Magazine et Edition

Volume 22 (2022): Edition 3 (September 2022)

Volume 22 (2022): Edition 2 (June 2022)

Volume 22 (2022): Edition 1 (March 2022)

Volume 21 (2021): Edition 4 (December 2021)

Volume 21 (2021): Edition 3 (September 2021)

Volume 21 (2021): Edition 2 (June 2021)

Volume 21 (2021): Edition 1 (March 2021)

Volume 20 (2020): Edition 6 (December 2020)
Special Edition on New Developments in Scalable Computing

Volume 20 (2020): Edition 5 (December 2020)
Special issue on Innovations in Intelligent Systems and Applications

Volume 20 (2020): Edition 4 (November 2020)

Volume 20 (2020): Edition 3 (September 2020)

Volume 20 (2020): Edition 2 (June 2020)

Volume 20 (2020): Edition 1 (March 2020)

Volume 19 (2019): Edition 4 (November 2019)

Volume 19 (2019): Edition 3 (September 2019)

Volume 19 (2019): Edition 2 (June 2019)

Volume 19 (2019): Edition 1 (March 2019)

Volume 18 (2018): Edition 5 (May 2018)
Special Thematic Edition on Optimal Codes and Related Topics

Volume 18 (2018): Edition 4 (November 2018)

Volume 18 (2018): Edition 3 (September 2018)

Volume 18 (2018): Edition 2 (June 2018)

Volume 18 (2018): Edition 1 (March 2018)

Volume 17 (2017): Edition 5 (December 2017)
Special Edition With Selected Papers From The Workshop “Two Years Avitohol: Advanced High Performance Computing Applications 2017

Volume 17 (2017): Edition 4 (November 2017)

Volume 17 (2017): Edition 3 (September 2017)

Volume 17 (2017): Edition 2 (June 2017)

Volume 17 (2017): Edition 1 (March 2017)

Volume 16 (2016): Edition 6 (December 2016)
Special issue with selection of extended papers from 6th International Conference on Logistic, Informatics and Service Science LISS’2016

Volume 16 (2016): Edition 5 (October 2016)
Edition Title: Special Edition on Application of Advanced Computing and Simulation in Information Systems

Volume 16 (2016): Edition 4 (December 2016)

Volume 16 (2016): Edition 3 (September 2016)

Volume 16 (2016): Edition 2 (June 2016)

Volume 16 (2016): Edition 1 (March 2016)

Volume 15 (2015): Edition 7 (December 2015)
Special Edition on Information Fusion

Volume 15 (2015): Edition 6 (December 2015)
Special Edition on Logistics, Informatics and Service Science

Volume 15 (2015): Edition 5 (April 2015)
Special Edition on Control in Transportation Systems

Volume 15 (2015): Edition 4 (November 2015)

Volume 15 (2015): Edition 3 (September 2015)

Volume 15 (2015): Edition 2 (June 2015)

Volume 15 (2015): Edition 1 (March 2015)

Volume 14 (2014): Edition 5 (December 2014)
Special Edition

Volume 14 (2014): Edition 4 (December 2014)

Volume 14 (2014): Edition 3 (September 2014)

Volume 14 (2014): Edition 2 (June 2014)

Volume 14 (2014): Edition 1 (March 2014)

Volume 13 (2013): Edition Special-Edition (December 2013)

Volume 13 (2013): Edition 4 (December 2013)
The publishing of the present issue (Volume 13, No 4, 2013) of the journal “Cybernetics and Information Technologies” is financially supported by FP7 project “Advanced Computing for Innovation” (ACOMIN), grant agreement 316087 of Call FP7 REGPOT-2012-2013-1.

Volume 13 (2013): Edition 3 (September 2013)

Volume 13 (2013): Edition 2 (June 2013)

Volume 13 (2013): Edition 1 (March 2013)

Volume 12 (2012): Edition 4 (December 2012)

Volume 12 (2012): Edition 3 (September 2012)

Volume 12 (2012): Edition 2 (June 2012)

Volume 12 (2012): Edition 1 (March 2012)

Détails du magazine
Format
Magazine
eISSN
1314-4081
Première publication
13 Mar 2012
Période de publication
4 fois par an
Langues
Anglais

Chercher

Volume 16 (2016): Edition 2 (June 2016)

Détails du magazine
Format
Magazine
eISSN
1314-4081
Première publication
13 Mar 2012
Période de publication
4 fois par an
Langues
Anglais

