A 3D object encryption scheme which maintains dimensional and spatial stability
- Jolfaei, Alireza, Wu, Xinwen, Muthukkumarasamy, Vallipuram
- Authors: Jolfaei, Alireza , Wu, Xinwen , Muthukkumarasamy, Vallipuram
- Date: 2015
- Type: Text , Journal article
- Relation: IEEE Transactions on Information Forensics and Security Vol. 10, no. 2 (2015), p. 409-422
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- Description: Due to widespread applications of 3D vision technology, the research into 3D object protection is primarily important. To maintain confidentiality, encryption of 3D objects is essential. However, the requirements and limitations imposed by 3D objects indicate the impropriety of conventional cryptosystems for 3D object encryption. This suggests the necessity of designing new ciphers. In addition, the study of prior works indicates that the majority of problems encountered with encrypting 3D objects are about point cloud protection, dimensional and spatial stability, and robustness against surface reconstruction attacks. To address these problems, this paper proposes a 3D object encryption scheme, based on a series of random permutations and rotations, which deform the geometry of the point cloud. Since the inverse of a permutation and a rotation matrix is its transpose, the decryption implementation is very efficient. Our statistical analyses show that within the cipher point cloud, points are randomly distributed. Furthermore, the proposed cipher leaks no information regarding the geometric structure of the plain point cloud, and is also highly sensitive to the changes of the plaintext and secret key. The theoretical and experimental analyses demonstrate the security, effectiveness, and robustness of the proposed cipher against surface reconstruction attacks.
- Authors: Jolfaei, Alireza , Wu, Xinwen , Muthukkumarasamy, Vallipuram
- Date: 2015
- Type: Text , Journal article
- Relation: IEEE Transactions on Information Forensics and Security Vol. 10, no. 2 (2015), p. 409-422
- Full Text:
- Reviewed:
- Description: Due to widespread applications of 3D vision technology, the research into 3D object protection is primarily important. To maintain confidentiality, encryption of 3D objects is essential. However, the requirements and limitations imposed by 3D objects indicate the impropriety of conventional cryptosystems for 3D object encryption. This suggests the necessity of designing new ciphers. In addition, the study of prior works indicates that the majority of problems encountered with encrypting 3D objects are about point cloud protection, dimensional and spatial stability, and robustness against surface reconstruction attacks. To address these problems, this paper proposes a 3D object encryption scheme, based on a series of random permutations and rotations, which deform the geometry of the point cloud. Since the inverse of a permutation and a rotation matrix is its transpose, the decryption implementation is very efficient. Our statistical analyses show that within the cipher point cloud, points are randomly distributed. Furthermore, the proposed cipher leaks no information regarding the geometric structure of the plain point cloud, and is also highly sensitive to the changes of the plaintext and secret key. The theoretical and experimental analyses demonstrate the security, effectiveness, and robustness of the proposed cipher against surface reconstruction attacks.
A comprehensive spectrum trading scheme based on market competition, reputation and buyer specific requirements
- Hassan, Md Rakib, Karmakar, Gour, Kamruzzaman, Joarder, Srinivasan, Bala
- Authors: Hassan, Md Rakib , Karmakar, Gour , Kamruzzaman, Joarder , Srinivasan, Bala
- Date: 2015
- Type: Text , Journal article
- Relation: Computer Networks Vol. 84, no. (2015), p. 17-31
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- Description: In the exclusive-use model of spectrum trading, cognitive radio devices or secondary users can buy spectrum resources from licensed users or primary users for a short or long period of time. Considering such spectrum access, a trading model is introduced where a buyer can select a set of candidate sellers based on their reputation and their offers in fulfilling its requirements, namely, offered signal quality, contract duration, coverage and bandwidth. Similarly, a seller can assess a buyer as a potential trading partner considering the buyer's reliability, which the seller can derive from the buyer's reputation and financial profile. In our scheme, seller reputation or buyer reliability can be either obtained from a reputation brokerage service, if one exists, or calculated using our model. Since in a competitive market, the price of a seller depends on that of other sellers, game theory is used to model the competition among multiple sellers. An optimization technique is used by a buyer to select the best seller(s) and optimize purchase to maximize its utility. This may result in buying from multiple sellers of certain amount of bandwidth from each, depending on price and meeting requirements and budget constraints. Stability of the model is analyzed and performance evaluation shows that it benefits sellers and buyers in terms of profit and throughput, respectively. © 2015 Elsevier B.V. All rights reserved.
- Authors: Hassan, Md Rakib , Karmakar, Gour , Kamruzzaman, Joarder , Srinivasan, Bala
- Date: 2015
- Type: Text , Journal article
- Relation: Computer Networks Vol. 84, no. (2015), p. 17-31
- Full Text:
- Reviewed:
- Description: In the exclusive-use model of spectrum trading, cognitive radio devices or secondary users can buy spectrum resources from licensed users or primary users for a short or long period of time. Considering such spectrum access, a trading model is introduced where a buyer can select a set of candidate sellers based on their reputation and their offers in fulfilling its requirements, namely, offered signal quality, contract duration, coverage and bandwidth. Similarly, a seller can assess a buyer as a potential trading partner considering the buyer's reliability, which the seller can derive from the buyer's reputation and financial profile. In our scheme, seller reputation or buyer reliability can be either obtained from a reputation brokerage service, if one exists, or calculated using our model. Since in a competitive market, the price of a seller depends on that of other sellers, game theory is used to model the competition among multiple sellers. An optimization technique is used by a buyer to select the best seller(s) and optimize purchase to maximize its utility. This may result in buying from multiple sellers of certain amount of bandwidth from each, depending on price and meeting requirements and budget constraints. Stability of the model is analyzed and performance evaluation shows that it benefits sellers and buyers in terms of profit and throughput, respectively. © 2015 Elsevier B.V. All rights reserved.
A critical review of intrusion detection systems in the internet of things : techniques, deployment strategy, validation strategy, attacks, public datasets and challenges
- Khraisat, Ansam, Alazab, Ammar
- Authors: Khraisat, Ansam , Alazab, Ammar
- Date: 2021
- Type: Text , Journal article
- Relation: Cybersecurity Vol. 4, no. 1 (2021), p.
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- Description: The Internet of Things (IoT) has been rapidly evolving towards making a greater impact on everyday life to large industrial systems. Unfortunately, this has attracted the attention of cybercriminals who made IoT a target of malicious activities, opening the door to a possible attack on the end nodes. To this end, Numerous IoT intrusion detection Systems (IDS) have been proposed in the literature to tackle attacks on the IoT ecosystem, which can be broadly classified based on detection technique, validation strategy, and deployment strategy. This survey paper presents a comprehensive review of contemporary IoT IDS and an overview of techniques, deployment Strategy, validation strategy and datasets that are commonly applied for building IDS. We also review how existing IoT IDS detect intrusive attacks and secure communications on the IoT. It also presents the classification of IoT attacks and discusses future research challenges to counter such IoT attacks to make IoT more secure. These purposes help IoT security researchers by uniting, contrasting, and compiling scattered research efforts. Consequently, we provide a unique IoT IDS taxonomy, which sheds light on IoT IDS techniques, their advantages and disadvantages, IoT attacks that exploit IoT communication systems, corresponding advanced IDS and detection capabilities to detect IoT attacks. © 2021, The Author(s).
- Authors: Khraisat, Ansam , Alazab, Ammar
- Date: 2021
- Type: Text , Journal article
- Relation: Cybersecurity Vol. 4, no. 1 (2021), p.
