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This paper presents a novel method for automatically detecting the onset of ventricular depolarization in electrocardiogram (ECG). In order to accommodate highly variable ECG morphologies in potentially noisy ECG signals, a weighted combination of factors that are consistent with the onset of ventricular depolarization is computed. Weight parameters are optimized to maximize the detection accuracy. The proposed method is evaluated against diverse datasets, yielding a bias of 1.69 ms, standard deviation of 10.55 ms, and mean absolute error of 6.68 ms.

The irreversible damage and eventual heart failure caused by untreated aortic stenosis (AS) can be prevented by early detection and timely intervention. Prior work in the field of phonocardiogram (PCG) signal analysis has provided proof of concept for using heart-sound data in AS diagnosis. However, such systems either require operation by trained technicians, fail to address a diverse subject set, or involve unwieldy configuration procedures that challenge real-world application. This paper presents an end-to-end, fully-automated system that uses noise-subtraction, heartbeat-segmentation and quality-assurance algorithms to extract physiologically-motivated features from PCG signals to diagnose AS. When tested on n=96 patients showing a diverse set of cardiac and non-cardiac conditions, the system was able to diagnose AS with 92% sensitivity and 95% specificity.

Human activity monitoring systems using inertial sensors have found wide applications in the field of health and wellness by providing valuable information for diagnostics and rehabilitation processes to doctors and clinicians. As the scales of studies increase, sensor orientation placement errors have become one of the most commonly seen difficulties for such systems. Assuming patients to wear sensors at the correct orientation is unrealistic and will result in a large amount of data loss or distortion. In order to tackle this problem, we propose a double layer classification model. The first layer, not assuming correct sensor orientation, uses orientation-invariant accelerometer magnitude to construct a highly conservative walking detection model. The detected walking beacons from this layer are used to compare to the training template to obtain the true sensor orientation. Then proper rotation matrix can be applied to the whole day data, and fed into the second layer of a finer classifier where orientation-variant features are used. In order to show validity of this method, we hired 7 healthy subjects and 2 stroke patients in the rehab process to wear the sensors for two days and at least 6 hours each day. Ground truth are labeled manually with a Matlab GUI tool. Precision and recall for walking detection in each day are reported and discussed.

Measuring the energy consumed by modern secondary storage devices and attributing it to the causative application's processes can guide optimization of application energy efficiency. Prior work has focused on hard disk drive or file system energy management using custom-built measurement systems and models, but suffers from limitations that prohibit the attribution of the hardware energy consumption to the causative processes. This paper presents a novel process-level disk drive energy estimation system that monitors the operating system kernel to predict the storage power consumption created by individual processes of a software application. Evaluation reveals that the system is accurate with prediction errors around 7% even for workloads involving simultaneous file system operations from multiple processes. The system creates virtually no power consumption overhead in the CPU and about 4% overhead in the RAM module. Results using I/O benchmarks demonstrate that a large portion of the energy usage (up to 100% in some cases) in modern disk drives can occur after the causative processes have already completed execution. Hence, results obtained using power meters or direct energy measurement systems based solely on a benchmark's execution lifetime can drastically underestimate the benchmark application's storage energy demands.

In this paper, we present a new gait analysis method using 3D body motion reconstruction with an activity-specific tracking protocol. A kinematic chain modeling the movement of lower extremities was constructed for general lower body activity monitoring. By exploring the nature of walking, a constrained forward-backward statistical linearized sigma-point Kalman Smoother with periodic state vector resetting was developed. This tracks the dynamic joint configuration during walking. Direct experimental evaluation was provided by step length computation as well as complete motion reconstruction. This method has demonstrated stable long term tracking of walking and yields greater than 95% accuracy for step length estimation.

Direct measurement of event and component resolved energy dissipation in computing systems is critical for energy optimization of computing and networking applications. Prior research focuses on development of energy consumption models and custom-built energy measurement systems, but suffers from critical drawbacks. This paper addresses these limitations and presents solutions using DEEP (Decision-support for Energy Efficient Processing). DEEP is a rapidly-deployed open-source energy measurement platform architecture. The platform provides an unprecedented ability to non-intrusively measure the energy consumption associated with execution of software application code. DEEP is implemented as both an online and offline version. Evaluation demonstrates processing and energy overheads less than 1% for offline and about 5% for the online implementation. The DEEP implementation investigates the impact of data compression on network data transport. An intelligent data compression and transport algorithm is developed using the decision-support capabilities of DEEP. The algorithm creates significant energy savings (38%) in network data transport using dynamic selection of compression schemes to adapt to varying system and wireless network conditions.

