Zeitschriften und Ausgaben

Volumen 22 (2022): Heft 3 (September 2022)

Volumen 22 (2022): Heft 2 (June 2022)

Volumen 22 (2022): Heft 1 (March 2022)

Volumen 21 (2021): Heft 4 (December 2021)

Volumen 21 (2021): Heft 3 (September 2021)

Volumen 21 (2021): Heft 2 (June 2021)

Volumen 21 (2021): Heft 1 (March 2021)

Volumen 20 (2020): Heft 6 (December 2020)
Special Heft on New Developments in Scalable Computing

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

Volumen 20 (2020): Heft 4 (November 2020)

Volumen 20 (2020): Heft 3 (September 2020)

Volumen 20 (2020): Heft 2 (June 2020)

Volumen 20 (2020): Heft 1 (March 2020)

Volumen 19 (2019): Heft 4 (November 2019)

Volumen 19 (2019): Heft 3 (September 2019)

Volumen 19 (2019): Heft 2 (June 2019)

Volumen 19 (2019): Heft 1 (March 2019)

Volumen 18 (2018): Heft 5 (May 2018)
Special Thematic Heft on Optimal Codes and Related Topics

Volumen 18 (2018): Heft 4 (November 2018)

Volumen 18 (2018): Heft 3 (September 2018)

Volumen 18 (2018): Heft 2 (June 2018)

Volumen 18 (2018): Heft 1 (March 2018)

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

Volumen 17 (2017): Heft 4 (November 2017)

Volumen 17 (2017): Heft 3 (September 2017)

Volumen 17 (2017): Heft 2 (June 2017)

Volumen 17 (2017): Heft 1 (March 2017)

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

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

Volumen 16 (2016): Heft 4 (December 2016)

Volumen 16 (2016): Heft 3 (September 2016)

Volumen 16 (2016): Heft 2 (June 2016)

Volumen 16 (2016): Heft 1 (March 2016)

Volumen 15 (2015): Heft 7 (December 2015)
Special Heft on Information Fusion

Volumen 15 (2015): Heft 6 (December 2015)
Special Heft on Logistics, Informatics and Service Science

Volumen 15 (2015): Heft 5 (April 2015)
Special Heft on Control in Transportation Systems

Volumen 15 (2015): Heft 4 (November 2015)

Volumen 15 (2015): Heft 3 (September 2015)

Volumen 15 (2015): Heft 2 (June 2015)

Volumen 15 (2015): Heft 1 (March 2015)

Volumen 14 (2014): Heft 5 (December 2014)
Special Heft

Volumen 14 (2014): Heft 4 (December 2014)

Volumen 14 (2014): Heft 3 (September 2014)

Volumen 14 (2014): Heft 2 (June 2014)

Volumen 14 (2014): Heft 1 (March 2014)

Volumen 13 (2013): Heft Special-Heft (December 2013)

Volumen 13 (2013): Heft 4 (December 2013)
The publishing of the present issue (Volumen 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.

Volumen 13 (2013): Heft 3 (September 2013)

Volumen 13 (2013): Heft 2 (June 2013)

Volumen 13 (2013): Heft 1 (March 2013)

Volumen 12 (2012): Heft 4 (December 2012)

Volumen 12 (2012): Heft 3 (September 2012)

Volumen 12 (2012): Heft 2 (June 2012)

Volumen 12 (2012): Heft 1 (March 2012)

Zeitschriftendaten
Format
Zeitschrift
eISSN
1314-4081
Erstveröffentlichung
13 Mar 2012
Erscheinungsweise
4 Hefte pro Jahr
Sprachen
Englisch

Suche

Volumen 17 (2017): Heft 4 (November 2017)

Zeitschriftendaten
Format
Zeitschrift
eISSN
1314-4081
Erstveröffentlichung
13 Mar 2012
Erscheinungsweise
4 Hefte pro Jahr
Sprachen
Englisch

Suche

10 Artikel
Uneingeschränkter Zugang

Exploring Security Issues and Solutions in Cloud Computing Services – A Survey

Online veröffentlicht: 30 Nov 2017
Seitenbereich: 3 - 31

Zusammenfassung

Abstract

Cloud computing is emerging as one of the powerful computing technologies in the field of Information Technology due to its flexibility and cost reduction. This paper provides a detailed survey on security issues of the services provided by cloud computing and solutions to mitigate them. The main objective of this paper is to empower a new researcher to figure out the concepts of cloud computing, the services provided by them, and the security issues in the services. It also provides solutions to avoid or mitigate the different security issues which occur in the services provided by cloud computing. Additionally, it provides insight into the cloud computing model proposed by the National Institute of Standards and Technology (NIST), data stages and data security basics in a multi-tenant environment. This paper explores the different security methods proposed by different researchers and analyzes them.

