Network industries in the new economy

In this paper we discuss two propositions: the supply and demand of knowledge, and network externalities. We outline the characteristics that distinguish knowledge- intensive industries from the general run of manufacturing and service businesses. Knowledge intensity and knowledge specialisation has developed as markets and globalisation have grown, leading to progressive incentives to outsource and for industries to deconstruct. The outcome has been more intensive competition. The paper looks at what is potentially the most powerful economic mechanism: positive feedback, alternatively known as demand-side increasing returns, network effects, or network externalities. We present alternative demand curves that incorporate positive feedback and discuss their potential economic and strategic consequences. We argue that knowledge supply and demand, and the dynamics of network externalities create new situations for our traditional industrial economy such that new types of economies of scale are emerging and "winner takes all" strategies are having more influence. This is the first of a pair of papers. A second paper will take the argument further and look at the nature of firms' strategies in the new world, arguing that technology standards, technical platforms, consumer networks, and supply chain strategies are making a significant contribution to relevant strategies within the new economy

Supervised ANN vs. unsupervised SOM to classify EEG data for BCI: why can GMDH do better?

Construction of a system for measuring the brain activity (electroencephalogram (EEG)) and recognising thinking patterns comprises significant challenges, in addition to the noise and distortion present in any measuring technique. One of the most major applications of measuring and understanding EGG is the brain-computer interface (BCI) technology. In this paper, ANNs (feedforward back -prop and Self Organising Maps) for EEG data classification will be implemented and compared to abductive-based networks, namely GMDH (Group Methods of Data Handling) to show how GMDH can optimally (i.e. noise and accuracy) classify a given set of BCI’s EEG signals. It is shown that GMDH provides such improvements. In this endeavour, EGG classification based on GMDH will be researched for comprehensible classification without scarifying accuracy. GMDH is suggested to be used to optimally classify a given set of BCI’s EEG signals. The other areas related to BCI will also be addressed yet within the context of this purpose

Network on Chip: a New Approach of QoS Metric Modeling Based on Calculus Theory

A NoC is composed by IP cores (Intellectual Propriety) and switches connected among themselves by communication channels. End-to-End Delay (EED) communication is accomplished by the exchange of data among IP cores. Often, the structure of particular messages is not adequate for the communication purposes. This leads to the concept of packet switching. In the context of NoCs, packets are composed by header, payload, and trailer. Packets are divided into small pieces called Flits. It appears of importance, to meet the required performance in NoC hardware resources. It should be specified in an earlier step of the system design. The main attention should be given to the choice of some network parameters such as the physical buffer size in the node. The EED and packet loss are some of the critical QoS metrics. Some real-time and multimedia applications bound up these parameters and require specific hardware resources and particular management approaches in the NoC switch. A traffic contract (SLA, Service Level Agreement) specifies the ability of a network or protocol to give guaranteed performance, throughput or latency bounds based on mutually agreed measures, usually by prioritizing traffic. A defined Quality of Service (QoS) may be required for some types of network real time traffic or multimedia applications. The main goal of this paper is, using the Network on Chip modeling architecture, to define a QoS metric. We focus on the network delay bound and packet losses. This approach is based on the Network Calculus theory, a mathematical model to represent the data flows behavior between IPs interconnected over NoC. We propose an approach of QoS-metric based on QoS-parameter prioritization factors for multi applications-service using calculus model

An Implementation of Intrusion Detection System Using Genetic Algorithm

Nowadays it is very important to maintain a high level security to ensure safe and trusted communication of information between various organizations. But secured data communication over internet and any other network is always under threat of intrusions and misuses. So Intrusion Detection Systems have become a needful component in terms of computer and network security. There are various approaches being utilized in intrusion detections, but unfortunately any of the systems so far is not completely flawless. So, the quest of betterment continues. In this progression, here we present an Intrusion Detection System (IDS), by applying genetic algorithm (GA) to efficiently detect various types of network intrusions. Parameters and evolution processes for GA are discussed in details and implemented. This approach uses evolution theory to information evolution in order to filter the traffic data and thus reduce the complexity. To implement and measure the performance of our system we used the KDD99 benchmark dataset and obtained reasonable detection rate