ALBERT

Description: Thermal infrared (TIR) time series are exploited by many methods based on Earth observation (EO), for such applications as agriculture, forest management, and meteorology. However, due to physical limitations, data acquired by a single sensor are often unsatisfactory in terms of spatial or temporal resolution. This issue can be tackled by using remotely sensed data acquired by multiple sensors with complementary features. When nonreal-time functioning or at least near real-time functioning is admitted, the measurements can be profitably fed to a sequential Bayesian algorithm, which allows to account for the correlation embedded in the successive acquisitions. In this work, we focus on applications that allow the batch processing of the whole data sequences acquired in a fixed time interval. In this case, multiple options for improving the final product are offered by the Bayesian framework, based on both sequential and smoothing techniques. We consider several such Bayesian strategies and comparatively assess their performances in practical applications and through real thermal data acquired by the SEVIRI and MODIS sensors, encompassing the presence of multiple disturbance source, e.g., the cloud coverage of the illuminated scene.

Description: Land surface albedo, qualifying the ratio of the radiant flux reflected from the land surface to the incident flux, is a key forcing parameter controlling the Earth’s energy budget. Previously, several BRDF archetypes were distilled from high-quality MODIS BRDF/Albedo products. In this study, we propose a method that largely relies on matching observed multiangular reflectances with the most appropriate of these prior BRDF archetypes to determine the amplitude and shape of the actual surface BRDFs, when directional signatures are insufficient. This method is first evaluated using an assortment of multisource BRDF data sets to demonstrate its viability for surface albedo estimates, and then is applied to airborne wide-angle infrared dual-mode line/area array scanner (WIDAS) from the Watershed Allied Telemetry Experimental Research (WATER) campaign in the Heihe River Basin of China in 2008. This algorithm makes use of the linear MODIS BRDF model to determine the BRDF archetypes needed as prior knowledge for intrinsic spectral albedo estimates. The intrinsic spectral albedos are then used to estimate actual spectral albedos by considering the proportion of direct and diffuse solar radiation. A spectral-to-broadband conversion is performed to generate the broadband albedo at shortwave regimes through the use of conversion coefficients derived from extensive radiative transfer simulations. A further validation confirms that the estimated albedos are consistent with in situ field measured albedos over available corn crop sites. This method provides a major advantage on utilizing generalized BRDF information derived from MODIS in conjunction with other instrument data that are acquired with less angular variation.

Description: Ocean current is highly related to the interaction between ocean and atmosphere. By measuring the speed and direction of the ocean current from space, we can investigate the ocean–atmosphere interaction on a global scale. The ocean–atmosphere interaction helps to maintain the balance that is essential for planet habitability. However, the conventional scatterometer is unable to measure the ocean current vector. To achieve this, a potentially feasible approach is to use a bigger antenna, a higher PRF, and measure the interferometric phase of two successive echoes. This paper derives four decorrelation factors, and provides the phase error model first. Then, an end-to-end simulation model is established, and it is used to analyze the feasibility of ocean surface current measurement from space. Based on the simulation model, the system parameters are optimized. The simulation results show that the current speed standard deviation (Std), which means the measurement accuracy, in along-track and cross-track direction is smaller than 0.1 m/s when the wind speed is larger than 4 m/s. The swath can be used for current vector inversion that is greater than 70% when the wind speed is larger than 7 m/s. Meanwhile, ${{bf K}_{{bf pc}}}$ of the modified scatterometer is computed and the results show that ${{bf K}_{{bf pc}}}$ is better than the traditional pencil-beam rotating scatterometer when the wind speed is larger than 6 m/s.

Description: High-resolution three-dimensional (3-D) radar imaging of space targets in micromotion plays a significant role in space target recognition and space situation awareness; thus, it has attracted extensive attention in recent years. Because of the fast rotation, some scattering centers are occluded by others, i.e., the scattering centers cannot be continuously illuminated by radar in the imaging interval, and their radar echoes are discontinuous. In this paper, a nonparametric 3-D imaging method based on scattering center trajectory association is proposed. It deals with target occlusion using the Riemannian manifold optimization and obtains focused imaging of targets in complex micromotion. The effectiveness of the proposed method is validated using simulated data.

Description: Recently, satellite-based systems have been introduced that utilize angle-of-arrival (AOA) measurements to geo-locate objects of interest. In the previous work, we considered the application of nonlinear optimization to AoA-based geolocation to these systems. This previous work, however, assumed that all noise sources were independent. In the case of fast-moving objects, however, there is a significant source of error due to the propagation time inherent in satellite-based observation of objects due to the difference between the location of the object when it is observed by a satellite, and the location of the object when it emitted the signal that is being measured. This introduces a systematic error into the system that cannot be resolved by the system proposed by Burchett et al. In this paper, we extend our prior work to account for the time-delay inherent in satellite-based geolocation systems, making this system accurate for fast-movers as well as fixed or slow-moving objects. Results demonstrating significant improvement in geolocation performance both in terms of accuracy and estimated error bounds are presented.

Description: Most of existing change detection methods could be classified into three groups, the traditional pixel-based change detection (PBCD), the object-based change detection (OBCD), and the hybrid change detection (HCD). Nevertheless, both PBCD and OBCD have disadvantages, and classical HCD methods belong to intuitive decision-level fusion schemes of PBCD and OBCD. There is no optimum HCD method as of yet. Analyzing the complementarities of PBCD and OBCD method, we propose a new unsupervised algorithm-level fusion scheme (UAFS-HCD) in this paper to improve the accuracy of PBCD using spatial context information through: 1) getting the preliminary change mask with PBCD at first to estimate some parameters for OBCD; 2) deriving the unchanged area mask to eliminate the areas without changes, reducing error amplification phenomenon of OBCD; and 3) obtaining the final change mask by means of OBCD method. Taking flood detection with multitemporal SAR data as an example, we compared the new scheme with some classical methods, including PBCD, OBCD, and HCD method and supervised manual trial-and-error procedure (MTEP). The experimental results of flood detection showed that the new scheme was efficient and robust, and its accuracy sometimes can even exceed MTEP.

Description: Low-energy technologies in the Internet of Things (IoTs) era are still unable to provide the reliability needed by the industrial world, particularly in terms of the wireless operation that pervasive deployments demand. While the industrial wireless performance has achieved an acceptable degree in communications, it is no easy task to determine an efficient energy-dimensioning of the device in order to meet the application requirements. This is especially true in the face of the uncertainty inherent in energy harvesting. Thus, it is of utmost importance to model and dimension the energy consumption of the IoT applications at the pre-deployment or pre-production stages, especially when considering critical factors, such as reduced cost, life-time, and available energy. This paper presents a comprehensive model for the power consumption of wireless sensor nodes. The model takes a system-level perspective to account for all energy expenditures: communications, acquisition and processing. Furthermore, it is based only on parameters that can empirically be quantified once the platform (i.e., technology) and the application (i.e., operating conditions) are defined. This results in a new framework for studying and analyzing the energy life-cycles in applications, and it is suitable for determining in advance the specific weight of application parameters, as well as for understanding the tolerance margins and tradeoffs in the system.

Description: With the development of Internet of Things (IoT), a large amount of machine-to-machine (M2M) devices produce the data from a huge number of M2M communications. A massive access congestion control scheme in M2M communication is important in a wireless M2M network, where a wireless M2M network is constituted by a large number of wireless M2M devices. In this paper, we developed a spiderweb-based massive access management protocol for M2M wireless networks. The wireless M2M network topology considered, in this paper, is a spiderweb-based topology, which belongs to a regular topology. The spiderweb-based topology is split into a number of sub-spiderweb, while each sub-spiderweb can be seen as a small reservoir. The main function of the small reservoir is to temporarily store the massive access if the congestion problem is occurred due to the massive assess has been suddenly happened from the M2M devices. When the congestion problem is alleviated gradually, the temporarily-stored massive assess can be digested from these small reservoirs. The contribution of the proposed scheme is to reduce the packet loss rate and prolong the wireless M2M network lifetime. In addition, a dynamically adjustment of the different sub-spiderweb size is developed based on the amount of different data traffics. Simulation result finally illustrates that the proposed mechanism significantly reduces the packet loss rate and keeps the maximum network lifetime.

Description: The paper presents the methodology including the deployment of a structure comprising of C-clamp magnetic stripes and a self-calibration scheme using a four-coil design to resolve the issues of the interference and misplacement problems existing in the flexible nonintrusive current sensor tag for the current detection of household two-wire power lines. Experimental results show that the stripe structure can effectively screen out the magnetic flux originating from the nearby power line for the reduction of sensing error caused by the electromagnetic field interference and the calibration scheme containing the correction of horizontal misplacement and the analysis of power cable thickness via the four-coil design can provide the information of sensor location for accurate current sensing. Less than 4% of sensing error indicates that the proposed methodology can indeed facilitate the flexible current sensor tag for practical use.

Description: Rapidly emerging batteryless sensors are creating tremendous opportunities for truly wearable sensors for activity recognition. However, data streams from such sensors are characterized by sparsity and noise, which make activity recognition a challenging task. In this paper, we study the feasibility of passive computational RFID sensors for ambulatory monitoring. In particular, we focus on recognizing transfers out of beds or chairs and walking. Ideally, all these activities need to be monitored by movement sensor alarm systems to alert caregivers to provide supervision during the ambulation of older people in hospitals and nursing homes to prevent a fall. Our novel approach to partition continuous sensor data on natural activity boundaries and to identify transfers out of beds or chairs and walking as transitions between sequences of movements overcomes issues posed by the sparsity and the noise. We demonstrate through in-depth experiments the high performance (F-score > 93%) and the responsiveness of our approach.

Description: This paper describes a methodology to extract a consistent human settlement extent layer using Landsat data and its implementation in the Google Earth Engine platform. The approach allows the extraction of human settlement extents by means of the existing Landsat 5 and 7 data sets, allowing to check their evolution at 30-m spatial resolution. Since human settlements are the main proxy to people geographical distribution and to building locations, this layer may serve as a mean to disaggregate people/building counts at the regional/national level. The approach is tested in several parts of the world against existing ground truth data at the same spatial resolution in Brazil and China, as well as against extents manually extracted from VHR data in three different geographical areas: 1) Brazil; 2) South East China; and 3) Indonesia.

Description: Remote sensing of urban areas is mainly conducted at high frequencies, to obtain highly resolved images for classification, target detection, or urban areas monitoring for instance. We propose on the contrary to investigate the use of VHF-band for observation, between 120 and 360 MHz. In particular, our concern is to allow target detection, when the object is not in the line of sight (LOS) of the radar, which is typically an issue in urban areas. The benefits of low frequencies are highligted using measurements and simulations over a simple metallic model of two buildings surrounding a street.

Description: A common-mode (CM) active filter was designed in a compact package to suppress CM conducted emissions at a switching mode power supply (SMPS). Based on the analytical expressions considering both stability and performance, the design and optimization rules for the proposed active filter have been presented. After verifying its performance by measurements using vector network analysis, the proposed filter was installed in a 200-W SMPS board with 64 and 110 kHz switching frequencies, demonstrating its usefulness by experiments. The performance degradation due to the magnetic saturation and the AEF grounding impedance was also analyzed and investigated.

Description: This paper proposes an approach to design multiple-input multiple-output radar waveforms that are orthogonal on both the transmitter and receiver. The proposed method jointly utilizes the direct sequence spread spectrum coding and orthogonal frequency division multiplexing (OFDM) chirp signaling techniques. We name it spread spectrum-coded OFDM chirp waveform diversity design. The performance of the designed waveforms is analyzed by examining the ambiguity function and correlation function. The influences of the spread spectrum code choice and the OFDM chirp parameters are also investigated. It is verified that the proposed design scheme can ensure these waveforms stay orthogonal on the receiver and have large time-bandwidth product which is beneficial to separate closely spaced targets.