Schlüsselwörter

  • Cloud computing
  • cloud services
  • cloud computing model
  • cloud security
  • cloud security solutions
Uneingeschränkter Zugang

Mitigation of Distributed Denial of Service Attacks in the Cloud

Online veröffentlicht: 30 Nov 2017
Seitenbereich: 32 - 51

Zusammenfassung

Abstract

Cybersecurity attacks resulting in loss of availability of cloud services can have significantly higher impact than those in the traditional stand-alone enterprise setups. Therefore, availability attacks, such as Denial of Service attacks (DoS); Distributed DoS attacks (DDoS) and Economical Denial of Sustainability (EDoS) attacks receive increasingly more attention. This paper surveys existing DDoS attacks analyzing the principles, ways of launching and their variants. Then, current mitigation systems are critically discussed. Based on the identification of the weak points, the paper proposes a new mitigation system named as DDoS-Mitigation System (DDoS-MS) that attempts to overcome the identified gap. The proposed framework is evaluated, and an enhanced version of the proposed system called Enhanced DDoS-MS is presented. In the end, the paper presents some future directions of the proposed framework.

Schlüsselwörter

  • Information processes
  • cloud computing
  • security
  • denial of service
  • distributed denial of service attacks
  • economical denial of sustainability
Uneingeschränkter Zugang

Fuzzy Bio-Inspired Hybrid Techniques for Server Consolidation and Virtual Machine Placement in Cloud Environment

Online veröffentlicht: 30 Nov 2017
Seitenbereich: 52 - 68

Zusammenfassung

Abstract

Cloud computing technology has transformed the information and communication technology industry by authorizing on-demand resource delivery to the cloud users. Datacenters are the major resource storage places from where the resources are disseminated to the requesters. When several requests are received by datacenters, the available resources are to be handled in an optimized way; otherwise the datacenters suffer from resource wastage. Virtualization is the technology that helps the cloud providers to handle several requests in an optimized way. In this regard, virtual machine placement, i.e., the process of mapping virtual machines to physical machines is considered to be the major research issue. In this paper, we propose to apply fuzzy hybrid bio-inspired meta-heuristic techniques for solving the virtual machine placement problem. The cuckoo search technique is hybridized with the fuzzy ant colony optimization and fuzzy firefly colony optimization technique. The experimental results obtained show competing performance of the proposed algorithms.

Schlüsselwörter

  • cloud computing
  • virtual machine placement
  • server consolidation
  • power consumption
  • resource wastage
  • cuckoo
  • ant colony system
  • firefly colony
Uneingeschränkter Zugang

Security Solution for ARP Cache Poisoning Attacks in Large Data Centre Networks

Online veröffentlicht: 30 Nov 2017
Seitenbereich: 69 - 86

Zusammenfassung

Abstract

The bridge protocol (Address Resolution Protocol) ARP, integrating Ethernet (Layer 2) and IP protocol (Layer 3) plays a vital role in TCP/IP communication since ARP packet is the first packet generated during any TCP/IP communications and they are the first traffic from the host. In the large data center, as the size of the broadcast domain (i.e., number of hosts on the network) increases consequently the broadcast traffic from the communication protocols like ARP also increases. This paper addresses the problem faced by Layer 2 protocols like insecured communication, scalability issues and VM migration issues. The proposed system addresses these issues by introducing two new types of messaging with traditional ARP and also combat the ARP Cache poisoning attacks like host impersonation, MITM, Distributed DoS by making ARP stateful. The components of the proposed methodology first start the process by decoding the packets, updates the invalid entry made by the user with Timestamp feature and messages being introduced. The system has been implemented and compared with various existing solutions.

