Efficient Estimation of Word Representations in Vector Space

We propose two novel model architectures for computing continuous vector representations of words from very large data sets. The quality of these representations is measured in a word similarity task, and the results are compared to the previously best performing techniques based on different types of neural networks. We observe large improvements in accuracy at much lower computational cost, i.e. it takes less than a day to learn high quality word vectors from a 1.6 billion words data set. Furthermore, we show that these vectors provide state-of-the-art performance on our test set for measuring syntactic and semantic word similarities

GPR propagation simulation and fat dipole antenna design

Word processed copy.Includes bibliographical references (leaves 67-69).Two applications of FEKO are reported. The first application is investigating how antennas propagate. reflect, and the difference in transmit and receive signals in various ground media. Results of the ground penetration simulations done in FEKO (MoM- Method of Moment) is compared to Finite Difference Time Domain (FDTD) results simulated by Mukhopadhyay with the same physical model. The second application is to model and fabricate an ultra wide-band antenna with implementation of the fat dipole design. The design considerations applied to improve antenna performance include antenna feed configurations, substrate width, aperture dimension, cavity implementation, terminating resistance. antenna impedance and balun matching. After the design process was completed, fabrication of the antenna took place and the design validated