Proliferation of low-cost nonintrusive wearable sensors enables researchers to explore capabilities in monitoring physiological parameters remotely expanding healthcare delivery and reducing costs. One of the parameters that is known to be important in rehabilitation and exercise physiology is human motion monitoring, such as analysis of the walking gait and corresponding characteristics. This paper presents a robust on-line methodology for computing clinically relevant metrics for assessing quality of the walking gait in normal subjects and subjects with gait abnormalities, e.g. in patients with stroke. Furthermore, this paper proposes a metric vector that enables characterization of spatiotemporal features of walking quality evolution for post-stroke patients during and after rehabilitation. This method enables visualization of the gait improvement or changes as a result of the rehabilitation or other treatment techniques.

Human motion monitoring and activity classification, specifically in the free-living conditions, are becoming increasingly important as preventative and rehabilitative measures in health and wellness applications. In contrast to gate analysis, wearable sensor-based evaluation of the upper body activities is not well studied. The work in this paper describes a novel system for upper body activity monitoring and classification. This paper focuses specifically on the application of motion classification to a complex task of automating rehabilitation evaluation, such as a Wolf Motor Function Test. The presented system consists of a novel wearable motion sensor platform and classification algorithms that convert motion data to an alphabet representation to form strings of primitives. A general string expression is then derived for each task and a regular expression-based searching method is developed. We present results from the successful application of the proposed system to upper extremity activity characterization.

Continued rapid progress in cost reduction, energy efficiency, and new data transport architectures for body worn sensors enables remote monitoring of patient activity with critical focus and impact on successful outcomes in healthcare. Monitoring systems, composed of both sensor and signal processing systems, seek to provide the capability to classify subject motion state and characteristics. Monitoring system progress has currently enabled classification of normal gait or abnormal gait within constrained laboratory operating conditions. However, monitoring of subjects in the community (specifically in residential environments remote from the laboratory or urban outdoor environments) has introduced fundamental challenges that have not been solved in the past. These challenges become profoundly more severe when monitoring subjects suffering from impaired gait due to conditions including stroke and other neurological disorders. One of the most important measures required in neurological rehabilitation is the accurate classification of walking speed in the community. Changes in absolute speed directly indicate rehabilitation progress and also directly determine whether an individual may remain safe and functional. Healthcare delivery practice requires that characterization of walking parameters and speed must be provided with reliance only on limited system training data acquisition and time. This paper reports on a primary advance in this capability through development of a novel architecture delivering required high rate, continuous sampling at low cost, with compact sensors and with rapidly deployable systems. Most importantly, this paper introduces a new hierarchical classification system applicable to subjects afflicted with hemi paresis due to stroke and disorders including multiple sclerosis. This system provides accurate classification and characterization of walking mobility invariant to other activities performed at the same time and in the presence of interfering signals induced by gait changes. Continued rapid progress in cost reduction, energy efficiency, and new data transport architectures for body worn sensors enables remote monitoring of patient activity with critical focus and impact on successful outcomes in healthcare. Monitoring systems, composed of both sensor and signal processing systems, seek to provide the capability to classify subject motion state and characteristics. Monitoring system progress has currently enabled classification of normal gait or abnormal gait within constrained laboratory operating conditions. However, monitoring of subjects in the community (specifically in residential environments remote from the laboratory or urban outdoor environments) has introduced fundamental challenges that have not been solved in the past. These challenges become profoundly more severe when monitoring subjects suffering from impaired gait due to conditions including stroke and other neurological disorders. One of the most important measures required in neurological rehabilitation is the accurate classification of walking speed in the community. Changes in absolute speed directly indicate rehabilitation progress and also directly determine whether an individual may remain safe and functional. Healthcare delivery practice requires that characterization of walking parameters and speed must be provided with reliance only on limited system training data acquisition and time. This paper reports on a primary advance in this capability through development of a novel architecture delivering required high rate, continuous sampling at low cost, with compact sensors and with rapidly deployable systems. Most importantly, this paper introduces a new hierarchical classification system applicable to subjects afflicted with hemi paresis due to stroke and disorders including multiple sclerosis. This system provides accurate classification and characterization of walking mobility invariant to other activities performed at the same time and in the presence of interfering signals induced by gait changes.

In this paper, we describe a physical activity classification system using a body sensor network (BSN) consisting of cost-sensitive tri-axial accelerometers. We focus on workspace activities (different motions and sitting postures). We use a Naive Bayes classifier and show that we can train the system simply and systematically. For each task, we find a set of features that separate the corresponding activities.

