Recent updates in the use of 0.5-2 GHz microwave radiometry for remotely sensing Earth’s cryosphere are reported. These updates concern measurements and modeling of the spectral properties of brightness temperatures for geophysical regions such as ice sheets, sea ice, ice shelves, and others. The results are intended to support the continued development of 0.5-2 GHz passive microwave mission concepts for future deployment in space.

Ice sheets are fundamental components of the Earth’s system that impact the climate through their contribution to global sea level. Knowledge of an ice sheet’s internal temperature is essential to improve understanding of its dynamics and hence its mass balance. We present an over-complete set – termed a dictionary of functions– for approximating the ISSM-simulated vertical temperature profiles obtained over the Greenland continent. Next, we present an algorithm to retrieve the internal ice-sheet temperature from brightness temperature measurements obtained. Finally, we compare the root mean square error (RMSE) between the recovered temperature profile using the Robin model, extended Robin model and Dictionary based method and the true temperature profile by varying the radiometric noise in the brightness temperature. We quantitatively establish that the proposed dictionary based method outperforms the Robin model in recovering the ice-sheet temperature profile from radiometic measurements.

Wireless biosensors enable continuous monitoring of physiology and can provide early signs of imminent problems allowing for quick intervention and improved outcomes. Wireless communication of the sensor data for remote storage and analysis dominates the device power budget and puts severe constraints on lifetime and size of these sensors. Traditionally, to minimize the wireless communication bandwidth, data compression at the sensor node and signal reconstruction at the remote terminal is utilized. Here we consider an alternative strategy of feature detection with compressed samples without the intermediate step of signal reconstruction. Specifically, we present a compressed matched subspace detection algorithm to detect fiducial points of ECG waveform from streaming random projections of the data. We provide a theoretical analysis to compare the performance of the compressed matched detector performance to that of a matched detector operating with uncompressed data. We present extensive experimental results with ECG data collected in the field illustrating that the proposed system can provide high quality heart rate variability indices and achieve an order of magnitude better RMSE in beat-to-beat heart rate estimation than the traditional filter/downsample solutions at low data rates.

Craving usually precedes a lapse for impulsive behaviors such as overeating, drinking, smoking, and drug use. Passive estimation of craving from sensor data in the natural environment can be used to assist users in coping with craving. In this paper, we take the first steps towards developing a computational model to estimate cigarette craving (during smoking abstinence) at the minute-level using mobile sensor data. We use 2,012 hours of sensor data and 1,812 craving self-reports from 61 participants in a smoking cessation study. To estimate craving, we first obtain a continuous measure of stress from sensor data. We find that during hours of day when craving is high, stress associated with self-reported high craving is greater than stress associated with low craving. We use this and other insights to develop feature functions, and encode them as pattern detectors in a Conditional Random Field (CRF) based model to infer craving probabilities.

Recent advances in mobile health have produced several new models for inferring stress from wearable sensors. But, the lack of a gold standard is a major hurdle in making clinical use of continuous stress measurements derived from wearable sensors. In this paper, we present a stress model (called cStress) that has been carefully developed with attention to every step of computational modeling including data collection, screening, cleaning, filtering, feature computation, normalization, and model training. More importantly, cStress was trained using data collected from a rigorous lab study with 21 participants and validated on two independently collected data sets --- in a lab study on 26 participants and in a week-long field study with 20 participants. In testing, the model obtains a recall of 89% and a false positive rate of 5% on lab data. On field data, the model is able to predict each instantaneous self-report with an accuracy of 72%.

Recent researches have demonstrated the feasibility of detecting smoking from wearable sensors, but their performance on real-life smoking lapse detection is unknown. In this paper, we propose a new model and evaluate its performance on 61 newly abstinent smokers for detecting a first lapse. We use two wearable sensors - breathing pattern from respiration and arm movements from 6-axis inertial sensors worn on wrists. In 10-fold cross-validation on 40 hours of training data from 6 daily smokers, our model achieves a recall rate of 96.9%, for a false positive rate of 1.1%. When our model is applied to 3 days of post-quit data from 32 lapsers, it correctly pinpoints the timing of first lapse in 28 participants. Only 2 false episodes are detected on 20 abstinent days of these participants. When tested on 84 abstinent days from 28 abstainers, the false episode per day is limited to 1/6.