Chercher

16 Articles
Accès libre

Metric Based Attribute Reduction Method in Dynamic Decision Tables

Publié en ligne: 22 Jun 2016
Pages: 3 - 15

Résumé

Abstract

Feature selection is a vital problem which needs to be effectively solved in knowledge discovery in databases and pattern recognition due to two basic reasons: minimizing costs and accurately classifying data. Feature selection using rough set theory is also called attribute reduction. It has attracted a lot of attention from researchers and numerous potential results have been gained. However, most of them are applied on static data and attribute reduction in dynamic databases is still in its early stages. This paper focuses on developing incremental methods and algorithms to derive reducts, employing a distance measure when decision systems vary in condition attribute set. We also conduct experiments on UCI data sets and the experimental results show that the proposed algorithms are better in terms of time consumption and reducts’ cardinality in comparison with non-incremental heuristic algorithm and the incremental approach using information entropy proposed by authors in [17].

Mots clés

  • Rough set
  • decision systems
  • attribute reduction
  • reduct
  • metric
Accès libre

Hybridization of Expectation-Maximization and K-Means Algorithms for Better Clustering Performance

Publié en ligne: 22 Jun 2016
Pages: 16 - 34

Résumé

Abstract

The present work proposes hybridization of Expectation-Maximization (EM) and K-means techniques as an attempt to speed-up the clustering process. Even though both the K-means and EM techniques look into different areas, K-means can be viewed as an approximate way to obtain maximum likelihood estimates for the means. Along with the proposed algorithm for hybridization, the present work also experiments with the Standard EM algorithm. Six different datasets, three of which synthetic datasets, are used for the experiments. Clustering fitness and Sum of Squared Errors (SSE) are computed for measuring the clustering performance. In all the experiments it is observed that the proposed algorithm for hybridization of EM and K-means techniques is consistently taking less execution time with acceptable Clustering Fitness value and less SSE than the standard EM algorithm. It is also observed that the proposed algorithm is producing better clustering results than the Cluster package of Purdue University.

Mots clés

  • Hybridization
  • clustering
  • K-means
  • mixture models
  • expectation maximization
  • clustering fitness
  • sum of squared errors
Accès libre

Mathematical Modeling and Examination of the Effects of Structural Redundancy in а Class of Computer-Based Fault Tolerant Systems

Publié en ligne: 22 Jun 2016
Pages: 35 - 45

Résumé

Abstract

The present article models and examines k˅n systems, in particular Triple modular redundancy (2˅3) and 3˅5. The aim of the study is to derive mathematical models, which are used for determining the impact of structural redundancy (the number of channels n and the threshold of the quorum function k) on the reliability of the system. The probability of failure-free operation p and the Mean Time Between Failures (MTBF) are used as reliability indicators.

Mots clés

  • Real time systems
  • fault-tolerant systems
  • Safety Critical Systems (SCS)
  • redundancy
  • triple modular redundancy
  • availability
Accès libre

A Fault-Tolerant Routing Algorithm for Wireless Sensor Networks Based on the Structured Directional de Bruijn Graph

Publié en ligne: 22 Jun 2016
Pages: 46 - 59

Résumé

Abstract

Wireless Sensor Network (WSNs) nodes with low energy, run out of energy easily and stop working, which results then in routing failures and communication blocking. The paper puts forward a FTRSDDB algorithm based on the structured directional de Bruijn graph to enhance the performance of faulttolerant routing for WSNs. The algorithm randomly deploys some super nodes with abundant energy and powerful performance in WSNs. These nodes are responsible for the collection of topology information from the WSNs to build redundant routing table, and provide data forwarding and routing update service for popular nodes. The FTRSDDB algorithm optimizes network topology structure using de Bruijn graph, and can quickly find neighbor nodes failure and invalid routing path, and then calculate new routing information with low cost, which greatly improves the performance of fault-tolerant routing of WSNs. Experiments show that the FTRSDDB algorithm takes on better performance compared with other faulttolerant routing algorithms, even that exist malicious nodes attack in the WSNs.

Mots clés

  • Wireless sensor networks
  • directional de Bruijn graph
  • fault-tolerant routing
Accès libre

TOPSIS Modification with Interval Type-2 Fuzzy Numbers

Publié en ligne: 22 Jun 2016
Pages: 60 - 68

Résumé

Abstract

This paper proposes a new TOPSIS with interval type-2 fuzzy numbers. The extension applies graded mean integration to compare normal fuzzy trapezoidal sets. It is demonstrated by a numerical example for ranking business intelligence software that the TOPSIS modification can operate with qualitative and quantitative criteria, reduces evaluation uncertainty and provides a feasible solution.

