Estimation Diversity and Energy Efficiency in Distributed Sensing

Distributed estimation based on measurements from multiple wireless sensors is investigated. It is assumed that a group of sensors observe the same quantity in independent additive observation noises with possibly different variances. The observations are transmitted using amplify-and-forward (analog) transmissions over non-ideal fading wireless channels from the sensors to a fusion center, where they are combined to generate an estimate of the observed quantity. Assuming that the Best Linear Unbiased Estimator (BLUE) is used by the fusion center, the equal-power transmission strategy is first discussed, where the system performance is analyzed by introducing the concept of estimation outage and estimation diversity, and it is shown that there is an achievable diversity gain on the order of the number of sensors. The optimal power allocation strategies are then considered for two cases: minimum distortion under power constraints; and minimum power under distortion constraints. In the first case, it is shown that by turning off bad sensors, i.e., sensors with bad channels and bad observation quality, adaptive power gain can be achieved without sacrificing diversity gain. Here, the adaptive power gain is similar to the array gain achieved in Multiple-Input Single-Output (MISO) multi-antenna systems when channel conditions are known to the transmitter. In the second case, the sum power is minimized under zero-outage estimation distortion constraint, and some related energy efficiency issues in sensor networks are discussed.Comment: To appear at IEEE Transactions on Signal Processin

Biologically Inspired Approaches to Automated Feature Extraction and Target Recognition

Ongoing research at Boston University has produced computational models of biological vision and learning that embody a growing corpus of scientific data and predictions. Vision models perform long-range grouping and figure/ground segmentation, and memory models create attentionally controlled recognition codes that intrinsically cornbine botton-up activation and top-down learned expectations. These two streams of research form the foundation of novel dynamically integrated systems for image understanding. Simulations using multispectral images illustrate road completion across occlusions in a cluttered scene and information fusion from incorrect labels that are simultaneously inconsistent and correct. The CNS Vision and Technology Labs (cns.bu.edulvisionlab and cns.bu.edu/techlab) are further integrating science and technology through analysis, testing, and development of cognitive and neural models for large-scale applications, complemented by software specification and code distribution.Air Force Office of Scientific Research (F40620-01-1-0423); National Geographic-Intelligence Agency (NMA 201-001-1-2016); National Science Foundation (SBE-0354378; BCS-0235298); Office of Naval Research (N00014-01-1-0624); National Geospatial-Intelligence Agency and the National Society of Siegfried Martens (NMA 501-03-1-2030, DGE-0221680); Department of Homeland Security graduate fellowshi

Data Transmission with Reduced Delay for Distributed Acoustic Sensors

This paper proposes a channel access control scheme fit to dense acoustic sensor nodes in a sensor network. In the considered scenario, multiple acoustic sensor nodes within communication range of a cluster head are grouped into clusters. Acoustic sensor nodes in a cluster detect acoustic signals and convert them into electric signals (packets). Detection by acoustic sensors can be executed periodically or randomly and random detection by acoustic sensors is event driven. As a result, each acoustic sensor generates their packets (50bytes each) periodically or randomly over short time intervals (400ms~4seconds) and transmits directly to a cluster head (coordinator node). Our approach proposes to use a slotted carrier sense multiple access. All acoustic sensor nodes in a cluster are allocated to time slots and the number of allocated sensor nodes to each time slot is uniform. All sensor nodes allocated to a time slot listen for packet transmission from the beginning of the time slot for a duration proportional to their priority. The first node that detect the channel to be free for its whole window is allowed to transmit. The order of packet transmissions with the acoustic sensor nodes in the time slot is autonomously adjusted according to the history of packet transmissions in the time slot. In simulations, performances of the proposed scheme are demonstrated by the comparisons with other low rate wireless channel access schemes.Comment: Accepted to IJDSN, final preprinted versio

Multimodal person recognition for human-vehicle interaction

Next-generation vehicles will undoubtedly feature biometric person recognition as part of an effort to improve the driving experience. Today's technology prevents such systems from operating satisfactorily under adverse conditions. A proposed framework for achieving person recognition successfully combines different biometric modalities, borne out in two case studies

Self-Organizing Information Fusion and Hierarchical Knowledge Discovery: A New Framework Using Artmap Neural Networks

Classifying novel terrain or objects from sparse, complex data may require the resolution of conflicting information from sensors woring at different times, locations, and scales, and from sources with different goals and situations. Information fusion methods can help resolve inconsistencies, as when eveidence variously suggests that and object's class is car, truck, or airplane. The methods described her address a complementary problem, supposing that information from sensors and experts is reliable though inconsistent, as when evidence suggests that an object's class is car, vehicle, and man-made. Underlying relationships among classes are assumed to be unknown to the autonomated system or the human user. The ARTMAP information fusion system uses distributed code representations that exploit the neural network's capacity for one-to-many learning in order to produce self-organizing expert systems that discover hierachical knowlege structures. The fusion system infers multi-level relationships among groups of output classes, without any supervised labeling of these relationships. The procedure is illustrated with two image examples, but is not limited to image domain.Air Force Office of Scientific Research (F49620-01-1-0423); National Geospatial-Intelligence Agency (NMA 201-01-1-2016, NMA 501-03-1-2030); National Science Foundation (SBE-0354378, DGE-0221680); Office of Naval Research (N00014-01-1-0624); Department of Homeland Securit

Self-Organizing Hierarchical Knowledge Discovery by an Artmap Information Fusion System

Classifying terrain or objects may require the resolution of conflicting information from sensors working at different times, locations, and scales, and from users with different goals and situations. Current fusion methods can help resolve such inconsistencies, as when evidence variously suggests that an object is a car, a truck, or an airplane. The methods described here define a complementary approach to the information fusion problem, considering the case where sensors and sources arc both nominally inconsistent and reliable, as when evidence suggests that an object is a car, a vehicle, and man-made. Underlying relationships among classes are assumed to be unknown to the automated system or the human user. The ARTMAP self-organizing rule discovery procedure is illustrated with an image example, but is not limited to the image domain.Air Force Office of Scientific Research (F49620-0 1-1-0423); National Geospatial-Intelligence Agency (NMA 201-01-1-2016, NMA 501-03-1-2030); National Science Foundation (SBE-0354378, DGE-0221680); Office of Naval Research (N00014-01-1-0624

Self-Organizing Hierarchical Knowledge Discovery by an ARTMAP Image Fusion System

Classifying novel terrain or objects front sparse, complex data may require the resolution of conflicting information from sensors working at different times, locations, and scales, and from sources with different goals and situations. Information fusion methods can help resolve inconsistencies, as when evidence variously suggests that an object's class is car, truck, or airplane. The methods described here consider a complementary problem, supposing that information from sensors and experts is reliable though inconsistent, as when evidence suggests that an object's class is car, vehicle, and man-made. Underlying relationships among objects are assumed to be unknown to the automated system or the human user. The ARTMAP information fusion system used distributed code representations that exploit the neural network's capacity for one-to-many learning in order to produce self-organizing expert systems that discover hierarchical knowledge structures. The system infers multi-level relationships among groups of output classes, without any supervised labeling of these relationships.Air Force Office of Scientific Research (F49620-01-1-0397, AFOSR F49620-01-1-0423); Office of Naval Research (N00014-01-1-0624); National Imagery and Mapping Agency and the National Science Foundation for Siegfried Martens (NMA501-03-1-2030, DGE-0221680); Department of Homeland Securit