We present a novel Weighted Barrier Function (WBF) method of efficiently computing optimal grasping force distributions for multifingered hands. Second-order conic friction constraints are not linearized, as in many previous works. The force distributions are smooth and rapidly computable, and they enable flexibility in selecting between firm, stable grasps or looser, more efficient grasps. Furthermore, fingers can be disengaged and re-engaged in a smooth manner, which is a critical capability for a large number of manipulation tasks. We present efficient solution methods that do not incur the increased computational complexity associated with solving the Semi-Definite Programming formulations presented in previous works. We present results from static and dynamic simulations which demonstrate the flexibility and computational efficiency associated with WBF force distributions.

AutoGait is a mobile platform that autonomously discovers a user's walking profile and accurately estimates the distance walked. The discovery is made by utilizing the GPS in the user's mobile device when the user is walking outdoors. This profile can then be used both indoors and outdoors to estimate the distance walked. To model the person's walking profile, we take advantage of the fact that a linear relationship exists between step frequency and stride length, which is unique to individuals and applies to everyone regardless of age. Autonomous calibration invisible to users allows the system to maintain a high level of accuracy under changing conditions. AutoGait can be integrated into any pedometer or indoor navigation software on handheld devices as long as they are equipped with GPS. The main contribution of this paper is two fold: (1) we propose an auto-calibration method that trains a person's walking profile by effectively processing noisy GPS readings, and (2) we build a prototype system and validate its performance by performing extensive experiments. Our experimental results confirm that the proposed auto-calibration method can accurately estimate a person's walking profile and thus significantly reduce the error rate.

Many sensing applications require monitoring phenomena with complex spatio-temporal dynamics spread over large spatial domains. Efficient monitoring of such phenomena would require an impractically large number of static sensors; therefore, actuated sensing - mobile robots carrying sensors - is required. Path planning for these robots, i.e., deciding on a subset of locations to observe, is critical for high fidelity monitoring of expansive areas with complex dynamics. We propose MUST - a MUltiscale approach with STochastic modeling. MUST is a hierarchical approach that models the phenomena as a stochastic Gaussian Process that is exploited to select a near-optimal subset of observation locations. We discuss in detail our proposed algorithm for the application of monitoring light intensity in a forest understory. We performed extensive empirical evaluations both in simulation using field data and on an actual cabled robotic system to validate the effectiveness of our proposed algorithm.

In this paper, we present results from a field-test of NIMS-PL, a redundantly actuated cable-driven robot for aquatic applications. We describe installation procedures that enable rapid deployability and present results demonstrating precise calibration capabilities. Detection and correction of positional drift, which is critical for long-term autonomous deployments, is demonstrated, and the rate of system drift is quantified. An example actuated sensing experiment is performed that accurately maps the spatiotemporal variation of the intensity of light incident on a swimming pool. Finally, we propose an upcoming, environmentally significant deployment in which we will map variations in aquatic phenomena such as salinity and pH through the confluence region of two rivers.

Interval training consists of interleaving high intensity exercises with rest periods. This training method is a well known exercise protocol which helps strengthen and improve one’s cardiovascular fitness. However, there is no known method for formulating and tailoring an optimized interval training protocol for a specific individual which maximizes the amount of work done while limiting fatigue. But by using data mining schemes with various attributes, conditions, and data gathered from an individual’s exercise session, we are able to efficiently formulate an optimized interval training method for an individual. Recent advances in wireless wearable sensors and smart phones have made available a new generation of fitness monitoring systems. With accelerometers embedded in an iPhone, a Bluetooth pulse oximeter, and the Weka data mining tool, we are able to formulate the optimized interval training protocols, which can increase the amount of calorie burned up to 29.54%, compared with the modified Tabata interval training protocol.

Cabled robotic systems have been used for a diverse set of applications such as environmental sensing, search and rescue, sports and entertainment and air vehicle simulators. In this paper, we introduce a new cabled robot- Networked Info Mechanical System for Planar actuation (NIMS-PL), with energy profiling capabilities. Accurate energy measurements supported by NIMS-PL enable path planning that optimizes the robotpsilas path subject to an upper bound on energy consumption. We performed extensive empirical validation of the optimized path planning approach in simulation using an environmental sensing application as an example. We also validated the simulation results using NIMS-PL, demonstrating significant improvements in the sensing task when accounting with accurate energy measurements as opposed to Euclidean distance, which is typically used for modeling energy spent in path traversal.

In this paper we describe an algorithm to generate energy efficient trajectories for NIMS3D, a three-dimensional cabled robotic platform. Optimized parabolic paths are used to exploit the relatively low I2R loss associated with operation in lower regions of the workspace. Trajectory optimization is sufficiently fast to enable real time operation. Experimental results on a physical system for a three cable deployment show substantial reductions in energy consumption as compared to linear trajectories.

