ALBERT

Beschreibung: Presents corrections to the paper, "Estimation of forest biomass from two-level model inversion of single-pass InSAR data" (Soja, M.J., et al.,Trans. Geosci. Remote Sens., vol. 53, no. 9, pp. 5083???5099, Sep. 2015).

Beschreibung: Commercial, industrial, and military aerospace designs are increasingly deploying MEMS micro inertial measurement unit (MIMU) for motion control, automation, and positioning applications, such as the unmanned aerial vehicle (UAV), robot, and smart phone. On the one hand, MIMU has the merit of low cost, small size, low-power consumption, and high shock resistance, but on the other hand, low-cost MIMU is affected by systematic error caused by the instability of the drift, scaling factors, and axes misalignment, which may lead to large errors in the position and attitude’s determination from time to time. That means calibration before use is an effective way to improve the practical precision of MIMU. However, many customers have no precise turntable to calibrate the MIMU before they use it. To address these problems, this paper presents an easy self-calibration method to implement calibration of the MIMU on a common table only with an inclined surface, no precise turntable is needed. The calibration method is based on the following principles. First, the module of the output vector of the orthogonal configured three-axis accelerometers is equal to unit gravity. Second, when IMU rotates to a known gesture with a stable axis, the angles can be calculated through integration. Third, when the accelerometers’ parameters are calculated, it can act as a level datum. Furthermore, the accelerometers on the inclined surface are used to determine the rotating heading datum. Finally, after a series static positions test and rotating test, the parameters can be extracted and estimated. To demonstrate the success and the convenience of the proposed method, comparison experiments with the precision turntable have been made on an ADI’s MIMU. The calibration results show that the accuracy and precision of this method is quite equivalent with the turntable-based calibration, and the scale factors error with an order of magnitude always equal or less than $10^{-5}$ . The observed static and dynamic yaw maximum angular error in a certain period is <0.8°, the pitch maximum angular error is <0.5°, and the roll maximum angular error is <0.3°.

Beschreibung: 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.

Beschreibung: 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.

Beschreibung: 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.

Beschreibung: 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.

Beschreibung: 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.

Beschreibung: 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.

Beschreibung: 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.

Beschreibung: 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.

Beschreibung: 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.

Beschreibung: 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.

Beschreibung: 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.

Beschreibung: 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.

Beschreibung: 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

Beschreibung: 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.

Beschreibung: 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.

Beschreibung: 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.

Beschreibung: 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.

Beschreibung: 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.

Beschreibung: 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.

Beschreibung: 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.

Beschreibung: 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.

Beschreibung: 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.

Beschreibung: 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.

Beschreibung: 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.

Beschreibung: 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.

Beschreibung: 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.

Beschreibung: Internet of Things (IoTs) is a technology to connect machines, objects, and people with electronic devices. Many researchers have focused their works on architecture design from different perspectives. The deployment of IoT applications contains a variety of things that are attached with end-point devices, such as sensors, actuators, and radio frequency identification tags to perform sensing, controlling, and/or identifying things. The GS1 EPCglobal Architecture and IEEE 1451 standards are adopted as the cornerstone of our IoT architecture framework. In this paper, we focus on how to resolve the heterogeneity and dynamic characteristics of this variety of things. A uniform expression mechanism based on electronic product coding (EPC) scheme is introduced. The practical facilities are presented and examples of how to generate EPC scheme for transducers are illustrated. By integrating the IEEE 1451 compatible transducers, based on transducer electronic data sheet, to the identify layer of the extended GS1 EPCglobal Architecture, we can process more versatile data of different things in IoT applications. The uniform EPC scheme of heterogeneous things in the identify layer will make a consistent expression of things and compatible with the upper layer. This mechanism plays a fundamental role to support transducer capability in GS1 EPCglobal Architecture.

Beschreibung: Hierarchical routing in wireless sensor networks (WSNs) is a very important topic that has been attracting the research community in the last decade. Typical hierarchical routing is called clustering routing, in which the network is divided into multiple clusters. Recently, some types of atypical hierarchical routing arise, including chain-based, tree-based, grid-based routing, and area-based routing. There are several survey papers that present and compare the hierarchical routing protocols from various perspectives, but a survey on atypical hierarchical routing is still missing. This paper makes a first attempt to provide a comprehensive review on atypical hierarchical routing. We offer a classification of atypical hierarchical routing of WSNs, and give detailed analysis of different logical topologies. The most representative atypical hierarchical routing protocols are described, discussed, and qualitatively compared. In particular, the advantages and disadvantages of different atypical hierarchical routing protocols are analyzed with respect to their significant performances and application scenarios. Finally, we put forward some open issues concerning the design of hierarchical WSNs. This survey aims to provide useful guidance for system designers on how to evaluate and select appropriate logical topologies and hierarchical routing protocols for specific applications.

Beschreibung: A novel fiber-optic relative humidity (RH) sensor with thermal compensation based on a fiber Bragg grating (FBG) is presented. Changes in the RH are transduced to the FBG properties by an ORMOCER coating. The cross sensitivity to temperature is compensated by inscribing the Bragg grating in a highly birefringent fiber and using the individual Bragg peaks of each polarization direction for separation (discrimination) of temperature and humidity influences.

Beschreibung: This paper focuses on the nondestructive dielectric measurement for thin dielectric material using open-ended coaxial probe. The probe calibration procedure requires only a measurement of a half-space air and three open standard kits. The measured reflection coefficient for thin sample, which is backed by metal plate, is taken with a vector network analyzer up to 7 GHz and the reflection coefficient is converted to relative dielectric constant and tangent loss via closed form capacitance model and simple calibration process.

Beschreibung: We present a physical model to study the dark current and noise characteristics of quantum-dot infrared photodetectors (QDIPs). This model considers both carrier’s thermoexcitaion and carrier’s field-assisted tunneling, which are the main origin of dark current and noise in QDIPs. We use this model to analyze the effect of different structural parameters, such as QDs lateral size and density of QD, and environmental parameters, such as bias voltage and operating temperature on the dark current and the noise of QDIP. The theoretical results obtained from our model are in excellent agreement with reported experimental data. Therefore, the validity of the model is proven for these reasons. This model can be extended to other quantum structure detectors to evaluate the noise and dark current characteristics.

Beschreibung: We present simplified expressions for the cross-polarized backscatter of a randomly rough surface predicted by the second-order small-slope approximation (SSA2). The simplification is based on appropriate polynomial approximations of the SSA2 kernel function. We obtain numerically efficient expressions for the cross-polarized backscattering amplitude of a deterministic surface in the form of a single space integral involving only the surface elevation and the second (mixed) derivative of the surface elevation. The ensemble average normalized radar cross section is then derived under a Gaussian random process assumption for the surface. The resulting expression has the form of a Kirchhoff integral involving the roughness correlation function and its second- and fourth-order cross-derivatives. Further simplification is achieved for off-nadir observations using a high-frequency approximation; the result is an analytical formula involving only the resonant curvature and the radar-filtered mean square slope in the out-of-plane direction. A numerical validation of the simplified expressions is provided by comparison with exact SSA2 predictions in representative test cases. The dependence of cross-polarized backscattering on the incidence angle as well as wind speed and direction is then investigated for the case of a directional sea surface model. At near nadir incidence, a clear maximum in azimuth of the cross-polarized backscatter is observed for radar look directions 45 $^{circ}$ from the wind direction.

Beschreibung: Wireless sensor networks (WSNs) are a key part of the Internet of Things vision which aims at bridging together the physical and the digital worlds in several application domains. In the building automation field, WSNs are widely adopted for energy optimization, safety and security purposes, and could greatly benefit from existing information already available in preexisting building information models (BIMs). Such BIMs are normally developed during the building design phase and reused continuously during the construction and operation phases of the building life-cycle. In current deployments, however, due to lack of interoperability such information (e.g., walls geometry, materials, and so on) must often be recollected and reinputed by WSN commissioning specialists. Open development and planning tools and methodologies can play a key role in fostering interoperability and convergence of BIM and WSN systems. This paper aims at demonstrating how increased interoperability between WSN development-support tools and BIM systems could provide advantages to developers, integrators, domain specialist, and BIM users. The methodology is validated by applying a newly proposed tool exploiting BIM interoperability to support the planning of the topology of a WSN based on ultrawideband technologies. The proposed approach is evaluated through a small-scale experimentation held in a historical building in Torino (Italy).

Beschreibung: We propose to exploit filter stopband for high sensitivity radio frequency (RF) interferometer operation by utilizing reflection scattering parameters. Combined with passband filter operation, the modified RF interferometer effectively expands its frequency coverage. A simple model is described to analyze and predict interferometer performance. A high-pass filter and a low-pass filter are designed and built to demonstrate the interferometer operation as well as to verify the model over a frequency of 1–4 GHz. Lossy materials are shown to significantly degrade filter sensitivity enhancement effects due to reduced group delay and lower RF fields. Further work is needed to address the issue.

Beschreibung: In this paper, we present a radar sensor system for real-time blast furnace burden surface imaging inside a fully operative blast furnace, called BLASTDAR, the blast furnace radar. The designed frequency-modulated continuous-wave (FMCW) radar sensor array operates in the frequency band around 77 GHz and consists of several nonuniformly spaced receive and transmit antennas, making it a multiple-input multiple-output radar system with large aperture. Mechanical steering is replaced by digital array processing techniques. Off-the-shelf automotive-qualified multichannel monolithic microwave integrated circuits are used. By means of this configuration, a virtual antenna array with 256 elements was developed that guarantees the desired angular resolution of better than 3°, and a range resolution of about 15 cm. Based on the single-channel FMCW signal model, this paper will derive a multichannel signal model in combination with a digital beamforming approach and further advanced signal processing algorithms. The implementation of a simulation tool covering the whole design process is shown. Based on these simulation results, a system configuration is chosen and the obtained setup is defined and presented. A description of the manufactured cost-efficient radio frequency and baseband boards together with the housing design shows the practical implementation of the sensor. For the system calibration, two different methods are listed and compared regarding their performance. Verification measurements confirm the predicted performance of the developed sensor. Several measurements inside a fully operational blast furnace demonstrate the proper long-term functionality of the system, to the best of our knowledge, for the first time worldwide. It is in continuous operation since about two years in blast furnace #5 of voestalpine Stahl GmbH, Linz.