- Full Text:
- Reviewed:
- Description: The Internet of Things (IoT) has been rapidly evolving towards making a greater impact on everyday life to large industrial systems. Unfortunately, this has attracted the attention of cybercriminals who made IoT a target of malicious activities, opening the door to a possible attack on the end nodes. To this end, Numerous IoT intrusion detection Systems (IDS) have been proposed in the literature to tackle attacks on the IoT ecosystem, which can be broadly classified based on detection technique, validation strategy, and deployment strategy. This survey paper presents a comprehensive review of contemporary IoT IDS and an overview of techniques, deployment Strategy, validation strategy and datasets that are commonly applied for building IDS. We also review how existing IoT IDS detect intrusive attacks and secure communications on the IoT. It also presents the classification of IoT attacks and discusses future research challenges to counter such IoT attacks to make IoT more secure. These purposes help IoT security researchers by uniting, contrasting, and compiling scattered research efforts. Consequently, we provide a unique IoT IDS taxonomy, which sheds light on IoT IDS techniques, their advantages and disadvantages, IoT attacks that exploit IoT communication systems, corresponding advanced IDS and detection capabilities to detect IoT attacks. © 2021, The Author(s).
A fully automated CAD system using multi-category feature selection with restricted recombination
- Ghosh, Ranadhir, Ghosh, Moumita, Yearwood, John, Mukherjee, Subhasis
- Authors: Ghosh, Ranadhir , Ghosh, Moumita , Yearwood, John , Mukherjee, Subhasis
- Date: 2007
- Type: Text , Conference paper
- Relation: Paper presented at 6th IEEE/ACIS International Conference on Computer and Information Science, ICIS 2007, Melbourne, Victoria : 11th-13th July 2007 p. 106-111
- Full Text:
- Description: In pattern recognition problems features plays an important role for classification results. It is very important which features are used and how many features are used for the classification process. Most of the real life classification problem uses different category of features. It is desirable to find the optimal combination of features that improves the performance of the classifier. There exists different selection framework that selects the features. Mostly do not incorporate the impact of one category of features on another. Even if they incorporate, they produce conflict between the categories. In this paper we proposed a restricted crossover selection framework which incorporate the impact of different categories on each other, as well as it restricts the search within the category which searching in the global region of the search space. The results obtained by the proposed framework are promising.
- Description: 2003005429
- Authors: Ghosh, Ranadhir , Ghosh, Moumita , Yearwood, John , Mukherjee, Subhasis
- Date: 2007
- Type: Text , Conference paper
- Relation: Paper presented at 6th IEEE/ACIS International Conference on Computer and Information Science, ICIS 2007, Melbourne, Victoria : 11th-13th July 2007 p. 106-111
- Full Text:
- Description: In pattern recognition problems features plays an important role for classification results. It is very important which features are used and how many features are used for the classification process. Most of the real life classification problem uses different category of features. It is desirable to find the optimal combination of features that improves the performance of the classifier. There exists different selection framework that selects the features. Mostly do not incorporate the impact of one category of features on another. Even if they incorporate, they produce conflict between the categories. In this paper we proposed a restricted crossover selection framework which incorporate the impact of different categories on each other, as well as it restricts the search within the category which searching in the global region of the search space. The results obtained by the proposed framework are promising.
- Description: 2003005429
A lightweight integrity protection scheme for low latency smart grid applications
- Jolfaei, Alireza, Kant, Krishna
- Authors: Jolfaei, Alireza , Kant, Krishna
- Date: 2019
- Type: Text , Journal article
- Relation: Computers and Security Vol. 86, no. (2019), p. 471-483
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- Reviewed:
- Description: The substation communication protocol used in smart grid allows the transmission of messages without integrity protection for applications that require very low communication latency. This leaves the real-time measurements taken by phasor measurement units (PMUs) vulnerable to man-in-the-middle attacks, and hence makes high voltage to medium voltage (HV/MV) substations vulnerable to cyber-attacks. In this paper, a lightweight and secure integrity protection algorithm has been proposed to maintain the integrity of PMU data, which fills the missing integrity protection in the IEC 61850-90-5 standard, when the MAC identifier is declared 0. The rigorous security analysis proves the security of the proposed integrity protection method against ciphertext-only attacks and known/chosen plaintext attacks. A comparison with existing integrity protection methods shows that our method is much faster, and is also the only integrity protection scheme that meets the strict timing requirement. Not only the proposed method can be used in power protection applications, but it also can be used in emerging anomaly detection scenarios, where a fast integrity check coupled with low latency communications is used for multiple rounds of message exchanges. This paper is an extension of work originally reported in Proceedings of 14th International Conference on Security and Cryptography (Jolfaei and Kant, 2017).
- Authors: Jolfaei, Alireza , Kant, Krishna
- Date: 2019
- Type: Text , Journal article
- Relation: Computers and Security Vol. 86, no. (2019), p. 471-483
- Full Text:
- Reviewed:
- Description: The substation communication protocol used in smart grid allows the transmission of messages without integrity protection for applications that require very low communication latency. This leaves the real-time measurements taken by phasor measurement units (PMUs) vulnerable to man-in-the-middle attacks, and hence makes high voltage to medium voltage (HV/MV) substations vulnerable to cyber-attacks. In this paper, a lightweight and secure integrity protection algorithm has been proposed to maintain the integrity of PMU data, which fills the missing integrity protection in the IEC 61850-90-5 standard, when the MAC identifier is declared 0. The rigorous security analysis proves the security of the proposed integrity protection method against ciphertext-only attacks and known/chosen plaintext attacks. A comparison with existing integrity protection methods shows that our method is much faster, and is also the only integrity protection scheme that meets the strict timing requirement. Not only the proposed method can be used in power protection applications, but it also can be used in emerging anomaly detection scenarios, where a fast integrity check coupled with low latency communications is used for multiple rounds of message exchanges. This paper is an extension of work originally reported in Proceedings of 14th International Conference on Security and Cryptography (Jolfaei and Kant, 2017).
A low-complexity equalizer for video broadcasting in cyber-physical social systems through handheld mobile devices
- Solyman, Ahmad, Attar, Hani, Khosravi, Mohammad, Menon, Varun, Jolfaei, Alireza, Balasubramanian, Venki, Selvaraj, Buvana, Tavallali, Pooya
- Authors: Solyman, Ahmad , Attar, Hani , Khosravi, Mohammad , Menon, Varun , Jolfaei, Alireza , Balasubramanian, Venki , Selvaraj, Buvana , Tavallali, Pooya
- Date: 2020
- Type: Text , Journal article
- Relation: IEEE Access Vol. 8, no. (2020), p. 67591-67602
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- Description: In Digital Video Broadcasting-Handheld (DVB-H) devices for cyber-physical social systems, the Discrete Fractional Fourier Transform-Orthogonal Chirp Division Multiplexing (DFrFT-OCDM) has been suggested to enhance the performance over Orthogonal Frequency Division Multiplexing (OFDM) systems under time and frequency-selective fading channels. In this case, the need for equalizers like the Minimum Mean Square Error (MMSE) and Zero-Forcing (ZF) arises, though it is excessively complex due to the need for a matrix inversion, especially for DVB-H extensive symbol lengths. In this work, a low complexity equalizer, Least-Squares Minimal Residual (LSMR) algorithm, is used to solve the matrix inversion iteratively. The paper proposes the LSMR algorithm for linear and nonlinear equalizers with the simulation results, which indicate that the proposed equalizer has significant performance and reduced complexity over the classical MMSE equalizer and other low complexity equalizers, in time and frequency-selective fading channels. © 2013 IEEE.
