Development of subsea robot nomad with a micro computer based intelligent control system

The goal of this PhD project was to design and develop a small inexpensive subsea robot with a micro computer based intelligent control system. The robot developed is called NOMAD. It could be the key element in a Distributed Marine Observation System(DMOS). Most Engineering PhDs are research oriented; this one has a design focus. -- NOMAD uses an air/water ballast tank instead of a battery/motor system to drive itself vertically. To facilitate mission requirements, great efforts were made to develop a high performance onboard micro computer based control system. To deal with the uncertainty and nonlinearity of the robot model, investigations were conducted to check the potential of strategies based on Neural Networks and Fuzzy Logic. A Real Time Kernel software and an onboard micro computer with a Z180 CPU were used to implement a Fuzzy Variable Structure Switching (FVSS) control scheme and a multiple-task, multiple-layered control structure. -- The design and development process for NOMAD are detailed in this thesis. The results of digital simulation, theoretical analysis and typical data recorded from tests in a deep water tank on the robot are presented. Successful tests and good agreement between data and analysis indicate great potential for industrial application of the technologies developed in this project

High-Fidelity Eye Animatable Neural Radiance Fields for Human Face

Face rendering using neural radiance fields (NeRF) is a rapidly developing research area in computer vision. While recent methods primarily focus on controlling facial attributes such as identity and expression, they often overlook the crucial aspect of modeling eyeball rotation, which holds importance for various downstream tasks. In this paper, we aim to learn a face NeRF model that is sensitive to eye movements from multi-view images. We address two key challenges in eye-aware face NeRF learning: how to effectively capture eyeball rotation for training and how to construct a manifold for representing eyeball rotation. To accomplish this, we first fit FLAME, a well-established parametric face model, to the multi-view images considering multi-view consistency. Subsequently, we introduce a new Dynamic Eye-aware NeRF (DeNeRF). DeNeRF transforms 3D points from different views into a canonical space to learn a unified face NeRF model. We design an eye deformation field for the transformation, including rigid transformation, e.g., eyeball rotation, and non-rigid transformation. Through experiments conducted on the ETH-XGaze dataset, we demonstrate that our model is capable of generating high-fidelity images with accurate eyeball rotation and non-rigid periocular deformation, even under novel viewing angles. Furthermore, we show that utilizing the rendered images can effectively enhance gaze estimation performance.Comment: Under revie

The effect of investment and financing optimization policies for developing photovoltaic power generation in Cameroon; a dynamic CGE model assessment

With less than a decade remaining until 2030, global investment in clean energy access falls short of the anticipated levels required to achieve the sustainable development goals. Notably, nations with the greatest gaps in electricity access, particularly those in Sub-Saharan Africa, have been largely excluded from energy access funding. Interestingly, the energy sector policy documents of these countries have neglected to incorporate financing strategies or plans for photovoltaic (PV) power generation. This discrepancy in the literature underscores the need to assess the economic impact of finance and investment policies that align with long-term PV power generation targets. To address this gap, our study employs a dynamic Computable General Equilibrium model to evaluate the macroeconomic consequences of achieving Cameroon’s Nationally Determined Contributions for PV power generation through optimized PV investment and finance. The model examines three policy scenarios: the Business-as-Usual, SC1 scenario involving a stable 100% increase in PV investment, and SC2 scenario featuring a stepwise 5%–100% increase in PV investment. By simulating these scenarios, we aim to shed light on their effects. The results reveal that SC1 and SC2 exhibit a 50% higher final demand for PV investment compared to the BAU scenario. Optimizing PV finance and investment in both scenarios leads to a slowdown in Cameroon’s economic growth, with SC1 showing a more pronounced impact. Additionally, SC2 encourages rapid decarbonization in energy-intensive sectors such as crude oil production and electricity generation industries. However, the SC1 policy scenario results in a rapid reduction in total investment expenditure for PV power generation. By 2035, PV power generation is projected to be three times higher in both SC1 and SC2 compared to the BAU scenario. The SC2 policy scenario also predicts relatively high levels of consumption among rural affluent and urban impoverished households. In conclusion, our study highlights the pressing need for enhanced investment and finance strategies to propel PV power generation, particularly in underserved regions. By leveraging the findings of this research, policymakers can make informed decisions and implement policies that promote sustainable and inclusive energy access, driving progress towards the fulfillment of SDGs