Mots clés

  • Multi-criteria decision making
  • TOPSIS
  • interval type-2 fuzzy sets
  • business intelligence
Accès libre

An Efficient Fault-Tolerant Multi-Bus Data Scheduling Algorithm Based on Replication and Deallocation

Publié en ligne: 22 Jun 2016
Pages: 69 - 84

Résumé

Abstract

The paper proposes a new reliable fault-tolerant scheduling algorithm for real-time embedded systems. The proposed scheduling algorithm takes into consideration only one bus fault in multi-bus heterogeneous architectures, caused by hardware faults and compensated by software redundancy solutions. The proposed algorithm is based on both active and passive backup copies, to minimize the scheduling length of data on buses. In the experiments, this paper evaluates the proposed methods in terms of data scheduling length for a set of DAG benchmarks. The experimental results show the effectiveness of our technique.

Mots clés

  • Fault-tolerance
  • scheduling
  • real time systems
  • active and passive redundancy
  • replication
  • deallocation
Accès libre

Multi-Partite Graphs and Verification of Software Applications for Real-Time Systems

Publié en ligne: 22 Jun 2016
Pages: 85 - 96

Résumé

Abstract

Aspects of static verification of software applications for real-time systems are considered. A verification method based on oriented multipartite graphs is suggested for checking whether mutual blockings (deadlocks or clinches) could occur in a real-time multitask application and estimate the duration of highpriority task blocking by lower-priority tasks due to the application structure.

Mots clés

  • Real-time systems
  • multi-task software applications
  • multi-core processors
  • feasibility analysis
  • shared resources
  • blocking factor
Accès libre

Evolutionary Computing Based on QoS Oriented Energy Efficient VM Consolidation Scheme for Large Scale Cloud Data Centers

Publié en ligne: 22 Jun 2016
Pages: 97 - 112

Résumé

Abstract

The high pace and increase in cloud computing technology and associated applications, especially large scale data centres, have demanded energy efficient and Quality of Service (QoS) oriented computing platform. To meet these requirements, virtualization and Virtual Machine (VM) consolidation has emerged as an effective solution. The optimization in VM consolidation by means of efficient dynamic resource-utilization prediction, VM selection and placement can achieve optimal solution for energy efficient and QoS oriented cloud computing system. In this paper, an evolutionary computing algorithm called Adaptive Genetic Algorithm (A-GA) based VM consolidation approach has been developed. A-GA based placement policy and its implementation with different VM selection policies like Minimum Migration Time (MMT), Maximum Correlation (MC) and Random Selection (RS), along with different CPU utilization estimation approaches like Inter Quartile Range (IQR), Local Regression (LR), Local Robust Regression (LRR), static THReshold (THR) and Median Absolute Deviation (MAD) has revealed that A-GA based consolidation with MMT selection policy and combined IQR and LRR can enable optimal VM consolidation for large scale infrastructures. In addition, the proposed A-GA policy has exhibited better performance as compared to other meta-heuristics such as Ant Colony Optimization (ACO) and Best Fit Decreasing. The proposed consolidation system can be used for large scale cloud infrastructures where energy conservation, minimal Service Level Agreement (SLA) violation and QoS assurance is inevitable.

Mots clés

  • Energy efficient cloud computing
  • virtual machine consolidation
  • adaptive genetic algorithm
  • minimum migration time
  • dynamic threshold
  • service level agreement violation
Accès libre

Reducing Energy Consumption by Using Smart Metering Intelligent Systems

Publié en ligne: 22 Jun 2016
Pages: 113 - 124

Résumé

Abstract

Smart metering is aimed at efficient energy management. Its potential may be revealed using recent advances in machine type communications. This paper presents an approach to design web services for residential power control with prepaid functionality. The reduction in energy consumption is estimated for typical households applying heating control.

