transformation model.

Description: Security and privacy issues in medical wireless body area networks (WBANs) constitute a major unsolved concern because of the challenges posed by the scarcity of resources in WBAN devices and the usability restrictions imposed by the healthcare domain. In this paper, we describe a WBAN architecture based on the well-known publish-subscribe paradigm. We present two protocols for publishing data and sending commands to a sensor that guarantee confidentiality and fine-grained access control. Both protocols are based on a recently proposed ciphertext policy attribute-based encryption (CP-ABE) scheme that is lightweight enough to be embedded into wearable sensors. We show how sensors can implement lattice-based access control (LBAC) policies using this scheme, which are highly appropriate for the eHealth domain. We report experimental results with a prototype implementation demonstrating the suitability of our proposed solution.

Description: Wireless Sensor Networks deliver valuable information for long periods, then it is desirable to have optimum performance, reduced delays, low overhead, and reliable delivery of information. In this work, proposed metrics that influence energy consumption are used for a performance comparison among our proposed routing protocol, called Multi-Parent Hierarchical (MPH), the well-known protocols for sensor networks, Ad hoc On-Demand Distance Vector (AODV), Dynamic Source Routing (DSR), and Zigbee Tree Routing (ZTR), all of them working with the IEEE 802.15.4 MAC layer. Results show how some communication metrics affect performance, throughput, reliability and energy consumption. It can be concluded that MPH is an efficient protocol since it reaches the best performance against the other three protocols under evaluation, such as 19.3% reduction of packet retransmissions, 26.9% decrease of overhead, and 41.2% improvement on the capacity of the protocol for recovering the topology from failures with respect to AODV protocol. We implemented and tested MPH in a real network of 99 nodes during ten days and analyzed parameters as number of hops, connectivity and delay, in order to validate our Sensors 2014, 14 22812 simulator and obtain reliable results. Moreover, an energy model of CC2530 chip is proposed and used for simulations of the four aforementioned protocols, showing that MPH has 15.9% reduction of energy consumption with respect to AODV, 13.7% versus DSR, and 5% against ZTR.

Description: An automated traffic measuring system for use on multiple drive lanes is proposed in this paper. This system, which uses ultrasonic sensors and a lateral scanning method, is suitable for use on real traffic roads. The proposed system can be easily established and maintained in various roadway environments. In addition, the system can be adjusted to measure traffic volumes according to the size and number of drive lanes. This paper describes the results of an experiment that the lateral scanning method can be easily applied to real traffic roads and provide a low error rate and real-time responses. This system can play an important role in accurately measuring traffic volumes as part of an intelligent transportation system.

Description: Given the advances in remotely sensed imagery and associated technologies, several global land cover maps have been produced in recent times including IGBP DISCover, UMD Land Cover, Global Land Cover 2000 and GlobCover 2009. However, the utility of these maps for specific applications has often been hampered due to considerable amounts of uncertainties and inconsistencies. A thorough review of these global land cover projects including evaluating the sources of error and uncertainty is prudent and enlightening. Therefore, this paper describes our work in which we compared, summarized and conducted an uncertainty analysis of the four global land cover mapping projects using an error budget approach. The results showed that the classification scheme and the validation methodology had the highest error contribution and implementation priority. A comparison of the classification schemes showed that there are many inconsistencies between the definitions of the map classes. This is especially true for the mixed type classes for which thresholds vary for the attributes/discriminators used in the classification process. Examination of these four global mapping projects provided quite a few important lessons for the future global mapping projects including the need for clear and uniform definitions of the classification scheme and an efficient, practical, and valid design of the accuracy assessment.

Description: High accuracy attitude and position determination is very important for underwater gliders. The cross-coupling among three attitude angles (heading angle, pitch angle and roll angle) becomes more serious when pitch or roll motion occurs. This cross-coupling makes attitude angles inaccurate or even erroneous. Therefore, the high accuracy attitude and position determination becomes a difficult problem for a practical underwater glider. To solve this problem, this paper proposes backing decoupling and adaptive extended Kalman filter (EKF) based on the quaternion expanded to the state variable (BD-AEKF). The backtracking decoupling can eliminate effectively the cross-coupling among the three attitudes when pitch or roll motion occurs. After decoupling, the adaptive extended Kalman filter (AEKF) based on quaternion expanded to the state variable further smoothes the filtering output to improve the accuracy and stability of attitude and position determination. In order to evaluate the performance of the proposed BD-AEKF method, the pitch and roll motion are simulated and the proposed method performance is analyzed and compared with the traditional method. Simulation results demonstrate the proposed BD-AEKF performs better. Furthermore, for further verification, a new underwater navigation system is designed, and the three-axis non-magnetic turn table experiments and the vehicle experiments are done. The results show that the proposed BD-AEKF is effective in eliminating cross-coupling and reducing the errors compared with the conventional method.