Beschreibung: Indoor lighting systems with wireless sensor-equipped luminaires are considered as an infrastructure for offering location-based illumination control. Wireless occupancy and light sensors transmit sensing information to a lighting controller, which then adapts artificial illumination from luminaires to user occupancy and daylight changes. Such wireless sensor transmissions can be used to collect received signal strength indicator (RSSI) values at a user device. We propose to use the RSSI measurements at the user device, in combination with accelerometer sensor data, to determine user location zone when a user seeks personalized light control. Based on its location zone, the user is granted control of associated luminaires. The performance of the proposed system and positioning method is evaluated in a large indoor lighting open-plan office model.

Beschreibung: In this paper, a Pd–TiO 2 nanofiber structure with photoactivation capability for H 2 gas sensing is made using the electrospinning procedure. The solution which was stacked into the syringe for electrospinning Pd–TiO 2 nanofibers consisted of titanium tetraisopropoxide, acetic acid, ethanol, polyvinyl pyrrolidone, and different amounts of Pd solution. The X-ray diffraction, field emission scanning microscopy, transmission electron microscopy, and spectrophotometry techniques were employed to analyze the crystalline structure and surface morphology of the nanofibers. Moreover, Fe 2 O 3 and HNO 3 were also used as additional additives and the effect of additives in the absorption spectrum shift towards the visible light spectrum was investigated. About 95-nm red shift toward visible light from 370 to 465 nm for TiO 2 /Pd/N/Fe 2 O 3 was observed in comparison with the pure TiO 2 nanofibers. By using additives and visible light irradiation, the operating temperature lowered from 290 °C to 130 °C and the response increased from 11 to 368. At an operating temperature of 150 °C, the response time also reduced from 25 to 0.9 s and recovery times reduced from 40 to 2 s. The response dropped only by $sim 30$ %, 12%, and 5% after nine months for the TiO 2 , TiO 2 /Pd, and TiO 2 /Pd/N/Fe 2 O 3 samples, respectively.

Beschreibung: Over the years, several QRS complex detection algorithms have been proposed with different features, but the remaining problem is their implementation in low-cost portable platforms for real-time applications, where hardware resources are limited, still providing the accuracy level required for medical applications. The proposed algorithm copes at the same time with both requirements: 1) accuracy and 2) low resource consumption. In this paper, a real-time QRS complex detector is proposed. This algorithm is based on a differentiation at the pre-processing stage combined with a dynamic threshold to detect R peaks. The thresholding stage is based on a finite-state machine, which modifies the threshold value according to the evolution of the signal and the previously detected peak. It has been evaluated on several databases, including the standard ones, thus resulting sensitivities and positive predictivities better than 99.3%. In order to analyze the computational complexity of the algorithm, it has been compared with the well-known Pan and Tompkins’ algorithm. As a result, the proposed detector achieves a reduction in processing time of almost 50% by using only the 25% of hardware resources (memory, adders, and multipliers).

Beschreibung: 1-D oxide nanostructures-based metal-insulator–metal structures represent potential gas sensor devices, owing to their vertical electron transport feature. In this paper, we demonstrate that for achieving optimum gas sensing by a TiO 2 nanotube (NT) array in vertical mode, tuning of NTs (1-D) surface area as well as carrier transport path length by tailoring the NT length can be a valuable approach. For anodization times of 1, 4, 8, 12, and 16 h, the corresponding NT length was found to be 280–320, 506–514, 1730–1790, 2000–2200, and 2380–2420 nm, respectively, with almost no variation in tube diameters or separations. The carrier concentrations of the NTs were found to be decreasing with increasing tube length. The vertical device structure, employing NT arrays of different lengths as the sensing layer, was investigated in the temperature range of 50 °C–250 °C for sensing acetone, as a test gas/vapor, in the concentration range of 10–1000 ppm. The response magnitude of the sensor was increased with increased NT length, possibly owing to the availability of larger amounts of gas interaction sites due to higher surface area at increased length. The response time and recovery time of the developed sensor also increased with increasing tube length and became excessively sluggish after the critical tube length exceeded 2250 nm owing to slower adsorption/desorption and diffusion kinetics.

Beschreibung: One of the key studies in the cognitive radio networks is maximizing some important metrics such as transmission/energy efficiencies or throughput with respect to network parameters such as sensing and transmission time. There is always a tradeoff between sensing time and transmission time. While longer sensing times results in higher detection accuracy, it loses transmission opportunities and hence decreases transmission efficiency. Many works have been done to find the best transmission and sensing time to maximize the efficiencies such as transmission and energy, but the interference and hence the probability of collision was not defined and formulated correctly. In this paper, we consider the trilateral tradeoff between sensing, transmission, and contention times. To do this, the interference between primary user (PU) and secondary user (SU) is formulated. We consider interference due to both imperfect sensing and PU-return. Furthermore, the interference among SUs has been also formulated. These metrics are used in defining transmission efficiency and probability of collision as a constraint.

Beschreibung: In this paper, a 2-D air holes in silicon slab photonic crystal-based structure with line defect has been designed and simulated for detection of high glucose concentration in urine from 0–15 to 10 gm/dl in the wavelength range of 1530–1565 nm. High glucose concentration in urine is referred as glycosuria. The proposed sensor structure is modeled and simulated in time domain by using Massachusetts Institute of Technology (MIT) electromagnetic equation propagation simulation tool. Finite-difference time-domain method has been used for the analysis. Band structure has been computed and eigen frequencies have been extracted for each k-point for the designed sensor structure by using (MIT photonic bands) simulation tool. The changes in the normalized output power levels, quality ( $Q$ )-factor, and resonant frequency have been observed for different concentrations of glucose present in urine. The calculated value of $Q$ -factor obtained is $Q=23,575$ . This paper has been done for normal urine and glycosuric urine. It is clear from the simulated graphs of transmission spectrum and band structure that for minute changes in the refractive index, the transmitted output power level range varies from 0.2298 to −0.091 dB and the resonant frequency range varies from 0.229259–0.22914 (in units of c/a) and hence it acts as a sensor for detection for glycosuria. Our designed sensor has achieved sensitivity of 638 nm /RIU.

Beschreibung: This paper reports a simple fluorescence-based tapered fiber optic probe for fluoride ion having a detection range of $2.08 times 10^{mathrm { {-6}}}$ – $2.005 times 10^{mathrm { {-4}}}$ M. The performance of the tapered probe is evaluated with respect to the probes that consist of combinations of bare uncladded multimode optical fibers. The effect of fluorescence quenching of a natural dye curcumin in the presence of fluoride ion is used in the implementation of the probes. The probe effectively uses multiple mechanisms for the excitation and collection of fluorescence from the medium enabling higher sensitivity compared with conventional spectrophotometry especially at very low concentrations of fluoride.

Beschreibung: In this paper, the arrayed flexible pH sensor and glucose biosensor modified by magnetic beads and graphene were proposed. The ruthenium dioxide (RuO 2 ) sensing films were deposited by radio frequency sputtering system and the screen-printed technique was used to construct the silver conducting wires and insulation layer of the RuO 2 electrodes. In order to enhance performance of the pH sensor and glucose biosensor, the microfluidic device had been utilized and developed. In the measurement processes, the different pH and glucose solutions were investigated in various flow rates. According to the experimental results, the average sensitivity of the pH sensor was enhanced from 52.280 to 57.981 mV/pH and the average sensitivity of the RuO 2 /graphene/magnetic bead-GO x -Nafion glucose biosensor was enhanced from 10.628 to 13.541 mV/mM. With regard to the wireless sensing measurements, the wireless sensing system which complied the ZigBee standard was employed to transmit the signals of the pH or glucose measurements in this investigation, and the system consisted of the Xbee device, Arduino Mega 2560, readout circuit, pH or glucose biosensor and computer. According to the experimental results, the average sensitivity and linearity of the pH sensor were 51.063 mV/pH and 0.988, respectively, and the average sensitivity and linearity of the RuO 2 /graphene/magnetic bead-GO x -Nafion glucose biosensor were 11.005 mV/mM and 0.995, respectively.

Beschreibung: This paper considers the throughput maximization of a secondary user (SU) in a realistic cognitive radio (CR) network where the battery suffers from constant energy leakage. We investigate two different CR scenarios where the primary user (PU) switches between idle and active states in a time-slotted manner. In the first scenario, the SU knows the exact state of the PU at the beginning of each time slot, whereas in the second scenario, the SU attempts to detect state of the PU by spectrum sensing. For both scenarios, we determine the maximum throughput of the SU with consideration of battery leakage of the SU and interference constraint of the PU. The optimal solutions of transmitting power and sensing duration are achieved by using golden section search method and a simplified brute-force search method. Finally, the theoretical analysis is verified through the Monte Carlo simulations.

