Synthetic Sensor Research Articles

FusIon Data Tracking System (FITS)

The field of generating movement profiles of individuals is valuable in many real-world applications (e.g., controlling disease spread or evaluating marketing engagement). Existing solutions often rely on global positioning systems (GPS) or similar systems, primarily targeted at outdoor use cases. However, the indoor tracking capabilities of current solutions either lack precision or are available in closed buildings only. The literature proposes sensor fusion approaches, but many of those are based on specific sensors. These approaches do not reveal implementation details or data to allow for their independent evaluation. Therefore, this article presents FusIon Data Tracking System ( <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">FITS</monospace> ) as an approach and proof-of-concept to facilitate the correlation of data from different indoor sensors to movement profiles of different individuals. Functionally, <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">FITS</monospace> does this by generating synthetic sensor measurement data based on real-world movement data and correlating objects tracked from distinct sensors by effectively solving clustering and position prediction tasks. This correlation is evaluated based on different metrics [multiple object tracker accuracy/precision (MOTA/MOTP)] in four different scenarios, for example, sparse data, high density of sensors, low density of sensors, and a base case. Finally, <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">FITS</monospace> ’s performance was evaluated by increasing the load test (dataset up to 100000 measurements and 1000 visitors) to assess whether near real-time processing is feasible under a high workload.

The protein scaffold calibrates metal specificity and activation in MerR sensors.

MerR metalloregulators are the central components of many biosensor platforms designed to report metal contamination. However, most MerR proteins are non-specific. This makes it difficult to apply these biosensors in the analysis of real environmental samples. On-demand implementation of molecular engineering to modify the MerR metal preferences is innovative, although it does not always yield the expected results. As the metal binding loop region (MBL) of these sensors has been proposed to be the major modulator of their specificity, we surgically switched this region for that of well-characterized specific and non-specific homologues. We found that identical modifications in different MerR proteins result in synthetic sensors displaying particular metal-detection patterns that cannot be predicted from the nature of the assembled modules. For instance, the MBL from a native Hg(II) sensor provided non-specificity or specificity toward Hg(II) or Cd(II) depending on the MerR scaffold into which it was integrated. These and other evidences reveal that residues outside the MBL are required to modulate ion recognition and transduce the input signal to the target promoter. Revealing their identity and their interactions with other residues is a critical step toward the design of more efficient biosensor devices for environmental metal monitoring.

Synergistic Use of the SRAL/MWR and SLSTR Sensors on Board Sentinel-3 for the Wet Tropospheric Correction Retrieval

The Sentinel-3 satellites are equipped with dual-band Microwave Radiometers (MWR) to derive the wet tropospheric correction (WTC) for satellite altimetry. The deployed MWR lack the 18 GHz channel, which mainly provides information on the surface emissivity. Currently, this information is considered using additional parameters, one of which is the sea surface temperature (SST) extracted from static seasonal tables. Recent studies show that the use of a dynamic SST extracted from Numerical Weather Models (ERA5) improves the WTC retrieval. Given that Sentinel-3 carries on board the Sea and Land Surface Temperature Radiometer (SLSTR), from which SST observations are derived simultaneously with those of the Synthetic Aperture Radar Altimeter and MWR sensors, this study aims to develop a synergistic approach between these sensors for the WTC retrieval over open ocean. Firstly, the SLSTR-derived SSTs are evaluated against the ERA5 model; secondly, their impact on the WTC retrieval is assessed. The results show that using the SST input from SLSTR, instead of ERA5, has no impact on the WTC retrieval, both globally and regionally. Thus, for the WTC retrieval, there seems to be no advantage in having collocated SST and radiometer observations. Additionally, this study reinforces the fact that the use of dynamic SST leads to a significant improvement over the current Sentinel-3 WTC operational algorithms.

