The paper describes the image superresolution method with aggregate divergence matrix and automatic detection of crossover. Formulation of the problem, building extreme optimization task and its solution for solving the automation determination of the crossover coefficient is presented. Different ways for building oversampling images algorithms based on the proposed method are shows. Based on practical experiments shows the effectiveness of the procedure of automatically the determination of the crossover coefficient. Experimentally established the effectiveness of the procedures oversampling images at high zoom resolution by the developed method.

Bezier curve is one of the most pragmatic curves that has vast application in computer aided geometry design. Unlike other normal curves, any Bezier curve model must follow the properties of Bezier curve. In our paper, we proposed the reconstruction of Bezier models by implementing satisfiability problem in Hopfield neural network as Bezier properties verification technique. We represent our logic construction to 2-satisfiability (2SAT) clauses in order to represent the properties of the Bezier curve model. The developed Bezier model will be integrated with Hopfield neural network in order to detect the existence of any non-Bezier curve. Microsoft Visual C++ 2013 is used as a platform for training, testing and validating of our proposed design. Hence, the performance of our proposed technique is evaluated based on global Bezier model and computation time. It has been observed that most of the model produced by HNN-2SAT are Bezier curve models.

In the near future, IP-based wireless sensor networks will play a key role in several application scenarios such as smart grid, smart home, healthcare, and building automation... An IPv6 routing protocol is expected to provide internet connectivity to any IP-based sensor node. In this paper, we propose IRPL protocol for IP-based wireless sensor networks. IRPL protocol uses a combination of two routing metrics that are the link quality and the remaining energy state of the preferred parent to select the optimal path. In IRPL protocol, we combine two metrics based on an alpha weight. IRPL protocol is implemented in ContikiOS and evaluated by using simulation and testbed experiments. The results show that IRPL protocol has achieved better network lifetime, data delivery ratio and energy balance compared to the traditional solution of RPL protocol.

Today, to use automatic image annotation in order to fill the semantic gap between low level features of images and understanding their information in retrieving process has become popular. Since automatic image annotation is crucial in understanding digital images several methods have been proposed to automatically annotate an image. One of the most important of these methods is instance-based image annotation. As these methods are vastly used in this paper, the most important instance-based image annotation methods are analyzed. First of all the main parts of instance-based automatic image annotation are analyzed. Afterwards, the main methods of instance-based automatic image annotation are reviewed and compared based on various features. In the end the most important challenges and open-ended fields in instance-based image annotation are analyzed.

Speech enhancement is a technique which processes the noisy speech signal. The aim of speech enhancement is to improve the perceived quality of speech and/or to improve its intelligibility. Due to its vast applications in mobile telephony, VOIP, hearing aids, Skype and speaker recognition, the challenges in speech enhancement have grown over the years. It is more challenging to suppress back ground noise that effects human communication in noisy environments like airports, road works, traffic, and cars. The objective of this survey paper is to outline the single channel speech enhancement methodologies used for enhancing the speech signal which is corrupted with additive background noise and also discuss the challenges and opportunities of single channel speech enhancement. This paper mainly focuses on transform domain techniques and supervised (NMF, HMM) speech enhancement techniques. This paper gives frame work for developments in speech enhancement methodologies.

Transient analysis is carried out for the single-phase grounding fault in small current grounding system, the transient grounding current expression is derived, and the influence factors are analyzed. Introduces a method for non-stationary and non-linear signal analysis method –Hilbert Huang transform (HHT) to analyze the single phase grounding fault in small current grounding system, HHT can be better used to extract the abundant transient time frequency information from the non-stationary and nonlinear fault current signals. The empirical mode decomposition (EMD) process and the normalized Hilbert Huang transform (NHHT) algorithm are presented, NHHT is used to analyze and verify an example of the nonlinear and non-stationary amplitude modulation signals. Build a small current grounding system in the EMTP_ATP environment, by selecting the appropriate time window to extract the transient signals, NHHT is used to analyze the transient current signals, and the Hilbert amplitude spectrum and the Hilbert marginal spectrum of the zero sequence transient current signals are obtained. Finally, the influences of the fault phase and the grounding resistance on the time-frequency characteristics of the signals are analyzed.

The article deals with the issues of the security of distributed and scalable computer systems based on the risk-based approach. The main existing methods for predicting the consequences of the dangerous actions of the intrusion agents are described. There is shown a generalized structural scheme of job manager in the context of a risk-based approach. Suggested analytical assessments for the security risk level in the distributed computer systems allow performing the critical time values forecast for the situation analysis and decision-making for the current configuration of a distributed computer system. These assessments are based on the number of used nodes and data links channels, the number of active security and monitoring mechanisms at the current period, as well as on the intensity of the security threats realization and on the activation intensity of the intrusion prevention mechanisms. The proposed comprehensive analytical risks assessments allow analyzing the dynamics of intrusions processes, the dynamics of the security level recovery and the corresponding dynamics of the risks level in the distributed computer system.

In the real world, most of the applications are inherently dynamic in nature i.e. their underlying data distribution changes with time. As a result, the concept drifts occur very frequently in the data stream. Concept drifts in data stream increase the challenges in learning as well, it also significantly decreases the accuracy of the classifier. However, recently many algorithms have been proposed that exclusively designed for data stream mining while considering drifting concept in the data stream.This paper presents an empirical evaluation of these algorithms on datasets having four possible types of concept drifts namely; sudden, gradual, incremental, and recurring drifts.