- Authors: Solyman, Ahmad , Attar, Hani , Khosravi, Mohammad , Menon, Varun , Jolfaei, Alireza , Balasubramanian, Venki , Selvaraj, Buvana , Tavallali, Pooya
- Date: 2020
- Type: Text , Journal article
- Relation: IEEE Access Vol. 8, no. (2020), p. 67591-67602
- Full Text:
- Reviewed:
- Description: In Digital Video Broadcasting-Handheld (DVB-H) devices for cyber-physical social systems, the Discrete Fractional Fourier Transform-Orthogonal Chirp Division Multiplexing (DFrFT-OCDM) has been suggested to enhance the performance over Orthogonal Frequency Division Multiplexing (OFDM) systems under time and frequency-selective fading channels. In this case, the need for equalizers like the Minimum Mean Square Error (MMSE) and Zero-Forcing (ZF) arises, though it is excessively complex due to the need for a matrix inversion, especially for DVB-H extensive symbol lengths. In this work, a low complexity equalizer, Least-Squares Minimal Residual (LSMR) algorithm, is used to solve the matrix inversion iteratively. The paper proposes the LSMR algorithm for linear and nonlinear equalizers with the simulation results, which indicate that the proposed equalizer has significant performance and reduced complexity over the classical MMSE equalizer and other low complexity equalizers, in time and frequency-selective fading channels. © 2013 IEEE.
A machine vision based automatic optical inspection system for measuring drilling quality of printed circuit boards
- Wang, Wei, Chen, Shang-Liang, Chen, Liang-Bi, Chang, Wan-Jung
- Authors: Wang, Wei , Chen, Shang-Liang , Chen, Liang-Bi , Chang, Wan-Jung
- Date: 2017
- Type: Text , Journal article
- Relation: IEEE Access Vol. 5, no. (2017), p. 10817-10833
- Full Text:
- Reviewed:
- Description: In this paper, we develop and put into practice an automatic optical inspection (AOI) system based on machine vision to check the holes on a printed circuit board (PCB). We incorporate the hardware and software. For the hardware part, we combine a PC, the three-axis positioning system, a lighting device, and charge-coupled device cameras. For the software part, we utilize image registration, image segmentation, drill numbering, drill contrast, and defect displays to achieve this system. Results indicated that an accuracy of 5 mu m could be achieved in errors of the PCB holes allowing comparisons to be made. This is significant in inspecting the missing, the multi-hole, and the incorrect location of the holes. However, previous work only focuses on one or other feature of the holes. Our research is able to assess multiple features: missing holes, incorrectly located holes, and excessive holes. Equally, our results could be displayed as a bar chart and target plot. This has not been achieved before. These displays help users to analyze the causes of errors and immediately correct the problems. In addition, this AOI system is valuable for checking a large number of holes and finding out the defective ones on a PCB. Meanwhile, we apply a 0.1-mm image resolution, which is better than others used in industry. We set a detecting standard based on 2-mm diameter of circles to diagnose the quality of the holes within 10 s.
- Authors: Wang, Wei , Chen, Shang-Liang , Chen, Liang-Bi , Chang, Wan-Jung
- Date: 2017
- Type: Text , Journal article
- Relation: IEEE Access Vol. 5, no. (2017), p. 10817-10833
- Full Text:
- Reviewed:
- Description: In this paper, we develop and put into practice an automatic optical inspection (AOI) system based on machine vision to check the holes on a printed circuit board (PCB). We incorporate the hardware and software. For the hardware part, we combine a PC, the three-axis positioning system, a lighting device, and charge-coupled device cameras. For the software part, we utilize image registration, image segmentation, drill numbering, drill contrast, and defect displays to achieve this system. Results indicated that an accuracy of 5 mu m could be achieved in errors of the PCB holes allowing comparisons to be made. This is significant in inspecting the missing, the multi-hole, and the incorrect location of the holes. However, previous work only focuses on one or other feature of the holes. Our research is able to assess multiple features: missing holes, incorrectly located holes, and excessive holes. Equally, our results could be displayed as a bar chart and target plot. This has not been achieved before. These displays help users to analyze the causes of errors and immediately correct the problems. In addition, this AOI system is valuable for checking a large number of holes and finding out the defective ones on a PCB. Meanwhile, we apply a 0.1-mm image resolution, which is better than others used in industry. We set a detecting standard based on 2-mm diameter of circles to diagnose the quality of the holes within 10 s.
A method to improve transparency of electronic election process without identification
- Alamuti, Roghayeh, Barjini, Hassan, Khandelwal, Manoj, Jafarabad, Mohammad
- Authors: Alamuti, Roghayeh , Barjini, Hassan , Khandelwal, Manoj , Jafarabad, Mohammad
- Date: 2015
- Type: Text , Conference proceedings
- Full Text:
- Description: Transparency of bank accounts, nowadays, is an undeniable necessity, but no one denies that definite transparency throughout election process is not realized thus far in the world. This calls for fundamental changes in traditional electronic election methods. The new method must close the way for any complaints by the candidate as to the voting process as the public completely trusts in the voting mechanism. Synchronizing voting and votes counting improves the public's trust in the results of election. The proposed secure room-corridor of electronic voting employs election watchers and reports real time results of election along with observance of confidentiality of the votes. © 2015 The Authors.
- Authors: Alamuti, Roghayeh , Barjini, Hassan , Khandelwal, Manoj , Jafarabad, Mohammad
- Date: 2015
- Type: Text , Conference proceedings
- Full Text:
- Description: Transparency of bank accounts, nowadays, is an undeniable necessity, but no one denies that definite transparency throughout election process is not realized thus far in the world. This calls for fundamental changes in traditional electronic election methods. The new method must close the way for any complaints by the candidate as to the voting process as the public completely trusts in the voting mechanism. Synchronizing voting and votes counting improves the public's trust in the results of election. The proposed secure room-corridor of electronic voting employs election watchers and reports real time results of election along with observance of confidentiality of the votes. © 2015 The Authors.
A model of the circadian clock in the cyanobacterium Cyanothece sp. ATCC 51142
- Nguyen, Vinh, Chetty, Madhu, Coppel, Ross, Gaudana, Sandeep, Wangikar, Pramod
- Authors: Nguyen, Vinh , Chetty, Madhu , Coppel, Ross , Gaudana, Sandeep , Wangikar, Pramod
- Date: 2013
- Type: Text , Journal article
- Relation: BMC Bioinformatics Vol. 14, no. (Supplement 2) (2013), p. s14-1-s14-9
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- Description: Background The over consumption of fossil fuels has led to growing concerns over climate change and global warming. Increasing research activities have been carried out towards alternative viable biofuel sources. Of several different biofuel platforms, cyanobacteria possess great potential, for their ability to accumulate biomass tens of times faster than traditional oilseed crops. The cyanobacterium Cyanothece sp. ATCC 51142 has recently attracted lots of research interest as a model organism for such research. Cyanothece can perform efficiently both photosynthesis and nitrogen fixation within the same cell, and has been recently shown to produce biohydrogen--a byproduct of nitrogen fixation--at very high rates of several folds higher than previously described hydrogen-producing photosynthetic microbes. Since the key enzyme for nitrogen fixation is very sensitive to oxygen produced by photosynthesis, Cyanothece employs a sophisticated temporal separation scheme, where nitrogen fixation occurs at night and photosynthesis at day. At the core of this temporal separation scheme is a robust clocking mechanism, which so far has not been thoroughly studied. Understanding how this circadian clock interacts with and harmonizes global transcription of key cellular processes is one of the keys to realize the inherent potential of this organism. Results In this paper, we employ several state of the art bioinformatics techniques for studying the core circadian clock in Cyanothece sp. ATCC 51142, and its interactions with other key cellular processes. We employ comparative genomics techniques to map the circadian clock genes and genetic interactions from another cyanobacterial species, namely Synechococcus elongatus PCC 7942, of which the circadian clock has been much more thoroughly investigated. Using time series gene expression data for Cyanothece, we employ gene regulatory network reconstruction techniques to learn this network de novo, and compare the reconstructed network against the interactions currently reported in the literature. Next, we build a computational model of the interactions between the core clock and other cellular processes, and show how this model can predict the behaviour of the system under changing environmental conditions. The constructed models significantly advance our understanding of the Cyanothece circadian clock functional mechanisms.