Monitoring of Coastal Aquaculture Sites in the Philippines through Automated Time Series Analysis of Sentinel-1 SAR Images

With the unprecedented expansion of aquaculture around the world, there is a critical need to monitor its progress. In Palawan, Philippines, coastal aquaculture is gaining momentum towards increasing fish production, a pressure that presents a challenge to the sustainability of these areas. In this paper, we explore the application of Earth observation methods to map coastal aquaculture development in Palawan and evaluate the extent of its change. The European Space Agency Sentinel-1 synthetic aperture radar and Sentinel-2 multispectral instrument sensor data were applied in fully automatic mode to build maps of Palawan coastal aquaculture. The maps were validated using Google Earth high-resolution optical images and in situ observations in Malampaya Sound, and demonstrated a successful detection rate of 72%, while the false alarm rate was less than 7.5%. Objects only 5 m across, four times smaller than the spatial resolution of the Sentinel-1 sensors, were successfully detected using the developed methodology, thus exceeding the capabilities of other published methods that are limited to detecting large groups of aquaculture structures. The maps revealed aquaculture structures in high quantities in Malampaya Sound, Taytay Bay, and other locations in the coastal waters of Palawan, Philippines. A significant change of aquaculture spatial distribution was identified by comparing aquaculture maps generated with an interval of three years. This new automated methodology was validated as robust for mapping aquaculture objects in Palawan, Philippines, and can be applied to aquaculture studies in other regions worldwide.

An Unsupervised Spectrogram Cross-Correlation Method to Assess ELM Triggering Efficiency by Pellets

The high confinement mode (H-mode) is considered the optimal regime for the production of energy through nuclear fusion for industrial purposes since it allows to increase the energy confinement time of the plasma roughly by a factor of two. Consequently, it has been selected at the moment as the standard scenario for the next generation of devices, such as ITER. However, pressure-driven edge instabilities, known as edge localized modes (ELMs), are a distinct feature of this plasma regime. Their extrapolated thermal and particle peak loads on the plasma-facing components (PFC) of the next generation of devices are expected to be so high as to damage such structures, compromising the normal operations of the reactors themselves. Consequently, the induced loads have to be controlled; this can be achieved by mitigating ELMs. A possibility then lays in increasing the ELMs frequency to lower the loads on the PFCs. As already demonstrated at JET, the pellet pacing of ELMs is considered one of the most promising techniques for such scope, and its optimization is therefore of great interest for present and future operations of nuclear fusion facilities. In this work, we suggest a method to access primary pieces of information to perform statistics, assess and characterize the pacing efficiency. The method, tested on JET data, is based on the clustering (k-means) of convoluted signals, using so-called spectrogram cross-correlation, between the measured pellets and ELMs time traces. Results have also been obtained by taking advantage of a new type of diagnostic for measuring the ELMs dynamic, based on synthetic diamond sensors, faster than the standard spectroscopic cameras used at JET.

Virtual training and commissioning of industrial bin picking systems using synthetic sensor data and simulation

ABSTRACT Defined handling of unsorted parts, known as bin picking, is a challenge in robotic automation. Available solution concepts for this problem are usually either costly or require considerable setup and tuning efforts. In this contribution, a setup for virtual commissioning of such automation systems is introduced. Using a physics-based simulation environment, a virtual stereo-camera simulation and robot controller integration, a full simulation of the bin picking cycle is possible. The setup is also used to generate realistic synthetic training data for learning-based computer vision routines. The functionality of the system is demonstrated for generating training data capable of enabling a real-life deployment of the pipeline. A simulation of both model-based and learning-based bin picking systems is also conducted. This simulation also involves the path planning and execution as well as the grasp itself, allowing for a full simulation of the bin picking cycle.

Multifrequency Spaceborne Synthetic Aperture Radar Data for Backscatter-Based Characterization of Land Use and Land Cover

Polarimetric synthetic aperture radar remote sensing extracts the information about the target using decomposition models to separate the polarimetric information into single-bounce (contributed by smooth surfaces), double-bounce (contributed by urban structure), and volume (mainly due to vegetation cover) scattering components. The penetration capacity of the electromagnetic wave into the surface increases with the decrease in its frequency. This study explores and compares the polarimetric decomposition models for scattering-based characterization of land use and cover using multifrequency spaceborne synthetic aperture radar sensor datasets that were acquired over San Francisco, CA, USA. The present work compares the scattering parameters of coherent (Pauli), roll-invariant (Barnes), eigenvalue–eigenvector (Cloude), and compact-polarimetric (Raney) decomposition modeling approaches for scattering-based characterization of urban structures, waterbody, and vegetation cover. The land use/cover classification was performed based on the scattering response of the scatterers using a support vector machine classifier. The outputs of the classification approach on multisensor, multifrequency, and multi-polarization polarimetric synthetic aperture radar data have shown reasonable accuracy in classifying the land use and land cover. The decomposition models fail to characterize the oriented urban structures that cause misclassification of urban structures as vegetation. The higher-order roll-invariant decomposition modeling approaches could improve the interpretation of different targets and accuracy in land use and land cover classification.