Beschreibung: A rotameter is a variable-area-type flow rate measuring instrument in which the position of a metallic float in a transparent conical tube is taken as a flow rate indicator. It has the disadvantage that it is a local indicating type instrument and special type of transducer is used for its remote indication. In this paper, a noncontact flow rate measurement technique using Hall probe sensor and rotameter is designed, developed, and tested. In this design, a float carrying a thin circular permanent magnet is used, and a Hall probe sensor placed outside the rotameter tube has been used to sense the variation of magnetic field of the magnet with the variation of float position. A signal conditioner unit has been used to convert the Hall probe sensor output into 1–5 V dc signal. This dc signal output of the signal conditioner has been sent to a PC-based flow indicator through optoisolator and analog input channel of a data acquisition system (DAS) card. The PC-based flow indicator has been designed using the Lab Tech Note Book Pro software and the PC-based supervisory control and DAS. A theoretical equation has been derived to explain the operation of the system. The performance of the system has been tested experimentally, and the experimental results are reported in this paper. A very good repeatability and linearity of results has been observed.

Beschreibung: Traditional wet or gel impedimetric electrodes for neuro-physiological signal (e.g., electroencephalography and electrocardiography) monitoring are usable for a short duration, as the performance of electrodes deteriorates rapidly when exposed to the environment. Dry impedimetric electrode is a promising alternative tool for long duration monitoring, however suffers from high interfacing impedance. This paper describes a novel dry interfacing electrode utilizing patterned vertical carbon nanotube (pvCNT) for impedimetric sensing. The electrodes were fabricated on circular stainless steel foil substrates (thickness = 2 mil) that are laser cut to circular discs (Ø = 10 mm). Pattern on the substrate was developed with a custom shadow mask while sputter coating the substrate with Al 2 O 3 and iron. Electrically conductive multiwalled CNTs were then grown vertically in pillar formation ( $100~mu text{m}^{}$ each side of square footprint) with various interpillar spacing (50, 100, and $200~mu text{m}$ for various masks). The heights of the CNT pillars were between 1 and 1.5 mm. The impedances of the electrodes were 1.92, 3.11, and $8.15~Omega $ for 50-, 100-, and 200- $mu text{m}$ spacing, respectively. A comparative in vitro study with commercial wet and gel electrodes showed pvCNT electrode has lower interfacial impedance, comparable signal capture quality, and ability to be used for stimulation. Long duration study showed minimal impedance degradation for pvCNT electrodes over a week. The results demonstrate pvCNT is a promising dry electrode for impedimetric sensing and stimulation of neurophysiological signals over - prolonged duration.

Beschreibung: In the case of an unavoidable frontal collision between a vehicle and a pedestrian, the activation of a protection system can reduce the severity of injuries caused by the collision. However, to fulfill this objective, it is necessary to determine when the shock will occur. To overcome this problem, we place inside the front bumper a metallic sheet that emits an electric field. This field is oriented toward the front of the vehicle by means of various guard screens. The intrusion into the emitted electric field induces a capacitive coupling with the sensor. Which will enable the electronic device to detect the presence of the pedestrian at a distance of 1 m. To avoid slow drifts in time, the change in capacity is measured every millisecond. The last hundred acquisitions are considered as a history. This history is compared with the signature of a pedestrian who was established in the laboratory. When the pedestrian is about a thirty centimeters of the vehicle and the shock is inevitable, the protection systems are activated. When the vehicle is at very low speed, this device can be used as parking assistance.

Beschreibung: The synoptic determination of ocean circulation using the data acquired from space, with a coherent depiction of its turbulent characteristics, remains a fundamental challenge in oceanography. This determination has the potential of revealing all aspects of the ocean dynamic variability on a wide range of spatiotemporal scales and will enhance our understanding of ocean–atmosphere exchanges at superresolution, as required in the present context of climate change. Here, we show a four-year time series of spatial superresolution (4 km) turbulent ocean dynamics generated from satellite data using emerging ideas in signal processing coming from nonlinear physics, low-resolution dynamics, and superresolution oceanic sea surface temperature data acquired from optical sensors. The method at its core consists in propagating across the scales the low-resolution dynamics in a multiresolution analysis computed on adimensional critical transition information.

Beschreibung: This paper presents the theory, algorithm, and results of a new bistatic interferometry synthetic aperture radar (InSAR) method. It employs the data acquired in an innovative bistatic configuration, which uses the orbital sensors as transmitters of opportunity and the stationary receivers on the ground, to generate a digital elevation model (DEM). In the bistatic spaceborne/stationary InSAR configuration, the interferometric phase only depends on the target-receiver range, which could not be obtained directly from the measured bistatic range. Therefore, the conventional transforming relationship between the interferometric phase and the topographic height is no longer practical. In order to solve the problem, we introduce a new conversion relationship between the interferometric phase and the topographic height, which is derived by the model of the ellipsoidal projection in the bistatic configuration. Meanwhile, the error analysis of the new conversion is carried out through a simulation. Both the simulated and measured data are used to test and verify the feasibility of the new bistatic InSAR method. In the spaceborne/stationary InSAR experiment, YaoGan-3 (an L-band spaceborne SAR system launched by China) was selected as the transmitter and two stationary receivers were mounted on the top of a tall building. The generated DEM of high quality shows that the presented method performs very well in the bistatic InSAR data process.

Beschreibung: We present a nonparametric Bayesian hierarchical model (HDP_IBPs) to classify very high resolution panchromatic satellite images in an unsupervised way, in which the hierarchical Dirichlet process (HDP) and Indian buffet process (IBP) are combined on multiple scenes. The main contribution of this paper is a novel application framework to solve the problems of traditional probabilistic topic models and achieve the effective unsupervised classification of very high resolution (VHR) panchromatic satellite images. In this framework, a VHR satellite image is first oversegmented into basic processing units and divided into a set of subimages. We use the Chinese restaurant franchise process as a construct method of the HDP to capture the latent semantic structures (i.e., classes) and the class proportion (i.e., co-occurrence of topics) for each subimage. Meanwhile, the subimages are grouped into different scenes based on the class proportion. Finally, the IBP is employed to select the most appropriate classes for each subimage from all of the classes based on different scenes in turn. The hierarchical structure of our model transmits the spatial information from the original image to the scene layer implicitly and provides useful cues of classification. The experimental results show that HDP_IBPs outperforms state-of-the-art models in terms of both qualitative and quantitative evaluations.

Beschreibung: This paper describes a linear-image-transform-based algorithm for reducing stripe noise, track line artifacts, and motion-induced errors in remote sensing data. Developed for multibeam bathymetry (MB), the method has also been used for removing scalloping in synthetic aperture radar images. The proposed image transform is the composition of an invertible edge detection operator and a fast discrete Radon transform (DRT) due to Götz, Druckmüller, and Brady. The inverse DRT is computed by using an iterative method and exploiting an approximate inverse algorithm due to Press. The edge operator is implemented by circular convolution with a Laplacian point spread function modified to render the operator invertible. In the transformed image, linear discontinuities appear as high-intensity spots, which may be reset to zero. In MB data, a second noise signature is linked to motion-induced errors. A Chebyshev approximation of the original image is subtracted before applying the transform, and added back to the denoised image; this is necessary to avoid boundary effects. It is possible to process data faster and suppress motion-induced noise further by filtering images in nonoverlapping blocks using a matrix representation for the inverse DRT. Processed test images from several MB data sets had less noise and distortion compared with those obtained with standard low-pass filters. Denoising also improved the accuracy in statistical classification of geomorphological type by 10–28% for two sets of invariant terrain features.

Beschreibung: Sparse graph embedding (SGE) is a promising technique useful for the nonlinear feature extraction (FE) of hyperspectral images (HSIs). However, such images exhibit spatial variability and spectral multimodality, presenting challenges to existing FE methods, including SGE. To address this issue, this paper presents two novel SGE methods for HSI classification. One method, which is termed simultaneous SGE (SSGE), is designed to consider the spatial variability of spectral signatures by using a simultaneous sparse representation (SSR) model integrated with a shape-adaptive neighborhood building approach. In addition, a sparse graph is constructed via matrix computation based on sparse codes. Then, low-dimensional features are produced by employing linear graph embedding (LGE) based on the constructed sparse graph. The other method, which is termed simultaneous sparse multimanifold learning (SSMML), is proposed to handle the multimodality of an HSI. In SSMML, multiple views are generated to represent different modalities. Then, multiview-oriented submanifolds are produced by adopting SSGE, and they are further integrated via coregularization. SSGE is capable of modeling both local and global data structures. Furthermore, SSMML serves as a prototype that can model multimodal data structures. The proposed methods are evaluated by using sparse multinomial logistic regression for HSI classification. Experimental results with two popular hyperspectral data sets validate the good performance of the two methods in producing more representative low-dimensional features and yielding superior classification results compared with other related approaches.

Beschreibung: We present a new algorithm that simultaneously retrieves aerosol properties and land surface bidirectional reflectance distribution function (BRDF) over Australian from Advanced Along-Track Scanning Radiometer images. Three key components are addressed: 1) an analytical radiative transfer formulation, based on Green's function, linking top-of-atmosphere (TOA) reflectance to the surface BRDF; 2) a novel approach to modeling BRDF using an extended compositing period; and 3) a set of representative aerosol models based on a published typology of Australian aerosols. Due to the generally low aerosol loadings and widespread bright surfaces over Australia, BRDF modeling is crucial. By using a 9-month compositing period, 90% of the Australian continent can be modeled with an error in the forward-to-nadir reflectance ratio of 2.5% or less. Comparison with suitably processed Moderate Resolution Imaging Spectroradiometer BRDF/albedo products demonstrates satisfactory agreement. For the studying period from 2002 to 2008, validation of aerosol optical depth (AOD) against eight sun photometers across Australia encompassing widely different atmospheric and surface regimes shows high accuracy, with a mean absolute error in AOD at 550 nm of 0.03 and a bias of 0.007. About 60% of the matchup points are within an absolute error of 0.03, 80% are within 0.05, and 96% are within 0.1. The algorithm selects for each cell an optimal aerosol type from a set of four predefined candidate models. Continental aerosol maps derived from the new method indicate broadscale agreement with known seasonal aerosol sources, while providing new insights into the spatial and temporal distributions of aerosol over the Australian continent.

