An Agricultural Spraying Robot Based on the Machine Vision

Accurate target spraying is a key technology in modern and intelligent agriculture. For solving the problems of pesticide waste and poisoning in the spraying process, a spraying robot based on binocular machine vision was proposed in this paper. A digital signal processor was used to identify and locate tomatoes as well as to control the nozzle spray. A stereoscopic vision model was established, and color normalization, 2G-R-B, was adopted to implement background segmentation between plants and soil. As for the tomatoes and plants, depth information and circularity depended on the nozzle’s target, and the plant shape area determined the amount of pesticide. Experiments shown that the recognition rate of this spraying robot was up to 92.5% for tomatoes

Numerical Methods for Learning Physical Constraints and their Applications

This thesis treats the theory and practice of learning physical constraints, and their use in interpolating and extrapolating motions. With the help of the evolving machine learning techniques, programs are capable of learning the physical properties and simulate future motions. To understand complex physical motions or make computationally expensive simulations much faster, there’s a pressing need to develop a model that can simplify this task. The contributions of this thesis begin with a theory that fits general physical motions into mass-spring models, followed by a neural particle simulator that leverages numerical methods that is end-to-end differentiable and trainable. Using this simulator, we can fit coordinates into this model to infer latent representation of collision radius, mass, spring constants in order to interpolate or extrapolate the motion. In the experiment section, we show that our method improves mean square error in chaotic motions by 100x compared to bilateral interpolation and the state-of-the-art and achieves similar performance in general physics motions.Bachelor of Scienc

Perovskite solar cells

The semiconductor perovskite CsPbBr2I was doped with Mn2+ to modulate its optical and photovoltaic performance. The Mn2+-doped CsPb0.9Mn0.1Br2I exhibited improved crystalline quality. Ultraviolet and visible spectroscopy of Mn2+-doped CsPb0.9Mn0.1Br2I revealed enhanced absorption capacity. Although the efficiency was not as good as desired, the enhanced light absorption of CsPb0.9Mn0.1Br2I still boosted the photovoltaic performance when it was utilized as a light absorber in perovskite solar cells, along with a low-cost carbon electrode. Compared with its counterpart CsPbBr2I, the doped CsPb0.9Mn0.1Br2I based solar cells demonstrated long-term air stability. Not only long-lasting stability was achieved by doping with Mn2+, the toxicity was also lessened by replacing the amount of hazardous lead in perovskite with harmless manganese

Cross-Camera Human Motion Transfer by Time Series Analysis

Along with advances in optical sensors is the increasingly common practice of building an imaging system with heterogeneous cameras. While high-resolution (HR) video acquisition and analysis benefit from hybrid sensors, the intrinsic characteristics of multiple cameras lead to a challenging motion transfer problem. In this paper, we propose an algorithm using time series analysis for motion transfer among multiple cameras. Specifically, we first identify seasonality in the motion data, and then build an additive time series model to extract patterns that could be transferred across different cameras. Our approach has a complete and clear mathematical formulation, and the algorithm is also efficient and interpretable. Through the experiment on real-world data, we demonstrate the effectiveness of our method. Furthermore, our motion transfer algorithm could combine with and facilitate downstream tasks, e.g., enhancing pose estimation on low-resolution (LR) videos with inherent patterns extracted from HR ones.Comment: 10 pages, 9 figure