Toward metrological trustworthiness for automated and connected mobility

The mobility of people and goods is moving into a new era of more automated services based on sensors networks and Artificial Intelligence. At present, Automated Mobility, in the broadest meaning of the terms includes Advanced Driver Assistance System (ADAS) and Autonomous Vehicle (AV), beyond being attractive for many practical advantages, ranging from safety to traffic flow management, still presents several concerns on the trustworthiness of sensor networks integrated into vehicles, especially regarding sensors calibration, data uncertainty and data fusion approaches. Currently, the trustworthiness of ADAS and AV functions is assessed with virtual and physical simulation of functions relying on synthetic sensor models, simulated and measured sensor data and equivalent environmental conditions. During the lifespan of vehicles, environmental effects including possible accidents and common usage, can have significant impact on the performance of ADAS sensors and customer functions. Current approach is to consider ADAS sensors output nominal, disregarding the uncertainty of sensors data and including all the possible tolerances and variability at sensors/system testing stage. At present day, market offers several facilities and commercial set-up promoted as being able to do ADAS calibration: usually are modular equipment allowing alignment and sensitivity check of different ADAS sensors, especially front sensors and camera. However, sensor calibration involves specific calibration facilities, procedures to establishing sensors sensitivity and most of all, associated uncertainty. Accuracy and traceability of ADAS sensors beyond being fundamental requirements in measurement science, allow the quantitative evaluation of the trustworthiness of sensors and customer functions. This paper suggests an approach to lay the foundation of Metrology of Trustworthiness for ADAS and AV complex sensors systems and provide a case study of IMU sensor trustworthiness.

Verification in Relevant Environment of a Physics-Based Synthetic Sensor for Flow Angle Estimation

In the area of synthetic sensors for flow angle estimation, the present work aims to describe the verification in a relevant environment of a physics-based approach using a dedicated technological demonstrator. The flow angle synthetic solution is based on a model-free, or physics-based, scheme and, therefore, it is applicable to any flying body. The demonstrator also encompasses physical sensors that provide all the necessary inputs to the synthetic sensors to estimate the angle-of-attack and the angle-of-sideslip. The uncertainty budgets of the physical sensors are evaluated to corrupt the flight simulator data with the aim of reproducing a realistic scenario to verify the synthetic sensors. The proposed approach for the flow angle estimation is suitable for modern and future aircraft, such as drones and urban mobility air vehicles. The results presented in this work show that the proposed approach can be effective in relevant scenarios even though some limitations can arise.

A wearable-HAR oriented sensory data generation method based on spatio-temporal reinforced conditional GANs

Human activity recognition based on wearable sensors plays an essential role in promoting many practical applications, such as healthcare, motion monitoring, medical examination, anomaly detection and human-computer interaction. It’s worth noting that longer temporal sensory sequences could reflect the characteristics of different daily activities more accurately. However, existing GANs-based time series generation methods could only synthesize uniaxial, multivariate or multidimensional sensor data over a relatively short span of time. These shorter synthetic time series could not effectively represent at least one complete daily activity cycle. To synthesize longer and more realistic multi-axial sensor data, this paper proposes a new customized GANs-based sensory data synthesizing method, which is dedicated to wearable activity recognition tasks, named Conditional SensoryGANs. Firstly, the elaborately designed MultiScale MultiDimensional (MSMD) spatiotemporal function module endows the proposed Conditional SensoryGANs with the capability of synthesizing longer sensory sequences, which could better characterize different behaviors with periodicity. Secondly, benefited from the well-designed Time-Frequency Enhancement (TFE) functional module, Conditional SensoryGANs could more accurately capture each axis’s spatiotemporal property and spatial correlation between different axes to improve the fidelity of synthetic sensor data. Thirdly, Conditional SensoryGANs could synthesize verisimilar wearable sensor data of the specified quantity and category under a unified framework with the embedded condition’s refined control. Qualitative visual evaluations demonstrate that the proposed method has more excellent capability for synthesizing verisimilar wearable multi-axial sensor data than the state-of-the-art GAN-based sensor data generation methods. Quantitative experiments also prove that it could achieve better results than off-the-shelf GANs-based time series methods for synthesizing wearable multi-axial sensor data. Meanwhile, empirical results demonstrate that synthetic sensor data from Conditional SensoryGANs can achieve comparatively approximate usability in the field of wearable human activity recognition than the real sensor data.

