A decentralized motion coordination strategy for dynamic target tracking

This paper presents a decentralized motion planning algorithm for the distributed sensing of a noisy dynamical process by multiple cooperating mobile sensor agents. This problem is motivated by localization and tracking tasks of dynamic targets. Our gradient-descent method is based on a cost function that measures the overall quality of sensing. We also investigate the role of imperfect communication between sensor agents in this framework, and examine the trade-offs in performance between sensing and communication. Simulations illustrate the basic characteristics of the algorithms

A Decision-Making Framework for Control Strategies in Probabilistic Search

This paper presents the search problem formulated as a decision problem, where the searcher decides whether the target is present in the search region, and if so, where it is located. Such decision-based search tasks are relevant to many research areas, including mobile robot missions, visual search and attention, and event detection in sensor networks. The effect of control strategies in search problems on decision-making quantities, namely time-to-decision, is investigated in this work. We present a Bayesian framework in which the objective is to improve the decision, rather than the sensing, using different control policies. Furthermore, derivations of closed-form expressions governing the evolution of the belief function are also presented. As this framework enables the study and comparison of the role of control for decision-making applications, the derived theoretical results provide greater insight into the sequential processing of decisions. Numerical studies are presented to verify and demonstrate these results

Search and Pursuit-Evasion in Mobile Robotics, A survey

This paper surveys recent results in pursuitevasion and autonomous search relevant to applications in mobile robotics. We provide a taxonomy of search problems that highlights the differences resulting from varying assumptions on the searchers, targets, and the environment. We then list a number of fundamental results in the areas of pursuit-evasion and probabilistic search, and we discuss field implementations on mobile robotic systems. In addition, we highlight current open problems in the area and explore avenues for future work

Scheduling for Distributed Sensor Networks

We examine the problem of distributed estimation when only one sensor can take a measurement per time step. The measurements are then exchanged among the sensors. The problem is motivated by the use of sonar range-finders used by the vehicles on the Caltech Multi-Vehicle Wireless Testbed. We solve for the optimal recursive estimation algorithm when the sensor switching schedule is given. Then we investigate several approaches for determining an optimal sensor switching strategy. We see that this problem involves searching a tree in general and propose and analyze two strategies for pruning the tree to keep the computation limited. The first is a sliding window strategy motivated by the Viterbi algorithm, and the second one uses thresholding. We also study a technique that employs choosing the sensors randomly from a probability distribution which can then be optimized. The performance of the algorithms are illustrated with the help of numerical examples

Multiscale search using probabilistic quadtrees

Abstract — We propose a novel framework to search for a static target using a multiscale representation. The algorithm we present is appropriate when the target detection sensor trades off accuracy versus covered area, e.g., when a UAV can fly and sense at different elevations. A structure based on quadtrees is used to propagate a posterior about the target location using a variable resolution representation that is dynamically refined in regions associated with higher probability of target presence. Probabilities are updated using a Bayesian approach accounting for erroneous sensor readings in the form of false positives and missed detections. The model we propose is coupled with a search and decision algorithm that determines where to sense next and with which accuracy. The search algorithm is based on an objective function accounting for both probability of detection and motion costs, thus aiming to minimize traveled distances while trying to localize the target. The paper is concluded with simulation results showing our approach outperforms commonly used methods based on uniform resolution grids. I

Linda-Smalltalk : parallel implementation of Little Smalltalk using tuple space communication of Linda

A new technique of implementing the object-oriented language Little Smalltalk is developed using tuple space communication of Linda. In this implementation, the language's abstract constructs such as object, class, and method are naturally mapped into tuple space. Since Linda's tuple space is designed to facilitate parallel processing, this implementation technique makes it particularly easy to introduce parallelism into the language. In other words, parallelism is achieved as a natural outgrowth of the implementation technique, without bringing excessive complexity to the system. Although it may be natural in abstract level, mapping the entire objects into the tuple space turned out to be unrealistic in implementation level owing the the inherent characteristics of the tuple space. A simple case of implicit parallelism can be detected by syntactic context of the language. General issues on object-oriented concurrent programming are also discussed

