Robot-Assisted Bridge Inspection

The Center for Robot-Assisted Search and Rescue (CRASAR®) deployed a customized AEOS man-portable unmanned surface vehicle and two commercially available underwater vehicles (the autonomous YSI EcoMapper and the tethered VideoRay) for inspection of the Rollover Pass bridge in the Bolivar peninsula of Texas in the aftermath of Hurricane Ike. A preliminary domain analysis with the vehicles identified key tasks in subsurface bridge inspection (mapping of the debris field and inspecting the bridge footings for scour), control challenges (navigation under loss of GPS, underwater obstacle avoidance, and stable positioning in high currents without GPS), possible improvements to human-robot interaction (having additional display units so that mission specialists can view and operate on imagery independently of the operator control unit, incorporating 2-way audio to allow operator and field personnel to communicate while launching or recovering the vehicle, and increased state sensing for reliability), and discussed the cooperative use of surface, underwater, and aerial vehicles. The article posits seven milestones in the development of a fully functional UMV for bridge inspection: standardize mission payloads, add health monitoring, improve teleoperation through better human-robot interaction, add 3D obstacle avoidance, improve station-keeping, handle large data sets, and support cooperative sensing.     

Setpoint regulation for stochastically interacting robots

We present an integral feedback controller that regulates the average copy number of an assembly in a system of stochastically interacting robots. The mathematical model for these robots is a tunable reaction network, which makes this approach applicable to a large class of other systems, including ones that exhibit stochastic self-assembly at various length scales. We prove that this controller works for a range of setpoints and how to compute this range both analytically and experimentally. Finally, we demonstrate these ideas on a physical testbed.     

Portfolios of buyer–supplier exchange relationships in an online marketplace for IT services

Most studies on the role of IT for economic exchange predicted that under a given set of exchange attributes buyers would choose a certain mode of relationship with suppliers. Our study of an online IT services marketplace revealed that buyers do not have a single, uniformly preferred type of relationship, but rather maintain a portfolio of relationships. Furthermore, different buyers arrange their portfolios of exchange relationships in different ways. We found four clusters of buyers' portfolios of relationships labeled Transactional buyers, Recurrent buyers, Small diversifiers and Large diversifiers, that differ in their usage of auction or negotiation mechanism, their supplier relations as well as their usage of preferred suppliers. Our results thus paint a richer picture of how buyers organize their supplier networks online.     

Discovery of sound sources by an autonomous mobile robot

In this work, we describe an autonomous mobile robotic system for finding, investigating, and modeling ambient noise sources in the environment. The system has been fully implemented in two different environments, using two different robotic platforms and a variety of sound source types. Making use of a two-step approach to autonomous exploration of the auditory scene, the robot first quickly moves through the environment to find and roughly localize unknown sound sources using the auditory evidence grid algorithm. Then, using the knowledge gained from the initial exploration, the robot investigates each source in more depth, improving upon the initial localization accuracy, identifying volume and directivity, and, finally, building a classification vector useful for detecting the sound source in the future.     

Evidential data mining: precise support and confidence

Associative classification has been shown to provide interesting results whenever of use to classify data. With the increasing complexity of new databases, retrieving valuable information and classifying incoming data is becoming a thriving and compelling issue. The evidential database is a new type of database that represents imprecision and uncertainty. In this respect, extracting pertinent information such as frequent patterns and association rules is of paramount importance task. In this work, we tackle the problem of pertinent information extraction from an evidential database. A new data mining approach, denoted EDMA, is introduced that extracts frequent patterns overcoming the limits of pioneering works of the literature. A new classifier based on evidential association rules is thus introduced. The obtained association rules, as well as their respective confidence values, are studied and weighted with respect to their relevance. The proposed methods are thoroughly experimented on several synthetic evidential databases and showed performance improvement.     

Microfluidic device with dual-channel fluorescence acquisition for quantification/identification of cancer cells

This paper presents an optofluidic device for cell discrimination with two independent interrogation regions. Pumping light is coupled to the device, and cell fluorescence is extracted from the two interrogation zones by using optical fibers embedded in the optofluidic chip. To test the reliability of this device, AU-565 cells—expressing EpCAM and HER2 receptors—and RAMOS cells were mixed in a controlled manner, confined inside a hydrodynamic focused flow in the microfluidic chip and detected individually so that they could be discriminated as positive (signal reception from fluorescently labeled antibodies from the AU-565 cells) or negative events (RAMOS cells). A correlation analysis of the two signals reduces the influence of noise on the overall data.     

Remotely sensed surface water extent as an indicator of short-term changes in ecohydrological processes in sub-Saharan Western Africa

The highly variable rainfall in the arid and semi-arid regions of sub-Saharan Western Africa drives both surface water availability and vegetation cover. Recent studies have established linkages between rainfall and vegetation cover at local to regional scales, but no study related yet remote sensing derived rainfall and vegetation cover to the available surface water. A new dataset based on SPOT VEGETATION (VGT) represents surface water bodies (SWB) in the arid and semi-arid regions of sub-Saharan Western Africa. Water bodies represent the integrated hydrological response of a catchment, and changes in their spatial extent involve complex interactions at the catchment scale. We analyzed time series of remotely sensed vegetation cover, rainfall and surface water extent for the period 1999–2008, and could detect and statistically demonstrate the links between these biophysical variables. Our findings for two regions in Mali and Burkina Faso suggest that vegetation cover is positively related to the amount of available surface water for those catchments that are mainly covered by annual plants. The observed relationships between remotely sensed variables allow developing ecological indicators that can indicate short-term changes in arid and semi-arid ecosystems at local to regional scales.