Beschreibung: This investigation examines the phenomenology effects of the squint angle on the morphology of moving target smears in spotlight synthetic aperture radar (SAR). This analysis includes both the smears resulting from standard image formation applied to simulated radar measurements as well as the theoretical predictions for the central contours of the signatures. In particular, this paper generates the down-range and cross-range components of the predicted central 2-D contours of mover signatures, including the locations of the cross-range offsets. The analytics for squinted geometry include additional contributions in the signature contour equations that do not arise for the case of broadside imaging. These terms can affect the overall contour morphology, particularly in terms of shape and extent. Numerous examples are presented to demonstrate that the signature prediction equations yield excellent agreement with standard image formation with simulated radar data. Therefore, this analysis can provide an effective tool in predicting the shape, extent, and location of smears due to arbitrarily moving surface targets for squinted spotlight SAR.

Beschreibung: In this paper, we consider signal subspace estimation based on low-rank representation for hyperspectral imagery. It is often assumed that major signal sources occupy a low-rank subspace. Due to the mixed nature of hyperspectral remote sensing data, the underlying data structure may include multiple subspaces instead of a single subspace. Therefore, in this paper, we propose the use of low-rank subspace representation to estimate the number of subspaces in hyperspectral imagery. In particular, we develop simple estimation approaches without user-defined parameters because these parameters can be fixed as constants. Both real data experiments and computer simulations demonstrate excellent performance of the proposed approaches over those currently in the literature.

Beschreibung: The conventional spaced-receiver approach uses amplitude scintillations to estimate equatorial ionospheric irregularity drift velocities. This approach is less applicable at high latitudes where there is a lack of substantial amplitude scintillations. This paper presents a method to estimate ionosphere irregularity horizontal drift velocities based on GPS signal carrier phase measurements. Joint time–frequency analysis of the carrier phase measurements using an adaptive periodogram technique generates time-varying spectrograms of ionospheric irregularity-induced phase fluctuations. Cross correlation of the spectrograms between antenna pairs provides time lag information on propagating radio signals through the same ionospheric structure. The time lag information is combined with known positions of the receiver array, satellite orbits, and assumed irregularity altitude to infer ionospheric irregularity horizontal drift velocity. This paper presents the methodology and demonstrates its feasibility using data collected by a GPS receiver array at Gakona, Alaska. The potential error sources of this method are also analyzed.

Beschreibung: In this paper, power allocation in distributed multiple-input multiple-output radar is investigated for range-only target tracking such that the determinant of Bayesian Fisher information matrix (B-FIM) is maximized. First, the B-FIM is derived for a signal model that incorporates the propagation path loss, the target reflectivity, the transmitted power, and the target prior density. Then, we model the problem as a cooperative game and exploit the solution concept of the Shapley value to distribute a given power budget among all transmitting radars for target tracking. In numerical examples, it is shown that uniform power allocation is not in general optimal. We illustrate the effects of the radar network geometry configuration, target prior density and number of antenna upon the power allocation results, and further demonstrate the superior performance of the proposed optimal power allocation scheme via Monte Carlo simulations.

Beschreibung: A fiber Bragg grating (FBG) accelerometer based on a push–pull elastic cylinder structure is demonstrated. First, the model based on the uniform cylinder structure is analyzed and the optimized accelerometer parameters are given. Then, by designing a radius-varying cylinder structure, the FBG strain becomes larger than the cylinder strain, which leads to enhanced sensitivity amplification for a small accelerometer size and relatively high resonant frequency. Meanwhile, the influence of the transverse force on the accelerometer is theoretically analyzed. These results indicate that the transverse-induced FBG deformation is very big so that a strict transverse constrain is needed. The formula of the strain magnification is derived and the design rules of the strain magnification are given. After structure optimization according to the rules, the FBG strain increases to 1.5 times, the sensitivity increases to 1.82 times, whereas the resonant frequency reduces to 0.9 times compared with the parameters of accelerometer based on uniform cylinder structure. Finally, the accelerometer size reduces to $Phi {20~{rm mm}times 34~{rm mm}}$ , the sensitivity increases to 623 pm/g, and the resonant frequency reduces to 449 Hz.

Beschreibung: The proliferation of radio frequency (RF) communication technology in biomedical signal transmission is frequently flustered by electromagnetic interference. Even though the flexibility and mobility of RF-based communication have much attraction, the radiation brings damage to hospital equipments and even harm to humans. In this letter, we propose a novel scheme for transmission of electroencephalography (EEG) biomedical signal using a visible light communication (VLC) link. The data transmission is performed in line of sight (LOS) condition using ON–OFF keying nonreturn-to-zero modulation by utilizing all three components, red, green, and blue, of RGB LED. Experiments are carried out for transmitting EEG signals using the VLC link. The transmitter consists of RGB LEDs, and at the receiver side, three photodiodes with red, green, and blue color filters are installed. The experimental results show excellent reliability and accuracy of the proposed scheme.

Beschreibung: Fractional vegetation cover (FVC) plays an important role in earth surface process simulations, climate modeling, and global change studies. Several global FVC products have been generated using medium spatial resolution satellite data. However, the validation results indicate inconsistencies, as well as spatial and temporal discontinuities of the current FVC products. The objective of this paper is to develop a reliable estimation algorithm to operationally produce a high-quality global FVC product from the Moderate Resolution Imaging Spectroradiometer (MODIS) surface reflectance. The high-spatial-resolution FVC data were first generated using Landsat TM/ETM+ data at the global sampling locations, and then, the general regression neural networks (GRNNs) were trained using the high-spatial-resolution FVC data and the reprocessed MODIS surface reflectance data. The direct validation using ground reference data from validation of land European Remote Sensing instruments sites indicated that the performance of the proposed method ( $mathbf{R}^{2}=0.809$ , $mathbf{RMSE} =0.157$ ) was comparable with that of the GEOV1 FVC product ( $mathbf{R}^{2}=0.775$ , $mathbf{RMSE} =0.166$ ) , which is currently considered to be the best global FVC product from SPOT VEGETATION data. Further comparison indicated that the spatial and temporal continuity of the estimates from the proposed method was superior to that of the GEOV1 FVC product.

Beschreibung: This paper proposes a region kernel to measure the region-to-region distance similarity for hyperspectral image (HSI) classification. The region kernel is designed to be a linear combination of multiscale box kernels, which can handle the HSI regions with arbitrary shape and size. Integrating labeled pixels and labeled regions, we further propose a region-kernel-based support vector machine (RKSVM) classification framework. In RKSVM, three different composite kernels are constructed to describe the joint spatial–spectral similarity. Particularly, we design a desirable stack composite kernel that consists of the point-based kernel, the region-based kernel, and the cross point-to-region kernel. The effectiveness of the proposed RKSVM is validated on three benchmark hyperspectral data sets. Experimental results show the superiority of our region kernel method over the classical point kernel methods.

Beschreibung: Atmospheric refraction in the troposphere causes the propagation speed of electromagnetic signals to be less than the light speed. This creates a difference between the actual propagation path delay and the distance of the geometrical straight-line path, i.e., a quantity known as the tropospheric delay. As classical imaging algorithms for spaceborne synthetic aperture radar (SAR) do not take the tropospheric delay into account, imaging filters are designed based on the assumption of rectilinear propagation with the light speed. Therefore, a residual phase exists in imaging results, which affects focusing quality under the condition of high resolution. In order to compensate for the impact of tropospheric delay on focusing performance, this paper modifies the spaceborne SAR echo model and then proposes an imaging compensation algorithm. The key to this algorithm is to fit a range delay coefficient based on the European Geostationary Navigation Overlay Service model of zenith delay and Niell mapping function, which projects the zenith delay onto the looking direction. After range compensation, classical imaging, and azimuth compensation, which compose the proposed algorithm, the processed results are well focused.

Beschreibung: Accurate correction of atmospheric effects on data captured by an infrared (IR) camera is crucial for several applications such as vegetation monitoring, temperature monitoring, satellite images, hyperspectral imaging, numerical model simulations, surface properties characterization, and IR measurement interpretation. Atmospheric effects depend on the temporal changes, i.e., year, season, day, hour, etc., and on the geometry between the camera and the measured scene, i.e., line of sight. The orientation and the optical depth of the camera significantly affect the variation of the geometry across the pixels. In this paper, we propose a method to estimate the range and zenith angle of each pixel using only the Global Positioning System (GPS) coordinates of the camera and a point of interest in the scene. The estimated geometry and measured meteorological data are used to obtain the spectral atmospheric transmittance and path radiance. Furthermore, we propose an atmospheric effects removal, i.e., atmospheric correction, method that considers the spectral characteristics of the detector, lens, and filter. The proposed atmospheric correction process is analyzed in detail with the simultaneous measurements of two IR cameras. In this process, an enhanced temperature calibration method is developed and it is shown that the temperature accuracy for the dynamic range of the IR camera is very close to the noise equivalent temperature difference (NETD) value of the camera.