Description: Studies have presented that the driver vigilance level has serious implication in the causation of road accidents. This paper focuses on integrating both the vehicle-based control behavior and physiological state to predict the driver vigilance index which is evaluated by using a smartwatch. The vehicle control behavior can be observed from the steering wheel movement. Our study utilized the smartwatch motion sensors to study the steering wheel behavior. Meanwhile, physiological state of driver reflects the driver capability of safety alert driving which is estimated by photoplethysmogram (PPG) and respiration signals in this paper. The PPG sensor is integrated in a sport wristband with a Bluetooth low energy module, transmitted the PPG signals to smartwatch in real time. The steering angle is derived by the reading from smartwatch built-in accelerometer and gyroscope sensors. On the other hand, the respiration is derived using the PPG peak baseline method. In order to utterly investigate the sleepiness-induced factors, the time, spectral, and phase space domain features are calculated. Considering the smartwatch processing capability, mutual-information technique is applied to designate the ten most descriptive features. Then, the extracted descriptive features are serve as parameters to a classifier to determine the driver aptitude status. The features are analyzed for their correlation with the subjective Koralinska sleepiness scale and through recorded video observations. The experimental results reveal that our system is capable of estimating driver hypervigilance at average of 96.5% accuracy rate by evaluating on both driving behavior and driver physiological state, provided a novel and low-cost implementation.

Description: A cavitating two-phase flow of water in a pipe with area shrinkage was experimentally investigated, acquiring at high sampling rate pressure signals and images of the cavitating flow field. The time series of the pressure fluctuations was analyzed in terms of power spectral density and related to the cavitation regimes. Furthermore, the fluctuations of the pressure measurements were also decomposed using the wavelet transform to analyze the frequency distribution of the signals energy with respect to the flow behavior. The energy content at each frequency band of the acquire signals is well related to cavitation flow-field behavior. Moreover, the artificial neural network and the least squares support vector machine (LS-SVM) were implemented to identify the cavitation regime, using, as inputs, the power spectral density distributions of the pressure fluctuations, and some features of the decomposed signals, as the wavelet energy for each decomposition level and wavelet entropy. Results indicate the most accurate model to be used in the cavitation regime identification, underlining the enhanced capability of LS-SVM trained with the input data set based on the wavelet decomposition features.

Description: In this paper, we present a novel silicon-on-insulator (SOI) complementary metal–oxide–semiconductor (CMOS) microelectromechanical-system thermal wall shear stress sensor based on a tungsten hot-wire and a single thermopile. Devices were fabricated using a commercial 1- $mu text{m}$ SOI-CMOS process followed by a deep reactive ion etching back-etch step to release a silicon dioxide membrane, which mechanically supports and thermally isolates heating and sensing elements. The sensors show an electrothermal transduction efficiency of $50~mu text{W}$ /°C, and a very small zero flow offset. Calibration for wall shear stress measurement in air in the range of 0–0.48 Pa was performed using a suction type, 2-D flow wind tunnel. The sensors were found to be extremely sensitive, up to 4 V/Pa for low wall shear stress values. Furthermore, we demonstrate the superior signal-to-noise ratio (up to five times higher) of a single thermopile readout configuration compared with a double thermopile readout configuration (embedded for comparison purposes within the same device). Finally, we verify that the output of the sensor is proportional to the cube root of the wall shear stress and we propose an accurate semiempirical formula for its modeling.

Description: A kind of low-cost electric and magnetic field simulators, which are used to produce simulative lightning electromagnetic pulse (LEMP) with adjustable waveforms and intensity output, are presented. The lightning electric field can be produced within the GTEM cell by feed-in of impulse voltage output by a Marx generator. The lightning magnetic field can be produced within the coil by feed-in of surge current from the lightning surge generator. The simulated magnetic field is measured by a “B-dot” coil. The measured results show that the lightning electric field simulator and magnetic field simulator could provide relatively homogeneous LEMP environment within certain area in the GTEM cell and the coil within 3 dB deviation. The effects of the simulated lightning electric fields on some electronic devices and the influence of the turns and size of the coil on the homogeneity of the simulated magnetic fields are also investigated.

Description: This paper presents a detailed model of devices utilizing many nanotubes and the coupling between them based on the electromagnetic model of a device using one nanotube. Empirical equations are proposed to link the device conductance with the number of nanotubes per device. Then, a circuit model is developed to predict the effect of the number of nanotubes per device on the overall conductance, capacitance, and the frequency response of the device. A prototype structure is fabricated. Its performance is tested and compared with the proposed model, and it shows promising agreements. The model is flexible and can be integrated with quantum transport models.

Description: A full-wave analysis of the fundamental quasi-TEM modes supported by multiple graphene nanoribbons above a ground plane is presented, aimed at characterizing crosstalk in graphene multiconductor lines. A method-of-moments discretization of the relevant electric-field integral equation is performed. Assuming first a local scalar conductivity, an efficient spatial-domain approach with subsectional basis functions is assuming first a local scalar conductivity, a spatial-domain approach with subsectional basis functions is developed. This allows for the efficient treatment of nanoribbons with wide transverse separations, and can be expanded to include in the simulation model spatial nonuniformity of the graphene conductivity. This spatial-domain formulation is then extended to treat the case of weakly nonlocal conductivity, via an original integro-differential approach derived by approximating a recent full spectral graphene conductivity model in the limit of low wavenumbers. Numerical results are provided for propagation constants and characteristic impedances of two identical coupled graphene nanoribbons; on this basis, a crosstalk analysis is performed by means of the modal decomposition method.

Description: We propose an optical fiber hydrostatic pressure sensor based on micro-cavities generated by the fiber fuse effect. The presented sensor is manufactured through the recycling of optical fiber destroyed by the fiber fuse effect, being, therefore, a cost-effective solution, when compared with other similar micro-cavity-based solutions. The developed sensor was characterized for pressures up to 20 kPa, showing a linear sensitivity coefficient of $0.47pm 0.03~{rm nm}cdot {rm kPa}^{-1}$ , for pressure values below 8 kPa. Furthermore, we propose a new theoretical model to describe the behavior of the microcavities embedded in optical fibers. This allows us to solve the discrepancies, already identified by other authors, between the experimental results and the ones attained with the flat mirrors Fabry-Perot model. By this way, we were able to describe the sensor response, within the full dynamic range.

Description: The monitoring and control of crops in precision agriculture sometimes requires a high collection frequency of information (e.g., temperature, humidity, and salinity) due to the variability in crops. Data acquisition and transmission are generally achieved thanks to wireless sensor networks. However, sensor nodes have limited resources. Thus, it is necessary to adapt the increase in sampling frequency for different crops, under application constraints (reliability, packet delay, and lifetime duration). In this paper, we propose to properly tune IEEE 802.15.4 MAC parameters ( macMinBE and macMaxCSMABackoffs ) and the sampling frequency of deployed sensor nodes. An analytical model of network performance is derived and used to perform the tuning of these tradeoff parameters. Simulation analysis shows that our scheme provides an efficient increase of sampling frequency of sensor nodes while satisfying application requirements.

Description: Sensors based on the magnetoelectric (ME) effect have the potential to be genuine alternatives for measuring bio-magnetic signals. Unfortunately, the sensor structure usually inhibits the problem that several non-magnetic types of noise couple mechanically into the sensor: in this contribution, we will focus on undesired acoustic coupling. Therefore, an adaptive cancellation approach based on a computationally efficient gradient estimation algorithm with a pseudo-optimally control scheme is proposed. The approach is using a microphone as a noise reference sensor and is implemented in real time. An evaluation in terms of measurements is performed inside a magnetically shielded chamber. For a particular scenario, which is characterized by double excitation, an algorithm with binary control-scheme improves the signal-to-noise ratio (SNR) only by around 4dB. If the proposed control scheme is used instead, an improvement of the SNR of around 13dB is achieved.

Description: Target detection is an important issue in the unattended ground sensors. In this paper, inspired by the idea of subspace-based direction of arrival estimation algorithms, a new target detection algorithm called subspace-based target detection (SBTD) is proposed to detect moving targets. The SBTD employs the SNR of the acoustic signals to decide whether moving targets are exiting or not. Although the SBTD has good detection performance, its cost maybe a little high for unattended sensors with low-cost hardware and long-term monitoring. To relieve the cost, we propose the hierarchical detection scheme and develop a two-stage detection method based on the SBTD for target detection in the wild, in which the first stage detection algorithm is chosen from current detection algorithms, while the second stage detection algorithm employs the SBTD. Experiments are conducted to verify the proposed detection method through acoustic signals gathered by the micro-electro-mechanical systems (MEMS) microphone array in the wild. Results show that the detector constructed by our two-stage detection method cannot only estimate the SNR of the acoustic signals but also can reduce the false alarm rate significantly with the detection rate almost unchanged in comparison with the detector chosen by its first-stage detection algorithm. The results indicate that a better detection performance is achieved in terms of the receiver operator characteristic curves.

Description: Crosstalk within cable bundles can degrade system performance. In aircraft systems that use shielded twisted pairs, the crosstalk occurs primarily in the connector where individual signal wires are not shielded or twisted. In many cases, the parameters which determine crosstalk within the connector are unknown because the connector is closed and wires cannot be easily accessed. Expanding on prior research [14] , a methodology for measuring coupling parameters and modeling crosstalk within aircraft cable connectors at low frequencies (〈400 MHz) was developed. The values of mutual inductance and capacitance were extracted from measurements made with a vector network analyzer (VNA). The characteristics of the individual wires were extracted from VNA-measured TDR response. The accuracy of the model was evaluated through comparison of simulated and measured results. Additionally, a closed-form solution was developed to estimate the worst-case envelope of the differential crosstalk. The calculated results match the measured peak values well. This worst-case crosstalk estimate allows effective evaluation of the impact of crosstalk within different connectors. The developed method can be effective for analyzing complex aircraft cable assemblies and connectors without requiring extensive knowledge of the assembly procedure.

Description: Surface roughness topography of printed circuit boards (PCBs) needs to be included in signal integrity simulations in order to accurately predict the insertion loss of the structure and its delay time. An effective roughness dielectric (ERD) model can be used to substitute an inhomogeneous interface between copper foil and laminate dielectric in a PCB. Herein, this approach is tested for verification using 3-D full-wave numerical simulations. These ERD layers with the appropriate complex permittivity are included in the modeling of stripline examples. The parameters of an ambient laminate dielectric refined from conductor roughness in the stripline are determined using differential extrapolation roughness measurement technique. The agreement of the results of 3-D full-wave modeling simulations and measurements on multiple test structures justifies the proposed approach. Based on the extracted ERD parameters “design curves” can be built and used in numerical simulations of PCB high-speed designs.

Description: Biometric systems provide a valuable service in helping to identify individuals from their stored personal details. Unfortunately, with the rapidly increasing use of such systems [1], there is a growing concern about the possible misuse of that information. To counteract the threat, the European Union (EU) has introduced comprehensive legislation [2] that seeks to regulate data collection and help strengthen an individual?s right to privacy. This article looks at the implications of the legislation for biometric system deployment. After an initial consideration of current privacy concerns, the definition of ?personal data? and its protection is examined in legislative terms. Also covered are the issues surrounding the storage of biometric data, including its accuracy, its security, and justification for what is collected. Finally, the privacy issues are illustrated through three biometric use cases: border security, online bank access control, and customer profiling in stores.