Mots clés

  • Smart metering
  • heating/cooling control
  • payment management
  • resource structure
  • RESTful web services
Accès libre

Hand Vein Image Enhancement Based on Multi-Scale Top-Hat Transform

Publié en ligne: 22 Jun 2016
Pages: 125 - 134

Résumé

Abstract

Traditional image contrast enhancement methods originally cannot improve the quality of vein images and may also import some unknown noise resulting in low recognition rate. To overcome the abovementioned disadvantages, the paper proposes an enhancement method based on the morphological filtering theory including three main procedures. Firstly, the algorithm extract the vein Region Of Interest (ROI), and then adopting the improved White Top-Hat transform (WTH) and Black Top-Hat transform (BTH) methods to get the features of vein in detail in both white and black pattern (vein information and background information); Secondly, to construct the filtering function with the self-designed controlling operator, representing the gradient changes of the vein edges, which well reflects the importance of local detail in multi-scale pattern; Finally, traditional nonlinear gray-level transformation function is imported with modality to the parameters to realize the gray normalization. We perform rigorous experiments with the proposed method and other state-of-the-art enhancement methods on the self-built dorsal vein image databases, and the experimental results illustrate that the multiscale top-hat theory-based enhancement methods improve the contrast of hand vein images with restrictions on the possibility of enhancement on existing noise information.

Mots clés

  • Contrast enhancement
  • morphological filtering
  • multi-scale
  • top-hat
  • vein recognition
Accès libre

A New Zero-Watermarking Algorithm Resisting Attacks Based on Differences Hashing

Publié en ligne: 22 Jun 2016
Pages: 135 - 147

Résumé

Abstract

Medical volume data containing patient information is often faced with various attacks in the transmission process. In order to enhance the medical information system security, and effectively solve the problem of medical volume data protection, a new zero-watermarking algorithm is proposed in the paper. The new zero-watermarking algorithm takes advantage of three-dimensional discrete wavelet transform multi-resolution analysis characteristics of space and time, threedimensional discrete cosine transform properties, and differences hashing robust characteristic. In order to enhance watermarking algorithm security, Legendre chaotic neural network is used for scrambling original watermark image. The medical volume data is made by three-dimensional discrete wavelet transform, threedimensional discrete cosine transform and three-dimensional discrete inverse cosine transform r to obtain the medical volume data feature matrix (4×5×4), which is converted to 64-bit binary feature sequence through difference hashing algorithm. The 64-bit binary feature sequence is used to construct the zero-watermarking. The experimental results prove that the new zero-watermarking has favorable security and robustness resisting various attacks. Therefore, the new zero-watermarking algorithm is more applicable to protect medical volume data.

Mots clés

  • Zero-watermarking algorithm
  • scrambling
  • medical volume data
  • three-dimensional discrete inverse cosine transform
  • difference hashing
Accès libre

An Indirected Recommendation Model for Chinese Microblog

Publié en ligne: 22 Jun 2016
Pages: 148 - 159

Résumé

Abstract

Microblog is a browser-based platform for web user’s information sharing and communication. With the rapidly increasing of microblog population, its recommendation function becomes necessary. This paper proposes the recommendation by the Latent Dirichlet Allocation topic model, which combines the user interests into the model to meet their needs. We also conduct a comparative analysis between indirect and direct recommendation algorithms. The experimental results show that the indirect recommendation is more effective for the micro-blog recommendation.

Mots clés

  • Microblog recommendation
  • the topic model
  • user interest model
Accès libre

Duplicate Literature Detection for Cross-Library Search

Publié en ligne: 22 Jun 2016
Pages: 160 - 178

Résumé

Abstract

The proliferation of online digital libraries offers users a great opportunity to search their desired literatures on Web. Cross-library search applications can help users search more literature information from multiple digital libraries. Duplicate literatures detection is always a necessary step when merging the search results from multiple digital libraries due to heterogeneity and autonomy of digital libraries. To this end, this paper proposes a holistic solution which includes achieving automatic training set, holistic attribute mapping, and weight of attribute training. The experiments on real digital libraries show that the proposed solution is highly effective.

Mots clés

  • Information integration
  • digital library
  • duplicate detection
  • schema mapping
  • data cleaning
Accès libre

Advanced Control of the Wood Thermal Treatment Processing

Publié en ligne: 22 Jun 2016
Pages: 179 - 197

Résumé

Abstract

An advanced control system is considered for wood Thermal Treatment Processing (TTP) in order to cope with the scarcity of on-line measurements as well as cope with a variety of unpredictable operational conditions arising due to disturbances and large uncertainties. The architecture of the proposed control system includes a number of Building Blocks (BB), functionally based on well-established algorithms, so that that the design and operation of the system use mainly parameterisation via simulation, optimization and coordination. In the created BB a set of advances are incorporated: first principle modelling of WEP, inference control, Run-to-Run optimization, Case-Based Reasoning (CBR). Some results of real applications in SMEs are presented.