- Authors: Nguyen, Vinh , Chetty, Madhu , Coppel, Ross , Gaudana, Sandeep , Wangikar, Pramod
- Date: 2013
- Type: Text , Journal article
- Relation: BMC Bioinformatics Vol. 14, no. (Supplement 2) (2013), p. s14-1-s14-9
- Full Text:
- Reviewed:
- Description: Background The over consumption of fossil fuels has led to growing concerns over climate change and global warming. Increasing research activities have been carried out towards alternative viable biofuel sources. Of several different biofuel platforms, cyanobacteria possess great potential, for their ability to accumulate biomass tens of times faster than traditional oilseed crops. The cyanobacterium Cyanothece sp. ATCC 51142 has recently attracted lots of research interest as a model organism for such research. Cyanothece can perform efficiently both photosynthesis and nitrogen fixation within the same cell, and has been recently shown to produce biohydrogen--a byproduct of nitrogen fixation--at very high rates of several folds higher than previously described hydrogen-producing photosynthetic microbes. Since the key enzyme for nitrogen fixation is very sensitive to oxygen produced by photosynthesis, Cyanothece employs a sophisticated temporal separation scheme, where nitrogen fixation occurs at night and photosynthesis at day. At the core of this temporal separation scheme is a robust clocking mechanism, which so far has not been thoroughly studied. Understanding how this circadian clock interacts with and harmonizes global transcription of key cellular processes is one of the keys to realize the inherent potential of this organism. Results In this paper, we employ several state of the art bioinformatics techniques for studying the core circadian clock in Cyanothece sp. ATCC 51142, and its interactions with other key cellular processes. We employ comparative genomics techniques to map the circadian clock genes and genetic interactions from another cyanobacterial species, namely Synechococcus elongatus PCC 7942, of which the circadian clock has been much more thoroughly investigated. Using time series gene expression data for Cyanothece, we employ gene regulatory network reconstruction techniques to learn this network de novo, and compare the reconstructed network against the interactions currently reported in the literature. Next, we build a computational model of the interactions between the core clock and other cellular processes, and show how this model can predict the behaviour of the system under changing environmental conditions. The constructed models significantly advance our understanding of the Cyanothece circadian clock functional mechanisms.
A multilevel longitudinal study of experiencing virtual presence in adolescence : The role of anxiety and openness to experience in the classroom
- Stavropoulos, Vasileios, Wilson, Peter, Kuss, Daria, Griffiths, Mark, Gentile, Douglas
- Authors: Stavropoulos, Vasileios , Wilson, Peter , Kuss, Daria , Griffiths, Mark , Gentile, Douglas
- Date: 2017
- Type: Text , Journal article
- Relation: Behaviour & Information Technology Vol. 36, no. 5 (2017), p. 524-539
- Full Text:
- Reviewed:
- Description: Presence describes the feeling of reality and immersion that users of virtual/Internet environments have. Importantly, it has been suggested that there are individual and contextual differences regarding susceptibility to presence. These aspects of presence have been linked to both beneficial and disadvantageous uses of the Internet, such as online therapeutic applications and addictive Internet behaviours. In the present study, presence was studied in relation to individual anxiety symptoms and classroom-level openness to experience (OTE) using a normative sample of 648 adolescents aged between 16 and 18 years. Presence was assessed with the Presence II questionnaire, anxiety symptoms with the relevant subscales of the SCL-90-R, and OTE with the Five-Factor Questionnaire. A three-level hierarchical linear model was calculated. Results showed that experiencing presence in virtual environments dropped between the ages of 16 and 18 years. Additionally, although anxiety symptoms were associated with higher presence at 16 years, this association decreased with age. Results also demonstrated that adolescents in classrooms higher on OTE reported reduced level of experiencing presence. The practical and theoretical implications of these findings are discussed.
- Authors: Stavropoulos, Vasileios , Wilson, Peter , Kuss, Daria , Griffiths, Mark , Gentile, Douglas
- Date: 2017
- Type: Text , Journal article
- Relation: Behaviour & Information Technology Vol. 36, no. 5 (2017), p. 524-539
- Full Text:
- Reviewed:
- Description: Presence describes the feeling of reality and immersion that users of virtual/Internet environments have. Importantly, it has been suggested that there are individual and contextual differences regarding susceptibility to presence. These aspects of presence have been linked to both beneficial and disadvantageous uses of the Internet, such as online therapeutic applications and addictive Internet behaviours. In the present study, presence was studied in relation to individual anxiety symptoms and classroom-level openness to experience (OTE) using a normative sample of 648 adolescents aged between 16 and 18 years. Presence was assessed with the Presence II questionnaire, anxiety symptoms with the relevant subscales of the SCL-90-R, and OTE with the Five-Factor Questionnaire. A three-level hierarchical linear model was calculated. Results showed that experiencing presence in virtual environments dropped between the ages of 16 and 18 years. Additionally, although anxiety symptoms were associated with higher presence at 16 years, this association decreased with age. Results also demonstrated that adolescents in classrooms higher on OTE reported reduced level of experiencing presence. The practical and theoretical implications of these findings are discussed.
A new cascaded multilevel inverter topology with galvanic isolation
- Hasan, Mubashwar, Abu-Siada, Ahmed, Islam, Syed, Dahidah, Mohamed
- Authors: Hasan, Mubashwar , Abu-Siada, Ahmed , Islam, Syed , Dahidah, Mohamed
- Date: 2018
- Type: Text , Journal article
- Relation: IEEE Transactions on Industry Applications Vol. 54, no. 4 (2018), p. 3463-3472
- Full Text:
- Reviewed:
- Description: IEEE This paper presents a new compact three-phase cascaded multilevel inverter (CMLI) topology with reduced device count and high frequency magnetic link. The proposed topology overcomes the predominant limitation of separate DC power supplies, which CMLI always require. The high frequency magnetic link also provides a galvanic isolation between the input and output sides of the inverter, which is essential for various grid-connected applications. The proposed topology utilizes an asymmetric inverter configuration that consists of cascaded H-bridge cells and a conventional three-phase two-level inverter. A toroidal core is employed for the high frequency magnetic link to ensure compact size and high-power density. Compared with counterpart CMLI topologies available in the literatures, the proposed inverter has the advantage of utilizing the least number of power electronic components without compromising the overall performance, particularly when a high number of output voltage levels is required. The feasibility of the proposed inverter is confirmed through extensive simulation and experimentally validated studies.