Mobile sensor data-to-knowledge (MD2K) was chosen as one of 11 Big Data Centers of Excellence by the National Institutes of Health, as part of its Big Data-to-Knowledge initiative. MD2K is developing innovative tools to streamline the collection, integration, management, visualization, analysis, and interpretation of health data generated by mobile and wearable sensors. The goal of the big data solutions being developed by MD2K is to reliably quantify physical, biological, behavioral, social, and environmental factors that contribute to health and disease risk. The research conducted by MD2K is targeted at improving health through early detection of adverse health events and by facilitating prevention. MD2K will make its tools, software, and training materials widely available and will also organize workshops and seminars to encourage their use by researchers and clinicians.

We consider the problem of distributed detection in a large network of sensors under network communication constraints. Sensor nodes are randomly deployed and follow a random sleep/wake schedule. When awake, sensor nodes perform local detection tests and communicate detections over a multiple-access channel to a fusion center. The fusion center can detect both successful communications and communication collisions in the channel. We show that the optimum fusion rule is a weighted sum of the number of detections received and the number of collisions detected by the fusion node. We derive analytical expressions that characterize the performance of the system. Simulation examples compare theoretical predictions with numerical results.

Predicting and bounding performance is a fundamental requirement in wireless sensor network (WSN) protocol development. To support a fast development cycle principled methods are required for experimentation with wireless protocols in testbed environments that can provide repeatable performance guarantees in the target environment. In this paper we propose a method to achieve performance repeatability across test and target environments, that relies on analytical prediction of expected protocol performance as a function of RF environment parameters and forwarding protocol. For the validation of the proposed method, we present analytical, simulation, and experimental results for one-dimensional networks deployed in indoor and outdoor propagation environments, and for two-dimensional networks on four major indoor WSN testbeds to validate the performance of the proposed method on achieving repeatable protocol behavior across diverse set of RF environments.

Effect of psychosocial stress on health has been a central focus area of public health research. This demonstration features a wireless sensor suite called AutoSense that collects and processes cardiovascular, respiratory, and thermoregularity measurements that can inform about the general stress state of test subjects in their natural environment.

The effect of psychosocial stress on health has been a central focus area of public health research. However, progress has been limited due a to lack of wearable sensors that can provide robust measures of stress in the field. In this paper, we present a wireless sensor suite called AutoSense that collects and processes cardiovascular, respiratory, and thermoregularity measurements that can inform about the general stress state of test subjects in their natural environment. AutoSense overcomes several challenges in the design of wearable sensor systems for use in the field. First, it is unobtrusively wearable because it integrates six sensors in a small form factor. Second, it demonstrates a low power design; with a lifetime exceeding ten days while continuously sampling and transmitting sensor measurements. Third, sensor measurements are robust to several sources of errors and confounds inherent in field usage. Fourth, it integrates an ANT radio for low power and integrated quality of service guarantees, even in crowded environments. The AutoSense suite is complemented with a software framework on a smart phone that processes sensor measurements received from AutoSense to infer stress and other rich human behaviors. AutoSense was used in a 20+ subject real-life scientific study on stress in both the lab and field, which resulted in the first model of stress that provides 90% accuracy.

Remote sensing with radar is typically an ill-posed linear inverse problem: a scene is to be inferred from limited measurements of scattered electric fields. Parsimonious models provide a compressed representation of the unknown scene and offer a means for regularizing the inversion task. The emerging field of compressed sensing combines nonlinear reconstruction algorithms and pseudorandom linear measurements to provide reconstruction guarantees for sparse solutions to linear inverse problems. This paper surveys the use of sparse reconstruction algorithms and randomized measurement strategies in radar processing. Although the two themes have a long history in radar literature, the accessible framework provided by compressed sensing illuminates the impact of joining these themes. Potential future directions are conjectured both for extension of theory motivated by practice and for modification of practice based on theoretical insights.

