106 research outputs found
Temporal Landscapes: A Graphical Temporal Logic for Reasoning
We present an elementary introduction to a new logic for reasoning about
behaviors that occur over time. This logic is based on temporal type theory.
The syntax of the logic is similar to the usual first-order logic; what differs
is the notion of truth value. Instead of reasoning about whether formulas are
true or false, our logic reasons about temporal landscapes. A temporal
landscape may be thought of as representing the set of durations over which a
statement is true. To help understand the practical implications of this
approach, we give a wide variety of examples where this logic is used to reason
about autonomous systems.Comment: 20 pages, lots of figure
Hearing the clusters in a graph: A distributed algorithm
We propose a novel distributed algorithm to cluster graphs. The algorithm
recovers the solution obtained from spectral clustering without the need for
expensive eigenvalue/vector computations. We prove that, by propagating waves
through the graph, a local fast Fourier transform yields the local component of
every eigenvector of the Laplacian matrix, thus providing clustering
information. For large graphs, the proposed algorithm is orders of magnitude
faster than random walk based approaches. We prove the equivalence of the
proposed algorithm to spectral clustering and derive convergence rates. We
demonstrate the benefit of using this decentralized clustering algorithm for
community detection in social graphs, accelerating distributed estimation in
sensor networks and efficient computation of distributed multi-agent search
strategies
Distributed H∞-consensus filtering in sensor networks with multiple missing measurements: The finite-horizon case
The official published version of the article can be found at the link below.This paper is concerned with a new distributed H∞-consensus filtering problem over a finite-horizon for sensor networks with multiple missing measurements. The so-called H∞-consensus performance requirement is defined to quantify bounded consensus regarding the filtering errors (agreements) over a finite-horizon. A set of random variables are utilized to model the probabilistic information missing phenomena occurring in the channels from the system to the sensors. A sufficient condition is first established in terms of a set of difference linear matrix inequalities (DLMIs) under which the expected H∞-consensus performance constraint is guaranteed. Given the measurements and estimates of the system state and its neighbors, the filter parameters are then explicitly parameterized by means of the solutions to a certain set of DLMIs that can be computed recursively. Subsequently, two kinds of robust distributed H∞-consensus filters are designed for the system with norm-bounded uncertainties and polytopic uncertainties. Finally, two numerical simulation examples are used to demonstrate the effectiveness of the proposed distributed filters design scheme.This work was supported in part by the Engineering and Physical Sciences Research Council (EPSRC) of the UK under Grant GR/S27658/01, the Royal Society of the UK, and the Alexander von Humboldt Foundation of Germany
Scalable Approach to Uncertainty Quantification and Robust Design of Interconnected Dynamical Systems
Development of robust dynamical systems and networks such as autonomous
aircraft systems capable of accomplishing complex missions faces challenges due
to the dynamically evolving uncertainties coming from model uncertainties,
necessity to operate in a hostile cluttered urban environment, and the
distributed and dynamic nature of the communication and computation resources.
Model-based robust design is difficult because of the complexity of the hybrid
dynamic models including continuous vehicle dynamics, the discrete models of
computations and communications, and the size of the problem. We will overview
recent advances in methodology and tools to model, analyze, and design robust
autonomous aerospace systems operating in uncertain environment, with stress on
efficient uncertainty quantification and robust design using the case studies
of the mission including model-based target tracking and search, and trajectory
planning in uncertain urban environment. To show that the methodology is
generally applicable to uncertain dynamical systems, we will also show examples
of application of the new methods to efficient uncertainty quantification of
energy usage in buildings, and stability assessment of interconnected power
networks
ECO: Egocentric Cognitive Mapping
We present a new method to localize a camera within a previously unseen
environment perceived from an egocentric point of view. Although this is, in
general, an ill-posed problem, humans can effortlessly and efficiently
determine their relative location and orientation and navigate into a
previously unseen environments, e.g., finding a specific item in a new grocery
store. To enable such a capability, we design a new egocentric representation,
which we call ECO (Egocentric COgnitive map). ECO is biologically inspired, by
the cognitive map that allows human navigation, and it encodes the surrounding
visual semantics with respect to both distance and orientation. ECO possesses
three main properties: (1) reconfigurability: complex semantics and geometry is
captured via the synthesis of atomic visual representations (e.g., image
patch); (2) robustness: the visual semantics are registered in a geometrically
consistent way (e.g., aligning with respect to the gravity vector,
frontalizing, and rescaling to canonical depth), thus enabling us to learn
meaningful atomic representations; (3) adaptability: a domain adaptation
framework is designed to generalize the learned representation without manual
calibration. As a proof-of-concept, we use ECO to localize a camera within
real-world scenes---various grocery stores---and demonstrate performance
improvements when compared to existing semantic localization approaches
Distributed state estimation for uncertain Markov-type sensor networks with mode-dependent distributed delays
This the post-print version of the Article. The official published version can be accessed from the link below - Copyright @ 2012 John Wiley & Sons, Ltd.In this paper, the distributed state estimation problem is investigated for a class of sensor networks described by uncertain discrete-time dynamical systems with Markovian jumping parameters and distributed time-delays. The sensor network consists of sensor nodes characterized by a directed graph with a nonnegative adjacency matrix that specifies the interconnection topology (or the distribution in the space) of the network. Both the parameters of the target plant and the sensor measurements are subject to the switches from one mode to another at different times according to a Markov chain. The parameter uncertainties are norm-bounded that enter into both the plant system as well as the network outputs. Furthermore, the distributed time-delays are considered, which are also dependent on the Markovian jumping mode. Through the measurements from a small fraction of the sensors, this paper aims to design state estimators that allow the nodes of the sensor network to track the states of the plant in a distributed way. It is verified that such state estimators do exist if a set of matrix inequalities is solvable. A numerical example is provided to demonstrate the effectiveness of the designed distributed state estimators.This work was supported in part by the Royal Society of the U.K., the National Natural Science Foundation of China under Grants 60804028 and 61028008, the Specialized Research Fund for the Doctoral Program of Higher Education for New Teachers in China under Grant 200802861044, the Teaching and Research Fund for Excellent Young Teachers at Southeast University of China, the International Science and Technology Cooperation Project of China under Grant No. 2009DFA32050, and the Alexander von Humboldt Foundation of Germany
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