Pub Date : 2019-02-01DOI: 10.1109/KBEI.2019.8734905
M. Rahmani, F. Faghihi
In this study a novel fuzzy cooperative frequency control method for islanding microgrids containing renewable energy resources is proposed. Frequency control, which comes from power balancing between generation and consumption components, plays an essential role to maintain the system stability. Frequency diversion is a result of a change in the microgrid. The best way to provide the balance is applying an energy storage system like a battery which could inject or take out the power from the system, immediately. In order to achieve the acceptable results, it is necessary to use the proper frequency control method which could guarantee the frequency stabilizing. The performance of the new approach is evaluated via two scenarios. Simulation results show that microgrid can be reached to the nominal frequency, efficiently.
{"title":"A Novel Fuzzy Cooperative Frequency Control Method for Islanding Microgrids comprising Renewable Energy Resources","authors":"M. Rahmani, F. Faghihi","doi":"10.1109/KBEI.2019.8734905","DOIUrl":"https://doi.org/10.1109/KBEI.2019.8734905","url":null,"abstract":"In this study a novel fuzzy cooperative frequency control method for islanding microgrids containing renewable energy resources is proposed. Frequency control, which comes from power balancing between generation and consumption components, plays an essential role to maintain the system stability. Frequency diversion is a result of a change in the microgrid. The best way to provide the balance is applying an energy storage system like a battery which could inject or take out the power from the system, immediately. In order to achieve the acceptable results, it is necessary to use the proper frequency control method which could guarantee the frequency stabilizing. The performance of the new approach is evaluated via two scenarios. Simulation results show that microgrid can be reached to the nominal frequency, efficiently.","PeriodicalId":339990,"journal":{"name":"2019 5th Conference on Knowledge Based Engineering and Innovation (KBEI)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131689207","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-02-01DOI: 10.1109/KBEI.2019.8734917
Ziba Imani, S. J. Kabudian
Fundamental frequency estimation is one of the most important issues in the field of speech processing. An accurate estimate of the fundamental frequency plays a key role in the field of speech and music analysis. So far, various methods have been proposed in the time- and frequency-domain. However, the main challenge is the strong noises in speech signals. In this paper, to improve the accuracy of fundamental frequency estimation, we propose a method for optimal nonlinear combination of fundamental frequency estimation methods, in noisy signals. In this method, to discriminate voiced frames from unvoiced frames in a better way, the Voiced/Unvoiced (V/U) scores of four pitch detection methods are combined with nonlinear fusion. These methods are: Autocorrelation (AC), Yin, YAAPT and SWIPE. After identifying the Voiced/Unvoiced label of each frame, the fundamental frequency (F0) of the frame is estimated using the SWIPE method. The optimal function for nonlinear combination is determined using Multi-Layer Perceptron (MLP) neural network (NN). To evaluate the proposed method, 10 speech files (5 female and 5 male voices) are selected from the PTDB-TUG standard database and the results are presented in terms of GPE, VDE, PTE and FFE standard error criteria. The results indicate that our proposed method relatively reduced the aforementioned criteria (averaged in various SNRs) by 25.06%, 20.92%, 13.94%, and 25.94% respectively, which demonstrate the effectiveness of the proposed method in comparison to state-of-the-art methods.
