Pub Date : 2015-11-01DOI: 10.1109/KBEI.2015.7436016
G. D'Antona, L. Perfetto
The State Estimation (SE) problem in electric power systems consists of three main functions: estimation, bad data detection and identification. D'Antona formalized the estimation procedure considering the contribution of both the measurement and the network parameters to uncertainty, in the so called extended SE. This paper presents an investigation of the effectiveness of data detection and identification in the extended SE. Some results ona simple three buses network are given as a test case of the proposed approach.
{"title":"Bad data detection and identification in power system state estimation with network parameters uncertainty","authors":"G. D'Antona, L. Perfetto","doi":"10.1109/KBEI.2015.7436016","DOIUrl":"https://doi.org/10.1109/KBEI.2015.7436016","url":null,"abstract":"The State Estimation (SE) problem in electric power systems consists of three main functions: estimation, bad data detection and identification. D'Antona formalized the estimation procedure considering the contribution of both the measurement and the network parameters to uncertainty, in the so called extended SE. This paper presents an investigation of the effectiveness of data detection and identification in the extended SE. Some results ona simple three buses network are given as a test case of the proposed approach.","PeriodicalId":168295,"journal":{"name":"2015 2nd International Conference on Knowledge-Based Engineering and Innovation (KBEI)","volume":"18 5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125619269","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 : 2015-11-01DOI: 10.1109/KBEI.2015.7436093
Navid Haghighat Nazar, Mohammad Reza Heydarinezhad
Wireless Sensor Network is a specific kind of Wireless Networks that has been taken into consideration by various sciences in recent years. These Networks could be used in environmental monitoring, agricultural science, industry, medical science, etc. position of sink could be static or mobile. Communication between nodes could be multi-hop or in clustering-mode and sending data to sink could be done single-step or multi-hop. A crucial issue of these networks is their limited lifetime due to using batteries with limited energy. As a consequence, various protocols have been proposed considering energy efficiency. In this paper, MDCA algorithm is proposed to integrate data using some mobile sinks. In proposed method, managing movement of sinks in network environment is done, led to load and energy regulation. We tried to improve network efficiency by determining an appropriate rout for sinks movement. Duty cycling mechanism is used for decreasing energy consumption. In this way, a percent of sensor nodes are going to sleep mode (in the network's runtime). Routing algorithm is considered based on distance and remaining energy of sensor node, so more appropriate routs are selected during runtime. Simulation and evaluation results show that our proposed method performs better than other methods that have been mentioned here.
{"title":"Enhancement lifetime of wireless sensor networks with mobile sink managed and improved routing and control Power Consumption","authors":"Navid Haghighat Nazar, Mohammad Reza Heydarinezhad","doi":"10.1109/KBEI.2015.7436093","DOIUrl":"https://doi.org/10.1109/KBEI.2015.7436093","url":null,"abstract":"Wireless Sensor Network is a specific kind of Wireless Networks that has been taken into consideration by various sciences in recent years. These Networks could be used in environmental monitoring, agricultural science, industry, medical science, etc. position of sink could be static or mobile. Communication between nodes could be multi-hop or in clustering-mode and sending data to sink could be done single-step or multi-hop. A crucial issue of these networks is their limited lifetime due to using batteries with limited energy. As a consequence, various protocols have been proposed considering energy efficiency. In this paper, MDCA algorithm is proposed to integrate data using some mobile sinks. In proposed method, managing movement of sinks in network environment is done, led to load and energy regulation. We tried to improve network efficiency by determining an appropriate rout for sinks movement. Duty cycling mechanism is used for decreasing energy consumption. In this way, a percent of sensor nodes are going to sleep mode (in the network's runtime). Routing algorithm is considered based on distance and remaining energy of sensor node, so more appropriate routs are selected during runtime. Simulation and evaluation results show that our proposed method performs better than other methods that have been mentioned here.","PeriodicalId":168295,"journal":{"name":"2015 2nd International Conference on Knowledge-Based Engineering and Innovation (KBEI)","volume":"428 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122797343","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 : 2015-11-01DOI: 10.1109/KBEI.2015.7436123
M. Hosseinzadeh, S. M. Hosseini Andargoli, S. H. Hojjati
Sensor localization in the sensor network with cooperative spectrum sensing is considered to detect a random geometric primary user (PU). The sensor's detection performance is an important issue which has attracted more attention in recent years. A crucial matter in this scenario is that we do not have any information about the position of primary user. Due to the random location of PU, we studied expectation of detection probability under OR fusion rule assumption and describe approximate theory for sensor's localization in the network. The simulation results confirm our theory on the various scenarios.
