Subspace Clustering for Situation Assessment in Aquatic Drones: A Sensitivity Analysis for State-Model Improvement

Abstract

In this paper, we propose the use of subspace clustering to detect the states of dynamical systems from sequences of observations. In particular, we generate sparse and interpretable models that relate the states of aquatic drones involved in autonomous water monitoring to the properties (e.g., statistical distribution) of data collected by drone sensors. The subspace clustering algorithm used is called SubCMedians. A quantitative experimental analysis is performed to investigate the connections between i) learning parameters and performance, ii) noise in the data and performance. The clustering obtained with this analysis outperforms those generated by previous approaches.     

A population-based iterated greedy algorithm for the minimum weight vertex cover problem

Given an undirected, vertex-weighted graph, the goal of the minimum weight vertex cover problem is to find a subset of the vertices of the graph such that the subset is a vertex cover and the sum of the weights of its vertices is minimal. This problem is known to be NP-hard and no efficient algorithm is known to solve it to optimality. Therefore, most existing techniques are based on heuristics for providing approximate solutions in a reasonable computation time.Population-based search approaches have shown to be effective for solving a multitude of combinatorial optimization problems. Their advantage can be identified as their ability to find areas of the space containing high quality solutions. This paper proposes a simple and efficient population-based iterated greedy algorithm for tackling the minimum weight vertex cover problem. At each iteration, a population of solutions is established and refined using a fast randomized iterated greedy heuristic based on successive phases of destruction and reconstruction. An extensive experimental evaluation on a commonly used set of benchmark instances shows that our algorithm outperforms current state-of-the-art approaches.     

The hyper-cube framework for ant colony optimization.

Ant colony optimization is a metaheuristic approach belonging to the class of model-based search algorithms. In this paper, we propose a new framework for implementing ant colony optimization algorithms called the hyper-cube framework for ant colony optimization. In contrast to the usual way of implementing ant colony optimization algorithms, this framework limits the pheromone values to the interval [0,1]. This is obtained by introducing changes in the pheromone value update rule. These changes can in general be applied to any pheromone value update rule used in ant colony optimization. We discuss the benefits coming with this new framework. The benefits are twofold. On the theoretical side, the new framework allows us to prove that in Ant System, the ancestor of all ant colony optimization algorithms, the average quality of the solutions produced increases in expectation over time when applied to unconstrained problems. On the practical side, the new framework automatically handles the scaling of the objective function values. We experimentally show that this leads on average to a more robust behavior of ant colony optimization algorithms.     

Concealed weapon detection and visualization in a synthesized image

Images acquired by heterogeneous image sensors may provide complementary information about the scene, for instance, the visual image can provide personal identification information like the facial pattern while the infrared (IR) or millimeter wave image can detect the suspicious regions of concealed weapons. Usually, a technique, namely multiresolution pixel-level image fusion is applied to integrate the information from multi-sensor images. However, when the images are significantly different, the performance of the multiresolution fusion algorithms is not always satisfactory. In this study, a new strategy consisting of two steps is proposed. The first step is to use an unsupervised fuzzy k-means clustering to detect the concealed weapon from the IR image. The detected region is embedded in the visual image in the second step and this process is implemented with a multiresolution mosaic technique. Therefore, the synthesized image retains the quality comparable to the visual image while the region of the concealed weapon is highlighted and enhanced. The experimental results indicate the efficiency of the proposed approach.This material is based on part of the work carried out at the SPCR laboratory of Lehigh University and the work is partially supported by the U. S. Army Research Office under grant number DAAD19-00-1-0431. The content of the information does not necessarily reflect the position or the policy of the federal government, and no official endorsement should be inferred.     

Particle Monte Carlo and lattice-Boltzmann methods for simulations of gas-particle flows

A numerical solution concept is presented for simulating the transport and deposition to surfaces of discrete, small (nano-)particles. The motion of single particles is calculated from the Langevin equation by Lagrangian integration under consideration of different forces such as drag force, van der Waals forces, electrical Coulomb forces and not negligible for small particles, under stochastic diffusion (Brownian diffusion). This so-called particle Monte Carlo method enables the computation of macroscopic filter properties as well the detailed resolution of the structure of the deposited particles. The flow force and the external forces depend on solutions of continuum equations, as the Navier-Stokes equations for viscous, incompressible flows or a Laplace equation of the electrical potential. Solutions of the flow and potential fields are computed here using lattice-Boltzmann methods. Essential advantage of these methods are the easy and efficient treatment of three-dimensional complex geometries, given by filter geometries or particle covered surfaces. A number of numerical improvements, as grid refinement or boundary fitting, were developed for lattice-Boltzmann methods in previous studies and applied to the present problem. The interaction between the deposited particle layer and the fluid field or the external forces is included by recomputing of these fields with changed boundaries. A number of simulation results show the influence of different effects on the particle motion and deposition.     