- Authors: Hasan, Mubashwar , Abu-Siada, Ahmed , Islam, Syed , Dahidah, Mohamed
- Date: 2018
- Type: Text , Journal article
- Relation: IEEE Transactions on Industry Applications Vol. 54, no. 4 (2018), p. 3463-3472
- Full Text:
- Reviewed:
- Description: IEEE This paper presents a new compact three-phase cascaded multilevel inverter (CMLI) topology with reduced device count and high frequency magnetic link. The proposed topology overcomes the predominant limitation of separate DC power supplies, which CMLI always require. The high frequency magnetic link also provides a galvanic isolation between the input and output sides of the inverter, which is essential for various grid-connected applications. The proposed topology utilizes an asymmetric inverter configuration that consists of cascaded H-bridge cells and a conventional three-phase two-level inverter. A toroidal core is employed for the high frequency magnetic link to ensure compact size and high-power density. Compared with counterpart CMLI topologies available in the literatures, the proposed inverter has the advantage of utilizing the least number of power electronic components without compromising the overall performance, particularly when a high number of output voltage levels is required. The feasibility of the proposed inverter is confirmed through extensive simulation and experimentally validated studies.
A new data driven long-term solar yield analysis model of photovoltaic power plants
- Ray, Biplob, Shah, Rakibuzzaman, Islam, Md Rabiul, Islam, Syed
- Authors: Ray, Biplob , Shah, Rakibuzzaman , Islam, Md Rabiul , Islam, Syed
- Date: 2020
- Type: Text , Journal article
- Relation: IEEE Access Vol. 8, no. (2020), p. 136223-136233
- Full Text:
- Reviewed:
- Description: Historical data offers a wealth of knowledge to the users. However, often restrictively mammoth that the information cannot be fully extracted, synthesized, and analyzed efficiently for an application such as the forecasting of variable generator outputs. Moreover, the accuracy of the prediction method is vital. Therefore, a trade-off between accuracy and efficacy is required for the data-driven energy forecasting method. It has been identified that the hybrid approach may outperform the individual technique in minimizing the error while challenging to synthesize. A hybrid deep learning-based method is proposed for the output prediction of the solar photovoltaic systems (i.e. proposed PV system) in Australia to obtain the trade-off between accuracy and efficacy. The historical dataset from 1990-2013 in Australian locations (e.g. North Queensland) are used to train the model. The model is developed using the combination of multivariate long and short-term memory (LSTM) and convolutional neural network (CNN). The proposed hybrid deep learning (LSTM-CNN) is compared with the existing neural network ensemble (NNE), random forest, statistical analysis, and artificial neural network (ANN) based techniques to assess the performance. The proposed model could be useful for generation planning and reserve estimation in power systems with high penetration of solar photovoltaics (PVs) or other renewable energy sources (RESs). © 2013 IEEE.
- Authors: Ray, Biplob , Shah, Rakibuzzaman , Islam, Md Rabiul , Islam, Syed
- Date: 2020
- Type: Text , Journal article
- Relation: IEEE Access Vol. 8, no. (2020), p. 136223-136233
- Full Text:
- Reviewed:
- Description: Historical data offers a wealth of knowledge to the users. However, often restrictively mammoth that the information cannot be fully extracted, synthesized, and analyzed efficiently for an application such as the forecasting of variable generator outputs. Moreover, the accuracy of the prediction method is vital. Therefore, a trade-off between accuracy and efficacy is required for the data-driven energy forecasting method. It has been identified that the hybrid approach may outperform the individual technique in minimizing the error while challenging to synthesize. A hybrid deep learning-based method is proposed for the output prediction of the solar photovoltaic systems (i.e. proposed PV system) in Australia to obtain the trade-off between accuracy and efficacy. The historical dataset from 1990-2013 in Australian locations (e.g. North Queensland) are used to train the model. The model is developed using the combination of multivariate long and short-term memory (LSTM) and convolutional neural network (CNN). The proposed hybrid deep learning (LSTM-CNN) is compared with the existing neural network ensemble (NNE), random forest, statistical analysis, and artificial neural network (ANN) based techniques to assess the performance. The proposed model could be useful for generation planning and reserve estimation in power systems with high penetration of solar photovoltaics (PVs) or other renewable energy sources (RESs). © 2013 IEEE.
A new global index for short term voltage stability assessment
- Alshareef, Abdulrhman, Shah, Rakibuzzaman, Mithulananthan, Nadarajah, Alzahrani, Saeed
- Authors: Alshareef, Abdulrhman , Shah, Rakibuzzaman , Mithulananthan, Nadarajah , Alzahrani, Saeed
- Date: 2021
- Type: Text , Journal article
- Relation: IEEE Access Vol. 9, no. (2021), p. 36114-36124
- Full Text:
- Reviewed:
- Description: The utility scale of non-conventional generators (NCGs), such as wind and photovoltaic (PV) plants, are competitive alternatives to synchronous machines (SMs) for power generation. Higher penetration of NCGs has been respondent of causing several recent incidents leading up to voltage collapse in power systems due to the distinct characteristics of NCGs under different operating conditions. Consequently, the so-called system strength has been reduced with higher NCGs penetration. A number of indices have been developed to quantify system strength from the short-term voltage stability (STVS) perspective. None of the indices capture the overall performances of power systems on dynamic voltage recovery. In this paper, an improvement in one of the STVS indices namely, the Voltage Recovery Index (VRI), is proposed to overcome shortcomings in the original index. Moreover, the improved index is globalized to establish a new index defined as system voltage recovery index (VRIsys) to quantify STVS at the system level. The amended VRI and developed VRIsys are used in systematic simulations to quantify the impact and interaction of various factors that could affect system strength. The assessment was conducted using time-domain simulation with direct connected induction motors (DCIMs) and a proliferation of converter-based technologies on both the generation and load sides, namely, NCGs and Variable Speed Drives (VSDs), respectively. © 2013 IEEE.
- Authors: Alshareef, Abdulrhman , Shah, Rakibuzzaman , Mithulananthan, Nadarajah , Alzahrani, Saeed
- Date: 2021
- Type: Text , Journal article
- Relation: IEEE Access Vol. 9, no. (2021), p. 36114-36124
- Full Text:
- Reviewed:
- Description: The utility scale of non-conventional generators (NCGs), such as wind and photovoltaic (PV) plants, are competitive alternatives to synchronous machines (SMs) for power generation. Higher penetration of NCGs has been respondent of causing several recent incidents leading up to voltage collapse in power systems due to the distinct characteristics of NCGs under different operating conditions. Consequently, the so-called system strength has been reduced with higher NCGs penetration. A number of indices have been developed to quantify system strength from the short-term voltage stability (STVS) perspective. None of the indices capture the overall performances of power systems on dynamic voltage recovery. In this paper, an improvement in one of the STVS indices namely, the Voltage Recovery Index (VRI), is proposed to overcome shortcomings in the original index. Moreover, the improved index is globalized to establish a new index defined as system voltage recovery index (VRIsys) to quantify STVS at the system level. The amended VRI and developed VRIsys are used in systematic simulations to quantify the impact and interaction of various factors that could affect system strength. The assessment was conducted using time-domain simulation with direct connected induction motors (DCIMs) and a proliferation of converter-based technologies on both the generation and load sides, namely, NCGs and Variable Speed Drives (VSDs), respectively. © 2013 IEEE.
A new scoring system in Cystic Fibrosis : Statistical tools for database analysis - A preliminary report
- Hafen, Gaudenz, Hurst, Cameron, Yearwood, John, Smith, Julie, Dzalilov, Zari, Robinson, P. J.
- Authors: Hafen, Gaudenz , Hurst, Cameron , Yearwood, John , Smith, Julie , Dzalilov, Zari , Robinson, P. J.
