Titlebook: Industrial Crystallization; Process Simulation A Narayan S. Tavare Book 1995 Springer Science+Business Media New York 1995 crystal.crystall

Range-Of-Motion

evince

Sleep-Paralysis

Crystallizer Design and Operation, the sizing of equipment for the desired functional duties under a given set of conditions, incorporating an element of economics in the procedure. Sizing of equipment, as discussed in this chapter, is restricted to the process engineering design. In the context of crystallizers and crystallization

Perceive

賞心悅目

prohibitive to perform on all sensory inputs. In computer vision, however, there has been relatively little exploration of . attention, where some information is selectively ignored, in spite of the success of . attention, where information is re-weighted and aggregated, but never filtered out. Her

prodrome

Narayan S. Tavarelution images which are artificially generated by simple bilinear down-sampling (or in a few cases by blurring followed by down-sampling). We show that such methods fail to produce good results when applied to real-world low-resolution, low quality images. To circumvent this problem, we propose a tw

莊嚴(yán)

Narayan S. Tavare, with applications ranging from modeling to robotics, through augmented reality. Nevertheless, their use is limited by their low resolution, with frames often corrupted with noise, missing data and temporal inconsistencies. Our approach, named ., consists in generating and updating through time a s

MANIA

Narayan S. Tavareed many state-of-the-art methods for 3D pose estimation to train deep networks end-to-end to predict from images directly, the top-performing approaches have shown the effectiveness of dividing the task of 3D pose estimation into two steps: using a state-of-the-art 2D pose estimator to estimate the

同義聯(lián)想法

champaign

Narayan S. Tavareative patterns. Nevertheless, a video sequence could also contain a lot of redundant and irrelevant frames. How to locate critical information of interest is a challenging task. In this paper, we propose a new network structure, known as Redundancy Reduction Attention (RRA), which learns to focus on