31,058 research outputs found
Minimizing the expected time to detect a randomly located lost target using 3-dimensional search technique
This paper considers a new model in search theory to find a randomly located target in the 3-dimensional space. An approximation algorithm that facilitates searching procedures for searchers or robots is presented. The expected time to detect the target is also proved. The statistical analysis by calculating the optimal search strategy which minimizes the time to detect the target, assuming trivariate standard normal distribution is provided, and the technique by flowcharts is designed as well. The effectiveness of this strategy is illustrated by introducing an application from real world
Efficient exploration of unknown indoor environments using a team of mobile robots
Whenever multiple robots have to solve a common task, they need to coordinate their actions to carry out the task efficiently and to avoid interferences between individual robots. This is especially the case when considering the problem of exploring an unknown environment with a team of mobile robots. To achieve efficient terrain coverage with the sensors of the robots, one first needs to identify unknown areas in the environment. Second, one has to assign target locations to the individual robots so that they gather new and relevant information about the environment with their sensors. This assignment should lead to a distribution of the robots over the environment in a way that they avoid redundant work and do not interfere with each other by, for example, blocking their paths. In this paper, we address the problem of efficiently coordinating a large team of mobile robots. To better distribute the robots over the environment and to avoid redundant work, we take into account the type of place a potential target is located in (e.g., a corridor or a room). This knowledge allows us to improve the distribution of robots over the environment compared to approaches lacking this capability. To autonomously determine the type of a place, we apply a classifier learned using the AdaBoost algorithm. The resulting classifier takes laser range data as input and is able to classify the current location with high accuracy. We additionally use a hidden Markov model to consider the spatial dependencies between nearby locations. Our approach to incorporate the information about the type of places in the assignment process has been implemented and tested in different environments. The experiments illustrate that our system effectively distributes the robots over the environment and allows them to accomplish their mission faster compared to approaches that ignore the place labels
Target Detection in a Known Number of Intervals Based on Cooperative Search Technique
Finding hidden/lost targets in a broad region costs strenuous effort and
takes a long time. From a practical view, it is convenient to analyze the
available data to exclude some parts of the search region. This paper discusses
the coordinated search technique of a one-dimensional problem with a search
region consisting of several mutual intervals. In other words, if the lost
target has a probability of existing in a bounded interval, then the successive
bounded interval has a far-fetched probability. Moreover, the search domain is
swept by two searchers moving in opposite directions, leading to three
categories of target distribution truncations: commensurate, uneven, and
symmetric. The truncated probability distributions are defined and applied
based on the proposed classification to calculate the expected value of the
elapsed time to find the hidden object. Furthermore, the optimization of the
associated expected time values of various cases is investigated based on
Newton's method. Several examples are presented to discuss the behavior of
various distributions under each case of truncation. Also, the associated
expected time values are calculated as their minimum values.Comment: 32 pages, 11 figure
Precision cosmology from future lensed gravitational wave and electromagnetic signals
The standard siren approach of gravitational wave cosmology appeals to the
direct luminosity distance estimation through the waveform signals from
inspiralling double compact binaries, especially those with electromagnetic
counterparts providing redshifts. It is limited by the calibration
uncertainties in strain amplitude and relies on the fine details of the
waveform. The Einstein Telescope is expected to produce
gravitational wave detections per year, of which will be lensed. Here
we report a waveform-independent strategy to achieve precise cosmography by
combining the accurately measured time delays from strongly lensed
gravitational wave signals with the images and redshifts observed in the
electromagnetic domain. We demonstrate that just 10 such systems can provide a
Hubble constant uncertainty of for a flat Lambda Cold Dark Matter
universe in the era of third generation ground-based detectors
Post-transcriptional knowledge in pathway analysis increases the accuracy of phenotypes classification
Motivation: Prediction of phenotypes from high-dimensional data is a crucial
task in precision biology and medicine. Many technologies employ genomic
biomarkers to characterize phenotypes. However, such elements are not
sufficient to explain the underlying biology. To improve this, pathway analysis
techniques have been proposed. Nevertheless, such methods have shown lack of
accuracy in phenotypes classification. Results: Here we propose a novel
methodology called MITHrIL (Mirna enrIched paTHway Impact anaLysis) for the
analysis of signaling pathways, which has built on top of the work of Tarca et
al., 2009. MITHrIL extends pathways by adding missing regulatory elements, such
as microRNAs, and their interactions with genes. The method takes as input the
expression values of genes and/or microRNAs and returns a list of pathways
sorted according to their deregulation degree, together with the corresponding
statistical significance (p-values). Our analysis shows that MITHrIL
outperforms its competitors even in the worst case. In addition, our method is
able to correctly classify sets of tumor samples drawn from TCGA. Availability:
MITHrIL is freely available at the following URL:
http://alpha.dmi.unict.it/mithril
Airborne chemical sensing with mobile robots
Airborne chemical sensing with mobile robots has been an active research areasince the beginning of the 1990s. This article presents a review of research work in this field,including gas distribution mapping, trail guidance, and the different subtasks of gas sourcelocalisation. Due to the difficulty of modelling gas distribution in a real world environmentwith currently available simulation techniques, we focus largely on experimental work and donot consider publications that are purely based on simulations
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