- Date: 2008
- Type: Text , Journal article
- Relation: BMC Medical Informatics and Decision Making Vol. 8, no. 44 (2008), p.1-11
- Full Text:
- Reviewed:
- Description: Background. Cystic fibrosis is the most common fatal genetic disorder in the Caucasian population. Scoring systems for assessment of Cystic fibrosis disease severity have been used for almost 50 years, without being adapted to the milder phenotype of the disease in the 21st century. The aim of this current project is to develop a new scoring system using a database and employing various statistical tools. This study protocol reports the development of the statistical tools in order to create such a scoring system. Methods. The evaluation is based on the Cystic Fibrosis database from the cohort at the Royal Children's Hospital in Melbourne. Initially, unsupervised clustering of the all data records was performed using a range of clustering algorithms. In particular incremental clustering algorithms were used. The clusters obtained were characterised using rules from decision trees and the results examined by clinicians. In order to obtain a clearer definition of classes expert opinion of each individual's clinical severity was sought. After data preparation including expert-opinion of an individual's clinical severity on a 3 point-scale (mild, moderate and severe disease), two multivariate techniques were used throughout the analysis to establish a method that would have a better success in feature selection and model derivation: 'Canonical Analysis of Principal Coordinates' and 'Linear Discriminant Analysis'. A 3-step procedure was performed with (1) selection of features, (2) extracting 5 severity classes out of a 3 severity class as defined per expert-opinion and (3) establishment of calibration datasets. Results. (1) Feature selection: CAP has a more effective "modelling" focus than DA. (2) Extraction of 5 severity classes: after variables were identified as important in discriminating contiguous CF severity groups on the 3-point scale as mild/moderate and moderate/severe, Discriminant Function (DF) was used to determine the new groups mild, intermediate moderate, moderate, intermediate severe and severe disease. (3) Generated confusion tables showed a misclassification rate of 19.1% for males and 16.5% for females, with a majority of misallocations into adjacent severity classes particularly for males. Conclusion. Our preliminary data show that using CAP for detection of selection features and Linear DA to derive the actual model in a CF database might be helpful in developing a scoring system. However, there are several limitations, particularly more data entry points are needed to finalize a score and the statistical tools have further to be refined and validated, with re-running the statistical methods in the larger dataset. © 2008 Hafen et al; licensee BioMed Central Ltd.
- Authors: Hafen, Gaudenz , Hurst, Cameron , Yearwood, John , Smith, Julie , Dzalilov, Zari , Robinson, P. J.
- Date: 2008
- Type: Text , Journal article
- Relation: BMC Medical Informatics and Decision Making Vol. 8, no. 44 (2008), p.1-11
- Full Text:
- Reviewed:
- Description: Background. Cystic fibrosis is the most common fatal genetic disorder in the Caucasian population. Scoring systems for assessment of Cystic fibrosis disease severity have been used for almost 50 years, without being adapted to the milder phenotype of the disease in the 21st century. The aim of this current project is to develop a new scoring system using a database and employing various statistical tools. This study protocol reports the development of the statistical tools in order to create such a scoring system. Methods. The evaluation is based on the Cystic Fibrosis database from the cohort at the Royal Children's Hospital in Melbourne. Initially, unsupervised clustering of the all data records was performed using a range of clustering algorithms. In particular incremental clustering algorithms were used. The clusters obtained were characterised using rules from decision trees and the results examined by clinicians. In order to obtain a clearer definition of classes expert opinion of each individual's clinical severity was sought. After data preparation including expert-opinion of an individual's clinical severity on a 3 point-scale (mild, moderate and severe disease), two multivariate techniques were used throughout the analysis to establish a method that would have a better success in feature selection and model derivation: 'Canonical Analysis of Principal Coordinates' and 'Linear Discriminant Analysis'. A 3-step procedure was performed with (1) selection of features, (2) extracting 5 severity classes out of a 3 severity class as defined per expert-opinion and (3) establishment of calibration datasets. Results. (1) Feature selection: CAP has a more effective "modelling" focus than DA. (2) Extraction of 5 severity classes: after variables were identified as important in discriminating contiguous CF severity groups on the 3-point scale as mild/moderate and moderate/severe, Discriminant Function (DF) was used to determine the new groups mild, intermediate moderate, moderate, intermediate severe and severe disease. (3) Generated confusion tables showed a misclassification rate of 19.1% for males and 16.5% for females, with a majority of misallocations into adjacent severity classes particularly for males. Conclusion. Our preliminary data show that using CAP for detection of selection features and Linear DA to derive the actual model in a CF database might be helpful in developing a scoring system. However, there are several limitations, particularly more data entry points are needed to finalize a score and the statistical tools have further to be refined and validated, with re-running the statistical methods in the larger dataset. © 2008 Hafen et al; licensee BioMed Central Ltd.
A scalable framework for healthcare monitoring application using the Internet of Medical Things
- Balasubramanian, Venki, Jolfaei, Alireza
- Authors: Balasubramanian, Venki , Jolfaei, Alireza
- Date: 2021
- Type: Text , Journal article
- Relation: Software - Practice and Experience Vol. 51, no. 12 (2021), p. 2457-2468
- Full Text:
- Reviewed:
- Description: Internet of Things (IoT) is finding application in many areas, particularly in health care where an IoT can be effectively used in the form of an Internet of Medical Things (IoMT) to monitor the patients remotely. The quality of life of the patients and health care outcomes can be improved with the deployment of an IoMT because health care professionals can monitor conditions; access the electronic medical records and communicates with each other. This remote monitoring and consultations might reduce the traditional stressful and costly exercise of frequent hospitalization. Also, the rising costs of health care in many developed countries have influenced the introduction of the Healthcare Monitoring Application (HMA) to their existing health care practices. To materialize the HMA concepts for successful deployment for civilian and commercial use with ease, application developers can benefit from a generic, scalable framework that provides significant components for building an HMA. In this chapter, a generic maintainable HMA is advanced by amalgamating the advantages of event-driven and the layered architecture. The proposed framework is used to establish an HMA with an end-to-end Assistive Care Loop Framework (ACLF) to provide a real-time alarm and assistance to monitor pregnant women. © 2020 John Wiley & Sons, Ltd.
- Authors: Balasubramanian, Venki , Jolfaei, Alireza
- Date: 2021
- Type: Text , Journal article
- Relation: Software - Practice and Experience Vol. 51, no. 12 (2021), p. 2457-2468
- Full Text:
- Reviewed:
- Description: Internet of Things (IoT) is finding application in many areas, particularly in health care where an IoT can be effectively used in the form of an Internet of Medical Things (IoMT) to monitor the patients remotely. The quality of life of the patients and health care outcomes can be improved with the deployment of an IoMT because health care professionals can monitor conditions; access the electronic medical records and communicates with each other. This remote monitoring and consultations might reduce the traditional stressful and costly exercise of frequent hospitalization. Also, the rising costs of health care in many developed countries have influenced the introduction of the Healthcare Monitoring Application (HMA) to their existing health care practices. To materialize the HMA concepts for successful deployment for civilian and commercial use with ease, application developers can benefit from a generic, scalable framework that provides significant components for building an HMA. In this chapter, a generic maintainable HMA is advanced by amalgamating the advantages of event-driven and the layered architecture. The proposed framework is used to establish an HMA with an end-to-end Assistive Care Loop Framework (ACLF) to provide a real-time alarm and assistance to monitor pregnant women. © 2020 John Wiley & Sons, Ltd.
