As a form of distance learning, e-learning has become a major instructional force in the world. I... more As a form of distance learning, e-learning has become a major instructional force in the world. In this chapter, initiatives regarding e-learning and its impacts on instructional design, on school management and on the community are described and discussed in order to show different aspects of e-learning environments and their impact on related individuals or institutions. Future trends in e-learning are presented in connection with expected technological improvements and key points needing special care in the development of future e-learning environments are mentioned in the light of diffusion theory.
In this paper we survey, consolidate, and present the state of the art in distributed database co... more In this paper we survey, consolidate, and present the state of the art in distributed database concurrency control. The heart of our analysts is a decomposition of the concurrency control problem into two major subproblems: read-write and write-write synchronization. We describe a series of synchromzation techniques for solving each subproblem and show how to combine these techniques into algorithms for solving the entire concurrency control problem. Such algorithms are called "concurrency control methods." We describe 48 principal methods, including all practical algorithms that have appeared m the literature plus several new ones. We concentrate on the structure and correctness of concurrency control algorithms. Issues of performance are given only secondary treatment.
Unsupervised source-free domain adaptation methods aim to train a model to be used in the target ... more Unsupervised source-free domain adaptation methods aim to train a model to be used in the target domain utilizing the pretrained source-domain model and unlabeled target-domain data, where the source data may not be accessible due to intellectual property or privacy issues. These methods frequently utilize self-training with pseudo-labeling thresholded by prediction confidence. In a source-free scenario, only supervision comes from target data, and thresholding limits the contribution of the self-training. In this study, we utilize self-training with a mean-teacher approach. The student network is trained with all predictions of the teacher network. Instead of thresholding the predictions, the gradients calculated from the pseudo-labels are weighted based on the reliability of the teacher's predictions. We propose a novel method that uses proxy-based metric learning to estimate reliability. We train a metric network on the encoder features of the teacher network. Since the teacher is updated with the moving average, the encoder feature space is slowly changing. Therefore, the metric network can be updated in training time, which enables end-to-end training. We also propose a metricbased online ClassMix method to augment the input of the student network where the patches to be mixed are decided based on the metric reliability. We evaluated our method in synthetic-to-real and cross-city scenarios. The benchmarks show that our method significantly outperforms the existing state-ofthe-art methods.
ABSTRACT In this study the classification of the EEG signals recorded during motor imagery for cu... more ABSTRACT In this study the classification of the EEG signals recorded during motor imagery for curser movement in brain computer interfaces is examined, in which the feature vectors obtained in frequency domain is used and then the linear transformations are applied for reducing the size of the feature vectors.
... Soner Büyükatalay 12 , Özlem Birgül 2 , Uğur Halıcı 1 ... Fotometrik stereo (FS) yönteminde s... more ... Soner Büyükatalay 12 , Özlem Birgül 2 , Uğur Halıcı 1 ... Fotometrik stereo (FS) yönteminde sabitduran yüzey ve kamera ile değişen ışık kaynakları kullanılarak çekimler yapılır [3]. Bu şekilde çekilen görüntülerin parlaklık değerlerindeki değişimler yüzey dikmesi ve yüzeyin ...
This study proposes a new method to detect road junctions from existing road masks obtained from ... more This study proposes a new method to detect road junctions from existing road masks obtained from geospatial databases. Moreover, this method can be used to extract junction points from the road masks generated by automatic or semiautomatic road extraction algorithms. The algorithm is intended to lower the false detection rate by refining the input road mask. Vector space analysis of the pruned road skeleton provides a simple yet robust detection and classification strategy. Empirical results demonstrate the success of the proposed junction extraction model.
Control of pH neutralization system for strong acid and strong base is studied utilising fuzzy lo... more Control of pH neutralization system for strong acid and strong base is studied utilising fuzzy logic, sliding mode and hybrid (fuzzy-sliding mode) control techniques. Models from literature is used in the simulation studies. The sliding mode controller is designed by using boundary layer and adaptive terms. The controllers designed are tested for set-point tracking and for disturbance rejection conditions.The results obtained for the fuzzy controller and fuzzy-sliding mode controller are satisfactory with robust performance.