Description: Clustering is a fundamental and effective technique for utilizing sensor nodes’ energy and extending the network lifetime for wireless sensor networks. In this paper, we propose a novel clustering protocol, LECP-CP (local energy consumption prediction-based clustering protocol), the core of which includes a novel cluster head election algorithm and an inter-cluster communication routing tree construction algorithm, both based on the predicted local energy consumption ratio of nodes. We also provide a more accurate and realistic cluster radius to minimize the energy consumption of the entire network. The global energy consumption can be optimized by the optimization of the local energy consumption, and the energy consumption among nodes can be balanced well. Simulation results validate our theoretical analysis and show that LECP-CP has high efficiency of energy utilization, good scalability and significant improvement in the network lifetime.

Description: The extended Kalman filter (EKF) is the nonlinear model of a Kalman filter (KF). It is a useful parameter estimation method when the observation model and/or the state transition model is not a linear function. However, the computational requirements in EKF are a difficulty for the system. With the help of cognition-based designation and the Taylor expansion method, a novel algorithm is proposed to ease the computational load for EKF in azimuth predicting and localizing under a nonlinear observation model. When there are nonlinear functions and inverse calculations for matrices, this method makes use of the major components (according to current performance and the performance requirements) in the Taylor expansion. As a result, the computational load is greatly lowered and the performance is ensured. Simulation results show that the proposed measure will deliver filtering output with a similar precision compared to the regular EKF. At the same time, the computational load is substantially lowered.

Description: This study investigated whether the relationship between muscle torque and m-waves remained constant after short recovery periods, between repeated intervals of isometric muscle contractions induced by functional electrical stimulation (FES). Eight subjects with spinal cord injury (SCI) were recruited for the study. All subjects had their quadriceps muscles group stimulated during three sessions of isometric contractions separated by 5 min of recovery. The evoked-electromyographic (eEMG) signals, as well as the produced torque, were synchronously acquired during the contractions and during short FES bursts applied during the recovery intervals. All analysed m-wave variables changed progressively throughout the three contractions, even though the same muscle torque was generated. The peak to peak amplitude (PtpA), and the m-wave area (Area) were significantly increased, while the time between the stimulus artefact and the positive peak (PosT) were substantially reduced when the muscles became fatigued. In addition, all m-wave variables recovered faster and to a greater extent than did torque after the recovery intervals. We concluded that rapid recovery intervals between FES-evoked exercise sessions can radically interfere in the use of m-waves as a proxy for torque estimation in individuals with SCI. This needs to be further investigated, in addition to seeking a better understanding of the mechanisms of muscle fatigue and recovery.

Description: Natural resource management literature has documented three paradigm shifts over the past decade: from co-management to adaptive co-management and adaptive governance respectively and, more recently, towards landscape governance. The latter is conceived as a governance approach towards negotiated land use at the landscape level to deal with global challenges such as food insecurity, climate change and biodiversity loss. There is not a lot of clarity about how co-management systems could actually evolve into landscape governance. This paper aims to address the gap by exploring how a stalled co-management system for the reforestation of degraded forest areas—the modified taungya system (MTS) in Ghana—could be revitalised and redesigned as a landscape approach. Drawing on case studies and expert consultation, the performance of the national MTS and the MTS under the Community Forestry Management Project is reviewed with regard to five principles (integrated approach, multi-stakeholder negotiation, polycentric governance, continual learning and adaptive capacity) and three enabling conditions (social capital, bridging organisations and long-term funding) distilled from the literature. The authors conclude that some of these principles and conditions were met under the Community Forestry Management Project, but that continual learning, transcending jurisdictional boundaries, developing adaptive capacity, and long-term funding and benefits still pose challenges.

Description: Negotiation, reconciliation of multiple scales through both ecological and social dimensions and minimization of power imbalances are considered critical challenges to overcome for effective governance of forest restoration. Finding the right mix of “command and control” in forest restoration vs. “environmental governance”, which includes non-state actors, regulatory flexibility, and market based instruments is at the heart of these challenges. This Special Issue attempts at shedding light on these challenges with case studies from South and Central America, Africa, and Asia. Some provide within-country as well as cross-country comparisons. A few others present case studies at the household level. Both policy and legal constraints towards implementing forest restoration are also discussed as a function of top down vs. bottom up approaches. The effectiveness of payments for environmental services is examined as catalyzers of forest restoration initiatives. Finally, two papers deal with the legal and policy constraints in making restoration through natural regeneration a viable and cost-effective tool. In the face of renewed perspectives for expanding forest restoration programs globally, governance issues will likely play a key role in eventually determining success. As many of the papers in this Special Issue suggest, the fate of forest restoration outcomes is, more often than not, associated with overall governance challenges, some of which are often overlooked particularly across multiple scales.