Mots clés

  • Advanced process control
  • Case-Based Reasoning (CBR)
  • inference control
  • Thermal Treatment Processing (TTP)
  • Run-to-Run optimization
Accès libre

Object Tracking Based on Online Semi-Supervised SVM and Adaptive-Fused Feature

Publié en ligne: 22 Jun 2016
Pages: 198 - 211

Résumé

Abstract

In order to improve the performance of tracking, we propose a new online tracking method based on classification and adaptive fused feature. We first label a few positive and negative samples, train the classifier by the online SSSM (Semi-Supervised Support Vector Machine) learning and these labelled samples, and then locate the position of the object from the next frame according to the trained classifier. In order to adapt more of the new samples, we need to update the classifier by finding new samples with high confident value obtained by the trained classifier and add them into the online SSSM. Finally we also update the object model by the online incremental PCA (Principal Component Analysis) because of background clutter, heavy occlusion and complicated object appearance changes. Compared with the basic mean shift tracking and the ensemble tracking method, experimental results show that our tracking method is able to effectively handle heavy occlusion and background clutter in some challenge videos including some thermal videos.

Mots clés

  • Visual tracking
  • SSSM
  • feature fusion
  • incremental PCA,online
Accès libre

Robot Simultaneous Localization and Mapping Based on Self-Detected Waypoint

Publié en ligne: 22 Jun 2016
Pages: 212 - 221

Résumé

Abstract

The point of interest in this paper is the main content of autonomous navigation of robots. An algorithm for robot Simultaneous Localization And Mapping (SLAM) based on self-detected waypoint is introduced to realize robot’s mapping in its area of interest. Robot’s next step waypoint is performed using characteristics of large information in the area of interest and dense landmark, clustering the landmark in the area of interest, and guiding robot’s movement with clustered central point. Robot clusters the observed area in its observation every time. It takes the clustered center based on the largest number of landmarks as the waypoint of the next step. Simulation experiment shows, that due to robot’s movement toward the area of dense landmarks, the proposed method increases the number of landmarks observed by the robot and frequency of observation is increased. The proposed method enhances accuracy of robot’s positioning and the robot realizes to detect its waypoint autonomously.

Mots clés

  • Robot
  • Simultaneous Localization And Mapping (SLAM)
  • K-means clustering
  • Extended Kalman Filter (EKF)
  • waypoint
16 Articles
Accès libre

Metric Based Attribute Reduction Method in Dynamic Decision Tables

Publié en ligne: 22 Jun 2016
Pages: 3 - 15

Résumé

Abstract

Feature selection is a vital problem which needs to be effectively solved in knowledge discovery in databases and pattern recognition due to two basic reasons: minimizing costs and accurately classifying data. Feature selection using rough set theory is also called attribute reduction. It has attracted a lot of attention from researchers and numerous potential results have been gained. However, most of them are applied on static data and attribute reduction in dynamic databases is still in its early stages. This paper focuses on developing incremental methods and algorithms to derive reducts, employing a distance measure when decision systems vary in condition attribute set. We also conduct experiments on UCI data sets and the experimental results show that the proposed algorithms are better in terms of time consumption and reducts’ cardinality in comparison with non-incremental heuristic algorithm and the incremental approach using information entropy proposed by authors in [17].

Mots clés

  • Rough set
  • decision systems
  • attribute reduction
  • reduct
  • metric
Accès libre

Hybridization of Expectation-Maximization and K-Means Algorithms for Better Clustering Performance

Publié en ligne: 22 Jun 2016
Pages: 16 - 34

Résumé

Abstract

The present work proposes hybridization of Expectation-Maximization (EM) and K-means techniques as an attempt to speed-up the clustering process. Even though both the K-means and EM techniques look into different areas, K-means can be viewed as an approximate way to obtain maximum likelihood estimates for the means. Along with the proposed algorithm for hybridization, the present work also experiments with the Standard EM algorithm. Six different datasets, three of which synthetic datasets, are used for the experiments. Clustering fitness and Sum of Squared Errors (SSE) are computed for measuring the clustering performance. In all the experiments it is observed that the proposed algorithm for hybridization of EM and K-means techniques is consistently taking less execution time with acceptable Clustering Fitness value and less SSE than the standard EM algorithm. It is also observed that the proposed algorithm is producing better clustering results than the Cluster package of Purdue University.

