This paper analyzes the influence of the Doppler Effect on the length of time to establish synchronization pseudorandom sequences in radio communications systems with an expanded spectrum. Also, this paper explores the possibility of using secure wireless communication for modular robots. Wireless communication could be used for local and global communication. We analyzed a radio communication system integrator, including the two effects of the Doppler signal on the duration of establishing synchronization of the received and locally generated pseudorandom sequence. The effects of the impact of the variability of the phase were analyzed between the said sequences and correspondence of the phases of these signals with the interval of time of acquisition of received sequences. An analysis of these impacts is essential in the transmission of signal and protection of the transfer of information in the communication systems with an expanded range (telecommunications, mobile telephony, Global Navigation Satellite System GNSS, and wireless communication). Results show that wireless communication can provide a safety approach for communication with mobile robots.

Charging the bottle of high pressure air isolation devices is performed by a high-pressure compressor. The charging time is in function of the compressor capacity and the intensity of the nominal pressure of the air in the bottle. However, in accident situations this time is long and therefore high-pressure accumulators are used where the filling time of the bottle of isolation apparatus has been drastically reduced. Due to the short filling time of the bottle through the air flow, there is a thermodynamic load of bottle material that could endanger the safety of users and other participants in the area. It is therefore necessary to determine the critical parameters of the rapid charge and their intensity.

Abstract In system identification, many structures and approaches have been proposed to deal with systems with non-linear behavior. When applicable, the prediction error method, analogously to the linear case, requires minimizing a cost function that is non-convex in general. The issue with non-convexity is more problematic for non-linear models, not only due to the increased complexity of the model, but also because methods to provide consistent initialization points may not be available for many model structures. In this paper, we consider a non-linear rational finite impulse response model. We observe how the prediction error method requires minimizing a non-convex cost function, and propose a three-step least-squares algorithm as an alternative procedure. This procedure is an extension of the Model Order Reduction Steiglitz-McBride method, which is asymptotically efficient in open loop for linear models. We perform a simulation study to illustrate the applicability and performance of the method, which suggests that it is asymptotically efficient.

: This paper analyzes the properties of Catalan numbers and their relation to the Lattice Path combinatorial problem in cryptography. Specifically, analyzes the application of the appropriate combinatorial problem based on Catalan-key in encryption and decryption of files and plaintext. Accordingly, we use Catalan numbers for generating keys and within the experimental part we have applied the NIST (National Institute of Standards and Technology) statistical battery of tests for assessing the quality of generated keys was applied. A total of 12 quality assurance tests for Catalan-key were applied. A Java application is presented which allows the encryption and decryption of plaintext based on the generated Catalan-key and combinatorial problem of movement in integer network or Lattice Path. Experimental study yields the comparison of results in text encryption speed for combinatorial encryption methods (such as: Ballot Problem, Stack permutations and Balanced Parentheses) in comparison with Lattice Path method (in Java programming language).

The paper describes some authentication techniques based on face recognition with emphasis on 2D and 3D facial recognition technologies. The paper presents analyzes that show that the success of a biometric system requires the measurement of its performances through several different types of parameters. Presented are the features and comparative analysis of the two120 | Zoran Lončarević , Muzafer Saračević, Muhedin Hadžić dimensional method of face recognition in relation to three-dimensional ones, based on numerous researches published in professional and scientific papers. The paper emphasize that it is necessary to analyze, in addition to the system performance, the errors that occur in biometric systems during the process of registration, verification and identification. An OpenCV library has been analyzed with it’s over 500 functions that cover several areas of Computer Vision and the area of automatic format recognition.

The aim of this paper is to summarize a large number of economic and demographic determinants that are used to predict the demand for life insurance into a smaller number of component variables (components) and to determine which component has a stronger influence on demand for life insurance. Data are collected for 150 countries during the period 2005-2010. Final cross-country database is consisted of six-year average values on variables for selected countries. The methodology includes techniques of multivariate analysis: principal component analysis (PCA) and multiple linear regression. Results show that initial determinants of demand for life insurance can be summarized into two components: economic and demographic. Both components have a statistically significant positive influence on the demand for life insurance. On the basis of standardized regression coefficients it can be concluded that economic component is stronger determinant of demand for life insurance in comparison with demographic.

: We use machine learning techniques to build predictive models for anomaly detection in the Border Gateway Protocol (BGP). Imbalanced datasets of network anomalies pose limitations to building predictive models for anomaly detection. In order to achieve better classification performance measures, we use resampling methods to balance classes in the datasets. We use undersampling, oversampling and combination techniques to change class distributions of the datasets. In this paper we build predictive models based on preprocessed network anomaly datasets of known Internet network anomalies and observe improvement in classifier performance measures compared to those reported in our previous work. We propose to use resampling combination techniques on datasets along with Decision Tree and Naïve Bayes classifiers in order to achieve the best trade-off between (1) the F-measure and the length of model training time, and (2) avoiding overfitting and loss of information.