Description: Forestry companies study the population dynamics of insect pests in Integrated Pest Management for cost effectiveness. The objective of this study was to obtain qualitative and quantitative information on population fluctuation of the Lepidopteran defoliators of Eucalyptus urophylla plants in the Brazilian Amazon rainforest. In all, 402 species were collected, of which 10 were primary pests, nine were secondary pests, and the remaining bore no definite relevance to eucalyptus. Primary pests formed a low percentage of the total species, although they recorded a high percentage of the total number of individuals. The abundance of secondary pests, except in Caracuru, was less than 150 specimens annually. Primary pests showed higher population peaks during periods of low precipitation. The small number of species and the high abundance of primary and secondary pests could be due to the availability of food, or a deficiency in natural biological control. This suggests the possibilities of population outbreaks in the eucalyptus plantations. The period of highest occurrence for insect species in these crops must be identified so that suitable strategies can be developed for Integrated Pest Management.

Description: Detecting and monitoring forest degradation in the tropics has implications for various fields of interest (biodiversity, emission calculations, self-sustenance of indigenous communities, timber exploitation). However, remote-sensing-based detection of forest degradation is difficult, as these subtle degradation signals are not easy to detect in the first place and quickly lost over time due to fast re-vegetation. To overcome these shortcomings, a time series analysis has been developed to map and monitor forest degradation over a longer period of time, with frequent updates based on Landsat data. This time series approach helps to reduce both the commission and the omission errors compared to, e.g., bi- or tri-temporal assessments. The approach involves a series of pre-processing steps, such as geometric and radiometric adjustments, followed by spectral mixture analysis and classification of spectral curves. The resulting pixel-based classification is then aggregated to degradation areas. The method was developed on a study site in Cameroon and applied to a second site in Central African Republic. For both areas, the results were finally evaluated against visual interpretation of very high-resolution optical imagery. Results show overall accuracies in both study sites above 85% for mapping degradation areas with the presented methods.

Description: Radar sensor networks, including bi- and multi-static radars, provide several operational advantages, like reduced vulnerability, good system flexibility and an increased radar cross-section. However, radar-to-radar interference suppression is a major problem in distributed radar sensor networks. In this paper, we present a cross-matched filtering-based radar-to-radar interference suppression algorithm. This algorithm first uses an iterative filtering algorithm to suppress the radar-to-radar interferences and, then, separately matched filtering for each radar. Besides the detailed algorithm derivation, extensive numerical simulation examples are performed with the down-chirp and up-chirp waveforms, partially overlapped or inverse chirp rate linearly frequency modulation (LFM) waveforms and orthogonal frequency division multiplexing (ODFM) chirp diverse waveforms. The effectiveness of the algorithm is verified by the simulation results.

Description: This methodology assesses the accuracy with which remote data characterizes a surface, as a function of Full Width at Half Maximum (FWHM). The purpose is to identify the best remote data that improves the characterization of a surface, evaluating the number of bands in the spectral range. The first step creates an accurate dataset of remote simulated data, using in situ hyperspectral reflectances. The second step evaluates the capability of remote simulated data to characterize this surface. The spectral similarity measurements, which are obtained using classifiers, provide this capability. The third step examines the precision of this capability. The assumption is that in situ hyperspectral reflectances are considered the “real” reflectances. They are resized with the same spectral range of the remote data. The spectral similarity measurements which are obtained from “real” resized reflectances, are considered “real” measurements. Therefore, the quantity and magnitude of “errors” (i.e., differences between spectral similarity measurements obtained from “real” resized reflectances and from remote data) provide the accuracy as a function of FWHM. This methodology was applied to evaluate the accuracy with which CHRIS-mode1, CHRIS-mode2, Landsat5-TM, MIVIS and PRISMA data characterize three coastal waters. Their mean values of uncertainty are 1.59%, 3.79%, 7.75%, 3.15% and 1.18%, respectively.

