Browsing by Author "Arsan, Taner"

Now showing 1 - 20 of 68

Citation - WoS: 8
Citation - Scopus: 12
3d Indoor Positioning With Spatial Modulation for Visible Light Communications
(Elsevier, 2023) Sen, Umit; Arsan, Taner; Yesilirmak, Yalin Evrim; Panayırcı, Erdal; Bayman, Irem Ozgur; Arsan, Taner; Panayırcı, Erdal; Stevens, Nobby
In this paper, a novel three-dimensional (3D) indoor visible light positioning (VLP) algorithm is proposed based on the spatial modulation (SM) and its error performance assessed as compared to the conventional received signal strength (RSS)-based 3D VLP systems. As contrasted to the traditional VLP system, the proposed SM-based 3D VLP system first estimates the optical channel gain between the transmitting light-emitting diodes (LEDs) and the two photo detectors (PDs) attached to the user by a pilot-based channel estimation technique. Then, unknown 3D positions of the receiver are determined by the trilateration algorithm with distances computed from the estimates of the channel gains. Consequently, the 3D VLP system achieves an interference -free transmission with increased spectral efficiency and without the need for a demultiplexing process at the receiving end. The algorithm's performance is evaluated regarding positioning error by applying the SM over four LEDs and the number of pilots selected as a function of the environmental signal-to-noise ratios (SNRs). The computer simulation results show that the positioning errors are obtained in an order of magnitude smaller than RSS-based techniques in an indoor industrial environment. This is mainly because the distances involved in determining the 3D positions can be determined more precisely by the pilot-aided channel estimation method without creating any data rate problem in transmission due to the higher spectral efficiency of the SM.
Citation - WoS: 4
Citation - Scopus: 6
Accurate Indoor Positioning With Ultra-Wide Band Sensors
(Tubitak, 2020) Arsan, Taner; Arsan, Taner
Ultra-wide band is one of the emerging indoor positioning technologies. In the application phase, accuracy and interference are important criteria of indoor positioning systems. Not only the method used in positioning, but also the algorithms used in improving the accuracy is a key factor. In this paper, we tried to eliminate the effects of off-set and noise in the data of the ultra-wide band sensor-based indoor positioning system. For this purpose, optimization algorithms and filters have been applied to the raw data, and the accuracy has been improved. A test bed with the dimensions of 7.35 m x 5.41 m and 50 cm x 50 cm grids has been selected, and a total of 27,000 measurements have been collected from 180 test points. The average positioning error of this test bed is calculated as 16.34 cm. Then, several combinations of algorithms are applied to raw data. The combination of Big Bang-Big Crunch algorithm for optimization, and then the Kalman Filter have yielded the most accurate results. Briefly, the average positioning error has been reduced from 16.34 cm to 7.43 cm.
Citation - Scopus: 1
Advancing Anomaly Detection in Time Series Data: a Knowledge Distillation Approach With Lstm Model
(Institute of Electrical and Electronics Engineers Inc., 2023) Kilinc,S.; Arsan, Taner; Camlidere,B.; Yildiz,E.; Guler,A.K.; Alsan,H.F.; Arsan,T.
This paper focuses on enhancing anomaly detection in time series data using deep learning techniques. Particularly, it investigates the integration of knowledge distillation with LSTM-based models for improved precision, efficiency, and interpretability. The study outlines objectives such as dataset preprocessing, developing a novel LSTM-knowledge distillation framework, incorporating Grafana, InfluxDB, Flask API with Docker, performance assessment, and practical implications. Results highlight the efficacy of knowledge distillation in enhancing student model performance. The proposed approach enhances anomaly detection, offering a viable solution for real-world applications. © 2023 IEEE.
Citation - WoS: 1
Citation - Scopus: 2
Analytical Expense Management System
(IEEE, 2009) Bozkuş, Zeki; Arsan, Taner; Bisson, Christophe; Bozkuş, Zeki; Arsan, Taner; A. Bısson, Chrıstophe Louıs
Although the development of communication technologies (e.g: UMTS ADSL) allowed the elaboration of multiple users' web applications (e.g. information storage) there are still many improvements on many applications to be done and uncovered areas. Expense management systems on web application area are still in their infancy. Expense management software is widely spread in companies and most of time supported by their intranet. These solutions are quite simple as they mainly collect the information related to the expenses and may propose a simple aggregation of these figures. The result is close to what an excel sheet provides.