A Sensor for Three-Dimensional Component Measurement of Synthetic Electric Field Vector in HVDC Transmission Lines Using Unidirectional Motion

High voltage direct current (HVDC) transmission lines have been widely used in China, and the electromagnetic effects of the synthetic electric field generated by them on the surrounding environment have attracted more and more attention. Especially when there are residential buildings around the transmission lines, the distortion of the electric field makes the research and development of synthetic electric field sensors with three-dimensional (3D) measurement capability an urgent technical demand. Based on the basic principle of the field mill electric field sensor, this paper presents a three-dimensional field mill structure to obtain the three-dimensional component of the electric field vector by using unidirectional motion. With the signal processing circuit, we realize the reading of three-dimensional electric field data. The feasibility of the proposed structure is verified by theoretical deduction analysis, numerical simulation, and prototype tests. The proposed structure provides a new scheme to solve the problems of three-dimensional synthetic electric field measurement and can enrich the existing methods.

State Estimation in a Hydraulically Actuated Log Crane Using Unscented Kalman Filter

Multibody system dynamics approaches together with state estimation methods can reduce the need for a large number of sensors, especially in the digital twin of working mobile machinery. To demonstrate this, a hydraulically actuated machine was modeled using the double-step semi-recursive multibody formulation and lumped fluid theory in terms of system independent states. Next, because of the high non-linearity of the modeled system and with respect to the reported performance degradation of the Extended Kalman Filters (EKF), which are mostly related to the linearization procedure of this filter, the Unscented Kalman Filter (UKF) was implemented to achieve high accuracy and performance. The methodology of the proposed approaches was applied to a mobile log crane model PATU 655. The implementation of the proposed estimation algorithms is demonstrated with three different multibody based simulation models: the synthetic real system producing the artificial measurements, the simulation model, and the estimation model. Encoders and pressure sensors, installed on the synthetic real system, provided synthetic sensor measurement data. To mimic real-world conditions, the estimation and simulation models used in the processing of the state estimation algorithm were assumed to have errors in the initial conditions and force model. The proposed UKF was applied to the estimation model with the synthetic sensor measurement data. The minimum percent normalized root mean square errors in the associated measured and unmeasured states of case example were 0.11% and 1.86%, respectively. The UKF using the multibody system dynamics formulations is able to estimate the case example states despite 15% and 60% errors in mass and inertial properties of bodies and Payload, respectively, confirming the accuracy and performance of the algorithm

A practical framework to generate and manage synthetic sensor data

A huge number of sensors are around us and they generate different kinds of data. Data owners, e.g. the companies need IT environments and applications to handle these datasets. The collected data often contain sensitive information about the operation of the companies and the production processes. Therefore, artificial sensor data are strongly needed in the development and testing phase of these applications. In this paper, we introduce a complex application with three main modules to manage synthetic sensor data. The first component is the data generator module, which is capable of creating synthetic sensor data according to the user-defined distributions and parameters. The second module is in charge of storing the generated data in a flexible relational database, developed by us. The third component ensures the filtering and the visualization of the collected or generated data. A common interface was created to bring together the components and to provide a unified interface for the users. The adequate user management was an important aspect of our work. Accordingly, four different user types and authorities were defined.

Synthetic Generation of Passive Infrared Motion Sensor Data Using a Game Engine.

Quantifying the number of occupants in an indoor space is useful for a wide variety of applications. Attempts have been made at solving the task using passive infrared (PIR) motion sensor data together with supervised learning methods. Collecting a large labeled dataset containing both PIR motion sensor data and ground truth people count is however time-consuming, often requiring one hour of observation for each hour of data gathered. In this paper, a method is proposed for generating such data synthetically. A simulator is developed in the Unity game engine capable of producing synthetic PIR motion sensor data by detecting simulated occupants. The accuracy of the simulator is tested by replicating a real-world meeting room inside the simulator and conducting an experiment where a set of choreographed movements are performed in the simulated environment as well as the real room. In 34 out of 50 tested situations, the output from the simulated PIR sensors is comparable to the output from the real-world PIR sensors. The developed simulator is also used to study how a PIR sensor’s output changes depending on where in a room a motion is carried out. Through this, the relationship between sensor output and spatial position of a motion is discovered to be highly non-linear, which highlights some of the difficulties associated with mapping PIR data to occupancy count.