Description: Highly reliable embedded systems have been widely applied in the fields of aerospace, nuclear power, high-speed rail, etc., which are related to security and economic development. The reliability of the power supply directly influences the security of the embedded system, and has been the research focus of numerous electronic information and energy studies. The degradation of power modules occupies a dominant position among the key factors affecting the power supply reliability. How to dynamically determine the degradation state and forecast the remaining useful life of working power modules is critical. Therefore, an online non-intrusive method of obtaining the degradation state of MOSFETs based on the Volterra series is proposed. It uses the self-driving signal of MOSFETs as a non-intrusive incentive, and extracts the degradation characteristics of MOSFETs by the frequency-domain kernel of the Volterra series. Experimental results show that the identification achieved by the method agrees well with the theoretical analysis.

Description: An algorithm based on a Bayesian network classifier was adapted to produce 10-day burned area (BA) maps from the Long Term Data Record Version 3 (LTDR) at a spatial resolution of 0.05° (~5 km) for the North American boreal region from 2001 to 2011. The modified algorithm used the Brightness Temperature channel from the Moderate Resolution Imaging Spectroradiometer (MODIS) band 31 T31 (11.03 μm) instead of the Advanced Very High Resolution Radiometer (AVHRR) band T3 (3.75 μm). The accuracy of the BA-LTDR, the Collection 5.1 MODIS Burned Area (MCD45A1), the MODIS Collection 5.1 Direct Broadcast Monthly Burned Area (MCD64A1) and the Burned Area GEOLAND-2 (BA GEOLAND-2) products was assessed using reference data from the Alaska Fire Service (AFS) and the Canadian Forest Service National Fire Database (CFSNFD). The linear regression analysis of the burned area percentages of the MCD64A1 product using 40 km × 40 km grids versus the reference data for the years from 2001 to 2011 showed an agreement of R2 = 0.84 and a slope = 0.76, while the BA-LTDR showed an agreement of R2 = 0.75 and a slope = 0.69. These results represent an improvement over the MCD45A1 product, which showed an agreement of R2 = 0.67 and a slope = 0.42. The MCD64A1, BA-LTDR and MCD45A1 products underestimated the total burned area in the study region, whereas the BA GEOLAND-2 product overestimated it by approximately five-fold, with an agreement of R2 = 0.05. Despite MCD64A1 showing the best overall results, the BA-LTDR product proved to be an alternative for mapping burned areas in the North American boreal forest region compared with the other global BA products, even those with higher spatial/spectral resolution.

Description: This paper presents a new spaceborne synthetic aperture radar (SAR) burst mode named “Extended Terrain Observation by Progressive Scans (ETOPS)” for wide swath imaged coverage. This scheme extends the imaging performance of the conventional Terrain Observation by Progressive Scans (TOPS) mode with a very limited azimuth beam steering capability. Compared with the TOPS mode with the same azimuth beam steering range for the same swath width, a finer azimuth resolution could be obtained. With the same system parameters, examples of four burst SAR imaging modes named ScanSAR, TOPS, inverse TOPS (ITOPS) and ETOPS are given, and their corresponding system performances are analyzed and compared. Simulation results show that the proposed ETOPS mode could obtain a better high-resolution wide-swath imaging performance under the same conditions.

Description: In bearing diagnostics using a data-driven modeling approach, a concern is the need for data from all possible scenarios to build a practical model for all operating conditions. This paper is a study on bearing diagnostics with the concurrent occurrence of multiple defect types. The authors are not aware of any work in the literature that studies this practical problem. A strategy based on one-versus-all (OVA) class binarization is proposed to improve fault diagnostics accuracy while reducing the number of scenarios for data collection, by predicting concurrent defects from training data of normal and single defects. The proposed OVA diagnostic approach is evaluated with empirical analysis using support vector machine (SVM) and C4.5 decision tree, two popular classification algorithms frequently applied to system health diagnostics and prognostics. Statistical features are extracted from the time domain and the frequency domain. Prediction performance of the proposed strategy is compared with that of a simple multi-class classification, as well as that of random guess and worst-case classification. We have verified the potential of the proposed OVA diagnostic strategy in performance improvements for single-defect diagnosis and predictions of BPFO plus BPFI concurrent defects using two laboratory-collected vibration data sets.

Description: The development of flexible polymer monofilament fiber strain sensors have many applications in both wearable computing (clothing, gloves, etc.) and robotics design (large deformation control). For example, a high-stretch monofilament sensor could be integrated into robotic arm design, easily stretching over joints or along curved surfaces. As a monofilament, the sensor can be woven into or integrated with textiles for position or physiological monitoring, computer interface control, etc. Commercially available conductive polymer monofilament sensors were tested alongside monofilaments produced from carbon black (CB) mixed with a thermo-plastic elastomer (TPE) and extruded in different diameters. It was found that signal strength, drift, and precision characteristics were better with a 0.3 mm diameter CB/TPE monofilament than thick (~2 mm diameter) based on the same material or commercial monofilaments based on natural rubber or silicone elastomer (SE) matrices.