Beschreibung: Multiple scattering within a mixed pixel results in a nonlinear effect on the measured spectra in remotely sensed imagery. This study provides a quantitative assessment of multiple scattering in the reflectance of semiarid shrublands and explores its relationship to the characteristics of shrubs (density and height) and imaging parameters (wavelength and viewing angles). Field measurements were conducted at the southern fringe of the Otindag Sandy Land in China. A Monte Carlo ray tracing model, the Forest LIGHT interaction model (FLIGHT), was applied to simulate the multiple scattering results. FLIGHT simulation results were first evaluated against field measurements and then compared with a Landsat-8 OLI image. Results show that: 1) the contribution of multiple scattering to the spectra of a scene increases linearly with the fractional cover of vegetation and crown height; 2) in general, multiple scattering has a stronger effect on the near-infrared (NIR) domain than on the visible bands; 3) shadows significantly strengthen the multiple scattering effect, specifically within the visible bands; and 4) 80 to 100% of the total multiple scattering is caused by the second-order scattering within the visible bands and 60% to 90% within the NIR band. This study helps to improve our understanding of the multiple scattering effect and to select between linear and nonlinear spectral unmixing models to solve the abundances of shrubs and soil in mixed pixels.

Beschreibung: The main objective of the Chinese Chang'E-3 (CE-3) lunar satellite is to achieve soft-landing and roving exploration on the lunar surface. A multibeam radar in the lunar lander is implemented to measure the echoes from the lunar rough surface during its descending and to derive the speed of the lander. In this paper, numerical simulation of multiangular radar echoes and speed inversions from Doppler frequency are presented. An area of the Lunar Sinus Iridum bay, as landing site, is specifically selected. The rough surface described with the real DTM data is first divided into triangular patches for numerical Kirchhoff approximation calculation. The radar echoes of multiangular radar beams of CE-3 during the landing are numerically simulated. The echo phase and the Doppler frequency are then derived to obtain the vertical speed.

Beschreibung: Light scattering models of snow are very important for the remote sensing of snow. Many previous models have used unrealistic assumptions about the snow particle shape and microstructure. In this paper, a new model is proposed, wherein a bicontinuous medium is used to simulate the snow microstructure, and geometric optics theory is used in combination with the Monte Carlo method to simulate the scattering properties of snow. Then, using the radiative transfer equation, the snow reflectance, including the polarized reflectance, can be simulated. Unlike other models that use Monte Carlo ray tracing, the new model is computationally efficient and can be used for massive simulations and practical applications. The simulation results of the new model are compared with the ground measurements and simulation results of a traditional model based on the Mie theory. Through validations and comparisons, the new model is shown to demonstrate a significantly improved capability in simulating the bidirectional reflectance of snow. The importance of the grain shape and microstructure modeling in the light scattering models of snow is confirmed by the comparison of the simulation results.

Beschreibung: In microwave radiometric remote sensing, undetected radio frequency interference (RFI) can adversely affect the accuracy of the science products. A method is presented to adaptively tune the parameters of an RFI detection algorithm which controls the equivalent brightness temperature of undetected RFI. The method is adaptive in the sense that it adjusts to variations in the RFI environment, e.g., from high RFI conditions near some population centers to low RFI conditions in the tropical Pacific Ocean. The RFI environment is characterized by inferring the distribution of low-level undetected RFI from that of high-level detected RFI using appropriate scaling arguments. The resulting tuned algorithm adjusts its detection threshold to equalize the brightness temperature calibration bias due to RFI at the expense of the now variable measurement precision (noise equivalent delta temperature). This tradeoff between calibration bias and measurement precision can be represented as a modified version of the classic receiver-operating-characteristic curve. The radiometer on the Aquarius/SAC-D mission is used as an example.

Beschreibung: Polarimetric technology has been one of the most important advances in microwave remote sensing during recent decades. H-alpha decomposition, which is a type of polarimetric analysis technique, has been common for terrain and land-use classification in polarimetric synthetic aperture radar. However, the technique has been less common in the ground penetrating radar (GPR) community. In this paper, we apply the H-alpha decomposition to analyze the surface GPR data to obtain polarimetric attributes for subsurface target classification. Also, by combining H-alpha decomposition and migration, we can obtain a subsurface H-alpha color-coded reconstructed target image, from which we can use both the polarimetric attributes and the geometrical features of the subsurface targets to enhance the ability of subsurface target classification of surface GPR. A 3-D full polarimetric GPR data set was acquired in a laboratory experiment, in which four targets, a scatterer with many branches, a ball, a plate, and a dihedral scatter, were buried in dry sand under flat ground surface, and used to test these techniques. As results, we obtained the subsurface H-alpha distribution and classified the subsurface targets. Also, we derived a subsurface H-alpha color-coded reconstructed target image and identified all four targets in the laboratory experiment.

Beschreibung: Snow microwave radiance assimilation (RA) or brightness temperature data assimilation (DA) has shown promise for improving snow water equivalent (SWE) estimation. A successful RA study requires, however, an analysis of the error characteristics of coupled land surface-radiative transfer models (LSM/RTMs). This paper focuses on the Community Land Model version 4 (CLM4) as the land-surface model and on the microwave emission model for layered snowpacks (MEMLS) and the dense media radiative transfer multilayer (DMRT-ML) model as RTMs. Using the National Aeronautics and Space Administration Cold Land Processes Field Experiment (CLPX) data sets and through synthetic experiments, the errors of the coupled CLM4/DMRT-ML and CLM4/MEMLS are characterized by: 1) evaluating the CLM4 snowpack state simulations; 2) assessing the performance of RTMs in simulating the brightness temperature $(T_{B} )$ ; and 3) analyzing the correlations between the SWE error $(varepsilon_text{SWE})$ and the $T_{B}$ error $(varepsilon_T_{B} ) $ from the RA perspective. The results using the CLPX data sets show that, given a large error of the snow grain radius $(varepsilon_r_{e} )$ under dry snowpack conditions (along with a small error of the snow temperature $(varepsilon_T_mathrm{snow})$ ), the correlations between $varepsilon_text{SWE}$ and $varepsilon_T_{B} $ are mainly determined by the relationship between $varepsilon_r_{e}$ and the snow depth error $(varepsilon_d_{mathrm{snow}})$ or the snow density error $(varepsilon_rho_{mathrm{snow}} ) $ . The synthetic experiments were carried out for the CLPX region (shallow snowpack conditions and the Rocky Mountains (deep snowpack conditions using the atmospheric ensemble reanalysis produced by the coupled DA Research Testbed/Community Atmospheric Model (CAM4. The synthetic experiments support the results from the CLPX data sets and show that the errors of soil (the water content and the temperature, snow wetness, and snow temperature mostly result in positive correlations between $varepsilon_text{SWE}$ and $varepsilon_T_{B} $ . CLM4/DMRT-ML and CLM4/MEMLS tend to produce varying RA performance, with more positive and negative correlations between $varepsilon_text{SWE}$ and $varepsilon_T_{B} $ , respectively. These results suggest the necessity of using multiple snowpack RTMs in RA to improve the SWE estimation at the continental scale. The results in this paper also show that the magnitude of $varepsilon_r_{e}$ and its relationship to $varepsilon_text{SWE} $ are important for the RA performance. Most of the SWE estimations in RA are improved when $varepsilon_text{SWE}$ and

Beschreibung: This paper addresses the important yet unresolved problem of estimating forest properties from polarimetric-interferometric radar images affected by temporal decorrelation. We approach the problem by formulating a physical model of the polarimetric-interferometric coherence that incorporates both volumetric and temporal decorrelation effects. The model is termed random-motion-over-ground (RMoG) model, as it combines the random-volume-over-ground (RVoG) model with a Gaussian-statistic motion model of the canopy elements. Key features of the RMoG model are: 1) temporal decorrelation depends on the vertical structure of forests; 2) volumetric and temporal coherences are not separable as simple multiplicative factors; and 3) temporal decorrelation is complex-valued and changes with wave polarization. This third feature is particularly important as it allows compensating for unknown levels of temporal decorrelation using multiple polarimetric channels. To estimate model parameters such as tree height and canopy motion, we propose an algorithm that minimizes the least square distance between model predictions and complex coherence observations. The algorithm was applied to L-band NASA's Uninhabited Aerial Vehicle Synthetic Aperture Radar data acquired over the Harvard Forest (Massachussetts, USA). We found that the RMS difference at stand level between estimated RMoG-model tree height and NASA's lidar Laser Vegetation and Ice Sensor tree height was within 12% of the lidar-derived height, which improved significantly the RMS difference of 37% obtained using the RVoG model and ignoring temporal decorrelation. This result contributes to our ability of estimating forest biomass using in-orbit and forthcoming polarimetric-interferometric radar missions.

Beschreibung: It is believed that it is essential to take the spatial adaptivity into the segmentation method for polarimetric synthetic aperture radar (PolSAR) images. The size and shape of each segment and the strength of the relationship of neighboring pixels need to depend on the local spatial complexity of the scene. The wedgelet framework provides a promising analysis tool for spatial information. The major advantage of the wedgelet analysis is that it captures the geometrical structure of images at multiple scales, with the local spatial complexity taken into consideration. Hence, in this paper, we propose a wedgelet approximation and analysis framework specially designed for PolSAR data. Based on this framework, a spatially adaptive representation and segmentation method is constructed and presented. It mainly consists of three parts: first, the multiscale wedgelet decomposition is applied to the PolSAR image, and the local geometrical information is captured in an optimal way; then, the image is segmented in a spatially adaptive manner by the multiscale wedgelet representation in the form of the regularized optimization, which keeps a balance between the approximation and parsimony of the representation; the final part is the spatial-complexity-adaptive segmentation refinement based on the Wishart Markov random field model. The performance of the proposed method is presented and analyzed on two experimental data sets, with visual presentation and numerical evaluation. It is also compared with an existing and theoretically well-founded segmentation method. The experiments and results demonstrate the availability and advantage of the proposed method.

