8,476 research outputs found
Memristor Crossbar-based Hardware Implementation of IDS Method
Ink Drop Spread (IDS) is the engine of Active Learning Method (ALM), which is
the methodology of soft computing. IDS, as a pattern-based processing unit,
extracts useful information from a system subjected to modeling. In spite of
its excellent potential in solving problems such as classification and modeling
compared to other soft computing tools, finding its simple and fast hardware
implementation is still a challenge. This paper describes a new hardware
implementation of IDS method based on the memristor crossbar structure. In
addition of simplicity, being completely real-time, having low latency and the
ability to continue working after the occurrence of power breakdown are some of
the advantages of our proposed circuit.Comment: 16 pages, 13 figures, Submitted to IEEE Transaction on Fuzzy System
A Spot Modeling Evolutionary Algorithm for Segmenting Microarray Images
cDNA microarrays is one of the most fundamental and powerful tools in biotechnology. Despite its relatively late discovery in 1995, it has since been utilized in many biomedical applications such as cancer research, infectious disease diagnosis and treatment, toxicology research, pharmacology research, and agricultural development. The reason for its broa
Inner and Inter Label Propagation: Salient Object Detection in the Wild
In this paper, we propose a novel label propagation based method for saliency
detection. A key observation is that saliency in an image can be estimated by
propagating the labels extracted from the most certain background and object
regions. For most natural images, some boundary superpixels serve as the
background labels and the saliency of other superpixels are determined by
ranking their similarities to the boundary labels based on an inner propagation
scheme. For images of complex scenes, we further deploy a 3-cue-center-biased
objectness measure to pick out and propagate foreground labels. A
co-transduction algorithm is devised to fuse both boundary and objectness
labels based on an inter propagation scheme. The compactness criterion decides
whether the incorporation of objectness labels is necessary, thus greatly
enhancing computational efficiency. Results on five benchmark datasets with
pixel-wise accurate annotations show that the proposed method achieves superior
performance compared with the newest state-of-the-arts in terms of different
evaluation metrics.Comment: The full version of the TIP 2015 publicatio
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