Recent progress in self-interference cancellation technology has demonstrated the in-band full-duplex wireless communication. However, the inter-client interference caused by the simultaneous co-channel uplink and downlink transmission in the asymmetric full-duplex communication can decrease the network throughput. The conventional solutions to this problem are finding the hidden stations by frequent inter-client channel probing, which caused a lot of signaling overhead. In this paper, we propose an asymmetric full-duplex medium access control (MAC) protocol using the beam divergence feature of the orbital angular momentum (OAM) waves. The noteworthy features of our protocol consist of two aspects. Firstly, the protocol avoids the inter-client interference through the divergence feature of the OAM wave, which does not need frequent inter-client channel probing and reduces the signaling overhead compared with the conventional solutions. Secondly, asymmetric full-duplex communication can be established in high probability with the appropriate multiplexing of OAM modes. Through the simulation results, the throughput of the asymmetric full-duplex network outperforms the other proposed full-duplex MAC protocol and half-duplex network.

We present a directed Markov random field (MRF) model that combinesn‐gram models, probabilistic context‐free grammars (PCFGs), and probabilistic latent semantic analysis (PLSA) for the purpose of statistical language modeling. Even though the composite directed MRF model potentially has an exponential number of loops and becomes a context‐sensitive grammar, we are nevertheless able to estimate its parameters in cubic time using an efficient modified Expectation‐Maximization (EM) method,the generalized inside–outside algorithm, which extends the inside–outside algorithm to incorporate the effects of then‐gram and PLSA language models. We generalize various smoothing techniques to alleviate the sparseness ofn‐gram counts in cases where there are hidden variables. We also derive an analogous algorithm to find the most likely parse of a sentence and to calculate the probability of initial subsequence of a sentence, all generated by the composite language model. Our experimental results on theWall Street Journalcorpus show that we obtain significant reductions in perplexity compared to the state‐of‐the‐art baseline trigram model with Good–Turing and Kneser–Ney smoothing techniques.

The Massachusetts Institute of Technology's iLab project has developed a distributed software toolkit and middleware service infrastructure to support Internet-accessible laboratories and promote their sharing among schools and universities on a worldwide scale. The project starts with the assumption that the faculty teaching with online labs and the faculty or academic departments that provide those labs are acting in two roles with different goals and concerns. The iLab architecture focuses on fast platform-independent lab development, scalable access for students, and efficient management for lab providers while preserving the autonomy of the faculty actually teaching the students. Over the past two years, the iLab architecture has been adopted by an increasing number of partner universities in Europe, Australia, Africa, Asia, and the United States. The iLab project has demonstrated that online laboratory use can scale to thousands of students dispersed on several continents.

We present a directed Markov random field (MRF) model that combines n-gram models, probabilistic context free grammars (PCFGs) and probabilistic latent semantic analysis (PLSA) for the purpose of statistical language modeling. Even though the composite directed MRF model potentially has an exponential number of loops and becomes a context sensitive grammar, we are nevertheless able to estimate its parameters in cubic time using an efficient modified EM method, the generalized inside-outside algorithm, which extends the inside-outside algorithm to incorporate the effects of the n-gram and PLSA language models. We generalize various smoothing techniques to alleviate the sparseness of n-gram counts in cases where there are hidden variables. We also derive an analogous algorithm to calculate the probability of initial subsequence of a sentence, generated by the composite language model. Our experimental results on the Wall Street Journal corpus show that we obtain significant reductions in perplexity compared to the state-of-the-art baseline trigram model with Good-Turing and Kneser-Ney smoothings.