As concern for water resource availability increases, so does the need for intelligent aquatic sensing applications. The requirements, and complexity of such applications has also increased due to demands for: 1) broad spatial coverage and high spatial resolution monitoring, 2) capability for resolving fine scale spatiotemporal dynamics and 3) the need for rapid system deployment with semi-autonomous operation. With these criteria in mind, we present the Aquatic Networked InfoMechanical System (NIMS-AQ). NIMS-AQ was developed based on experience gained from engineering research and collaboration with aquatic scientists and environmental engineers during several in-field measurement campaigns [1], [2], [3]. In this paper we demonstrate the effectiveness of NIMS- AQ through two experimental sensing campaigns encompassing both river and lake environments. Each campaign is centered around critical water resource monitoring objectives such as temperature, flow and contaminant levels. Experimental results for autonomous depth profiling using a submersible sonar system as well as adaptive sampling algorithms guided by phenomena models are presented herein. The found results conform with our objectives for rapid and systematic operation. Preliminary studies also indicate the systems viability for use with an autonomous iterative experiment design for environmental applications (A-IDEA) methodology that is currently under development. The IDEA methodology [1] provides effective characterization of spatiotemporal dynamics in aquatic environments. A-IDEA, as it is to be implemented on the NIMS-AQ platform, is also described.

The usage of conventional assistive cane devices is critical in reducing the risk of falls, which are particularly detrimental for the elderly and disabled. Many of the individuals that experience the greatest risk of falling rely on cane devices for support of ambulation. However, the results of many studies have shown that incorrect cane usage is prevalent among cane users. The original SmartCane assistive system [4] has been developed to provide a method for acquiring detailed motion data from cane usage. The cane itself, however, lacks any type of programmability as well as real-time data processing algorithms to provide feedback to the cane user. In this demonstration, we have incorporated an embedded computing platform into SmartCane [2] and developed a real-time sensor information processing algorithm that provides direct detection of cane usage characteristics. The new system provides local data processing capability by classifying whether an individual is executing a stride with proper cane motion and applied forces. It also provides direct feedback information to the individual, thereby guiding the subject towards proper cane usage and reducing the risk of falls.

This paper describes a new embedded networked sensor platform architecture that combines hardware and software tools providing detailed, fine-grained real-time energy usage information. We introduce the LEAP2 platform, a qualitative step forward over the previously developed LEAP and other similar platforms. LEAP2 is based on a new low power ASIC system and generally applicable supporting architecture that provides unprecedented capabilities for directly observing energy usage of multiple subsystems in real-time. Real-time observation with microsecond-scale time resolution now enables direct accounting of energy dissipation for each computing task as well as for each hardware subsystem. This new hardware architecture is exploited with our new software tools, etop and endoscope. A series of experimental investigations provide high-resolution power information in networking, storage, memory and processing for primary embedded networked sensing applications. Using these results obtained in real-time we show that for a large class of wireless sensor network nodes, there exist several interdependencies in energy consumption between different subsystems. Through the use of our measurement tools we demonstrate that by carefully selecting the system operating points, energy savings of over 60% can be achieved while retaining system performance.

Over the past decades, the aging infrastructure of wastewater collection system (WCS) has endangered the public health by contaminating the sources of drinking water and by polluting the natural environment. The dangers caused by discharging untreated wastewater have been repeatedly proclaimed by academia, industries, and public sectors. As numerous studies have pointed out, wastewater escaped from the wastewater collection system (WCS) has been a major source of impairment of water quality. In order to identify untreated wastewater escaping from WCS, various schemes have been designed and developed. However, only a small fraction of the WCS is inspected each year due to the cost and complications associated with the current inspection techniques. In an effort to solve this environmental problem, we designed and developed a new methodology for detecting functional deficiencies in WCS. The new methodology, which is utilizing the wireless floating sensors as well as interdisciplinary researches, is not only by far simpler than any current industry methods, but also significantly more efficient. Moreover, this is the first known WCS monitoring methodology utilizing a wireless mobile sensing system.

The usage of conventional assistive cane devices is critical in reducing the risk of falls, which are particularly detrimental to the elderly and disabled. Individuals that experience the greatest risks rely on cane devices for support of ambulation. Results of many studies, however, have shown that incorrect cane usage is prevalent among cane users. The original SmartCane assistive system has been developed to provide a method for acquiring detailed motion data from cane usage. The cane itself, however, lacks any type of programmability as well as real-time data processing algorithms to provide feedback to the cane user. This paper describes the development of a real-time sensor information processing algorithm that provides direct detection of cane usage characteristics. Specifically, it supports direct feedback to the cane user, permitting guidance for proper cane usage and reducing the risk of falls. This paper also aims to improve upon the existing system by incorporating MicroLEAP, an energy-aware embedded computing platform. The new system provides local data processing capability by classifying whether an individual is executing a stride with proper cane motion and applied forces.