Mots clés

  • Hybridization
  • clustering
  • K-means
  • mixture models
  • expectation maximization
  • clustering fitness
  • sum of squared errors
Accès libre

Mathematical Modeling and Examination of the Effects of Structural Redundancy in а Class of Computer-Based Fault Tolerant Systems

Publié en ligne: 22 Jun 2016
Pages: 35 - 45

Résumé

Abstract

The present article models and examines k˅n systems, in particular Triple modular redundancy (2˅3) and 3˅5. The aim of the study is to derive mathematical models, which are used for determining the impact of structural redundancy (the number of channels n and the threshold of the quorum function k) on the reliability of the system. The probability of failure-free operation p and the Mean Time Between Failures (MTBF) are used as reliability indicators.

Mots clés

  • Real time systems
  • fault-tolerant systems
  • Safety Critical Systems (SCS)
  • redundancy
  • triple modular redundancy
  • availability
Accès libre

A Fault-Tolerant Routing Algorithm for Wireless Sensor Networks Based on the Structured Directional de Bruijn Graph

Publié en ligne: 22 Jun 2016
Pages: 46 - 59

Résumé

Abstract

Wireless Sensor Network (WSNs) nodes with low energy, run out of energy easily and stop working, which results then in routing failures and communication blocking. The paper puts forward a FTRSDDB algorithm based on the structured directional de Bruijn graph to enhance the performance of faulttolerant routing for WSNs. The algorithm randomly deploys some super nodes with abundant energy and powerful performance in WSNs. These nodes are responsible for the collection of topology information from the WSNs to build redundant routing table, and provide data forwarding and routing update service for popular nodes. The FTRSDDB algorithm optimizes network topology structure using de Bruijn graph, and can quickly find neighbor nodes failure and invalid routing path, and then calculate new routing information with low cost, which greatly improves the performance of fault-tolerant routing of WSNs. Experiments show that the FTRSDDB algorithm takes on better performance compared with other faulttolerant routing algorithms, even that exist malicious nodes attack in the WSNs.

Mots clés

  • Wireless sensor networks
  • directional de Bruijn graph
  • fault-tolerant routing
Accès libre

TOPSIS Modification with Interval Type-2 Fuzzy Numbers

Publié en ligne: 22 Jun 2016
Pages: 60 - 68

Résumé

Abstract

This paper proposes a new TOPSIS with interval type-2 fuzzy numbers. The extension applies graded mean integration to compare normal fuzzy trapezoidal sets. It is demonstrated by a numerical example for ranking business intelligence software that the TOPSIS modification can operate with qualitative and quantitative criteria, reduces evaluation uncertainty and provides a feasible solution.

Mots clés

  • Multi-criteria decision making
  • TOPSIS
  • interval type-2 fuzzy sets
  • business intelligence
Accès libre

An Efficient Fault-Tolerant Multi-Bus Data Scheduling Algorithm Based on Replication and Deallocation

Publié en ligne: 22 Jun 2016
Pages: 69 - 84

Résumé

Abstract

The paper proposes a new reliable fault-tolerant scheduling algorithm for real-time embedded systems. The proposed scheduling algorithm takes into consideration only one bus fault in multi-bus heterogeneous architectures, caused by hardware faults and compensated by software redundancy solutions. The proposed algorithm is based on both active and passive backup copies, to minimize the scheduling length of data on buses. In the experiments, this paper evaluates the proposed methods in terms of data scheduling length for a set of DAG benchmarks. The experimental results show the effectiveness of our technique.

Mots clés

  • Fault-tolerance
  • scheduling
  • real time systems
  • active and passive redundancy
  • replication
  • deallocation
Accès libre

Multi-Partite Graphs and Verification of Software Applications for Real-Time Systems

Publié en ligne: 22 Jun 2016
Pages: 85 - 96

Résumé

Abstract

Aspects of static verification of software applications for real-time systems are considered. A verification method based on oriented multipartite graphs is suggested for checking whether mutual blockings (deadlocks or clinches) could occur in a real-time multitask application and estimate the duration of highpriority task blocking by lower-priority tasks due to the application structure.

Mots clés

  • Real-time systems
  • multi-task software applications
  • multi-core processors
  • feasibility analysis
  • shared resources
  • blocking factor
Accès libre

Evolutionary Computing Based on QoS Oriented Energy Efficient VM Consolidation Scheme for Large Scale Cloud Data Centers

