Technology Incubators as Nodes in Knowledge Networks

It is widely accepted that new knowledge underpinned innovation and growth influences economic activities. Economic agents rely not only on their own knowledge but also knowledge from others, whether it be codified and ’transferred via ICT’ or in tacit form. Moreover, it has long been argued that the acquisition of latter type of knowledge is influenced by geographic proximity. Based on this argument, it follows that the part firms’ supply of knowledge depends on how close, in terms of physical distance, to other firms, suppliers, customers, and research institutions, they are located. They are all can be categorize as a pool of knowledge that important for the firms’ growth and innovation capacity. Today, we witness many initiatives from policy makers around the world to compete in an increasingly technology- driven global economy through the establishing of technology incubators. Technology incubators can be conceived as organizations and/or facilities to enhance high-technology firm establishment and survival. Mostly they are located near the university or research center. There are many success stories on the contribution of incubators to the regional growth. At the same time, technology incubators have been widely criticized in the academic literature when judged in terms of regional innovation and knowledge development. The critics include the relying on an outdated, linear, model of innovation, which assumes that knowledge can be transferred directly from university to firms. However, innovation is now widely recognized as a complex non-linear process involving feedback loops and the creation of synergies through a diverse range of knowledge networks. Therefore, our understanding about knowledge spillover processes connected with incubator is yet poor. Very little is known about the mechanisms of knowledge exchange and spillover initiated by incubator and their role in supporting the growth of the firm. In this study we draw on the current body of literature, mainly agglomeration theories, and use the concepts of tacit knowledge and context to understand how knowledge spillovers actually take place. Our objective is to build a conceptual framework that describes how technology incubators operate as a mediator of knowledge for their tenants. In addition, based on empirical data of high-technology start-ups at TU Delft (The Netherlands), this study tests the proposition that not only geographic proximity to the university, but also that relations with other firms, particularly customers and suppliers matters. We also consider the function of ICT in shaping the new role of technology incubators in providing knowledge support. By explicitly analyzing the knowledge spillovers and mediation role offered by technology incubators, we seek to open up the ‘black box’ of the incubation process as a source of learning and gaining knowledge resources. We conclude the paper with a few recommendations for policymaking and further research.

Uncertainty Management of Intelligent Feature Selection in Wireless Sensor Networks

Wireless sensor networks (WSN) are envisioned to revolutionize the paradigm of monitoring complex real-world systems at a very high resolution. However, the deployment of a large number of unattended sensor nodes in hostile environments, frequent changes of environment dynamics, and severe resource constraints pose uncertainties and limit the potential use of WSN in complex real-world applications. Although uncertainty management in Artificial Intelligence (AI) is well developed and well investigated, its implications in wireless sensor environments are inadequately addressed. This dissertation addresses uncertainty management issues of spatio-temporal patterns generated from sensor data. It provides a framework for characterizing spatio-temporal pattern in WSN. Using rough set theory and temporal reasoning a novel formalism has been developed to characterize and quantify the uncertainties in predicting spatio-temporal patterns from sensor data. This research also uncovers the trade-off among the uncertainty measures, which can be used to develop a multi-objective optimization model for real-time decision making in sensor data aggregation and samplin

Software tools for the cognitive development of autonomous robots

Robotic systems are evolving towards higher degrees of autonomy. This paper reviews the cognitive tools available nowadays for the fulfilment of abstract or long-term goals as well as for learning and modifying their behaviour.Peer ReviewedPostprint (author's final draft

Investigation on soft computing techniques for airport environment evaluation systems

Spatial and temporal information exist widely in engineering fields, especially in airport environmental management systems. Airport environment is influenced by many different factors and uncertainty is a significant part of the system. Decision support considering this kind of spatial and temporal information and uncertainty is crucial for airport environment related engineering planning and operation. Geographical information systems and computer aided design are two powerful tools in supporting spatial and temporal information systems. However, the present geographical information systems and computer aided design software are still too general in considering the special features in airport environment, especially for uncertainty. In this thesis, a series of parameters and methods for neural network-based knowledge discovery and training improvement are put forward, such as the relative strength of effect, dynamic state space search strategy and compound architecture. [Continues.

EGNN-C+: Interpretable Evolving Granular Neural Network and Application in Classification of Weakly-Supervised EEG Data Streams

We introduce a modified incremental learning algorithm for evolving Granular Neural Network Classifiers (eGNN-C+). We use double-boundary hyper-boxes to represent granules, and customize the adaptation procedures to enhance the robustness of outer boxes for data coverage and noise suppression, while ensuring that inner boxes remain flexible to capture drifts. The classifier evolves from scratch, incorporates new classes on the fly, and performs local incremental feature weighting. As an application, we focus on the classification of emotion-related patterns within electroencephalogram (EEG) signals. Emotion recognition is crucial for enhancing the realism and interactivity of computer systems. We extract features from the Fourier spectrum of EEG signals obtained from 28 individuals engaged in playing computer games -- a public dataset. Each game elicits a different predominant emotion: boredom, calmness, horror, or joy. We analyze individual electrodes, time window lengths, and frequency bands to assess the accuracy and interpretability of resulting user-independent neural models. The findings indicate that both brain hemispheres assist classification, especially electrodes on the temporal (T8) and parietal (P7) areas, alongside contributions from frontal and occipital electrodes. While patterns may manifest in any band, the Alpha (8-13Hz), Delta (1-4Hz), and Theta (4-8Hz) bands, in this order, exhibited higher correspondence with the emotion classes. The eGNN-C+ demonstrates effectiveness in learning EEG data. It achieves an accuracy of 81.7% and a 0.0029 II interpretability using 10-second time windows, even in face of a highly-stochastic time-varying 4-class classification problem.Comment: 10 pages, IEEE International Conference on Evolving and Adaptive Intelligent Systems 2024 (IEEE EAIS 2024

An overview of decision table literature 1982-1995.

This report gives an overview of the literature on decision tables over the past 15 years. As much as possible, for each reference, an author supplied abstract, a number of keywords and a classification are provided. In some cases own comments are added. The purpose of these comments is to show where, how and why decision tables are used. The literature is classified according to application area, theoretical versus practical character, year of publication, country or origin (not necessarily country of publication) and the language of the document. After a description of the scope of the interview, classification results and the classification by topic are presented. The main body of the paper is the ordered list of publications with abstract, classification and comments.

Mobile Robots Navigation

Mobile robots navigation includes different interrelated activities: (i) perception, as obtaining and interpreting sensory information; (ii) exploration, as the strategy that guides the robot to select the next direction to go; (iii) mapping, involving the construction of a spatial representation by using the sensory information perceived; (iv) localization, as the strategy to estimate the robot position within the spatial map; (v) path planning, as the strategy to find a path towards a goal location being optimal or not; and (vi) path execution, where motor actions are determined and adapted to environmental changes. The book addresses those activities by integrating results from the research work of several authors all over the world. Research cases are documented in 32 chapters organized within 7 categories next described

Neurocognitive Informatics Manifesto.

Informatics studies all aspects of the structure of natural and artificial information systems. Theoretical and abstract approaches to information have made great advances, but human information processing is still unmatched in many areas, including information management, representation and understanding. Neurocognitive informatics is a new, emerging field that should help to improve the matching of artificial and natural systems, and inspire better computational algorithms to solve problems that are still beyond the reach of machines. In this position paper examples of neurocognitive inspirations and promising directions in this area are given