BMDO materials testing in the EOIM-3 experiment

The NASA Evaluation of Oxygen Interactions with Materials-3 (EOIM-3) experiment served as a testbed for a variety of materials that are candidates for Ballistic Missile Defense Organization (BMDO) space assets. The materials evaluated on this flight experiment were provided by BMDO contractors and technology laboratories. A parallel ground-based exposure evaluation was conducted using the Fast Atom Sample Tester (FAST) atomic-oxygen simulation facility at Physical Sciences, Inc. The EOIM-3 flight materials were exposed to an atomic oxygen fluence of approximately 2.3 x 10(exp 20) atoms/sq cm. The ground-based exposure fluence of 2.0 - 2.5 x 10(exp 20) atoms/sq cm permits direct comparison with that of the flight-exposed specimens. The results from the flight test conducted aboard STS-46 and the correlative ground-based exposure are summarized here. A more detailed correlation study is presented in the JPL Publication 93-31 entitled 'Flight-and Ground-Test Correlation Study of BMDO SDS Materials: Phase 1 Report'. In general, the majority of the materials survived the AO environment with their performance tolerances maintained for the duration of the exposure. Optical materials, baffles, and coatings performed extremely well as did most of the thermal coatings and tribological materials. A few of the candidate radiator, threat shielding, and structural materials showed significant degradation. Many of the coatings designed to protect against AO erosion of sensitive materials performed this function well

DNA repair gene XRCC1 polymorphisms and bladder cancer risk

BACKGROUND: Cigarette smoking and chemical occupational exposure are the main known risk factors for bladder transitional cell carcinoma (TCC). Oxidative DNA damage induced by carcinogens present in these exposures requires accurate base excision repair (BER). The XRCC1 protein plays a crucial role in BER by acting as a scaffold for other BER enzymes. Variants in the XRCC1 gene might alter protein structure or function or create alternatively spliced proteins which may influence BER efficiency and hence affect individual susceptibility to bladder cancer. Recent epidemiological studies have shown inconsistent associations between these polymorphisms and bladder cancer. To clarify the situation, we conducted a comprehensive analysis of 14 XRCC1 polymorphisms in a case-control study involving more than 1100 subjects. RESULTS: We found no evidence of an association between any of the 14 XRCC1 polymorphisms and bladder cancer risk. However, we found carriage of the variant Arg280His allele to be marginally associated with increased bladder cancer risk compared to the wild-type genotype (adjusted odds ratio [95% confidence interval], 1.50 [0.98–2.28], p = 0.06). The association was stronger for current smokers such that individuals carrying the variant 280His allele had a two to three-fold increased risk of bladder cancer compared to those carrying the wildtype genotype (p = 0.09). However, the evidence for gene-environment interaction was not statistically significant (p = 0.45). CONCLUSION: We provide no evidence of an association between polymorphisms in XRCC1 and bladder cancer risk, although our study had only limited power to detect the association for low frequency variants, such as Arg280His

Central Neurocytoma: A Review of Clinical Management and Histopathologic Features.

Central neurocytoma (CN) is a rare, benign brain tumor often located in the lateral ventricles. CN may cause obstructive hydrocephalus and manifest as signs of increased intracranial pressure. The goal of treatment for CN is a gross total resection (GTR), which often yields excellent prognosis with a very high rate of tumor control and survival. Adjuvant radiosurgery and radiotherapy may be considered to improve tumor control when GTR cannot be achieved. Chemotherapy is also not considered a primary treatment, but has been used as a salvage therapy. The radiological features of CN are indistinguishable from those of other brain tumors; therefore, many histological markers, such as synaptophysin, can be very useful for diagnosing CNs. Furthermore, the MIB-1 Labeling Index seems to be correlated with the prognosis of CN. We also discuss oncogenes associated with these elusive tumors. Further studies may improve our ability to accurately diagnose CNs and to design the optimal treatment regimens for patients with CNs

Impact of Trucking Network Flow on Preferred Biorefinery Locations in the Southern United States

The impact of the trucking transportation network flow was modeled for the southern United States. The study addresses a gap in existing research by applying a Bayesian logistic regression and Geographic Information System (GIS) geospatial analysis to predict biorefinery site locations. A one-way trucking cost assuming a 128.8 km (80-mile) haul distance was estimated by the Biomass Site Assessment model. The median family income, timberland annual growth-to-removal ratio, and transportation delays were significant in determining mill location. Transportation delays that directly impacted the costs of trucking are presented. A logistic model with Bayesian inference was used to identify preferred site locations, and locations not preferential for a mill location. The model predicted that higher probability locations for smaller biomass mills (feedstock capacity, the size of sawmills) were in southern Alabama, southern Georgia, southeast Mississippi, southern Virginia, western Louisiana, western Arkansas, and eastern Texas. The higher probability locations for large capacity mills (feedstock capacity, the size for pulp and paper mills) were in southeastern Alabama, southern Georgia, central North Carolina, and the Mississippi Delta regions