Subclass Discriminant Analysis of Morphological and Textural Features for HEp-2 Staining Pattern Classification

Classifying HEp-2 fluorescence patterns in Indirect Immunofluorescence (IIF) HEp-2 cell imaging is important for the differential diagnosis of autoimmune diseases. The current technique, based on human visual inspection, is time-consuming, subjective and dependent on the operator's experience. Automating this process may be a solution to these limitations, making IIF faster and more reliable. This work proposes a classification approach based on Subclass Discriminant Analysis (SDA), a dimensionality reduction technique that provides an effective representation of the cells in the feature space, suitably coping with the high within-class variance typical of HEp-2 cell patterns. In order to generate an adequate characterization of the fluorescence patterns, we investigate the individual and combined contributions of several image attributes, showing that the integration of morphological, global and local textural features is the most suited for this purpose. The proposed approach provides an accuracy of the staining pattern classification of about 90%

Towards an Iterative Algorithm for the Optimal Boundary Coverage of a 3D Environment

This paper presents a new optimal algorithm for locating a set of sensors in 3D able to see the boundaries of a polyhedral environment. Our approach is iterative and is based on a lower bound on the sensors' number and on a restriction of the original problem requiring each face to be observed in its entirety by at least one sensor. The lower bound allows evaluating the quality of the solution obtained at each step, and halting the algorithm if the solution is satisfactory. The algorithm asymptotically converges to the optimal solution of the unrestricted problem if the faces are subdivided into smaller part

Optimizing source and receiver placement in multistatic sonar networks to monitor fixed targets

17 USC 105 interim-entered record; under review.The article of record as published may be found at https://doi.org/10.1016/j.ejor.2018.02.006Multistatic sonar networks consisting of non-collocated sources and receivers are a promising develop ment in sonar systems, but they present distinct mathematical challenges compared to the monostatic case in which each source is collocated with a receiver. This paper is the first to consider the optimal placement of both sources and receivers to monitor a given set of target locations. Prior publications have only considered optimal placement of one type of sensor, given a fixed placement of the other type. We first develop two integer linear programs capable of optimally placing both sources and receivers within a discrete set of locations. Although these models are capable of placing both sources and receivers to any degree of optimality desired by the user, their computation times may be unacceptably long for some applications. To address this issue, we then develop a two-step heuristic process, Adapt-LOC, that quickly selects positions for both sources and receivers, but with no guarantee of optimality. Based on this, we also create an iterative approach, Iter-LOC, which leads to a locally optimal placement of both sources and receivers, at the cost of larger computation times relative to Adapt-LOC. Finally, we perform compu tational experiments demonstrating that the newly developed algorithms constitute a powerful portfolio of tools, enabling the user to slect an appropriate level of solution quality, given the available time to perform computations. Our experiments include three real-world case studies.Office of Naval Research

A Nearly Optimal Algorithm for covering the interior of an Art Gallery

The problem of locating visual sensors can be often modeled as 2D Art Gallery problems. In particular, tasks such as surveillance require observing the interior of a polygonal environment (interior covering, IC), while for inspection or image based rendering observing the boundary (edge covering, EC) is sufficient. Both problems are NP-hard, and no technique is known for transforming one problem into the other. Recently, an incremental algorithm for EC has been proposed, and its near-optimality has been demonstrated experimentally. In this paper we show that, with some modification, the algorithm is nearly optimal also for IC. The algorithm has been implemented and tested over several hundreds of random polygons with and without holes. The cardinality of the solutions provided is very near to, or coincident with, a polygon-specific lower bound, and then suboptimal or optimal. In addition, our algorithm has been compared, for all the test polygons, with recent heuristic sensor location algorithms. In all cases, the cardinality of the set of guards provided by our algorithm was less than or equal to that of the set computed by the other algorithms. An enhanced version of the algorithm, also taking into account range and incidence constraints, has also been implemente

Optimizing source and receiver placement in multistatic sonar

17 USC 105 interim-entered record; under review.Multistatic sonar networks consisting of non-collocated sources and receivers are a promising development in sonar systems, but they present distinct mathematical challenges compared to the monostatic case in which each source is collocated with a receiver. This paper is the first to consider the optimal placement of both sources and receivers to monitor a given set of target locations. Prior publications have only considered optimal placement of one type of sensor, given a fixed placement of the other type. We first develop two integer linear programs capable of optimally placing both sources and receivers within a discrete set of locations. Although these models are capable of placing both sources and receivers to any degree of optimality desired by the user, their computation times may be unacceptably long for some applications. To address this issue, we then develop a two-step heuristic process, Adapt-LOC, that quickly selects positions for both sources and receivers, but with no guarantee of optimality. Based on this, we also create an iterative approach, Iter-LOC, which leads to a locally optimal placement of both sources and receivers, at the cost of larger computation times relative to Adapt-LOC. Finally, we perform computational experiments demonstrating that the newly developed algorithms constitute a powerful portfolio of tools, enabling the user to slect an appropriate level of solution quality, given the available time to perform computations. Our experiments include three real-world case studies.Dr. Craparo is funded by the Office of Naval Research

An efficient genetic algorithm for large-scale planning of robust industrial wireless networks

An industrial indoor environment is harsh for wireless communications compared to an office environment, because the prevalent metal easily causes shadowing effects and affects the availability of an industrial wireless local area network (IWLAN). On the one hand, it is costly, time-consuming, and ineffective to perform trial-and-error manual deployment of wireless nodes. On the other hand, the existing wireless planning tools only focus on office environments such that it is hard to plan IWLANs due to the larger problem size and the deployed IWLANs are vulnerable to prevalent shadowing effects in harsh industrial indoor environments. To fill this gap, this paper proposes an overdimensioning model and a genetic algorithm based over-dimensioning (GAOD) algorithm for deploying large-scale robust IWLANs. As a progress beyond the state-of-the-art wireless planning, two full coverage layers are created. The second coverage layer serves as redundancy in case of shadowing. Meanwhile, the deployment cost is reduced by minimizing the number of access points (APs); the hard constraint of minimal inter-AP spatial paration avoids multiple APs covering the same area to be simultaneously shadowed by the same obstacle. The computation time and occupied memory are dedicatedly considered in the design of GAOD for large-scale optimization. A greedy heuristic based over-dimensioning (GHOD) algorithm and a random OD algorithm are taken as benchmarks. In two vehicle manufacturers with a small and large indoor environment, GAOD outperformed GHOD with up to 20% less APs, while GHOD outputted up to 25% less APs than a random OD algorithm. Furthermore, the effectiveness of this model and GAOD was experimentally validated with a real deployment system

Transfer Operator Theoretic Framework for Monitoring Building Indoor Environment in Uncertain Operating Conditions

Dynamical system-based linear transfer Perron- Frobenius (P-F) operator framework is developed to address analysis and design problems in the building system. In particular, the problems of fast contaminant propagation and optimal placement of sensors in uncertain operating conditions of indoor building environment are addressed. The linear nature of transfer P-F operator is exploited to develop a computationally efficient numerical scheme based on the finite dimensional approximation of P-F operator for fast propagation of contaminants. The proposed scheme is an order of magnitude faster than existing methods that rely on simulation of an advection-diffusion partial differential equation for contami- nant transport. Furthermore, the system-theoretic notion of observability gramian is generalized to nonlinear flow fields using the transfer P-F operator. This developed notion of observability gramian for nonlinear flow field combined with the finite dimensional approximation of P-F operator is used to provide a systematic procedure for optimal placement of sensors under uncertain operating conditions. Simulation results are presented to demonstrate the applicability of the developed framework on the IEA-annex 2D benchmark problem