Role of quantum coherence in chromophoric energy transport

The role of quantum coherence and the environment in the dynamics of excitation energy transfer is not fully understood. In this work, we introduce the concept of dynamical contributions of various physical processes to the energy transfer efficiency. We develop two complementary approaches, based on a Green's function method and energy transfer susceptibilities, and quantify the importance of the Hamiltonian evolution, phonon-induced decoherence, and spatial relaxation pathways. We investigate the Fenna-Matthews-Olson protein complex, where we find a contribution of coherent dynamics of about 10% and of relaxation of 80%.Comment: 5 pages, 3 figures, included static disorder, correlated environmen

Development of new intelligent autonomous robotic assistant for hospitals

Continuous technological development in modern societies has increased the quality of life and average life-span of people. This imposes an extra burden on the current healthcare infrastructure, which also creates the opportunity for developing new, autonomous, assistive robots to help alleviate this extra workload. The research question explored the extent to which a prototypical robotic platform can be created and how it may be implemented in a hospital environment with the aim to assist the hospital staff with daily tasks, such as guiding patients and visitors, following patients to ensure safety, and making deliveries to and from rooms and workstations. In terms of major contributions, this thesis outlines five domains of the development of an actual robotic assistant prototype. Firstly, a comprehensive schematic design is presented in which mechanical, electrical, motor control and kinematics solutions have been examined in detail. Next, a new method has been proposed for assessing the intrinsic properties of different flooring-types using machine learning to classify mechanical vibrations. Thirdly, the technical challenge of enabling the robot to simultaneously map and localise itself in a dynamic environment has been addressed, whereby leg detection is introduced to ensure that, whilst mapping, the robot is able to distinguish between people and the background. The fourth contribution is geometric collision prediction into stabilised dynamic navigation methods, thus optimising the navigation ability to update real-time path planning in a dynamic environment. Lastly, the problem of detecting gaze at long distances has been addressed by means of a new eye-tracking hardware solution which combines infra-red eye tracking and depth sensing. The research serves both to provide a template for the development of comprehensive mobile assistive-robot solutions, and to address some of the inherent challenges currently present in introducing autonomous assistive robots in hospital environments.Open Acces

A Comprehensive Survey of Deep Learning in Remote Sensing: Theories, Tools and Challenges for the Community

In recent years, deep learning (DL), a re-branding of neural networks (NNs), has risen to the top in numerous areas, namely computer vision (CV), speech recognition, natural language processing, etc. Whereas remote sensing (RS) possesses a number of unique challenges, primarily related to sensors and applications, inevitably RS draws from many of the same theories as CV; e.g., statistics, fusion, and machine learning, to name a few. This means that the RS community should be aware of, if not at the leading edge of, of advancements like DL. Herein, we provide the most comprehensive survey of state-of-the-art RS DL research. We also review recent new developments in the DL field that can be used in DL for RS. Namely, we focus on theories, tools and challenges for the RS community. Specifically, we focus on unsolved challenges and opportunities as it relates to (i) inadequate data sets, (ii) human-understandable solutions for modelling physical phenomena, (iii) Big Data, (iv) non-traditional heterogeneous data sources, (v) DL architectures and learning algorithms for spectral, spatial and temporal data, (vi) transfer learning, (vii) an improved theoretical understanding of DL systems, (viii) high barriers to entry, and (ix) training and optimizing the DL.Comment: 64 pages, 411 references. To appear in Journal of Applied Remote Sensin

Collective Gradient Sensing in Fish Schools

Throughout the animal kingdom, animals frequently benefit from living in groups. Models of collective behaviour show that simple local interactions are sufficient to generate group morphologies found in nature (swarms, flocks and mills). However, individuals also interact with the complex noisy environment in which they live. In this work, we experimentally investigate the group performance in navigating a noisy light gradient of two unrelated freshwater species: golden shiners (Notemigonuscrysoleucas) and rummy nose tetra (Hemigrammus bleheri). We find that tetras outperform shiners due to their innate individual ability to sense the environmental gradient. Using numerical simulations, we examine how group performance depends on the relative weight of social and environmental information. Our results highlight the importance of balancing of social and environmental information to promote optimal group morphologies and performance

Electrometry Using Coherent Exchange Oscillations in a Singlet-Triplet-Qubit

Two level systems that can be reliably controlled and measured hold promise in both metrology and as qubits for quantum information science (QIS). When prepared in a superposition of two states and allowed to evolve freely, the state of the system precesses with a frequency proportional to the splitting between the states. In QIS,this precession forms the basis for universal control of the qubit,and in metrology the frequency of the precession provides a sensitive measurement of the splitting. However, on a timescale of the coherence time, $T_2$, the qubit loses its quantum information due to interactions with its noisy environment, causing qubit oscillations to decay and setting a limit on the fidelity of quantum control and the precision of qubit-based measurements. Understanding how the qubit couples to its environment and the dynamics of the noise in the environment are therefore key to effective QIS experiments and metrology. Here we show measurements of the level splitting and dephasing due to voltage noise of a GaAs singlet-triplet qubit during exchange oscillations. Using free evolution and Hahn echo experiments we probe the low frequency and high frequency environmental fluctuations, respectively. The measured fluctuations at high frequencies are small, allowing the qubit to be used as a charge sensor with a sensitivity of $2 \times 10^{-8} e/\sqrt{\mathrm{Hz}}$, two orders of magnitude better than the quantum limit for an RF single electron transistor (RF-SET). We find that the dephasing is due to non-Markovian voltage fluctuations in both regimes and exhibits an unexpected temperature dependence. Based on these measurements we provide recommendations for improving $T_2$ in future experiments, allowing for higher fidelity operations and improved charge sensitivity

The Pacifican, December 1, 1972

https://scholarlycommons.pacific.edu/pacifican/2079/thumbnail.jp