{"title":"Sensor localization for cooperative spectrum sensing in the network with random geometric primary","authors":"M. Hosseinzadeh, S. M. Hosseini Andargoli, S. H. Hojjati","doi":"10.1109/KBEI.2015.7436123","DOIUrl":"https://doi.org/10.1109/KBEI.2015.7436123","url":null,"abstract":"Sensor localization in the sensor network with cooperative spectrum sensing is considered to detect a random geometric primary user (PU). The sensor's detection performance is an important issue which has attracted more attention in recent years. A crucial matter in this scenario is that we do not have any information about the position of primary user. Due to the random location of PU, we studied expectation of detection probability under OR fusion rule assumption and describe approximate theory for sensor's localization in the network. The simulation results confirm our theory on the various scenarios.","PeriodicalId":168295,"journal":{"name":"2015 2nd International Conference on Knowledge-Based Engineering and Innovation (KBEI)","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127781397","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 : 2015-11-01DOI: 10.1109/KBEI.2015.7436104
Somayeh Mohseni, A. F. Jahromi
The way of searching For outlier data based on distance is one of the attractive study in data mining during recent two decades, due to the wide range of applications, has been always investigated. But this wide scope has been limited to certain data, while the valuable ability of fuzzy data in analyzing and applying has been proven. Considering the effective performance of LDOF method as a distance based approach in identifying outlier data, the almost this article is to use the famous vortex metric, to provide a universalization of LDOF method for identifying outlier dataset of fuzzy data. Also performance and efficiency of the proposed method has been investigated in simulation.
{"title":"A new local distace-based outlier detection approach for fuzzy data by vertex metric","authors":"Somayeh Mohseni, A. F. Jahromi","doi":"10.1109/KBEI.2015.7436104","DOIUrl":"https://doi.org/10.1109/KBEI.2015.7436104","url":null,"abstract":"The way of searching For outlier data based on distance is one of the attractive study in data mining during recent two decades, due to the wide range of applications, has been always investigated. But this wide scope has been limited to certain data, while the valuable ability of fuzzy data in analyzing and applying has been proven. Considering the effective performance of LDOF method as a distance based approach in identifying outlier data, the almost this article is to use the famous vortex metric, to provide a universalization of LDOF method for identifying outlier dataset of fuzzy data. Also performance and efficiency of the proposed method has been investigated in simulation.","PeriodicalId":168295,"journal":{"name":"2015 2nd International Conference on Knowledge-Based Engineering and Innovation (KBEI)","volume":"110 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128003704","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 : 2015-11-01DOI: 10.1109/KBEI.2015.7436205
Farzad Paridar, S. J. Mirabedini
Studies based on newly documented statistics suggest that a plethora of people suffer from psychological disorders. Violent behaviors are of symptoms reflecting these psychological disorders. In this paper, we design a gadget connecting to a smart operating system and counseling the one instantly and immediately in case of their violent behaviors. Using WBAN sensors we could measure the patients' vital signs including blood pressure and pulses, body temperature and heart beats and level of these measures leads the system to figure out their moods and provide them with a counseling option mitigating their fretful senses and moving them to relaxation.
{"title":"Using WBAN as an intelligent adviser gadget","authors":"Farzad Paridar, S. J. Mirabedini","doi":"10.1109/KBEI.2015.7436205","DOIUrl":"https://doi.org/10.1109/KBEI.2015.7436205","url":null,"abstract":"Studies based on newly documented statistics suggest that a plethora of people suffer from psychological disorders. Violent behaviors are of symptoms reflecting these psychological disorders. In this paper, we design a gadget connecting to a smart operating system and counseling the one instantly and immediately in case of their violent behaviors. Using WBAN sensors we could measure the patients' vital signs including blood pressure and pulses, body temperature and heart beats and level of these measures leads the system to figure out their moods and provide them with a counseling option mitigating their fretful senses and moving them to relaxation.","PeriodicalId":168295,"journal":{"name":"2015 2nd International Conference on Knowledge-Based Engineering and Innovation (KBEI)","volume":"66 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133447646","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 : 2015-11-01DOI: 10.1109/KBEI.2015.7436118
M. A. Naji, A. Aghagolzadeh
Multi-focus image fusion is used to collect useful and necessary information from input images with different focus depths in order to create an output image that ideally has all information from input images. In this article, an efficient, new and simple method is proposed for multi-focus image fusion which is based on correlation coefficient calculation in the discrete cosine transform (DCT) domain. Image fusion algorithms which are based on DCT are very appropriate, and they consume less time and energy, especially when JPEG images are used in visual sensor networks (VSN). The proposed method evaluates the amount of changes of the input multi-focus images when they pass through a low pass filter, and then selects the block which has been changed more. In order to assess the algorithm performance, a lot of pair multi-focused images which are coded as JPEG were used. The results show that the output image quality is better than that of the previous methods.