Description: The articles in this special section were conceived to champion recent developments in the rapidly evolving field of biometrics and also to encourage research in new signal processing solutions to security and privacy protection. Biometrics is the science of recognizing individuals based on their behavioral and biological characteristics such as face, fingerprints, iris, voice, gait, and signature. The past decade has witnessed a rapid increase in biometrics research in addition to the deployment of large-scale biometrics solutions in both civilian and law enforcement applications. Example applications that incorporate biometric recognition include: logical and physical access systems; surveillance operations to fight against fraud and organized crime; immigration control and border security systems; national identity programs; identity management systems; and the determination of friend or foe in military installations.

Description: A small amount of jitter can quickly eat up timing budgets and create timing issues. Precise characterization of deterministic and crosstalk-induced jitter can help isolate and solve issues within high-speed links. Characterizing deterministic and crosstalk-induced jitter is challenging, however, because many types of jitter work together to create the overall jitter profile. Methods are presented in this paper to characterize the deterministic and crosstalk-induced jitter from measurements of total jitter. An improved tail-fit deconvolution method is proposed for characterizing the impact of deterministic jitter in the presence of random jitter. The contribution of random jitter to total jitter is found first, and then that contribution is accounted for to find deterministic jitter. A Wiener filter deconvolution method is also presented for extracting the characteristics of crosstalk-induced jitter from measurements of total jitter made when the crosstalk sources were and were not present. The Wiener filter allows for accurate deconvolution of the measured histograms for total jitter even in the presence of the measurement noise. The proposed techniques are shown to work well both in simulations and in measurements of a high-speed link.

Description: This paper proposes a new control structure for tasks where explicit disturbance compensation is not only critical for overcoming poor feedback performance but is also challenging due to the complexity and nonrepetitive nature of the interaction between the plant and the environment. The approach proposed uses a particular form of iterative learning control (ILC) to estimate the previous disturbances, which are used as a preview of the disturbance in the next iteration. A disturbance observer is used to compensate for the difference between the ILC prediction and the true disturbance. The controller is evaluated and compared with a proportional controller, with ILC, and with an observer-based controller in extensive field trials using an automated excavator.

Description: This paper explored the possibility of building channel emulators by utilizing fused deposition modeling (FDM) three-dimensional (3-D) printing technology. The FDM 3-D printing provides a rapid and economic method to produce parts with different shapes. An optimizing algorithm was developed for obtaining the printing pattern and loss profile. Parts with different dielectric constants and loss tangents can be printed. Those parts will be placed or directly printed on a low-loss transmission line to modify its transmission or reflection. As a result, different channel emulators can be built to emulate the S -parameter and eye diagrams of a target channel with the advantage of avoiding complicated electronic components and only being limited in the frequency range by the transmission line and attached connectors.

Description: In this paper, we present general considerations for the application of the multiconductor transmission-line theory for simulating shielded spacecraft harness cable assemblies. Some of the practical issues that occur in the modeling process of various components of cable assemblies in spacecraft applications, namely connectors, pigtails, and backshells are discussed. The overall transfer impedance of the assembly measured through a current injection with a pseudomicrostrip line is used as a figure of merit to evaluate the total shielding provided by the harness. Some of the parameters of the model were available from the manufacturers’ datasheets, while others were determined either experimentally or empirically. The position of the cables along the cross section was randomly assigned by the simulation tool. In general, the obtained simulation results are found to be in reasonable agreement with the experimental data. It is found that the generally overlooked contact impedances between the cable shields and connectors play a significant role, especially at low frequencies. The presented simulation results also emphasize the importance of the inductance and resistance of the connector backshell interconnection to the spacecraft chassis.

Description: Encryptions are used in almost all standards to ensure the confidentiality of the data. Encryptions can be and indeed are implemented in the different layers of a network protocol stack. Conventional encryption performs the bitwise XOR operation between one message bit and one key stream bit to generate one ciphertext bit. Huo et al. have recently proposed to provide confidentialities on the user data by performing the phase encryption on the time domain OFDM samples in LTE system. Phase encryption is performed on the modulated symbols, different from the bit level of XOR encryption, i.e., stream cipher encryption. In this paper, we extend their study. We first generalize the phase encryption to general communication systems independent of the underlying modulation scheme. Then, we formulate the mathematical models for XOR and phase encryptions. Based on our model, we compare these two encryption methods in terms of their security and encryption efficiency. We also show phase encryption can resist traffic analysis attack when implemented in the physical layer. Finally, we conduct simulations to compare the performance of these two methods in terms of their decoding symbol error rate.

Description: The temporal variations (diurnal and annual) in arboreal $(varepsilon_mathrm{Tree})$ and bare soil $(varepsilon_mathrm{Soil})$ dielectric constants and their correlation with precipitation were examined for several trees in Japan. A significant (1 $sigma$ (standard deviation) and 2 $sigma$ ) $varepsilon_mathrm{Tree}$ increase is observed after rainfall at 89.8% and 90.5% probability. However, rainfall does not always induce significant $varepsilon_mathrm{Tree}$ increases. Rainfall of more than 5 mm/day can induce 1 $sigma$ $varepsilon_mathrm{Tree}$ Tree increase at a 59.6% probability. In order to examine whether the increase in $varepsilon_mathrm{Tree}$ affects the L-band $sigma^{0}$ variation in a forest, the four-year temporal variation of the L-band backscattering coefficient $(sigma^{0})$ was estimated from observations by the Advanced Land Observing Satellite Phased Array type L-band Synthetic Aperture Radar. Observed maximum absolute deviations from the mean over the forest area were 1.0 and 1.2 dB for $sigma_{mathrm{HH}}^{0}$ and $sigma_mathrm{HV}^{0}$ , respectively, and 4.0 and 3.0 dB over open land. $sigma^{0}$ and rainfall correlations show that $varepsilon_mathrm{Tree}$ and $sigma_mathrm{Forest}^{0}$ are proportional to precipitation integrated over seven or eight days; $varepsilon_mathrm{Soil}$ and $sigma_mathrm{Open land}^{0}$ are proportional to precipitation integrated over three days. This finding indicates that $varepsilon_mathrm{Tree}$ variations influence $sigma_{mathrm{Forest areas}}^{0}$ . A stronger correlation between $sigma_mathrm{HV}^{0}$ and precipitation is observed in several sites with low $sigma_mathrm{HV}^{0}$ , where less biomass is expected, and several sites with high $sigma_mathrm{HV}^{0}$ , where more biomass is expected. A weaker correlation between $sigma_mathrm{HV}^{0}$ and precipitation is observed for several sites with high $sigma_mathrm{HV}^{0}$ . These differences may be explained by the different contributions of double bounce scattering and potential transpiration, which is a measure of the ability of the atmosphere to remove water from the surface th

Description: The spectral and radiometric quality of airborne imaging spectrometer data is affected by the anisotropic reflectance behavior of the imaged surface. Illumination and observation angle-dependent patterns of surface reflected radiation propagate into products, hinder quantitative assessment of biophysical/biochemical parameters, and decrease the comparability of data from multiple flight lines. The Ross–Li model, originally developed for multiangular observations, can be inverted to estimate and correct for surface anisotropy effects. This requires land cover be stratified into distinct types of scattering behavior. When the observations subsumed in these classes cover a range of view angles, a pseudo multiangular view on the surface can be employed to invert the Ross–Li model. A discrete land cover classification, however, bears the risk of inappropriate scattering correction resulting in spatial artifacts in the corrected data, predominantly in transition regions of two land cover types (e.g., soil and sparse vegetation with varying fractions). We invert the Ross–Li model on continuous land cover fraction layers. We decompose land cover in dominating structural types using linear spectral unmixing. Ross–Li kernel weights and formulations are estimated for each type independently; the correction is then applied pixel-wise according to the fractional distribution. The corrected Airborne Prism EXperiment imaging spectrometer data show significant reduction of anisotropic reflectance effects of up to 90% (average 60% to 75%, $p=0.05$ ), measured in the overlapping regions of adjacent flight lines. No spatial artifacts or spectral irregularities are observed after correction.

Description: Most spaceborne sensors have a tradeoff between high spatial and high temporal resolutions. This tradeoff limits the use of remote sensing data in various applications that require images in both the high spatial and high temporal resolutions. In this paper, we propose a novel technique to create a fine spatial and high temporal resolution images at a ground-based data processing system. Resourcesat-2 is one of the Indian Space Research Organization missions, and it carries the Linear Imaging Self-Scanning sensors (LISS III and LISS IV) and an Advanced Wide-Field Sensor (AWiFS) onboard. The spatial resolution of LISS III is 23.5 m, and that of AWiFS is 56 m. The temporal resolution of LISS III is 24 days, and that of AWiFS is five days. The proposed method creates a synthetic LISS-III image at 23.5-m spatial and five-day temporal resolutions. It is based on the subpixel relationship between a single AWiFS–LISS-III image pair, which is acquired before or after the prediction date. In temporal data composition, spurious spatial discontinuities are inevitable for land-cover type changes. These discontinuities were identified with temporal edge primitives and were smoothed with a spatial-profile-averaging method. A synthetic LISS-III image for time $t_{k}$ is predicted from an AWiFS image at time $t_{k}$ and a single AWiFS–LISS-III image pair at time $t_{0}$ , where $t_{0}ne t_{k}$ . Experimental results demonstrated that the proposed method is superior in terms of the computational efficiency and prediction accuracy with the other existing methods.

Description: Glacier avalanches are natural hazards that could damage infrastructures and threaten lives in high-altitude mountainous terrains. On April 7, 2012, a massive ice avalanche struck a Pakistani base at Gayari sector, Saltoro Valley, and buried/killed 148 soldiers and civilians. Keeping in view the catastrophe, a study was designed with the objectives to: 1) model and simulate the Gayari sector glacier avalanche incident in terms of height, extent, velocity, pressure, and momentum; 2) generate hazard risk assessment of possible other glacier avalanches in the Saltoro Valley through modeling and simulation; and 3) suitability analysis of current camp sites and recommendation of new safe camps sites locations in the Saltoro valley. To simulate the Gayari sector glacier event and other Glacier possible avalanches, a physical process-based rapid mass movements (RAMMS) was used. The RAMMS has two main components, i.e., Voellmy–Salm (VS) model and random kinetic energy, which deals with variables such as avalanche height and the mean avalanche velocity during the course of simulation. The suitability analysis of current camp sites were achieved using weighted overlay analysis with different constraints in ArcGIS Spatial Analyst. The RAMMS model simulation of the Gayari avalanche event predicted a maximum velocity of 74 ms -1 , generating a pressure of 5074 kPa and attaining a height of 45 m, whereas the predicted debris volume on the ground was 3.8145 million m 3 . A good agreement was found between actual debris height and extent, as compared with the RAMMS model output. The RAMMS model simulated all the potential tributary glacier avalanches of Saltoro valley very well. The weighted overlay analyses in ArcGIS Spatial Analyst revealed that the existing camp sites are safe and were not threatened by the glacier avalanche hazard. However, it was recommended that the Gayari camp should not be constructed at the same location and should be relocated- to the proposed safe camp sites identified in this research study. The proposed methodology developed in the current study could be applied in the Siachen conflict zone for avalanche hazard/risk analysis of all the camp sites located in the valley.