Description: Mountain chains of Central Asia host a large number of glaciated areas that provide critical water supplies to the semi-arid populated foothills and lowlands of this region. Spatio-temporal variations of glacier flows are a key indicator of the impact of climate change on water resources as the glaciers react sensitively to climate. Satellite remote sensing using optical imagery is an efficient method for studying ice-velocity fields on mountain glaciers. In this study, temporal and spatial changes in surface velocity associated with the Inylchek glacier in Kyrgyzstan are investigated. We present a detailed map for the kinematics of the Inylchek glacier obtained by cross-correlation analysis of Landsat images, acquired between 2000 and 2011, and a set of ASTER images covering the time period between 2001 and 2007. Our results indicate a high-velocity region in the elevated part of the glacier, moving up to a rate of about 0.5 m/day. Time series analysis of optical data reveals some annual variations in the mean surface velocity of the Inylchek during 2000–2011. In particular, our findings suggest an opposite trend between periods of the northward glacial flow in Proletarskyi and Zvezdochka glacier, and the rate of westward motion observed for the main stream of the Inylchek.

Description: Wireless Body Sensor Networks (WBSNs) constitute a subset of Wireless Sensor Networks (WSNs) responsible for monitoring vital sign-related data of patients and accordingly route this data towards a sink. In routing sensed data towards sinks, WBSNs face some of the same routing challenges as general WSNs, but the unique requirements of WBSNs impose some more constraints that need to be addressed by the routing mechanisms. This paper identifies various issues and challenges in pursuit of effective routing in WBSNs. Furthermore, it provides a detailed literature review of the various existing routing protocols used in the WBSN domain by discussing their strengths and weaknesses.

Description: While the use of timber harvests is generally accepted as an effective approach to controlling bark beetles during outbreaks, in reality there has been a dearth of monitoring to assess outcomes, and failures are often not reported. Additionally, few studies have focused on how these treatments affect forest structure and function over the long term, or our forests’ ability to adapt to climate change. Despite this, there is a widespread belief in the policy arena that timber harvesting is an effective and necessary tool to address beetle infestations. That belief has led to numerous proposals for, and enactment of, significant changes in federal environmental laws to encourage more timber harvests for beetle control. In this review, we use mountain pine beetle as an exemplar to critically evaluate the state of science behind the use of timber harvest treatments for bark beetle suppression during outbreaks. It is our hope that this review will stimulate research to fill important gaps and to help guide the development of policy and management firmly based in science, and thus, more likely to aid in forest conservation, reduce financial waste, and bolster public trust in public agency decision-making and practice.

Description: An efficient method for the continuous extraction of subway tunnel cross sections using terrestrial point clouds is proposed. First, the continuous central axis of the tunnel is extracted using a 2D projection of the point cloud and curve fitting using the RANSAC (RANdom SAmple Consensus) algorithm, and the axis is optimized using a global extraction strategy based on segment-wise fitting. The cross-sectional planes, which are orthogonal to the central axis, are then determined for every interval. The cross-sectional points are extracted by intersecting straight lines that rotate orthogonally around the central axis within the cross-sectional plane with the tunnel point cloud. An interpolation algorithm based on quadric parametric surface fitting, using the BaySAC (Bayesian SAmpling Consensus) algorithm, is proposed to compute the cross-sectional point when it cannot be acquired directly from the tunnel points along the extraction direction of interest. Because the standard shape of the tunnel cross section is a circle, circle fitting is implemented using RANSAC to reduce the noise. The proposed approach is tested on terrestrial point clouds that cover a 150-m-long segment of a Shanghai subway tunnel, which were acquired using a LMS VZ-400 laser scanner. The results indicate that the proposed quadric parametric surface fitting using the optimized BaySAC achieves a higher overall fitting accuracy (0.9 mm) than the accuracy (1.6 mm) obtained by the plain RANSAC. The results also show that the proposed cross section extraction algorithm can achieve high accuracy (millimeter level, which was assessed by comparing the fitted radii with the designed radius of the cross section and comparing corresponding chord lengths in different cross sections) and high efficiency (less than 3 s/section on average).