{"title":"A Neural Network-Based Optimal Nonlinear Fusion of Speech Pitch Detection Algorithms","authors":"Ziba Imani, S. J. Kabudian","doi":"10.1109/KBEI.2019.8734917","DOIUrl":"https://doi.org/10.1109/KBEI.2019.8734917","url":null,"abstract":"Fundamental frequency estimation is one of the most important issues in the field of speech processing. An accurate estimate of the fundamental frequency plays a key role in the field of speech and music analysis. So far, various methods have been proposed in the time- and frequency-domain. However, the main challenge is the strong noises in speech signals. In this paper, to improve the accuracy of fundamental frequency estimation, we propose a method for optimal nonlinear combination of fundamental frequency estimation methods, in noisy signals. In this method, to discriminate voiced frames from unvoiced frames in a better way, the Voiced/Unvoiced (V/U) scores of four pitch detection methods are combined with nonlinear fusion. These methods are: Autocorrelation (AC), Yin, YAAPT and SWIPE. After identifying the Voiced/Unvoiced label of each frame, the fundamental frequency (F0) of the frame is estimated using the SWIPE method. The optimal function for nonlinear combination is determined using Multi-Layer Perceptron (MLP) neural network (NN). To evaluate the proposed method, 10 speech files (5 female and 5 male voices) are selected from the PTDB-TUG standard database and the results are presented in terms of GPE, VDE, PTE and FFE standard error criteria. The results indicate that our proposed method relatively reduced the aforementioned criteria (averaged in various SNRs) by 25.06%, 20.92%, 13.94%, and 25.94% respectively, which demonstrate the effectiveness of the proposed method in comparison to state-of-the-art methods.","PeriodicalId":339990,"journal":{"name":"2019 5th Conference on Knowledge Based Engineering and Innovation (KBEI)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132562644","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-02-01DOI: 10.1109/KBEI.2019.8734928
Rasoul Kiani, M. Montazeri, B. Minaei-Bidgoli
Outliers are data with anomalous behaviors to other datasets. There are three different types of outliers, namely point anomaly, collective anomaly, and conditional anomaly. Different density-, clustering-, distance-, and distribution-based methods are used to detect outliers. It is obvious that before testing detection algorithms, a dataset that encompasses different types of outliers is required. In this paper an intelligent clustering algorithm is presented to produce a dataset consisting of different outliers. The other important point in this paper is the probability of two uninvestigated types of collective data among datasets that the anomalies are called type I and II. Results show that the proposed algorithm is capable of producing a dataset including different types of outliers. This dataset can be used in all outlier detection techniques. In addition to detection of point anomalies, it can detect all collective anomalies.
{"title":"Intelligent Production and Detection Template of Outlier Dataset Using Clustering","authors":"Rasoul Kiani, M. Montazeri, B. Minaei-Bidgoli","doi":"10.1109/KBEI.2019.8734928","DOIUrl":"https://doi.org/10.1109/KBEI.2019.8734928","url":null,"abstract":"Outliers are data with anomalous behaviors to other datasets. There are three different types of outliers, namely point anomaly, collective anomaly, and conditional anomaly. Different density-, clustering-, distance-, and distribution-based methods are used to detect outliers. It is obvious that before testing detection algorithms, a dataset that encompasses different types of outliers is required. In this paper an intelligent clustering algorithm is presented to produce a dataset consisting of different outliers. The other important point in this paper is the probability of two uninvestigated types of collective data among datasets that the anomalies are called type I and II. Results show that the proposed algorithm is capable of producing a dataset including different types of outliers. This dataset can be used in all outlier detection techniques. In addition to detection of point anomalies, it can detect all collective anomalies.","PeriodicalId":339990,"journal":{"name":"2019 5th Conference on Knowledge Based Engineering and Innovation (KBEI)","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115468825","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-02-01DOI: 10.1109/KBEI.2019.8734939
M. Akbari, Habib Izadkhah
Clustering techniques are usually utilized to partition a software system, aiming to understand it. Understanding a program helps to maintain the legacy source code. Since the partitioning of a software system is an NP-hard problem, using the evolutionary approaches seems reasonable. Krill herd (KH) evolutionary algorithm is an effective algorithm for solving optimization problems with continuous state space which imitates the individual and group behavior of krill. According to its nature, is unable to solve the discrete space problems. The main advantage of this algorithm is to keep the information flow between different individuals during the evolutionary process. Genetic algorithm (GA) is an evolutionary algorithm utilizing the search techniques to find the closest solution to optimal; however, its main problem is a lack of strong effective information flow between different generations. This paper proposes a new evolutionary method, named GAKH, for software clustering inspired by Krill herd and Genetic algorithm. In the proposed evolutionary algorithm, the strengths of these two algorithms have been utilized and better results have been achieved in software clustering by changing GA cycle and operators, adding swarm intelligence and inspiring from Krill movements. The initial results achieved from the application of the proposed algorithm on ten software systems indicate higher quality results of clustering compared to other algorithms.
