Bu calismada, onceden tasarlanmis olan ozgun KNT/MOSFET tabanli aktif elektrotun ve yaygin olarak... more Bu calismada, onceden tasarlanmis olan ozgun KNT/MOSFET tabanli aktif elektrotun ve yaygin olarak kullanilan islak elektrotun elektrik devresi esdegerlerine, farkli biyopotansiyeller uygulanarak benzetimleri gerceklestirilmistir. Elektrotlarin giris sinyallerine verdigi yanitlar, belirli cilt yuzeyi kosullarinda karsilastirilmistir. Elektrotlari degerlendirmek icin elde edilen biyopotansiyellerin Fourier donusumleri ve toplam harmonik bozulmalari incelenmistir. Simulasyon sonuclari, KNT/MOSFET tabanli elektrotun islak elektrottan daha iyi sonuclar verdigini ve biyopotansiyelleri yuksek kalitede olcebilecegini gostermektedir.
Particularly in operating systems, a quite a few of researches are focused on solving deadlock pr... more Particularly in operating systems, a quite a few of researches are focused on solving deadlock problems. Deadlock is a common problem bounded efficiency of the system and very often occurs where multi-processes compete for some sharable resources.. Deadlocked status means that in among of processes which each of them is waiting for an event while another process can cause it switches to ready state and then runs. The necessary conditions for deadlock are mutual exclusion, hold and wait, preemption, and circular wait. There are four strategies to handle deadlocks as if include ignorance, detection, avoidance and prevention. Deadlock Prevention (DP) is a true real-time solution; however some researchers see Deadlock Avoidance (DA) as less restrictive. In DP, the concern is to condition a system to remove any possibility of deadlocks occurring.This paper is a review of the literature of techniques for solving deadlock problems in operating system. The solution methods for DP were selected according to a set of criteria of work relevancy their internal architecture techniques were reviewed. Our work addresses the issue of DP via these methods. A classification considering the main goal of the methods has been made methods to handle deadlocks. A discussion is presented for the suitability of resolution methods of deadlock problems in operating systems. Keyworks: Deadlock Prevention, Deadlock Avoidance, Deadlock Detection, Deadlock Recovery, Operating Systems;
... Guo, Zhang, and Xu (2009), mechanical properties of CNTs studied by Alzubi and Cosby (2008), ... more ... Guo, Zhang, and Xu (2009), mechanical properties of CNTs studied by Alzubi and Cosby (2008), CNT-metal junctions investigated by Khazaei, Lee, Picherri, and Kawazoe (2008), transport properties of CNTs with crystal defects studied by Neophytou, Ahmed, and Klimeck ...
Impedance measurement has been widely used as an effective indicator for characterizing samples. ... more Impedance measurement has been widely used as an effective indicator for characterizing samples. Traditional high accuracy impedance analyzers are complicated, expensive, and non-portable. Many kinds of research require low cost, portable, and high precision impedance analyzer devices. AD5933 impedance analyzer integrated circuit has been popularly used for fulfilling these requirements. There are many successful applications of the AD5933 integrated circuit; however, the most significant drawback is nonlinear calibration requirements for high precision measurement in a specified range. The calibration and unknown impedances must be close enough to each other for better measurement accuracy. In the literature, calibration impedance arrays increasing the complexity and processing time are commonly used for high accuracy measurements. In this study, an artificial neural network-based signal post-processing algorithm is proposed to overcome the calibration requirements of the AD5933 in...
