A novel cognitive computing architecture is conceptualized for processing multiple channels of multi-modal sensory data streams simultaneously, and fusing the information in real time to generate intelligent reaction sequences. This unique architecture is capable of assimilating parallel data streams that could be analog, digital, synchronous/asynchronous, and could be programmed to act as a knowledge synthesizer and/or an “intelligent perception” processor. In this architecture, the bio-inspired models of visual pathway and olfactory receptor processing are combined as processing components, to achieve the composite function of “searching for a source of food while avoiding the predator.” The architecture is particularly suited for scene analysis from visual data and odorant signature identification in a heterogeneous environment.

In this architecture, there are four basic blocks: input, output, processing, and storage. The input block consists of sensing devices including IR, lidar, radar, visual, chemical, and biosensors, at their various sampling data rates. Based on application scenario, selected sensory streams are sent by the input block to the subsequent “processing” block in a fully parallel fashion. Feature data is extracted from the analog/digital sensory streams and is accumulated in the storage block for enriching the “knowledge base” as a situation unfolds. The incoming raw data is not stored as is the usual approach in current computer architecture, and is reconstructed if required during the process in real time. The output block sends the output signal to various interfaces (actuating interfaces), such as other machines, humans, or RF devices. The processing block consists of several mathematical constructs including Principal Component Analysis (PCA), Independent Component Analysis (ICA), Neural Network (NN), Genetic Algorithm (GA), etc., and is controlled by a hierarchy of logical rules to enact reasoning, reconfiguring, and adapting as required when the target is changing in the dynamic environment. There fore, the processing block can select an architecture for each particular application as needed, dynamically, and still remain compatible with a digital environment. The conceptualized architecture, capable of extracting knowledge from information and using the knowledge for reasoning, adapting, and reacting therefore qualifies as a cognitive architecture for real-time data fusion in a dynamic environment. Furthermore, its dynamic autonomous reconfigurability makes it versatile as a “general- purpose” intelligent system to accomplish the “searching for a source of food while avoiding the predator” function.

This work was done by Tuan A. Duong and Vu A. Duong of Caltech for NASA’s Jet Propulsion Laboratory.

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