Schlüsselwörter

  • Large data center networks
  • Broadcast storms
  • VM migration
  • Timestamp
  • ARP cache poisoning attacks
Uneingeschränkter Zugang

New Formal Description of Expert Views of Black-Litterman Asset Allocation Model

Online veröffentlicht: 30 Nov 2017
Seitenbereich: 87 - 98

Zusammenfassung

Abstract

The general contribution of this research is the implementation of new formal type of relative view, which has been added to the Black-Litterman Model (BLM) for asset management. It is well known that the BLM integrates both historical data about the assets’ returns and subjective views given by experts and investors. Such complicated model is expected to give more realistic assessment about the dynamical behavior of the stock exchanges. The BLM applies both absolute and relative views about the asset returns. The paper proves that the currently applied relative views with equal weights are equivalent to assess the risk of a virtual portfolio with these assets of the view which participate with equal weights. The paper extends this form of views, applying non-equal weights of the assets. This new formal description has been tested on a market, containing ten world known indices for a 10 years period. The calculations which have been provided give benefits to the suggested non-equal weighted form of subjective views. It gives more conservative results and decreases the portfolio risk supporting the same level of returns, provided by the average market behavior.

Schlüsselwörter

  • optimization of assets allocation
  • modeling market behavior
  • assessment of portfolio risks and returns
  • formal description of subjective views
Uneingeschränkter Zugang

Application of Genetic Algorithm Based Intuitionistic Fuzzy k-Mode for Clustering Categorical Data

Online veröffentlicht: 30 Nov 2017
Seitenbereich: 99 - 113

Zusammenfassung

Abstract

In present times a great number of clustering algorithms are available which group objects having similar features. But most of the datasets have data values that are categorical, which makes it difficult to implement these algorithms. The concept of genetic algorithm on intuitionistic fuzzy k-Mode method is proposed in the paper to cluster categorical data. This model is an extension of intuitionistic fuzzy k-Mode in which the notion of fitness related objective functions, crossovers, mutations and probability has been added to provide better clusters for the data objects. Also the intuitionistic parameter has been retained for the calculation of membership values of element x in a given cluster. UCI repository datasets were used for assessing efficacy of algorithms. The qualified analysis and results depict much consistent performance, where a significant improvement is achieved as compared to intuitionistic fuzzy k-Mode and simulated annealing based intuitionistic fuzzy k-mode. Genetic Algorithm based intuitionistic fuzzy k-Mode is very efficient when clustering is applied on large datasets that are categorical in nature, which proves to be very critical for data mining processes.

Schlüsselwörter

  • Categorical data
  • clustering
  • Data Mining
  • intuitionistic fuzzy k-Mode
  • simulated annealing
  • Genetic Algorithm
Uneingeschränkter Zugang

LTSD and GDMD features for Telephone Speech Endpoint Detection

Online veröffentlicht: 30 Nov 2017
Seitenbereich: 114 - 133

Zusammenfassung

Abstract

This paper proposes a new contour-based speech endpoint detector which combines the log-Group Delay Mean-Delta (log-GDMD) feature, an adaptive twothreshold scheme and an eight-state automaton. The adaptive thresholds scheme uses two pairs of thresholds - for the starting and for the ending points, respectively. Each pair of thresholds is calculated by using the contour characteristics in the corresponded region of the utterance. The experimental results have shown that the proposed detector demonstrates better performance compared to the Long-Term Spectral Divergence (LTSD) one in terms of endpoint accuracy. Additional fixed-text speaker verification tests with short phrases of telephone speech based on the Dynamic Time Warping (DTW) and left-to-right Hidden Markov Model (HMM) frameworks confirm the improvements of the verification rate due to the better endpoint accuracy.

Schlüsselwörter

  • endpoint detection
  • long-term spectral divergence
  • group delay spectrum
Uneingeschränkter Zugang

A Survey on Key(s) and Keyless Image Encryption Techniques

Online veröffentlicht: 30 Nov 2017
Seitenbereich: 134 - 164

Zusammenfassung

Abstract

As in recent years digital data transmission and image application have been increasing, maintaining secure transmission of image is of high importance. Image Encryption is implemented to achieve security on image applications. This paper exhibits a survey on various existing image encryption techniques. The paper mainly focuses on two types: Image encryption with Key(s) and Image Encryption without Key(s). In addition it also describes several properties of a good image encryption technique. The paper presents a survey of most popular algorithms and research papers that are related with different image encryption techniques.