Citation - Scopus: 0
Audience Tracking and Cheering Content Control in Sports Events
(IEEE, 2020) Yeşilyurt, Gözdenur; Arsan, Taner; Dursun, Sefa; Kumas, Osman; Çakir, Nagehan; Arsan, Taner
Swearing cheers encountered in sports competitions do not comply with sports ethics and morals. Even if this kind of cheering is a group, the entire tribune block is penalized in accordance with the current rules. This method is not preventive and individual punishment should be used. The aim of this study is to determine the individuals who cheer with swearing content. In this study, the person detection is made with the multi-task cascaded convolutional neural network. Moreover, facial landmarks representing the facial regions and the regions related to them are determined as a result of this process. The mouth region is also determined by means of these important points removed, and finally the mouth is determined according to the equation. The face recognition is carried out because the person would be in a state of yelling if the mouth opening ratio exceeds the threshold value by determining the rate of opening. Landmarks extracted from the facial regions for the face recognition are transformed into feature vectors by FaceNet, and the model is created by classifying these vectors with classifiers to use in recognition process. When evaluated in terms of industry, face recognition and detection systems find a wide field of study.
Bandwidth Allocation and Traffic Shaping in Mobile Broadband Networks Using Deep Packet Inspection
(Kadir Has Üniversitesi, 2015) Özbilen, Ramazan; Arsan, Taner; Arsan, Taner
In this thesis, it is intended to estimate bandwidth and control mobile data usage by utilizing PCC (Policy and Charging Control) function. According to increase in number of mobile devices, data explosion occurs. It is becoming a must to analyze traffic and sharing resources between subscribers according to their usage habits. It is aimed to provide better a better connected world with service assurance by sharing available bandwidth and estimate it to users according to their needs by protocol level and service based QoS. Due to increase in amount of services like Facebook, Twitter, Mobile TV, in general IP networks, providing service assurance becomes more important day by day. That's why the issue of controlling bandwidth is raised. In the most basic sense, system architecture consists of three main components: A cell phone to generate user based traffic, Gateway GPRS Support Node (GGSN) for Deep Packet Inspection (DPI), and Policy and Charging Rule Function (PCRF) for initiating PCC or Non-PCC rules to GGSN according to services that are needed by user. Shortly, the main idea in this thesis is assigning service based QoS to subscribers to provide better service assurance according to their usage. As thought, the reason of preparing this study is to show the dramatical increase in service based traffic, to explain insufficiency in current bandwidth estimation approaches, and the idea of what can be used in the work of providing better service assurance to an end user. PCRF is the best component for providing required bandwidth when they need.
Citation - Scopus: 10
Big Data Platform Development With a Domain Specific Language for Telecom Industries
(IEEE Computer Society, 2013) Senbalci,C.; Arsan, Taner; Altuntas,S.; Bozkuş, Zeki; Bozkus,Z.; Arsan,T.
This paper introduces a system that offer a special big data analysis platform with Domain Specific Language for telecom industries. This platform has three main parts that suggests a new kind of domain specific system for processing and visualization of large data files for telecom organizations. These parts are Domain Specific Language (DSL), Parallel Processing/Analyzing Platform for Big Data and an Integrated Result Viewer. In addition to these main parts, Distributed File Descriptor (DFD) is designed for passing information between these modules and organizing communication. To find out benefits of this domain specific solution, standard framework of big data concept is examined carefully. Big data concept has special infrastructure and tools to perform for data storing, processing, analyzing operations. This infrastructure can be grouped as four different parts, these are infrastructure, programming models, high performance schema free databases, and processing-analyzing. Although there are lots of advantages of Big Data concept, it is still very difficult to manage these systems for many enterprises. Therefore, this study suggest a new higher level language, called as DSL which helps enterprises to process big data without writing any complex low level traditional parallel processing codes, a new kind of result viewer and this paper also presents a Big Data solution system that is called Petaminer. © 2013 IEEE.
