Media-streaming applications experience limited performance and perceptible quality degradation in the presence of random wireless errors, as the underlying congestion control typically interprets packet loss as the outcome of congestion. In this context, we propose a selective rate control, namely AIAMD, which manages to differentiate congestive and non-congestive loss by utilizing history in its control rules. AIAMD combines the most

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and offers several other advantages, complications still occur and there is a need for a more efficient stent-graft. Here, the early steps performed in the development of a novel stent-graft are described, and desired features for an ‘ideal ’ device are pointed out. The implementation

correction for "faking good " or "faking bad " (Meehl & Hathaway, 1946), the analysis of response sets (Cron-bach, 1946,1950), and ratings of the social de-sirability of personality test items (Edwards, 19 5 7). A further distinction can be made, how-ever, which results in a somewhat

In the feature subset selection problem, a learning algorithm is faced with the problem of selecting a relevant subset of features upon which to focus its attention, while ignoring the rest. To achieve the best possible performance with a particular learning algorithm on a particular training set

We present a technique for constructing random fields from a set of training samples. The learning paradigm builds increasingly complex fields by allowing potential functions, or features, that are supported by increasingly large subgraphs. Each feature has a weight that is trained by minimizing

The factorization method described in this series of reports requires an algorithm to track the motion of features in an image stream. Given the small inter-frame displacement made possible by the factorization approach, the best tracking method turns out to be the one proposed by Lucas and Kanade

ADL are particularly well suited for component-based model frameworks that support hierarchical composition, such as DEVS with coupled models. In this paper we present some features found in the ADL of another hierarchical component model, namely the Fractal Component Model (FCM). To our best

This paper is a comparative study of feature selection methods in statistical learning of text categorization. The focus is on aggressive dimensionality reduction. Five methods were evaluated, including term selection based on document frequency (DF), information gain (IG), mutual information (MI

Recently Viola et al. [5] have introduced a rapid object detection scheme based on a boosted cascade of simple feature classifiers. In this paper we introduce a novel set of rotated haar-like features. These novel features significantly enrich the simple features of [5] and can also be calculated