A secured framework for SDN-based edge computing in IoT-enabled healthcare system
- Li, Junxia, Cai, Jinjin, Khan, Fazlullah, Rehman, Ateeq, Balasubramanian, Venki
- Authors: Li, Junxia , Cai, Jinjin , Khan, Fazlullah , Rehman, Ateeq , Balasubramanian, Venki
- Date: 2020
- Type: Text , Journal article
- Relation: IEEE Access Vol. 8, no. (2020), p. 135479-135490
- Full Text:
- Reviewed:
- Description: The Internet of Things (IoT) consists of resource-constrained smart devices capable to sense and process data. It connects a huge number of smart sensing devices, i.e., things, and heterogeneous networks. The IoT is incorporated into different applications, such as smart health, smart home, smart grid, etc. The concept of smart healthcare has emerged in different countries, where pilot projects of healthcare facilities are analyzed. In IoT-enabled healthcare systems, the security of IoT devices and associated data is very important, whereas Edge computing is a promising architecture that solves their computational and processing problems. Edge computing is economical and has the potential to provide low latency data services by improving the communication and computation speed of IoT devices in a healthcare system. In Edge-based IoT-enabled healthcare systems, load balancing, network optimization, and efficient resource utilization are accurately performed using artificial intelligence (AI), i.e., intelligent software-defined network (SDN) controller. SDN-based Edge computing is helpful in the efficient utilization of limited resources of IoT devices. However, these low powered devices and associated data (private sensitive data of patients) are prone to various security threats. Therefore, in this paper, we design a secure framework for SDN-based Edge computing in IoT-enabled healthcare system. In the proposed framework, the IoT devices are authenticated by the Edge servers using a lightweight authentication scheme. After authentication, these devices collect data from the patients and send them to the Edge servers for storage, processing, and analyses. The Edge servers are connected with an SDN controller, which performs load balancing, network optimization, and efficient resource utilization in the healthcare system. The proposed framework is evaluated using computer-based simulations. The results demonstrate that the proposed framework provides better solutions for IoT-enabled healthcare systems. © 2013 IEEE. **Please note that there are multiple authors for this article therefore only the name of the first 5 including Federation University Australia affiliate “Venki Balasubramaniam” is provided in this record**
- Authors: Li, Junxia , Cai, Jinjin , Khan, Fazlullah , Rehman, Ateeq , Balasubramanian, Venki
- Date: 2020
- Type: Text , Journal article
- Relation: IEEE Access Vol. 8, no. (2020), p. 135479-135490
- Full Text:
- Reviewed:
- Description: The Internet of Things (IoT) consists of resource-constrained smart devices capable to sense and process data. It connects a huge number of smart sensing devices, i.e., things, and heterogeneous networks. The IoT is incorporated into different applications, such as smart health, smart home, smart grid, etc. The concept of smart healthcare has emerged in different countries, where pilot projects of healthcare facilities are analyzed. In IoT-enabled healthcare systems, the security of IoT devices and associated data is very important, whereas Edge computing is a promising architecture that solves their computational and processing problems. Edge computing is economical and has the potential to provide low latency data services by improving the communication and computation speed of IoT devices in a healthcare system. In Edge-based IoT-enabled healthcare systems, load balancing, network optimization, and efficient resource utilization are accurately performed using artificial intelligence (AI), i.e., intelligent software-defined network (SDN) controller. SDN-based Edge computing is helpful in the efficient utilization of limited resources of IoT devices. However, these low powered devices and associated data (private sensitive data of patients) are prone to various security threats. Therefore, in this paper, we design a secure framework for SDN-based Edge computing in IoT-enabled healthcare system. In the proposed framework, the IoT devices are authenticated by the Edge servers using a lightweight authentication scheme. After authentication, these devices collect data from the patients and send them to the Edge servers for storage, processing, and analyses. The Edge servers are connected with an SDN controller, which performs load balancing, network optimization, and efficient resource utilization in the healthcare system. The proposed framework is evaluated using computer-based simulations. The results demonstrate that the proposed framework provides better solutions for IoT-enabled healthcare systems. © 2013 IEEE. **Please note that there are multiple authors for this article therefore only the name of the first 5 including Federation University Australia affiliate “Venki Balasubramaniam” is provided in this record**
A smart proxy for a next generation web services transaction
- Pradhan, Sunam, Zaslavsky, Arkady
- Authors: Pradhan, Sunam , Zaslavsky, Arkady
- Date: 2007
- Type: Text , Conference paper
- Relation: Paper presented at 6th IEEE/ACIS International Conference on Computer and Information Science, ICIS 2007, Melbourne, Victoria : 11th-13th July 2007 p. 646-651
- Full Text:
- Description: In this paper, we propose and describe sProxy - smart proxy, a software tool in Web Services transaction. sProxy acts as a gateway between transaction management systems and Web services which implements a key abstraction of proxy management systems. This enables to perform transactions in the loosely coupled environment i.e. loose coupling among services. Proxies are useful to invoke Web services to allow an easy programming model that facilitates the serialization and transmission of service invocations. Our proposed model supports relaxation of traditional ACID properties with existing commit and recovery protocols. The model works on non-ACID type of transactions which encapsulates Web services. It also uses multithreading proxies to check and update transaction simultaneously. The proposed model solves the current problems with distributed computational activities which involves both transactions and Web Services. The proposed model is more abstract and generic as demonstrated in the paper.
- Description: 2003005442
- Authors: Pradhan, Sunam , Zaslavsky, Arkady
- Date: 2007
- Type: Text , Conference paper
- Relation: Paper presented at 6th IEEE/ACIS International Conference on Computer and Information Science, ICIS 2007, Melbourne, Victoria : 11th-13th July 2007 p. 646-651
- Full Text:
- Description: In this paper, we propose and describe sProxy - smart proxy, a software tool in Web Services transaction. sProxy acts as a gateway between transaction management systems and Web services which implements a key abstraction of proxy management systems. This enables to perform transactions in the loosely coupled environment i.e. loose coupling among services. Proxies are useful to invoke Web services to allow an easy programming model that facilitates the serialization and transmission of service invocations. Our proposed model supports relaxation of traditional ACID properties with existing commit and recovery protocols. The model works on non-ACID type of transactions which encapsulates Web services. It also uses multithreading proxies to check and update transaction simultaneously. The proposed model solves the current problems with distributed computational activities which involves both transactions and Web Services. The proposed model is more abstract and generic as demonstrated in the paper.
- Description: 2003005442
A Survey on Behavioral Pattern Mining from Sensor Data in Internet of Things
- Rashid, Md Mamunur, Kamruzzaman, Joarder, Hassan, Mohammad, Shahriar Shafin, Sakib, Bhuiyan, Md Zakirul
- Authors: Rashid, Md Mamunur , Kamruzzaman, Joarder , Hassan, Mohammad , Shahriar Shafin, Sakib , Bhuiyan, Md Zakirul
- Date: 2020
- Type: Text , Journal article
- Relation: IEEE Access Vol. 8, no. (2020), p. 33318-33341
- Full Text:
- Reviewed:
- Description: The deployment of large-scale wireless sensor networks (WSNs) for the Internet of Things (IoT) applications is increasing day-by-day, especially with the emergence of smart city services. The sensor data streams generated from these applications are largely dynamic, heterogeneous, and often geographically distributed over large areas. For high-value use in business, industry and services, these data streams must be mined to extract insightful knowledge, such as about monitoring (e.g., discovering certain behaviors over a deployed area) or network diagnostics (e.g., predicting faulty sensor nodes). However, due to the inherent constraints of sensor networks and application requirements, traditional data mining techniques cannot be directly used to mine IoT data streams efficiently and accurately in real-time. In the last decade, a number of works have been reported in the literature proposing behavioral pattern mining algorithms for sensor networks. This paper presents the technical challenges that need to be considered for mining sensor data. It then provides a thorough review of the mining techniques proposed in the recent literature to mine behavioral patterns from sensor data in IoT, and their characteristics and differences are highlighted and compared. We also propose a behavioral pattern mining framework for IoT and discuss possible future research directions in this area. © 2013 IEEE.
