Arm Motion Classification Using Curve Matching of Maximum Instantaneous Doppler Frequency Signatures

Hand and arm gesture recognition using the radio frequency (RF) sensing modality proves valuable in manmachine interface and smart environment. In this paper, we use curve matching techniques for measuring the similarity of the maximum instantaneous Doppler frequencies corresponding to different arm gestures. In particular, we apply both Frechet and dynamic time warping (DTW) distances that, unlike the Euclidean (L2) and Manhattan (L1) distances, take into account both the location and the order of the points for rendering two curves similar or dissimilar. It is shown that improved arm gesture classification can be achieved by using the DTW method, in lieu of L2 and L1 distances, under the nearest neighbor (NN) classifier.Comment: 6 pages, 7 figures, 2020 IEEE radar conference. arXiv admin note: substantial text overlap with arXiv:1910.1117

Speeding up biphasic reactions with surface nanodroplets

Biphasic chemical reactions compartmentalized in small droplets offer advantages, such as streamlined procedures for chemical analysis, enhanced chemical reaction efficiency and high specificity of conversion. In this work, we experimentally and theoretically investigate the rate for biphasic chemical reactions between acidic nanodroplets on a substrate surface and basic reactants in a surrounding bulk flow. The reaction rate is measured by droplet shrinkage as the product is removed from the droplets by the flow. In our experiments, we determine the dependence of the reaction rate on the flow rate and the solution concentration. The theoretical analysis predicts that the life time $\tau$ of the droplets scales with Peclet number $Pe$ and the reactant concentration in the bulk flow $c_{re,bulk}$ as $\tau\propto Pe^{-3/2}c_{re,bulk}^{-1}$, in good agreement with our experimental results. Furthermore, we found that the product from the reaction on an upstream surface can postpone the droplet reaction on a downstream surface, possibly due to the adsorption of interface-active products on the droplets in the downstream. The time of the delay decreases with increasing $Pe$ of the flow and also with increasing reactant concentration in the flow, following the scaling same as that of the reaction rate with these two parameters. Our findings provide insight for the ultimate aim to enhance droplet reactions under flow conditions

The Power of Visual Texture in Aesthetic Perception: An Exploration of the Predictability of Perceived Aesthetic Emotions

How to interpret the relationship between the low-level features, such as some statistical characteristics of color and texture, and the high-level aesthetic properties, such as warm or cold, soft or hard, has been a hot research topic of neuroaesthetics. Contrary to the black-box method widely used in the fields of machine learning and pattern recognition, we build a white-box model with the hierarchical feed-forward structure inspired by neurobiological mechanisms underlying the aesthetic perception of visual art. In the experiment, the aesthetic judgments for 8 pairs of aesthetic antonyms are carried out for a set of 151 visual textures. For each visual texture, 106 low-level features are extracted. Then, ten more useful and effective features are selected through neighborhood component analysis to reduce information redundancy and control the complexity of the model. Finally, model building of the beauty appreciation of visual textures using multiple linear or nonlinear regression methods is detailed. Compared with our previous work, a more robust feature selection algorithm, neighborhood component analysis, is used to reduce information redundancy and control computation complexity of the model. Some nonlinear models are also adopted and achieved higher prediction accuracy when compared with the previous linear models. Additionally, the selection strategy of aesthetic antonyms and the selection standards of the core set of them are also explained. This research also suggests that the aesthetic perception and appreciation of visual textures can be predictable based on the computed low-level features

Speeding up biphasic reactions with surface nanodroplets

Biphasic chemical reactions compartmentalized in small droplets offer advantages, such as streamlined procedures for chemical analysis, enhanced chemical reaction efficiency and high specificity of conversion. In this work, we experimentally and theoretically investigate the rate for biphasic chemical reactions between acidic nanodroplets on a substrate surface and basic reactants in a surrounding bulk flow. The reaction rate is measured by droplet shrinkage as the product is removed from the droplets by the flow. In our experiments, we determine the dependence of the reaction rate on the flow rate and the solution concentration. The theoretical analysis predicts that the life time τ of the droplets scales with Peclet number Pe and the reactant concentration in the bulk flow cre,bulk as τ∝ Pe-3/2cre,bulk-1, in good agreement with our experimental results. Furthermore, we found that the product from the reaction on an upstream surface can postpone the droplet reaction on a downstream surface, possibly due to the adsorption of interface-active products on the droplets in the downstream. The time of the delay decreases with increasing Pe of the flow and also with increasing reactant concentration in the flow, following the scaling same as that of the reaction rate with these two parameters. Our findings provide insight for the ultimate aim to enhance droplet reactions under flow conditions