Soliton propagation and polarisation mode-locking in birefringent optical fibres

Soliton propagation in polarization-preserving fibres is analysed. Based on the coupled nonlinear Schrodinger equations we derive an analytical approximation for such type of soliton propagation. Exploitation of soliton polarization properties for passive mode-locking in fibre lasers is also considered

A Machine Learning Approach for Automated Fine-Tuning of Semiconductor Spin Qubits

While spin qubits based on gate-defined quantum dots have demonstrated very favorable properties for quantum computing, one remaining hurdle is the need to tune each of them into a good operating regime by adjusting the voltages applied to electrostatic gates. The automation of these tuning procedures is a necessary requirement for the operation of a quantum processor based on gate-defined quantum dots, which is yet to be fully addressed. We present an algorithm for the automated fine-tuning of quantum dots, and demonstrate its performance on a semiconductor singlet-triplet qubit in GaAs. The algorithm employs a Kalman filter based on Bayesian statistics to estimate the gradients of the target parameters as function of gate voltages, thus learning the system response. The algorithm's design is focused on the reduction of the number of required measurements. We experimentally demonstrate the ability to change the operation regime of the qubit within 3 to 5 iterations, corresponding to 10 to 15 minutes of lab-time

Caracterização, modelação e compensação de efeitos de memória lenta em amplificadores de potência baseados em GAN HEMTS

Gallium nitride (GaN) high-electron-mobility transistors (HEMTs) have emerged as the most compelling technology for the transmission of highpower radio-frequency (RF) signals for cellular mobile communications and radar applications. However, despite their remarkable power capabilities, the deployment of GaN HEMT-based RF power amplifiers (PAs) in the mobile communications infrastructure is often ruled out in favor of alternative siliconbased technologies. One of the main reasons for this is the pervasiveness of nonlinear long-term memory effects in GaN HEMT technology caused by thermal and charge-trapping phenomena. While these effects can be compensated for using sophisticated digital predistortion algorithms, their implementation and model-extraction complexity—as well as the power necessary for their real-time execution—make them unsuitable for modern small cells and large-scale multiple-input multiple-output transceivers, where the power necessary for the linearization of each amplification element is of great concern. In order to address these issues and further the deployment of high-powerdensity high-efficiency GaN HEMT-based RF PAs in next-generation communications and radar applications, in this thesis we propose novel methods for the characterization, modeling, and compensation of long-term memory effects in GaN HEMT-based RF PAs. More specifically, we propose a method for the characterization of the dynamic self-biasing behavior of GaN HEMTbased RF PAs; multiple behavioral models of charge trapping and their implementation as analog electronic circuits for the accurate real-time prediction of the dynamic variation of the threshold voltage of GaN HEMTs; a method for the compensation of the pulse-to-pulse instability of GaN HEMT-based RF PAs for radar applications; and a hybrid analog/digital scheme for the linearization of GaN HEMT-based RF PAs for next-generation communications applications.Os transístores de alta mobilidade eletrónica de nitreto de gálio (GaN HEMTs) são considerados a tecnologia mais atrativa para a transmissão de sinais de radiofrequência de alta potência para comunicações móveis celulares e aplicações de radar. No entanto, apesar das suas notáveis capacidades de transmissão de potência, a utilização de amplificadores de potência (PAs) baseados em GaN HEMTs é frequentemente desconsiderada em favor de tecnologias alternativas baseadas em transístores de silício. Uma das principais razões disto acontecer é a existência pervasiva na tecnologia GaN HEMT de efeitos de memória lenta causados por fenómenos térmicos e de captura eletrónica. Apesar destes efeitos poderem ser compensados através de algoritmos sofisticados de predistorção digital, estes algoritmos não são adequados para transmissores modernos de células pequenas e interfaces massivas de múltipla entrada e múltipla saída devido à sua complexidade de implementação e extração de modelo, assim como a elevada potência necessária para a sua execução em tempo real. De forma a promover a utilização de PAs de alta densidade de potência e elevada eficiência baseados em GaN HEMTs em aplicações de comunicação e radar de nova geração, nesta tese propomos novos métodos de caracterização, modelação, e compensação de efeitos de memória lenta em PAs baseados em GaN HEMTs. Mais especificamente, nesta tese propomos um método de caracterização do comportamento dinâmico de autopolarização de PAs baseados em GaN HEMTs; vários modelos comportamentais de fenómenos de captura eletrónica e a sua implementação como circuitos eletrónicos analógicos para a previsão em tempo real da variação dinâmica da tensão de limiar de condução de GaN HEMTs; um método de compensação da instabilidade entre pulsos de PAs baseados em GaN HEMTs para aplicações de radar; e um esquema híbrido analógico/digital de linearização de PAs baseados em GaN HEMTs para comunicações de nova geração.Programa Doutoral em Telecomunicaçõe

Rate-Accuracy Trade-Off In Video Classification With Deep Convolutional Neural Networks