In this paper, a neural network structure based on Self Organizing Feature Maps (SOM) is proposed... more In this paper, a neural network structure based on Self Organizing Feature Maps (SOM) is proposed for fingerprint classification. In order to be able to deal with fingerprint images having distorted regions, the SOM learning and classifi cation algorithms are modified. For this purpose, the concept of "certainty" is introduced and used in the modified algorithms. This jingeuprint classifier together with a fingerprint identi+-, constitute subsystems of an automated fingerprint identification system named HALafis. ' Our results show that a network that is trained with a suflciently large and representative set of samples can be used as an indexing mechanism for a fingerprint database, so that it does not need to be retrained for each fingerprint added to the database.
Abstract This paper presents an application of the multi-agent system approach to a service mobil... more Abstract This paper presents an application of the multi-agent system approach to a service mobile manipulator robot that interacts with a human during an object delivery and hand-over task in two dimensions. The base, elbow and shoulder of the robot are identified as three different agents, and are controlled using fuzzy control. The fuzzy rules of each agent are written considering the state of other agents besides its own state. While writing the rules effective delivery and avoiding the contact with human not to cause physical harm is considered. The membership functions of the fuzzy controllers are tuned using genetic algorithms. In tuning, the performance is calculated considering the deviation from the optimum path, time spent to reach the human hand and energy consumed by the actuators. The proposed multi agent system structure based on fuzzy control for the object delivery task succeeded in both effective and safe delivery.
As a form of distance learning, e-learning has become a major instructional force in the world. I... more As a form of distance learning, e-learning has become a major instructional force in the world. In this chapter, initiatives regarding e-learning and its impacts on instructional design, on school management and on the community are described and discussed in order to show different aspects of e-learning environments and their impact on related individuals or institutions. Future trends in e-learning are presented in connection with expected technological improvements and key points needing special care in the development of future e-learning environments are mentioned in the light of diffusion theory.
Brain Computer Interface (BCI) systems provide control of external devices by using only brain ac... more Brain Computer Interface (BCI) systems provide control of external devices by using only brain activity. In recent years, there has been a great interest in developing BCI systems for different applications. These systems are capable of solving daily life problems for both healthy and disabled people. One of the most important applications of BCI is to provide communication for disabled people that are totally paralysed. In this paper, different parts of a BCI system and different methods used in each part are reviewed. Neuroimaging devices, with an emphasis on EEG (electroencephalography), are presented and brain activities as well as signal processing methods used in EEG-based BCIs are explained in detail. Current methods and paradigms in BCI based speech communication are considered.
ABSTRACT A method for concurrency control in distributed database management systems that increas... more ABSTRACT A method for concurrency control in distributed database management systems that increases the level of concurrent execution of transactions, called ordering by serialization numbers (OSN), is proposed. The OSN method works in the certifier model and uses time-interval techniques in conjunction with short-term locks to provide serializability and prevent deadlocks. The scheduler is distributed, and the standard transaction execution policy is assumed, that is, the read and write operations are issued continuously during transaction execution. However, the write operations are copied into the database only when the transaction commits. The amount of concurrency provided by the OSN method is demonstrated by log classification. It is shown that the OSN method provides more concurrency than basic timestamp ordering and two-phase locking methods and handles successfully some logs which cannot be handled by any of the past methods. The complexity analysis of the algorithm indicates that the method works in a reasonable amount of time
IEEE Transactions on Software Engineering, Jul 1, 1991
Abstruct-An optimistic scheme, called ODL, which uses dummy locks to test the validity of a trans... more Abstruct-An optimistic scheme, called ODL, which uses dummy locks to test the validity of a transaction for concurrency control in distributed database systems, is suggested. The dummy locks are long-term locks; however, they do not conflict with any other lock. By the use of long-term ...
This study investigated the use of deep convolutional neural networks (CNNs) in providing a solut... more This study investigated the use of deep convolutional neural networks (CNNs) in providing a solution for the problem of airport detection in remote sensing images (RSIs). In recent years, Deep CNNs have gained much attention with numerous applications having been undertaken in the area of computer vision. Researchers generally approach airport detection as a pattern recognition problem, in which first various distinctive features are extracted, and then a classifier is adopted to detect airports. CNNs not only ensure a tuned feature vector, but also yield better classification accuracy. The method proposed in this study first detects various regions on RSIs and then uses these candidate regions to train CNN architecture. The CNN model used has five convolution and three fully connected layers. Normalization and dropout layers were employed in order to build efficient architecture. A data augmentation strategy was used to reduce overfitting. Several experiments were performed to evaluate the performance of CNNs. Comparative work validated the efficiency of the proposed method and yielded an accuracy of 95.21%.