{"title":"Hybrid of genetic algorithm and krill herd for software clustering problem","authors":"M. Akbari, Habib Izadkhah","doi":"10.1109/KBEI.2019.8734939","DOIUrl":"https://doi.org/10.1109/KBEI.2019.8734939","url":null,"abstract":"Clustering techniques are usually utilized to partition a software system, aiming to understand it. Understanding a program helps to maintain the legacy source code. Since the partitioning of a software system is an NP-hard problem, using the evolutionary approaches seems reasonable. Krill herd (KH) evolutionary algorithm is an effective algorithm for solving optimization problems with continuous state space which imitates the individual and group behavior of krill. According to its nature, is unable to solve the discrete space problems. The main advantage of this algorithm is to keep the information flow between different individuals during the evolutionary process. Genetic algorithm (GA) is an evolutionary algorithm utilizing the search techniques to find the closest solution to optimal; however, its main problem is a lack of strong effective information flow between different generations. This paper proposes a new evolutionary method, named GAKH, for software clustering inspired by Krill herd and Genetic algorithm. In the proposed evolutionary algorithm, the strengths of these two algorithms have been utilized and better results have been achieved in software clustering by changing GA cycle and operators, adding swarm intelligence and inspiring from Krill movements. The initial results achieved from the application of the proposed algorithm on ten software systems indicate higher quality results of clustering compared to other algorithms.","PeriodicalId":339990,"journal":{"name":"2019 5th Conference on Knowledge Based Engineering and Innovation (KBEI)","volume":"19 3","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114102251","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-02-01DOI: 10.1109/KBEI.2019.8735077
S. M. K. Sangdehi, S. E. Abdollahi, S. Gholamian
Hybrid excited flux switching machines (HEFSM) are appropriate candidates for hybrid electric vehicles (HEV) application owing to their high torque/power density, robust rotor structure as well as flux weakening capability by DC excitation windings. However they exhibit relatively high cogging torque due to their high air-gap flux density and their doubly salient structure which is a major problem of employing HEFSM in HEVs application; hence the cogging torque reduction is a required approach in HEFSM design. Due to the relatively simple rotor structure of HEFSM in comparison with stator structure, optimal rotor design techniques are much more preferable to reduce the cogging torque and torque ripple of flux switching machine. In this paper, the rotor step skewing method is employed to reduce cogging torque and torque ripple of 6/13 HEFSM. In order to evaluate the effectiveness of the employed method finite element analysis is employed.