The transmembrane protein AaSho1 is essential for appressorium formation and secondary metabolism but dispensable for vegetative growth in pear fungal Alternaria alternata

The high-osmolarity glycerol response (HOG) pathway is pivotal in environmental stress response, differentiation and virulence of Alternaria alternata. The synthetic high osmolarity sensitive sensor Sho1 has been postulated to regulate the HOG pathway. To determine the regulatory role of transmembrane protein Sho1 on vegetative growth, secondary metabolism and infection structure formation, a gene (AaSho1) encoding Sho1 was cloned and characterized from A. alternata (JT-03). Sequence analysis showed that AaSho1 has all four characteristic transmembrane domains and the SH3 domain present in another Sho1 gene from several filamentous fungal. The quantitative RT-PCR analysis showed that fruit wax extract significantly up-regulated AaSho1 gene expression in vitro. Pharmacological experiments showed that A. alternata treated with nystatin, a specific AaSho1 inhibitor, had no significant effect on the morphology of A. alternata and the invasive growth in pear fruit. However, nystatin treatment significantly reduced spore germination rates on different wax-coated hydrophobic surfaces, with 58.00, 46.70 and 83.72% reduced for fruit wax, beeswax and paraffin coated. Meanwhile, the secondary metabolism altenuene (ALT), tentoxin (TEN) toxin, and melanin content were also affected by nystatin treatment. These findings suggest that AaSho1 is required for the infection structure differentiation and secondary metabolism of A. alternata in response to physiochemical signals on the host surfaces.

Highly Elastic and Fatigue-Resistant Graphene-Wrapped Lamellar Wood Sponges for High-Performance Piezoresistive Sensors

Three-dimensional (3D) conductive aerogels with structural robustness and mechanical resilience are highly attractive for sensitive and stable pressure sensing. However, the fabrication of such 3D aerogels often relies on complicated bottom-up assembly processes that involve costly raw materials or intensive energy consumption or directly coating synthetic polymer sponges (e.g., polyurethane) with conductive materials, which may pose environmental concerns for their disposal. Herein, a simple and sustainable strategy is proposed to fabricate a reduced graphene oxide-coated wood sponge (RGO@WS) with a lamellar structure for high-performance piezoresistive sensors. The introduced RGO nanosheets endow the RGO@WS not only with high conductivity but also with high elasticity and excellent fatigue resistance. These features make it an ideal piezoresistive sensor with a high sensitivity of 0.32 kPa–1 (superior to most polymeric sponge-based sensors), high working stability over 10 000 cycles, and excellent sensing reproducibility at ultralow temperatures. Thanks to its prominent sensing performance, the RGO@WS-based sensor can serve as a wearable device for detecting human motions and physiological signals and allows for spatially resolved pressure mapping via integrating the sensors into a large-area sensing array. The developed highly elastic and fatigue-resistant RGO@WS represents a promising and sustainable alternative to the synthetic polymer-based piezoresistive sensors.

Sense and sensibility: of synthetic biology and the redesign of bioreporter circuits.

Sense and sensibility: of synthetic biology and the redesign of bioreporter circuits.

Modeling sub-boreal forest canopy bulk density in Minnesota, USA, using synthetic aperture radar and optical satellite sensor data

