Applications of Intelligent Vision in Low-Cost Mobile Robots

With the development of intelligent information technology, we have entered an era of 5G and AI. Mobile robots embody both of these technologies, and as such play an important role in future developments. However, the development of perception vision in consumer-grade low-cost mobile robots is still in its infancies. With the popularity of edge computing technology in the future, high-performance vision perception algorithms are expected to be deployed on low-power edge computing chips. Within the context of low-cost mobile robotic solutions, a robot intelligent vision system is studied and developed in this thesis. The thesis proposes and designs the overall framework of the higher-level intelligent vision system. The core system includes automatic robot navigation and obstacle object detection. The core algorithm deployments are implemented through a low-power embedded platform. The thesis analyzes and investigates deep learning neural network algorithms for obstacle object detection in intelligent vision systems. By comparing a variety of open source object detection neural networks on high performance hardware platforms, combining the constraints of hardware platform, a suitable neural network algorithm is selected. The thesis combines the characteristics and constraints of the low-power hardware platform to further optimize the selected neural network. It introduces the minimize mean square error (MMSE) and the moving average minmax algorithms in the quantization process to reduce the accuracy loss of the quantized model. The results show that the optimized neural network achieves a 20-fold improvement in inference performance on the RK3399PRO hardware platform compared to the original network. The thesis concludes with the application of the above modules and systems to a higher-level intelligent vision system for a low-cost disinfection robot, and further optimization is done for the hardware platform. The test results show that while achieving the basic service functions, the robot can accurately identify the obstacles ahead and locate and navigate in real time, which greatly enhances the perception function of the low-cost mobile robot

Towards Understanding the Condensation of Neural Networks at Initial Training

Implicit regularization is important for understanding the learning of neural networks (NNs). Empirical works show that input weights of hidden neurons (the input weight of a hidden neuron consists of the weight from its input layer to the hidden neuron and its bias term) condense on isolated orientations with a small initialization. The condensation dynamics implies that the training implicitly regularizes a NN towards one with much smaller effective size. In this work, we utilize multilayer networks to show that the maximal number of condensed orientations in the initial training stage is twice the multiplicity of the activation function, where "multiplicity" is multiple roots of activation function at origin. Our theoretical analysis confirms experiments for two cases, one is for the activation function of multiplicity one with arbitrary dimension input, which contains many common activation functions, and the other is for the layer with one-dimensional input and arbitrary multiplicity. This work makes a step towards understanding how small initialization implicitly leads NNs to condensation at initial training stage, which lays a foundation for the future study of the nonlinear dynamics of NNs and its implicit regularization effect at a later stage of training

Temperature- and field angular-dependent helical spin period characterized by magnetic dynamics in a chiral helimagnet MnNb3S6MnNb_3S_6

The chiral magnets with topological spin textures provide a rare platform to explore topology and magnetism for potential application implementation. Here, we study the magnetic dynamics of several spin configurations on the monoaxial chiral magnetic crystal $MnNb_3S_6$ via broadband ferromagnetic resonance (FMR) technique at cryogenic temperature. In the high-field forced ferromagnetic state (FFM) regime, the obtained frequency f vs. resonance field Hres dispersion curve follows the well-known Kittel formula for a single FFM, while in the low-field chiral magnetic soliton lattice (CSL) regime, the dependence of Hres on magnetic field angle can be well-described by our modified Kittel formula including the mixture of a helical spin segment and the FFM phase. Furthermore, compared to the sophisticated Lorentz micrograph technique, the observed magnetic dynamics corresponding to different spin configurations allow us to obtain temperature- and field-dependent proportion of helical spin texture and helical spin period ratio L(H)/L(0) via our modified Kittel formula. Our results demonstrated that field- and temperature-dependent nontrivial magnetic structures and corresponding distinct spin dynamics in chiral magnets can be an alternative and efficient approach to uncovering and controlling nontrivial topological magnetic dynamics.Comment: 29 pages (including Supporting Information), 7 figures, accepted by SCIENCE CHINA Physics, Mechanics & Astronom