Citation - WoS: 7
Big Data Platform Development With a Domain Specific Language for Telecom Industries
(IEEE, 2013) Şenbalcı, Cüneyt; Arsan, Taner; Altuntaş, Serkan; Bozkuş, Zeki; Bozkuş, Zeki; Arsan, Taner
This paper introduces a system that offer a special big data analysis platform with Domain Specific Language for telecom industries. This platform has three main parts that suggests a new kind of domain specific system for processing and visualization of large data files for telecom organizations. These parts are Domain Specific Language (DSL) Parallel Processing/Analyzing Platform for Big Data and an Integrated Result Viewer. hi addition to these main parts Distributed File Descriptor (DFD) is designed for passing information between these modules and organizing communication. To find out benefits of this domain specific solution standard framework of big data concept is examined carefully. Big data concept has special infrastructure and tools to perform for data storing processing analyzing operations. This infrastructure can be grouped as four different parts these are infrastructure programming models high performance schema free databases and processing-analyzing. Although there are lots of advantages of Big Data concept it is still very difficult to manage these systems for many enterprises. Therefore this study suggest a new higher level language called as DSL which helps enterprises to process big data without writing any complex low level traditional parallel processing codes a new kind of result viewer and this paper also presents a Big Data solution system that is called Petaminer.
Citation - Scopus: 0
Building Damage Assessment To Facilitate Post-Earthquake Search and Rescue Missions by Leveraging a Machine Learning Algorithm
(Institute of Electrical and Electronics Engineers Inc., 2024) Arsan, Taner; Alsan, H.F.; Arsan, T.
Earthquakes have a severe impact on people's lives and infrastructure. Many emergency institutes and search and rescue missions need accurate post-earthquake response strategies, particularly in building damage assessment. Traditional methods, relying on manual inspections, are inefficient compared to Machine Learning (ML) algorithms. Thus, Random Forest (RF) algorithms stand out because they handle diverse datasets effectively and minimize overfitting. The study outlines the methodology encompassing data preparation, exploratory analysis, feature engineering, and model building, employing a preprocessing pipeline integrating numerical and categorical features. Additionally, Principal Component Analysis (PCA) is applied to reduce dimensionality. The results of the RF model showed an accuracy of 94% and the highest F1-score of 97% among all the grades, demonstrating its efficacy in predicting damage grades post-earthquake. The results can help support better disaster management plans by helping to prioritize rescue operations and allocate resources wisely. © 2024 IEEE.
Büyük Patlama – Büyük Çöküş Optimizasyon Yöntemi Kullanılarak Bluetooth Tabanlı İç Mekan Konum Belirleme Sisteminin Doğruluğunun İyileştirilmesi
(Süleyman Demirel Üniversitesi, 2018) Arsan, Taner; Arsan, Taner
Düşük enerjili Bluetooth işaretçi (Bluetooth low energy - BLE beacon) teknolojisi, iç mekan konum belirleme sistemlerinde başarılı ve düşük maliyetli çözümler sunan gelişmekte olan bir teknolojidir. Bu çalışmada, BLE işaretçileri (beacons) kullanan bir iç mekan konum belirleme sistemi geliştirilmiş, kullanılan ilave algoritmalarla standart sensörlerden elde edilen konum değerlerinin doğruluğunun artırılması amaçlanmıştır. Bunun için, deneysel iç mekan konum algılama sisteminden elde edilen konum bilgilerine Büyük Patlama – Büyük Çöküş (Big Bang – Big Crunch (BB-BC)) optimizasyon yöntemi uygulanmış ve konum doğruluğunun geliştirildiği yapılan testlerle kanıtlanmıştır. Test alanı olarak, 9,60 m × 3,90 m boyutundaki 37,44 m2'lik alan seçilmiş ve 2,40 m × 1,30 m boyutundaki oniki tane ızgara alanına ayak izi (fingerprinting) algoritması uygulanmıştır. Test alanına dört tane BLE işaretçi (beacon) yerleştirilmiş, on iki test alanından 150 saniye boyunca toplam 9.000 ölçüm yapılmıştır. Ölçüm sonuçları Büyük Patlama – Büyük Çöküş optimizasyon yöntemi ile Öklid uzaklık eşleştirme yöntemi ve Kalman Filtresi kullanılarak iyileştirilmiş, bu sayede konum doğruluğu %26,62'den %75,69'a arttırılmıştır.
