Pub Date : 2016-05-01DOI: 10.1109/AIC-MITCSA.2016.7759925
A. Sallomi, Sulaiman Ahmed
In this paper an artificial Feed Forward Neural Network (FFNN) is applied for smart antenna adaptive beamforming. The neural network is used to calculate the optimum weights of the uniform linear antenna array to steer the radiation pattern of the toward the desired users and make nulling in the direction of interference sources. Levenberg Marquardt (LM) algorithm and Resilient Backpropagation (Rprop) algorithm are used to train the FFNN. Five element uniform linear array is used with spacing between element equal to the half wavelength. The simulation results of FFNN training using LM and Rprop algorithms showed that the Neural Network (NN) trained by LM training algorithm gives better performance than Rprop training algorithm, since it considers the fastest backpropagation training algorithm but it takes more memory than other algorithms.
{"title":"Multi-layer feed forward neural network application in adaptive beamforming of smart antenna system","authors":"A. Sallomi, Sulaiman Ahmed","doi":"10.1109/AIC-MITCSA.2016.7759925","DOIUrl":"https://doi.org/10.1109/AIC-MITCSA.2016.7759925","url":null,"abstract":"In this paper an artificial Feed Forward Neural Network (FFNN) is applied for smart antenna adaptive beamforming. The neural network is used to calculate the optimum weights of the uniform linear antenna array to steer the radiation pattern of the toward the desired users and make nulling in the direction of interference sources. Levenberg Marquardt (LM) algorithm and Resilient Backpropagation (Rprop) algorithm are used to train the FFNN. Five element uniform linear array is used with spacing between element equal to the half wavelength. The simulation results of FFNN training using LM and Rprop algorithms showed that the Neural Network (NN) trained by LM training algorithm gives better performance than Rprop training algorithm, since it considers the fastest backpropagation training algorithm but it takes more memory than other algorithms.","PeriodicalId":315179,"journal":{"name":"2016 Al-Sadeq International Conference on Multidisciplinary in IT and Communication Science and Applications (AIC-MITCSA)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127674722","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 : 2016-05-01DOI: 10.1109/AIC-MITCSA.2016.7759919
O. Almiahi, V. Kanapelka
We propose a method of progressive segmentation of gray scale images based on wave quasi parallel region growing. In contrast to known methods of segmentation of the proposed method allows to divide the area with smoothing drops of brightness and adapt to the constraints time of segmentation.
{"title":"Progressive image segmentation based on the wave region growing","authors":"O. Almiahi, V. Kanapelka","doi":"10.1109/AIC-MITCSA.2016.7759919","DOIUrl":"https://doi.org/10.1109/AIC-MITCSA.2016.7759919","url":null,"abstract":"We propose a method of progressive segmentation of gray scale images based on wave quasi parallel region growing. In contrast to known methods of segmentation of the proposed method allows to divide the area with smoothing drops of brightness and adapt to the constraints time of segmentation.","PeriodicalId":315179,"journal":{"name":"2016 Al-Sadeq International Conference on Multidisciplinary in IT and Communication Science and Applications (AIC-MITCSA)","volume":"88 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132562635","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 : 2016-05-01DOI: 10.1109/AIC-MITCSA.2016.7759929
A. H. Mary, T. Kara, A. H. Miry
This paper presents a new general and fast scheme for solving the inverse kinematics problem (IKP) of the multilink robot arm. The proposed strategy is general as in it is independent of the geometry of the robot arm or its number of degrees of freedom (DOF) and only the forward kinematics is required. The proposed method is a closed-loop strategy in which the IKP is restated as a control problem for a dynamic system and the objective is providing a good trajectory tracking performance. Therefore, PD-like fuzzy controller is used as the controller for this system. Different Cartesian trajectories with different configurations of robotic arm are simulated to demonstrate the effectiveness and generality of the proposed method.
