Stabilizing optical microcavities in 3D

Optical (micro-)cavities are the workhorse for studying light-matter interactions with important applications in lasing, sensing, and quantum simulations, to name a few. Open resonators in particular offer great versatility due to their tunability but pose challenges in terms of control. This concerns, on the one hand, the control of their length, and on the other hand, the relative orientation (tilt) of the mirror planes to each other. The latter becomes particularly important when working with optically unstable resonators, such as plane-parallel resonators.There are numerous strategies to enhance stability using passive techniques, such as material selection, mechanical damping, or thermal compensation. But especially for tuneable microcavities often an active stabilization method with feedback control systems must be employed. Here, we present a novel method for tilt measurement and stabilization using inverse solving of the Schrödinger equation arising in the paraxial description of the cavity modes. Our method enables the highly precise determination of absolute tilt angles, making it suitable for microcavity applications that require the highest level of cavity parallelism

Single photons in an interferometer:Forbidden outcomes

The well-known Hong-Ou-Mandel (HOM) effect is the first demonstration of perfect destructive interference of quantum amplitudes in a linear optical system with single photon Fock-states at input. A natural question to ask is whether we are able to predict similar behavior for systems of a general size, that is, predicting forbidden transitions for a given input state and a given linear optical system. Previous studies have found that certain symmetries between the input and output configuration in combination with a symmetric interferometer will always result into so called suppressions. But recently, a few examples have been found of suppressions which do not obey the suppression laws constructed in previous studies. In this work, weparametrize a general three-mode interferometer and we use a numerical optimization algorithm in order to find all three-mode interferometers that demonstrate forbidden transitions. These results help us to gain a better understanding of the fundamental reason behind suppressions. In addition, we compare the quality of the different interferometers for applicationsin quantum tomography

Time-domain Physical Unclonable Functions

One can replace one-way functions (also known as hash functions) commonlyfound in cryptography with physical processes, known as physical unclonable functions (PUFs). Optical PUFs have been devised based on the complex response ofscattering media to the spatial wavefront. However, such PUFs are intrinsicallyunpractical for use over larger distances. In this project, we design PUFs with atime-domain scattering response (tPUF), whose readout can be performed over asingle spatial mode, in our case an optical fiber. These tPUFs are networks of microring resonators, whose transfer function is highly complex and vary strongly from realization to realization as a result of manufacturing imperfections. Thesedevices are developed for pulses with a very low number of average photons perpulse which negates any attempts at reading out the pulse shape in transit, therebyeliminating eavesdropping. These PUFs can then be used for a variety of applicationsin asymmetric cryptography, such as proof of identity and secure messaging

A performance analysis of distributed filtering algorithms for indoor pedestrian tracking

Trabalho de Conclusão de Curso (graduação)—Universidade de Brasília, Faculdade de Tecnologia, Departamento de Engenharia Elétrica, 2017.A literatura recente indica que algoritmos de filtragem distribuída podem ter várias vantagens em relação a algoritmos centralizados, especialmente no que se refere a robustez, escalabilidade, uso de recursos de comunicação e flexibilidade. Neste contexto, este trabalho objetiva fornecer uma análise da performance de algoritmos de filtragem distribuída aplicados à estimação da posição de pesdestres em ambientes fechados. Particularmente, duas formas de algoritmos distribuídos serão testados: consenso por média ponderada e difusão. Um esquema de localização baseado em RFID é utilizado como teste para avaliar a performance dos algoritmos. Em especial, técnicas baseadas na diferença da fase de chegada são utilizadas para fornecer medições das distâncias e ângulos relativos entre as antenas e a etiqueta RFID. Simulações realizadas no software MATLAB indicam que algoritmos de difusão tem performance superior do que algoritmos de consenso na aplicação tratada neste trabalho. Além disso, a pequena diferença entre os erros quadráticos médios dos algoritmos centralizados e de difusão inspiram a utilização do último por seus outros benefícios.Recent literature suggests that distributed filtering algorithms may have several advantages over centralized ones, especially concerning robustness, scalability, use of communication resources and flexibility. In this context, this work aims to provide a performance analysis of distributed filtering algorithms for indoor pedestrian tracking, where a set of sensors are employed to estimate the position of a person in a room. More specifically, two forms of distributed algorithms will be investigated: weighted average consensus and diffusion. A localization scheme based on RFID is used as a testbed for the assessment of the performance of the algorithms. Particularly, phase difference of arrival techniques are applied to provide measurements of the relative distance and angle from the antennas to the RFID tag. Simulation experiments carried out in MATLAB indicate that the diffusion algorithm has superior performance than the weighted average consensus algorithm. Furthermore, the small difference in the root-mean-square error of centralized and diffusion algorithms inspires the use of the latter for its other benefits

