


Vol 50, No 3 (2016)
- Year: 2016
- Articles: 7
- URL: https://journal-vniispk.ru/0146-4116/issue/view/10660
Article
Markov-modulated continuous time finite Markov chain as the model of hybrid wireless communication channels operation
Abstract
Problems of the development of hybrid communication systems based on laser and radio wave technologies are discussed. A novel method for assessing the reliability characteristics of such systems, operating in random environment, is presented. The method is based on the theory of Markov- modulated processes. An algorithm of stationary distribution calculation for the systems state probabilities is elaborated. Numerical examples illustrate the suggested approach.



Robust control design for uncertain objects with time delay on the state
Abstract
This paper is devoted to developing methodology for designing robust control systems with large gain coefficients. The problem of robust control of uncertain objects with time-delayed states is solved based on the Lyapunov method. An unlimited increase in the gain coefficient of a controller allows the general components of an uncertain model to be suppressed to the maximum extent possible without a loss of stability. Within the limits, the system is described by a hyperplane equation. The quality parameters are determined by tuning the hyperplane coefficients. This equivalent-to-robust control enables one to track a reference signal with the desired accuracy for a wide class of uncertainties. The simulation results illustrate the effectiveness and efficiency of the proposed technique.



Distributed two-level cloud-based multimedia task scheduling
Abstract
Recently, Multimedia cloud is emerging as a promising technology to effectively process multimedia services. A key problem in multimedia cloud is how to deal with task scheduling and load balancing to satisfy the quality of service demands of users. In this paper, we propose a two levels task scheduling mechanism for multimedia cloud to addresses the problem. The first level scheduling is from the users’ multimedia application to the data centers, and the second is from the data centers to servers. The data centers and virtual machines both are modeled as M/M/1 queuing systems. The algorithm proposed formulates the task-scheduling problem as cooperative game among data centers. Then we allocate the tasks received by a data center to servers using cooperative game again among servers. Various simulations are conducted to validate the efficiency of the proposed task scheduling approaches. The results showed that the proposed solutions provided better performance as compared to the existing approaches.



F-SIFT and FUZZY-RVM based efficient multi-temporal image segmentation approach for remote sensing applications
Abstract
Image segmentation plays a most important role in the remote sensing applications, for the efficient detection of the Earth surface. The main objective of the segmentation process is to modify and simplify the representation of an image into an easier form for efficient analysis. The performance of the image segmentation process reduces due to the occurrence of noise and disturbances in the image. Existing segmentation approaches suffer from the performance degradation in the segmentation accuracy owing to the quality of the acquired satellite image. To overcome these drawbacks, this paper proposes an efficient image segmentation process for the clear view of the multi-temporal satellite image. Gaussian Filter (GF) is used for filtering the image to remove the noises present in the image. PSO-Affine based image registration is applied for the extraction of the pixel points and registration of the multi-temporal image. Removal of cloud from the image is performed to get a clear view of the image. Feature extraction is performed by using the Fast-Scale Invariant Feature Transform (F-SIFT) approach. The feature points of the image are extracted to form the cluster including six different classes such as building area, road area, vegetation area, tree area, water area and land area. The classes of the cluster are recognized by using the Fuzzy-Relevance Vector Machine (F-RVM) algorithm. The proposed approach achieves better performance in the cloud removal and efficient image segmentation.



Key Derivation Policy for data security and data integrity in cloud computing
Abstract
Cloud computing is currently emerging as a promising next-generation architecture in the Information Technology (IT) industry and education sector. The encoding process of state information from the data and protection are governed by the organizational access control policies. An encryption technique protects the data confidentiality from the unauthorized access leads to the development of fine-grained access control policies with user attributes. The Attribute-Based Encryption (ABE) verifies the intersection of attributes to the multiple sets. The handling of adding or revoking the users is difficult with respect to changes in policies. The inclusion of multiple encrypted copies for the same key raised the computational cost. This paper proposes an efficient Key Derivation Policy (KDP) for improvement of data security and integrity in the cloud and overcomes the problems in traditional methods. The local key generation process in proposed method includes the data attributes. The secret key is generated from the combination of local keys with the user attribute by a hash function. The original text is recovered from the ciphertext by the decryption process. The key sharing between data owner and user validates the data integrity referred MAC verification process. The proposed efficient KDP with MAC verification analyze the security issues and compared with the Cipher Text–Attribute-Based Encryption (CP-ABE) schemes on the performance parameters of encryption time, computational overhead and the average lifetime of key generation. The major advantage of proposed approach is the updating of public information and easy handling of adding/revoking of users in the cloud.



Application of computer image processing in office automation system
Abstract
With the development of science and technology, a variety of office automation systems (OAS) has been extensively applied in various occasions. Moreover, digital image processing technology has made great progress. The emergence of a series of excellent algorithms represented by Adaboost human face detection algorithm extends the application space of digital image processing in daily work and study. Besides, the operational capability of existing personal computers enables them to run smoothly these algorithms, which further contributes to the technological maturity of the digital image processing associated office automation systems. To keep up with the pace of information technology, this study selects high definition (HD) technology for paper archives in OAS, which is related to digital image processing as the research content. Automatic high definition demonstration of paper archives can reduce the burden on staff. This paper solved the problems of correction of slanted document image, automatic extraction of identification photo and color enhancement of seal and verified the feasibility of the scheme.



A review of channel estimation and security techniques for CRNS
Abstract
Cognitive Radio Network (CRN) is an intelligent wireless communication system that adapts itself to variations in the incoming radio frequency stimuli by modifying the operating parameters. Using the spectrum sensing techniques, the idle channels are detected, and allocated to the Secondary Users (SUs). The existing cooperative spectrum sensing techniques such as centralized sensing technique, Distributed sensing technique, and External sensing technique exploit efficient prediction models for allocating the frequency spectrum to SUs. For an optimal assignment of the channel using channel parameters, the channel estimation techniques such as pilot-assisted channel estimation, blind and semi blind estimation technique, and decision directed channel estimation technique are analyzed. The flexible nature of the CRN introduces various security attacks such as Primary User Emulation Attack, Objective Function Attack, Jamming Attack, Spectrum Sensing Data Falsification (SSDF), Control Channel Saturation DoS Attack (CCSD), Selfish Channel Negotiation (SCN), Sinkhole Attacks, HELLO Flood Attacks and Lion Attack. From the surveyed results, it is observed that the existing spectrum sensing, and prediction-based techniques consume more energy, and minimal data transmission rate for detecting the idle channel. Further, the end-to-end delay, energy consumption, end-to-end delay, and bandwidth are not minimized by the existing techniques.


