Silicon Photonics Optical Beamformer for Broadband Phased Array Antennas

Este projecto tem como objectivo o estudo, desenho e simulação de um beamformer óptico, a operar na banda Ka (26 - 40 GHz) implementado num circuito fotónico integrado. Este dispositivo deve ser capaz de gerar atrasos TTD (True Time Delay) de modo a evitar deformações de feixe, denominadas beamsquint, que afectam negativamente a qualidade do sinal transmitido. Esta funcionalidade tem como objectivo melhorar a performance de sistemas baseados em agrupamentos de antenas (Phased Array Antennas) para sinais com elevada largura de banda. Outro aspecto importante do projecto é a integração do circuito óptico num chip fotónico baseado na tecnologia de fotónica integrada em Silício, que permite o fabrico de dispositivos ópticos compactos e de baixo custo.This project proposes the design of a Photonic Integrated Circuit, or PIC, Optical Beaformer for broadband Phased Array Antennas, or PAA, operating in the Ka band (26 - 40 GHz). The beamformer circuit should implement a True Time Delay device that enables seamless phase tuning for each radiating element that is independent from frequency. The frequency independence of the generated delays avoids a recurring phenomenon on PAA systems known as beamsquint, which consists in deformation of the array radiation pattern that deteriorates the quality of the transmitted signal. Therefore, by eliminating beamsquint, this technology should allow PAA based systems to be used in broadband communications, which are becoming evermore pervasive, due to modern day demands for high-speed data tranfer. This project also aims to take advantage of recent integrated phtonics techonology, in order to fabricate compact and cheaper optical circuit devices

Simultaneous localization and map-building using active vision

An active approach to sensing can provide the focused measurement capability over a wide field of view which allows correctly formulated Simultaneous Localization and Map-Building (SLAM) to be implemented with vision, permitting repeatable long-term localization using only naturally occurring, automatically-detected features. In this paper, we present the first example of a general system for autonomous localization using active vision, enabled here by a high-performance stereo head, addressing such issues as uncertainty-based measurement selection, automatic map-maintenance, and goal-directed steering. We present varied real-time experiments in a complex environment.Published versio

Deep Learning for Distant Speech Recognition

Deep learning is an emerging technology that is considered one of the most promising directions for reaching higher levels of artificial intelligence. Among the other achievements, building computers that understand speech represents a crucial leap towards intelligent machines. Despite the great efforts of the past decades, however, a natural and robust human-machine speech interaction still appears to be out of reach, especially when users interact with a distant microphone in noisy and reverberant environments. The latter disturbances severely hamper the intelligibility of a speech signal, making Distant Speech Recognition (DSR) one of the major open challenges in the field. This thesis addresses the latter scenario and proposes some novel techniques, architectures, and algorithms to improve the robustness of distant-talking acoustic models. We first elaborate on methodologies for realistic data contamination, with a particular emphasis on DNN training with simulated data. We then investigate on approaches for better exploiting speech contexts, proposing some original methodologies for both feed-forward and recurrent neural networks. Lastly, inspired by the idea that cooperation across different DNNs could be the key for counteracting the harmful effects of noise and reverberation, we propose a novel deep learning paradigm called network of deep neural networks. The analysis of the original concepts were based on extensive experimental validations conducted on both real and simulated data, considering different corpora, microphone configurations, environments, noisy conditions, and ASR tasks.Comment: PhD Thesis Unitn, 201