Description: In this paper, an adaptive hierarchical sparse grid collocation (ASGC) method combined with the discontinuous Galerkin time-domain method is leveraged to quantify the impacts of random parameters on the electromagnetics systems. The ASGC method approximates the stochastic observables of interest using interpolation functions over a set of collocation points determined by the Smolyak's algorithm integrated with an adaptive strategy. Instead of resorting to a full-tensor product sense, the Smolyak's algorithm constructs the collocation points in a hierarchical scheme with the interpolation level. Enhanced by an adaptive strategy, the Smolyak's algorithm will sample more points along important dimensions with sharp variations or discontinuities, resulting in a nonuniform sampling scheme. To flexibly handle different stochastic systems, either piecewise linear or Lagrange polynomial basis functions are applied. With these strategies, the number of collocation points is significantly reduced. The statistical knowledge of stochastic observables including the expected value, variance, probability density function, and cumulative distribution function are presented. The accuracy and robustness of the algorithm are demonstrated by various examples.

Description: Object-oriented remote sensing image classification is becoming more and more popular because it can integrate spatial information from neighboring regions of different shapes and sizes into the classification procedure to improve the mapping accuracy. However, object identification itself is difficult and challenging. Superpixels, which are groups of spatially connected similar pixels, have the scale between the pixel level and the object level and can be generated from oversegmentation. In this paper, we establish a new classification framework using a superpixel-based graphical model. Superpixels instead of pixels are applied as the basic unit to the graphical model to capture the contextual information and the spatial dependence between the superpixels. The advantage of this treatment is that it makes the classification less sensitive to noise and segmentation scale. The contribution of this paper is the application of a graphical model to remote sensing image semantic segmentation. It is threefold. 1) Gradient fusion is applied to multispectral images before the watershed segmentation algorithm is used for superpixel generation. 2) A probabilistic fusion method is designed to derive node potential in the superpixel-based graphical model to address the problem of insufficient training samples at the superpixel level. 3) A boundary penalty between the superpixels is introduced in the edge potential evaluation. Experiments on three real data sets were conducted. The results show that the proposed method performs better than the related state-of-the-art methods tested.

Description: We introduce a practical and accurate model, referred to as “GO4,” to describe near-nadir microwave scattering from the sea surface, and at the same time, we address the issue of the filtered mean square slope (mss) conventionally used in the geometrical optics model. GO4 is a simple correction of this last model, taking into account the diffraction correction induced by the rough surface through what we call an effective mean square curvature (msc). We evaluate the effective msc as a function of the surface wavenumber spectrum and the radar frequency and show that GO4 reaches the same accuracy as the physical optics model in a wide range of incidence and frequency bands with the sole knowledge of the mss and msc parameters. The key point is that the mss entering in GO4 is not the filtered but the total slope. We provide estimation of the effective msc on the basis of classical sea spectrum models. We also evaluate the effective msc from near-nadir satellite data in various bands and show that it is consistent with model predictions. Non-Gaussian effects are discussed and shown to be incorporated in the effective msc. We give some applications of the method, namely, the estimation of the total sea surface mss and the recalibration of relative radar cross sections.

Description: This paper evaluates the radiometric accuracy of observations from the Advanced Technology Microwave Sounder (ATMS) onboard Suomi National Polar-orbiting Partnership and Sondeur Atmospherique du Profil d' Humidité Intropicale par Radiométrie (SAPHIR) onboard Megha-Tropiques through intercalibration and validation versus in situ radiosonde and Global Positioning System Radio Occultation (GPS-RO) observations. SAPHIR and ATMS water vapor channels operate at slightly different frequencies. We calculated the bias due to radiometric errors as the difference between the observed and simulated differences between the two instruments. This difference, which is often referred to as double difference, ranges between 0.3 and 0.7 K, which shows good consistency between the instruments. We used a radiative transfer model to simulate the satellite brightness temperatures (Tbs) using radiosonde and GPS-RO profiles and then compared simulated and observed Tbs. The difference between radiosonde and ATMS Tbs for the middle and upper tropospheric temperature sounding channels was less than 0.5 K at most stations, but the difference between radiosonde and ATMS/SAPHIR Tbs for water vapor channels was between 0.5 and 2.0 K. The larger bias for the water vapor channels is mainly due to several errors in radiosonde humidity observations. The mean differences between the ATMS observations and the Tbs simulated using GPS-RO profiles were 0.2, 0.3, 0.4, 0.2, and −0.2 K for channels 10–14, respectively; and the uncertainty increases from 0.02 K for channel 10 to 0.07 K for channel 14.

Description: Signal integrity (SI) analysis based on state-of-the-art measurements can be difficult to perform especially when the structures of interest are on inner layers of multilayer boards or are enclosed by IC packages. To enable an SI analysis in such cases the authors have recently developed a method that is based on the extraction of accurate simulation models from computed tomography (CT) scans. These models can be used in electromagnetic (EM) field simulators for computer-aided SI analyses. Such CT-based models include geometry variations or defects due to the manufacturing process so that computed EM field simulation results have a good correlation with common measurements. In order to identify the potential of the method an analysis of the required voxel resolution for the extraction of single-ended and differential striplines is presented. The analysis is based on the measurement uncertainty of length measurements in CT scans and an analysis of the propagation of uncertainty for the characteristic impedances of single-ended and differential striplines. This analysis shows that the voxel resolution of industrial CT scans is well suited for the extraction of accurate simulation models which can be used for an SI analysis.

Description: This paper presents a completely automatic processing chain for orthorectification of optical pushbroom sensors. The procedure is robust and works without manual intervention from raw satellite image to orthoimage. It is modularly divided in four main steps: metadata extraction, automatic ground control point (GCP) extraction, geometric modeling, and orthorectification. The GCP extraction step uses georeferenced vector roads as a reference and produces a file with a list of points and their accuracy estimation. The physical geometric model is based on collinearity equations and works with sensor-corrected (level 1) optical satellite images. It models the sensor position and attitude with second-order piecewise polynomials depending on the acquisition time. The exterior orientation parameters are estimated in a least squares adjustment, employing random sample consensus and robust estimation algorithms for the removal of erroneous points and fine-tuning of the results. The images are finally orthorectified using a digital elevation model and positioned in a national coordinate system. The usability of the method is presented by testing three RapidEye images of regions with different terrain configurations. Several tests were carried out to verify the efficiency of the procedure and to make it more robust. Using the geometric model, subpixel accuracy on independent check points was achieved, and positional accuracy of orthoimages was around one pixel. The proposed procedure is general and can be easily adapted to various sensors.

Description: This paper presents a new ground moving target imaging (GMTIm) algorithm for airborne synthetic aperture radar (SAR) based on a novel time-frequency representation (TFR), Lv's distribution (LVD). We first analyze generic moving target signatures for a multichannel SAR and then derive the analytical spectrum of a point target moving at a constant velocity by a polar format algorithm for SAR image formation. SAR motion deviation from a predetermined flight track is considered to facilitate airborne SAR applications. LVD, as a recently developed TFR for the analysis of multicomponent linear-frequency-modulated signal, is adopted to represent the target kinematic spectrum in the Doppler centroid frequency and chirp rate domain. As a result, the proposed SAR-GMTIm algorithm is capable of imaging multiple moving targets even when they are located at the same range resolution cell. Some practical issues such as imaging maneuvering targets and small/weak targets are discussed to enhance the applicability of the proposed algorithm. Simulation results with isotropic point moving targets are presented to validate the effectiveness and superiority of the proposed algorithm. Raw data collected by an airborne multichannel SAR are also used to verify the performance improvement made by the proposed algorithm.

Description: Satellite soil moisture estimates have received increasing attention over the past decade. This paper examines the applicability of estimating soil moisture states and soil hydraulic parameters through two particle filter (PF) methods: The PF with commonly used sampling importance resampling (PF-SIR) and the PF with recently developed Markov chain Monte Carlo sampling (PF-MCMC) methods. In a synthetic experiment, the potential of assimilating remotely sensed near-surface soil moisture measurements into a 1-D mechanistic soil water model (HYDRUS-1D) using both the PF-SIR and PF-MCMC algorithms is analyzed. The effects of satellite temporal resolution and accuracy, soil type, and ensemble size on the assimilation of soil moisture are analyzed. In a real data experiment, we first validate the Advanced Microwave Scanning Radiometer--Earth Observing System (AMSR-E) soil moisture products in the Oklahoma Little Washita Watershed. Aside from rescaling the remotely sensed soil moisture, a bias correction algorithm is implemented to correct the deep soil moisture estimate. Both the ascending and descending AMSR-E soil moisture data are assimilated into the HYDRUS-1D model. The synthetic assimilation results indicated that, whereas both updating schemes showed the ability to correct the soil moisture state and estimate hydraulic parameters, the PF-MCMC scheme is consistently more accurate than PR-SIR. For real data case, the quality of remotely sensed soil moisture impacts the benefits of their assimilation into the model. The PF-MCMC scheme brought marginal gains than the open-loop simulation in RMSE at both surface and root-zone soil layer, whereas the PF-SIR scheme degraded the open-loop simulation.

Description: In the millimeter wave (mmW) frequency range, the root mean square height of the through silicon via (TSV) sidewall roughness is comparable to the skin depth, and hence, becomes a critical factor for TSV modeling and analysis. In this paper, the impact of the TSV sidewall roughness on electrical performance, such as the loss and impedance alteration in the mmW frequency range, is examined and analyzed. The second-order small analytical perturbation method is applied to obtain a simple closed-form expression for the power absorption enhancement factor of the TSV. In this study, we propose an electrical model of the TSV, which considers the TSV sidewall roughness effect, the skin effect, and the metal oxide semiconductor effect. The parameters of the proposed circuit model can be determined analytically; the accuracy of the model is validated through a comparison of circuit model behavior for full wave electromagnetic field simulations up to 100 GHz.

Description: We propose a new deterministic approach for remote sensing retrieval, called modified total least squares (MTLS), built upon the total least squares (TLS) technique. MTLS implicitly determines the optimal regularization strength to be applied to the normal equation first-order Newtonian retrieval using all of the noise terms embedded in the residual vector. The TLS technique does not include any constraint to prevent noise enhancement in the state space parameters from the existing noise in measurement space for an inversion with an ill-conditioned Jacobian. To stabilize the noise propagation into parameter space, we introduce an additional empirically derived regularization proportional to the logarithm of the condition number of the Jacobian and inversely proportional to the L2-norm of the residual vector. The derivation, operational advantages and use of the MTLS method are demonstrated by retrieving sea surface temperature from GOES-13 satellite measurements. An analytic equation is derived for the total retrieval error, and is shown to agree well with the observed error. This can also serve as a quality indicator for pixel-level retrievals. We also introduce additional tests from the MTLS solutions to identify contaminated pixels due to residual clouds, error in the water vapor profile and aerosols. Comparison of the performances of our new and other methods, namely, optimal estimation and regression-based retrieval, is performed to understand the relative prospects and problems associated with these methods. This was done using operational match-ups for 42 months of data, and demonstrates a relatively superior temporally consistent performance of the MTLS technique.