Publié en ligne: 22 Jun 2016
Pages: 97 - 112

Résumé

Abstract

The high pace and increase in cloud computing technology and associated applications, especially large scale data centres, have demanded energy efficient and Quality of Service (QoS) oriented computing platform. To meet these requirements, virtualization and Virtual Machine (VM) consolidation has emerged as an effective solution. The optimization in VM consolidation by means of efficient dynamic resource-utilization prediction, VM selection and placement can achieve optimal solution for energy efficient and QoS oriented cloud computing system. In this paper, an evolutionary computing algorithm called Adaptive Genetic Algorithm (A-GA) based VM consolidation approach has been developed. A-GA based placement policy and its implementation with different VM selection policies like Minimum Migration Time (MMT), Maximum Correlation (MC) and Random Selection (RS), along with different CPU utilization estimation approaches like Inter Quartile Range (IQR), Local Regression (LR), Local Robust Regression (LRR), static THReshold (THR) and Median Absolute Deviation (MAD) has revealed that A-GA based consolidation with MMT selection policy and combined IQR and LRR can enable optimal VM consolidation for large scale infrastructures. In addition, the proposed A-GA policy has exhibited better performance as compared to other meta-heuristics such as Ant Colony Optimization (ACO) and Best Fit Decreasing. The proposed consolidation system can be used for large scale cloud infrastructures where energy conservation, minimal Service Level Agreement (SLA) violation and QoS assurance is inevitable.

Mots clés

  • Energy efficient cloud computing
  • virtual machine consolidation
  • adaptive genetic algorithm
  • minimum migration time
  • dynamic threshold
  • service level agreement violation
Accès libre

Reducing Energy Consumption by Using Smart Metering Intelligent Systems

Publié en ligne: 22 Jun 2016
Pages: 113 - 124

Résumé

Abstract

Smart metering is aimed at efficient energy management. Its potential may be revealed using recent advances in machine type communications. This paper presents an approach to design web services for residential power control with prepaid functionality. The reduction in energy consumption is estimated for typical households applying heating control.

Mots clés

  • Smart metering
  • heating/cooling control
  • payment management
  • resource structure
  • RESTful web services
Accès libre

Hand Vein Image Enhancement Based on Multi-Scale Top-Hat Transform

Publié en ligne: 22 Jun 2016
Pages: 125 - 134

Résumé

Abstract

Traditional image contrast enhancement methods originally cannot improve the quality of vein images and may also import some unknown noise resulting in low recognition rate. To overcome the abovementioned disadvantages, the paper proposes an enhancement method based on the morphological filtering theory including three main procedures. Firstly, the algorithm extract the vein Region Of Interest (ROI), and then adopting the improved White Top-Hat transform (WTH) and Black Top-Hat transform (BTH) methods to get the features of vein in detail in both white and black pattern (vein information and background information); Secondly, to construct the filtering function with the self-designed controlling operator, representing the gradient changes of the vein edges, which well reflects the importance of local detail in multi-scale pattern; Finally, traditional nonlinear gray-level transformation function is imported with modality to the parameters to realize the gray normalization. We perform rigorous experiments with the proposed method and other state-of-the-art enhancement methods on the self-built dorsal vein image databases, and the experimental results illustrate that the multiscale top-hat theory-based enhancement methods improve the contrast of hand vein images with restrictions on the possibility of enhancement on existing noise information.

Mots clés

  • Contrast enhancement
  • morphological filtering
  • multi-scale
  • top-hat
  • vein recognition
Accès libre

A New Zero-Watermarking Algorithm Resisting Attacks Based on Differences Hashing

Publié en ligne: 22 Jun 2016
Pages: 135 - 147

Résumé

Abstract

Medical volume data containing patient information is often faced with various attacks in the transmission process. In order to enhance the medical information system security, and effectively solve the problem of medical volume data protection, a new zero-watermarking algorithm is proposed in the paper. The new zero-watermarking algorithm takes advantage of three-dimensional discrete wavelet transform multi-resolution analysis characteristics of space and time, threedimensional discrete cosine transform properties, and differences hashing robust characteristic. In order to enhance watermarking algorithm security, Legendre chaotic neural network is used for scrambling original watermark image. The medical volume data is made by three-dimensional discrete wavelet transform, threedimensional discrete cosine transform and three-dimensional discrete inverse cosine transform r to obtain the medical volume data feature matrix (4×5×4), which is converted to 64-bit binary feature sequence through difference hashing algorithm. The 64-bit binary feature sequence is used to construct the zero-watermarking. The experimental results prove that the new zero-watermarking has favorable security and robustness resisting various attacks. Therefore, the new zero-watermarking algorithm is more applicable to protect medical volume data.