Description: The use of wireless body sensor networks is gaining popularity in monitoring and communicating information about a person’s health. In such applications, the amount of data transmitted by the sensor node should be minimized. This is because the energy available in these battery powered sensors is limited. In this paper, we study the wireless transmission of electroencephalogram (EEG) signals. We propose the use of a compressed sensing (CS) framework to efficiently compress these signals at the sensor node. Our framework exploits both the temporal correlation within EEG signals and the spatial correlations amongst the EEG channels. We show that our framework is up to eight times more energy efficient than the typical wavelet compression method in terms of compression and encoding computations and wireless transmission. We also show that for a fixed compression ratio, our method achieves a better reconstruction quality than the CS-based state-of-the art method. We finally demonstrate that our method is robust to measurement noise and to packet loss and that it is applicable to a wide range of EEG signal types.

Description: We investigated the self-oscillating behaviors of two types of polymer chains induced by the Belousov–Zhabotinsky (BZ) reaction. One consisted of N-isopropylacrylamide (NIPAAm) and the Ru catalyst of the BZ reaction, and the other consisted of NIPAAm, the Ru catalyst, and acrylamide-2-methylpropanesulfonic acid (AMPS) with a negatively charged domain as a solubility control site. A comparison of the two types of self-oscillation systems showed that the anionic AMPS portion of the polymer chain significantly affected the self-oscillating behavior under strongly acidic condition. The periods of self-oscillation for the two types of self-oscillating polymer chains were investigated by changing the initial concentrations of the three BZ substrates and the temperature. As a result, it was demonstrated that the period of self-oscillation could be controlled by the concentration of the BZ substrates and the temperature. Furthermore, the activation energies of the two types of the self-oscillating polymer chains gave similar values as normal BZ reactions, i.e., not including the self-oscillating polymer system with a Ru moiety. In addition, it was clarified the activation energy was hardly affected by the initial concentration of the three BZ substrates.

Description: The integrated navigation system with strapdown inertial navigation system (SINS), Beidou (BD) receiver and Doppler velocity log (DVL) can be used in marine applications owing to the fact that the redundant and complementary information from different sensors can markedly improve the system accuracy. However, the existence of multisensor asynchrony will introduce errors into the system. In order to deal with the problem, conventionally the sampling interval is subdivided, which increases the computational complexity. In this paper, an innovative integrated navigation algorithm based on a Cubature Kalman filter (CKF) is proposed correspondingly. A nonlinear system model and observation model for the SINS/BD/DVL integrated system are established to more accurately describe the system. By taking multi-sensor asynchronization into account, a new sampling principle is proposed to make the best use of each sensor’s information. Further, CKF is introduced in this new algorithm to enable the improvement of the filtering accuracy. The performance of this new algorithm has been examined through numerical simulations. The results have shown that the positional error can be effectively reduced with the new integrated navigation algorithm. Compared with the traditional algorithm based on EKF, the accuracy of the SINS/BD/DVL integrated navigation system is improved, making the proposed nonlinear integrated navigation algorithm feasible and efficient.

Description: The non-contact magnetostrictive sensor (MsS) has been widely used in the guided wave testing of pipes, cables, and so on. However, it has a disadvantage of low excitation efficiency. A new method for enhancing the excitation efficiency of the non-contact MsS for pipe inspection using guided waves, by adjusting the axial length of the excitation magnetic field, is proposed. A special transmitter structure, in which two copper rings are added beside the transmitter coil, is used to adjust the axial length at the expense of weakening the excitation magnetic field. An equivalent vibration model is presented to analyze the influence of the axial length variation. The final result is investigated by experiments. Results show that the excitation efficiency of the non-contact MsS is enhanced in the whole inspection frequency range of the L(0,2) mode if the axial length is adjusted to a certain value. Moreover that certain axial length is the same for pipes of different sizes but made of the same material.

Description: In extremely low-light conditions, random telegraph signal (RTS) noise and dark current white defects become visible. In this paper, a multi-aperture imaging system and selective averaging method which removes the RTS noise and the dark current white defects by minimizing the synthetic sensor noise at every pixel is proposed. In the multi-aperture imaging system, a very small synthetic F-number which is much smaller than 1.0 is achieved by increasing optical gain with multiple lenses. It is verified by simulation that the effective noise normalized by optical gain in the peak of noise histogram is reduced from 1.38e⁻ to 0.48 e⁻ in a 3 × 3-aperture system using low-noise CMOS image sensors based on folding-integration and cyclic column ADCs. In the experiment, a prototype 3 × 3-aperture camera, where each aperture has 200 × 200 pixels and an imaging lens with a focal length of 3.0 mm and F-number of 3.0, is developed. Under a low-light condition, in which the maximum average signal is 11e⁻ per aperture, the RTS and dark current white defects are removed and the peak signal-to-noise ratio (PSNR) of the image is increased by 6.3 dB.