Previous studies of statistical MIMO radar detection performance have used a target model that consists of a large number of point scatterers located within a rectangular target area. These point scatterers have scattering amplitudes that are complex random variables and are spatially uncorrelated, so that the target is a white noise process in space. Spatial correlations are introduced into the target model in this paper, and the impact of these correlations on MIMO radar system detection performance is analyzed.

We consider the problem of joint enhancement of multichannel images with pixel based constraints on the multichannel data. Previous work by Cetin and Karl introduced nonquadratic regularization methods for SAR image enhancement using sparsity enforcing penalty terms. We formulate an optimization problem that jointly enhances complex-valued multichannel images while preserving the cross-channel information, which we include as constraints tying the multichannel images together. We pose this problem as a joint optimization problem with constraints. We first reformulate it as an equivalent (unconstrained) dual problem and develop a numerically-efficient method for solving it. We develop the Dual Descent method, which has low complexity, for solving the joint optimization problem. The algorithm is applied to both an interferometric synthetic aperture radar (IFSAR) problem, in which the relative phase between two complex-valued images indicate height, and to a synthetic multimodal medical image example.

We consider characterization of building interior structure from two-pass interferometric circular synthetic aperture radar data. We model returns from complex building structures as the sum of the responses from scattering primitives-plates, dihedrals, and trihedrals-observed through transfer functions that characterize both the transmission through and reflection from dielectric surfaces. Maximum-likelihood estimates for the location of primitive features and wall propagation/reflection parameters are obtained through a two-stage detection-and-estimation algorithm. We employ a sparse reconstruction algorithm to detect primitive signatures in the presence of wall effects and refine our estimates using a nonlinear minimization of local cost functions. We show that using simplified wall and primitive models, we can extract geometrically relevant features which can be combined to reveal 3-D floorplan estimates of buildings. Examples illustrate the effectiveness of the feature extraction approach.

We consider characterization of building interior structure from two-pass interferometric circular SAR data. We extract dihedral and trihedral scattering primitives that dominate mono-static returns from buildings. The 3D location of each scattering primitive is estimated using the phase information in two-pass data. Using simplified wall and primitive models, we match model signatures to the extracted features using a hypothesis selection and refinement loop. Examples illustrate the effectiveness of the feature extraction approach.

Despite recent theory development, methods of calibration that accurately recover signals from biased sensor readings remain limited in their applicability. Acoustic sensors, for instance, which have been popular in low power wireless sensor networks, are difficult to calibrate in this manner, given their significant hardware variability, large dynamic range, sensitivity to battery power level, and complex spatial/temporal environmental variations. In this paper, we submit that the applicability of calibration is broadened by lifting the calibration problem from the level of sensors to that of sensing applications. We show feasibility of adaptive, easy, and accurate calibration at the level of application-specific features, via an example of recovering the feature of acoustic signal-to-noise ratio (SNR) that is useful in event-detection applications. By easy, we mean there is an efficient, purely local, and stimulus-free procedure for recovering SNR (that compares measured variances for multiple randomly chosen sensitivities, effected via acoustic sensor hardware support); unlike extant calibration methods, the procedure does not need to rely on any synchronization among nodes, long-term correlation between their respective environments, or assumptions about training events. And by accurate, we mean the procedure yields low error in SNR estimation. We provide experimental validation of the difficulty of directly calibrating acoustic signals and the accuracy of our SNR calibration procedure.

Surveillance systems based on wireless sensor network technology have been shown to successfully detect, classify and track evaders over a large area. State information collected via the sensor network also enables these systems to actuate mobile agents so as to achieve surveillance goals, such as target capture and asset protection. But satisfying these goals is complicated by the fact that the track information in a sensor network is routed to mobile agents through multihop wireless communication links and is thus subject to message delays and losses. Stabilization must also be considered in designing pursuer strategies so as to deal with state corruption as well as suboptimal evader strategies. In this article, we formulate optimal pursuit control strategies in the presence of network effects, assuming that target track information has been established locally in the sensor network. We adapt ideas from the theory of differential games to networked games—including ones involving nonperiodic track updates, message losses and message delays—to derive optimal strategies, bounds on the information requirements, and scaling properties of these bounds. We show the inherent stabilization features of our pursuit strategies, both in terms of implementation as well as the strategies themselves.