Extended system lifetime is a critical requirement for wearable sensor platforms. However, these platforms must also accommodate local data processing, data storage, and broadband wireless communications. Since compact battery storage capacity is constrained, there exists a fundamental tradeoff between energy optimization and performance. Furthermore, biomedical transducers may also demand high peak power dissipation during active operations. Energy management, therefore, must be introduced through new hardware architecture and enabled through software in the overall system design. To effectively optimize energy dissipation for biomedical sensing applications, a new wearable sensor platform, MicroLEAP, has been developed. The MicroLEAP platform supports per-task real-time energy profiling to permit adaptive applications that select platform components to best match dynamically-varying measurement requirements. MicroLEAP design, implementation, and example of energy-aware operation are demonstrated.

In this paper we present algorithms that enable precise trajectory control of NIMS3D, an underconstrained, three-dimensional cabled robot intended for use in actuated sensing. We begin by offering a brief system overview and then describe methods to determine the range of operation of the robot. Next, a discrete-time model of the system is presented. Thereafter, we present an online algorithm for modeling motor behavior. The majority of the paper is dedicated to describing three feedback control laws used to enable accurate trajectory tracking for both linear and non-linear motion profiles. We present experimental results that highlight the strengths and weaknesses of these mechanisms and conclude by offering a series of future plans for NIMS3D.

Recent advancement in microsensor technology permits miniaturization of conventional physiological sensors. Combined with low-power, energy-aware embedded systems and low power wireless interfaces, these sensors now enable patient monitoring in home and workplace environments in addition to the clinic. Low energy operation is critical for meeting typical long operating lifetime requirements. Some of these physiological sensors, such as electrocardiographs (ECG), introduce large energy demand because of the need for high sampling rate and resolution, and also introduce limitations due to reduced user wearability. In this paper, we show how context-aware sensing can provide the required monitoring capability while eliminating the need for energy-intensive continuous ECG signal acquisition. We have implemented a wearable system based on standard widely-used handheld computing hardware components. This system relies on a new software architecture and an embedded inference engine developed for these standard platforms. The performance of the system is evaluated using experimental data sets acquired for subjects wearing this system during an exercise sequence. This same approach can be used in context-aware monitoring of diverse physiological signals in a patient's daily life.

Low-cost sensors and wireless systems can now create a constantly vigilant and pervasive monitoring capability at home, at work, and in conventional point-of-care environments. While progress in this area is underway in sensor technology, mobile computing platforms, and data transport, barriers to large scale application remain ahead, particularly in the area of patient disease diagnosis, which generally requires a diverse set of sensors and instruments that are applied at proper times in response to patient state/behavior. As these sensors may be numerous, and may not be worn comfortably and practicably at all times, a solution is required for the systematic selection of sensors at the point of use. We describe the Incremental Diagnosis Method (IDM) system, an embedded decision support system based on Bayesian statistics and decision analysis theory developed to select or deselect available sensors so that the diagnostic certainty of patient condition best improved while the set of sensors used on the patient body is minimized. IDM has been evaluated in a testbed, the Medical Embedded Device for Individualized Care (MEDIC) system, based on standard, ubiquitous wireless platforms. MEDIC supports local sensing and signal processing, autonomous decision support, and remote reconfiguration and control of wearable components. A detailed evaluation of IDM operation and performance for patient gait analysis is also given in this paper. Finally, we also discuss the many new opportunities provided by IDM and the related future research introduced by this capability.

Dual-radio, dual-processor nodes are an emerging class of Wireless Sensor Network devices that provide both low- energy operation as well as substantially increased computational performance and communication bandwidth for applications. In such systems, the secondary radio and processor operates with sufficiently low power that it may remain always vigilant, while the the main processor and primary, high-bandwidth radio remain off until triggered by the application. By exploiting the high energy efficiency of the main processor and primary radio along with proper usage, net operating energy benefits are enabled for applications. The secondary radio provides a constantly available multi-hop network, while paths in the primary network exist only when required. This paper describes a topology control mechanism for establishing an end-to-end path in a network of dual-radio nodes using the secondary radios as a control channel to selectively wake up nodes along the required end-to-end path. Using numerical models as well as testbed experimentation, we show that our proposed mechanism provides significant energy savings of more than 60% compared to alternative approaches, and that it incurs only moderately greater application latency.

In this paper, we present NIMS RD, a rapidly deployable cable based robotic system developed for environmental monitoring applications. NIMS technology has been under continuous development resulting in several architectures including the NIMS RD system. This is an advance over previous systems in that its operation performance is improved, total system volume and mass is reduced, reliability is increased, and its deployment requires a smaller field team than for previous systems. The NIMS RD design will be described to highlight its new features and innovations. Also, NIMS RD field deployments will be discussed and some of the collected results displayed. Finally, future development directions for the NIMS RD system will also be discussed.