Schlüsselwörter

  • Chaotic sequence
  • image scrambling
  • error diffusion
  • visual cryptography
  • random grid
Uneingeschränkter Zugang

Robust Active Contour Model Guided by Local Binary Pattern Stopping Function

Online veröffentlicht: 30 Nov 2017
Seitenbereich: 165 - 182

Zusammenfassung

Abstract

Edge based active contour models are adequate to some extent in segmenting images with intensity inhomogeneity but often fail when applied to images with poorly defined or noisy boundaries. Instead of the classical and widely used gradient or edge stopping function which fails to stop contour evolution at such boundaries, we use local binary pattern stopping function to construct a robust and effective active contour model for image segmentation. In fact, comparing to edge stopping function, local binary pattern stopping function accurately distinguishes object’s boundaries and determines the local intensity variation dint to the local binary pattern textons used to classify the image regions. Moreover, the local binary pattern stopping function is applied using a variational level set formulation that forces the level set function to be close to a signed distance function to eliminate costly re-initialization and speed up the motion of the curve. Experiments on several gray level images confirm the advantages and the effectiveness the proposed model.

Schlüsselwörter

  • Active contour models
  • edge stopping function
  • image segmentation
  • local binary pattern
Uneingeschränkter Zugang

ISAR Image Recognition Algorithm and Neural Network Implementation

Online veröffentlicht: 30 Nov 2017
Seitenbereich: 183 - 199

Zusammenfassung

Abstract

The image recognition and identification procedures are comparatively new in the scope of ISAR (Inverse Synthetic Aperture Radar) applications and based on specific defects in ISAR images, e.g., missing pixels and parts of the image induced by target’s aspect angles require preliminary image processing before identification. The present paper deals with ISAR image enhancement algorithms and neural network architecture for image recognition and target identification. First, stages of the image processing algorithms intended for image improving and contour line extraction are discussed. Second, an algorithm for target recognition is developed based on neural network architecture. Two Learning Vector Quantization (LVQ) neural networks are constructed in Matlab program environment. A training algorithm by teacher is applied. Final identification decision strategy is developed. Results of numerical experiments are presented.

Schlüsselwörter

  • Inverse synthetic aperture radar
  • ISAR imaging
  • image processing
  • neural network recognition
10 Artikel
Uneingeschränkter Zugang

Exploring Security Issues and Solutions in Cloud Computing Services – A Survey

Online veröffentlicht: 30 Nov 2017
Seitenbereich: 3 - 31

Zusammenfassung

Abstract

Cloud computing is emerging as one of the powerful computing technologies in the field of Information Technology due to its flexibility and cost reduction. This paper provides a detailed survey on security issues of the services provided by cloud computing and solutions to mitigate them. The main objective of this paper is to empower a new researcher to figure out the concepts of cloud computing, the services provided by them, and the security issues in the services. It also provides solutions to avoid or mitigate the different security issues which occur in the services provided by cloud computing. Additionally, it provides insight into the cloud computing model proposed by the National Institute of Standards and Technology (NIST), data stages and data security basics in a multi-tenant environment. This paper explores the different security methods proposed by different researchers and analyzes them.

Schlüsselwörter

  • Cloud computing
  • cloud services
  • cloud computing model
  • cloud security
  • cloud security solutions
Uneingeschränkter Zugang

Mitigation of Distributed Denial of Service Attacks in the Cloud

Online veröffentlicht: 30 Nov 2017
Seitenbereich: 32 - 51

Zusammenfassung

Abstract

Cybersecurity attacks resulting in loss of availability of cloud services can have significantly higher impact than those in the traditional stand-alone enterprise setups. Therefore, availability attacks, such as Denial of Service attacks (DoS); Distributed DoS attacks (DDoS) and Economical Denial of Sustainability (EDoS) attacks receive increasingly more attention. This paper surveys existing DDoS attacks analyzing the principles, ways of launching and their variants. Then, current mitigation systems are critically discussed. Based on the identification of the weak points, the paper proposes a new mitigation system named as DDoS-Mitigation System (DDoS-MS) that attempts to overcome the identified gap. The proposed framework is evaluated, and an enhanced version of the proposed system called Enhanced DDoS-MS is presented. In the end, the paper presents some future directions of the proposed framework.

