Surface Modeling and Analysis Using Range Images: Smoothing, Registration, Integration, and Segmentation

This dissertation presents a framework for 3D reconstruction and scene analysis, using a set of range images. The motivation for developing this framework came from the needs to reconstruct the surfaces of small mechanical parts in reverse engineering tasks, build a virtual environment of indoor and outdoor scenes, and understand 3D images. The input of the framework is a set of range images of an object or a scene captured by range scanners. The output is a triangulated surface that can be segmented into meaningful parts. A textured surface can be reconstructed if color images are provided. The framework consists of surface smoothing, registration, integration, and segmentation. Surface smoothing eliminates the noise present in raw measurements from range scanners. This research proposes area-decreasing flow that is theoretically identical to the mean curvature flow. Using area-decreasing flow, there is no need to estimate the curvature value and an optimal step size of the flow can be obtained. Crease edges and sharp corners are preserved by an adaptive scheme. Surface registration aligns measurements from different viewpoints in a common coordinate system. This research proposes a new surface representation scheme named point fingerprint. Surfaces are registered by finding corresponding point pairs in an overlapping region based on fingerprint comparison. Surface integration merges registered surface patches into a whole surface. This research employs an implicit surface-based integration technique. The proposed algorithm can generate watertight models by space carving or filling the holes based on volumetric interpolation. Textures from different views are integrated inside a volumetric grid. Surface segmentation is useful to decompose CAD models in reverse engineering tasks and help object recognition in a 3D scene. This research proposes a watershed-based surface mesh segmentation approach. The new algorithm accurately segments the plateaus by geodesic erosion using fast marching method. The performance of the framework is presented using both synthetic and real world data from different range scanners. The dissertation concludes by summarizing the development of the framework and then suggests future research topics

G protein-coupled receptors mediate neural regulation of innate immune responses in caenorhabditis elegans

G protein-coupled receptors (GPCRs) are a large family of transmembrane proteins that perceive many extracellular signals and transduce them into cellular physiological responses. GPCRs regulate immunity in both vertebrates and invertebrates. However, the mechanisms responsible for such regulation are not fully understood. Recent research using the genetically tractable model organism Caenorhabditis elegans has led to the identification of specific GPCRs, neurotransmitters, neurons and non-neural cells in the regulation of innate immunity. Several neural circuits have been demonstrated to function in GPCR-dependent immuno-regulatory pathways. Besides being essential in neural-immune interactions, GPCRs also regulate innate immune response in non-neural tissues cell-autonomously through mechanisms independent of neural circuits. Here we review GPCR-mediated neural control of innate immunity in C. elegans and briefly discuss GPCR-dependent immune regulation via non-neural mechanisms

A Sliding Mode based Cascade Observer for Estimation and Compensation Controller

The sliding mode observer can estimate the system state and the unknown disturbance, while the traditional single-layer one might still suffer from high pulse when the output measurement is mixed with noise. To improve the estimation quality, a new cascade sliding mode observer containing multiple discontinuous functions is proposed in this letter. It consists of two layers: the first layer is a traditional sliding mode observer, and the second layer is a cascade observer. The measurement noise issue is considered in the source system model. An alternative method how to design the observer gains of the two layers, together with how to examine the effectiveness of the compensator based closed-loop system, are offered. A numerical example is provided to demonstrate the effectiveness of the proposed method. The observation structure proposed in this letter not only smooths the estimated state but also reduces the control consumption

A multifunctional tripodal fluorescent probe for the recognition of Cr3+, Al3+, Zn2+ and F− with controllable ESIPT processes