Efficient computation based on stochastic spikes

The speed and reliability of mammalian perception indicate that cortical computations can rely on very few action potentials per involved neuron. Together with the stochasticity of single-spike events in cortex, this appears to imply that large populations of redundant neurons are needed for rapid computations with action potentials. Here we demonstrate that very fast and precise computations can be realized also in small networks of stochastically spiking neurons. We present a generative network model for which we derive biologically plausible algorithms that perform spike-by-spike updates of the neuron's internal states and adaptation of its synaptic weights from maximizing the likelihood of the observed spike patterns. Paradigmatic computational tasks demonstrate the online performance and learning efficiency of our framework. The potential relevance of our approach as a model for cortical computation is discussed.     

Transformation of Na2O-CeO2-B2O3 glass into a material with interconnected pores

Substitution of SiO₂ with CeO₂ in the ternary sodium borosilicate system was found to produce phase-separable glasses. Heat treatment of these glasses resulted in separation into two different phases. The one phase enriched in sodium borate was then leached out leaving a CeO₂-rich framework. The structure of the leached material was crystalline (Pt/Rh crucible melt) which changed to a rather net-like appearance if Al₂O₃ resulting from erosion of alumina crucibles was added. B₂O₃ remained partially in the insoluble CeO₂-skeleton. X-ray diffraction analysis of leached material proved the presence of crystalline cubic CeO₂ and cerium borate (metaborate of the aragonite type) in Pt/Rh crucible melts, whereas cubic CeO₂, 2Al₂O₃ · B₂O₃ and traces of sodium borate were detected in Al₂O₃ containing melts. The specific surface areas of the leached materials ranged between 25 and 120m²g^–1 while the main radii of interconnected pores were calculated to be between 0.5 and 17nm. A sintering temperature of about 1500° C was estimated from void volume and bulk density measurements.     

From IMS Management to SOA Based NGN Management

Although IP Multimedia Subsystem (IMS) based Next Generation Networks (NGNs) are already emerging as the common session control platform for converging fixed, mobile and cable networks, harmonized solutions for the management of these converged platforms have still got to be developed. This document describes a hands-on approach to NGN Management. Started with IMS specific management systems, succeeding research had to take into account the importance of the management of NGN SDPs as well. This work shows that the hybrid nature of an NGN, where services can be delivered at the IMS layer, by SIP signaling mechanisms, as well as at the SDP, via Web Services, requires a harmonized management approach. Taking into account Service Oriented Architecture (SOA) principles and policy based model driven architectures, this work shows that a unification of service composition and service management already at the workflow creation level, bares significant benefits in terms of automation and harmonization. Following the SOA paradigm, the approach presented here does not differentiate between business process management (BPM) and management process management. Focusing on Telemanagement Forum’s enhanced Telecom Operations Map service fulfillment and service assurance operations, this document describes an New Generation Software and Services (NGOSS) based implementation of a unified Operation Support System (OSS) for NGNs that encompasses many problems of former stovepipe management solutions in terms of automation, flexibility and manageability.     

Parallel Execution of Iterative Computations on Workstation Clusters

Methods are developed for transforming sequential programs for iterative computations into parallel-distributed versions which execute in parallel on a cluster of workstation or PC nodes on a local area network. We focus on communication issues and present algorithms for interprocess communication implemented by UNIX TCP/IP socket commands. Results of performance tests on several application problems, such as simulation of neural networks and the Jacobi method for solving linear equations, representative of a large class of application problems are presented. Analysis indicates that, for problems with rather intensive computation, speedups of better than 2p/3 are possible with an optimal numberpof nodes on a single Ethernet bus segment. Preliminary tests on small clusters show efficient speedups even for nonoptimalp.