{"title":"Multi-focus image fusion in DCT domain based on correlation coefficient","authors":"M. A. Naji, A. Aghagolzadeh","doi":"10.1109/KBEI.2015.7436118","DOIUrl":"https://doi.org/10.1109/KBEI.2015.7436118","url":null,"abstract":"Multi-focus image fusion is used to collect useful and necessary information from input images with different focus depths in order to create an output image that ideally has all information from input images. In this article, an efficient, new and simple method is proposed for multi-focus image fusion which is based on correlation coefficient calculation in the discrete cosine transform (DCT) domain. Image fusion algorithms which are based on DCT are very appropriate, and they consume less time and energy, especially when JPEG images are used in visual sensor networks (VSN). The proposed method evaluates the amount of changes of the input multi-focus images when they pass through a low pass filter, and then selects the block which has been changed more. In order to assess the algorithm performance, a lot of pair multi-focused images which are coded as JPEG were used. The results show that the output image quality is better than that of the previous methods.","PeriodicalId":168295,"journal":{"name":"2015 2nd International Conference on Knowledge-Based Engineering and Innovation (KBEI)","volume":"85 11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134128921","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 : 2015-11-01DOI: 10.1109/KBEI.2015.7436055
Farshad Rafiei, G. Hossein-Zadeh
In a brain decoding study, using the functional magnetic resonance imaging (fMRI) data we determined the facial expression of the visual stimulus that the subject perceived. fMRI data acquired from a healthy right-handed adult volunteer who participated in three separate sessions. Participant viewed blocks of emotionally expressive faces alternating with blocks of neutral faces and scrambled images. Multi-voxel pattern analyses are then used to decode different expressions using the activity pattern of most active parts of brain. We used multi-class support vector machine (SVM) to distinct five brain states corresponding to neutral, happy, sad, angry and surprised. Results show that these facial expressions can be classified from fMRI data with the average sensitivity of 90 percent.
{"title":"fMRI brain decoding of facial expressions based on multi-voxel pattern analysis","authors":"Farshad Rafiei, G. Hossein-Zadeh","doi":"10.1109/KBEI.2015.7436055","DOIUrl":"https://doi.org/10.1109/KBEI.2015.7436055","url":null,"abstract":"In a brain decoding study, using the functional magnetic resonance imaging (fMRI) data we determined the facial expression of the visual stimulus that the subject perceived. fMRI data acquired from a healthy right-handed adult volunteer who participated in three separate sessions. Participant viewed blocks of emotionally expressive faces alternating with blocks of neutral faces and scrambled images. Multi-voxel pattern analyses are then used to decode different expressions using the activity pattern of most active parts of brain. We used multi-class support vector machine (SVM) to distinct five brain states corresponding to neutral, happy, sad, angry and surprised. Results show that these facial expressions can be classified from fMRI data with the average sensitivity of 90 percent.","PeriodicalId":168295,"journal":{"name":"2015 2nd International Conference on Knowledge-Based Engineering and Innovation (KBEI)","volume":"99 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134637215","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 : 2015-11-01DOI: 10.1109/KBEI.2015.7436164
H. Karrari, E. N. Aghdam
In this paper, a 2.8-10.6GHz low-noise amplifier for ultra-wideband applications is presented. The proposed UWB-LNA uses inter-stage technique (current reuse topology with a peaking inductor) to achieve low power consumption. It is designed using TSMC 0.18 μm CMOS technology. Simulation results show the LNA achieves flat S21 of 12.32 ± 1.07 dB, S11 below -7.45 dB, S22 below -8.45 dB, S12 below -47 dB and flat NF of 3.2 ± 0.2 dB over the 2.8-10.6-GHz band of interest, with only power consumption of 5.74mW.
{"title":"A 2.8-10.6GHz Low-Power Low-Noise Amplifier for Ultra-Wideband Recivers","authors":"H. Karrari, E. N. Aghdam","doi":"10.1109/KBEI.2015.7436164","DOIUrl":"https://doi.org/10.1109/KBEI.2015.7436164","url":null,"abstract":"In this paper, a 2.8-10.6GHz low-noise amplifier for ultra-wideband applications is presented. The proposed UWB-LNA uses inter-stage technique (current reuse topology with a peaking inductor) to achieve low power consumption. It is designed using TSMC 0.18 μm CMOS technology. Simulation results show the LNA achieves flat S21 of 12.32 ± 1.07 dB, S11 below -7.45 dB, S22 below -8.45 dB, S12 below -47 dB and flat NF of 3.2 ± 0.2 dB over the 2.8-10.6-GHz band of interest, with only power consumption of 5.74mW.","PeriodicalId":168295,"journal":{"name":"2015 2nd International Conference on Knowledge-Based Engineering and Innovation (KBEI)","volume":"321 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132202833","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 : 2015-11-01DOI: 10.1109/KBEI.2015.7436084
Elham Mohebbian, M. Hariri
Digital images are easy to manipulate and edit due to advances in computers and image editing software. Copy-move forgery is one of the most popular tampering artifacts in digital images that used by image forgers. In this paper, a DCT-based method is developed to detect image forgery considering the complexity of input image. To detect duplicate region, images are divided into two categories: smooth and complex. To extract features discrete cosine transform (DCT) is applied to each block. Experimental results show that our proposed method is able to precisely detect duplicated regions even when the image was undergone several image manipulations like lossy JPEG compression, Gaussian blur filtering and Gaussian white noise contamination.
