Two-Dimensional Code Acquisition in Environments With a Spatially Nonuniform Distribution of Interference: Algorithms and Performance

2004 ◽  
Vol 3 (1) ◽  
pp. 1-7 ◽  
Author(s):  
M. Katz ◽  
J. Iinatti ◽  
S. Glisic
2006 ◽  
Vol 31 (2) ◽  
pp. 20-31
Author(s):  
Jia Beisi

Habraken points out that the architectural studio failed to bring students to basic questions in the architecture of everyday environments. Till criticizes that in a studio, it is only the professional value represented by the teachers that prevails. To investigate the reasons of the allegation, this paper introduces a learning model defined by David A. Kolb, in which a learning process consists of two dimensional movements: i.e., prehension (concrete experiences vs. abstract conceptualization) and transformation (reflection and experiment). The paper then inquires into Schön's observation in the studio learning mode characterized as reflection-in-action. It is found that this studio is mainly dealing with the transformation dimension, and prehension dimension is either suppressed or represented by the teacher's experiences and conceptions. The paper discovers that the cause of problems raised by Habraken and Till is the inherent lack of substance in the prehension dimension. The paper assesses a studio programme in which the basic questions of built environment were systematically introduced. It analyzes the students' reactions and performance in line with students' learning styles found using Kolb's Learning Style Inventory (LSI). It suggests that the students' learning activities are more diversified than what Schön could perceive. There is a possibility to adapt students' personal experience and abstract conceptualization which may play into the studio. By enhancing diversity of learning styles rather than letting one's learning style (reflection-in-action) prevail, the studio may become a platform in which students may learn from each other.


2016 ◽  
Vol 850 ◽  
pp. 144-151 ◽  
Author(s):  
Mehmet Fidan ◽  
Ömer Nezih Gerek

The Mycielski method is a prospering prediction algorithm which is based on searching and finding largest repeated binary patterns. It uses infinite-past data to devise a rule based prediction method on a time series. In this work, a novel two-dimensional (image processing) version of the Mycielski algorithm is proposed. Since the dimensionality definition of “past” data increases in two-dimensional signals, the proposed algorithm also needs to handle how the boundaries of the pixel cliques are iteratively extended in the neighborhood of a current pixel. The clique extension invokes novel similarity search strategies that depend on the chosen physical distance metric. The proposed prediction algorithm is used for predictive image compression and performance comparisons with other predictive coding methods are presented.


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