Open Access. Powered by Scholars. Published by Universities.®
Physical Sciences and Mathematics Commons™
Open Access. Powered by Scholars. Published by Universities.®
- Keyword
-
- Machine learning (11)
- Classification (7)
- Convolutional neural networks (6)
- Deep learning (6)
- Genetic algorithm (6)
-
- Optimization (6)
- Artificial neural networks (4)
- Adaptive control (3)
- Feature extraction (3)
- Feature selection (3)
- Genetic algorithms (3)
- Security (3)
- Support vector machine (3)
- Synthetic aperture radar (3)
- Wideband (3)
- Wireless sensor network (3)
- Wireless sensor networks (3)
- 5G (2)
- Artificial bee colony (2)
- Artificial neural network (2)
- CAD (2)
- Clustering (2)
- Conic functions (2)
- Convolutional neural network (2)
- Decision tree (2)
- Detection (2)
- Diversity (2)
- Efficiency (2)
- Energy efficiency (2)
- Energy harvesting (2)
Articles 211 - 230 of 230
Full-Text Articles in Physical Sciences and Mathematics
Impulse Noise Removal By K-Means Clustering Identified Fuzzy Filter: A Newapproach, Aritra Bandyopadhyay, Kaustuv Deb, Atanu Das, Rajib Bag
Impulse Noise Removal By K-Means Clustering Identified Fuzzy Filter: A Newapproach, Aritra Bandyopadhyay, Kaustuv Deb, Atanu Das, Rajib Bag
Turkish Journal of Electrical Engineering and Computer Sciences
Removal of impulse noise from corrupted digital images has been a hitch in the field of image processing. Random nature of impulse noise makes the task of noise removal more critical. Different filters have been designed for noise removal purpose and have shown formidable results mostly for low and medium level noise densities. In this paper, a new two-stage technique called k-means clustering identified fuzzy filter (KMCIFF) is proposed for de-noising gray-scale images. KMCIFF consists of a k-Means clustering-based high density impulse noise detection, followed by a fuzzy logic-oriented noise removal mechanism. In the detection process, a 5 $\times$ 5 …
Detection Of Hand Osteoarthritis From Hand Radiographs Using Convolutionalneural Networks With Transfer Learning, Kemal Üreten, Hasan Erbay, Hadi̇ Hakan Maraş
Detection Of Hand Osteoarthritis From Hand Radiographs Using Convolutionalneural Networks With Transfer Learning, Kemal Üreten, Hasan Erbay, Hadi̇ Hakan Maraş
Turkish Journal of Electrical Engineering and Computer Sciences
Osteoarthritis is the most common type of arthritis. Hand osteoarthritis leads to specific structural changes in the joints, such as asymmetric joint space narrowing and osteophytes (bone spurs). Conventional radiography has traditionally been the primary method of visualizing these structural changes and diagnosing osteoarthritis. We aimed to develop a computerized method that is capable of determining the structural changes seen in radiography of the hand and to assist practitioners in interpreting radiographic changes and diagnosing the disease. In this retrospective study, transfer-learning-based convolutional neural networks were trained on a randomly selected dataset containing 332 radiography images of hands from an …
Exhaustive Hard Triplet Mining Loss For Person Re-Identification, Chao Xu, Xiang Sun, Ziliang Chen, Shoubiao Tan
Exhaustive Hard Triplet Mining Loss For Person Re-Identification, Chao Xu, Xiang Sun, Ziliang Chen, Shoubiao Tan
Turkish Journal of Electrical Engineering and Computer Sciences
Person reidentification (Re-ID) is an important task in computer vision and has many applications in videobased surveillance. Recently, the triplet loss has been popular in the deep learning framework for person Re-ID. It is particularly important to note that the selection of hard triplets has significant influence on the performance of the learned deep model. However, the existing triplet losses only focus on some specific forms of hard triplets, thus leading to weaker generalization capability. To address this issue, we propose a novel variant of the triplet loss, named exhaustive hard triplet mining loss (EHTM), which is able to deal …
Optimum Reference Distance Based Path Loss Exponent Determination Forvehicle-To-Vehicle Communication, Kenan Kuzulugi̇l, Zeynep Hasirci, İsmai̇l Hakki Çavdar
Optimum Reference Distance Based Path Loss Exponent Determination Forvehicle-To-Vehicle Communication, Kenan Kuzulugi̇l, Zeynep Hasirci, İsmai̇l Hakki Çavdar