2017 IEEE International Conference on INnovations in Intelligent SysTems and Applications (INISTA), 2017
Increase in popularity of deep convolutional neural networks in many different areas leads to inc... more Increase in popularity of deep convolutional neural networks in many different areas leads to increase in the use of these networks in reinforcement learning. Training a huge deep neural network structure by using simple gradient descent learning can take quite a long time. Some additional learning approaches should be utilized to solve this problem. One of these techniques is use of momentum which accelerates gradient descent learning. Although momentum techniques are mostly developed for supervised learning problems, it can also be used for reinforcement learning problems. However, its efficiency may vary due to the dissimilarities in two training learning processes. In this paper, the performances of different momentum techniques are compared for one of the reinforcement learning problems; Othello game benchmark. Test results show that the Nesterov accelerated momentum technique provided a more effective generalization on benchmark
Technological improvements lead big data producing, processing and storing systems. These systems... more Technological improvements lead big data producing, processing and storing systems. These systems must contain extraordinary capabilities to overcome complexity of the big data. Therefore, the methodologies utilized for data analysis have been evolved due to the increase in importance of extracting information from big data. Healthcare systems are important systems dealing with big data analysis. Deep learning is the most applied data analysis method. It becomes one of the most popular and up-to-date artificial neural network types with deep representation ability. Another powerful ability of deep learning is providing feature learning through convolutional neural networks. Deep learning has wide implementation areas in medical applications from diagnosis to treatment. Various deep learning methods are applied to the biomedical problems. In many applications, deep learning solutions are modified in accordance with the requirements of the problems. Through this chapter the most popul...
Up to date technological implementations of deep convolutional neural networks are at the forefro... more Up to date technological implementations of deep convolutional neural networks are at the forefront of many issues, such as autonomous device control, effective image and pattern recognition solutions. Deep neural networks generally utilize a hybrid topology of a feature extractor containing convolutional layers followed by a fully connected classifier network. The characteristic and quality of the produced features differ according to the deep learning structure. In order to get high performance, it is necessary to choose an effective topology. In this study, a novel topology based hybrid structure named as Deep Convolutional Generalized Classifier Neural Network and its learning algoritm are introduced. This novel structure allows the deep learning network to extract features with the desired characteristics. This ensures high performance classification, even for relatively small deep learning networks. This has led to many novelties such as principal feature analysis, better learning ability, one-pass learning for classifier part, new error computation and backpropagation approach for filter weights. Two experiment sets were performed to measure the performance of DC-GCNN. In the first experiment set, DC-GCNN was compared with clasical approach on 10 different datasets. DC-GCNN performed better up to 44.45% for precision, 39.69% for recall and 42.57% for F1-score. In the second experiment set, DC-GCNN’s performance was compared with alternative methods on larger datasets. Proposed structure performed better than alternative deep learning based classifier structures on CIFAR-10 and MNIST datasets with 89.12% and 99.28% accuracy values.
Abstract Standard electrodes for electrophysiological signal acquisition in clinical applications... more Abstract Standard electrodes for electrophysiological signal acquisition in clinical applications such as electrocardiography or electromyography require the use of electrolytic gel and skin abrasion for better electrical interface between electrode and skin. Since gel dries out over a period of time, signal deterioration takes place on long duration with wet electrodes. Dry electrodes are promising alternative for long duration recording; nevertheless, they suffer from high contact impedance and motion artifacts. The proposed work introduces a novel multi-walled carbon nanotube (MWCNT) modified metal oxide semiconductor field effect transistor (MOSFET) based electrode for electrophysiological measurements on human skin. Vertically aligned metallic MWCNTs grown on the gate of MOSFETs form the contact surface of the electrode. MWCNTs penetrate the outer layer of skin for stable and improved electrical contact without the gel. The proposed electrode utilizes advantages of the MOSFET such as direct charge–current conversion, insulation between skin and instrumentation unit, and low noise pre-amplification of electrophysiological signals. Electrical equivalent of MWCNTs, design, and microfabrication of convenient MOSFET are reported. MOSFET parameters are obtained from technology computer-aided design simulation environment and combined with MWCNT parameters in the simulation program for integrated circuits emphasis. Simulated results of the proposed electrode exhibited lower contact impedance and high quality signal capture with respect to wet electrodes. The results show that the proposed electrode can be used for long duration recording of biopotentials with very high stable performance.
This paper introduces an artificial neural network (ANN) approach for estimating monthly mean dai... more This paper introduces an artificial neural network (ANN) approach for estimating monthly mean daily values of global sunshine duration (SD) for Turkey. Three different ANN models, namely, GRNN, MLP, and RBF, were used in the estimation processes. A climatic variable (cloud cover) and two geographical variables (day length and month) were used as input parameters in order to obtain monthly mean SD as output. The datasets of 34 stations which spread across Turkey were split into two parts. First part covering 21 years (1980–2000) was used for training and second part covering last six years (2001–2006) was used for testing. Statistical indicators have shown that, GRNN and MLP models produced better results than the RBF model and can be used safely for the estimation of monthly mean SD.
