Using heuristic algorithms for capacity leasing and task allocation issues in telecommunication networks under fuzzy quality of service constraints

Nowadays, every firm uses telecommunication networks in different amounts and ways in order to complete their daily operations. In this article, we investigate an optimisation problem that a firm faces when acquiring network capacity from a market in which there exist several network providers offering different pricing and quality of service (QoS) schemes. The QoS level guaranteed by network providers and the minimum quality level of service, which is needed for accomplishing the operations are denoted as fuzzy numbers in order to handle the non-deterministic nature of the telecommunication network environment. Interestingly, the mathematical formulation of the aforementioned problem leads to the special case of a well-known two-dimensional bin packing problem, which is famous for its computational complexity. We propose two different heuristic solution procedures that have the capability of solving the resulting nonlinear mixed integer programming model with fuzzy constraints. In conclusion, the efficiency of each algorithm is tested in several test instances to demonstrate the applicability of the methodology.     

Bailout forward contracts for edge-to-edge internet services

Despite the huge success of the Internet in providing basic communication services, its economic architecture needs to be upgraded so as to provide end-to-end guaranteed or more reliable services to its customers. Currently, a user or an enterprise that needs end-to-end bandwidth guarantees between two arbitrary points in the Internet for a short period of time has no way of expressing its needs. To allow these much needed basic services, we propose a single-domain edge-to-edge (g2g) dynamic capacity contracting mechanism, where a network customer can enter into a bandwidth contract on a g2g path at a future time, at a predetermined price. For practical and economic viability, such forward contracts must involve a bailout option to account for bandwidth becoming unavailable at service delivery time, and must be priced appropriately to enable Internet Service Providers (ISPs) manage risks in their contracting and investments. Our design allows ISPs to advertise point-to-point different prices for each of their g2g paths instead of the current point-to-anywhere prices, allowing discovery of better end-to-end paths, temporal flexibility and efficiency of bandwidth usage. We compute the risk-neutral prices for these g2g bailout forward contracts (BFCs), taking into account correlations between different contracts due to correlated demand patterns and overlapping paths. We apply this multiple g2g BFC framework on network models with Rocketfuel topologies. We evaluate our contracting mechanism in terms of key network performance metrics like fraction of bailouts, revenue earned by the provider, and adaptability to link failures. We also explore the tradeoffs between complexity of pricing and performance benefits of our BFC mechanism.     

Decentralised controller design based on structured singular values

In this study, structured singular values are used in a different way from those commonly used in the robust control literature. It is shown that subject to conditions based on structured singular values, each local area controller can be designed independently. A MATLAB? program is developed to plot inverse structured singular values of multi input multi output (MIMO) system relative error matrix. This plot can be used to predict the stability of the global system with decentralised controller. Therefore decentralised controller design problem can be translated into an equivalent problem of decentralized controller design for a MIMO control system.     

Modified particle swarm optimization and fuzzy regularization for pseudo de-convolution of spatially variant blurs

We propose a modified particle swarm optimization (MPSO) based method for Pseudo De-convolution of the ill-posed inverse problem namely, the space-variant image degradation (SVD). In this paper, SVD is simulated by the pseudo convolution of different sub-regions of the image with different known blurring kernels and additive random noise with unknown variance. Two heuristic modifications are proposed in PSO: 1) Initialization of the swarm and 2) Mutation of the global best. Fuzzy logic is applied for the computation of regularization parameter (RP) to cater for the sensitivity of the problem. The computation of RP is crucial due to the additive noise in the SVD image. Thus mathematical morphology (MM) is applied for better extraction of spatial activity from the distorted image. The performance of the proposed method is evaluated with different test images and noise powers. Comparative analysis demonstrates the superiority of proposed restoration, in terms of quantitative measures, over well-known existing and state-of-the-art SVD approaches.     

A fuzzy long-term investment planning model for a GenCo in a hybrid electricity market considering climate change impacts

We study the long-term generation capacity investment problem of an independent power generation company (GenCo) that functions in an environment where GenCos perform business with both bilateral contracts (BC) and transactions in the day-ahead market (DAM). A fuzzy mixed integer linear programming model with a fuzzy objective and fuzzy constraints is developed to incorporate the impacts of imprecision/uncertainty in the economic environment on the calculation of the optimal value of the GenCo’s objective function. In formulating the fuzzy objective function we also include the potential impacts of climate change on the energy output of hydroelectric power plants. In addition to formulating and solving the capacity planning/investment problem, we also performed scenario-based (sensitivity) analysis to explore how investment decisions of the GenCos change when fuzziness (tolerance) in the maximum energy output of hydroelectric units and/or drought expectation increases. The proposed model is novel and investigates the effects of factors like drought expectations of climate changes, hydroelectric power plant investments, and other power generation technology investment options.     

Parallel interval-based reasoning in medical knowledge-based system Clinaid

The paper continues a series of papers and a monograph [33], where we have described the conceptual structures as well as the basic architecture of a knowledge-based systemCunaid.     

Structure and reversibility of 2D hexagonal cellular automata

Cellular automata are used to model dynamical phenomena by focusing on their local behavior which depends on the neighboring cells in order to express their global behavior. The geometrical structure of the models suggests the algebraic structure of cellular automata. After modeling the dynamical phenomena, it is sometimes an important problem to be able to move backwards in order to understand it better. This is only possible if cellular automata is reversible. In this paper, 2D finite cellular automata defined by local rules based on hexagonal cell structure are studied. Rule matrix of the hexagonal finite cellular automaton is obtained. The rank of rule matrices representing the 2D hexagonal finite cellular automata via an algorithm is computed. It is a well known fact that determining the reversibility of a 2D cellular automata is a very difficult problem in general. Here, the reversibility problem of this family of 2D hexagonal cellular automata is also resolved completely.     

Two methods for the calculation of the degree of an approximate greatest common divisor of two inexact polynomials

The calculation of the degree d of an approximate greatest common divisor of two inexact polynomials f(y) and g(y) reduces to the determination of the rank loss of a resultant matrix, the entries of which are functions of the coefficients of f(y) and g(y). This paper considers this issue by describing two methods to calculate d, such that knowledge of the noise level imposed on the coefficients of f(y) and g(y) is not assumed. One method uses the residual of a linear algebraic equation whose coefficient matrix and right hand side vector are derived from the Sylvester resultant matrix S(f,g), and the other method uses the first principal angle between a line and a hyperplane, the equations of which are calculated from S(f,g). Computational results on inexact polynomials whose exact forms have multiple roots of high degree are shown and very good results are obtained. These results are compared with the rank loss of S(f,g) for the calculation of d, and it is shown that this method yields incorrect results for these examples.     

Unsupervised and simultaneous training of multiple object detectors from unlabeled surveillance video

Object detection is an essential component in automated vision-based surveillance systems. In general, object detectors are constructed using training examples obtained from large annotated data sets. The inevitable limitations of typical training data sets make such supervised methods unsuitable for building generic surveillance systems applicable to a wide variety of scenes and camera setups. In our previous work we proposed an unsupervised method for learning and detecting the dominant object class in a general dynamic scene observed by a static camera. In this paper, we investigate the possibilities to expand the applicability of this method to the problem of multiple dominant object classes. We propose an idea on how to approach this expansion, and perform an evaluation of this idea using two representative surveillance video sequences.