7,407 research outputs found

    Optical Spectroscopy of 3d and 4d correlated electron systems.

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    In the context of this work, three different materials are studied via optical spec- troscopy methods. The three materials are La2Cu2O5, Fe3O4, and Ca2RuO4, where the first one is investigated via Fourier spectroscopy, while the latter two are stud- ied via spectroscopic ellipsometry

    The development of liquid crystal lasers for application in fluorescence microscopy

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    Lasers can be found in many areas of optical medical imaging and their properties have enabled the rapid advancement of many imaging techniques and modalities. Their narrow linewidth, relative brightness and coherence are advantageous in obtaining high quality images of biological samples. This is particularly beneficial in fluorescence microscopy. However, commercial imaging systems depend on the combination of multiple independent laser sources or use tuneable sources, both of which are expensive and have large footprints. This thesis demonstrates the use of liquid crystal (LC) laser technology, a compact and portable alternative, as an exciting candidate to provide a tailorable light source for fluorescence microscopy. Firstly, to improve the laser performance parameters such that high power and high specification lasers could be realised; device fabrication improvements were presented. Studies exploring the effect of alignment layer rubbing depth and the device cell gap spacing on laser performance were conducted. The results were the first of their kind and produced advances in fabrication that were critical to repeatedly realising stable, single-mode LC laser outputs with sufficient power to conduct microscopy. These investigations also aided with the realisation of laser diode pumping of LC lasers. Secondly, the identification of optimum dye concentrations for single and multi-dye systems were used to optimise the LC laser mixtures for optimal performance. These investigations resulted in novel results relating to the gain media in LC laser systems. Collectively, these advancements yielded lasers of extremely low threshold, comparable to the lowest reported thresholds in the literature. A portable LC laser system was integrated into a microscope and used to perform fluorescence microscopy. Successful two-colour imaging and multi-wavelength switching ability of LC lasers were exhibited for the first time. The wavelength selectivity of LC lasers was shown to allow lower incident average powers to be used for comparable image quality. Lastly, wavelength selectivity enabled the LC laser fluorescence microscope to achieve high enough sensitivity to conduct quantitative fluorescence measurements. The development of LC lasers and their suitability to fluorescence microscopy demonstrated in this thesis is hoped to push towards the realisation of commercialisation and application for the technology

    Reliable Sensor Intelligence in Resource Constrained and Unreliable Environment

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    The objective of this research is to design a sensor intelligence that is reliable in a resource constrained, unreliable environment. There are various sources of variations and uncertainty involved in intelligent sensor system, so it is critical to build reliable sensor intelligence. Many prior works seek to design reliable sensor intelligence by developing robust and reliable task. This thesis suggests that along with improving task itself, task reliability quantification based early warning can further improve sensor intelligence. DNN based early warning generator quantifies task reliability based on spatiotemporal characteristics of input, and the early warning controls sensor parameters and avoids system failure. This thesis presents an early warning generator that predicts task failure due to sensor hardware induced input corruption and controls the sensor operation. Moreover, lightweight uncertainty estimator is presented to take account of DNN model uncertainty in task reliability quantification without prohibitive computation from stochastic DNN. Cross-layer uncertainty estimation is also discussed to consider the effect of PIM variations.Ph.D

    LIPIcs, Volume 251, ITCS 2023, Complete Volume

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    LIPIcs, Volume 251, ITCS 2023, Complete Volum

    Automated identification and behaviour classification for modelling social dynamics in group-housed mice

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    Mice are often used in biology as exploratory models of human conditions, due to their similar genetics and physiology. Unfortunately, research on behaviour has traditionally been limited to studying individuals in isolated environments and over short periods of time. This can miss critical time-effects, and, since mice are social creatures, bias results. This work addresses this gap in research by developing tools to analyse the individual behaviour of group-housed mice in the home-cage over several days and with minimal disruption. Using data provided by the Mary Lyon Centre at MRC Harwell we designed an end-to-end system that (a) tracks and identifies mice in a cage, (b) infers their behaviour, and subsequently (c) models the group dynamics as functions of individual activities. In support of the above, we also curated and made available a large dataset of mouse localisation and behaviour classifications (IMADGE), as well as two smaller annotated datasets for training/evaluating the identification (TIDe) and behaviour inference (ABODe) systems. This research constitutes the first of its kind in terms of the scale and challenges addressed. The data source (side-view single-channel video with clutter and no identification markers for mice) presents challenging conditions for analysis, but has the potential to give richer information while using industry standard housing. A Tracking and Identification module was developed to automatically detect, track and identify the (visually similar) mice in the cluttered home-cage using only single-channel IR video and coarse position from RFID readings. Existing detectors and trackers were combined with a novel Integer Linear Programming formulation to assign anonymous tracks to mouse identities. This utilised a probabilistic weight model of affinity between detections and RFID pickups. The next task necessitated the implementation of the Activity Labelling module that classifies the behaviour of each mouse, handling occlusion to avoid giving unreliable classifications when the mice cannot be observed. Two key aspects of this were (a) careful feature-selection, and (b) judicious balancing of the errors of the system in line with the repercussions for our setup. Given these sequences of individual behaviours, we analysed the interaction dynamics between mice in the same cage by collapsing the group behaviour into a sequence of interpretable latent regimes using both static and temporal (Markov) models. Using a permutation matrix, we were able to automatically assign mice to roles in the HMM, fit a global model to a group of cages and analyse abnormalities in data from a different demographic

