A Neural Network Measurement of Relative Military Security: The Case of Greece and Cyprus

This paper aims at introducing a relative security measure, applicable to evaluating the impact of arms races on the military security of allies. This measure is based on demographic criteria, which play a dominant role in a number of arms races involving military alliances. The case of Greece and Cyprus, on one hand, and Turkey on the other, is the one to which our relative security measure is applied and tested. Artificial neural networks were trained to forecast the future behaviour of relative security. The high forecasting performance permitted the application of alternative scenarios for predicting the impact of the Greek - Turkish arms race on the relative security of the Greek - Cypriot alliance.Arms Race, Neural Networks, Relative Military Security

Computational Intelligence in Exchange-Rate Forecasting

This paper applies computational intelligence methods to exchange rate forecasting. In particular, it employs neural network methodology in order to predict developments of the Euro exchange rate versus the U.S. Dollar and the Japanese Yen. Following a study of our series using traditional as well as specialized, non-parametric methods together with Monte Carlo simulations we employ selected Neural Networks (NNs) trained to forecast rate fluctuations. Despite the fact that the data series have been shown by the Rescaled Range Statistic (R/S) analysis to exhibit random behaviour, their internal dynamics have been successfully captured by certain NN topologies, thus yielding accurate predictions of the two exchange-rate series.Exchange - rate forecasting, Neural networks

Financial Versus Human Resources in the Greek-Turkish Arms Race: A Forecasting Investigation Using Artificial Neural Networks

This paper aims at forecasting the burden on the Greek economy resulting from the arms race against Turkey and at concentrating on the leading determinants of this burden. The military debt and the defence share of GDP are employed alternatively in order to approximate the measurement of the arms race pressure on Greece, and the method used is that of artificial neural networks. The use of a wide variety of explanatory variables in combination with the promising results derived, suggest that the impact on the Greek economy resulting from this arms race is determined, to a large extent, by demographic factors which strongly favour the Turkish side. Prediction on both miltary debt and defence expenditure exhibited highly satisfactory accuracy, while the estimation of input significance, indicates that variables describing the Turkish side are often dominant over the corresponding Greek ones.Greek Military Debt, Defence Expenditure, Neural Networks

In-Situ Characterization of Carrier Mobility in Field Effect Transistors

The Field Effect Transistor (FET) is today the basic element of Very Large Scale Integrated (VLSI) digital systems. FETs are also used in analog circuits for high frequency (microwave) applications. Different types of FETs are the Metal Oxide Semiconductor Field Effect Transistors (MOSFETs), the MEtal Semiconductor Field Effect Transistors (MESFETs) and the Modulation Doped Field Effect Transistors (MODFETs)

The Greek Current Account Deficit:Is it Sustainable after all?

The large Greek current account deficit figures reported during the past few years have become the source of increasing concern regarding its sustainability. Bearing in mind the variety of techniques employed and the views expressed as regards the analysis and the assessment of the size of the current account deficit, this paper resorts to using neural network architectures to demonstrate that, despite its size, the current account deficit of Greece can be considered sustainable. This conclusion, however, is not meant to neglect the structural weaknesses that lead to such a deficit. In fact, even in the absence of any financing requirements these high deficit figures point to serious competitiveness losses with everything that these may entail for the future performance of the Greek economy.Neural Networks; Current Account Deficit Sustainability

A programmable VLSI filter architecture for application in real-time vision processing systems

An architecture is proposed for the realization of real-time edge-extraction filtering operation in an Address-Event-Representation (AER) vision system. Furthermore, the approach is valid for any 2D filtering operation as long as the convolutional kernel F(p,q) is decomposable into an x-axis and a y-axis component, i.e. F(p,q)=H(p)V(q), for some rotated coordinate system [p,q]. If it is possible to find a coordinate system [p,q], rotated with respect to the absolute coordinate system a certain angle, for which the above decomposition is possible, then the proposed architecture is able to perform the filtering operation for any angle we would like the kernel to be rotated. This is achieved by taking advantage of the AER and manipulating the addresses in real time. The proposed architecture, however, requires one approximation: the product operation between the horizontal component H(p) and vertical component V(q) should be able to be approximated by a signed minimum operation without significant performance degradation. It is shown that for edge-extraction applications this filter does not produce performance degradation. The proposed architecture is intended to be used in a complete vision system known as the Boundary-Contour-System and Feature-Contour-System Vision Model, proposed by Grossberg and collaborators. The present paper proposes the architecture, provides a circuit implementation using MOS transistors operated in weak inversion, and shows behavioral simulation results at the system level operation and electrical simulation and experimental results at the circuit level operation of some critical subcircuits

The Adequacy of Accounting Mandatory Disclosures: A Field Study in Cyprus

The research paper explores professional accountants and investors' perceptions on the adequacy of the quality and quantity of current accounting mandatory disclosure, within the Cyprus environment

Very wide range tunable CMOS/bipolar current mirrors with voltage clamped input

In low power current mode signal processing circuits it is often necessary to use current mirrors to replicate and amplify/attenuate current signals and clamp the voltage of nodes with high parasitic capacitances so that the smallest currents do not introduce unacceptable delays. The use of tunable active-input current mirrors would meet both requirements. In conventional active-input current mirrors, stability compensation is required. Furthermore, once stabilized, the input current cannot be made arbitrarily small. In this paper we introduce two new active-input current mirrors that clamp their input node to a given voltage. One of them does not require compensation, while the other may under some circumstances. However, for both, the input current may take any value. The mirrors can operate with their transistors biased in strong inversion, weak inversion, or even as CMOS compatible lateral bipolar devices. If it is biased in weak inversion or as lateral bipolars, the current mirror gain can be tuned over a very wide range. According to the experimental measurements provided in this paper, the input current may spawn beyond nine decades and the current mirror gain can be tuned over 11 decades. As an application example, a sinusoidal gm-C-based VCO has been fabricated, whose oscillation frequency could be tuned for over seven decades, between 74 mHz and 1 MHz.Office of Naval Research (USA) N00014-95-1-040