{"title":"Inverse kinematics solution for robotic manipulators based on fuzzy logic and PD control","authors":"A. H. Mary, T. Kara, A. H. Miry","doi":"10.1109/AIC-MITCSA.2016.7759929","DOIUrl":"https://doi.org/10.1109/AIC-MITCSA.2016.7759929","url":null,"abstract":"This paper presents a new general and fast scheme for solving the inverse kinematics problem (IKP) of the multilink robot arm. The proposed strategy is general as in it is independent of the geometry of the robot arm or its number of degrees of freedom (DOF) and only the forward kinematics is required. The proposed method is a closed-loop strategy in which the IKP is restated as a control problem for a dynamic system and the objective is providing a good trajectory tracking performance. Therefore, PD-like fuzzy controller is used as the controller for this system. Different Cartesian trajectories with different configurations of robotic arm are simulated to demonstrate the effectiveness and generality of the proposed method.","PeriodicalId":315179,"journal":{"name":"2016 Al-Sadeq International Conference on Multidisciplinary in IT and Communication Science and Applications (AIC-MITCSA)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131835176","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 : 2016-05-01DOI: 10.1109/AIC-MITCSA.2016.7759910
M. Rashid, A. Rashid
In this days, the field of designing swimming robot takes the interest of researchers due to its intervention in many applications that required diving processes. There are several modes of swimming mechanism like carangiform and labriform modes. In this paper, swimming robot has been designed and implemented based on labriform mode. The forward motion and control of robot direction in horizontal plane has been achieved by pectoral fins, while the swimming robot performs diving process by using center of gravity control system. Proposed swimming robot model has been graphically simulated by MATLAB, also this robot is implemented by KKmulticontroller V.5.5 development kit and several experiments have been performed in order to testing swimming robot.
{"title":"Design and implementation of swimming robot based on labriform model","authors":"M. Rashid, A. Rashid","doi":"10.1109/AIC-MITCSA.2016.7759910","DOIUrl":"https://doi.org/10.1109/AIC-MITCSA.2016.7759910","url":null,"abstract":"In this days, the field of designing swimming robot takes the interest of researchers due to its intervention in many applications that required diving processes. There are several modes of swimming mechanism like carangiform and labriform modes. In this paper, swimming robot has been designed and implemented based on labriform mode. The forward motion and control of robot direction in horizontal plane has been achieved by pectoral fins, while the swimming robot performs diving process by using center of gravity control system. Proposed swimming robot model has been graphically simulated by MATLAB, also this robot is implemented by KKmulticontroller V.5.5 development kit and several experiments have been performed in order to testing swimming robot.","PeriodicalId":315179,"journal":{"name":"2016 Al-Sadeq International Conference on Multidisciplinary in IT and Communication Science and Applications (AIC-MITCSA)","volume":"182 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133550136","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 : 2016-05-01DOI: 10.1109/AIC-MITCSA.2016.7759954
Hikmat N. Abdullah, H. Abed
High energy consumption is one of the main challenges faced in cognitive radio (CR) networks, which may limit their implementation especially in battery-powered devices. In these networks, significant part of the energy is consumed in the energy detector during spectrum sensing for possible vacancy for secondary user transmission. In this paper, we have investigated reducing the energy consumption of single cognitive user (CU) by reducing the number of sensed samples. Also we have explained the optimization criteria for improving energy consumption by controlling number of sensed samples, detection probability and threshold of energy detector. The performance of energy detection system is evaluated in AWGN and Rayleigh fading channels. The simulation results show that at Eb/No of 10 dB 50% and 46 % of the energy consumed in detection are saved when the number of sensed samples is reduced by 50% with acceptable loss in detection probability of 5% and 12% in AWGN and Rayleigh channel respectively. The results also shows the impact of changing the threshold of energy detector on energy consumed in spectrum sensing.
