Abstract

Abstract
The current quality control methodology adopted by the water distribution service provider in the metropolitan region of Porto - Portugal, is based on simple heuristics and empirical knowledge. Based on the domain complexity and data volume, this application is a perfect candidate to apply data mining process. In this paper, we propose a new methodology to predict the range of normality for the values of different water quality parameters. These intervals of normality are of key importance to decide on costly inspection activities. Our experimental evaluation confirms that our proposal achieves good results on the task of forecasting the normal distribution of values for the following 30 days. The proposed method can be applied to other domains with similar network monitoring objectives.

Abstract
Microscopy cell image analysis is a fundamental tool for biological research. In particular, multivariate fluorescence microscopy is used to observe different aspects of cells in cultures. It is still common practice to perform analysis tasks by visual inspection of individual cells which is time consuming, exhausting and prone to induce subjective bias. This makes automatic cell image analysis essential for large scale, objective studies of cell cultures. Traditionally the task of automatic cell analysis is approached through the use of image segmentation methods for extraction of cells' locations and shapes. Image segmentation, although fundamental, is neither an easy task in computer vision nor is it robust to image quality changes. This makes image segmentation for cell detection semi-automated requiring frequent tuning of parameters. We introduce a new approach for cell detection and shape estimation in multivariate images based on the sliding band filter (SBF). This filter's design makes it adequate to detect overall convex shapes and as such it performs well for cell detection. Furthermore, the parameters involved are intuitive as they are directly related to the expected cell size. Using the SBF filter we detect cells' nucleus and cytoplasm location and shapes. Based on the assumption that each cell has the same approximate shape center in both nuclei and cytoplasm fluorescence channels, we guide cytoplasm shape estimation by the nuclear detections improving performance and reducing errors. Then we validate cell detection by gathering evidence from nuclei and cytoplasm channels. Additionally, we include overlap correction and shape regularization steps which further improve the estimated cell shapes. The approach is evaluated using two datasets with different types of data: a 20 images benchmark set of simulated cell culture images, containing 1000 simulated cells; a 16 images Drosophila melanogaster Kc167 dataset containing 1255 cells, stained for DNA and actin. Both image datasets present a difficult problem due to the high variability of cell shapes and frequent cluster overlap between cells. On the Drosophila dataset our approach achieved a precision/recall of 95%/69% and 82%/90% for nuclei and cytoplasm detection respectively and an overall accuracy of 76%.

Abstract

Abstract
In service-oriented systems, composition of services is required to build new, distributed and more complex services, based on the logic behavior of individual ones. This paper discusses the formal composition of Petri nets models used for the process description and control in service-oriented automation systems. The proposed approach considers two forms for the composition of services, notably the offline composition, applied during the design phase, and the online composition, related to the synchronization of Petri nets models on the fly. An experimental case study is used to illustrate the proposed composition approach. © 2010 IEEE.

Abstract
This paper introduces a novel method for the specification and selection of criteria-weighted operation modes for the orchestration of services in industrial automation using Petri nets. The objective is to provide to the internal decision support system of a service-oriented automation device or of another applicable computational system the capability to select the best path in a Petri net orchestration model considering different criteria to evaluate the quality of services, such as the time, energy efficiency and reliability. The transition-invariants obtained from the Petri net represent the set of possible modi operandi and these are then weighted with decision criteria. The result will be afterwards evaluated in order to select the optimal modus operandi to be executed by the device. Based on the experiments, this method permits the dynamic optimization of processes in real-time, considering available parameters from devices and other resources. © 2010 IEEE.