BackgroundNational estimates of canopy bulk density (CBD; kg m−3) for fire behavior modeling are generated and supported by the LANDFIRE program. However, locally derived estimates of CBD at finer scales are preferred over national estimates if they exist, as the absolute accuracy of the LANDFIRE CBD product is low and varies regionally. Active sensors (e.g., lidar or radar) are better suited for this task, as passive sensors are ill equipped to detect differences among key vertical fuel structures, such as coniferous surface fuels (≤2 m high) and canopy fuels above this threshold—a key categorical fuel distinction in fire behavior modeling. However, previous efforts to map CBD using lidar sensor data in the Superior National Forest (SNF) of Minnesota, USA, yielded substandard results. Therefore, we use a combination of dormant-season synthetic aperture radar (SAR) and optical satellite sensor data to (1) expand detectability of coniferous fuels among mixed forest canopies to improve the accuracy of CBD modeling and (2) better understand the influence of surface fuels in this regard. Response variables included FuelCalc output and indirect estimates of maximum burnable fuel based on canopy gap fraction (CGF) measured at ground level and 2 m above ground level.ResultsSAR variables were important predictors of CBD and total fuel density (TFD) in all independent model calibrations with ground data, in which we define TFD as the sum of CBD and primarily live coniferous surface fuel density (SFD) 0 to 2 m above ground. Exploratory estimates of TFD appeared biased to the presence of sapling-stage conifer fuel on measures of CGF at the ground level. Thus, modeling efforts to calibrate SFD with satellite sensor data failed. Both CGF-based and FuelCalc-based field estimates of CBD yielded close unity with satellite-calibrated estimates, although substantial differences in data distributions existed. Estimates of CBD from the widest CGF zenith angle range (0 to 38°) correlated best with FuelCalc-based CBD estimates, while both resulted in maximum biomass values that exceeded those considered typical for the SNF. Model results from the narrowest zenith angle range (0 to 7°) produced estimates of CBD that were more in line with values considered typical. LANDFIRE’s estimates of CBD were weakly, but significantly (P = 0.05), correlated to both narrow- and wide-angle CGF-based estimates of CBD, but not with FuelCalc-based estimates.ConclusionsThe combined use of field estimates of CBD, based on indirect measures of CGF according to Keane et al. (Canadian Journal of Forest Research 35:724–739, 2005), with SAR and optical satellite sensor data demonstrates the potential of this method for mapping CBD in the Upper Midwest, USA. Results suggested that the presence of live, coniferous surface fuels neither confounds remote detection nor precludes mapping of CBD in this region using SAR satellite sensor data, as C- and L-band idiosyncrasies likely limit the visibility of these smaller understory fuels from space. Nevertheless, research using direct measures of burnable SFD for calibrations with SAR satellite sensor data should be conducted to more definitively answer this remote detection question, as we suspect substantial bias among measures of CGF from ground level when estimating SFD as the difference between TFD and CBD.

The requirements for automation systems based on Boeing 737 MAX crashes

PurposeThis paper aims to show the current situation and additional requirements for the aircraft automation systems based on the lessons learned from the two 737 MAX crashes.Design/methodology/approachIn this study, the Swiss cheese model was used to find the real root causes of the 737 MAX accidents. Then, the results have been compared with the actions taken by the manufacturers and authorities. Based on the comparison, the necessary improvements to prevent such accidents are defined. Regarding the faulty sensor that forms the accidents, a synthetic sensor was developed using an aerodynamic model.FindingsIt has been proven that the safety-critical automation systems should not be designed by relying on a single set of sensor data. Automation levels should be defined in a standard way. Depending on the defined automation level, the system must be designed as either fail-safe or fail-operational system. When designing backup systems, it should be decided by looking at not only whether it has power but also the accuracy of the incoming signals.Practical implicationsAviation certification requirements related to automation systems need to be revised and improved. With this context, it was revealed that the certification processes for automation systems should be re-evaluated and updated by aviation authorities, especially Federal Aviation Administration and European Union Aviation Safety Agency.Originality/valueTask sharing between automation system and pilot based on the classification of automation levels and determining certification requirements accordingly has been brought to the agenda. A synthetic Angle of Attack sensor was developed by using an aerodynamic model for fault detection and diagnosis.

Probing Lanmodulin's Lanthanide Recognition via Sensitized Luminescence Yields a Platform for Quantification of Terbium in Acid Mine Drainage.

Lanmodulin is the first natural, selective macrochelator for f elements-a protein that binds lanthanides with picomolar affinity at 3 EF hands, motifs that instead bind calcium in most other proteins. Here, we use sensitized terbium luminescence to probe the mechanism of lanthanide recognition by this protein as well as to develop a terbium-specific biosensor that can be applied directly in environmental samples. By incorporating tryptophan residues into specific EF hands, we infer the order of metal binding of these three sites. Despite lanmodulin's remarkable lanthanide binding properties, its coordination of approximately two solvent molecules per site (by luminescence lifetime) and metal dissociation kinetics (koff = 0.02-0.05 s-1, by stopped-flow fluorescence) are revealed to be rather ordinary among EF hands; what sets lanmodulin apart is that metal association is nearly diffusion limited (kon ≈ 109 M-1 s-1). Finally, we show that Trp-substituted lanmodulin can quantify 3 ppb (18 nM) terbium directly in acid mine drainage at pH 3.2 in the presence of a 100-fold excess of other rare earths and a 100 000-fold excess of other metals using a plate reader. These studies not only yield insight into lanmodulin's mechanism of lanthanide recognition and the structures of its metal binding sites but also show that this protein's unique combination of affinity and selectivity outperforms synthetic luminescence-based sensors, opening the door to rapid and inexpensive methods for selective sensing of individual lanthanides in the environment and in-line monitoring in industrial operations.