In this paper, four artificial neural network (ANN) models [i.e., feed-forward neural network (FF... more In this paper, four artificial neural network (ANN) models [i.e., feed-forward neural network (FFNN), function fitting neural network (FITNET), cascade-forward neural network (CFNN) and generalized regression neural network] have been developed for atomic coordinate prediction of carbon nanotubes (CNTs). The research reported in this study has two primary objectives: (1) to develop ANN prediction models that calculate atomic coordinates of CNTs instead of using any simulation software and (2) to use results of the ANN models as an initial value of atomic coordinates for reducing number of iterations in calculation process. The dataset consisting of 10,721 data samples was created by combining the atomic coordinates of elements and chiral vectors using BIOVIA Materials Studio CASTEP (CASTEP) software. All prediction models yield very low mean squared normalized error and mean absolute error rates. Multiple correlation coefficient (R) results of FITNET, FFNN and CFNN models are close to 1. Compared with CASTEP, calculation times decrease from days to minutes. It would seem possible to predict CNTs’ atomic coordinates using ANN models can be successfully used instead of mathematical calculations.
Generalized classifier neural network introduced as a kind of radial basis function neural networ... more Generalized classifier neural network introduced as a kind of radial basis function neural network, uses gradient descent based optimized smoothing parameter value to provide efficient classification. However, optimization consumes quite a long time and may cause a drawback. In this work, one pass learning for generalized classifier neural network is proposed to overcome this disadvantage. Proposed method utilizes standard deviation of each class to calculate corresponding smoothing parameter. Since different datasets may have different standard deviations and data distributions, proposed method tries to handle these differences by defining two functions for smoothing parameter calculation. Thresholding is applied to determine which function will be used. One of these functions is defined for datasets having different range of values. It provides balanced smoothing parameters for these datasets through logarithmic function and changing the operation range to lower boundary. On the other hand, the other function calculates smoothing parameter value for classes having standard deviation smaller than the threshold value. Proposed method is tested on 14 datasets and performance of one pass learning generalized classifier neural network is compared with that of probabilistic neural network, radial basis function neural network, extreme learning machines, and standard and logarithmic learning generalized classifier neural network in MATLAB environment. One pass learning generalized classifier neural network provides more than a thousand times faster classification than standard and logarithmic generalized classifier neural network. Due to its classification accuracy and speed, one pass generalized classifier neural network can be considered as an efficient alternative to probabilistic neural network. Test results show that proposed method overcomes computational drawback of generalized classifier neural network and may increase the classification performance.
Memory fragmentation is a serious obstacle preventing efficient memory usage. Garbage collectors ... more Memory fragmentation is a serious obstacle preventing efficient memory usage. Garbage collectors may solve the problem; however, they cause serious performance impact, memory and energy consumption. Therefore, various memory allocators have been developed. Software developers must test memory allocators, and find an efficient one for their programs. Instead of this cumbersome method, we propose a novel approach for dynamically deciding the best memory allocator for every application. The proposed solution tests each process with various memory allocators. After the testing, it selects an efficient memory allocator according to condition of operating system (OS). If OS runs out of memory, then it selects the most memory efficient allocator for new processes. If most of the CPU power was occupied, then it selects the fastest allocator. Otherwise, the balanced allocator is selected. According to test results, the proposed solution offers up to 58% less fragmented memory, and 90% faster memory operations. In average of 107 processes, it offers 7.16?2.53% less fragmented memory, and 1.79?7.32% faster memory operations. The test results also prove the proposed approach is unbeatable by any memory allocator. In conclusion, the proposed method is a dynamic and efficient solution to the memory fragmentation problem. HighlightsOur solution is an intelligent memory allocator selector for operating systems.The solution selects an efficient and fastest memory allocator for each process.The approach reduces memory fragmentation, and increases system performance.Our solution is a dynamic and efficient solution to memory fragmentation problem.