Beschreibung: In inverse synthetic aperture radar (ISAR) imaging of nonuniformly rotating targets, such as highly maneuvering airplanes and ships fluctuating with oceanic waves, azimuth echoes have to be modeled as cubic phase signals (CPSs) after the range migration compensation and the translational-induced phase error correction. For the CPS model, the chirp rate and the quadratic chirp rate, which deteriorate the azimuth focusing quality due to the Doppler frequency shift, need to be estimated with a parameter estimation algorithm. In this paper, by employing the proposed generalized scaled Fourier transform (GSCFT) and the nonuniform fast Fourier transform (NUFFT), a fast parameter estimation algorithm is presented and utilized in the ISAR imaging of the nonuniformly rotating target. Compared to the scaled Fourier transform-based algorithm, advantages of the fast parameter estimation algorithm include the following: 1) the computational cost is lower due to the utilization of the NUFFT, and 2) the GSCFT has a wider applicability in ISAR imaging applications. The CPS model and the algorithm implementation are verified with the real radar data of a ship target. In addition, the cross-term, which plays an important role in correlation algorithms, is analyzed for the fast parameter estimation algorithm. Through simulations of the synthetic data and the real radar data, we verify the effectiveness of the fast parameter estimation algorithm and the corresponding ISAR imaging algorithm.

Beschreibung: Classification is one of the most important techniques to the analysis of hyperspectral remote sensing images. Nonetheless, there are many challenging problems arising in this task. Two common issues are the curse of dimensionality and the spatial information modeling. In this paper, we present a new general framework to train series of effective classifiers with spatial information for classifying hyperspectral data. The proposed framework is based on the two key observations: 1) the curse of dimensionality and the high feature-to-instance ratio can be alleviated by using random subspace (RS) ensembles; and 2) the spatial–contextual information is modeled by the extended multiattribute profiles (EMAPs). Two fast learning algorithms, i.e., decision tree (DT) and extreme learning machine (ELM), are selected as the base classifiers. Six RS ensemble methods, namely, RS with DT, random forest (RF), rotation forest, rotation RF (RoRF), RS with ELM (RSELM), and rotation subspace with ELM (RoELM), are constructed by the multiple base learners. Experimental results on both simulated and real hyperspectral data verify the effectiveness of the RS ensemble methods for the classification of both spectral and spatial information (EMAPs). On the University of Pavia Reflective Optics Spectrographic Imaging System image, our proposed approaches, i.e., both RSELM and RoELM with EMAPs, achieve the state-of-the-art performances, which demonstrates the advantage of the proposed methods. The key parameters in RS ensembles and the computational complexity are also investigated in this paper.

Beschreibung: The Climate Absolute Radiance and Refractivity Observatory (CLARREO) mission was recommended by the National Research Council in 2007 to conduct highly accurate and International System of Unit-traceable decadal change observations and provide an on-orbit intercalibration standard with high accuracy for relevant Earth observing sensors. The goal of reference intercalibration is to enable rigorous observations of critical climate change variables, including reflected broadband radiation, cloud properties, and changes in surface albedo, including snow and ice albedo feedback, to be made consistently among different sensors. This requires the CLARREO Reflected Solar Spectrometer (RSS) to provide highly accurate spectral reflectance measurements to establish an on-orbit reference with a radiometric accuracy requirement better than 0.3% $(mathrm{k} =2) $ for existing sensors. In this paper, MODTRAN-simulated top-of-atmosphere spectral data and spectral measurements from the SCIAMACHY instrument on Envisat are used to determine sensitivity of intercalibration uncertainty on key design parameters of the CLARREO spectrometer: spectral range, sampling and resolution. Their impact on intercalibration uncertainty for MODIS and VIIRS imagers is estimated for various surface types (ocean, vegetation, desert, snow, deep convective clouds, clouds and all-sky) . Results indicate that for the visible to near-infrared spectral region (465–856 nm) , the RSS instrument under current design concept produces uncertainties of 0.16% for the spectral range and 0.3% for the sampling and resolution. However, for the water vapor absorption bands in the short wavelength infrared region (1242–1629 nm) , the same requirement is not met for sampling and resolution due to their high sensitivity to the influence of atmospheric water vapor.

Beschreibung: Generally, some object-based features are more relevant to a thematic class than other features. These strongly relevant features, termed as class-specific features, would significantly contribute to thematic information extraction for very high resolution (VHR) images. However, many existing feature selection methods have been designed to select a good feature subset for all classes, rather than an independent feature subset for the thematic class. The latter might better meet the requirement of thematic information extraction than the former. In addition, the lack of quantitative evaluation of the contribution of the selected features to thematic classes also weakens our understandability of these features. To address the problems, class-specific feature selection methods are developed to measure the effectiveness of features for extracting thematic information from VHR images. First, the one-versus-all scheme is combined with traditional feature selection methods, such as ReliefF and LeastC. Also, one-versus-one scheme is utilized for alleviating the negative impact of a class imbalance problem arising from the one-versus-all scheme. Then, the relative contributions of features to thematic classes are obtained by the class-specific feature selection methods to describe the effectiveness of features for thematic information extraction. Finally, the class-specific feature selection methods are compared with the original methods on three different VHR image data sets by the nearest neighbor and support vector machine. Experimental results show that the class-specific feature selection methods outperform the corresponding conventional methods, and the one-versus-one scheme surpasses one-versus-all scheme. Additionally, many features are evaluated by the class-specific feature selection methods, to provide end users advice on effectiveness of the features.

Beschreibung: The conditional random field (CRF) model is suitable for the image segmentation because this model relaxes the assumption of conditional independence of the observed data and models the data-dependent label interaction in the image modeling. However, this model has a limited ability to capture the global and local image information from the perspective of multiresolution analysis. Moreover, for synthetic aperture radar (SAR) image segmentation, SAR scattering statistics that are essential to SAR image processing are not considered in the CRF model. In this paper, we propose a hierarchical CRF (HIECRF) model for SAR image segmentation. The HIECRF model belongs to the discriminative models according to the semantic structure. While inheriting the advantages of the CRF model, the HIECRF model achieves the integration of the image features and SAR scattering statistics and captures the contextual structure information in the spatial and scale spaces. Moreover, we derive a hierarchical inference algorithm for the HIECRF model in virtue of the mean-field approximation (MFA) to provide the maximization of the posterior marginal (MPM) estimate of the HIECRF model. Then, by the bottom-up and the top-down recursions in the hierarchical inference procedure, the HIECRF model effectively exploits the global and local image information, including the contextual structures, the image features, and the scattering statistics, to achieve the MPM segmentation. The effectiveness of the HIECRF model is demonstrated by the application to the semisupervised segmentation of the simulated images and the real SAR images.

Beschreibung: Due to the limited spatial resolution of multispectral/hyperspectral data, mixed pixels widely exist and various spectral unmixing techniques have been developed for information extraction at the subpixel level in recent years. One of the challenging problems in spectral mixture analysis is how to model the data of a primary class. Given that the within-class spectral variability (WSV) is inevitable, it is more realistic to associate a group of representative spectra with a pure class. The unmixing method using the extended support vector machines (eSVMs) has handled this problem effectively. However, it has simplified WSV in the mixed cases. In this paper, a further development of eSVMs is presented to address two problems in multiple-endmember spectral mixture analysis: 1) one mixed pixel may be unmixed into different fractions (model overlap); and 2) one fraction may correspond to a group of mixed pixels (fraction overlap). Then, spectral unmixing resolution (SUR) is introduced to characterize how finely the mixture in a mixed pixel can be quantified. The quantitative relationship between SUR and WSV of endmembers is derived via a geometry analysis in support vector machine feature space. Thus, the possible SUR can be estimated when multiple endmembers for each class are given. Moreover, if the requirement of SUR is fixed, the acceptance level of WSV is then limited, which can be used as a guide to remove outliers and purify endmembers for each primary class. Experiments are presented to illustrate model and fraction overlap problems and the application of SUR in uncertainty analysis of spectral unmixing.

Beschreibung: We have studied thin ice detection using Advanced Microwave Scanning Radiometer–Earth Observing System (AMSR-E) and Special Sensor Microwave Imager/Sounder radiometer data acquired over the Barents and Kara Seas during three winters (November–April) in 2008–2011. Moderate Resolution Imaging Spectroradiometer-based ice thickness charts were used as reference data. Thin ice detection was studied using polarization and spectral gradient ratios (PR and GR) calculated from the 36/37 and 89/91 GHz radiometer data. Thresholds for thin ice detection and maximum thicknesses for the detected thin ice ( $hT$ ) were determined, as were error rates for misdetections. The results for different 1-D PR and GR parameters led to the conclusion that the AMSR-E PR36 and H-polarized GR8936 would be the best parameters for a 2-D classifier. We adopted the linear discrimination analysis (LDA) as a statistical tool. Thin ice areas with $hT$ of 30 cm could be separated from thicker ice fields with approximately 20% error level. In our large data set, the estimation of thin ice thickness was not possible with reasonable accuracy due to the large scatter between ice thickness and the PR and GR signatures. This is likely due to a large data set, besides thin ice in polynyas also thin ice in the marginal ice zone and thin ice from freeze-up period. The optimal LDA parameters in the classifier and $hT$ depended on the daily mean air temperature ( $(T_{am} )$ ). We could not yet parameterize the classifier optimally according to $(T_{am} ) $ , but the constructed classifier worked rather robustly as indicated by the relat- ve small error rate variation between the three analyzed winters.

