Most literature on knowledge management (KM) focuses on large firms – the domain in which KM was originally developed – and most KM literature on entrepreneurship focuses on entrepreneurial activities in post-revenue firms. The domain of the startup, however, is traditionally very different from these, characterized by a lack of tangible assets and validated value proposition. The authors review the literature on KM and entrepreneurship with a particular focus on young micro-enterprises that have yet to cross the “valley of death” stage of maturation. Using the Dynamic Knowledge Creation Process as a guide, they elaborate on the challenges facing the implementation of KM in startups, and on the subsequent opportunities for startup growth. Finally, the authors reflect upon research questions that may engage future researchers in proposing strategies that better integrate KM as a discipline into the fabric of entrepreneurship and the startup domain.

This paper presents a method for detecting and classifying a target from its foveal (graded resolution) imagery using a multiresolution neural network. Target identification decisions are based on minimizing an energy function. This energy function is evaluated by comparing a candidate blob with a library of target models at several levels of resolution simultaneously available in the current foveal image. For this purpose, a concurrent (top-down-and-bottom-up) matching procedure is implemented via a novel multilayer Hopfield neural network. The associated energy function supports not only interactions between cells at the same resolution level, but also between sets of nodes at distinct resolution levels. This permits features at different resolution levels to corroborate or refute one another contributing to an efficient evaluation of potential matches. Gaze control, refoveation to more salient regions of the available image space, is implemented as a search for high resolution features which will disambiguate the candidate blob. Tests using real two-dimensional (2-D) objects and their simulated foveal imagery are provided.

A multiple instruction multiple data (MIMD) parallel computing platform built upon a network of TMS320C40/44s (C40/C44) for real-time image processing of a hierarchical foveal machine vision (HFMV) system is described in this paper. The architecture of the system, the parallel algorithm development environment, and strategies to map tasks into the computing platform are described. The platform supports both static and dynamic computing resource allocation. The performance of the computing platform is illustrated by examples.

This paper proposes a method for identifying and classifying a target from its foveal imagery using a neural network. The method's criterion for identifying a target is based on finding the global minimum of an energy function. This energy function is characterized by matching the candidate target and a library of target models at several levels of resolution of nonuniformly sampled foveal image data. For this purpose, a top-down and bottom-up (concurrent) matching procedure is implemented via a multi-layer Hopfield neural network. The corresponding energy function supports not only connections between cells at the same resolution level, but also interconnections between two sets of nodes at two different resolution levels. The proposed method also utilizes a feature analysis at the higher resolution levels of the target to relocate the center of the fovea to a more salient region of the target (gaze control). The results of an experimental scenario for foveal target recognition are presented.

A class of machine vision systems is proposed, called foveal vision systems. These systems, modeled after advanced biological vision, feature space-variant (variable-resolution) imager topologies and a closed-loop system architecture. The imager topology is characterized by resolution which is high at the center of the sampling lattice and which decreases with distance from the center. The central axis is controlled by feedback from higher-level algorithms, allowing the allocation of sampling resources to the region(s) of interest and resulting in greater relevant information from the imager yet permitting considerable reduction in data. Preliminary investigations have demonstrated reductions in data structure size and computations of several orders of magnitude relative to conventional implementations. The savings factors increase with field-of-view and resolution.>