As a form of distance learning, e-learning has become a major instructional force in the world. I... more As a form of distance learning, e-learning has become a major instructional force in the world. In this chapter, initiatives regarding e-learning and its impacts on instructional design, on school management and on the community are described and discussed in order to show different aspects of e-learning environments and their impact on related individuals or institutions. Future trends in e-learning are presented in connection with expected technological improvements and key points needing special care in the development of future e-learning environments are mentioned in the light of diffusion theory.
In this paper we survey, consolidate, and present the state of the art in distributed database co... more In this paper we survey, consolidate, and present the state of the art in distributed database concurrency control. The heart of our analysts is a decomposition of the concurrency control problem into two major subproblems: read-write and write-write synchronization. We describe a series of synchromzation techniques for solving each subproblem and show how to combine these techniques into algorithms for solving the entire concurrency control problem. Such algorithms are called "concurrency control methods." We describe 48 principal methods, including all practical algorithms that have appeared m the literature plus several new ones. We concentrate on the structure and correctness of concurrency control algorithms. Issues of performance are given only secondary treatment.
Unsupervised source-free domain adaptation methods aim to train a model to be used in the target ... more Unsupervised source-free domain adaptation methods aim to train a model to be used in the target domain utilizing the pretrained source-domain model and unlabeled target-domain data, where the source data may not be accessible due to intellectual property or privacy issues. These methods frequently utilize self-training with pseudo-labeling thresholded by prediction confidence. In a source-free scenario, only supervision comes from target data, and thresholding limits the contribution of the self-training. In this study, we utilize self-training with a mean-teacher approach. The student network is trained with all predictions of the teacher network. Instead of thresholding the predictions, the gradients calculated from the pseudo-labels are weighted based on the reliability of the teacher's predictions. We propose a novel method that uses proxy-based metric learning to estimate reliability. We train a metric network on the encoder features of the teacher network. Since the teacher is updated with the moving average, the encoder feature space is slowly changing. Therefore, the metric network can be updated in training time, which enables end-to-end training. We also propose a metricbased online ClassMix method to augment the input of the student network where the patches to be mixed are decided based on the metric reliability. We evaluated our method in synthetic-to-real and cross-city scenarios. The benchmarks show that our method significantly outperforms the existing state-ofthe-art methods.
ABSTRACT In this study the classification of the EEG signals recorded during motor imagery for cu... more ABSTRACT In this study the classification of the EEG signals recorded during motor imagery for curser movement in brain computer interfaces is examined, in which the feature vectors obtained in frequency domain is used and then the linear transformations are applied for reducing the size of the feature vectors.
... Soner Büyükatalay 12 , Özlem Birgül 2 , Uğur Halıcı 1 ... Fotometrik stereo (FS) yönteminde s... more ... Soner Büyükatalay 12 , Özlem Birgül 2 , Uğur Halıcı 1 ... Fotometrik stereo (FS) yönteminde sabitduran yüzey ve kamera ile değişen ışık kaynakları kullanılarak çekimler yapılır [3]. Bu şekilde çekilen görüntülerin parlaklık değerlerindeki değişimler yüzey dikmesi ve yüzeyin ...
This study proposes a new method to detect road junctions from existing road masks obtained from ... more This study proposes a new method to detect road junctions from existing road masks obtained from geospatial databases. Moreover, this method can be used to extract junction points from the road masks generated by automatic or semiautomatic road extraction algorithms. The algorithm is intended to lower the false detection rate by refining the input road mask. Vector space analysis of the pruned road skeleton provides a simple yet robust detection and classification strategy. Empirical results demonstrate the success of the proposed junction extraction model.
Control of pH neutralization system for strong acid and strong base is studied utilising fuzzy lo... more Control of pH neutralization system for strong acid and strong base is studied utilising fuzzy logic, sliding mode and hybrid (fuzzy-sliding mode) control techniques. Models from literature is used in the simulation studies. The sliding mode controller is designed by using boundary layer and adaptive terms. The controllers designed are tested for set-point tracking and for disturbance rejection conditions.The results obtained for the fuzzy controller and fuzzy-sliding mode controller are satisfactory with robust performance.