Distributed, high-density spatiotemporal observations are proposed for answering many river related questions, including those pertaining to hydraulics and multi-dimensional river modeling, geomorphology, sediment transport and riparian habitat restoration. In spite of the recent advancements in technology, currently available systems have many constraints that preclude long term, remote, autonomous, high resolution monitoring in the real environment. We present here a case study of an autonomous, high resolution robotic spatial mapping of cross-sectional velocity and salt concentration in a river basin. The scientific objective of this investigation was to characterize the transport and mixing phenomena at the confluence of two distinctly different river streams - San Joaquin River and its tributary Merced River. Several experiments for analyzing the spatial and temporal trends at multiple cross-sections of the San Joaquin River were performed during the campaign from August 21-25, 2006. These include deterministic dense raster scans and in-field adapted experimental design. Preliminary analysis from these experiments illustrating the range of investigations is presented with the focus on adaptive experiments that enable sparse sampling to provide larger spatial coverage without discounting the dynamics in the phenomena. Lessons learned during the campaign are discussed to provide useful insights for similar robotic investigations in aquatic environments.

In this paper, we present NIMS3D, a novel, rapidly deployable cable based robotic system capable of accurate opsitioning within its 3-dimensional span. The system is designed for indoor and outdoor use. In NIMS3D, a node moves via three cables which enable navigation in the 3D volume spanned by the system. The hardware is composed primarily of commercially available components and the software consists of three tiers: low level motor control, motion planning, and user interface. The proposed system has health monitoring capabilities that seek to ensure that robot integrity is not compromised. We provide theoretical and empirical analysis of system characteristics and present results that advocate its use for a variety of applications such as topographical and optical intensity mapping. Finally, we propose a number of future enhancements and plans for the system.

Many environmental applications require high temporal frequency (rapidly changing) and spatially distributed phenomena to be sampled with high fidelity. This requires mobile sensing elements to perform guided sampling in regions of high variability. We propose a multiscale approach for efficiently sampling such phenomena. This approach introduces a hierarchy of sensors according to the sampling fidelity, spatial coverage, and mobility characteristics. In this paper, we report the development of a two-tier multiscale system where information from a low-fidelity, high spatial (global) sensor actuates a mobile robotic node, carrying a high-fidelity, low spatial coverage (spot measurement) sensor, to perform guided sampling in the regions of high phenomenon variability. As a case study of the proposed multiscale paradigm, we investigated the spatiotemporal distribution of the light intensity in a forest understory. The performance of the multiscale approach is verified in simulation and on a physical system. Results suggest that our approach is adequate for the problem of high-frequency spatiotemporal phenomena sampling and significantly outperforms traditional sampling approaches such as a raster scan.

Environment reconstruction through sampling is a difficult task and usually requires a large amount of resources. In this paper, a sampling technique is presented that approaches exhaustive sampling performance with only sparse samples. The goal is achieved by combining information from sensors of different types and resolutions. Image processing techniques are employed to extract global information. This information is passed on to the local sensors to optimize the number and locations of low-level sampling points. The sampled values are then applied back to the image to reconstruct the whole field. The technique is tested in the lab setup and shown to achieve a better result than traditional sampling methods.

Wireless sensor networks (WSNs) are a relatively new and rapidly developing technology; they have a wide range of applications including environmental monitoring, agriculture, and public health. Shared technology is a common usage model for technology adoption in developing countries. WSNs have great potential to be utilized as a shared resource due to their on-board processing and ad-hoc networking capabilities, however their deployment as a shared resource requires that the technical community first address several challenges. The main challenges include enabling sensor portability: (1) the frequent movement of sensors within and between deployments, and rapidly deployable systems; (2) systems that are quick and simple to deploy. We first discuss the feasibility of using sensor networks as a shared resource, and then describe our research in addressing the various technical challenges that arise in enabling such sensor portability and rapid deployment. We also outline our experiences in developing and deploying water quality monitoring wireless sensor networks in Bangladesh and California

The quality of sensing in practical sensor net- work deployments suffers due to the presence of obstacles in the sensing medium. The obstacles may not be known before deployment and may change over time. This makes it hard to provide any estimate on the reliability of sensor data. Hence, it is of interest to develop methods which enable a sensor network to determine the presence and extent of sensing occlusions. We present one such method based on the use of a range sensor to map the obstacles in the medium. A network architecture to support efficient medium mapping facilities is presented, along with several design choices in the acquisition and update of the medium map data. We also present algorithms to rapidly acquire this data and share it among multiple nodes. All algorithms presented are implemented on prototype hardware con- sisting of an actuated laser and an embedded processing platform.