Bu calismada, onceden tasarlanmis olan ozgun KNT/MOSFET tabanli aktif elektrotun ve yaygin olarak... more Bu calismada, onceden tasarlanmis olan ozgun KNT/MOSFET tabanli aktif elektrotun ve yaygin olarak kullanilan islak elektrotun elektrik devresi esdegerlerine, farkli biyopotansiyeller uygulanarak benzetimleri gerceklestirilmistir. Elektrotlarin giris sinyallerine verdigi yanitlar, belirli cilt yuzeyi kosullarinda karsilastirilmistir. Elektrotlari degerlendirmek icin elde edilen biyopotansiyellerin Fourier donusumleri ve toplam harmonik bozulmalari incelenmistir. Simulasyon sonuclari, KNT/MOSFET tabanli elektrotun islak elektrottan daha iyi sonuclar verdigini ve biyopotansiyelleri yuksek kalitede olcebilecegini gostermektedir.
Particularly in operating systems, a quite a few of researches are focused on solving deadlock pr... more Particularly in operating systems, a quite a few of researches are focused on solving deadlock problems. Deadlock is a common problem bounded efficiency of the system and very often occurs where multi-processes compete for some sharable resources.. Deadlocked status means that in among of processes which each of them is waiting for an event while another process can cause it switches to ready state and then runs. The necessary conditions for deadlock are mutual exclusion, hold and wait, preemption, and circular wait. There are four strategies to handle deadlocks as if include ignorance, detection, avoidance and prevention. Deadlock Prevention (DP) is a true real-time solution; however some researchers see Deadlock Avoidance (DA) as less restrictive. In DP, the concern is to condition a system to remove any possibility of deadlocks occurring.This paper is a review of the literature of techniques for solving deadlock problems in operating system. The solution methods for DP were selected according to a set of criteria of work relevancy their internal architecture techniques were reviewed. Our work addresses the issue of DP via these methods. A classification considering the main goal of the methods has been made methods to handle deadlocks. A discussion is presented for the suitability of resolution methods of deadlock problems in operating systems. Keyworks: Deadlock Prevention, Deadlock Avoidance, Deadlock Detection, Deadlock Recovery, Operating Systems;
... Guo, Zhang, and Xu (2009), mechanical properties of CNTs studied by Alzubi and Cosby (2008), ... more ... Guo, Zhang, and Xu (2009), mechanical properties of CNTs studied by Alzubi and Cosby (2008), CNT-metal junctions investigated by Khazaei, Lee, Picherri, and Kawazoe (2008), transport properties of CNTs with crystal defects studied by Neophytou, Ahmed, and Klimeck ...
Impedance measurement has been widely used as an effective indicator for characterizing samples. ... more Impedance measurement has been widely used as an effective indicator for characterizing samples. Traditional high accuracy impedance analyzers are complicated, expensive, and non-portable. Many kinds of research require low cost, portable, and high precision impedance analyzer devices. AD5933 impedance analyzer integrated circuit has been popularly used for fulfilling these requirements. There are many successful applications of the AD5933 integrated circuit; however, the most significant drawback is nonlinear calibration requirements for high precision measurement in a specified range. The calibration and unknown impedances must be close enough to each other for better measurement accuracy. In the literature, calibration impedance arrays increasing the complexity and processing time are commonly used for high accuracy measurements. In this study, an artificial neural network-based signal post-processing algorithm is proposed to overcome the calibration requirements of the AD5933 in...
