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Most current language recognition systems model different levels of information such as acoustic, prosodic, phonotactic, etc. independently and combine the model likelihoods in order to make a decision. However, these are single level systems that treat all languages identically and hence incapable of exploiting any similarities that may exist within groups of languages.

Atmospheric conditions are known to affect the Received Sig- nal Level (RSL) in commercial microwave links (MWLs), that operate at frequencies of tens of GHz. Study of these ef- fects is of great importance both for communication engineers and for environmental monitoring. In this paper we study the phenomenon of a wet antenna. During periods of high rela- tive humidity (RH), a thin layer of water may collect on the outside cover of the microwave units, resulting in increased signal attenuation. Here, we focus on the estimation of the signal power loss caused due to this phenomenon.

The High Efficiency Video Coding (HEVC) standard provides a substantial improvement in coding efficiency over previous video coding standards at the cost of a higher computational complexity. HEVC employs a quadtree based image structure by partitioning the image into coding units (CUs). Finding the optimal CU size in terms of rate-distortion is one of the most computationally challenging parts of any HEVC encoder.

In this research we present a new approach for calibration of the Power-Law parameters, using the available commercial microwave links min/max attenuation measurements, and measurements from standard rain-gauges. This calibration process was tested in a real world scenario, and showed promising results.

Person reidentification refers to the task of recognizing the same person
under different non-overlapping camera views. Presently, person
reidentification based on metric learning is proved to be effective among
various techniques, which exploits the labeled data to learn
a subspace that maximizes the inter-person divergence while minimizes
the intra-person divergence. However, these methods fail to
take the different impacts of various instances and local features into
account. To address this issue, we propose to learn a projection matrix