Rapidly emerging applications for microaccelerometer and microgyroscope position sensors require a major advance in stability and resolution. A highly sensitive, stable, and robust sensor interface circuit that can be applied to inertial navigation sensors is presented in this paper. The circuit can be applied to any fully-differential capacitive sensor or to a differential piezoresistive one. The high sensitivity is achieved by a double-sampling technique, compensating switch, and feedback method with a high loop gain. The charge injection by the MOS switches was reduced below the thermal noise level of the opamp. Resolution and stability levels of 10/sup -8/ with a supply variation of 10% are achieved.

We present a radio telemetry solution for remote sensor networks that uses rotational actuation of directional antennas. The benefits of this solution over either omnidirectional or fixed directional antennas for intra-nodal communications include greater energy efficiency, sparse node deployment, and robustness to node failure. Four strategies for minimizing link acquisition time are presented. Each strategy uses a search algorithm tailored to a unique combination of radio hardware to provide nodes with coordination and/or geolocation capabilities. These strategies are implemented on a two-node testbed and their performance is measured in a realistic field trial. Results show that the use of secondary radios for search coordination and geolocation provide an order of magnitude reduction in search duration. In particular, the strategy using only search coordination yields performance that is independent of node separation while performance of the strategy using only geolocation is strongly dependent on range.

A key challenge in sensor networks is ensuring the sustainability of the system at the required performance level, in an autonomous manner. Sustainability is a major concern because of severe resource constraints in terms of energy, bandwidth and sensing capabilities in the system. In this paper, we envision the use of a new design dimension to enhance sustainability in sensor networks - the use of controlled mobility. We argue that this capability can alleviate resource limitations and improve system performance by adapting to deployment demands. While opportunistic use of external mobility has been considered before, the use of controlled mobility is largely unexplored. We also outline the research issues associated with effectively utilizing this new design dimension. Two system prototypes are described to present first steps towards realizing the proposed vision.

Monitoring environmental phenomena by distributed sensor sampling confronts the challenge of unpredictable variability in the spatial distribution of phenomena often coupled with demands for a high spatial sampling rate. The introduction of actuation-enabled robotics sensors permits a system to optimize the sampling distribution through runtime adaptation. However, such systems must efficiently dispense sampling points or otherwise suffer from poor temporal response. In this paper, we propose and characterize an active modeling system. In our approach, as the robotic sensor acquires measurement samples of the environment, it builds a model of the phenomenon. Our algorithm is based on an incremental optimization process where the robot supports a continuous, iterative process of 1) collecting samples with maximal coverage in the design space; 2) building the environmental model; 3) predicting sampling point locations that contribute the greatest certainty regarding the phenomenon; and 4) sampling the environment based on a combined measure of information gain and navigation and sampling cost. This can provide significant reductions in the magnitude of field estimation error with a modest navigational trajectory time. We evaluate our algorithm through a simulation, using a combination of static and mobile sensors sampling light illumination field.

Monitoring of environmental phenomena with embedded networked sensing confronts the challenges of both unpredictable variability in the spatial distribution of phenomena, coupled with demands for a high spatial sampling rate in three dimensions. For example, low distortion mapping of critical solar radiation properties in forest environments may require two-dimensional spatial sampling rates of greater than 10 samples/m2 over transects exceeding 1000 m2 . Clearly, adequate sampling coverage of such a transect requires an impractically large number of sensing nodes. A new approach, Networked Infomechanical System (NIMS), has been introduced to combine autonomous-articulated and static sensor nodes enabling sufficient spatiotemporal sampling density over large transects to meet a general set of environmental mapping demands. This paper describes our work on a critical parts of NIMS, the Task Allocation module. We present our methodologies and the two basic greedy Task Allocation policies - based on time of the task arrival (Time policy) and distance from the robot to the task (Distance policy). We present results from NIMS deployed in a forest reserve and from a lab testbed. The results show that both policies are adequate for the task of spatiotemporal sampling, but also complement each other. Finally, we suggest the future direction of research that would both help us better quantify the performance of our system and create more complex policies combining time, distance, information gain, etc.

Distributed embedded sensor networks are now being successfully deployed in environmental monitoring of natural phenomena as well as for applications in commerce and physical security. Distributed architectures have been developed for cooperative detection, scalable data transport, and other capabilities and services. However, the complexity of environmental phenomena has introduced a new set of challenges related to sensing uncertainty associated with the unpredictable presence of obstacles to sensing that appear in the environment. These obstacles may dramatically reduce the effectiveness of distributed monitoring. Thus, a new distributed, embedded, computing attribute, self-awareness, must be developed and provided to distributed sensor systems. Self-awareness must provide the ability for a deployed system to autonomously detect and reduce its own sensing uncertainty. The physical constraints encountered by sensing require physical reconfiguration for detection and reduction of sensing uncertainty. Networked Infomechanical Systems (NIMS) consisting of distributed, embedded computing systems provide autonomous physical configuration through controlled mobility. The requirements that lead to NIMS, the implementation of NIMS technology, and its first applications are discussed here.