{"title":"Improvement of energy consumption in cognitive radio by reducing the number of sensed samples","authors":"Hikmat N. Abdullah, H. Abed","doi":"10.1109/AIC-MITCSA.2016.7759954","DOIUrl":"https://doi.org/10.1109/AIC-MITCSA.2016.7759954","url":null,"abstract":"High energy consumption is one of the main challenges faced in cognitive radio (CR) networks, which may limit their implementation especially in battery-powered devices. In these networks, significant part of the energy is consumed in the energy detector during spectrum sensing for possible vacancy for secondary user transmission. In this paper, we have investigated reducing the energy consumption of single cognitive user (CU) by reducing the number of sensed samples. Also we have explained the optimization criteria for improving energy consumption by controlling number of sensed samples, detection probability and threshold of energy detector. The performance of energy detection system is evaluated in AWGN and Rayleigh fading channels. The simulation results show that at Eb/No of 10 dB 50% and 46 % of the energy consumed in detection are saved when the number of sensed samples is reduced by 50% with acceptable loss in detection probability of 5% and 12% in AWGN and Rayleigh channel respectively. The results also shows the impact of changing the threshold of energy detector on energy consumed in spectrum sensing.","PeriodicalId":315179,"journal":{"name":"2016 Al-Sadeq International Conference on Multidisciplinary in IT and Communication Science and Applications (AIC-MITCSA)","volume":"79 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114438762","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 : 2016-05-01DOI: 10.1109/AIC-MITCSA.2016.7759933
Mahnoosh Fatahi, F. Mardukhi
Profit Maximization is the problem of finding an optimal strategy to maximize the expected total profit earned by the end of an influence diffusion process under a given propagation model. In the previous works the strategy of influencing the most profitable (influential) users in a social network in order to start the viral marketing campaign has been proposed. These initial users are called the Seed Set. In this work, we introduce a new problem namely Location-Based Profit Maximization (LBPM) and provide a model to solve it. LBPM is the problem of finding the most profitable Seed Set in a Location-Based Social Network (LBSN) in such a way that the expected profit for the owners of a specific venue would be maximized under a certain propagation model considering a limited budget. The proposed model encompasses two main steps. In the first step, we provide Profit Calculation Algorithm to estimate the profitability of each user for a particular venue, considering social aspect, spatial aspect, and users' opinion aspect. Then, in the second step we utilize Linear Threshold (LT) diffusion model under our proposed P-Greedy algorithm to find the final Seed Set.
{"title":"A model for profit maximization in LBSNs","authors":"Mahnoosh Fatahi, F. Mardukhi","doi":"10.1109/AIC-MITCSA.2016.7759933","DOIUrl":"https://doi.org/10.1109/AIC-MITCSA.2016.7759933","url":null,"abstract":"Profit Maximization is the problem of finding an optimal strategy to maximize the expected total profit earned by the end of an influence diffusion process under a given propagation model. In the previous works the strategy of influencing the most profitable (influential) users in a social network in order to start the viral marketing campaign has been proposed. These initial users are called the Seed Set. In this work, we introduce a new problem namely Location-Based Profit Maximization (LBPM) and provide a model to solve it. LBPM is the problem of finding the most profitable Seed Set in a Location-Based Social Network (LBSN) in such a way that the expected profit for the owners of a specific venue would be maximized under a certain propagation model considering a limited budget. The proposed model encompasses two main steps. In the first step, we provide Profit Calculation Algorithm to estimate the profitability of each user for a particular venue, considering social aspect, spatial aspect, and users' opinion aspect. Then, in the second step we utilize Linear Threshold (LT) diffusion model under our proposed P-Greedy algorithm to find the final Seed Set.","PeriodicalId":315179,"journal":{"name":"2016 Al-Sadeq International Conference on Multidisciplinary in IT and Communication Science and Applications (AIC-MITCSA)","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124782729","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 : 2016-05-01DOI: 10.1109/AIC-MITCSA.2016.7759921
A. Shauchuk, V. Tsviatkou
In this paper propose a method of normalization in thickness of the contour lines based on the analysis by mask of the local orientations of fragments. Comparison of the proposed method with known methods of thinning is held. It is shown that the proposed method is superior to the known methods of thinning on speed and quality.
{"title":"Method of normalization of the contour line in thickness based on binary masks","authors":"A. Shauchuk, V. Tsviatkou","doi":"10.1109/AIC-MITCSA.2016.7759921","DOIUrl":"https://doi.org/10.1109/AIC-MITCSA.2016.7759921","url":null,"abstract":"In this paper propose a method of normalization in thickness of the contour lines based on the analysis by mask of the local orientations of fragments. Comparison of the proposed method with known methods of thinning is held. It is shown that the proposed method is superior to the known methods of thinning on speed and quality.","PeriodicalId":315179,"journal":{"name":"2016 Al-Sadeq International Conference on Multidisciplinary in IT and Communication Science and Applications (AIC-MITCSA)","volume":"48 4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129279860","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 : 2016-05-01DOI: 10.1109/AIC-MITCSA.2016.7759942
Eng Sattar B. Sadkhan, Baheeja K. Al-Shukur, Ali K. Mattar
Security of cryptosystems became more important issues in several applications, and need to be focused. Traditional cryptosystem used traditional ways to secure the data (pin, password, etc.) while biometric features extracted from single biometric model or from merging more than one biometric features to produce strong keys for security. This paper provides a survey we tried to explore different method to construct keys (that's lead to a secure cryptosystem) with more resistance against attackers and how the system react with it.