Mots clés

  • Zero-watermarking algorithm
  • scrambling
  • medical volume data
  • three-dimensional discrete inverse cosine transform
  • difference hashing
Accès libre

An Indirected Recommendation Model for Chinese Microblog

Publié en ligne: 22 Jun 2016
Pages: 148 - 159

Résumé

Abstract

Microblog is a browser-based platform for web user’s information sharing and communication. With the rapidly increasing of microblog population, its recommendation function becomes necessary. This paper proposes the recommendation by the Latent Dirichlet Allocation topic model, which combines the user interests into the model to meet their needs. We also conduct a comparative analysis between indirect and direct recommendation algorithms. The experimental results show that the indirect recommendation is more effective for the micro-blog recommendation.

Mots clés

  • Microblog recommendation
  • the topic model
  • user interest model
Accès libre

Duplicate Literature Detection for Cross-Library Search

Publié en ligne: 22 Jun 2016
Pages: 160 - 178

Résumé

Abstract

The proliferation of online digital libraries offers users a great opportunity to search their desired literatures on Web. Cross-library search applications can help users search more literature information from multiple digital libraries. Duplicate literatures detection is always a necessary step when merging the search results from multiple digital libraries due to heterogeneity and autonomy of digital libraries. To this end, this paper proposes a holistic solution which includes achieving automatic training set, holistic attribute mapping, and weight of attribute training. The experiments on real digital libraries show that the proposed solution is highly effective.

Mots clés

  • Information integration
  • digital library
  • duplicate detection
  • schema mapping
  • data cleaning
Accès libre

Advanced Control of the Wood Thermal Treatment Processing

Publié en ligne: 22 Jun 2016
Pages: 179 - 197

Résumé

Abstract

An advanced control system is considered for wood Thermal Treatment Processing (TTP) in order to cope with the scarcity of on-line measurements as well as cope with a variety of unpredictable operational conditions arising due to disturbances and large uncertainties. The architecture of the proposed control system includes a number of Building Blocks (BB), functionally based on well-established algorithms, so that that the design and operation of the system use mainly parameterisation via simulation, optimization and coordination. In the created BB a set of advances are incorporated: first principle modelling of WEP, inference control, Run-to-Run optimization, Case-Based Reasoning (CBR). Some results of real applications in SMEs are presented.

Mots clés

  • Advanced process control
  • Case-Based Reasoning (CBR)
  • inference control
  • Thermal Treatment Processing (TTP)
  • Run-to-Run optimization
Accès libre

Object Tracking Based on Online Semi-Supervised SVM and Adaptive-Fused Feature

Publié en ligne: 22 Jun 2016
Pages: 198 - 211

Résumé

Abstract

In order to improve the performance of tracking, we propose a new online tracking method based on classification and adaptive fused feature. We first label a few positive and negative samples, train the classifier by the online SSSM (Semi-Supervised Support Vector Machine) learning and these labelled samples, and then locate the position of the object from the next frame according to the trained classifier. In order to adapt more of the new samples, we need to update the classifier by finding new samples with high confident value obtained by the trained classifier and add them into the online SSSM. Finally we also update the object model by the online incremental PCA (Principal Component Analysis) because of background clutter, heavy occlusion and complicated object appearance changes. Compared with the basic mean shift tracking and the ensemble tracking method, experimental results show that our tracking method is able to effectively handle heavy occlusion and background clutter in some challenge videos including some thermal videos.

Mots clés

  • Visual tracking
  • SSSM
  • feature fusion
  • incremental PCA,online
Accès libre

Robot Simultaneous Localization and Mapping Based on Self-Detected Waypoint

Publié en ligne: 22 Jun 2016
Pages: 212 - 221

Résumé

Abstract

The point of interest in this paper is the main content of autonomous navigation of robots. An algorithm for robot Simultaneous Localization And Mapping (SLAM) based on self-detected waypoint is introduced to realize robot’s mapping in its area of interest. Robot’s next step waypoint is performed using characteristics of large information in the area of interest and dense landmark, clustering the landmark in the area of interest, and guiding robot’s movement with clustered central point. Robot clusters the observed area in its observation every time. It takes the clustered center based on the largest number of landmarks as the waypoint of the next step. Simulation experiment shows, that due to robot’s movement toward the area of dense landmarks, the proposed method increases the number of landmarks observed by the robot and frequency of observation is increased. The proposed method enhances accuracy of robot’s positioning and the robot realizes to detect its waypoint autonomously.

Mots clés

  • Robot
  • Simultaneous Localization And Mapping (SLAM)
  • K-means clustering
  • Extended Kalman Filter (EKF)
  • waypoint

Planifiez votre conférence à distance avec Sciendo