Description: A major challenge related to caring for patients with chronic conditions is the early detection of exacerbations of the disease. Medical personnel should be contacted immediately in order to intervene in time before an acute state is reached, ensuring patient safety. This paper proposes an approach to an ambient intelligence (AmI) framework supporting real-time remote monitoring of patients diagnosed with congestive heart failure (CHF). Its novelty is the integration of: (i) personalized monitoring of the patients health status and risk stage; (ii) intelligent alerting of the dedicated physician through the construction of medical workflows on-the-fly; and (iii) dynamic adaptation of the vital signs’ monitoring environment on any available device or smart phone located in close proximity to the physician depending on new medical measurements, additional disease specifications or the failure of the infrastructure. The intelligence lies in the adoption of semantics providing for a personalized and automated emergency alerting that smoothly interacts with the physician, regardless of his location, ensuring timely intervention during an emergency. It is evaluated on a medical emergency scenario, where in the case of exceeded patient thresholds, medical personnel are localized and contacted, presenting ad hoc information on the patient’s condition on the most suited device within the physician’s reach.

Description: We propose and demonstrate a wireless, passive, metamaterial-based sensor that allows for remotely monitoring submicron displacements over millimeter ranges. The sensor comprises a probe made of multiple nested split ring resonators (NSRRs) in a double-comb architecture coupled to an external antenna in its near-field. In operation, the sensor detects displacement of a structure onto which the NSRR probe is attached by telemetrically tracking the shift in its local frequency peaks. Owing to the NSRR’s near-field excitation response, which is highly sensitive to the displaced comb-teeth over a wide separation, the wireless sensing system exhibits a relatively high resolution (〈1 µm) and a large dynamic range (over 7 mm), along with high levels of linearity (R2 > 0.99 over 5 mm) and sensitivity (>12.7 MHz/mm in the 1–3 mm range). The sensor is also shown to be working in the linear region in a scenario where it is attached to a standard structural reinforcing bar. Because of its wireless and passive nature, together with its low cost, the proposed system enabled by the metamaterial probes holds a great promise for applications in remote structural health monitoring.

Description: In this research we introduce a wearable sensory system for motion intention estimation and control of exoskeleton robot. The system comprises wearable inertial motion sensors and shoe-embedded force sensors. The system utilizes an instrumented cane as a part of the interface between the user and the robot. The cane reflects the motion of upper limbs, and is used in terms of human inter-limb synergies. The developed control system provides assisted motion in coherence with the motion of other unassisted limbs. The system utilizes the instrumented cane together with body worn sensors, and provides assistance for start, stop and continuous walking. We verified the function of the proposed method and the developed wearable system through gait trials on treadmill and on ground. The achievement contributes to finding an intuitive and feasible interface between human and robot through wearable gait sensors for practical use of assistive technology. It also contributes to the technology for cognitively assisted locomotion, which helps the locomotion of physically challenged people.

Description: We describe a chemical sensor based on a simple synthesis of zinc oxide nanorods (ZNRs) for the detection of dopamine molecules by a potentiometric approach. The polar nature of dopamine leads to a change of surface charges on the ZNR surface via metal ligand bond formation which results in a measurable electrical signal. ZNRs were grown on a gold-coated glass substrate by a low temperature aqueous chemical growth (ACG) method. Polymeric membranes incorporating β-cyclodextrin (β-CD) and potassium tetrakis (4-chlorophenyl) borate was immobilized on the ZNR surface. The fabricated electrodes were characterized by X-ray diffraction (XRD) and scanning electron microscopy (SEM) techniques. The grown ZNRs were well aligned and exhibited good crystal quality. The present sensor system displays a stable potential response for the detection of dopamine in 10−2 mol·L−1 acetic acid/sodium acetate buffer solution at pH 5.45 within a wide concentration range of 1 × 10−6 M–1 × 10−1 M, with sensitivity of 49 mV/decade. The electrode shows a good response time (less than 10 s) and excellent repeatability. This finding can contribute to routine analysis in laboratories studying the neuropharmacology of catecholamines. Moreover, the metal-ligand bonds can be further exploited to detect DA receptors, and for bio-imaging applications.

Description: The kitchen environment is one of the scenarios in the home where users can benefit from Ambient Assisted Living (AAL) applications. Moreover, it is the place where old people suffer from most domestic injuries. This paper presents a novel design, implementation and assessment of a Smart Kitchen which provides Ambient Assisted Living services; a smart environment that increases elderly and disabled people’s autonomy in their kitchen-related activities through context and user awareness, appropriate user interaction and artificial intelligence. It is based on a modular architecture which integrates a wide variety of home technology (household appliances, sensors, user interfaces, etc.) and associated communication standards and media (power line, radio frequency, infrared and cabled). Its software architecture is based on the Open Services Gateway initiative (OSGi), which allows building a complex system composed of small modules, each one providing the specific functionalities required, and can be easily scaled to meet our needs. The system has been evaluated by a large number of real users (63) and carers (31) in two living labs in Spain and UK. Results show a large potential of system functionalities combined with good usability and physical, sensory and cognitive accessibility.