    Neutron scattering studies of heterogeneous catalysis

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    Understanding the structural dynamics/evolution of catalysts and the related surface chemistry is essential for establishing structure–catalysis relationships, where spectroscopic and scattering tools play a crucial role. Among many such tools, neutron scattering, though less-known, has a unique power for investigating catalytic phenomena. Since neutrons interact with the nuclei of matter, the neutron–nucleon interaction provides unique information on light elements (mainly hydrogen), neighboring elements, and isotopes, which are complementary to X-ray and photon-based techniques. Neutron vibrational spectroscopy has been the most utilized neutron scattering approach for heterogeneous catalysis research by providing chemical information on surface/bulk species (mostly H-containing) and reaction chemistry. Neutron diffraction and quasielastic neutron scattering can also supply important information on catalyst structures and dynamics of surface species. Other neutron approaches, such as small angle neutron scattering and neutron imaging, have been much less used but still give distinctive catalytic information. This review provides a comprehensive overview of recent advances in neutron scattering investigations of heterogeneous catalysis, focusing on surface adsorbates, reaction mechanisms, and catalyst structural changes revealed by neutron spectroscopy, diffraction, quasielastic neutron scattering, and other neutron techniques. Perspectives are also provided on the challenges and future opportunities in neutron scattering studies of heterogeneous catalysis

    Synthetic Aperture Radar (SAR) Meets Deep Learning

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    This reprint focuses on the application of the combination of synthetic aperture radars and depth learning technology. It aims to further promote the development of SAR image intelligent interpretation technology. A synthetic aperture radar (SAR) is an important active microwave imaging sensor, whose all-day and all-weather working capacity give it an important place in the remote sensing community. Since the United States launched the first SAR satellite, SAR has received much attention in the remote sensing community, e.g., in geological exploration, topographic mapping, disaster forecast, and traffic monitoring. It is valuable and meaningful, therefore, to study SAR-based remote sensing applications. In recent years, deep learning represented by convolution neural networks has promoted significant progress in the computer vision community, e.g., in face recognition, the driverless field and Internet of things (IoT). Deep learning can enable computational models with multiple processing layers to learn data representations with multiple-level abstractions. This can greatly improve the performance of various applications. This reprint provides a platform for researchers to handle the above significant challenges and present their innovative and cutting-edge research results when applying deep learning to SAR in various manuscript types, e.g., articles, letters, reviews and technical reports

    Observation of Josephson Harmonics in Tunnel Junctions

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    Superconducting quantum processors have a long road ahead to reach fault-tolerant quantum computing. One of the most daunting challenges is taming the numerous microscopic degrees of freedom ubiquitous in solid-state devices. State-of-the-art technologies, including the world's largest quantum processors, employ aluminum oxide (AlOx_x) tunnel Josephson junctions (JJs) as sources of nonlinearity, assuming an idealized pure sinφ\sin\varphi current-phase relation (Cφ\varphiR). However, this celebrated sinφ\sin\varphi Cφ\varphiR is only expected to occur in the limit of vanishingly low-transparency channels in the AlOx_x barrier. Here we show that the standard Cφ\varphiR fails to accurately describe the energy spectra of transmon artificial atoms across various samples and laboratories. Instead, a mesoscopic model of tunneling through an inhomogeneous AlOx_x barrier predicts %-level contributions from higher Josephson harmonics. By including these in the transmon Hamiltonian, we obtain orders of magnitude better agreement between the computed and measured energy spectra. The reality of Josephson harmonics transforms qubit design and prompts a reevaluation of models for quantum gates and readout, parametric amplification and mixing, Floquet qubits, protected Josephson qubits, etc. As an example, we show that engineered Josephson harmonics can reduce the charge dispersion and the associated errors in transmon qubits by an order of magnitude, while preserving anharmonicity
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