Description: Radar-based remote sensing for measurement of ocean surface waves presents advantages over conventional point sensors such as wave buoys. As its use becomes more widespread, it is important to understand the sensitivity of the extracted wave parameters to the characteristics of the radar and the scatterers. To examine such issues, experiments were performed offshore of the Scripps Institution of Oceanography pier in July 2010. Radar measurements in low wind speeds were performed with a dual-polarized high-resolution X-band pulse-Doppler radar at low grazing angles along with two independent measurements of the surface waves using conventional sensors, a GPS-based buoy, and an ultrasonic array. Comparison between radar cross section (RCS) and Doppler modulations shows peak values occurring nearly in-phase, in contrast with tilt modulation theory. Spectral comparisons between Doppler-based and RCS-based spectra show that Doppler-based spectra demonstrate greater sensitivity to swell-induced modulations, whereas RCS-based spectra show greater sensitivity to small-scale modulations (or generally have more noise at high frequency), and they equally capture energy at the wind wave peak. Doppler estimates of peak period were consistent with the conventional sensors, whereas the RCS differed in assignment of peak period to wind seas rather than swell in a couple of cases. Higher order period statistics of both RCS and Doppler were consistent with the conventional sensors. Radar-based significant wave heights are lower than buoy-based values and contain nontrivial variability of ∼33%. Comparisons between HH and VV polarization data show that VV data more accurately represent the wave field, particularly as the wind speeds decrease.

Description: Polarimetric synthetic aperture radar satellite and ground-based Ku- and X-band scatterometer measurements are used to explore the scattering mechanism for ice in shallow Arctic lakes, wherein strong radiometric responses are seen for floating ice, and low returns are evident where the ice has grounded. Scatterometer measurements confirm that high backscatter is from the ice/water interface, whereas polarimetric decomposition suggests that the dominant scattering mechanism from that interface is single bounce. Using Fresnel equations, a simple model for surface bounce from the ice/water interface is proposed, and its predictions are supported by experimental parameters such as co-pol phase difference, co-pol ratio, and the results of rigorous numerical modeling. Despite early research suggesting double-bounce scattering from columnar air bubbles and the ice/water interface as the dominant scattering mechanism in shallow lakes, this paper strongly supports a single-bounce model.

Description: The on-orbit radiometric calibration of the reflective solar bands (RSBs) of the Visible Infrared Imaging Radiometer Suite (VIIRS) aboard the Suomi National Polar-orbiting Partnership satellite is carried out primarily through observations of a fully illuminated solar diffuser (SD) panel. Accurate knowledge of the solar spectral radiance scattered from the SD is available. The sensor aperture spectral radiance is assumed to be a quadratic polynomial function of a VIIRS detector's background-subtracted response in digital number. The coefficients of the polynomial were initially determined prelaunch. Once on orbit, we assume that these coefficients change uniformly by a common calibration factor, which is referred to as the $F$ -factor. The known solar spectral radiance scattered from a fully illuminated SD allows for the determination of these $F$ -factors. We describe the methodology and the associated algorithms used in the calculation of the RSB $F$ -factors. Our results show that the $F$ -factors change over time, with the largest change occurring at a wavelength of 862 nm (with a value of about 1.55 on day 950 after the satellite launch, relative to its value at the beginning of the launch) . In addition, we estimate the relative error standard deviations of the computed top-of-the-atmosphere reflectance at the detector pixel level. On day 950 of the mission, the relative error standard deviations are all less or equal to 0.016, except for the M11 band (band central wavelength of 2257 nm) , which has a relative error standard deviation of about 0.049 due to a very low signal-to-noise ratio.

Description: Histopathological grading of cancer not only offers an insight to the patients’ prognosis but also helps in making individual treatment plans. Mitosis counts in histopathological slides play a crucial role for invasive breast cancer grading using the Nottingham grading system. Pathologists perform this grading by manual examinations of a few thousand images for each patient. Hence, finding the mitotic figures from these images is a tedious job and also prone to observer variability due to variations in the appearances of the mitotic cells. We propose a fast and accurate approach for automatic mitosis detection from histopathological images. We employ area morphological scale space for cell segmentation. The scale space is constructed in a novel manner by restricting the scales with the maximization of relative-entropy between the cells and the background. This results in precise cell segmentation. The segmented cells are classified in mitotic and non-mitotic category using the random forest classifier. Experiments show at least 12% improvement in $F_{1}$ score on more than 450 histopathological images at $40times $ magnification.

Description: This paper proposes a fast multi-band image fusion algorithm, which combines a high-spatial low-spectral resolution image and a low-spatial high-spectral resolution image. The well admitted forward model is explored to form the likelihoods of the observations. Maximizing the likelihoods leads to solving a Sylvester equation. By exploiting the properties of the circulant and downsampling matrices associated with the fusion problem, a closed-form solution for the corresponding Sylvester equation is obtained explicitly, getting rid of any iterative update step. Coupled with the alternating direction method of multipliers and the block coordinate descent method, the proposed algorithm can be easily generalized to incorporate prior information for the fusion problem, allowing a Bayesian estimator. Simulation results show that the proposed algorithm achieves the same performance as the existing algorithms with the advantage of significantly decreasing the computational complexity of these algorithms.

Description: In recent years, baggage screening at airports has included the use of dual-energy X-ray computed tomography (DECT), an advanced technology for nondestructive evaluation. The main challenge remains to reliably find and identify threat objects in the bag from DECT data. This task is particularly hard due to the wide variety of objects, the high clutter, and the presence of metal, which causes streaks and shading in the scanner images. Image noise and artifacts are generally much more severe than in medical CT and can lead to splitting of objects and inaccurate object labeling. The conventional approach performs object segmentation and material identification in two decoupled processes. Dual-energy information is typically not used for the segmentation, and object localization is not explicitly used to stabilize the material parameter estimates. We propose a novel learning-based framework for joint segmentation and identification of objects directly from volumetric DECT images, which is robust to streaks, noise and variability due to clutter. We focus on segmenting and identifying a small set of objects of interest with characteristics that are learned from training images, and consider everything else as background. We include data weighting to mitigate metal artifacts and incorporate an object boundary field to reduce object splitting. The overall formulation is posed as a multilabel discrete optimization problem and solved using an efficient graph-cut algorithm. We test the method on real data and show its potential for producing accurate labels of the objects of interest without splits in the presence of metal and clutter.

Description: Feature point matching is a fundamental and challenging problem in many computer vision applications. In this paper, a robust feature point matching algorithm named spatial order constraints bilateral-neighbor vote (SOCBV) is proposed to remove outliers for a set of matches (including outliers) between two images. A directed ${k}$ nearest neighbor ( knn ) graph of match sets is generated, and the problem of feature point matching is formulated as a binary discrimination problem. In the discrimination process, the class labeled matrix is built via the spatial order constraints defined on the edges that connect a point to its knn . Then, the posterior inlier class probability of each match is estimated with the knn density estimation and spatial order constraints. The vote of each match is determined by averaging all posterior class probabilities that originate from its associative inliers set and is used for removing outliers. The algorithm iteratively removes outliers from the directed graph and recomputes the votes until the stopping condition is satisfied. Compared with other popular algorithms, such as RANSAC, RSOC, GTM, SOC and WGTM, experiments under various testing data sets demonstrate strong robustness for the proposed algorithm.

Description: Acoustic localization is an essential technique in speech capturing, speech enhancement, video conferencing, and human–robot interaction. However, in practical situations, localization has to be performed in abominable environments, where the presence of reverberation and noise degrades the performance of available position estimates. Besides, the designed systems should be adaptive to locomotion of targets with low computational complexity. In the context, this paper introduces a robust hierarchical acoustic localization method via time-delay compensation (TDC) and interaural matching filter (IMF). Firstly, interaural time-delay (ITD) and interaural level difference (ILD), which are cues involved in first two layers, respectively, are yielded by TDC all at once. Then, a novel feature named IMF, which can eliminate the difference between binaural signals, is proposed in the third layer. The final decision making is based on a Bayesian rule. The relationships among the three layers are that the former layer provides candidate directions for later ones such that the searching space becomes gradually smaller to reduce matching time. Experiments using both a public database and a real scenario verify that TDC and IMF are robust for acoustic localization, and hierarchical system has less consumption time.

Description: Canonical correlation analysis (CCA) is a widely used data analysis tool that allows to assess the correlation between two distinct sets of signals. It computes optimal linear combinations of the signals in both sets such that the resulting signals are maximally correlated. The weight vectors defining these optimal linear combinations are referred to as “principal CCA directions”. In addition to this particular type of data analysis, CCA is also often used as a blind source separation (BSS) technique, i.e., under certain assumptions, the principal CCA directions have certain demixing properties. In this paper, we propose a distributed CCA (DCCA) algorithm that can operate in wireless sensor networks (WSNs) with a fully connected or a tree topology. The algorithm estimates the $Q$ principal CCA directions from the sensor signal observations collected by the different nodes in the WSN and extracts the corresponding sources. These network-wide principal CCA directions are estimated in a time-recursive fashion without explicitly constructing the corresponding network-wide correlation matrices, i.e., without the need for data centralization. Instead, each node locally computes smaller CCA problems and only transmits compressed sensor signal observations (of dimension $Q$ ), which significantly reduces the bit rate over the wireless links of the WSN. We prove convergence and optimality of the DCCA algorithm, and we demonstrate its performance by means of numerical simulations in a blind source separation scenario.

Description: This installment of Computer's series highlighting the work published in IEEE Computer Society journals comes from IEEE Transactions on Visualization and Computer Graphics. The Web extra at http://youtu.be/E1PVTitj7h0 is a video demonstration of a novel solution to multivariate data visualization that helps users interactively explore data by combining standard presentations, from detailed views to high-level overviews.

Description: The data rearrangement engine (DRE) performs in-memory data restructuring to accelerate irregular, data-intensive applications. An emulation on a field-programmable gate array shows how the DRE could improve speedup, memory bandwidth, and energy consumption on three representative benchmarks.

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Description: Background: Prokaryotic translation initiation involves the proper docking, anchoring, and accommodation of mRNA to the 30S ribosomal subunit. Three initiation factors (IF1, IF2, and IF3) and some ribosomal proteins mediate the assembly and activation of the translation initiation complex. Although the interaction between Shine-Dalgarno (SD) sequence and its complementary sequence in the 16S rRNA is important in initiation, some genes lacking an SD ribosome binding site (RBS) are still well expressed. The objective of this study is to examine the pattern of distribution and diversity of RBS in fully sequenced bacterial genomes. The following three hypotheses were tested: SD motifs are prevalent in bacterial genomes; all previously identified SD motifs are uniformly distributed across prokaryotes; and genes with specific cluster of orthologous gene (COG) functions differ in their use of SD motifs. Results: Data for 2,458 bacterial genomes, previously generated by Prodigal (PROkaryotic DYnamic programming Gene-finding ALgorithm) and currently available at the National Center for Biotechnology Information (NCBI), were analyzed. Of the total genes examined, ~77.0 % use an SD RBS, while ~23.0 % have no RBS. Majority of the genes with the most common SD motifs are distributed in a manner that is representative of their abundance for each COG functional category, while motifs 13 (5′-GGA-3′/5′-GAG-3′/5′-AGG-3′) and 27 (5′-AGGAGG-3′) appear to be predominantly used by genes for information storage and processing, and translation and ribosome biogenesis, respectively. Conclusion: These findings suggest that an SD sequence is not obligatory for translation initiation; instead, other signals, such as the RBS spacer, may have an overarching influence on translation of mRNAs. Subsequent analyses of the 5′ secondary structure of these mRNAs may provide further insight into the translation initiation mechanism.

