On-Demand Electro Spraying or Single Event Electrospraying (SEE) is a method to jet small amounts of fluid out of a nozzle with a relatively large diameter by switching on and off an electrical field between nozzle and substrate. The jet decomposes in many fl droplets, the total amount of volume deposited is up to 5 pl. A pipette is considered in which an electrode is mounted close to the exit of the nozzle. The electrode is connected to a high voltage power amplifier. By switching on the electrical field the apparent surface tension drops, the meniscus deforms into a cone and fluid starts to flow towards the nozzles. The moment the cone has reached the Taylor cone dimensions, from the tip of the cone a jet emerges and a number of charged fl droplets fly towards the substrate. This process stops when the pulse is switched off. After switching off the meniscus returns slowly to its equilibrium position. The process is controlled by different time constants, such as the slew rate of the power amplifier and the RC time of the electrical circuit composed of the electrical resistance in the fluid contained in the nozzle between electrode and meniscus, and the capacitance of the gap between the meniscus and the flat substrate. Another time constant deals with the fluid flow during the growth of the meniscus, directly after switching on the pulse. This fluid flow is driven by hydrostatic pressure and opposed by viscous drag in the nozzle. The final fluid flow during droplet formation is governed by the balance between by the drag of the charge carriers inside the fluid, caused by the current associated with the charged droplets leaving the meniscus and the viscous drag. These different phenomena will be discussed theoretically and compared to experimental results.
Cone-beam computed tomography (CT) is an attractive tool for many kinds of non-destructive evaluation (NDE). Model-based iterative reconstruction (MBIR) has been shown to improve reconstruction quality and reduce scan time. However, the computational burden and storage of the system matrix is challenging. In this paper we present a separable representation of the system matrix that can be completely stored in memory and accessed cache-efficiently. This is done by quantizing the voxel position for one of the separable subproblems. A parallelized algorithm, which we refer to as zipline update, is presented that speeds up the computation of the solution by about 50 to 100 times on 20 cores by updating groups of voxels together. The quality of the reconstruction and algorithmic scalability are demonstrated on real cone-beam CT data from an NDE application. We show that the reconstruction can be done from a sparse set of projection views while reducing artifacts visible in the conventional filtered back projection (FBP) reconstruction. We present qualitative results using a Markov Random Field (MRF) prior and a Plug-and-Play denoiser.
In this paper, we will present a model of color images that provides insight into how the color channels are related. We will show experimental results that illustrate the efficacy of this model. We will then demonstrate how this model can be used to design a simple chrominance based image denoising system.