{"title":"Survey of biometrie based key generation to enhance security of cryptosystems","authors":"Eng Sattar B. Sadkhan, Baheeja K. Al-Shukur, Ali K. Mattar","doi":"10.1109/AIC-MITCSA.2016.7759942","DOIUrl":"https://doi.org/10.1109/AIC-MITCSA.2016.7759942","url":null,"abstract":"Security of cryptosystems became more important issues in several applications, and need to be focused. Traditional cryptosystem used traditional ways to secure the data (pin, password, etc.) while biometric features extracted from single biometric model or from merging more than one biometric features to produce strong keys for security. This paper provides a survey we tried to explore different method to construct keys (that's lead to a secure cryptosystem) with more resistance against attackers and how the system react with it.","PeriodicalId":315179,"journal":{"name":"2016 Al-Sadeq International Conference on Multidisciplinary in IT and Communication Science and Applications (AIC-MITCSA)","volume":"2143 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127467304","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 : 2016-05-01DOI: 10.1109/AIC-MITCSA.2016.7759936
Ali H. Ali, Raed S. H. AL-Musawi
The use of a consumer-grade Brain-Computer Interface (BCI) has seen significant interests among researchers and hobbyists like communities. It has been suggested as a viable mean to control robots, improve learning experience and even to classify thought patterns. This paper investigates the possibility of using the NeuroSky Mindwave headset, a very cheap and popular single electrode BCI, for such endeavors by means of unsupervised machine learning algorithms. Firstly, the raw Electroencephalography (EEG) signals from 10 different subjects were acquired while they performed various mental activities. The mental activities ranged from listening to relaxing music to doing mathematical calculations. Secondly, the EEG signals were filtered to obtain the Gamma, Beta, Alpha, Theta and Delta brainwaves. Finally, k-means, fuzzy c-means and Self-Organizing Maps (SOMs) clustering algorithms have been applied to group the brainwaves according to their similarities. The performance of the cluster algorithms was benchmarked using distance metric maps, cluster silhouettes, Calinski-Harabasz index and Davies-Bouldin index. K-means clustering algorithm has showed some power of separating different mental activities into groups. The minimum Mean Silhouette Value has been found to be 0.475 when the number of clusters is 3 and the highest CH-index registered has been 65.7. These results show an interesting possibility for using the MindWave headset in applications where the number of mental activities to be harvested may not be greater than 2 or 3 at most.
{"title":"Investigating the possibility of using a single electrode brain-computer interface device for human machine interaction by means of cluster analysis","authors":"Ali H. Ali, Raed S. H. AL-Musawi","doi":"10.1109/AIC-MITCSA.2016.7759936","DOIUrl":"https://doi.org/10.1109/AIC-MITCSA.2016.7759936","url":null,"abstract":"The use of a consumer-grade Brain-Computer Interface (BCI) has seen significant interests among researchers and hobbyists like communities. It has been suggested as a viable mean to control robots, improve learning experience and even to classify thought patterns. This paper investigates the possibility of using the NeuroSky Mindwave headset, a very cheap and popular single electrode BCI, for such endeavors by means of unsupervised machine learning algorithms. Firstly, the raw Electroencephalography (EEG) signals from 10 different subjects were acquired while they performed various mental activities. The mental activities ranged from listening to relaxing music to doing mathematical calculations. Secondly, the EEG signals were filtered to obtain the Gamma, Beta, Alpha, Theta and Delta brainwaves. Finally, k-means, fuzzy c-means and Self-Organizing Maps (SOMs) clustering algorithms have been applied to group the brainwaves according to their similarities. The performance of the cluster algorithms was benchmarked using distance metric maps, cluster silhouettes, Calinski-Harabasz index and Davies-Bouldin index. K-means clustering algorithm has showed some power of separating different mental activities into groups. The minimum Mean Silhouette Value has been found to be 0.475 when the number of clusters is 3 and the highest CH-index registered has been 65.7. These results show an interesting possibility for using the MindWave headset in applications where the number of mental activities to be harvested may not be greater than 2 or 3 at most.","PeriodicalId":315179,"journal":{"name":"2016 Al-Sadeq International Conference on Multidisciplinary in IT and Communication Science and Applications (AIC-MITCSA)","volume":"39 4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128775463","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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