{"title":"Cogging Torque Reduction of 6/13 Hybrid Excited Flux Switching Machine with Rotor Step Skewing","authors":"S. M. K. Sangdehi, S. E. Abdollahi, S. Gholamian","doi":"10.1109/KBEI.2019.8735077","DOIUrl":"https://doi.org/10.1109/KBEI.2019.8735077","url":null,"abstract":"Hybrid excited flux switching machines (HEFSM) are appropriate candidates for hybrid electric vehicles (HEV) application owing to their high torque/power density, robust rotor structure as well as flux weakening capability by DC excitation windings. However they exhibit relatively high cogging torque due to their high air-gap flux density and their doubly salient structure which is a major problem of employing HEFSM in HEVs application; hence the cogging torque reduction is a required approach in HEFSM design. Due to the relatively simple rotor structure of HEFSM in comparison with stator structure, optimal rotor design techniques are much more preferable to reduce the cogging torque and torque ripple of flux switching machine. In this paper, the rotor step skewing method is employed to reduce cogging torque and torque ripple of 6/13 HEFSM. In order to evaluate the effectiveness of the employed method finite element analysis is employed.","PeriodicalId":339990,"journal":{"name":"2019 5th Conference on Knowledge Based Engineering and Innovation (KBEI)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121359125","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-02-01DOI: 10.1109/KBEI.2019.8735008
T. Gandomani, Z. Tavakoli, Mina Ziaei Nafchi, Mona Najafi Sarpiri
Today, Agile software development is one of the main approaches of software development. In this area adherence to the Agile principles and values has led to the removal of comprehensive documentation in the development process. This has replaced the explicit knowledge with implicit knowledge. In fact, the foundation of Agile software development is based on implicit knowledge. This has led knowledge management in the Agile software development as a challenging point. However, the nature of implicit knowledge is such that it can be used beneficially with optimal management. Considering this challenge, this study attempts to adapt the Scrum framework as the most popular Agile method with 7C model as one of the well-known implicit knowledge management models. The results of this study showed that this model would be successful in Scrum framework.
{"title":"Adapting Scrum Process with 7C Knowledge Management Model","authors":"T. Gandomani, Z. Tavakoli, Mina Ziaei Nafchi, Mona Najafi Sarpiri","doi":"10.1109/KBEI.2019.8735008","DOIUrl":"https://doi.org/10.1109/KBEI.2019.8735008","url":null,"abstract":"Today, Agile software development is one of the main approaches of software development. In this area adherence to the Agile principles and values has led to the removal of comprehensive documentation in the development process. This has replaced the explicit knowledge with implicit knowledge. In fact, the foundation of Agile software development is based on implicit knowledge. This has led knowledge management in the Agile software development as a challenging point. However, the nature of implicit knowledge is such that it can be used beneficially with optimal management. Considering this challenge, this study attempts to adapt the Scrum framework as the most popular Agile method with 7C model as one of the well-known implicit knowledge management models. The results of this study showed that this model would be successful in Scrum framework.","PeriodicalId":339990,"journal":{"name":"2019 5th Conference on Knowledge Based Engineering and Innovation (KBEI)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116258597","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-02-01DOI: 10.1109/KBEI.2019.8734965
Abdolreza Azizi Koochaksaraei, H. Izadfar
Solar energy is considered as one of the promising renewable sources due to to the availability of sunlight and cleanness performance compared to fossil fuels. The transferred energy from the sun to the Earth changes during the day. So, absorbing the maximum energy by the solar panel and transferring it to the load is essential. Consequently, maximum power point tracking (MPPT) techniques are proposed in numerous research papers. In this paper, an Adaptive Neuro-Fuzzy Inference System (ANFIS) based MPPT controller has been introduced. To transfer maximum power to the load, the duty cycle of the two-switch flyback inverter, which has been connected between the solar panel and the load, must be generated with the aid of the proposed ANFIS method. This tracker takes irradiance level and operating temperature as inputs and current at maximum power point as an output. Then Fuzzy controller must be tunned to generate an appropriate duty cycle. For validation, the proposed model was analyzed in different situations by MATLAB-PSIM Co-Simulation, and results show the accuracy and high efficiency of the proposed tracker.