Advanced video classification systems decode video frames to derive the necessary texture and motion representations for ingestion and analysis by spatio-temporal deep convolutional neural networks (CNNs). However, when considering visual Internet-of-Things applications, surveillance systems and semantic crawlers of large video repositories, the video capture and the CNN-based semantic analysis parts do not tend to be co-located. This necessitates the transport of compressed video over networks and incurs significant overhead in bandwidth and energy consumption, thereby significantly undermining the deployment potential of such systems. In this paper, we investigate the trade-off between the encoding bitrate and the achievable accuracy of CNN-based video classification models that directly ingest AVC/H.264 and HEVC encoded videos. Instead of retaining entire compressed video bitstreams and applying complex optical flow calculations prior to CNN processing, we only retain motion vector and select texture information at significantly-reduced bitrates and apply no additional processing prior to CNN ingestion. Based on three CNN architectures and two action recognition datasets, we achieve 11%-94% saving in bitrate with marginal effect on classification accuracy. A model-based selection between multiple CNNs increases these savings further, to the point where, if up to 7% loss of accuracy can be tolerated, video classification can take place with as little as 3 kbps for the transport of the required compressed video information to the system implementing the CNN models

Few-cycle Pulses Amplification For Attosecond Science Applications Modeling And Experiments

The emergence of mode-locked oscillators providing pulses with durations as short as a few electric-field cycles in the near infra-red has paved the way toward electric-field sensitive physics experiments. In addition, the control of the relative phase between the carrier and the pulse envelope, developed in the early 2000’s and rewarded by a Nobel price in 2005, now provides unprecedented control over the pulse behaviour. The amplification of such pulses to the millijoule level has been an on-going task in a few world-class laboratories and has triggered the dawn of attoscience, the science of events happening on an attosecond timescale. This work describes the theoretical aspects, modeling and experimental implementation of HERACLES, the Laser Plasma Laboratory optical parametric chirped pulse amplifier (OPCPA) designed to deliver amplified carrier-envelope phase stabilized 8-fs pulses with energy beyond 1 mJ at repetition rates up to 10 kHz at 800 nm central wavelength. The design of the hybrid fiber/solid-state amplifier line delivering 85-ps pulses with energy up to 10 mJ at repetition rates in the multi-kHz regime tailored for pumping the optical parametric amplifier stages is presented. The novel stretcher/compressor design of HERACLES, suitable for handling optical pulses with spectra exceeding 300 nm of bandwidth with unprecedented flexibility, is fully modeled and also presented in the frame of this thesis. Finally, a 3D model of the multistage non-collinear optical parametric amplifier is also reported. The current and foreseen overall performances of HERACLES are presented. This facility is designed to enable attosecond physics experiments, high-harmonic generation and physics of plasma studies

Respiratory organ motion in interventional MRI : tracking, guiding and modeling

Respiratory organ motion is one of the major challenges in interventional MRI, particularly in interventions with therapeutic ultrasound in the abdominal region. High-intensity focused ultrasound found an application in interventional MRI for noninvasive treatments of different abnormalities. In order to guide surgical and treatment interventions, organ motion imaging and modeling is commonly required before a treatment start. Accurate tracking of organ motion during various interventional MRI procedures is prerequisite for a successful outcome and safe therapy. In this thesis, an attempt has been made to develop approaches using focused ultrasound which could be used in future clinically for the treatment of abdominal organs, such as the liver and the kidney. Two distinct methods have been presented with its ex vivo and in vivo treatment results. In the first method, an MR-based pencil-beam navigator has been used to track organ motion and provide the motion information for acoustic focal point steering, while in the second approach a hybrid imaging using both ultrasound and magnetic resonance imaging was combined for advanced guiding capabilities. Organ motion modeling and four-dimensional imaging of organ motion is increasingly required before the surgical interventions. However, due to the current safety limitations and hardware restrictions, the MR acquisition of a time-resolved sequence of volumetric images is not possible with high temporal and spatial resolution. A novel multislice acquisition scheme that is based on a two-dimensional navigator, instead of a commonly used pencil-beam navigator, was devised to acquire the data slices and the corresponding navigator simultaneously using a CAIPIRINHA parallel imaging method. The acquisition duration for four-dimensional dataset sampling is reduced compared to the existing approaches, while the image contrast and quality are improved as well. Tracking respiratory organ motion is required in interventional procedures and during MR imaging of moving organs. An MR-based navigator is commonly used, however, it is usually associated with image artifacts, such as signal voids. Spectrally selective navigators can come in handy in cases where the imaging organ is surrounding with an adipose tissue, because it can provide an indirect measure of organ motion. A novel spectrally selective navigator based on a crossed-pair navigator has been developed. Experiments show the advantages of the application of this novel navigator for the volumetric imaging of the liver in vivo, where this navigator was used to gate the gradient-recalled echo sequence