Topological structures of energy flow: Poynting vector skyrmions

Topological properties of energy flow of light are fundamentally interesting and have rich practical applications in optical manipulations. Here, skyrmion-like structures formed by Poynting vectors are unveiled in the focal region of a pair of counter-propagating cylindrical vector vortex beams in free space. A N\'eel-Bloch-N\'eel skyrmion type transformation of Poynting vectors is observed along the light propagating direction within a volume with subwavelength feature sizes. The corresponding skyrmion type can be determined by the phase singularities of the individual components of the coherently superposed electromagnetic field in the focal region. This work reveals a new family member of optical skyrmions and may introduce novel physical phenomena associated with light scattering and optical force

A sustainable biochar catalyst synergized with copper heteroatoms and CO2 for singlet oxygenation and electron transfer routes

We have developed a wood waste-derived biochar as a sustainable graphitic carbon catalyst for environmental remediation through catalytic pyrolysis under the synergistic effects between Cu heteroatoms and CO2, which for the first time are found to significantly enhance the oxygen functionalities, defective sites, and highly ordered sp2-hybridized carbon matrix. The copper-doped graphitic biochars (Cu-GBCs) were further characterized by XRD, FTIR, Raman, XPS, etc., revealing that the modified specific surface area, pore structure, graphitization, and active sites (i.e., defective sites and ketonic group) on the Cu-GBCs corresponded to the synergistic Cu species loading and Cu-induced carbon-matrix reformation in CO2 environment during pyrolysis. The catalytic ability of Cu-GBCs was evaluated using the ubiquitous peroxydisulfate (PDS) activation system for the removal of various organic contaminants (i.e., rhodamine B, phenol, bisphenol A, and 4-chlorophenol), and gave the highest degradation rate of 0.0312 min-1 in comparison with those of pristine GBCs and N2-pyrolyzed Cu-GBCs ranging from 0.0056 to 0.0094 min-1. The synergistic effects were attributed to the encapsulated Cu heteroatoms, evolved ketonic groups, and abundant unconfined π electrons within the carbon lattice. According to scavenger experiments, ESR analysis, and two-chamber experiments, selective and sustainable non-radical pathways (i.e., singlet oxygenation and electron transfer) mediated by the Cu-induced metastable surface complex were achieved in the Cu-GBC/PDS system. This study offers the first insights into the efficacy, sustainability, and mechanistic roles of Cu-GBCs as an emerging carbon-based catalyst for green environmental remediation

Agricultural biomass/waste as adsorbents for toxic metal decontamination of aqueous solutions

Toxic metals can be present in the environment, causing negative effects on the ecosystem and human health. Although several technologies have been used for decontamination purposes, biosorption is an environmentally friendly and cost-effective alternative to remove toxic metals from wastewater. Agricultural biomasses are a class of biosorbents that offer several advantages, including their low cost, availability in nature, simplicity to be obtained and used as adsorbents. This review article is focused on the use of agricultural biomass materials for the removal of toxic metal(oid)s from contaminated aqueous matrices. In addition, raw and modified forms of these biosorbents are considered as precursors for the preparation of other adsorbents like biochar. Following agricultural biomasses are discussed: i) watermelon, ii) potato, iii) cucumber, iv) peanut, v) almond, vi) walnut and hazelnut, vii) pistachio, and viii) tea waste-based biosorbents. The adsorption potential of the biomasses is exhibited under the optimum experimental conditions, and their characterization and possibility to reuse is also considered. Moreover, isotherm and equilibrium parameters of the metal(oid) adsorption by the biomasses are discussed. Specifically, thermodynamic studies are described in order to better understand the nature of the biosorption process between contaminant and biomass. All these considerations reflect the high potential of agricultural waste-based adsorbents for toxic metal(oid)s removal related to wastewater treatment technologies.Fil: Anastopoulos, Ioannis. University Of Cyprus; ChipreFil: Pashalidis, Ioannis. University Of Cyprus; ChipreFil: Hosseini Bandegharaei, Ahmad. Sabzevar University Of Medical Sciences; Irán. Islamic Azad University; IránFil: Giannakoudakis, Dimitrios A.. Polish Academy of Sciences; PoloniaFil: Robalds, Artis. Animal Health And Environment Bior; LetoniaFil: Usman, Muhammad. University Of Agriculture; PakistánFil: Escudero, Leticia Belén. Universidad Nacional de Cuyo. Facultad de Ciencias Exactas y Naturales. Laboratorio de Química Analítica para Investigación y Desarrollo; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mendoza; ArgentinaFil: Zhou, Yaoyu. Hunan Agricultural University; ChinaFil: Colmenares, Juan Carlos. Polish Academy of Sciences; PoloniaFil: Núñez Delgado, Avelino. Universidad de Santiago de Compostela; EspañaFil: Lima, Éder Claudio. Universidade Federal do Rio Grande do Sul; Brasi