{"title":"Increase the efficiency of DCT method for detection of copy-move forgery in complex and smooth images","authors":"Elham Mohebbian, M. Hariri","doi":"10.1109/KBEI.2015.7436084","DOIUrl":"https://doi.org/10.1109/KBEI.2015.7436084","url":null,"abstract":"Digital images are easy to manipulate and edit due to advances in computers and image editing software. Copy-move forgery is one of the most popular tampering artifacts in digital images that used by image forgers. In this paper, a DCT-based method is developed to detect image forgery considering the complexity of input image. To detect duplicate region, images are divided into two categories: smooth and complex. To extract features discrete cosine transform (DCT) is applied to each block. Experimental results show that our proposed method is able to precisely detect duplicated regions even when the image was undergone several image manipulations like lossy JPEG compression, Gaussian blur filtering and Gaussian white noise contamination.","PeriodicalId":168295,"journal":{"name":"2015 2nd International Conference on Knowledge-Based Engineering and Innovation (KBEI)","volume":"397 ","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133586865","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 : 2015-11-01DOI: 10.1109/KBEI.2015.7436077
Raoof Masoomi, Ali Khadem
Due to the potential applications of Brain-Computer Interfaces (BCI), like producing rehabilitation systems for disabled people, many researches have been aimed at minimizing the error of BCI systems. In this paper, we used left and right hand motor imagery EEG data provided by Graz University of Technology for the BCI Competition II. We attempted to achieve a better misclassification rate while selecting less features compared with various former reported researches on this dataset. We used linear discriminant analysis (LDA) as the classifier due to its low computational cost and previously reported promising results. Furthermore, we investigated what features have major impacts on local or global minimization of the misclassification rate. Also, we briefly assessed the effect of changing window length on the misclassification rate. In this paper first, a set of various statistical, spectral, wavelet-based, connectivity, and chaotic features was extracted from EEG data. Subsequently, an LDA-based wrapper Sequential Forward Selection (SFS) scheme was used for selecting optimum subset of features for each data window. Finally, data windows were classified by LDA. We achieved less misclassification rate using less features compared with previous LDA-based researches and the winner of BCI competition II on the same dataset. Also, the absolute mean of the third-level wavelet detail coefficients (related to μ-band) and the skewness were the two features that together yielded the best local discrimination results.
{"title":"Enhancing LDA-based discrimination of left and right hand motor imagery: Outperforming the winner of BCI Competition II","authors":"Raoof Masoomi, Ali Khadem","doi":"10.1109/KBEI.2015.7436077","DOIUrl":"https://doi.org/10.1109/KBEI.2015.7436077","url":null,"abstract":"Due to the potential applications of Brain-Computer Interfaces (BCI), like producing rehabilitation systems for disabled people, many researches have been aimed at minimizing the error of BCI systems. In this paper, we used left and right hand motor imagery EEG data provided by Graz University of Technology for the BCI Competition II. We attempted to achieve a better misclassification rate while selecting less features compared with various former reported researches on this dataset. We used linear discriminant analysis (LDA) as the classifier due to its low computational cost and previously reported promising results. Furthermore, we investigated what features have major impacts on local or global minimization of the misclassification rate. Also, we briefly assessed the effect of changing window length on the misclassification rate. In this paper first, a set of various statistical, spectral, wavelet-based, connectivity, and chaotic features was extracted from EEG data. Subsequently, an LDA-based wrapper Sequential Forward Selection (SFS) scheme was used for selecting optimum subset of features for each data window. Finally, data windows were classified by LDA. We achieved less misclassification rate using less features compared with previous LDA-based researches and the winner of BCI competition II on the same dataset. Also, the absolute mean of the third-level wavelet detail coefficients (related to μ-band) and the skewness were the two features that together yielded the best local discrimination results.","PeriodicalId":168295,"journal":{"name":"2015 2nd International Conference on Knowledge-Based Engineering and Innovation (KBEI)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133334779","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：