Description: Background: Retention of sister centromere cohesion during meiosis I and its dissolution at meiosis II is necessary for balanced chromosome segregation and reduction of chromosome number. PATRONUS1 (PANS1) has recently been proposed to regulate centromere cohesion in Arabidopsis after meiosis I, during interkinesis. pans1 mutants lose centromere cohesion prematurely during interkinesis and segregate randomly at meiosis II. PANS1 protein interacts with components of the Anaphase Promoting Complex/Cyclosome (APC/C). Results: We show here that PANS1 protein is found mainly in prophase I of meiosis, with its level declining late in prophase I during diplotene. PANS1 also shows expression in dividing tissues. We demonstrate that, in addition to the previously reported premature loss of centromere cohesion during interkinesis, pans1 mutants show partially penetrant defects in centromere cohesion during meiosis I. We also determine that pans1 shows synthetic lethality at the level of the sporophyte, with Omission of Second Division 1 (osd1), which encodes a known inhibitor of the APC/C that is required for cell cycle progression during mitosis, as well as meiosis I and II. Conclusions: Our results show that PANS1 is expressed mainly in meiosis I where it has an important function and together with previous studies indicate that PANS1 and OSD1 are part of a network linking centromere cohesion and cell cycle progression through control of APC/C activity.

Description: Background: Perception and transduction of temperature changes result in altered growth enabling plants to adapt to increased ambient temperature. While PHYTOCHROME-INTERACTING FACTOR4 (PIF4) has been identified as a major ambient temperature signaling hub, its upstream regulation seems complex and is poorly understood. Here, we exploited natural variation for thermo-responsive growth in Arabidopsis thaliana using quantitative trait locus (QTL) analysis. Results: We identified GIRAFFE2.1, a major QTL explaining ~18 % of the phenotypic variation for temperature-induced hypocotyl elongation in the Bay-0 x Sha recombinant inbred line population. Transgenic complementation demonstrated that allelic variation in the circadian clock regulator EARLY FLOWERING3 (ELF3) is underlying this QTL. The source of variation could be allocated to a single nucleotide polymorphism in the ELF3 coding region, resulting in differential expression of PIF4 and its target genes, likely causing the observed natural variation in thermo-responsive growth. Conclusions: In combination with other recent studies, this work establishes the role of ELF3 in the ambient temperature signaling network. Natural variation of ELF3-mediated gating of PIF4 expression during nightly growing periods seems to be affected by a coding sequence quantitative trait nucleotide that confers a selective advantage in certain environments. In addition, natural ELF3 alleles seem to differentially integrate temperature and photoperiod information to induce architectural changes. Thus, ELF3 emerges as an essential coordinator of growth and development in response to diverse environmental cues and implicates ELF3 as an important target of adaptation.

Description: Background: In Eucalyptus genus, studies on genome composition and transposable elements (TEs) are particularly scarce. Nearly half of the recently released Eucalyptus grandis genome is composed by retrotransposons and this data provides an important opportunity to understand TE dynamics in Eucalyptus genome and transcriptome. Results: We characterized nine families of transcriptionally active LTR retrotransposons from Copia and Gypsy superfamilies in Eucalyptus grandis genome and we depicted genomic distribution and copy number in two Eucalyptus species. We also evaluated genomic polymorphism and transcriptional profile in three organs of five Eucalyptus species. We observed contrasting genomic and transcriptional behavior in the same family among different species. RLC_egMax_1 was the most prevalent family and RLC_egAngela_1 was the family with the lowest copy number. Most families of both superfamilies have their insertions occurring

Description: Background: The historical orogenesis and associated climatic changes of mountain areas have been suggested to partly account for the occurrence of high levels of biodiversity and endemism. However, their effects on dispersal, differentiation and evolution of many groups of plants are still unknown. In this study, we examined the detailed diversification history of Primula sect. Armerina, and used biogeographic analysis and macro-evolutionary modeling to investigate a series of different questions concerning the evolution of the geographical and ecological distribution of the species in this section. Results: We sequenced five chloroplast and one nuclear genes for species of Primula sect. Armerina. Neither chloroplast nor nuclear trees support the monophyly of the section. The major incongruences between the two trees occur among closely related species and may be explained by hybridization. Our dating analyses based on the chloroplast dataset suggest that this section began to diverge from its relatives around 3.55 million years ago, largely coinciding with the last major uplift of the Qinghai-Tibet Plateau (QTP). Biogeographic analysis supports the origin of the section in the Himalayan Mountains and dispersal from the Himalayas to Northeastern QTP, Western QTP and Hengduan Mountains. Furthermore, evolutionary models of ecological niches show that the two P. fasciculata clades have significantly different climatic niche optima and rates of niche evolution, indicating niche evolution under climatic changes and further providing evidence for explaining their biogeographic patterns. Conclusion: Our results support the hypothesis that geologic and climatic events play important roles in driving biological diversification of organisms in the QTP area. The Pliocene uplift of the QTP and following climatic changes most likely promoted both the inter- and intraspecific divergence of Primula sect. Armerina. This study also illustrates how niche evolution under climatic changes influences biogeographic patterns.

Description: Background: Acidity is a major contributor to fruit quality. Several organic acids are present in apple fruit, but malic acid is predominant and determines fruit acidity. The trait is largely controlled by the Malic acid (Ma) locus, underpinning which Ma1 that putatively encodes a vacuolar aluminum-activated malate transporter1 (ALMT1)-like protein is a strong candidate gene. We hypothesize that fruit acidity is governed by a gene network in which Ma1 is key member. The goal of this study is to identify the gene network and the potential mechanisms through which the network operates. Results: Guided by Ma1, we analyzed the transcriptomes of mature fruit of contrasting acidity from six apple accessions of genotype Ma_ (MaMa or Mama) and four of mama using RNA-seq and identified 1301 fruit acidity associated genes, among which 18 were most significant acidity genes (MSAGs). Network inferring using weighted gene co-expression network analysis (WGCNA) revealed five co-expression gene network modules of significant (P 

Description: Background: Squaliform sharks represent approximately 27 % of extant shark diversity, comprising more than 130 species with a predominantly deep-dwelling lifestyle. Many Squaliform species are highly specialized, including some that are bioluminescent, a character that is reported exclusively from Squaliform sharks within Chondrichthyes. The interfamiliar relationships within the order are still not satisfactorily resolved. Herein we estimate the phylogenetic interrelationships of a generic level sampling of “squaloid” sharks and closely related taxa using aligned sequences derived from a targeted gene capture approach. The resulting phylogenetic estimate is further used to evaluate the age of first occurrence of bioluminescence in Squaliformes. Results: Our dataset comprised 172 putative ortholog exon sequences. Phylogenetic estimates result in a fully resolved tree supporting a monophyletic lineage of Squaliformes excluding Echinorhinus. Non-luminous Squalidae are inferred to be the sister to a clade comprising all remaining Squaliform families. Our results suggest that the origin of photophores is coincident with an elevated diversification rate and the splitting of families Dalatiidae, Etmopteridae, Oxynotidae and Somniosidae at the transition of the Lower to the Upper Cretaceous. The presence of luminous organs was confirmed for the Sleeper shark genus Zameus. These results indicate that bioluminescence in sharks is not restricted solely to the families Etmopteridae and Dalatiidae as previously believed. Conclusions: The sister-clade to non-luminous Squalidae comprises five families. The presence of photophores is reported for extant members of three out of these five families based on results of this study, i.e. Lantern sharks (Etmopteridae), Kitefin sharks (Dalatiidae) and Sleeper sharks (Somniosidae). Our results suggest that the origin of luminous organs arose during the rapid diversification event that gave rise to the extant Squaliform families. These inferences are consistent with the idea of diversification of Squaliform sharks being associated with the emergence of new deep-sea habitats in the Lower Cretaceous, which may have been facilitated by the evolution of bioluminescence.

Description: Background: Copy number variation was found to be a frequent type of DNA polymorphism in the human genome often associated with diseases but its importance in crops and the effects on agronomic traits are still largely unknown. Results: Here, we employed a large worldwide panel of 1110 winter wheat varieties to assess the frequency and the geographic distribution of copy number variants at the Photoperiod-B1 (Ppd-B1) and the Vernalization-A1 (Vrn-A1) loci as well as their effects on flowering time under field conditions. We identified a novel four copy variant of Vrn-A1 and based on the phylogenetic relationships among the lines show that the higher copy variants at both loci are likely to have arisen independently multiple times. In addition, we found that the frequency of the different copy number variants at both loci reflects the environmental conditions in the varieties’ region of origin and based on multi-location field trials show that Ppd-B1 copy number has a substantial effect on the fine-tuning of flowering time. Conclusions: In conclusion, our results show the importance of copy number variation at Ppd-B1 and Vrn-A1 for the global adaptation of wheat making it a key factor for wheat success in a broad range of environments and in a wider context substantiate the significant role of copy number variation in crops.

Description: Background: The study evaluated genetic progress of a breeding program for common carp undergoing four generations of selection for increased harvest body weight from 2004 to 2014. The pedigree included 17,351 individual fish which were offspring of 342 sires and 352 dams. Genetic parameters for body weight at about two years of age and survival rate during grow-out period were also estimated using the residual maximum likelihood method applied to a two-trait linear mixed model. Direct response in body weight and correlated changes in survival were measured as the differences in: i) estimated breeding values (EBVs) between the two lines; and ii) EBVs of the selection line between successive generations. Results: Direct gain in body weight ranged from 0.20 to 0.90 genetic standard deviation units and averaged 7 % of the base population per generation (two years per generation). Correlated changes in survival were negligible, indicating that the selection program for high growth did not have any adverse effect on this trait in the present population. The heritability for body weight was moderate (0.17, s.e. 0.05), whereas the estimate for survival was low (0.05–0.17) but significantly different from zero across linear mixed and threshold generalised statistical models. Our results predict that body weight or/and other growth related traits will continue to respond to selection and that there is potential to improve survival through direct genetic means. Correlated improvement in survival to selection for increased body weight was hardly achieved, as the genetic correlation between the two traits was not different from zero. Conclusions: It is concluded that selection for increased harvest body weight resulted in significant improvement in growth performance of the present population of common carp Cyprinus carpio.

Description: Background: Spine surgery is one of the most difficult areas in which to achieve a good clinical outcome and pain medication is often used for a long period of time after surgery. The purpose of this study was to investigate whether pain medication use after spine surgery has been assessed previously with respect to clinical outcome. Methods: A systematic review of PubMed/MEDLINE databases was conducted from Jan 1st 2000 to Dec 31st 2009 using the search key words, “spine surgery” and “clinical outcome.” All publications reporting clinical outcomes were examined and analyzed for outcome measures and data with respect to pain medication use after spine surgery. Results: In total 990 articles met the inclusion criteria. Among them, 56 articles (5.7%) described definitive pain medication use after spine surgery; 98 articles (9.9%) used clinical outcome measures that incorporate pain medication assessment, although only one such study included a definitive description of pain medication use. Conclusions: Pain medication use after spine surgery was assessed in 15.5% of articles published during the last decade. The use of pain medication following spine surgery can affect clinical outcome and, therefore, needs to be taken into consideration for clinical assessment. In future studies, a detailed description of pain medication use and/or clinical outcome measures that incorporate pain medication assessment are advocated when reporting clinical outcomes after spine surgery so that it can be better assessed.