Turkish Journal of Electrical Engineering and Computer Sciences
Vehicle-to-vehicle (V2V) communication environment differs from classical wireless communication with respect to low antenna heights and high mobility. Therefore, V2V channel modeling based on real measurements is still crucial to get the channel parameters for the various road environments. One of the most extracted parameters from measurements is path loss exponent and selecting a fixed reference distance value in obtaining this parameter may also cause remarkable fitting errors. Thus, in this study, least square method-based approach for the best-fitted path loss exponent calculation was proposed by determining the optimum reference distance value from the V2V channel measurements. First, V2V channel …
Optimization Of Real-Time Wireless Sensor Based Big Data With Deep Autoencoder Network: A Tourism Sector Application With Distributed Computing, Beki̇r Aksoy, Utku Kose
Optimization Of Real-Time Wireless Sensor Based Big Data With Deep Autoencoder Network: A Tourism Sector Application With Distributed Computing, Beki̇r Aksoy, Utku Kose
Turkish Journal of Electrical Engineering and Computer Sciences
Internet usage has increased rapidly with the development of information communication technologies. The increase in internet usage led to the growth of data volumes on the internet and the emergence of the big data concept. Therefore, it has become even more important to analyze the data and make it meaningful. In this study, 690 million queries and approximately 5.9 quadrillion data collected daily from different servers were recorded on the Redis servers by using real-time big data analysis method and load balance structure for a company operating in the tourism sector. Here, wireless networks were used as a triggering factor …
An Intelligent Diagnostic Method Based On Optimizing B-Cell Pool Clonal Selection Classification Algorithm, Chao Lan, Hongli Zhang, Xin Sun, Zhongyuan Ren
An Intelligent Diagnostic Method Based On Optimizing B-Cell Pool Clonal Selection Classification Algorithm, Chao Lan, Hongli Zhang, Xin Sun, Zhongyuan Ren
Turkish Journal of Electrical Engineering and Computer Sciences
The trend of intellectualization and complication of mechanical equipment makes the demand for intelligent diagnostic methods more and more intense in industry. In view of the difficulty of obtaining mechanical fault samples and the requirement of clear and reliable diagnosis results, intelligent diagnosis methods need to adapt to the learning of small samples and have the interpretability of white box model. In this paper, inspired by biological immunity, an intelligent fault diagnosis method was proposed-optimizing b-cell pool clonal selection classification algorithm (OBPCSCA). The OBPCSCA provides a method to construct unique B-cell pools corresponding to specific antigen pools, and uses greedy …
Gated Recurrent Unit Based Demand Response For Preventing Voltage Collapse In A Distribution System, Venkateswarlu Gundu, Sishaj Pulikottil Simon, Kinattingal Sundareswaran, Srinivasa Rao Nayak Panugothu
Gated Recurrent Unit Based Demand Response For Preventing Voltage Collapse In A Distribution System, Venkateswarlu Gundu, Sishaj Pulikottil Simon, Kinattingal Sundareswaran, Srinivasa Rao Nayak Panugothu
Turkish Journal of Electrical Engineering and Computer Sciences
This paper presents the application of deep learning algorithms towards demand response management. Demand limit violation and voltage stability are the major problems associated with a secondary distribution system. These problems are solved using demand response models by day ahead scheduling loads at every 15 min interval through linear integer programming and based on short term forecasting of load (kW). A new architecture for short term load forecasting is presented namely gated recurrent unit in which statistical analysis is carried out to get the optimal architecture of the neural network model. Reliability indices such as loss of load probability (LOLP) …
Quantum Key Distribution Over Free Space Optic (Fso) Channel Using Higher Order Gaussian Beam Spatial Modes, Muhammad Kamran, Dr. Muhammad Mubashir Khan, Tahir Malik, Asad Arfeen
Quantum Key Distribution Over Free Space Optic (Fso) Channel Using Higher Order Gaussian Beam Spatial Modes, Muhammad Kamran, Dr. Muhammad Mubashir Khan, Tahir Malik, Asad Arfeen