Performance Analysis of Bearings-only Tracking Problems for Maneuvering Target and Heterogeneous Sensor Applications

State estimation, i.e. determining the trajectory, of a maneuvering target from noisy measurements collected by a single or multiple passive sensors (e.g. passive sonar and radar) has wide civil and military applications, for example underwater surveillance, air defence, wireless communications, and self-protection of military vehicles. These passive sensors are listening to target emitted signals without emitting signals themselves which give them concealing properties. Tactical scenarios exists where the own position shall not be revealed, e.g. for tracking submarines with passive sonar or tracking an aerial target by means of electro-optic image sensors like infrared sensors. This estimation process is widely known as bearings-only tracking. On the one hand, a challenge is the high degree of nonlinearity in the estimation process caused by the nonlinear relation of angular measurements to the Cartesian state. On the other hand, passive sensors cannot provide direct target location measurements, so bearings-only tracking suffers from poor target trajectory estimation accuracy due to marginal observability from sensor measurements. In order to achieve observability, that means to be able to estimate the complete target state, multiple passive sensor measurements must be fused. The measurements can be recorded spatially distributed by multiple dislocated sensor platforms or temporally distributed by a single, moving sensor platform. Furthermore, an extended case of bearings-only tracking is given if heterogeneous measurements from targets emitting different types of signals, are involved. With this, observability can also be achieved on a single, not necessarily moving platform. In this work, a performance bound for complex motion models, i.e. piecewisely maneuvering targets with unknown maneuver change times, by means of bearings-only measurements from a single, moving sensor platform is derived and an efficient estimator is implemented and analyzed. Furthermore, an observability analysis is carried out for targets emitting acoustic and electromagnetic signals. Here, the different signal propagation velocities can be exploited to ensure observability on a single, not necessarily moving platform. Based on the theoretical performance and observability analyses a distributed fusion system has been realized by means of heterogeneous sensors, which shall detect an event and localize a threat. This is performed by a microphone array to detect sound waves emitted by the threat as well as a radar detector that detects electromagnetic emissions from the threat. Since multiple platforms are involved to provide increased observability and also redundancy against possible breakdowns, a WiFi mobile ad hoc network is used for communications. In order to keep up the network in a breakdown OLSR (optimized link state routing) routing approach is employed

Advanced Integration of GNSS and External Sensors for Autonomous Mobility Applications

