Tiling mechanisms of the compound eye through geometrical tessellation

Tilling patterns are observed in many biological structures. Hexagonal tilling, commonly observed in the compound eyes of wild-type Drosophila, is dominant in nature; this dominance can probably be attributed to physical restrictions such as structural robustness, minimal boundary length, and space filling efficiency. Surprisingly, tetragonal tiling patterns are also observed in some Drosophila small eye mutants and aquatic crustaceans. Herein, geometrical tessellation is shown to determine the ommatidial tiling patterns. In small eye mutants, the hexagonal pattern is transformed into a tetragonal pattern as the relative positions of neighboring ommatidia are stretched along the dorsal-ventral axis. Hence, the regular distribution of ommatidia and their uniform growth collectively play an essential role in the establishment of tetragonal and hexagonal tiling patterns in compound eyes.

Supramolecular tessellations by the exo-wall interactions of pagoda[4]arene

Supramolecular tessellation has gained increasing interest in supramolecular chemistry for its structural aesthetics and potential applications in optics, magnetics and catalysis. In this work, a new kind of supramolecular tessellations (STs) have been fabricated by the exo-wall interactions of pagoda[4]arene (P4). ST with rhombic tiling pattern was first constructed by P4 itself through favorable π···π interactions between anthracene units of adjacent P4. Notably, various highly ordered STs with different tiling patterns have been fabricated based on exo-wall charge transfer interactions between electron-rich P4 and electron-deficient guests including 1,4-dinitrobenzene, terephthalonitrile and tetrafluoroterephthalonitrile. Interestingly, solvent modulation and guest selection played a crucial role in controlling the molecular arrangements in the co-crystal superstructures. This work not only proves that P4 is an excellent macrocyclic building block for the fabrication of various STs, but also provides a new perspective and opportunity for the design and construction of supramolecular two-dimensional organic materials.

Statistical characterization of tile decoding time of HEVC-encoded 360° video

In this paper, we present a statistical characterization of tile decoding time of 360° videos encoded via HEVC that considers different tiling patterns and quality levels (i.e., bitrates). In particular, we present results for probability density function estimation of tile decoding time based on a series of experiments carried out over a set of 360° videos with different spatial and temporal characteristics. Additionally, we investigate the extent to which tile decoding time is correlated with tile bitrate (at chunk level), so that DASH-based video streaming can make possible use of such an information to infer tile decoding time. The results of this work may help in the design of queueing or control theory-based adaptive bitrate (ABR) algorithms for 360° video streaming.

Evolutionary Algorithm-Based Complete Coverage Path Planning for Tetriamond Tiling Robots

Tiling robots with fixed morphology face major challenges in terms of covering the cleaning area and generating the optimal trajectory during navigation. Developing a self-reconfigurable autonomous robot is a probable solution to these issues, as it adapts various forms and accesses narrow spaces during navigation. The total navigation energy includes the energy expenditure during locomotion and the shape-shifting of the platform. Thus, during motion planning, the optimal navigation sequence of a self-reconfigurable robot must include the components of the navigation energy and the area coverage. This paper addresses the framework to generate an optimal navigation path for reconfigurable cleaning robots made of tetriamonds. During formulation, the cleaning environment is filled with various tiling patterns of the tetriamond-based robot, and each tiling pattern is addressed by a waypoint. The objective is to minimize the amount of shape-shifting needed to fill the workspace. The energy cost function is formulated based on the travel distance between waypoints, which considers the platform locomotion inside the workspace. The objective function is optimized based on evolutionary algorithms such as the genetic algorithm (GA) and ant colony optimization (ACO) of the traveling salesman problem (TSP) and estimates the shortest path that connects all waypoints. The proposed path planning technique can be extended to other polyamond-based reconfigurable robots.