Beschreibung: Hyperspectral images (HSIs) can be very noisy, and the amount of noise may differ from band to band. While some spectral bands may be dominated by low signal-independent noise levels, others have mixed noise levels, which may include high levels of Gaussian, Poisson, and Spike noises. When a denoising algorithm is globally applied to the whole data set, it usually affects the low-noise bands adversely. Therefore, it is better to use different criteria for denoising different bands. In this paper, we propose a new denoising strategy to do so. The method is based on a 2-D nonsubsampled shearlet transform, applied to each spectral band of the HSI. We propose an effective method to distinguish between bands with low levels of Gaussian noise (LGN bands) and bands with mixed noise (MN bands) based on spectral correlation. LGN bands are denoised using a thresholding technique on the shearlet coefficients. On the MN bands, a local noise reduction method is applied, in which the detail shearlet coefficients of adjacent LGN bands are employed. This targeted approach is prone to reduce spectral distortions during denoising compared with global denoising methods. This advantage is shown in experiments where the proposed method is compared with state-of-the-art denoising methods on synthetic and real hyperspectral data sets. To assess the effect of denoising, classification and spectral unmixing tasks are applied to the denoised data. Obtained results show the superiority of the proposed approach.

Beschreibung: Linear spectral unmixing aims at estimating the number of pure spectral substances, also called endmembers , their spectral signatures, and their abundance fractions in remotely sensed hyperspectral images. This paper describes a method for unsupervised hyperspectral unmixing called minimum volume simplex analysis (MVSA) and introduces a new computationally efficient implementation. MVSA approaches hyperspectral unmixing by fitting a minimum volume simplex to the hyperspectral data, constraining the abundance fractions to belong to the probability simplex. The resulting optimization problem, which is computationally complex, is solved in this paper by implementing a sequence of quadratically constrained subproblems using the interior point method, which is particularly effective from the computational viewpoint. The proposed implementation (available online: www.lx.it.pt/%7ejun/DemoMVSA.zip) is shown to exhibit state-of-the-art performance not only in terms of unmixing accuracy, particularly in nonpure pixel scenarios, but also in terms of computational performance. Our experiments have been conducted using both synthetic and real data sets. An important assumption of MVSA is that pure pixels may not be present in the hyperspectral data, thus addressing a common situation in real scenarios which are often dominated by highly mixed pixels. In our experiments, we observe that MVSA yields competitive performance when compared with other available algorithms that work under the nonpure pixel regime. Our results also demonstrate that MVSA is well suited to problems involving a high number of endmembers (i.e., complex scenes) and also for problems involving a high number of pixels (i.e., large scenes).

Beschreibung: Stereo images have long been the main practical data source for the high-accuracy retrieval of 3-D information over large areas. However, stereoscopy has been surpassed by laser scanning (LS) techniques in recent years, particularly in forested areas, because the reflection of laser points from object surfaces directly provides 3-D geometric features and because the laser beam has good penetration capacity through forest canopies. In the last few years, image-based point clouds have become a more widely available data source because of advances in matching algorithms and computer hardware. This paper explores the possibility of using consumer cameras for forest field data collection and presents an application of terrestrial image-based point clouds derived from a handheld camera to forest plot inventories. In the experiment, the sample forest plot was photographed in a stop-and-go mode using different routes and camera settings. Five data sets were generated from photographs taken in the field, representing different photographic conditions. The stem detection accuracy ranged between 60% and 84%, and the root-mean-square errors of the estimated diameters at breast height were between 2.98 and 6.79 cm. The performance of image-based point clouds in forest data collection was compared with that of point clouds derived from two LS techniques, i.e., terrestrial LS (the professional level) and personal LS (an emerging technology). The study indicates that the construction of image-based point clouds of forest field data requires only low-cost, low-weight, and easy-to-use equipment and automated data processing. Photographic measurement is easy and relatively fast. The accuracy of tree attribute estimates is close to an acceptable level for forest field inventory but is lower than that achieved with the tested LS techniques.

Beschreibung: Ice sounding radars are able to measure ice sheets by profiling their glaciological features from the surface to the bedrock. The current airborne and, in particular, future space-based systems are suffering from off-nadir surface clutter, which can mask the depth signal of interest. The most recent surface clutter suppression techniques are based on multi-phase-center systems combined with sophisticated coherent postprocessing. The performance of the techniques can be improved by accurate direction-of-arrival (DOA) estimates of the surface clutter. This paper deals with data-driven DOA estimation for surface clutter signals, which includes a formulation of the mathematical foundation of spatial aliasing. DOA estimation is applied to data acquired with the P-band POLarimetric Airborne Radar Ice Sounder at the Jutulstraumen Glacier, Antarctica. The effects of spatial aliasing related to a large phase center spacing are analyzed, and an unwrapping procedure is presented and applied to the data. Finally, DOA estimation of full-scene data is analyzed and used to show an along-track and incidence (off-nadir) angle dependent variation of the effective scattering center of the surface return, which is caused by a varying penetration depth.

Beschreibung: A model for aboveground biomass estimation from single-pass interferometric synthetic aperture radar (InSAR) data is presented. Forest height and canopy density estimates $Delta h$ and $eta_0$ , respectively, obtained from two-level model (TLM) inversion, are used as biomass predictors. Eighteen bistatic VV-polarized TanDEM-X (TDM) acquisitions are used, made over two Swedish test sites in the summers of 2011, 2012, and 2013 (nominal incidence angle: 41 $^{circ} $ ; height-of-ambiguity: 32–63 m) . Remningstorp features a hemiboreal forest in southern Sweden, with flat topography and where 32 circular plots have been sampled between 2010 and 2011 (area: 0.5 ha; biomass: 42–242 t/ha; height: 14–32 m) . Krycklan features a boreal forest in northern Sweden, 720-km north–northeast from Remningstorp, with significant topography and where 31 stands have been sampled in 2008 (area: 2.4–26.3 ha; biomass: 23–183 t/ha; height: 7–21 m) . A high-resolution digital terrain model has been used as ground reference during InSAR processing. For the aforementioned plots and stands and if the same acquisition is used for model training and validation, the new model explains 65%–89% of the observed variance, with root-mean-square error (RMSE) of 12%–19% (median: 15%) . By fixing two of the three model parameters, accurate biomass estimation can also be done when different acquisitions or different test sites are used for model training and validation, with RMSE of 12%–56% (median: 17%) . Compared with a simple scaling model computing biomass from the phase center elevation above ground, the proposed model shows significantly better- performance in Remningstorp, as it accounts for the large canopy density variations caused by active management. In Krycklan, the two models show similar performance.

Beschreibung: Snow is important to the ecological and climate systems; however, current snowfall and snow depth in situ observations are only available sparsely on the globe. By making use of the networks of Global Positioning System (GPS) stations established for geodetic applications, it is possible to monitor snow distribution on a global scale in an inexpensive way. In this paper, we propose a new snow depth estimation approach using a geodetic GPS station, multipath reflectometry and a linear combination of phase measurements of GPS triple-frequency (L1, L2, and L5) signals. This phase combination is geometry free and is not affected by ionospheric delays. Analytical linear models are first established to describe the relationship between antenna height and spectral peak frequency of combined phase time series, which are calculated based on theoretical formulas. When estimating snow depth in real time, the spectral peak frequency of the phase measurements is obtained, and then the model is used to determine snow depth. Two experimental data sets recorded in two different environments were used to test the proposed method. The results demonstrate that the proposed method shows an improvement with respect to existing methods on average.

Beschreibung: The light rain (less than or equal to a few mm hr -1 ) is critical to the Earth's ecosystem due to the high occurrence rate, particularly in middle and high latitude (over 80%). However, it is challenging to use rainfall gauge to measure light rain due to the sampling time and bucket volume resolution. Dual-polarization radar has become an important tool for quantitative precipitation estimation because of its relatively large covering area and ability to fill the sampling void. This paper presents the application of Ku-band dual-polarization radar for light rainfall estimation. The Ku-band radar rainfall algorithms and their error structure are described. The Ku-band observations from the National Aeronautics and Space Administration (NASA) Dual-frequency Dual-polarization Doppler Radar (D3R) during the NASA Iowa Flood Studies (IFoodS) field campaign are used to derive the rainfall products. The comparisons are performed between radar rainfall estimates and ground rainfall measurements from rain gauge and Autonomous Parsivel Unit (APU). It is shown here that the radar rainfall measurements at different timescales (i.e., 5, 10, and 15 min) agree with the APU observations very well. The normalized difference error is about 26.1%, 24.8%, and 23.7%, for 5-min, 10-min, and 15-min rainfall accumulations, respectively. The excellent performance of Ku-band rainfall algorithm for light rain estimation indicates the great potential of using D3R as a ground validation tool for the Global Precipitation Measurement (GPM) satellite precipitation product evaluations.

Beschreibung: In this paper, we propose a monocular multiframe high dynamic range (HDR) monocular vision system to improve the imaging quality of traditional CMOS/charge-coupled device (CCD)-based vision system for advanced driver assistance systems (ADASs). Conventional CMOS/CCD image sensors are confined to limited dynamic range that it impairs the imaging quality under undesirable environments for ADAS (e.g., strong contrast of bright and darkness, strong sunlight, headlights at night, and so on). Contrary to current HDR video solutions relying on expensive specially designed sensors, we implement a multiframe HDR algorithm to enable one common CMOS/CCD sensor capturing HDR video. Key parts of the realized HDR vision system are: 1) circular exposure control; 2) latent image calculation; and 3) exposure fusion. We have successfully realized a prototype of monocular HDR vision system and mounted it on our SetCar platform. The effectiveness of this technique is demonstrated by our experimental results, while its bottleneck is the processing time. By exploring the capability of the proposed method in the future, a low-cost HDR vision system can be achieved for ADAS.

Beschreibung: In this paper, a fiber-optic surface plasmon resonance (SPR) biosensor is presented, in which a sheet of graphene acting as a sensing layer is coated around the gold film. A theoretical study of the proposed fiber-optic biosensor has been carried out by applying four-layer modal, which shows that by incorporating a graphene sensing layer, the sensitivity of the proposed SPR fiber biosensor can be greatly enhanced than the conventional gold film SPR fiber sensors. The relationship between resonance wavelengths and sensitivity of the proposed graphene sensing layer-based SPR fiber biosensor with the number of sensing layer has also been studied.