Turkish Journal of Electrical Engineering and Computer Sciences
Quantum key distribution(QKD) has emerged as a secure solution of secret key distribution utilizing the well established theories of modern physics. Since its introduction in 1984, many interesting and innovative ideas have been proposed for QKD in order to improve the security and efficiency of the scheme keeping in view of its applications and practicalimplementation. High error rate QKD scheme for long distance communication-theso-called KMB09 protocol is one such scheme which was designed to achieve longer communication distance in QKD, without compromising its security,by allowing the utilisation of higher dimensional photon states which is not possible with standard BB84 scheme. …
Reducing Computational Complexity In Fingerprint Matching, Mubeen Sabir, Tariq Mahmood Khan, Munazza Arshad, Sana Munawar
Reducing Computational Complexity In Fingerprint Matching, Mubeen Sabir, Tariq Mahmood Khan, Munazza Arshad, Sana Munawar
Turkish Journal of Electrical Engineering and Computer Sciences
The performance of cross-correlation functions can decrease computational complexity under optimal fingerprint feature selection. In this paper, a technique is proposed to perform alignment of fingerprints followed by their matching in fewer computations. Minutiae points are extracted and alignment is performed on the basis of their spatial locations and orientation fields. Unlike traditional cross-correlation based matching algorithms, ridges are not included in the matching process to avoid redundant computations. However, optimal cross-correlation is chosen by correlating feature vectors accompanying x-y locations of minutiae points and their aligned orientation fields. As a result, matching time is significantly reduced with much improved …
A Review On Embedded Field Programmable Gate Array Architectures And Configuration Tools, Khouloud Bouaziz, Abdulfattah M. Obeid, Sonda Chtourou, Mohamed Abid
A Review On Embedded Field Programmable Gate Array Architectures And Configuration Tools, Khouloud Bouaziz, Abdulfattah M. Obeid, Sonda Chtourou, Mohamed Abid
Turkish Journal of Electrical Engineering and Computer Sciences
Nowadays, systems-on-chip have reached a level where nonrecurring engineering costs have become a great challenge due to the increase of design complexity and postfabrication errors. Embedded field programmable gate arrays (eFPGAs) represent a viable alternative to overcome these issues since they provide postmanufacturing flexibility that can reduce the number of chip redesigns and amortize chip fabrication cost. In this paper, we present an overview on eFPGAs and their architectures, computer aided design (CAD) tools, and design challenges. An eFPGA must be well-designed and accompanied by an optimized CAD tool suite to respond to target application's requirements in terms of power …
A Symmetric-Based Framework For Securing Cloud Data At Rest, Mohammed Mohammed, Fadhil Abed
A Symmetric-Based Framework For Securing Cloud Data At Rest, Mohammed Mohammed, Fadhil Abed
Turkish Journal of Electrical Engineering and Computer Sciences
Cloud computing is the umbrella term for delivering services via the Internet. It enables enterprises and individuals to access services such as virtual machines, storage, or applications on demand. It allows them to achieve more by paying less, and it removes the barrier of installing physical infrastructure. However, due to its openness and availability over the Internet, the issue of ensuring security and privacy arises. This requires careful consideration from enterprises and individuals before the adoption of cloud computing. In order to overcome security issues, cloud service providers are required to use strong security measures to secure their storage and …
Rms Frequency Error Performance And Spurious Signals In Two-Point Modulators Due To Path Imbalances, Gökhun Selçuk, Kaan Üçel
Rms Frequency Error Performance And Spurious Signals In Two-Point Modulators Due To Path Imbalances, Gökhun Selçuk, Kaan Üçel
Turkish Journal of Electrical Engineering and Computer Sciences
