Wireless Network Card (WNC) manufacturers are capable of implementing algorithms in the IEEE 802.11 protocol dynamically. This fact may allow some WNCs to occupy channels in illegal ways with the purpose of delivering data at higher rates. In order to prevent this occurrence, it is important to develop methodologies to identify anomalies of WNC behavior. In the experiments discussed here, we demonstrate how the behaviors of WNCs can be characterized by analyzing some signatures of wireless traffic. The paper then proposes an approach to identify the traffic patterns of different brands of WNCs and thus classifies the data traffic according to these signatures. Therefore, the work described here is a contribution toward the foundation of a general validation framework for WNCs' access strategies, an evaluation of their level of protocol adherence, as well as their overall performance in wireless networks.

We propose a novel semi-supervised classifier for handwritten digit recognition problems that is based on the assumption that any digit can be obtained as a slight transformation of another sufficiently close digit. Given a number of labeled and unlabeled images, it is possible to determine the class membership of each unlabeled image by creating a sequence of such image transformations that connect it, through other unlabeled images, to a labeled image. In order to measure the total transformation, a robust and reliable metric of the path length is proposed, which combines a local dissimilarity between consecutive images along the path with a global connectivity-based metric. For the local dissimilarity we use a symmetrized version of the zero-order image deformation model (IDM) proposed by Keysers et al. in [1]. For the global distance we use a connectivity-based metric proposed by Chapelle and Zien in [2]. Experimental results on the MNIST benchmark indicate that the proposed classifier out-performs current state-of-the-art techniques, especially when very few labeled patterns are available.

We describe a trainable system for analyzing videos of developing C. elegans embryos. The system automatically detects, segments, and locates cells and nuclei in microscopic images. The system was designed as the central component of a fully automated phenotyping system. The system contains three modules 1) a convolutional network trained to classify each pixel into five categories: cell wall, cytoplasm, nucleus membrane, nucleus, outside medium; 2) an energy-based model, which cleans up the output of the convolutional network by learning local consistency constraints that must be satisfied by label images; 3) a set of elastic models of the embryo at various stages of development that are matched to the label images.