Beschreibung: We report the development of cantilever-based cold cathode ionization pressure sensor that works on the principle of dielectric breakdown of gaseous medium. The high electric field ( $E_{m}=1$ MV/m) developed at the corners of the microtip produced dielectric breakdown of the medium, sufficient to produce ionization current at a relatively low bias voltage $V_{b} 〈10$ V. With increase in pressure, the availability of molecules in the vicinity of the micro-tip increased which when ionized by the application of $V_{b}$ , produced enhanced ionization current through the device. The devices with cantilever lengths $L=50$ , 150, 250, 350, and $450~mu text{m}$ were designed with interelectrode spacing ( ${d}_{textrm {int}} )$ of 1250 nm, which was further reduced by the application of suitable $V_{b}$ to enhance the sensor performance. The device with $L=450~mu text{m}$ provides the widest dynamic range of $10^{mathrm {mathbf {-4}}}-10^{mathrm {mathbf {2}}}$ mbar and the highest sensitivity of 2.44 $mu text{A}$ /mbar with power consumption of $1.32times 10^{-1}~mu text{W}$ at 10 V, which is $10^{4}$ - times lower than previously reported device. The device offers a service life of 60 cycles assuming a tolerance of 10% in device performance.

Beschreibung: A simple noncontact displacement microfiber sensor using adiabatic U-shaped tapered fiber is proposed and demonstrated. The microfiber is fabricated using a systematic fiber flame brushing technique, where the fiber waist diameter is proportional to the duration of the heating cycles. The sensor is capable of measuring a wide displacement distance up to 12 mm. A sensitivity of 0.2 dB/mm is recorded at a minimum tapered diameter of 8 $mu text{m}$ . In comparison with the previous works of using Fresnel reflection method, the results of our proposed method show significant improvement in sensing range, which is indicated by distinct inclination of the loss slope. The microfiber probe shows a promise for a sensitive sensing at low development cost.

Beschreibung: In this paper, wireless sensing in the presence of complex electromagnetic media created by combinations of reinforcing bars and concrete is investigated. The wireless displacement sensing system, primarily designed for use in structural health monitoring (SHM), is composed of a comb-like nested split-ring resonator (NSRR) probe and a transceiver antenna. Although each complex medium scenario is predicted to have a detrimental effect on sensing in principle, it is demonstrated that the proposed sensor geometry is able to operate fairly well in all scenarios except one. In these scenarios that mimic real-life SHM, it is shown that this sensor exhibits a high displacement resolution of 1 $mu text{m}$ , a good sensitivity of 7 MHz/mm in average, and a high dynamic range extending over 20 mm. For the most disruptive scenario of placing concrete immediately behind NSRR, a solution based on employing a separator behind the probe is proposed to overcome the handicaps introduced by the medium. In order to obtain a one-to-one mapping from the measured frequency shift to the displacement, a numerical fit is proposed and used. The effects of several complex medium scenarios on this fit are discussed. These results indicate that the proposed sensing scheme works well in real-life SHM applications.

Beschreibung: Due to electromagnetic silence, passive tracking systems for emitter targets usually produce track segments (i.e., tracklets) rather than an entire trajectory of the target. Therefore, a multistage method for emitter target tracking is proposed in this paper. In the stage of tracklet generation, the Gaussian mixture-probability hypothesis density tracker with adaptive estimation of target birth intensity is applied to generate reliable tracklets of the emitter targets. After that, in the stage of tracklet association, the multipoint motion information and emitter signal information are integrated to compute the similarities between the tracklets. The affinity propagation algorithm, which does not impose the constraint of one-to-one correspondence, is then used to cluster the tracklets. In the stage of association refining, the clustering result is adjusted to refine the final trajectories according to the spatial-temporal constraint of the tracklets. The simulation results show that the proposed method is robust and performs well.

Beschreibung: Entomologists need to observe and monitor insects in their natural habitats for various reasons. One of the most common techniques in use today is to attach low-cost harmonic sensor or transponder to insects and to track them using an associated harmonic radar. Existing harmonic transponders based on monopoles, loop-dipoles, and Minkowski loops have been successfully attached to small low-flying insects. However, they offer significant constraints from a mounting perspective, weight and size considerations, aerodynamic drag, and the elevation of the insect center of gravity. This paper addresses these issues by presenting novel design methodologies for millimeter wave harmonic sensors and radar. The sensors address the challenges of ease of design and fabrication, robustness, conversion efficiency, miniaturization, antenna entangling, and attachment problems that are generally encountered with other transponders.

Beschreibung: This paper presents an ultra-low power DC–DC converter to be embedded into body sensor nodes. The proposed DC–DC converter is used to step-up the voltage at the output of a micro-thermoelectric generator up to a usable supply voltage between 0.42 and 1.05 V. Measurements have been conducted on ten available samples. The resulting mean value of the minimum input voltage is 96 mV. At the minimum input voltage, the power consumption is only 0.23 $mu text{W}$ . The operating temperature ranges from −40 °C to 100 °C.

Beschreibung: Various jamming techniques have been developed to prevent interferometric synthetic aperture radar from effective detection and observation. In this paper, a thorough analysis of the jamming effects on correlation and interferometric phase is provided. To derive the jamming result, a general signal model for the interference is first presented and the corresponding imaging results are produced through the range-Doppler algorithm. Then, the impacts of the interference on correlation are analyzed. The non-center located jammer decreases the correlation seriously due to the low correlation of the interference. However, the center located jammer clearly increases the correlation when the input jamming-to-signal ratio is large enough. Finally, the jammed interferometric phases for different jammer positions are discussed. It shows that the non-center located jammer results in large phase errors, while for the center located jammer, the interferometric phase approaches a constant. The effects of interference are demonstrated by simulated data based on the TerraSAR system.

Beschreibung: This paper demonstrates the measurement of respiration waveform during sleep with a noncontact radar sensor. Instead of measuring only the respiration rate, the methods that allow monitoring the absolute respiration displacement were studied. Absolute respiration displacement can in theory be measured with a quadrature microwave Doppler radar sensor and using the nonlinear demodulation as the channel combining method. However, in this paper, relative respiration displacement measures were used as a reference. This is the first time that longer data sets have been analyzed successfully with the nonlinear demodulation method. This paper consists of whole-night recordings of three patients in an uncontrolled environment. The reference respiration data were obtained from a full polysomnography recorded simultaneously. The feasibility of the nonlinear demodulation in a real-life setting has been unclear. However, this paper shows that it is successful most of the time. The coverage of successfully demodulated radar data was $sim 58$ %–78%. The use of the nonlinear demodulation is not possible in the following cases: 1) if the chest wall displacement is too small compared with the wavelength of the radar; 2) if the radar data do not form an arc-like shape in the $IQ$ -plot; or 3) if there are large movement artifacts present in the data. Both in academic literature and in commercial radar devices, the data are processed based on the presumption that it forms either an arc or a line in the $IQ$ -plot. Our measurements show that the presumption is not always valid.

Beschreibung: This paper presents a demodulation algorithm based on the synchronous integrator circuit for the MEMS tuning fork gyroscope utilizing the digital processing technology. The synchronous integral demodulator (SID) is adopted to demodulate the drive-detection signal and sense-axis output signal separately. Combining with automatic gain control and phase-locked loop technology, the closed-loop control of drive mode has been implemented, and high precision output of the gyroscope has been achieved. The simulation results have verified the effectiveness of the synchronous integrator circuit, which is in good agreement with the theoretical analysis. Compared with other demodulation algorithms, the SID has the advantages of the lower noise level, a better ability to attenuate the harmonics, and the best hardware efficiency. The frequency characteristic of the synchronous integrator is also analyzed, which is vital to the bandwidth of gyroscope. The print circuit board based on field-programmable gate array digital circuit is manufactured and the corresponding experiment is carried out. The experimental results show that the SID algorithm of digital control system for the gyroscope developed in our laboratory has achieved a good performance. The bias instability of tested gyroscope is measured to be 0.2°/h with the angle random walk of 0.14°/ $surd text{h}$ and the nonlinearity of the scale factor is <60 ppm with the measurement range of ±100°/s.

Beschreibung: We report the observed bifurcation in extraordinary electroconductance (EEC) sensor response to direct reverse bias based on measurement lead location as well as a dramatic enhancement in responsivity achieved via a modification of the shunt geometry. A maximum percent change in the four-point resistance of 130856% was achieved under a direct reverse bias of −1 V using an enhanced shunt design, a 325 fold increase over the conventional EEC square shunt design. This result was accompanied by an observed bifurcation in sensor response, driven by a rotation of the four-point measurement leads.

Beschreibung: This paper introduces joint neighbor discovery (ND) and coarse time-of-arrival (ToA) estimation in wireless sensor networks (WSNs) via orthogonal frequency-division multiple access. In the proposed technique, each sensor node exploits at least one orthogonal sub-carrier as its allocated signature, to respond the ND and ToA estimation requests transmitted by target nodes. The target node utilizes the orthogonality across sub-carriers to detect the transmitted signatures and their corresponding delays. This technique is energy efficient as it avoids multiple transmissions and receptions inherent in traditional ND protocols and ToA estimation techniques in WSN. Moreover, in this technique, network initiation process does not require channel information or time synchronization across sensor nodes. The performance of the proposed method is studied by evaluating the probabilities of false alarm and miss detection of the ND. In addition, ToA estimation error is calculated theoretically and via simulations. Moreover, the impact of available bandwidth on the performance and energy efficiency of ND and ToA estimation are investigated. Simulation results confirm the energy efficiency and the feasibility of the proposed method even at low signal-to-noise ratio regimes and in multi-path and frequency selective channels.