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UBC Theses and Dissertations

ELSA: an intelligent multisensor integration architecture for industrial grading tasks Naish, Michael David


The Extended Logical Sensor Architecture (ELSA) for multisensor integration has been developed for industrial applications, particularly, the on-line grading and classification of non-uniform food products. It addresses a number of issues specific to industrial inspection. The system must be modular and scalable to accommodate new processes and changing customer demands. It must be easy to understand so that non-expert users can construct, modify, and maintain the system. The object model used by ELSA is particularly suited to the representation of non-uniform products, which do not conform to an easily specified template. Objects are represented by a connected graph structure; object nodes represent salient features of the object. Object classifications are defined by linking to primary features, each primary feature may be composed of a number of lower-level subfeatures. Sensors and processing algorithms are encapsulated by a logical sensor model, providing robustness and flexibility. This is achieved by separating sensors from their functional use within a system. The hierarchical structure of the architecture allows for modification with minimal disturbance to other components. The construction methodology enables domain experts, who often lack signal processing knowledge, to design and understand a sensor system for their particular application. This is achieved through a formal design process that addresses functional requirements in a systematic way. Each stage involves the extraction and utilization of the user's expert knowledge about the process and desired outcomes. Specification of the requirements leads to the identification of primary features and object classifications. Primary features are expanded into subfeatures. Logical sensors are then chosen to provide each of the features defined by the object model; this in turn determines what physical sensors are required by the system. The object classifications determine the rulebase used by the inference engine to infer process decisions.

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