Büyük Patlama Büyük Çöküş Optimizasyon Yöntemi ile Ultra Geniş Band Sensörlerinin İç Mekân Konum Belirleme Doğruluklarının İyileştirilmesi
(Pamukkale Üniversitesi, 2018) Arsan, Taner; Arsan, Taner
Ultra geniş band teknolojisi, birçok iç mekân konum belirleme sisteminde başarılı çözümler sunan, diğer yöntemlere kıyasla daha iyi performans gösteren, gelişmekte olan bir teknolojidir. Bu çalışmada, ultra geniş band (Ultra Wide Band-UWB) sensörler kullanılarak bir iç mekân konum belirleme sistemi geliştirilmiş ve kullanılan ek algoritmalarla, standart donanımların sağladığı doğruluk düzeyi arttırılırken aynı zamanda ortalama hatayı azaltmak hedeflenmiştir. Bu amaçla Büyük Patlama - Büyük Çöküş (Big Bang-Big Crunch veya BB-BC) optimizasyon yöntemi deneysel iç mekân konumlandırma sistemine uygulanmış ve ölçüm doğruluğu üzerindeki olumlu etkisi yapılan testlerle kanıtlanmıştır. Test alanı olarak 7.35 m × 5.41 m boyutlarında 39.76 m2 'lik bir alan seçilmiş ve özel olarak tasarlanmış bir tavan sistemine yerden 2.85 m yüksekliğe üç farklı UWB alıcı yerleştirilmiş ve 182 adet test noktasından 60 sn.süreyle toplam 10.920 ölçüm alınmıştır. Ölçüm sonuçları Büyük Patlama - Büyük Çöküş optimizasyon algoritması ile düzeltilerek, ortalama hatası önceki 20.72 cm değerinden 15.02 cm’ye düşürülmüş, böylelikle ölçüm sonuçlarının doğruluğu arttırılmıştır.
Citation - Scopus: 0
C# Based Media Center
(2013) Arsan, Taner; Arsan, Taner; Sen, Rasim; Ersoy, Barkan; Devri, Kadir Kadirhan
In this paper, we design and implement a novel all-in-one Media Center that can be directly connected to a high-definition television (HDTV). C# programming is used for developing modular structured media center for home entertainment. Therefore it is possible and easy to add new limitless number of modules and software components. The most importantly, user interface is designed by considering two important factors; simplicity and tidiness. Proposed media center provides opportunities to users to have an experience on listening to music/radio, watching TV, connecting to Internet, online Internet videos, editing videos, Internet connection to pharmacy on duty, checking weather conditions, song lyrics, CD/DVD burning, connecting to Wikipedia. All the modules and design steps are explained in details for user friendly cost effective all-in-one media center.
Citation - Scopus: 0
Capacity Planning for Electricity Utility Call Centers: a Time Series Analysis Approach
(Institute of Electrical and Electronics Engineers Inc., 2024) Arsan, Taner; Alsan, H.F.; Arsan, T.
Electric power systems are crucial for modern society, yet their reliability can be challenged by unforeseen disruptions, causing electricity supply disruptions. Call centers are essential for managing customer inquiries during such outages, acting as communication hubs for electricity utility companies. Effective capacity planning is vital for these call centers to maintain efficient operations and meet customer demands promptly. Proper workforce management ensures that enough skilled agents can handle calls effectively and maintain high service quality. Capacity planning begins with analyzing historical data to understand call volumes, patterns, and peak times. This data analysis identifies trends and factors influencing call patterns, enabling accurate forecasting of future demand and optimizing staffing levels. This paper provides a comprehensive overview of quantitative forecasting methods, focusing on Time Series Analysis applied to a dataset from a Turkish electric utility company that exhibits typical seasonal fluctuations. Specifically, the study examines the performance of AutoRegressive Integrated Moving Average and Seasonal AutoRegressive Integrated Moving Average models. Results indicate that both models perform well, with the Seasonal AutoRegressive Integrated Moving Average model demonstrating slightly superior performance compared to the AutoRegressive Integrated Moving Average model. This suggests that the Seasonal AutoRegressive Integrated Moving Average model may be more suitable for forecasting inbound calls at electricity utility call centers. This paper's detailed analysis and methodology offer valuable insights for optimizing operational efficiency, reducing costs, and enhancing customer satisfaction in dynamic and challenging operational scenarios. © 2024 IEEE.
