2 results
Air entrapment of a neutral drop impacting onto a flat solid surface in electric fields
- Yu Tian, Yanchu Liu, Zihan Peng, Chenghao Xu, Dong Ye, Yin Guan, Xinping Zhou, Weiwei Deng, YongAn Huang
-
- Journal:
- Journal of Fluid Mechanics / Volume 946 / 10 September 2022
- Published online by Cambridge University Press:
- 04 August 2022, A21
-
- Article
- Export citation
-
When a charge neutral drop impacts on a flat solid substrate, a small air bubble is always trapped underneath due to the lubrication pressure coming from the viscous stress in the squeezed air film. Herein we find experimentally and numerically that the process of the air entrapment and the initial contact state of the drop with the substrate can be profoundly altered via an external electric field. In an electric field, the induced electric stresses at the bottom of the drop increase drastically right before the drop contacts the substrate, which acts against the lubrication pressure, resulting in reduced initial contact radius and air bubble size. When the external electric field reaches a critical value, the electrical stress accelerates the flow near the bottom of the drop and generates a conical tip quickly instead of a dimple, resulting in a centre contact and eliminating the air bubble entrapment. Based on the dipole mirror charge model, we find the dimensionless strength of critical electric field scales with the square root of capillary number based on the air viscosity. This scaling law of the critical electric field for eliminating the air bubble entrapment is verified experimentally and numerically. This work may offer a new way to mitigate defects caused by air bubble entrapment for inkjet printing and droplet-based additive manufacturing.
12 - Face recognition by sparse representation
-
- By Arvind Ganesh, University of Illinois, USA, Andrew Wagner, University of Illinois, USA, Zihan Zhou, University of Illinois, USA, Allen Y. Yang, University of California, USA, Yi Ma, Microsoft Research Asia, China, John Wright, Microsoft Research Asia, China
- Edited by Yonina C. Eldar, Weizmann Institute of Science, Israel, Gitta Kutyniok, Technische Universität Berlin
-
- Book:
- Compressed Sensing
- Published online:
- 05 November 2012
- Print publication:
- 17 May 2012, pp 515-539
-
- Chapter
- Export citation
-
Summary
In this chapter, we present a comprehensive framework for tackling the classical problem of face recognition, based on theory and algorithms from sparse representation. Despite intense interest in the past several decades, traditional pattern recognition theory still stops short of providing a satisfactory solution capable of recognizing human faces in the presence of real-world nuisances such as occlusion and variabilities in pose and illumination. Our new approach, called sparse representation-based classification (SRC), is motivated by a very natural notion of sparsity, namely, one should always try to explain a query image using a small number of training images from a single subject category. This sparse representation is sought via ℓ1 minimization. We show how this core idea can be generalized and extended to account for various physical variabilities encountered in face recognition. The end result of our investigation is a full-fledged practical system aimed at security and access control applications. The system is capable of accurately recognizing subjects out of a database of several hundred subjects with state-of-the-art accuracy.
Introduction
Automatic face recognition is a classical problem in the computer vision community. The community's sustained interest in this problem is mainly due to two reasons. First, in face recognition, we encounter many of the common variabilities that plague vision systems in general: illumination, occlusion, pose, and misalignment. Inspired by the good performance of humans in recognizing familiar faces [38], we have reason to believe that effective automatic face recognition is possible, and that the quest to achieve this will tell us something about visual recognition in general.