Schlüsselwörter

  • Information processes
  • cloud computing
  • security
  • denial of service
  • distributed denial of service attacks
  • economical denial of sustainability
Uneingeschränkter Zugang

Fuzzy Bio-Inspired Hybrid Techniques for Server Consolidation and Virtual Machine Placement in Cloud Environment

Online veröffentlicht: 30 Nov 2017
Seitenbereich: 52 - 68

Zusammenfassung

Abstract

Cloud computing technology has transformed the information and communication technology industry by authorizing on-demand resource delivery to the cloud users. Datacenters are the major resource storage places from where the resources are disseminated to the requesters. When several requests are received by datacenters, the available resources are to be handled in an optimized way; otherwise the datacenters suffer from resource wastage. Virtualization is the technology that helps the cloud providers to handle several requests in an optimized way. In this regard, virtual machine placement, i.e., the process of mapping virtual machines to physical machines is considered to be the major research issue. In this paper, we propose to apply fuzzy hybrid bio-inspired meta-heuristic techniques for solving the virtual machine placement problem. The cuckoo search technique is hybridized with the fuzzy ant colony optimization and fuzzy firefly colony optimization technique. The experimental results obtained show competing performance of the proposed algorithms.

Schlüsselwörter

  • cloud computing
  • virtual machine placement
  • server consolidation
  • power consumption
  • resource wastage
  • cuckoo
  • ant colony system
  • firefly colony
Uneingeschränkter Zugang

Security Solution for ARP Cache Poisoning Attacks in Large Data Centre Networks

Online veröffentlicht: 30 Nov 2017
Seitenbereich: 69 - 86

Zusammenfassung

Abstract

The bridge protocol (Address Resolution Protocol) ARP, integrating Ethernet (Layer 2) and IP protocol (Layer 3) plays a vital role in TCP/IP communication since ARP packet is the first packet generated during any TCP/IP communications and they are the first traffic from the host. In the large data center, as the size of the broadcast domain (i.e., number of hosts on the network) increases consequently the broadcast traffic from the communication protocols like ARP also increases. This paper addresses the problem faced by Layer 2 protocols like insecured communication, scalability issues and VM migration issues. The proposed system addresses these issues by introducing two new types of messaging with traditional ARP and also combat the ARP Cache poisoning attacks like host impersonation, MITM, Distributed DoS by making ARP stateful. The components of the proposed methodology first start the process by decoding the packets, updates the invalid entry made by the user with Timestamp feature and messages being introduced. The system has been implemented and compared with various existing solutions.

Schlüsselwörter

  • Large data center networks
  • Broadcast storms
  • VM migration
  • Timestamp
  • ARP cache poisoning attacks
Uneingeschränkter Zugang

New Formal Description of Expert Views of Black-Litterman Asset Allocation Model

Online veröffentlicht: 30 Nov 2017
Seitenbereich: 87 - 98

Zusammenfassung

Abstract

The general contribution of this research is the implementation of new formal type of relative view, which has been added to the Black-Litterman Model (BLM) for asset management. It is well known that the BLM integrates both historical data about the assets’ returns and subjective views given by experts and investors. Such complicated model is expected to give more realistic assessment about the dynamical behavior of the stock exchanges. The BLM applies both absolute and relative views about the asset returns. The paper proves that the currently applied relative views with equal weights are equivalent to assess the risk of a virtual portfolio with these assets of the view which participate with equal weights. The paper extends this form of views, applying non-equal weights of the assets. This new formal description has been tested on a market, containing ten world known indices for a 10 years period. The calculations which have been provided give benefits to the suggested non-equal weighted form of subjective views. It gives more conservative results and decreases the portfolio risk supporting the same level of returns, provided by the average market behavior.

Schlüsselwörter

  • optimization of assets allocation
  • modeling market behavior
  • assessment of portfolio risks and returns
  • formal description of subjective views
Uneingeschränkter Zugang

Application of Genetic Algorithm Based Intuitionistic Fuzzy k-Mode for Clustering Categorical Data

Online veröffentlicht: 30 Nov 2017
Seitenbereich: 99 - 113

Zusammenfassung

Abstract

In present times a great number of clustering algorithms are available which group objects having similar features. But most of the datasets have data values that are categorical, which makes it difficult to implement these algorithms. The concept of genetic algorithm on intuitionistic fuzzy k-Mode method is proposed in the paper to cluster categorical data. This model is an extension of intuitionistic fuzzy k-Mode in which the notion of fitness related objective functions, crossovers, mutations and probability has been added to provide better clusters for the data objects. Also the intuitionistic parameter has been retained for the calculation of membership values of element x in a given cluster. UCI repository datasets were used for assessing efficacy of algorithms. The qualified analysis and results depict much consistent performance, where a significant improvement is achieved as compared to intuitionistic fuzzy k-Mode and simulated annealing based intuitionistic fuzzy k-mode. Genetic Algorithm based intuitionistic fuzzy k-Mode is very efficient when clustering is applied on large datasets that are categorical in nature, which proves to be very critical for data mining processes.