L'abstract è presente nell'allegato / the abstract is in the attachmen

Vehicle trajectory prediction for safe navigation of autonomous vehicles

Trajectory prediction of the other road users in the vicinity of an autonomous vehicle is important for safe navigation in dense traffic. Once an autonomous vehicle anticipates how the other road actors will react in the near future, path planning is a lot more simpler and safer. Moreover, the knowledge of future movement of other road actors allows control of sudden jerks in the planned ego vehicle’s path and thus makes travel smoother. This trajectory prediction stage can be used at any level, from restricted driver assistance to full vehicle autonomy. In this thesis two novel trajectory prediction models have been developed. In the first model, the spatio-temporal features that form the basis of behaviour prediction were captured using a Convolutional Long Short Term Memory (Conv-LSTM) neural network architecture consisting of three modules: 1) Interaction Learning to capture the motion of and interaction with surrounding cars, 2) Temporal Learning to identify the dependency on past movements and 3) Motion Learning to convert the extracted features from these two modules into future positions. In addition, a novel feedback scheme was introduced in which the current predicted positions of each car are leveraged to update future motion, encapsulating the effect of the surrounding cars. In the second model a conventional Long Short Term Memory (LSTM) cell based encoder-decoder architecture was developed which uses not only the historical observations but also the associated map features. Moreover, unlike existing architectures, the proposed method incorporates and updates the surrounding vehicle information in both the encoder and decoder, making use of dynamically predicted new data for accurate prediction in longer time horizons. This seamlessly performs four tasks: first, it encodes a feature given the past observations, second, it estimates future maneuvers given the encoded state, third, it predicts the future motion given the estimated maneuvers and the initially encoded states, and fourth, it estimates future trajectory given the encoded state and the predicted maneuvers and motions. Both the developed models were evaluated extensively on two publicly available datasets which include both multi-lane highway and signalled intersections, to benchmark the prediction accuracy with the state-of-the-art models. Later, the conventional encoder-decoder model was also evaluated with a newly collected “Radiate” dataset which includes two intersections, the Kingussie T-junction and the Edinburgh four-way junction, both without traffic signals. The accuracy of the predicted trajectories on the benchmark datasets are comparable with state-of-the-art methods. Moreover, evaluation on the latter dataset (“Radiate”) made it possible to understand better the effect of inter-vehicle interactions on future motion without any influence from mandatory traffic signals.Engineering and Physical Sciences Research Council (EPSRC) funding

Dynamics of DNA Breathing and Folding for Molecular Recognition and Computation

This thesis is centered on the development of the molecular beacon, as a new DNA probe for DNA genotyping, D N A computation and biophysical studies of DNA conformations. Molecular beacons are single-stranded DNA molecules that form a stem-and-loop structure. A fluorophore and a quencher are grafted at their two ends to report their conformations: when the molecular beacon is closed, fluorophore and quencher are held in close proximity and the fluorescence is quenched; when the molecular beacon is open, fluorophore and quencher are far apart, and the fluorescence is restored. Molecular beacons are ideal DNA probes coupling conformational switch with fluorescence signal turning-ON. We use molecular beacons to study the molecular recognition of single-stranded DNA (ssDNA) oligonucleotide. We present a thermodynamic diagram to show that structural constraints make the molecular beacon highly sensitive to the presence of mismatches in its target. We introduce a sequence sensitivity parameter to quantitatively compare different DNA probes, and propose an algorithm to optimally tune the probe\u27s structure for enhanced sequence discrimination. Logic gates (OR and AND gates) using molecular beacons are designed to carry most elementary molecular computations. The conformational changes associated with such computations can be used to concatenate many chemical reactions, and carry out complex molecular computations. Molecular beacons are also ideal probes to study DNA secondary structures and their fluctuations. We develop the fluorescence correlation spectroscopy (FCS) technique to monitor the dynamics of relaxation of DNA conformational fluctuations. We first measure the opening and closing timescales of DNA hairpin-loops. Activation barriers for opening and closing for different loop lengths and sequences are analyzed to better account for the stability of DNA secondary structures. A sequence dependent rigidity of ssDNA has been discovered, and analyzed in terms of base stacking. We then use F C S to study the dynamics of double-stranded DNA (dsDNA) breathing modes with synthetic DNA constructs. The analysis of the base pairing fluctuation dynamics, monitored by fluorescence, unravels lifetimes of breathing modes ranging from 1/us to 1ms. Long-range distortions of the d s DNA have been unraveled for purine-rich sequences, of relevance to the specificity of transcription initiation in prokaryotes