{"title":"High-Efficiency MPPT Controller Using ANFIS-reference Model For Solar Systems","authors":"Abdolreza Azizi Koochaksaraei, H. Izadfar","doi":"10.1109/KBEI.2019.8734965","DOIUrl":"https://doi.org/10.1109/KBEI.2019.8734965","url":null,"abstract":"Solar energy is considered as one of the promising renewable sources due to to the availability of sunlight and cleanness performance compared to fossil fuels. The transferred energy from the sun to the Earth changes during the day. So, absorbing the maximum energy by the solar panel and transferring it to the load is essential. Consequently, maximum power point tracking (MPPT) techniques are proposed in numerous research papers. In this paper, an Adaptive Neuro-Fuzzy Inference System (ANFIS) based MPPT controller has been introduced. To transfer maximum power to the load, the duty cycle of the two-switch flyback inverter, which has been connected between the solar panel and the load, must be generated with the aid of the proposed ANFIS method. This tracker takes irradiance level and operating temperature as inputs and current at maximum power point as an output. Then Fuzzy controller must be tunned to generate an appropriate duty cycle. For validation, the proposed model was analyzed in different situations by MATLAB-PSIM Co-Simulation, and results show the accuracy and high efficiency of the proposed tracker.","PeriodicalId":339990,"journal":{"name":"2019 5th Conference on Knowledge Based Engineering and Innovation (KBEI)","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116433034","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-02-01DOI: 10.1109/KBEI.2019.8735000
Marziyeh Movahedi, F. Zare-Mirakabad, A. Ramazani, N. Konduru, S. Arab
the key role of protein-nanoparticle (NP) interactions in biological mediums has begun to emerge recently with the development of the concept of NP-protein ‘corona’. A dynamic layer of proteins- referred to as corona- adsorb on to NP surfaces immediately upon entering a biological milieu. This layer of protein is mainly constructed via hydrophobic interactions in addition to the entropy-driven mechanisms. The unique fingerprint of protein corona for each NP type arises from the differences in the characteristics of NPs including SSA, Dxrd, ρ, Dh, PdI and Zeta. Therefore, in this paper, according to the characteristics of four different NPs and their corresponding quantifications of nine corona proteins taken from a study by Konduru et al., we computationally analyze the effect of the characteristics of NPs, and accordingly present a computational model to predict the quantification of the formed corona proteins around the NPs. For this, a multiple linear regression model is developed to investigate the effect of selective physicochemical characteristics of NPs on the protein corona formation. This model could be used as a predictive model in addition to the computational models to determine the percentage of proteins interacting with NPs.
{"title":"Computational Analysis of Nanoparticle Features on Protein Corona Composition in Biological Nanoparticle-Protein Interactions","authors":"Marziyeh Movahedi, F. Zare-Mirakabad, A. Ramazani, N. Konduru, S. Arab","doi":"10.1109/KBEI.2019.8735000","DOIUrl":"https://doi.org/10.1109/KBEI.2019.8735000","url":null,"abstract":"the key role of protein-nanoparticle (NP) interactions in biological mediums has begun to emerge recently with the development of the concept of NP-protein ‘corona’. A dynamic layer of proteins- referred to as corona- adsorb on to NP surfaces immediately upon entering a biological milieu. This layer of protein is mainly constructed via hydrophobic interactions in addition to the entropy-driven mechanisms. The unique fingerprint of protein corona for each NP type arises from the differences in the characteristics of NPs including SSA, Dxrd, ρ, Dh, PdI and Zeta. Therefore, in this paper, according to the characteristics of four different NPs and their corresponding quantifications of nine corona proteins taken from a study by Konduru et al., we computationally analyze the effect of the characteristics of NPs, and accordingly present a computational model to predict the quantification of the formed corona proteins around the NPs. For this, a multiple linear regression model is developed to investigate the effect of selective physicochemical characteristics of NPs on the protein corona formation. This model could be used as a predictive model in addition to the computational models to determine the percentage of proteins interacting with NPs.","PeriodicalId":339990,"journal":{"name":"2019 5th Conference on Knowledge Based Engineering and Innovation (KBEI)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123683465","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-02-01DOI: 10.1109/KBEI.2019.8735033
Milad Abbasi, Babak Majidi, Moahmmad Eshghi, E. Abbasi
In the past few years, visual information collection and transmission is increased significantly for various applications. Smart vehicles, service robotic platforms and surveillance cameras for the smart city applications are collecting a large amount of visual data. The preservation of the privacy of people presented in this data is an important factor in storage, processing, sharing and transmission of visual data across the Internet of Robotic Things (IoRT). In this paper, a novel anonymisation method for information security and privacy preservation in visual data in sharing layer of the Web of Robotic Things (WoRT) is proposed. The proposed framework uses deep neural network based semantic segmentation to preserve the privacy in video data base of the access level of the applications and users. The data is anonymised to the applications with lower level access but the applications with higher legal access level can analyze and annotated the complete data. The experimental results show that the proposed method while giving the required access to the authorities for legal applications of smart city surveillance, is capable of preserving the privacy of the people presented in the data.
