Rough approximation of shapes in pattern recognition
Computer Vision, Graphics, and Image Processing
A rough grammar for a linguistic recognition of image patches
Signal Processing
Rough Grammar for Efficient and Fault-Tolerant Computing on a Distributed System
IEEE Transactions on Software Engineering
Task scheduling in parallel and distributed systems
Task scheduling in parallel and distributed systems
Rough Sets: Theoretical Aspects of Reasoning about Data
Rough Sets: Theoretical Aspects of Reasoning about Data
Load Balancing: An Automated Learning Approach
Load Balancing: An Automated Learning Approach
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A novel methodology for high performance computing in real time is presented based on a rough grammar approach. Using the rough grammar we sequence dynamically all the tasks in a processor net in a pipeline fashion. The duration of each pipeline stage is normalized to the duration of one of the shorter tasks, by which a reduction of the idle and wait times of nodes to a minimum is achieved. The Simulations show that the node busyness is almost one hundred percent at each node for an arbitrary algorithm and without knowing the task execution times in advance. Higher linear improvement is achieved by allocating the next nodes for a smaller quantun (e.g., at 10% of tasks completed) and passing all the tasks forward when the quantum expires.