Informatica

In 2008, based on the two-party Diffie–Hellman technique, Biswas proposed a contributory group key exchange protocol called the Group-DH protocol. This contributory property is an important one of group key agreement. Unfortunately, in this paper we show that the proposed Group-DH protocol is not a contributory group key exchange protocol. Therefore, we propose an improved group key exchange protocol with verifiably contributory property based on the same Diffie–Hellman technique. When an identical group key is constructed, each participant can confirm that his/her contribution is actually included in the group key. We show that the improved protocol is provably secure against passive attacks under the decisional Diffie–Hellman assumption. As compared to the previously proposed group key exchange protocols, our protocol provides contributiveness and the required computational cost is suitable for low-power participants in a network environment.

One of the major activities in software design is defining software architecture. Before designing software structure and software behavior we have to define its architecture. In this paper we have proposed three-tiered software architecture. This software architecture extends application logic tier with security. We have implemented two important security issues: authentication and authorization processes. These processes are implemented through software patterns. The software patterns have the particular place in the Proposed Software Architecture (PSA). In this paper, we have presented these software patterns and explained why they are important in PSA.

The asymmetric cipher protocol, based on decomposition problem in matrix semiring ℳ over semiring of natural numbers 𝒩 is presented. The security of presented cipher protocol is based on matrix decomposition problem (MDP), which is linked to the problem of solution of multivariate polynomial system of equations. Compromitation of proposed scheme relies on the solution of system of multivariate polynomial system of equations over the semiring of natural numbers 𝒩. The security parameters are defined, security analysis and implementation is presented.

We address the issue of inapproximability of the wavelength assignment problem in wavelength division multiplexing (WDM) optical networks. We prove that in an n-node WDM optical network with m lightpaths and maximum load L, if NP ≠ ZPP, for any constant δ>0, no polynomial time algorithm can achieve approximation ratio n^1/2−δ or m^1−δ, where NP is the class of problems which can be solved by nondeterministic polynomial time algorithms, and ZPP is the class of problems that can be solved by polynomial randomized algorithms with zero probability of error. Furthermore, the above result still holds even when L=2. We also prove that no algorithm can guarantee the number of wavelengths to be less than $(\sqrt{n}/2)L$ or (m/2)L. This is the first time inapproximability results are established for the wavelength assignment problem in WDM optical networks. We also notice the following fact, namely, there is a polynomial time algorithm for wavelength assignment which achieves approximation ratio of O(m(log log m)²/(log m)³). Therefore, the above lower bound of m^1−δ is nearly tight.

Transient evoked otoacoustic emissions (TEOAEs) have been analyzed for objective assessment of hearing function and monitoring of the influence of noise exposure and ototoxic drugs. This paper presents a novel application of the Hilbert–Huang transform (HHT) for detection and time-frequency mapping of TEOAEs. Since the HHT does not distinguish between signal and noise, it is combined with ensemble correlation in order to extract signal information in intervals with correlated activity. High resolution time-frequency mapping could predict 30 dBHL, or higher hearing loss, at different audiological frequencies in 63–90% of the cases and normal hearing in 75–90% of the cases. The proposed method offers TEOAE time-frequency mapping by constraining the analysis to regions with high signal-to-noise ratios. The results suggest that the HHT is suitable for hearing loss detection at individual frequencies and characterization of the fine structures of TEOAEs.

This paper presents an improved differential evolution (IDE) method for the solution of large-scale unit commitment (UC) problems. The objective of the proposed scheme is to determine the generation schedule which minimizes the total operating cost over a given time horizon subject to a variety of constraints. Through its use of enhanced acceleration and migration processes, the IDE method limits the population size required in the search procedure and is therefore an ideal candidate for the solution of large-scale combinatorial optimization problems. The effectiveness of the proposed approach is verified by performing a series of simulations based upon the practical Tai-Power System (TPS) and various other power systems presented in the literature. In general, the results show that the IDE scheme outperforms existing deterministic and stochastic optimization methods both in terms of the quality of the solutions obtained and the computational cost. Furthermore, it is found that the magnitude of the cost savings achieved by the IDE scheme compared to that obtained by the other optimization techniques increases as the number of generating units within the power system increases. Therefore, the proposed scheme represents a particularly effective technique for the solution of large-scale UC problems.

Least-squares method is the most popular method for parameter estimation. It is easy applicable, but it has considerable drawback. Under well-known conditions in the presence of noise, the LS method produces asymptotically biased and inconsistent estimates. One way to overcome this drawback is the implementation of the instrumental variable method. In this paper several modifications of this method for closed-loop system identification are considered and investigated. The covariance matrix of the instrumental variable estimates is discussed. A simulation is carried out in order to illustrate the obtained results.

The optimization problems occurring in nonlinear regression normally cannot be proven unimodal. In the present paper applicability of global optimization algorithms to this problem is investigated with the focus on interval arithmetic based algorithms.

The paper deals with the recursive identification of dynamic systems having noninvertible output characteristics, which can be represented by the Wiener model. A special form of the model is considered where the linear dynamic block is given by its transfer function and the nonlinear static block is characterized by such a description of the piecewise-linear characteristic, which is appropriate for noninvertible nonlinearities. The proposed algorithm is a direct application of the known recursive least squares method extended with the estimation of internal variables and enables the on-line estimation of both the linear block parameters and the parameters of some noninvertible nonlinearities and their changes. The feasibility of the proposed method is illustrated on examples of time-varying systems.

In stochastic programming and decision analysis, an important issue consists in the approximate representation of the multidimensional stochastic underlying process in the form of scenario tree. This paper presents the approach to generate the multistage multidimensional scenario tree out of a set of scenario fans. For this purpose, the multistage K-means clustering algorithm is developed. The presented scenario tree generation algorithm is motivated by the stability results for optimal values of a multistage stochastic program. The time complexity of developed multistage K-means clustering algorithm is proved to be linear in regard to the number of scenarios in the fan. The algorithm to determine the branches with nonduplicate information in the multistage scenario tree is also presented as an intermediate result of research.