Motivated by the emerging needs for low power narrow-band wireless communication systems, the first micro-power RFIC front-end has been designed using weak inversion CMOS techniques. The front-end, a low-noise amplifier (LNA) combined with a downconversion mixer, has been implemented in a standard 0.8 /spl mu/m CMOS process. The front-end supply current is less than 110 /spl mu/A at 3 V supply bias for operation at 450 MHz. High-Q inductors, used in the front-end design, have been manufactured using low-temperature cofired ceramic technology. The front-end's gain is 25 dB with an IP3 of -15 dBm. This is the lowest current consumption reported to date for a CMOS front-end operating at this frequency.

The authors describe the development of an improved Golay cell. This sensor is constructed entirely from micromachined silicon components. In this device, a silicon oxynitride (SiO/sub x/N/sub y/) membrane is deflected by the thermal expansion of a small volume of trapped gas. To detect the motion of the membrane, an electron tunneling displacement transducer is used. An improved infrared sensor in which the cantilever was dispensed with altogether, and the rebalance force from the feedback circuit applied to the membrane directly, is described. >

In this paper, a working prototype of a /spl mu/-magnetometer based on electron tunneling is described. The tunneling /spl mu/-magnetometer offers a unique combination of advantages including high resolution (/spl sim/10/sup -9/ Tesla//spl radic/Hz), vector sensitivity, wide bandwidth (>10 kHz), low power ( 100 dB), and small temperature coefficient. The preliminary noise equivalent magnetic field (NEB) for a prototype is 6 /spl mu/T//spl radic/Hz, in good agreement with a device performance model. Linear response to increasing DC and AC magnetic fields is also demonstrated. Finally, work in progress to improve the NEB to 4 nT//spl radic/Hz is described.

An electron tunneling sensor that takes advantage of the extreme position sensitivity of electron tunneling and the unique properties of micromachined silicon is presented. Anisotropic etchants, such as EDP, are used to create micrometer- and millimeter-scale structures such as single-crystal silicon springs, electrodes, and tunneling tips, which are then incorporated in the sensor. The electrostatic force between a pair of planar electrodes is used to control the separation between the tunneling tip and the surface. The thermal drift, hysteresis, and creep of conventional piezoelectric actuators is thereby avoided. Because the structure is composed entirely of silicon, the usual problems associated with differential thermal expansion in piezoelectric-based tunnel devices are greatly reduced. >

A wide range of new applications have recently appeared for a low power, low cost, "embedded radio". These wireless interfaces for handheld mobile nodes and Wireless Integrated Network Sensors (WINS) must provide spread spectrum signaling for multi-user operation at 902-928 MHz. Conventional low power RF systems have been implemented in bipolar technology. However, cost considerations motivate the development of complete micropower CMOS RF circuits and systems operating at previously unexplored low power levels. Micropower CMOS VCO and mixer circuits, developed for these emerging narrow-band communication systems, are reported here. Design methods combining high-Q inductors and weak inversion MOSFET operation enable the lowest reported operating power for RF front end components including a mixer and voltage-controlled oscillator (VCO) operating at frequencies of 400-900 MHz. In addition, the VCO, by virtue of its high-Q inductive components, displays the lowest reported phase noise for 1 GHz CMOS VCO systems for any power dissipation.

Low power wireless sensor networks provide a new monitoring and control capability for civil and military applications in transportation, manufacturing, biomedical technology, environmental management, and safety and security systems. Low power integrated CMOS systems are being developed for microsensors, signal processors, microcontrollers, communication transceivers and network access control. This paper covers recent advances in CMOS-based microsensor systems, low power signal processing and RF communication circuits. Communication circuits include the demonstration of a 20 /spl mu/A supply current, 860 MHz, low phase noise CMOS local oscillator.

A 900 MHz PLL frequency synthesizer implemented in 0.6 /spl mu/m CMOS technology is developed for WINS (Wireless Integrated Network Sensors) applications. It incorporates an automatic SC discrete-tuning loop to extend the frequency tuning range to 20% while the VCO gain from the CMOS varactor continuous-tuning is kept low at only 20 MHz/V, to minimize the reference spurs. This frequency synthesizer achieves a phase noise of -102 dBc/Hz at 100 kHz offset and reference spurs below -55 dBc. The synthesizer, including an on-chip VCO, dissipates only 2.5 mA from a 3 V supply.