2017 IEEE International Conference on INnovations in Intelligent SysTems and Applications (INISTA), 2017
Increase in popularity of deep convolutional neural networks in many different areas leads to inc... more Increase in popularity of deep convolutional neural networks in many different areas leads to increase in the use of these networks in reinforcement learning. Training a huge deep neural network structure by using simple gradient descent learning can take quite a long time. Some additional learning approaches should be utilized to solve this problem. One of these techniques is use of momentum which accelerates gradient descent learning. Although momentum techniques are mostly developed for supervised learning problems, it can also be used for reinforcement learning problems. However, its efficiency may vary due to the dissimilarities in two training learning processes. In this paper, the performances of different momentum techniques are compared for one of the reinforcement learning problems; Othello game benchmark. Test results show that the Nesterov accelerated momentum technique provided a more effective generalization on benchmark
Technological improvements lead big data producing, processing and storing systems. These systems... more Technological improvements lead big data producing, processing and storing systems. These systems must contain extraordinary capabilities to overcome complexity of the big data. Therefore, the methodologies utilized for data analysis have been evolved due to the increase in importance of extracting information from big data. Healthcare systems are important systems dealing with big data analysis. Deep learning is the most applied data analysis method. It becomes one of the most popular and up-to-date artificial neural network types with deep representation ability. Another powerful ability of deep learning is providing feature learning through convolutional neural networks. Deep learning has wide implementation areas in medical applications from diagnosis to treatment. Various deep learning methods are applied to the biomedical problems. In many applications, deep learning solutions are modified in accordance with the requirements of the problems. Through this chapter the most popul...
Up to date technological implementations of deep convolutional neural networks are at the forefro... more Up to date technological implementations of deep convolutional neural networks are at the forefront of many issues, such as autonomous device control, effective image and pattern recognition solutions. Deep neural networks generally utilize a hybrid topology of a feature extractor containing convolutional layers followed by a fully connected classifier network. The characteristic and quality of the produced features differ according to the deep learning structure. In order to get high performance, it is necessary to choose an effective topology. In this study, a novel topology based hybrid structure named as Deep Convolutional Generalized Classifier Neural Network and its learning algoritm are introduced. This novel structure allows the deep learning network to extract features with the desired characteristics. This ensures high performance classification, even for relatively small deep learning networks. This has led to many novelties such as principal feature analysis, better learning ability, one-pass learning for classifier part, new error computation and backpropagation approach for filter weights. Two experiment sets were performed to measure the performance of DC-GCNN. In the first experiment set, DC-GCNN was compared with clasical approach on 10 different datasets. DC-GCNN performed better up to 44.45% for precision, 39.69% for recall and 42.57% for F1-score. In the second experiment set, DC-GCNN’s performance was compared with alternative methods on larger datasets. Proposed structure performed better than alternative deep learning based classifier structures on CIFAR-10 and MNIST datasets with 89.12% and 99.28% accuracy values.
Abstract Standard electrodes for electrophysiological signal acquisition in clinical applications... more Abstract Standard electrodes for electrophysiological signal acquisition in clinical applications such as electrocardiography or electromyography require the use of electrolytic gel and skin abrasion for better electrical interface between electrode and skin. Since gel dries out over a period of time, signal deterioration takes place on long duration with wet electrodes. Dry electrodes are promising alternative for long duration recording; nevertheless, they suffer from high contact impedance and motion artifacts. The proposed work introduces a novel multi-walled carbon nanotube (MWCNT) modified metal oxide semiconductor field effect transistor (MOSFET) based electrode for electrophysiological measurements on human skin. Vertically aligned metallic MWCNTs grown on the gate of MOSFETs form the contact surface of the electrode. MWCNTs penetrate the outer layer of skin for stable and improved electrical contact without the gel. The proposed electrode utilizes advantages of the MOSFET such as direct charge–current conversion, insulation between skin and instrumentation unit, and low noise pre-amplification of electrophysiological signals. Electrical equivalent of MWCNTs, design, and microfabrication of convenient MOSFET are reported. MOSFET parameters are obtained from technology computer-aided design simulation environment and combined with MWCNT parameters in the simulation program for integrated circuits emphasis. Simulated results of the proposed electrode exhibited lower contact impedance and high quality signal capture with respect to wet electrodes. The results show that the proposed electrode can be used for long duration recording of biopotentials with very high stable performance.
This paper introduces an artificial neural network (ANN) approach for estimating monthly mean dai... more This paper introduces an artificial neural network (ANN) approach for estimating monthly mean daily values of global sunshine duration (SD) for Turkey. Three different ANN models, namely, GRNN, MLP, and RBF, were used in the estimation processes. A climatic variable (cloud cover) and two geographical variables (day length and month) were used as input parameters in order to obtain monthly mean SD as output. The datasets of 34 stations which spread across Turkey were split into two parts. First part covering 21 years (1980–2000) was used for training and second part covering last six years (2001–2006) was used for testing. Statistical indicators have shown that, GRNN and MLP models produced better results than the RBF model and can be used safely for the estimation of monthly mean SD.
