A Formal Methods Approach to Pattern Synthesis in Reaction Diffusion Systems

We propose a technique to detect and generate patterns in a network of locally interacting dynamical systems. Central to our approach is a novel spatial superposition logic, whose semantics is defined over the quad-tree of a partitioned image. We show that formulas in this logic can be efficiently learned from positive and negative examples of several types of patterns. We also demonstrate that pattern detection, which is implemented as a model checking algorithm, performs very well for test data sets different from the learning sets. We define a quantitative semantics for the logic and integrate the model checking algorithm with particle swarm optimization in a computational framework for synthesis of parameters leading to desired patterns in reaction-diffusion systems

A Distributed Newton Method for Network Utility Maximization

Most existing work uses dual decomposition and subgradient methods to solve Network Utility Maximization (NUM) problems in a distributed manner, which suffer from slow rate of convergence properties. This work develops an alternative distributed Newton-type fast converging algorithm for solving network utility maximization problems with self-concordant utility functions. By using novel matrix splitting techniques, both primal and dual updates for the Newton step can be computed using iterative schemes in a decentralized manner with limited information exchange. Similarly, the stepsize can be obtained via an iterative consensus-based averaging scheme. We show that even when the Newton direction and the stepsize in our method are computed within some error (due to finite truncation of the iterative schemes), the resulting objective function value still converges superlinearly to an explicitly characterized error neighborhood. Simulation results demonstrate significant convergence rate improvement of our algorithm relative to the existing subgradient methods based on dual decomposition.Comment: 27 pages, 4 figures, LIDS report, submitted to CDC 201

Feedback Controlled Software Systems

Software systems generally suffer from a certain fragility in the face of disturbances such as bugs, unforeseen user input, unmodeled interactions with other software components, and so on. A single such disturbance can make the machine on which the software is executing hang or crash. We postulate that what is required to address this fragility is a general means of using feedback to stabilize these systems. In this paper we develop a preliminary dynamical systems model of an arbitrary iterative software process along with the conceptual framework for stabilizing it in the presence of disturbances. To keep the computational requirements of the controllers low, randomization and approximation are used. We describe our initial attempts to apply the model to a faulty list sorter, using feedback to improve its performance. Methods by which software robustness can be enhanced by distributing a task between nodes each of which are capable of selecting the best input to process are also examined, and the particular case of a sorting system consisting of a network of partial sorters, some of which may be buggy or even malicious, is examined

Adaptive Neuro-Fuzzy Inference System for Dynamic Load Balancing in 3GPP LTE

ANFIS is applicable in modeling of key parameters when investigating the performance and functionality of wireless networks. The need to save both capital and operational expenditure in the management of wireless networks cannot be over-emphasized. Automation of network operations is a veritable means of achieving the necessary reduction in CAPEX and OPEX. To this end, next generations networks such WiMAX and 3GPP LTE and LTE-Advanced provide support for self-optimization, self-configuration and self-healing to minimize human-to-system interaction and hence reap the attendant benefits of automation. One of the most important optimization tasks is load balancing as it affects network operation right from planning through the lifespan of the network. Several methods for load balancing have been proposed. While some of them have a very buoyant theoretical basis, they are not practically implementable at the current state of technology. Furthermore, most of the techniques proposed employ iterative algorithm, which in itself is not computationally efficient. This paper proposes the use of soft computing, precisely adaptive neuro-fuzzy inference system for dynamic QoS-aware load balancing in 3GPP LTE. Three key performance indicators (i.e. number of satisfied user, virtual load and fairness distribution index) are used to adjust hysteresis task of load balancing

Knowledge acquisition for the internationalization of the smaller firm: content and sources

Internationalization process research emphasizes accumulated experience and networks as sources of knowledge for internationalization. Our understanding, however, as to what this knowledge is in practice for smaller firms, the challenges they face in acquiring it, and how they address those challenges is limited. Integrating organizational learning concepts with our theoretical understanding of the small firm internationalization process, we develop a new framework for understanding knowledge acquisition processes, which are examined with a case study of 10 Scottish internationalizing firms. We find smaller firms may not have relevant experience or useful networks, and rely on sources rarely recognised before. Firms used recruitment, government advisors and consultants to acquire indirect experience. Recruitment is a source of market and technological knowledge and government advisors and consultants a source of internationalization knowledge. Accessing internal information is important for firms that have internationalized. Our integrated theoretical framework identifies knowledge content and sources that are critical for internationalization, but that may be absent