Three 4-(benzo[d]thiazol-2-yl)-2,5-dihydroxybenzaldehyde fluorophores were introduced to construct a tripodal multifunctional ESPIT fluorescence probe L. The fluorescent analysis revealed that probe L exhibited excellent recognition capabilities towards Cr3+, Al3+, Zn2+ and F− ions with large Stokes shifts. Furthermore, under optimal conditions, the detection limit of probe L towards Cr3+, Al3+, Zn2+ and F− were low, of the order of 10−8 M, which indicated that probe L was sensitive to these four ions. Interestingly, the fluorescent and 1H NMR titration experiments revealed that the recognition mechanism of probe L towards the ions Cr3+, Al3+, Zn2+ and F− were different. The presence of Cr3+ and Al3+ recovered the ESIPT, but the presence of Zn2+ trigger a moderate deprotonation of the phenolic OH and induced an ESIPT red-shifted (60 nm) emission wavelength. Finally, the presence of F− completely deprotonated the free phenolic OH and a remarkable red-shifted (130 nm) ESIPT emission was observed. In other words, the ESIPT process of probe L is controllable. Furthermore, the utility of probe L as a biosensor in living cells (PC3 cells) towards Cr3+, Al3+ and Zn2+ ions has been demonstrated

Submarine groundwater discharge in Dongshan Bay, China: A master regulator of nutrients in spring and potential national significance of small bays

Despite over 90% of China’s coastal bays have an area less than 500 km2, the geochemical effects of SGD on those ecosystems are ambiguous. Based on mapping and time-series observations of Ra isotopes and nutrients, a case study of small bays (<500 km2), we revealed that submarine groundwater discharge (SGD) predominately regulated the distribution of nutrients and fueled algal growth in Dongshan Bay, China. On the bay-wide scale, the SGD rate was estimated to be 0.048 ± 0.022 m day−1 and contributed over 95% of the nutrients. At the time-series site where the bay-wide highest Ra activities in the bottom water marked an SGD hotspot with an average rate an order of magnitude greater, the maximum chlorophyll concentration co-occurred, suggesting that SGD may support the algal bloom. The ever-most significant positive correlations between 228Ra and nutrients throughout the water column (P< 0.01, R2 > 0.90 except for soluble reactive phosphorus in the surface) suggested the predominance of SGD in controlling nutrient distribution in the bay. Extrapolated to a national scale, the SGD-carried dissolved inorganic nitrogen flux in small bays was twice as much as those in large bays (>2,000 km2). Thus, the SGD-carried nutrients in small bays merit immediate attention in environmental monitoring and management

Optimal mathematical programming and variable neighborhood search for k-modes categorical data clustering

The conventional k-modes algorithm and its variants have been extensively used for categorical data clustering. However, these algorithms have some drawbacks, e.g., they can be trapped into local optima and sensitive to initial clusters/modes. Our numerical experiments even showed that the k-modes algorithm could not identify the optimal clustering results for some special datasets regardless the selection of the initial centers. In this paper, we developed an integer linear programming (ILP) approach for the k-modes clustering, which is independent to the initial solution and can obtain directly the optimal results for small-sized datasets. We also developed a heuristic algorithm that implements iterative partial optimization in the ILP approach based on a framework of variable neighborhood search, known as IPO-ILP-VNS, to search for near-optimal results of medium and large sized datasets with controlled computing time. Experiments on 38 datasets, including 27 synthesized small datasets and 11 known benchmark datasets from the UCI site were carried out to test the proposed ILP approach and the IPO-ILP-VNS algorithm. The experimental results outperformed the conventional and other existing enhanced k-modes algorithms in literature, updated 9 of the UCI benchmark datasets with new and improved results

G protein-coupled receptors mediate neural regulation of innate immune responses in caenorhabditis elegans

G protein-coupled receptors (GPCRs) are a large family of transmembrane proteins that perceive many extracellular signals and transduce them into cellular physiological responses. GPCRs regulate immunity in both vertebrates and invertebrates. However, the mechanisms responsible for such regulation are not fully understood. Recent research using the genetically tractable model organism Caenorhabditis elegans has led to the identification of specific GPCRs, neurotransmitters, neurons and non-neural cells in the regulation of innate immunity. Several neural circuits have been demonstrated to function in GPCR-dependent immuno-regulatory pathways. Besides being essential in neural-immune interactions, GPCRs also regulate innate immune response in non-neural tissues cell-autonomously through mechanisms independent of neural circuits. Here we review GPCR-mediated neural control of innate immunity in C. elegans and briefly discuss GPCR-dependent immune regulation via non-neural mechanisms