Description: Background: Matrix-metalloproteinases 9 (MMP-9) belongs to the class of matrix metalloproteinases whose main function is to degrade and remodel the extracellular matrix (ECM). MMP-9 has been shown to be an integral part of many diseases where modulation of the ECM is a key step such as cancer, osteoporosis and fibrosis. MMP-9 is secreted as a latent pro-enzyme that requires activation in the extracellular space. Therefore, identifying physiological and molecular contexts, which can activate MMP-9 is important. Results: Acidification of osteoclast-conditioned media to pH 5 resulted in a fragment with a size corresponding to active MMP-9. Also, treatment of recombinant proMMP-9 with recombinant cathepsin K (CTSK) at pH 5 yielded a fragment that corresponded to the molecular weight of active MMP-9, and showed MMP-9 activity. This activation was abrogated in the presence of CTSK inhibitor indicating that CTSK was responsible for the activation of pro-MMP-9. Knocking down CTSK in MDA-MB-231 cells also diminished MMP-9 activity compared to wild type control. Conclusions: Here we provide the first evidence that CTSK can cleave and activate MMP-9 in acidic environments such as seen in tumors and during bone resorption. This finding provides a key link between CTSK expression in tumors and bone and ECM remodeling, through MMP-9 activation. This novel mechanism to activate MMP-9 through extracellular physiological changes elucidated in this study reveals a protease-signaling network involving CTSK and MMP-9 and provides the impetus to explore ECM proteases as physiological markers and pharmacological targets.

Description: Background: Several epidemiologic studies indicate that maternal gestational weight gain (GWG) influences health outcomes in offspring. Any underlying mechanisms have, however, not been established. A recent study of 88 children based on the Avon Longitudinal Study of Parents and Children (ALSPAC) cohort examined the methylation levels at 1,505 Cytosine-Guanine methylation (CpG) loci and found several to be significantly associated with maternal weight gain between weeks 0 and 18 of gestation. Since these results could not be replicated we wanted to examine associations between 0 and 18 week GWG and genome-wide methylation levels using the Infinium HumanMethylation450 BeadChip (450K) platform on a larger sample size, i.e. 729 newborns sampled from the Norwegian Mother and Child Cohort Study (MoBa). Results: We found no CpG loci associated with 0–18 week GWG after adjusting for the set of covariates used in the ALSPAC study (i.e. child’s sex and maternal age) and for multiple testing (q 〉 0.9, both 1,505 and 473,731 tests). Hence, none of the CpG loci linked with the genes found significantly associated with 0–18 week GWG in the ALSPAC study were significant in our study. Conclusions: The inconsistency in the results with the ALSPAC study with regards to the 0–18 week GWG model may arise for several reasons: sampling from different populations, dissimilar methylome coverage, sample size and/or false positive findings.

Description: Background: High disease burden and scarcity of healthcare resources present complex ethical dilemmas for nurses working in developing countries. We assessed nurses’ knowledge in ethics and their perceptions about Continuous Nurses’ Ethics Education (CNEE) for in-service nurses. Methods: Using an anonymous, pre-tested self-administered questionnaire, we assessed nurses’ knowledge in basic ethics concepts at three regional hospitals in Uganda. Adequate knowledge was measured by a score ≥50% in the knowledge assessment test. Nurses’ perceptions on CNEE were assessed using a six-point Likert scale. Results: Of 114 nurses, 91% were female; with mean age 44.7 (SD 10) years. Half were diploma, 47 (41%) certificates, 6 (5%) bachelors’ degrees and one masters’ level training. Overall, 18 (16%) scored ≥50% in the ethics knowledge test. Nurses with diploma or higher level of nursing training were less likely to fail the ethics knowledge than certificate-level nurses (OR 0.14, 95% CI: 0.02–0.7). Only 45% had ever attended at least one CNEE session and up to 93% agreed that CNEE is required to improve nurses’ ethics knowledge and practice. Conclusions: Nurses exhibited low knowledge in ethics and positive attitudes towards CNEE. We recommend structured CNEE programs to address basic concepts in nursing ethics and their application in clinical practice.

Description: Background: Pentalogy of Cantrell is a rare syndrome, first described by Cantrell and co-workers in 1958. The syndrome is characterized by the presence of five major congenital defects involving the diaphragm, abdominal wall, the diaphragmatic pericardium, lower sternum and various congenital intra-cardiac abnormalities. The syndrome has never been reported in Tanzania, although may have been reported from other African countries. Survival rate of the complete form of pentalogy of Cantrell is as low as 20%, but recent studies have reported normal growth achieved by 6 years of age where corrective surgeries were done; showing that surgical repair early in life is essential for survival.Case presentationThe African baby residing in Tanzania was referred from a district hospital on the second day of life. She was noted to have a huge omphalocele and ectopia cordis covered by a thin membrane, with bowels visible through the membrane and the cardiac impulse visible just below the epigastrium. Despite the physical anomaly, she appeared to saturate well in room air and had stable vitals. Her chest X-ray revealed the absence of the lower segments of the sternum and echocardiography showed multiple intra-cardiac defects. Based on these findings, the diagnosis of pentalogy of Cantrell was reached. On her fifth day of life, the neonate was noted to have signs of cardiac failure characterized by easy fatigability and restlessness during feeding. Cardiac failure treatment was initiated and she was discharged on parents’ request on the second week of life. Due to inadequate facilities to undertake this complex corrective surgery, arrangements were being made to refer her abroad. In the meantime, her growth and development was satisfactory until the age of 9 months, when she ran out of the medications and succumbed to death. Her parents could no longer afford transport cost to attend the monthly clinic visits, where the infant was getting free medication refill. Conclusions: The case reported here highlights that in resource limited settings; poor outcome in infants with complex congenital anomalies is a function of multiple factors. However, we believe that surgery would have averted mortality in this 9-month-old female infant. We hope to be able to manage these cases better in future following the recent establishment of cardiac surgery facilities at Muhimbili National Hospital.

Description: This paper proposes the improved design of an ultraviolet (UV)- and-blue-light-inhibited white light-emitting diode for use as a safe and practical light source. Covered with a glass substrate coated with a photocatalyst resin on one side and a reflectance film on the other side, wavelengths below 400 nm are reflected back to re-excite the red–green–blue phosphors and, consequently, enhance luminous efficiency. The absorption spectrum of bismuth oxide photocatalysts is below 521 nm, and the leaked UV and blue light can be absorbed, thereby exciting electron–hole pairs and producing the photocatalytic effect. Thus, blue light and UV leakage can be suppressed appreciably, and the luminous efficiency can be increased markedly. Experimental results showed a UV suppression ratio of 88.43% and a visible light increasing ratio of 21.66%. The Commission International de L'Eclairage chromaticity coordinates $(x,y)$ were (0.343, 0.404), the correlated color temperature and the deviation from de Blackbody locus were (5201 K, 0.0250), and the color rendering index was 93.16. In addition, the photocatalyst coating layer can act as a diffuser to provide a comfortable visual experience and facilitate environmental purification.

Description: Metamaterial-based perfect absorbers have attracted considerable attention due to their potential for practical applications. The existing absorbers, however, are mostly polarization insensitive or only sensitive to one direction, which is inapplicable in some areas. Polarization tunable or high absorption in two orthogonal directions is very useful and necessary. Herein, we present a polarization tunable absorber formed by an asymmetric patch and a dielectric layer on top of a metallic board. With this structure, the frequency of the absorber can be tuned by merely changing the polarization of the incident. The tunable mechanism originates from the different length of the patch along the two orthogonal directions. The concept is rather general and applicable to various absorbers, as long as the asymmetric design is valid. The absorber can find practical applications in manipulation of the polarization of the light and detecting waves with specific polarization.

Description: The major activity during speech production is glottal activity and is earlier detected using strength of excitation (SoE). This work uses the normalized autocorrelation peak strength (NAPS) and higher order statistics (HOS) as additional features for detecting glottal activity. The three features, namely, SoE, NAPS, and HOS, are, respectively indicators of different attributes of glottal activity, namely, energy, periodicity, and asymmetrical nature of the resulting source signal. The effectiveness of these features is analyzed using the differential electroglottograph signal, zero-frequency filtered signal, and integrated linear prediction residual, as representatives of source signal. The combination of glottal activity information from the three features outperforms any single of them, demonstrating different information represented by each of these features.

Description: This letter addresses the texture classification problem through a pixel-based local binary pattern (LBP) statistics aggregation mechanism. Real-world texture images often present challenges for classification algorithms in terms of intra-class variability due, among others, to variable illumination. The LBP operator, a state-of-the-art texture descriptor, possesses key properties for tackling real-world texture images: discriminative power and invariance against monotonic gray level changes. We propose a novel texture classification approach that increases the robustness of LBP-based methods with respect to any type of intra-class variations. The method locally characterizes each pixel with an LBP code histogram and globally computes the label of a textured image by aggregating pixel labels through a voting process. Our approach can be in principle applied to any LBP version, as it focuses on how statistics are computed from LBP codes. We show that the proposed pixel-based approach improves upon traditional LBP block-based approaches in terms of classification accuracy by up to 5.1 p.p. on the public Outex database for the classic LBP with various neighborhoods as well as for various LBP extensions.

Description: The atmospheric condition parameters used in the radiative transfer-based atmospheric correction (AC) are often uncertain. This uncertainty propagates to the estimated reflectance. The reflectance, is, however, not equally sensitive to all the parameters. A sensitivity analysis (SA) helps in prioritizing the parameters. The objective of this study was to perform an SA of reflectance to water vapor concentration ( $wv$ ) and aerosol optical thickness ( $AOT$ ). SA was performed using the Fourier amplitude sensitivity test (FAST) method, which computes sensitivity indices ( $text{SI}$ ) of these parameters. Besides variation in the two parameters, we also studied the effect of surface albedo on the $text{SI}$ by quantifying $text{SI}$ for three target surfaces (in the spectral range $text{0.44{-}0.96};upmu$ m): 1) a dark target (water); 2) a bright target (bare soil); and 3) a target having low albedo in the visible and high albedo in near-infrared range (forest). For $AOT$ , high ( $approx!text{0.9}$ ) $text{SI}$ values were observed at the nonwater absorption wavelengths. For $wv$ , high $text{SI}$ values were observed at wavelengths, where strong absorption features are loca- ed and when the surface albedo was high. For the dark target, the effect of $AOT$ was prominent throughout the spectral range. We found that the sensitivity of reflectance to $wv$ and $AOT$ is a function of wavelength, strength of the absorption features, and surface albedo. We conclude that $AOT$ is a more important parameter for dark targets than $wv$ even at the principal absorption feature. For bright targets, the importance of $wv$ and $AOT$ depends on the strength of the absorption feature.

Description: The feasibility study of the HALESIS (High-Altitude Luminous Events Studied by Infrared Spectro-imagery) project is presented. The purpose of this experiment is to measure the atmospheric perturbation in the minutes following the occurrence of transient luminous events (TLEs) from a stratospheric balloon in the altitude range of 20–40 km. The instrumentation will include a spectro-imager embedded in a pointing gondola. Infrared signatures of a single blue jet were simulated under the assumption of local thermodynamic equilibrium (LTE), and were then compared with a panel of commercially available instrument specifications. The sensitivity of the signatures with a local perturbation of the main vibrational energy level populations of ${mathbf{rm CO}_{2}}$ , CO, NO, ${mathbf{rm O}_{3}}$ , and ${mathbf{rm H}_{2}}{rm O}$ was measured and the infrared signatures of a single blue jet taking into account non-LTE hypotheses were compared with the same panel of commercially available instrument specifications. Lastly, the feasibility of the study is discussed.

Description: To improve accuracy and efficiency of object detection and classification with hyperspectral imagery (HSI), we propose a novel smoothing algorithm by coupling of a Laplacian-based reaction term to a classical divergence-based anisotropic diffusion partial differential equation (PDE). In addition, an adaptive parameter is introduced to regularize this nonlinear reaction-diffusion PDE by explicitly integrating the interband correlations with the noise level of each band in HSI. It is also well-known that the interband correlations can be implicitly embedded into the diffusion coefficient of the divergence-based PDE, to allow a selective smoothing that reduces the local homogeneous area variability while preventing smoothing across edges. Therefore, the interband correlations in HSI are exploited in the proposed method in both explicit and implicit ways. As a result, our algorithm is more effective at controlling the behavior of the diffusion evolution when compared to previous multi/hyperspectral diffusion algorithms. The simulations based on both synthetic data and real hyperspectral remote sensing data show that our algorithm can improve the hyperspectral data quality in terms of both visual inspection and image quality indices.