In this study the authors introduce an analysis of rms frequency error performance and spurious signals generated by two-point modulators. The analysis is not limited to a constant delay and magnitude imbalance between modulation paths but allows frequency-dependent group delay and amplitude variations as well. Moreover, a discrete time phase frequency detector model is incorporated in Laplace domain analysis that takes into account the sampling nature of a phase-locked loop (PLL). Using the spectrum of pulse width modulated charge pump pulses, the spurious signals at the output of the PLL are evaluated. The proposed formulae are tested on a practical …
A Low Dropout Voltage Regulator With A Transient Voltage Spikes Reducer And Improved Figure Of Merit, Guru Prasad, Kumara Shama
A Low Dropout Voltage Regulator With A Transient Voltage Spikes Reducer And Improved Figure Of Merit, Guru Prasad, Kumara Shama
Turkish Journal of Electrical Engineering and Computer Sciences
An area efficient output capacitor-free low dropout [LDO] voltage regulator with an improved figure of merit is presented in this paper. The proposed LDO regulator consists of a novel, dynamically biased error amplifier that reduces overshoot and undershoot voltage spikes arising from abrupt load changes. Source bulk modulation is employed to enhance the current driving capability of the pass transistor. An adaptive biasing scheme is also used along with dynamic biasing to improve the current efficiency of the system. The on-chip capacitor required for proper working of the LDO regulator is only 35 pF. The proposed LDO regulator is designed …
Dynamically Updated Diversified Ensemble-Based Approach For Handling Concept Drift, Kanu Goel, Shalini Batra
Dynamically Updated Diversified Ensemble-Based Approach For Handling Concept Drift, Kanu Goel, Shalini Batra
Turkish Journal of Electrical Engineering and Computer Sciences
Concept drift is the phenomenon where underlying data distribution changes over time unexpectedly. Examining such drifts and getting insight into the executing processes at that instance of time is a big challenge. Prediction models should be capable of handling drifts in scenarios where statistical properties show abrupt changes. Various strategies exist in the literature to deal with such challenging scenarios but the majority of them are limited to the identification of a particular kind of drift pattern. The proposed approach uses online drift detection in a diversified adaptive setting with pruning techniques to formulate a concept drift handling approach, named …
Reconfiguration-Based Hierarchical Energy Management In Multimicrogrid Systems Considering Power Losses, Reliability Index, And Voltage Enhancement, Farid Hamzeh Aghdam, Navid Taghizadegan Kalantari, Sajad Najafi Ravadanegh
Reconfiguration-Based Hierarchical Energy Management In Multimicrogrid Systems Considering Power Losses, Reliability Index, And Voltage Enhancement, Farid Hamzeh Aghdam, Navid Taghizadegan Kalantari, Sajad Najafi Ravadanegh
Turkish Journal of Electrical Engineering and Computer Sciences
This paper presents a reconfiguration-based hierarchical energy management for interconnected microgrids known as multimicrogrid system. The goal is to minimize the operational costs of different entities alongside with finding the best topology to minimize the active power loss, enhance reliability index, and improve the voltage level. The distribution network (DN) includes several dispatchable and undispatchable distributed energy resources, energy storage devices, and multiple microgrids (MGs). The first layer of the optimization process is executed by each MG operator and each MG performs local energy management. Each MG operator informs the DN operator about its optimal schedules. Finally, global energy management …
Low Power And Low Phase Noise Vco With Dual Current Shaping For Iotapplications, Sajad Nejadhasan, Narges Moazenian, Ebrahim Abiri, Mohammah Reza Salehi
Low Power And Low Phase Noise Vco With Dual Current Shaping For Iotapplications, Sajad Nejadhasan, Narges Moazenian, Ebrahim Abiri, Mohammah Reza Salehi
Turkish Journal of Electrical Engineering and Computer Sciences
In this paper, two low phase noise and power consumption VCO circuits, which are suitable for Internet of things (IoT) applications, are proposed. In the first structure, in order to have more control of the current consumption, the current shaping technique is used in the PMOS and NMOS biasing circuit. In the second structure, for increasing the oscillation amplitude and reducing the phase noise, independent biasing for the NMOS section is used. In both structures, to increase the frequency tuning range (FTR), without using a capacitor bank, the varactor is used in the biasing structure. In the first structure the …