- Authors: Rashid, Md Mamunur , Kamruzzaman, Joarder , Hassan, Mohammad , Shahriar Shafin, Sakib , Bhuiyan, Md Zakirul
- Date: 2020
- Type: Text , Journal article
- Relation: IEEE Access Vol. 8, no. (2020), p. 33318-33341
- Full Text:
- Reviewed:
- Description: The deployment of large-scale wireless sensor networks (WSNs) for the Internet of Things (IoT) applications is increasing day-by-day, especially with the emergence of smart city services. The sensor data streams generated from these applications are largely dynamic, heterogeneous, and often geographically distributed over large areas. For high-value use in business, industry and services, these data streams must be mined to extract insightful knowledge, such as about monitoring (e.g., discovering certain behaviors over a deployed area) or network diagnostics (e.g., predicting faulty sensor nodes). However, due to the inherent constraints of sensor networks and application requirements, traditional data mining techniques cannot be directly used to mine IoT data streams efficiently and accurately in real-time. In the last decade, a number of works have been reported in the literature proposing behavioral pattern mining algorithms for sensor networks. This paper presents the technical challenges that need to be considered for mining sensor data. It then provides a thorough review of the mining techniques proposed in the recent literature to mine behavioral patterns from sensor data in IoT, and their characteristics and differences are highlighted and compared. We also propose a behavioral pattern mining framework for IoT and discuss possible future research directions in this area. © 2013 IEEE.
A technique for parallel share-frequent sensor pattern mining from wireless sensor networks
- Rashid, Md. Mamunur, Gondal, Iqbal, Kamruzzaman, Joarder
- Authors: Rashid, Md. Mamunur , Gondal, Iqbal , Kamruzzaman, Joarder
- Date: 2014
- Type: Text , Conference paper
- Relation: 14th Annual International Conference on Computational Science, ICCS 2014; Cairns, Australia; 10th-12th June 2014; published in Procedia Computer Science p. 124-133
- Full Text:
- Reviewed:
- Description: WSNs generate huge amount of data in the form of streams and mining useful knowledge from these streams is a challenging task. Existing works generate sensor association rules using occurrence frequency of patterns with binary frequency (either absent or present) or support of a pattern as a criterion. However, considering the binary frequency or support of a pattern may not be a sufficient indicator for finding meaningful patterns from WSN data because it only reflects the number of epochs in the sensor data which contain that pattern. The share measure of sensorsets could discover useful knowledge about numerical values associated with sensor in a sensor database. Therefore, in this paper, we propose a new type of behavioral pattern called share-frequent sensor patterns by considering the non-binary frequency values of sensors in epochs. To discover share-frequent sensor patterns from sensor dataset, we propose a novel parallel technique. In this technique, we develop a novel tree structure, called parallel share-frequent sensor pattern tree (PShrFSP-tree) that is constructed at each local node independently, by capturing the database contents to generate the candidate patterns using a pattern growth technique with a single scan and then merges the locally generated candidate patterns at the final stage to generate global share-frequent sensor patterns. Comprehensive experimental results show that our proposed model is very efficient for mining share-frequent patterns from WSN data in terms of time and scalability.
- Authors: Rashid, Md. Mamunur , Gondal, Iqbal , Kamruzzaman, Joarder
- Date: 2014
- Type: Text , Conference paper
- Relation: 14th Annual International Conference on Computational Science, ICCS 2014; Cairns, Australia; 10th-12th June 2014; published in Procedia Computer Science p. 124-133
- Full Text:
- Reviewed:
- Description: WSNs generate huge amount of data in the form of streams and mining useful knowledge from these streams is a challenging task. Existing works generate sensor association rules using occurrence frequency of patterns with binary frequency (either absent or present) or support of a pattern as a criterion. However, considering the binary frequency or support of a pattern may not be a sufficient indicator for finding meaningful patterns from WSN data because it only reflects the number of epochs in the sensor data which contain that pattern. The share measure of sensorsets could discover useful knowledge about numerical values associated with sensor in a sensor database. Therefore, in this paper, we propose a new type of behavioral pattern called share-frequent sensor patterns by considering the non-binary frequency values of sensors in epochs. To discover share-frequent sensor patterns from sensor dataset, we propose a novel parallel technique. In this technique, we develop a novel tree structure, called parallel share-frequent sensor pattern tree (PShrFSP-tree) that is constructed at each local node independently, by capturing the database contents to generate the candidate patterns using a pattern growth technique with a single scan and then merges the locally generated candidate patterns at the final stage to generate global share-frequent sensor patterns. Comprehensive experimental results show that our proposed model is very efficient for mining share-frequent patterns from WSN data in terms of time and scalability.
Adaptive weighted non-parametric background model for efficient video coding
- Chakraborty, Subrata, Paul, Manoranjan, Murshed, Manzur, Ali, Mortuza
- Authors: Chakraborty, Subrata , Paul, Manoranjan , Murshed, Manzur , Ali, Mortuza
- Date: 2017
- Type: Text , Journal article
- Relation: Neurocomputing Vol. 226, no. (2017), p. 35-45
- Full Text:
- Reviewed:
- Description: Dynamic background frame based video coding using mixture of Gaussian (MoG) based background modelling has achieved better rate distortion performance compared to the H.264 standard. However, they suffer from high computation time, low coding efficiency for dynamic videos, and prior knowledge requirement of video content. In this paper, we introduce the application of the non-parametric (NP) background modelling approach for video coding domain. We present a novel background modelling technique, called weighted non-parametric (WNP) which balances the historical trend and the recent value of the pixel intensities adaptively based on the content and characteristics of any particular video. WNP is successfully embedded into the latest HEVC video coding standard for better rate-distortion performance. Moreover, a novel scene adaptive non-parametric (SANP) technique is also developed to handle video sequences with high dynamic background. Being non-parametric, the proposed techniques naturally exhibit superior performance in dynamic background modelling without a priori knowledge of video data distribution.
- Authors: Chakraborty, Subrata , Paul, Manoranjan , Murshed, Manzur , Ali, Mortuza
- Date: 2017
- Type: Text , Journal article
- Relation: Neurocomputing Vol. 226, no. (2017), p. 35-45
- Full Text:
- Reviewed:
- Description: Dynamic background frame based video coding using mixture of Gaussian (MoG) based background modelling has achieved better rate distortion performance compared to the H.264 standard. However, they suffer from high computation time, low coding efficiency for dynamic videos, and prior knowledge requirement of video content. In this paper, we introduce the application of the non-parametric (NP) background modelling approach for video coding domain. We present a novel background modelling technique, called weighted non-parametric (WNP) which balances the historical trend and the recent value of the pixel intensities adaptively based on the content and characteristics of any particular video. WNP is successfully embedded into the latest HEVC video coding standard for better rate-distortion performance. Moreover, a novel scene adaptive non-parametric (SANP) technique is also developed to handle video sequences with high dynamic background. Being non-parametric, the proposed techniques naturally exhibit superior performance in dynamic background modelling without a priori knowledge of video data distribution.