A Fermi-LAT Study of Globular Cluster Dynamical Evolution in Milky Way Galaxy: Millisecond Pulsars as the Probe

Using archival {\it Fermi}-LAT data with a time span of $\sim12$ years, we study the population of Millisecond Pulsars (MSPs) in Globular Clusters (GlCs) and investigate their dependence on cluster dynamical evolution in the Milky Way Galaxy. We show that the $\gamma$-ray luminosity ($L_{\gamma}$) and emissivity ($\epsilon_{\gamma}=L_{\gamma}/M$) are good indicators of the population and abundance of MSPs in GlCs, and they are highly dependent on the dynamical evolution history of the host clusters. Specifically speaking, the dynamically older GlCs with more compact structures are more likely to have larger $L_{\gamma}$ and $\epsilon_{\gamma}$, and these trends can be summarized as strong correlations with cluster stellar encounter rate $\Gamma$ and the specific encounter rate ($\Lambda=\Gamma/M$), with $L_{\gamma}\propto \Gamma^{0.70\pm0.11}$ and $\epsilon_{\gamma}\propto \Lambda^{0.73\pm0.13}$ for dynamically normal GlCs. However, as GlCs evolve into deep core collapse, these trends are found to be reversed, implying that strong encounters may have lead to the ejection of MSPs from core-collapsed Systems. Besides, the GlCs are found to exhibit larger $\epsilon_{\gamma}$ with increasing stellar mass function slope, decreasing tidal radius and distances from the Galactic Center (GC). These correlations indicate that, as GlCs losing kinetic energy and spiral in towards GC, tidal stripping and mass segregation have a preference in leading to the loss of normal stars from GlCs, while MSPs are more likely to concentrate to cluster center and be deposited into the GC. Moreover, we gauge $\epsilon_{\gamma}$ of GlCs is $\sim10-1000$ times larger than the Galactic bulge, the latter is thought to reside thousands of unresolved MSPs and may responsible for the GC $\gamma$-ray excess, which support that GlCs are generous contributors to the population of MSPs in the GC.Comment: 23 pages, 9 figures, 3 tables, accepted for publication in RA

Category-Level 6D Object Pose Estimation with Flexible Vector-Based Rotation Representation

In this paper, we propose a novel 3D graph convolution based pipeline for category-level 6D pose and size estimation from monocular RGB-D images. The proposed method leverages an efficient 3D data augmentation and a novel vector-based decoupled rotation representation. Specifically, we first design an orientation-aware autoencoder with 3D graph convolution for latent feature learning. The learned latent feature is insensitive to point shift and size thanks to the shift and scale-invariance properties of the 3D graph convolution. Then, to efficiently decode the rotation information from the latent feature, we design a novel flexible vector-based decomposable rotation representation that employs two decoders to complementarily access the rotation information. The proposed rotation representation has two major advantages: 1) decoupled characteristic that makes the rotation estimation easier; 2) flexible length and rotated angle of the vectors allow us to find a more suitable vector representation for specific pose estimation task. Finally, we propose a 3D deformation mechanism to increase the generalization ability of the pipeline. Extensive experiments show that the proposed pipeline achieves state-of-the-art performance on category-level tasks. Further, the experiments demonstrate that the proposed rotation representation is more suitable for the pose estimation tasks than other rotation representations.Comment: revised from CVPR2021 paper FS-NET. arXiv admin note: substantial text overlap with arXiv:2103.0705