Sharif Digital Repository

This paper describes an approach to mid-term probabilistic conflict detection between two aircraft flying at the same altitude. The key distinguishing feature of the proposed method is that it analytically estimates the conflict probability, in the presence of a fully correlated wind field. The method uses a two-step approach. First, the mean and the covariance of the aircraft relative position are computed analytically. Then, the conflict probability is estimated such that a desired level of accuracy is obtained. Numerical results, conducted using a simulator incorporating a fully correlated wind field model, show the effectiveness of the approach versus the competitors. Finally,... 

The key element of a spoken dialogue system is a spoken language understanding (SLU) unit. Hidden Vector State (HVS) is one of the most popular statistical approaches employed to implement the SLU unit. This paper presents a two-step approach for Persian language understanding. First, a goal detector is used to identify the main goal of the input utterance. Second, after restricting the search space for semantic tagging, an extended hidden vector state (EHVS) parser is used to extract the remaining semantics in each subspace. This will mainly improve the performance of semantic tagger, while reducing the model complexity and training time. Moreover, the need for large amount of data will be... 

We fabricated a high rate capability supercapacitor based on fluorine-doped graphene-carbon nanotubes aerogel network (G-CNT-F). Based on the electrochemical impedance spectroscopy data, the fluorination decreases the charge transfer resistance of graphene sheets, while CNTs act as spacer in the 3D structure. Therefore, both treatments improved the electrochemical properties of the resulted aerogel. Based on the Fourier transform infrared spectroscopy and XPS results, these excellent performances are attributed to semi-ionic bonds between fluorine and carbon. The specific capacitance of the graphene aerogel showed 78% decrease, when discharge current increases from 2 to 40 mA, while the... 

This paper presents efficient algorithms for learning low-coherence dictionaries. First, a new algorithm based on proximal methods is proposed to solve the dictionary learning (DL) problem regularized with the mutual coherence of dictionary. This is unlike the previous approaches that solve a regularized problem where an approximate incoherence promoting term, instead of the mutual coherence, is used to encourage low-coherency. Then, a new solver is proposed for constrained low-coherence DL problem, i.e., a DL problem with an explicit constraint on the mutual coherence of the dictionary. As opposed to current methods, which follow a suboptimal two-step approach, the new algorithm directly...