Citation - Scopus: 0
Career Center System Software Architecture
(Springer Verlag, 2015) Arsan, Taner; Arsan, Taner; Çimenli, Safa; Güneş, Erhan
In today’s world, thousands of job seekers are looking for a new job. On the other hand, thousands of employers are trying to find new employees. So, this is a chaotic matching problem and it does not have a certain answer. Companies are searching career centers and web-based career software to find an answer for the question of “”Should we find a convenient worker for a certain role and hire this person or not?” Solution is that simple; just have a look at the beginning of the story: university career centers. In this study, a Career Center System Software has been designed and implemented for matching students with their ideal job. Career Center System Software (CCSS) is programmed on C#, MS-SQL and.NET platform. CCSS has been developed on Visual Studio 2010. CCSS is implemented in a way so as to enable the user to apply for the job announcements and to monitor courses and to call for conferences and seminars. Furthermore, CCSS enables companies to view the applicant’s curriculum vitae. All job announcements, educations, seminars and CVs are stored on the database. Software quality and testing shows that CCSS is implemented successfully and ready to use tool as Career Center Software.
Citation - WoS: 1
Chaotic - Deterministic or Random Nature of Earthquakes: a Phase Space Analysis
(Symmetrion, 2023) Pekcan, Onder; Arsan, Taner; Arsan, Taner; Pekcan, Mehmet Önder
Using the phase space approach, time series analysis of high EV1 and low EV2 intense two different earthquakes that occurred at the nearly same precise spot, at different times, and were measured with the same sensor of a broadband station were studied. Time series data of strong, large (EV1) and weak, small (EV2) two earthquake events were analyzed by dividing them into three different regions. Fractal dimensions of the EV1 and EV2 were produced using the box-counting algorithm for east-west (BHE), north-south (BHN), and vertical (BHZ) components. The small, weak earthquake, EV2, created a larger fractal dimension in phase space by implying its random nature in all regions. However, EV1 is a strong, large earthquake that presents deterministic oscillatory behavior at a long-time region. Oscillatory behavior can be named surface wave. EV2 exhibits weak, high-frequency ground oscillations similar to fibrillation before and after the earthquake in the long-term areas.
Citation - Scopus: 0
CHAOTIC – DETERMINISTIC OR RANDOM NATURE OF EARTHQUAKES: A PHASE SPACE ANALYSIS
(Symmetrion, 2023) Arsan, Taner; Arsan,T.
Using the phase space approach, time series analysis of high EV1 and low EV2 intense two different earthquakes that occurred at the nearly same precise spot, at different times, and were measured with the same sensor of a broadband station were studied. Time series data of strong, large (EV1) and weak, small (EV2) two earthquake events were analyzed by dividing them into three different regions. Fractal dimensions of the EV1 and EV2 were produced using the box-counting algorithm for east-west (BHE), north-south (BHN), and vertical (BHZ) components. The small, weak earthquake, EV2, created a larger fractal dimension in phase space by implying its random nature in all regions. However, EV1 is a strong, large earthquake that presents deterministic oscillatory behavior at a long-time region. Oscillatory behavior can be named surface wave. EV2 exhibits weak, high-frequency ground oscillations similar to fibrillation before and after the earthquake in the long-term areas. © 2023, Symmetrion. All rights reserved.
Churn Analysis and Prediction With Decision Tree and Artificial Neural Network
(Kadir Has Üniversitesi, 2015) Çimenli, Safa; Arsan, Taner; Arsan, Taner
Nowadays market competition is increased in logistic sector. Retention of customers due to the competition has gained importance. Retention of customers is more advantageous than gaining new customers. Gaining new customer has 5 units more than retention of existing customer. Especially telecommunication companies use churn analyzing and prediction. However competition increased in different sectors and they need churn analysis. In recent years logistic companies increased in Turkey, so retention of customer and churn analysis are important for logistic companies. Some logistic companies have churn committees and work on customer loyalty. This article includes churn analysis and prediction with decision tree and artificial neural network. In addition, this article includes comparison of 2 different methods for churn analysis. Article results show neural network better than decision for prediction. Because decision tree churn prediction rate is %81, Artificial Neural Networks rate is 97%.