Surveillance systems based on sensor network technology have been shown to successfully detect, classify and track targets of interest over a large area. State information collected via the sensor network also enables these systems to actuate mobile agents so as to achieve surveillance goals such as target capture and asset protection. But satisfying these goals is complicated by the fact that track information in a sensor network is routed to mobile agents through multi-hop communication links and is thus subject to delays and losses. In addition, as the sensor network is scaled in size, high throughput rates for all pursuers cannot be sustained at all times, which necessitates a network communication strategy that adapts to pursuer information requirements.In this paper, we concentrate on the formulation of optimal pursuit control strategies in the presence of network effects, assuming that target track information has been established locally in the sensor network. We adapt ideas from the theory of differential games to networked games --including ones involving non-periodic track updates, message losses and message delays-- to derive optimal strategies, bounds on the information requirements, and scaling properties of these bounds. Moreover, we present a specific network communication protocol which has the required scalable information characteristics and conclude with the results of experimental studies.

The Kansei testbed at the Ohio State University is designed to facilitate research on networked sensing applications at scale. Kansei embodies a unique combination of characteristics as a result of its design focus on sensing and scaling: (i) Heterogeneous hardware infrastructure with dedicated node resources for local computation, storage, data exfiltration and back-channel communication, to support complex experimentation, (ii) Time accurate hybrid simulation engine for simulating substantially larger arrays using testbed hardware resources, (iii) High fidelity sensor data generation and real-time data and event injection, (iv) Software components and associated job control language to support complex multi-tier experiments utilizing real hardware resources and data generation and simulation engines. In this paper, we present the elements of Kansei testbed architecture, including its hardware and software platforms as well as its hybrid simulation and sensor data generation engines.

Research in sensor networks has begun to address the use of mobility to improve the reachability of the network, but a number of network principles and application patterns remain to be explored in this context. We propose here a network architecture that uses energy constrained devices for enabling new campus wide applications. Specifically, our demonstration illustrates a new network stack and application framework for a class of locality specific applications. The locality specific nature favors exploiting the limited, slow and regional mobility pattern present in large campuses, as opposed to exclusively exploiting the Internet or the cellular network.

Project ExScal (for extreme scale) fielded a 1000+ node wireless sensor network and a 200+ node peer-to-peer ad hoc network of 802.11 devices in a 13km by 300m remote area in Florida, USA during December 2004. In comparison with previous deployments, the ExScal application is relatively complex and its networks are the largest ones of either type fielded to date. In this paper, we overview the key requirements of ExScal, the corresponding design of the hardware/software platform and application, and some results of our experiments.

Distributed sensing systems fuse information from various local measurements to form joint estimates of physical phenomena. We study Bayesian parameter estimation with measurement failures, where the probability of failing to make an observation depends on the unknown parameters. Both the absence and the existence of the measurements provide information about the parameters. We present lower bounds for minimum mean square error estimation. The bounds are applied to the analysis of sensor arrays for source localization and the distributed estimation problem with binary detectors.

The problem of estimating the channel parameters of a new user in a multiuser code-division multiple-access (CDMA) communication system is addressed. It is assumed that the new user transmits training data over a slowly fading multipath channel. The proposed algorithm is based on maximum-likelihood estimation of the channel parameters. First, an asymptotic expression for the likelihood function of channel parameters is derived and a re-parametrization of this likelihood function is proposed. In this re-parametrization, the channel parameters are combined into a discrete time channel filter of symbol period length. Then, expectation-maximization algorithm and alternating projection algorithm-based techniques are considered to extract channel parameters from the estimated discrete channel filter, to maximize the derived asymptotic likelihood function. The performance of the proposed algorithms is evaluated through simulation studies. In addition, the proposed algorithms are compared to previously suggested subspace techniques for multipath channel estimation.