Coding for discrete memoryless two-way channels

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering, 1962.Vita.Includes bibliographical references (leaf 253).by Frederick Jelinek.Ph.D

Large-scale random forest language models for speech recognition

The random forest language model (RFLM) has shown encouraging results in several automatic speech recognition (ASR) tasks but has been hindered by practical limitations, notably the space-complexity of RFLM estimation from large amounts of data. This paper addresses large-scale training and testing of the RFLM via an efficient disk-swapping strategy that exploits the recursive structure of a binary decision tree and the local access property of the tree-growing algorithm, redeeming the full potential of the RFLM, and opening avenues of further research, including useful comparisons with n-gram models. Benefits of this strategy are demonstrated by perplexity reduction and lattice rescoring experiments using a state-of-the-art ASR system. Index Terms: random forest language model, large-scale training, data scaling, speech recognitio

Characteristic Modes of Frequency-Selective Surfaces and Metasurfaces from S-parameter Data

Characteristic modes of arbitrary two-dimensional periodic systems are analyzed using scattering parameter data. This approach bypasses the need for periodic integral equations and allows for characteristic modes to be computed from generic simulation or measurement data. Example calculations demonstrate the efficacy of the method through comparison against a periodic method of moments formulation for a simple, single-layer conducting unit cell. The effect of vertical structure and electrical size on the number of modes is studied and its discrete nature is verified with example calculations. % Additional examples verify the binary impact of vertical structure on the number of radiating characteristic modes. A multiband polarization-selective surface and a beamsteering metasurface are presented as additional examples.Comment: 11 pages, 11 figure