Description: The goal of cross-domain matching (CDM) is to find correspondences between two sets of objects in different domains in an unsupervised way. CDM has various interesting applications, including photo album summarization where photos are automatically aligned into a designed frame expressed in the Cartesian coordinate system, and temporal alignment which aligns sequences such as videos that are potentially expressed using different features. In this paper, we propose an information-theoretic CDM framework based on squared-loss mutual information (SMI). The proposed approach can directly handle non-linearly related objects/sequences with different dimensions, with the ability that hyper-parameters can be objectively optimized by cross-validation. We apply the proposed method to several real-world problems including image matching, unpaired voice conversion, photo album summarization, cross-feature video and cross-domain video-to-mocap alignment, and Kinect -based action recognition, and experimentally demonstrate that the proposed method is a promising alternative to state-of-the-art CDM methods.

Description: We present a new trigonometric basis function that is capable of perfectly reproducing circles, spheres and ellipsoids while at the same time being interpolatory. Such basis functions have the advantage that they allow to construct shapes through a sequence of control points that lie on their contour (2-D) or surface (3-D) which facilitates user-interaction, especially in 3-D. Our piecewise exponential basis function has finite support, which enables local control for shape modification. We derive and prove all the necessary properties of the kernel to represent shapes that can be smoothly deformed and show how idealized shapes such as ellipses and spheres can be constructed.

Description: The skeleton of a 2D shape is an important geometric structure in pattern analysis and computer vision. In this paper we study the skeleton of a 2D shape in a two-manifold $mathcal {M}$ , based on a geodesic metric. We present a formal definition of the skeleton $S(Omega )$ for a shape $Omega$ in $mathcal {M}$ and show several properties that make $S(Omega )$ distinct from its Euclidean counterpart in $mathbb {R}^2$ . We further prove that for a shape sequence $lbrace Omega _irbrace$ that converge to a shape $Omega$ in $mathcal {M}$ , the mapping $Omega righta- row overline{S}(Omega )$ is lower semi-continuous. A direct application of this result is that we can use a set $P$ of sample points to approximate the boundary of a 2D shape $Omega$ in $mathcal {M}$ , and the Voronoi diagram of $P$ inside $Omega subset mathcal {M}$ gives a good approximation to the skeleton $S(Omega )$ . Examples of skeleton computation in topography and brain morphometry are illustrated.

Description: A widely used approach for locating points on deformable objects in images is to generate feature response images for each point, and then to fit a shape model to these response images. We demonstrate that Random Forest regression-voting can be used to generate high quality response images quickly. Rather than using a generative or a discriminative model to evaluate each pixel, a regressor is used to cast votes for the optimal position of each point. We show that this leads to fast and accurate shape model matching when applied in the Constrained Local Model framework. We evaluate the technique in detail, and compare it with a range of commonly used alternatives across application areas: the annotation of the joints of the hands in radiographs and the detection of feature points in facial images. We show that our approach outperforms alternative techniques, achieving what we believe to be the most accurate results yet published for hand joint annotation and state-of-the-art performance for facial feature point detection.

Description: We present a novel method to recognise planar structures in a single image and estimate their 3D orientation. This is done by exploiting the relationship between image appearance and 3D structure, using machine learning methods with supervised training data. As such, the method does not require specific features or use geometric cues, such as vanishing points. We employ general feature representations based on spatiograms of gradients and colour, coupled with relevance vector machines for classification and regression. We first show that using hand-labelled training data, we are able to classify pre-segmented regions as being planar or not, and estimate their 3D orientation. We then incorporate the method into a segmentation algorithm to detect multiple planar structures from a previously unseen image.

Description: Multiple view segmentation consists in segmenting objects simultaneously in several views. A key issue in that respect and compared to monocular settings is to ensure propagation of segmentation information between views while minimizing complexity and computational cost. In this work, we first investigate the idea that examining measurements at the projections of a sparse set of 3D points is sufficient to achieve this goal. The proposed algorithm softly assigns each of these 3D samples to the scene background if it projects on the background region in at least one view, or to the foreground if it projects on foreground region in all views. Second, we show how other modalities such as depth may be seamlessly integrated in the model and benefit the segmentation. The paper exposes a detailed set of experiments used to validate the algorithm, showing results comparable with the state of art, with reduced computational complexity. We also discuss the use of different modalities for specific situations, such as dealing with a low number of viewpoints or a scene with color ambiguities between foreground and background.

Description: This paper proposes a novel approach to classify hyperspectral (HS) images using both spectral and spatial information. It first consists of a supervised spectral dimension reduction step that transforms the HS image into a score image that has fewer channels. These channels are chosen so as to enhance distances between classes to be discriminated and to reduce background variability, thus leading to edges that correspond to actual class borders. In the second step, applying an edge-preserving spatial regularization on this score image leads to a lowered background variability. Therefore, in the third step, the pixel-wise classification of the regularized score image is greatly improved. We implement this approach using the partial least squares (PLS) method for spectral dimension reduction and the anisotropic diffusion for spatial regularization. We then compare linear discriminant analysis (LDA), K-nearest neighbors (KNN), and support vector machine (SVM) for the class decision. The effectiveness of our method was evaluated with three remotely sensed HS images. Its robustness was also assessed for different training sets, since the latter has a crucial influence on classification performance. On average, our method gave better results in terms of classification accuracy and was more robust than other classification methods tested with the same images.

Description: Due to its simple, fast, and good generalization ability, extreme learning machine (ELM) has recently drawn increasing attention in the pattern recognition and machine learning fields. To investigate the performance of ELM on the hyperspectral images (HSIs), this paper proposes two spatial–spectral composite kernel (CK) ELM classification methods. In the proposed CK framework, the single spatial or spectral kernel consists of activation–function-based kernel and general Gaussian kernel, respectively. The proposed methods inherit the advantages of ELM and have an analytic solution to directly implement the multiclass classification. Experimental results on three benchmark hyperspectral datasets demonstrate that the proposed ELM with CK methods outperform the general ELM, SVM, and SVM with CK methods.

Description: This paper proposes a framework for hyperspectral images (HSIs) classification with composite kernels discriminant analysis (CKDA). The CKDA uses the spectral and spatial information extracted by Gaussian weighted local mean operator (GWLM) and is suitable to solve few labeled samples classification problem of HSI, which has very important practical significance for the case that training samples are insufficient due to high cost. Experimental results show that the spatial information extracted by GWLM can greatly improve the performance, and demonstrate the superiority of CKDA for HSI classification in the case of few labeled samples. Compared with other state-of-the-art spectral–spatial kernel methods, the proposed methods also show very good advantages, especially the parallel kernel method.

Description: Accurate generation of a land cover map using hyperspectral data is an important application of remote sensing. Multiple classifier system (MCS) is an effective tool for hyperspectral image classification. However, most of the research in MCS addressed the problem of classifier combination, while the potential of selecting classifiers dynamically is least explored for hyperspectral image classification. The goal of this paper is to assess the potential of dynamic classifier selection/dynamic ensemble selection (DCS/DES) for classification of hyperspectral images, which consists in selecting the best (subset of) optimal classifier(s) relative to each input pixel by exploiting the local information content of the image pixel. In order to have an accurate as well as computationally fast DCS/DES, we proposed a new DCS/DES framework based on extreme learning machine (ELM) regression and a new spectral–spatial classification model, which incorporates the spatial contextual information by using the Markov random field (MRF) with the proposed DES method. The proposed classification framework can be considered as a unified model to exploit the full spectral and spatial information. Classification experiments carried out on two different airborne hyperspectral images demonstrate that the proposed method yields a significant increase in the accuracy when compared to the state-of-the-art approaches.

Description: Advanced classifiers, e.g., partial least squares discriminant analysis (PLS-DA) and random forests (RF), have been recently used to model reflectance spectral data in general, and of soil properties in particular, since their spectra are multivariate and highly collinear. Preprocessing transformations (PPTs) can improve the classification accuracy by increasing the variability between classes while decreasing the variability within classes. Such PPTs are common practice prior to a PLS-DA, but are rarely used for RF. The objectives of this paper are twofold: to compare the performances of PLS-DA and RF for modeling the spectral reflectance of soil in changed land-uses with different treatments and to compare the effects of nine different PPTs on the prediction accuracy of each of these classification methods. Differences in six physical, biological, and chemical soil properties of changed land-uses from the northern Negev Desert in Israel were evaluated. Significant differences were found between soil properties, which are used to classify land-uses and treatments. Depending on the dataset, different PPTs improved the classification accuracy by 11%–24% and 32%–42% for PLS-DA and RF, respectively, in comparison to the spectra without PPT. Out of the PPTs tested, the generalized least squares weighting (GLSW)-based transformations were found to be the most effective for most classifications using both PLS-DA and RF. Our results show that both PLS-DA and RF are suitable classifiers for spectral data, provided that an appropriate PPT is applied.

Description: This paper contributes the concept of spectral–spatial kernel-based multivariate analysis (KMVSSA) based on the statistical principle of multivariate statistics. The essence of proposed framework is to expose the inherent structure and meaning revealed within spectral and spatial features through various statistical methods in hyperspectral remotely sensed data. This kernel-based framework is investigated to incorporate the spectral and spatial information simultaneously for dimension reduction and classification of hyperdimensional datasets. The method uses multivariate analysis to choose and apply a transform matrix that the transformed components are as orthogonal as possible. This nonlinear framework is derived by means of the theory of complete orthonormal systems. KMVSSA exhibits great flexibility by the combination of spectral and spatial features. We investigate the possibility of using KMVSSA for the classification of hyperspectral images and dimension reduction. The proposed framework is examined and compared in different merits with several hyperspectral images in different conditions (urban/agricultural area and size of the training set). Experimental results show that the proposed framework can meaningfully enhance the dimensionality reduction and also it greatly improves the overall as well as per class classification accuracies. We demonstrate a comprehensive comparison of some state of the art hyperspectral image classification methods.

Description: Development of intelligent decision-making systems for complex problems, such as land covers classification of hyperspectral remote sensing (HSRS) images, requires efficient interpretation of available information through conceptual rather than numerical level. Granular neural network (GNN) in combination with the granular representation of information using linguistic terms is one such system. GNN takes the fuzzified input information and processes them with neural network (NN) architecture, where the network structure is transparent enough to interpret the processing steps. Further, knowledge encoding has been considered as one of the principal elements of intelligent decision-making systems. This paper proposes a new model of knowledge-encoded GNNs for land cover classification of HSRS images. Knowledge encoding is done using neighborhood rough sets (NRSs) that explore the local/contextual information. The encoded knowledge using NRS is obtained in the form of dependency rules with respect to the output class labels of land covers and these rules determine appropriate number of hidden nodes of GNNs. The dependency factors obtained during rule generation are used for initializing the connecting weights of GNNs. NRS is also used here in the selection of a subset of features for reducing the burden of high-dimensional fuzzy-granulated feature space of HSRS image. The proposed model thus explores jointly the advantages of fuzzy granulation, GNNs, and feature selection and knowledge encoding using NRS. Superiority of the model to similar other methods are justified in land covers classification of two HSRS images acquired by different remotely placed sensors.