Automatic discovery of Web Query Interfaces using machine learning techniques

The amount of information contained in databases available on the Web has grown explosively in the last years. This information, known as the Deep Web, is heterogeneous and dynamically generated by querying these back-end (relational) databases through Web Query Interfaces (WQIs) that are a special type of HTML forms. The problem of accessing to the information of Deep Web is a great challenge because the information existing usually is not indexed by general-purpose search engines. Therefore, it is necessary to create efficient mechanisms to access, extract and integrate information contained in the Deep Web. Since WQIs are the only means to access to the Deep Web, the automatic identification of WQIs plays an important role. It facilitates traditional search engines to increase the coverage and the access to interesting information not available on the indexable Web. The accurate identification of Deep Web data sources are key issues in the information retrieval process. In this paper we propose a new strategy for automatic discovery of WQIs. This novel proposal makes an adequate selection of HTML elements extracted from HTML forms, which are used in a set of heuristic rules that help to identify WQIs. The proposed strategy uses machine learning algorithms for classification of searchable (WQIs) and non-searchable (non-WQI) HTML forms using a prototypes selection algorithm that allows to remove irrelevant or redundant data in the training set. The internal content of Web Query Interfaces was analyzed with the objective of identifying only those HTML elements that are frequently appearing provide relevant information for the WQIs identification. For testing, we use three groups of datasets, two available at the UIUC repository and a new dataset that we created using a generic crawler supported by human experts that includes advanced and simple query interfaces. The experimental results show that the proposed strategy outperforms others previously reported works.     

SLAC: Statistical total lesion metabolic activity computation by fuzzy unsupervised learning of PET images

Accurate lesion metabolic response estimation is imperative for efficient tumor staging and follow-up studies. Positron emission tomography (PET) successfully images the lesion metabolic activity. Nonetheless, on course of accurate delineation, chances are high to end up with activity underestimation as, due to the limited resolution, the PET images suffer from partial volume effects. Recently, PET images were modeled as a fuzzy mixture to delineate lesions accurately. We extend this work by proposing a statistical lesion activity computation (SLAC) approach to robustly estimate the total lesion activity (TLA) directly from the modeled partial volume mixtures, without an explicit delineation. To evaluate the proposed method, PET scans of phantoms containing spherical and non-spherical lesions with increased activity uptake were simulated. The PET images were reconstructed with the standard clinically used maximum likelihood expectation maximization and an edge preserving maximum a posteriori (MAP) algorithm, both with resolution recovery. From these images, the TLA was estimated in each lesion using the proposed method and compared to the TLA estimation in the tumor delineations obtained with three state-of-the-art PET delineation schemes. SLAC outperformed TLA estimation via tumor delineation and showed robust against variation in reconstruction parameters. With reference to the ground truth knowledge, SLAC gives median $\delta $ TLA $~\approx $  5 % for spherical lesions. For more realistic non-spherical lesions, median $\delta $ TLA $~\approx $  15 %.     

A fast and simple algorithm for evaluation of minimum zone straightness error from coordinate data

Various methods have been suggested in the past to determine the minimum zone straightness error, but suffer from various drawbacks. A new, fast and simple algorithm is proposed to calculate the straightness error from planar coordinate data. It guarantees the minimum zone solution. An example and test data are provided. Results of simulation experiments to establish the time computational complexity of the algorithm are also presented.     

Rapid hyperspectral fluorescence lifetime imaging

We report a rapid hyperspectral fluorescence lifetime imaging (FLIM) instrument that exploits high-speed FLIM technology in a line-scanning microscope. We demonstrate the acquisition of whole-field optically sectioned hyperspectral fluorescence lifetime image stacks (with 32 spectral bins) in less than 40 s and illustrate its application to unstained biological tissue.     

Methodology for generating thermal and electric load profiles for designing a cogeneration system

Cogeneration will be always an important concept for energy conversion in the future, since it proposes to optimize the use of the energy resources. In the transformation of conventional systems for operation in the cogeneration mode, there exists the necessity to estimate the electric and thermal load profiles hour by hour, so that the cogeneration system can be optimally designed and thus displace the electric energy that would be used by the conventional systems. This work develops a methodology for estimating the electric and thermal load profiles, hour by hour for each month of the year, from the few normally available data. For the electric profile, annual consumption data of electric energy measured at PUC-Rio, every 15 min in the period of 1 year, has been used to validate this methodology. For the thermal profile, a methodology was developed; it discusses how the input thermal energy can be estimated from values of ambient temperature, internal thermal loads and solar radiation incident on the buildings. As an example of this methodology, a thermal load calculation is detailed for a business building and the results compared to those obtained from an existing methodology. The results obtained with these models, allow more accurate predictions for estimating the electric energy with a generator, over a month period, when its capacity is smaller than the building peak demand. A model was also developed to calculate the contribution of the air conditioning electric energy consumption to the total electric energy load.     

Citizen monitoring: Testing hypotheses about the interactive influences of eutrophication and mussel invasion on a cyanobacterial toxin in lakes

An existing volunteer monitoring network in the state of Michigan was exploited to conduct a statewide survey of the cyanobacterial toxin, microcystin, and to test hypotheses about the interactive influences of eutrophication and dreissenid mussel invasion. A total of 77 lakes were sampled by citizen volunteers for microcystin, total phosphorus (TP) and chlorophyll a. Microcystin was measured in depth-integrated samples collected from the euphotic zone as well as in surface-water samples collected along the shoreline. Average microcystin in samples collected by volunteers was not different from samples collected side-by-side by professionals. Euphotic-zone microcystin was positively related to TP in lakes without dreissenids (uninvaded) but not in lakes with dreissenids (invaded). Regression-tree analysis indicated that euphotic-zone microcystin was eight times higher in the presence of dreissenids for lakes with TP between 5 and 10 μg L⁻¹. In contrast, euphotic-zone microcystin was almost identical in invaded and uninvaded lakes with TP between 10 and 26 μg L⁻¹. Across all lakes, microcystin concentrations at the surface were on average more than double, and in some cases an order-of-magnitude greater than, concentrations in the euphotic-zone. Given these results, it seems prudent to include dreissenid invasion status in forecasting models for microcystin, and to include shoreline sampling in monitoring programs aimed at assessing recreational exposure to cyanobacterial toxins.     

Quadrotors Formation Control

In this paper, the impact of the medium access protocols on the average consensus problem over wireless networks for a group of quadrotors is established. The stabilization of each helicopter is guaranteed using a simple and bounded nonlinear control strategy. We study the case of a group of quadrotors communicating over a wireless network considering both directed and undirected graphs of information flow. It turns out that the media access control (MAC) protocols have a direct impact in both convergence time and average consensus solution, i.e., the solution of the average consensus is no longer the average of the initial conditions. It will be shown that the solution for the average consensus problem over a wireless network depends directly on the MAC algorithm. Simulations are provided to demonstrate the theoretical results. In addition, to validate the control strategy some experimental tests have been carried out to control the yaw angle of two quadrotors.     

An indirect feedforward composition controller for distillation columns

This paper addresses the problem of designing an indirect feedforward controller, also called cascade control, to regulate the product composition of distillation columns to overcome load disturbances. The overall controller is composed of two cascaded controllers. The primary controller is basically a low-gain controller, which uses slow (delayed) composition measurements to provide servo responses (composition control). The secondary controller has the structure of a high-gain proportional-integral (PI) controller with an antireset windup scheme, which uses fast temperature measurements at a given distillation tray to track the output of the primary controller. In this way, the secondary controller also provides protection against load disturbances. A simulated example is used to illustrate the functioning of the proposed controller.