Classification of Heart Diseases With Convolutional Neural Networks
(Kadir Has Üniversitesi, 2021) Koç, Bekir Yavuz; Arsan, Taner; Arsan, Taner
Günümüzde kalp hastalıklarının sayısı ve sıklığı artmaktadır. Bu alanda iyileştirmeler yapılabilmesi için yüksek miktarda harcama yapılmaktadır. Kalbin elektriksel iletimindeki atımlar özel cihazlarla kaydedilebilir ve EKG (Elektrokardiyogram) oluşturulabilir. EKG'den üretilen veriler, Taylor Series algoritması ile faz uzaylarına dönüştürülebilir. Kalp hastalığının tespiti için 44 farklı kişiden alınan verilerle MLII sinyallerinden EKG ve faz uzayları oluşturuldu. Bu kayıtların kalp durumunu belirlemek için hem EKG görüntüleri hem de faz uzayı görüntüleri kullanıldı. Kayıtların kalp durumu görüntülere ve sonuçlara Convolutional Neural Networks (CNNs) yöntemi uygulandı ve SVM (Support Vector Machine) algoritması ile karşılaştırılarak başarı oranı ölçüldü. Ayrıca aynı kayıtlar üzerinden eğitim ve test seti değiştirilerek farklı modellerin başarı oranları karşılaştırıldı. EKG ile faz uzayı görüntülerine CNN algoritmasının verdiği sonuçlardaki farklılık tespit edildi. Nowadays, the number and frequency of heart diseases is increasing. High amounts of expenses are incurred in order to make improvements in this area. The beats in the electrical conduction of the heart can be recorded by special devices and ECG (Electrocardiogram) can be created. Data generated from ECG can be transformed into phase spaces with Taylor Series algorithm. In order to determine the detection of heart disease, ECG and phase spaces were created from MLII signals based on 44 different records. Both ECG images and phase space images were used to determine the heart conditions of these recordings. The heart status of the recordings was measured by applying Convolutional Neural Networks (CNNs) method to the images and results compared with the SVM (Support Vector Machine) algorithm. In addition, the success rates of different models were compared by changing the training and test set over the same records. The success rate between ECG and phase space was also determined.
Citation - WoS: 12
Citation - Scopus: 15
A Clustering-Based Approach for Improving the Accuracy of Uwb Sensor-Based Indoor Positioning System
(Hindawi LTD, 2019) Arsan, Taner; Arsan, Taner; Hameez, Mohammed Muwafaq Noori
There are several methods which can be used to locate an object or people in an indoor location. Ultra-wideband (UWB) is a specifically promising indoor positioning technology because of its high accuracy, resistance to interference, and better penetration. This study aims to improve the accuracy of the UWB sensor-based indoor positioning system. To achieve that, the proposed system is trained by using the K-means algorithm with an additional average silhouette method. This helps us to define the optimal number of clusters to be used by the K-means algorithm based on the value of the silhouette coefficient. Fuzzy c-means and mean shift algorithms are added for comparison purposes. This paper also introduces the impact of the Kalman filter while using the measured UWB test points as an input for the Kalman filter in order to obtain a better estimation of the position. As a result, the average localization error is reduced by 43.26% (from 16.3442 cm to 9.2745 cm) when combining the K-means algorithm with the Kalman filter in which the Kalman-filtered UWB-measured test points are used as an input for the proposed system.
Citation - Scopus: 0
A Data Science Perspective on Global Trends in Energy Production
(Institute of Electrical and Electronics Engineers Inc., 2024) Arsan, Taner; Alsan, H.F.; Arsan, T.
As global demand for energy continues to rise, understanding the trends and dynamics of energy generation is crucial to ensure a sustainable and efficient energy future. This study employs data science techniques to analyze global energy production data from 48 countries spanning 2010 to 2023. Initially, we use clustering methods to categorize countries based on their energy production profiles into three distinct groups: high, medium, and low production. This clustering provides insights into the diverse energy strategies and capacities across different regions. Subsequently, we apply and compare two classification models, specifically Random Forest and Gradient Boosting, to predict the dominant energy source for each cluster. Furthermore, we perform a comparative analysis of two forecasting models, SARIMA and Prophet, to predict future renewable energy production for countries with high production profiles, such as the USA and China. The forecasting results show the efficacy of these models in capturing seasonal trends and providing accurate predictions. © 2024 IEEE.