Modelling Sensor Ontology With The Sosa/Ssn Frameworks:A Case Study For Laboratory Parameters, Özlem Aktaş, Mehmet Mi̇lli̇, Sanaz Lakestani̇, Musa Mi̇lli̇
Modelling Sensor Ontology With The Sosa/Ssn Frameworks:A Case Study For Laboratory Parameters, Özlem Aktaş, Mehmet Mi̇lli̇, Sanaz Lakestani̇, Musa Mi̇lli̇
Turkish Journal of Electrical Engineering and Computer Sciences
Recently, the use of sensor-based systems in many areas has led to an exponential increase in the raw sensor data. However, the lack of neither syntactic nor semantic integrity between these sensor data limited their sharing, reusability, and interpretation. These inabilities can cause some problems. For example, different wireless sensor networks may not work together due to the subtle variations in their sensing methods, operating systems, syntax, and data structure. In recent years, to cope with these inabilities, the semantic sensor web approach, which enables us to enrich the meaning of sensor data, has been seen as the critical technology …
Revised Polyhedral Conic Functions Algorithm For Supervised Classification, Gürhan Ceylan, Gürkan Öztürk
Revised Polyhedral Conic Functions Algorithm For Supervised Classification, Gürhan Ceylan, Gürkan Öztürk
Turkish Journal of Electrical Engineering and Computer Sciences
In supervised classification, obtaining nonlinear separating functions from an algorithm is crucial for prediction accuracy. This paper analyzes the polyhedral conic functions (PCF) algorithm that generates nonlinear separating functions by only solving simple subproblems. Then, a revised version of the algorithm is developed that achieves better generalization and fast training while maintaining the simplicity and high prediction accuracy of the original PCF algorithm. This is accomplished by making the following modifications to the subproblem: extension of the objective function with a regularization term, relaxation of a hard constraint set and introduction of a new error term. Experimental results show that …
Time Series Forecasting On Multivariate Solar Radiation Data Using Deep Learning (Lstm), Murat Ci̇han Sorkun, Özlem Durmaz İncel, Christophe Paoli
Time Series Forecasting On Multivariate Solar Radiation Data Using Deep Learning (Lstm), Murat Ci̇han Sorkun, Özlem Durmaz İncel, Christophe Paoli
Turkish Journal of Electrical Engineering and Computer Sciences
Energy management is an emerging problem nowadays and utilization of renewable energy sources is an efficient solution. Solar radiation is an important source for electricity generation. For effective utilization, it is important to know precisely the amount from different sources and at different horizons: minutes, hours, and days. Depending on the horizon, two main classes of methods can be used to forecast the solar radiation: statistical time series forecasting methods for short to midterm horizons and numerical weather prediction methods for medium- to long-term horizons. Although statistical time series forecasting methods are utilized in the literature, there are a limited …
Fast Texture Classification Of Denoised Sar Image Patches Using Glcm On Spark, Caner Özcan, Kadri̇ Okan Ersoy, İskender Ülgen Oğul
Fast Texture Classification Of Denoised Sar Image Patches Using Glcm On Spark, Caner Özcan, Kadri̇ Okan Ersoy, İskender Ülgen Oğul
Turkish Journal of Electrical Engineering and Computer Sciences
Classification of a synthetic aperture radar (SAR) image is an essential process for SAR image analysis and interpretation. Recent advances in imaging technologies have allowed data sizes to grow, and a large number of applications in many areas have been generated. However, analysis of high-resolution SAR images, such as classification, is a time-consuming process and high-speed algorithms are needed. In this study, classification of high-speed denoised SAR image patches by using Apache Spark clustering framework is presented. Spark is preferred due to its powerful open-source cluster-computing framework with fast, easy-to-use, and in-memory analytics. Classification of SAR images is realized on …