Schlüsselwörter

  • Categorical data
  • clustering
  • Data Mining
  • intuitionistic fuzzy k-Mode
  • simulated annealing
  • Genetic Algorithm
Uneingeschränkter Zugang

LTSD and GDMD features for Telephone Speech Endpoint Detection

Online veröffentlicht: 30 Nov 2017
Seitenbereich: 114 - 133

Zusammenfassung

Abstract

This paper proposes a new contour-based speech endpoint detector which combines the log-Group Delay Mean-Delta (log-GDMD) feature, an adaptive twothreshold scheme and an eight-state automaton. The adaptive thresholds scheme uses two pairs of thresholds - for the starting and for the ending points, respectively. Each pair of thresholds is calculated by using the contour characteristics in the corresponded region of the utterance. The experimental results have shown that the proposed detector demonstrates better performance compared to the Long-Term Spectral Divergence (LTSD) one in terms of endpoint accuracy. Additional fixed-text speaker verification tests with short phrases of telephone speech based on the Dynamic Time Warping (DTW) and left-to-right Hidden Markov Model (HMM) frameworks confirm the improvements of the verification rate due to the better endpoint accuracy.

Schlüsselwörter

  • endpoint detection
  • long-term spectral divergence
  • group delay spectrum
Uneingeschränkter Zugang

A Survey on Key(s) and Keyless Image Encryption Techniques

Online veröffentlicht: 30 Nov 2017
Seitenbereich: 134 - 164

Zusammenfassung

Abstract

As in recent years digital data transmission and image application have been increasing, maintaining secure transmission of image is of high importance. Image Encryption is implemented to achieve security on image applications. This paper exhibits a survey on various existing image encryption techniques. The paper mainly focuses on two types: Image encryption with Key(s) and Image Encryption without Key(s). In addition it also describes several properties of a good image encryption technique. The paper presents a survey of most popular algorithms and research papers that are related with different image encryption techniques.

Schlüsselwörter

  • Chaotic sequence
  • image scrambling
  • error diffusion
  • visual cryptography
  • random grid
Uneingeschränkter Zugang

Robust Active Contour Model Guided by Local Binary Pattern Stopping Function

Online veröffentlicht: 30 Nov 2017
Seitenbereich: 165 - 182

Zusammenfassung

Abstract

Edge based active contour models are adequate to some extent in segmenting images with intensity inhomogeneity but often fail when applied to images with poorly defined or noisy boundaries. Instead of the classical and widely used gradient or edge stopping function which fails to stop contour evolution at such boundaries, we use local binary pattern stopping function to construct a robust and effective active contour model for image segmentation. In fact, comparing to edge stopping function, local binary pattern stopping function accurately distinguishes object’s boundaries and determines the local intensity variation dint to the local binary pattern textons used to classify the image regions. Moreover, the local binary pattern stopping function is applied using a variational level set formulation that forces the level set function to be close to a signed distance function to eliminate costly re-initialization and speed up the motion of the curve. Experiments on several gray level images confirm the advantages and the effectiveness the proposed model.

Schlüsselwörter

  • Active contour models
  • edge stopping function
  • image segmentation
  • local binary pattern
Uneingeschränkter Zugang

ISAR Image Recognition Algorithm and Neural Network Implementation

Online veröffentlicht: 30 Nov 2017
Seitenbereich: 183 - 199

Zusammenfassung

Abstract

The image recognition and identification procedures are comparatively new in the scope of ISAR (Inverse Synthetic Aperture Radar) applications and based on specific defects in ISAR images, e.g., missing pixels and parts of the image induced by target’s aspect angles require preliminary image processing before identification. The present paper deals with ISAR image enhancement algorithms and neural network architecture for image recognition and target identification. First, stages of the image processing algorithms intended for image improving and contour line extraction are discussed. Second, an algorithm for target recognition is developed based on neural network architecture. Two Learning Vector Quantization (LVQ) neural networks are constructed in Matlab program environment. A training algorithm by teacher is applied. Final identification decision strategy is developed. Results of numerical experiments are presented.

Schlüsselwörter

  • Inverse synthetic aperture radar
  • ISAR imaging
  • image processing
  • neural network recognition

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