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System-level approaches to power efficiency in FPGA-based designs (data reduction algorithms case study)

Czapski P. P., Śluzek A.

Journal of Automation Mobile Robotics and Intelligent Systems

2011

Vol. 5, No. 2

49-59

In this paper we present preliminary results on systemlevel analysis of power efficiency in FPGA-based designs. Advanced FPGA devices allow implementation of sophisticated systems (e.g. embedded sensor nodes). However, designing such complex applications is prohibitively expensive at lower levels so that, moving the designing process to higher abstraction layers, i.e. system-levels of design, is a rational decision. This paper shows that at least a certain level of power awareness is achievable at these higher abstractions. A methodology and preliminary results for a power-aware, system-level algorithm partitioning is presented. We select data reduction algorithms as the case study because of their importance in wireless sensor networks (WSN's). Although, the research has been focused on WSN applications of FPGA, it is envisaged that the presented ideas are applicable to other untethered embedded systems based on FPGA's and other similar programmable devices.

Corner-based keypoints for scale-invariant detection of partially visible objects

Śluzek A.

Machine Graphics and Vision

2006

Vol. 15, No. 3/4

639-647

Local features (also known as interest points, keypoints, etc.) are a popular and powerful tool for matching images and detecting partially occluded objects. While the problems of photometric distortions of images and rotational invariance of the features have satisfactory solutions, satisfactorily simple scale-invariant algorithms do not exist yet. Generally, either computationally complex methods of scale-space (multi-scale approach) are used, or the correct scale is estimated using additional mechanisms. The paper proposes a new category of keypoints that can be used to develop a simple scale-invariant method for detecting known objects in analyzed images. Keypoints are defined as locations at which selected moment-based parameters are consistent over a wide range of different-size circular patches around the keypoint. While the database of known objects (i.e. the keypoints and their descriptions) is still built using a multi-scale approach, analyzed images are scanned using only a single-scale window and its sub-window. The paper focuses on the keypoint building and keypoint matching principles. Higher-level issues of hypotheses building and verification (regarding the presence of objects in analyzed images) are only briefly discussed.

Moment-based contour segmentation using multiple segmentation primitives

Śluzek A.

Machine Graphics and Vision

1998

Vol. 7, No. 1/2

269-279

The paper presents a new technique of segmentating digital contours using multiple segmentation primitives. Therefore segmentation results are a combination of line segments, arcs. corners,etc., depending on how well these shapesmath the orginal contour. In yhr first step, the prospective instances of all avaible segmentation primitives are detected within the input digital contours. Then, the detected instances are ranked according to how accurately they fit the corresponding fragment of the contour. Finally, the top-rank segmantation primitives are selected one by one until the whole contour is approximeted. The algorithms has relatively low computational complexity, and it allows paraqllel implementation. Moreover, the algorithm is not sensitive to the performance of edge detectors so that similar results are produced no matter what edge detector has extracted contours from the original image. Therefore, if the quality of camera-captured images is satisfactofily high, the algorithm can analyse them whitout any pre- processing required (except, of course, edge detection).