In this paper, a neural network structure based on Self Organizing Feature Maps (SOM) is proposed... more In this paper, a neural network structure based on Self Organizing Feature Maps (SOM) is proposed for fingerprint classification. In order to be able to deal with fingerprint images having distorted regions, the SOM learning and classifi cation algorithms are modified. For this purpose, the concept of "certainty" is introduced and used in the modified algorithms. This jingeuprint classifier together with a fingerprint identi+-, constitute subsystems of an automated fingerprint identification system named HALafis. ' Our results show that a network that is trained with a suflciently large and representative set of samples can be used as an indexing mechanism for a fingerprint database, so that it does not need to be retrained for each fingerprint added to the database.
Abstract This paper presents an application of the multi-agent system approach to a service mobil... more Abstract This paper presents an application of the multi-agent system approach to a service mobile manipulator robot that interacts with a human during an object delivery and hand-over task in two dimensions. The base, elbow and shoulder of the robot are identified as three different agents, and are controlled using fuzzy control. The fuzzy rules of each agent are written considering the state of other agents besides its own state. While writing the rules effective delivery and avoiding the contact with human not to cause physical harm is considered. The membership functions of the fuzzy controllers are tuned using genetic algorithms. In tuning, the performance is calculated considering the deviation from the optimum path, time spent to reach the human hand and energy consumed by the actuators. The proposed multi agent system structure based on fuzzy control for the object delivery task succeeded in both effective and safe delivery.
As a form of distance learning, e-learning has become a major instructional force in the world. I... more As a form of distance learning, e-learning has become a major instructional force in the world. In this chapter, initiatives regarding e-learning and its impacts on instructional design, on school management and on the community are described and discussed in order to show different aspects of e-learning environments and their impact on related individuals or institutions. Future trends in e-learning are presented in connection with expected technological improvements and key points needing special care in the development of future e-learning environments are mentioned in the light of diffusion theory.
Brain Computer Interface (BCI) systems provide control of external devices by using only brain ac... more Brain Computer Interface (BCI) systems provide control of external devices by using only brain activity. In recent years, there has been a great interest in developing BCI systems for different applications. These systems are capable of solving daily life problems for both healthy and disabled people. One of the most important applications of BCI is to provide communication for disabled people that are totally paralysed. In this paper, different parts of a BCI system and different methods used in each part are reviewed. Neuroimaging devices, with an emphasis on EEG (electroencephalography), are presented and brain activities as well as signal processing methods used in EEG-based BCIs are explained in detail. Current methods and paradigms in BCI based speech communication are considered.
ABSTRACT A method for concurrency control in distributed database management systems that increas... more ABSTRACT A method for concurrency control in distributed database management systems that increases the level of concurrent execution of transactions, called ordering by serialization numbers (OSN), is proposed. The OSN method works in the certifier model and uses time-interval techniques in conjunction with short-term locks to provide serializability and prevent deadlocks. The scheduler is distributed, and the standard transaction execution policy is assumed, that is, the read and write operations are issued continuously during transaction execution. However, the write operations are copied into the database only when the transaction commits. The amount of concurrency provided by the OSN method is demonstrated by log classification. It is shown that the OSN method provides more concurrency than basic timestamp ordering and two-phase locking methods and handles successfully some logs which cannot be handled by any of the past methods. The complexity analysis of the algorithm indicates that the method works in a reasonable amount of time
IEEE Transactions on Software Engineering, Jul 1, 1991
Abstruct-An optimistic scheme, called ODL, which uses dummy locks to test the validity of a trans... more Abstruct-An optimistic scheme, called ODL, which uses dummy locks to test the validity of a transaction for concurrency control in distributed database systems, is suggested. The dummy locks are long-term locks; however, they do not conflict with any other lock. By the use of long-term ...
This study investigated the use of deep convolutional neural networks (CNNs) in providing a solut... more This study investigated the use of deep convolutional neural networks (CNNs) in providing a solution for the problem of airport detection in remote sensing images (RSIs). In recent years, Deep CNNs have gained much attention with numerous applications having been undertaken in the area of computer vision. Researchers generally approach airport detection as a pattern recognition problem, in which first various distinctive features are extracted, and then a classifier is adopted to detect airports. CNNs not only ensure a tuned feature vector, but also yield better classification accuracy. The method proposed in this study first detects various regions on RSIs and then uses these candidate regions to train CNN architecture. The CNN model used has five convolution and three fully connected layers. Normalization and dropout layers were employed in order to build efficient architecture. A data augmentation strategy was used to reduce overfitting. Several experiments were performed to evaluate the performance of CNNs. Comparative work validated the efficiency of the proposed method and yielded an accuracy of 95.21%.
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