A broad-bandwidth persistent spectral hole-burning organic material is used as a fast optical processor that compares an input temporal-frequency profile with a recorded reference spectral shape. This patternrecognition procedure relies on a subpicosecond temporal cross-correlation process. The size of the phaseencoding spectral interval exceeds 1 THz. The storage material, the spectral encoder...

The family of artificial neural networks based on Adaptive Resonance Theory (ART) forms a collection of distinct mathematical patternrecognition methods. The classification of sensor signals, process data analysis, spectral interpretation, and image analysis are discussed as applications of ART outside and within chemistry. The advantages of ART are considered. They include its use as a built-i...

Support Vector Machine (SVM) as an important theory of machine learning and patternrecognition has been well applied to small sample clustering learning, nonlinear problems, outlierdetection and so on. The license plate automation recognition system has received extensiveattention as an important application of machine learning and pattern recognition in intelligent transport.The license plate ...

Fuzzy-Attribute Graph (FAG) was proposed to handle fuzziness in the pattern primitives in structural pattern recogni~~on [l]. FAG has the advantage that we can combine several possible definition into a single template, and hence only one matching is required instead of one for each definition. Also, each vertex or edge of the graph can contain fuzzy attributes to model real-life situation. How...

C-B4 Monitoring is a new method for automatically monitoring and analyzing complex processes. At the heart of the C-B4 Monitoring concept is the analysis of data patterns by the C-B4 network. Once the network is constructed, it captures all the significant dynamics and dependencies in the data. This information can be used by various applications. This presentation focuses on the integration be...

Adaptive models are complex systems that are especially difficult to investigate. In particular, when researching such systems it easy to blur the line between model and domain, given that the former, the later, and both are vital adaptive system research pursuits. When investigating a model, one may become ‘caught-up’ on model performance resulting in a vicious cycle of increasing the complexi...

Our research goal is to retarget image processing programs written in sequential languages (e.g., C) to architectures with data-parallel processing capabilities. Image processing algorithms are often inherently dataparallel, but the artifacts imposed by the sequential programming language (e.g., loops, pointer variables, linear address spaces) can obscure the parallelism and prohibit generation...

We address the problem of featureless patternrecognition under the assumption that pair-wise comparison of objects is arbitrarily scored by real numbers. Such a linear embedding is much more general than the traditional kernel-based approach, which demands positive semi-definiteness of the matrix of object comparisons. This demand is frequently prohibitive and is further complicated if there ex...

Text-mining commonly consigns to the development of mining attractive patterns and non-trivial information from the database and gets acquaintance from non-arrangement text. Generally text mining covers several computer science restraints with a physically powerful orientation towards artificial intelligence in wide-ranging, together with but not maximum valued of given attractive patterns to r...

Motivation-Two decision-making processes have been identified: an analytical process and an intuitive process. One conceptual model of the latter is the Recognition Primed Decision (RPD) model (Klein, 2008). According to this model, decision making in naturalistic contexts entails a situational patternrecognition process which, if subsequent expectancies are confirmed, lead the decision maker t...