In this paper, four artificial neural network (ANN) models [i.e., feed-forward neural network (FF... more In this paper, four artificial neural network (ANN) models [i.e., feed-forward neural network (FFNN), function fitting neural network (FITNET), cascade-forward neural network (CFNN) and generalized regression neural network] have been developed for atomic coordinate prediction of carbon nanotubes (CNTs). The research reported in this study has two primary objectives: (1) to develop ANN prediction models that calculate atomic coordinates of CNTs instead of using any simulation software and (2) to use results of the ANN models as an initial value of atomic coordinates for reducing number of iterations in calculation process. The dataset consisting of 10,721 data samples was created by combining the atomic coordinates of elements and chiral vectors using BIOVIA Materials Studio CASTEP (CASTEP) software. All prediction models yield very low mean squared normalized error and mean absolute error rates. Multiple correlation coefficient (R) results of FITNET, FFNN and CFNN models are close to 1. Compared with CASTEP, calculation times decrease from days to minutes. It would seem possible to predict CNTs’ atomic coordinates using ANN models can be successfully used instead of mathematical calculations.
Generalized classifier neural network introduced as a kind of radial basis function neural networ... more Generalized classifier neural network introduced as a kind of radial basis function neural network, uses gradient descent based optimized smoothing parameter value to provide efficient classification. However, optimization consumes quite a long time and may cause a drawback. In this work, one pass learning for generalized classifier neural network is proposed to overcome this disadvantage. Proposed method utilizes standard deviation of each class to calculate corresponding smoothing parameter. Since different datasets may have different standard deviations and data distributions, proposed method tries to handle these differences by defining two functions for smoothing parameter calculation. Thresholding is applied to determine which function will be used. One of these functions is defined for datasets having different range of values. It provides balanced smoothing parameters for these datasets through logarithmic function and changing the operation range to lower boundary. On the other hand, the other function calculates smoothing parameter value for classes having standard deviation smaller than the threshold value. Proposed method is tested on 14 datasets and performance of one pass learning generalized classifier neural network is compared with that of probabilistic neural network, radial basis function neural network, extreme learning machines, and standard and logarithmic learning generalized classifier neural network in MATLAB environment. One pass learning generalized classifier neural network provides more than a thousand times faster classification than standard and logarithmic generalized classifier neural network. Due to its classification accuracy and speed, one pass generalized classifier neural network can be considered as an efficient alternative to probabilistic neural network. Test results show that proposed method overcomes computational drawback of generalized classifier neural network and may increase the classification performance.
Memory fragmentation is a serious obstacle preventing efficient memory usage. Garbage collectors ... more Memory fragmentation is a serious obstacle preventing efficient memory usage. Garbage collectors may solve the problem; however, they cause serious performance impact, memory and energy consumption. Therefore, various memory allocators have been developed. Software developers must test memory allocators, and find an efficient one for their programs. Instead of this cumbersome method, we propose a novel approach for dynamically deciding the best memory allocator for every application. The proposed solution tests each process with various memory allocators. After the testing, it selects an efficient memory allocator according to condition of operating system (OS). If OS runs out of memory, then it selects the most memory efficient allocator for new processes. If most of the CPU power was occupied, then it selects the fastest allocator. Otherwise, the balanced allocator is selected. According to test results, the proposed solution offers up to 58% less fragmented memory, and 90% faster memory operations. In average of 107 processes, it offers 7.16?2.53% less fragmented memory, and 1.79?7.32% faster memory operations. The test results also prove the proposed approach is unbeatable by any memory allocator. In conclusion, the proposed method is a dynamic and efficient solution to the memory fragmentation problem. HighlightsOur solution is an intelligent memory allocator selector for operating systems.The solution selects an efficient and fastest memory allocator for each process.The approach reduces memory fragmentation, and increases system performance.Our solution is a dynamic and efficient solution to memory fragmentation problem.
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