Memory MAM (Multi-mode Access Memory) Untuk Pengolahan Citra Paralel: Prinsip Aplikasi Dan Performansi

This paper presents a new design of memory called MAM (Multi-mode Access Memory). According to its name, this memory can be accessed using some modes e.g. RAM, CAM, and Shift. This type of memory provides a significant benefit in the field of image processing particularly to process the local (e.g. filtering, edge detection, etc) and regional image problems (e.g. labeling, area and perimeter of objects, etc). For an image of nxn pixels, the complexities obtained were very excellent, in O(n). However, these complexities could reach O(n2) when RAM memory was used. In this paper, the principle of MAM, its applications, and its performances were discussed. This research was a part of the optimal parallel architecture for image processing development project granted by ITSF (Indonesia Toray Science Foundation)

A characterization of parallel systems

technical reporta taxonomy for parallel processing systems is presented which has some advantages over previous taxonomies. The taxonomy characterizes parallel processing systems using four parameters: topology, communication, granularity, and operation. These parameters and used repetitively in a hierarchical fashion to produce a taxonomic structure which is extensible to the level of detail desired. Topology describes the structure of the priniciple interconnections. Communication describes the flow of data and programs through the system. Granularity describes the size of the largest repeated element, or grain. Operation describes the important functional properties of each grain, especially the ratio of storage to logic circuitry. Granularity and topology are structural parameters, while operation and communication are functional parameters which describe the behavior of the system components. A final section of this paper includes examples of the application of the taxonomy to several parallel processing systems

Key Issues in the Analysis of Remote Sensing Data: A report on the workshop

The procedures of a workshop assessing the state of the art of machine analysis of remotely sensed data are summarized. Areas discussed were: data bases, image registration, image preprocessing operations, map oriented considerations, advanced digital systems, artificial intelligence methods, image classification, and improved classifier training. Recommendations of areas for further research are presented