{"title":"Deep Visual Privacy Preserving for Internet of Robotic Things","authors":"Milad Abbasi, Babak Majidi, Moahmmad Eshghi, E. Abbasi","doi":"10.1109/KBEI.2019.8735033","DOIUrl":"https://doi.org/10.1109/KBEI.2019.8735033","url":null,"abstract":"In the past few years, visual information collection and transmission is increased significantly for various applications. Smart vehicles, service robotic platforms and surveillance cameras for the smart city applications are collecting a large amount of visual data. The preservation of the privacy of people presented in this data is an important factor in storage, processing, sharing and transmission of visual data across the Internet of Robotic Things (IoRT). In this paper, a novel anonymisation method for information security and privacy preservation in visual data in sharing layer of the Web of Robotic Things (WoRT) is proposed. The proposed framework uses deep neural network based semantic segmentation to preserve the privacy in video data base of the access level of the applications and users. The data is anonymised to the applications with lower level access but the applications with higher legal access level can analyze and annotated the complete data. The experimental results show that the proposed method while giving the required access to the authorities for legal applications of smart city surveillance, is capable of preserving the privacy of the people presented in the data.","PeriodicalId":339990,"journal":{"name":"2019 5th Conference on Knowledge Based Engineering and Innovation (KBEI)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122888231","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-02-01DOI: 10.1109/KBEI.2019.8734926
M. Nezhadshahbodaghi, M. Mosavi
The Global Positioning System (GPS) receivers often encounter the problem of navigation bit sign transitions which decrease the receiver performance. In order to enhance the sensitivity of GPS receivers, increasing the integration time in the acquisition process is necessary. We propose a new method to extend differential integration for weak GPS signal acquisition. In our proposed method, Fast Fourier Transform (FFT) operation is applied to the presented algorithm to search the code delay in parallel. Meanwhile, the presented method has the capability of extending the differential integration time to multiple navigation data bit duration. Experimental results demonstrate that the proposed method outperforms compared to the conventional methods such as coherent, noncoherent, differential integration. This improvement is more than 69 % in the amount of output SNR to detect in the acquisition section.
{"title":"A New Method to Extend Differential Integration for Weak GPS Signal Acquisition","authors":"M. Nezhadshahbodaghi, M. Mosavi","doi":"10.1109/KBEI.2019.8734926","DOIUrl":"https://doi.org/10.1109/KBEI.2019.8734926","url":null,"abstract":"The Global Positioning System (GPS) receivers often encounter the problem of navigation bit sign transitions which decrease the receiver performance. In order to enhance the sensitivity of GPS receivers, increasing the integration time in the acquisition process is necessary. We propose a new method to extend differential integration for weak GPS signal acquisition. In our proposed method, Fast Fourier Transform (FFT) operation is applied to the presented algorithm to search the code delay in parallel. Meanwhile, the presented method has the capability of extending the differential integration time to multiple navigation data bit duration. Experimental results demonstrate that the proposed method outperforms compared to the conventional methods such as coherent, noncoherent, differential integration. This improvement is more than 69 % in the amount of output SNR to detect in the acquisition section.","PeriodicalId":339990,"journal":{"name":"2019 5th Conference on Knowledge Based Engineering and Innovation (KBEI)","volume":"259 1-2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123709828","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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