The use of constructed wetland for mitigating nitrogen and phosphorus from agricultural runoff: a review

The loss of nitrogen and phosphate fertilizers in agricultural runoff is a global environmental problem, attracting worldwide attention. In the last decades, the constructed wetland has been increasingly used for mitigating the loss of nitrogen and phosphate from agricultural runoff, while the substrate, plants, and wetland structure design remain far from clearly understood. In this paper, the optimum substrates and plant species were identified by reviewing their treatment capacity from the related studies. Specifically, the top three suitable substrates are gravel, zeolite, and slag. In terms of the plant species, emergent plants are the most widely used in the constructed wetlands. Eleocharis dulcis, Typha orientalis, and Scirpus validus are the top three optimum emergent plant species. Submerged plants (Hydrilla verticillata, Ceratophyllum demersum, and Vallisneria natans), free-floating plants (Eichhornia crassipes and Lemna minor), and floating-leaved plants (Nymphaea tetragona and Trapa bispinosa) are also promoted. Moreover, the site selection methods for constructed wetland were put forward. Because the existing research results have not reached an agreement on the controversial issue, more studies are still needed to draw a clear conclusion of effective structure design of constructed wetlands. This review has provided some recommendations for substrate, plant species, and site selections for the constructed wetlands to reduce nutrients from agricultural runoff

Alveolar Epithelial Type II Cells Activate Alveolar Macrophages and Mitigate P. Aeruginosa Infection

Although alveolar epithelial type II cells (AECII) perform substantial roles in the maintenance of alveolar integrity, the extent of their contributions to immune defense is poorly understood. Here, we demonstrate that AECII activates alveolar macrophages (AM) functions, such as phagocytosis using a conditioned medium from AECII infected by P. aeruginosa. AECII-derived chemokine MCP-1, a monocyte chemoattractant protein, was identified as a main factor in enhancing AM function. We proposed that the enhanced immune potency of AECII may play a critical role in alleviation of bacterial propagation and pneumonia. The ability of phagocytosis and superoxide release by AM was reduced by MCP-1 neutralizing antibodies. Furthermore, MCP-1−/− mice showed an increased bacterial burden under PAO1 and PAK infection vs. wt littermates. AM from MCP-1−/− mice also demonstrated less superoxide and impaired phagocytosis over the controls. In addition, AECII conditioned medium increased the host defense of airway in MCP-1−/− mice through the activation of AM function. Mechanistically, we found that Lyn mediated NFκB activation led to increased gene expression and secretion of MCP-1. Consequently Lyn−/− mice had reduced MCP-1 secretion and resulted in a decrease in superoxide and phagocytosis by AM. Collectively, our data indicate that AECII may serve as an immune booster for fighting bacterial infections, particularly in severe immunocompromised conditions