Description: This study aimed at monitoring the changes of fluorescent components in wastewater samples from 22 Korean biological wastewater treatment plants and exploring their prediction capabilities for total organic carbon (TOC), dissolved organic carbon (DOC), biochemical oxygen demand (BOD), chemical oxygen demand (COD), and the biodegradability of the wastewater using an optical sensing technique based on fluorescence excitation emission matrices and parallel factor analysis (EEM-PARAFAC). Three fluorescent components were identified from the samples by using EEM-PARAFAC, including protein-like (C1), fulvic-like (C2) and humic-like (C3) components. C1 showed the highest removal efficiencies for all the treatment types investigated here (69% ± 26%–81% ± 8%), followed by C2 (37% ± 27%–65% ± 35%), while humic-like component (i.e., C3) tended to be accumulated during the biological treatment processes. The percentage of C1 in total fluorescence (%C1) decreased from 54% ± 8% in the influents to 28% ± 8% in the effluents, while those of C2 and C3 (%C2 and %C3) increased from 43% ± 6% to 62% ± 9% and from 3% ± 7% to 10% ± 8%, respectively. The concentrations of TOC, DOC, BOD, and COD were the most correlated with the fluorescence intensity (Fmax) of C1 (r = 0.790–0.817), as compared with the other two fluorescent components. The prediction capability of C1 for TOC, BOD, and COD were improved by using multiple regression based on Fmax of C1 and suspended solids (SS) (r = 0.856–0.865), both of which can be easily monitored in situ. The biodegradability of organic matter in BOD/COD were significantly correlated with each PARAFAC component and their combinations (r = −0.598–0.613, p 〈 0.001), with the highest correlation coefficient shown for %C1. The estimation capability was further enhanced by using multiple regressions based on %C1, %C2 and C3/C2 (r = −0.691).

Description: Human topoisomerase I (hTopI) is an essential cellular enzyme. The enzyme is often upregulated in cancer cells, and it is a target for chemotherapeutic drugs of the camptothecin (CPT) family. Response to CPT-based treatment is dependent on hTopI activity, and reduction in activity, and mutations in hTopI have been reported to result in CPT resistance. Therefore, hTOPI gene copy number, mRNA level, protein amount, and enzyme activity have been studied to explain differences in cellular response to CPT. We show that Rolling Circle Enhanced Enzyme Activity Detection (REEAD), allowing measurement of hTopI cleavage-religation activity at the single molecule level, may be used to detect posttranslational enzymatic differences influencing CPT response. These differences cannot be detected by analysis of hTopI gene copy number, mRNA amount, or protein amount, and only become apparent upon measuring the activity of hTopI in the presence of CPT. Furthermore, we detected differences in the activity of the repair enzyme tyrosyl-DNA phosphodiesterase 1, which is involved in repair of hTopI-induced DNA damage. Since increased TDP1 activity can reduce cellular CPT sensitivity we suggest that a combined measurement of TDP1 activity and hTopI activity in presence of CPT will be the best determinant for CPT response.

Description: The spruce bark beetle, Ips typographus (L.) (Coleoptera: Curculionidae, Scolytinae), is one of the most destructive insects infesting spruce forests in Europe. Data concerning infestations of I. typographus occurring over the last 19 years (1994–2012) on the Southern Alps were analyzed in seven spruce forest types: (1) pure spruce plantations; (2) pure spruce reforestations; (3) pure spruce mountain forests; (4) pure spruce alpine forests; (5) spruce-conifer mixed forests; (6) spruce-broadleaf mixed forests; and (7) spruce-conifer-broadleaf mixed forests. The collected data included the amount of I. typographus damage and the location and composition of the infested forests. The results indicate that different forest types are differently susceptible to I. typographus. Plantations, reforestations and mountain spruce forests show mean damage and mean number of infestations higher than other forest types. Within pure spruce forests, alpine forests growing at high elevations (>1300 m) suffer low damage. Furthermore, the mean number of infestation spots recorded annually in the different spruce forest types is negatively correlated with a Naturality Index value. The results suggest that forest composition and elevation are the main factors driving the risk of I. typographus damage. A new management strategy for some spruce forest types is needed, with a progressive reduction of pure spruce forests at low altitude and an increase of broadleaf composition.