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Erden, Fatih
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Name Variants
Erden, Fatih
Erden,F.
E.,Fatih
F., Erden
Fatih, Erden
E., Fatih
F.,Erden
Erden,F.
E.,Fatih
F., Erden
Fatih, Erden
E., Fatih
F.,Erden
Job Title
Doktor Öğretim Üyesi
Email Address
erdenfatih@gmail.com
ORCID ID
Scopus Author ID
Turkish CoHE Profile ID
Google Scholar ID
WoS Researcher ID
Scholarly Output
8
Articles
3
Citation Count
128
Supervised Theses
0
8 results
Scholarly Output Search Results
Now showing 1 - 8 of 8
Article Citation Count: 9A Robust System for Counting People Using an Infrared Sensor and a Camera(Elsevier, 2015) Erden, Fatih; Erden, Fatih; Alkar, Ali Ziya; Cetin, Ahmet Enis; Department of Electrical & Electronics EngineeringIn this paper, a multi-modal solution to the people counting problem in a given area is described. The multi-modal system consists of a differential pyro-electric infrared (PIR) sensor and a camera. Faces in the surveillance area are detected by the camera with the aim of counting people using cascaded AdaBoost classifiers. Due to the imprecise results produced by the camera-only system, an additional differential PIR sensor is integrated to the camera. Two types of human motion: (i) entry to and exit from the surveillance area and (ii) ordinary activities in that area are distinguished by the PIR sensor using a Markovian decision algorithm. The wavelet transform of the continuous-time real-valued signal received from the PIR sensor circuit is used for feature extraction from the sensor signal. Wavelet parameters are then fed to a set of Markov models representing the two motion classes. The affiliation of a test signal is decided as the class of the model yielding higher probability. People counting results produced by the camera are then corrected by utilizing the additional information obtained from the PIR sensor signal analysis. With the proof of concept built, it is shown that the multi-modal system can reduce false alarms of the camera-only system and determines the number of people watching a TV set in a more robust manner. (c) 2015 Elsevier B.V. All rights reserved.Conference Object Citation Count: 1A Distributed Smart Pev Charging Algorithm Based on Forecasted Mobility Energy Demand(Institute of Electrical and Electronics Engineers Inc., 2017) Kisacikoglu,M.C.; Erden, Fatih; Erden,F.; Erdoğan, Nuh; Erdogan,N.; Department of Electrical & Electronics EngineeringThis study proposes a new distributed control strategy for the grid integration of plug-in electric vehicles. The proposed strategy consists of two stages: (i) an offline process to determine an aggregated reference charge power level based on mobility estimation and base load profile, and (ii) a real-time operation based on the distributed control approach. The control algorithm manages PEV charge load profiles in order to flatten the residential distribution transformer loading while ensuring the desired state of the charge (SOC) level. The proposed algorithm is tested on real distribution transformer loading data, and compared with heuristic charging scenarios. The numerical results are presented to demonstrate the impact of the proposed algorithm. © 2016 IEEE.Conference Object Citation Count: 4Respiratory Rate Monitoring Using Infrared Sensors(Ieee, 2015) Erden, Fatih; Erden, Fatih; Cetin, A. Enis; Department of Electrical & Electronics EngineeringRespiratory rate is an essential parameter in many practical applications such as patient and elderly people monitoring. In this paper, a novel contact-free system is introduced to detect the human breathing activity. The system, which consists of two pyro-electric infrared (PIR) sensors, is capable of estimating the respiratory rate and detecting the sleep apnea. Sensors' signals corresponding to the thoracic movements of a human being are sampled using a microprocessor and analyzed on a general-purpose computer. Sampled signals are processed using empirical mode decomposition (EMD) and a new average magnitude difference function (AMDF) is used to detect the periodicity and the period of the processed signals. The resulting period, by using the fact that breathing is almost a periodic activity, is monitored as the respiratory rate. The new AMDF provides a way to fuse the data from the multiple sensors and generate a more reliable estimation of the respiratory rate.Article Citation Count: 20Contact-Free Measurement of Respiratory Rate Using Infrared and Vibration Sensors(Elsevier Science Bv, 2015) Erden, Fatih; Erden, Fatih; Alkar, Ali Ziya; Cetin, Ahmet Enis; Department of Electrical & Electronics EngineeringRespiratory rate is an essential parameter in many practical applications such as apnea detection, patient monitoring, and elderly people monitoring. In this paper, we describe a novel method and a contact-free multi-modal system which is capable of detecting human breathing activity. The multimodal system, which uses both differential pyro-electric infrared (PIR) and vibration sensors, can also estimate the respiratory rate. Vibration sensors pick up small vibrations due to the breathing activity. Similarly, PIR sensors pick up the thoracic movements. Sensor signals are sampled using a microprocessor board and analyzed on a laptop computer. Sensor signals are processed using wavelet analysis and empirical mode decomposition (EMD). Since breathing is almost periodic, a new multi-modal average magnitude difference function (AMDF) is used to detect the periodicity and the period in the processed signals. By fusing the data of two different types of sensors we achieve a more robust and reliable contact-free human breathing activity detection system compared to systems using only one specific type of sensors. (C) 2015 Elsevier B.V. All rights reserved.Conference Object Citation Count: 17Examination of Ev-Grid Integration Using Real Driving and Transformer Loading Data(Institute of Electrical and Electronics Engineers Inc., 2016) Erden,F.; Erden, Fatih; Kisacikoglu,M.C.; Gurec,O.H.; Department of Electrical & Electronics EngineeringThe growing environmental concerns and the increase in oil prices will lead to the proliferation of electric vehicles (EVs) in the near future. The increase in the number of EVs, while providing green and inexpensive solutions to transportation needs, may cause constraints on the operation of the utility grid that should be investigated. In this paper, the real user driving information is collected from individual data tracking devices of passenger vehicle owners instead of assuming randomly distributed trip characteristics. The collected trip data are first analyzed to generate a statistical model of the trip characteristics in terms of home arrival times and state of charge (SOC) levels. The resulting model is then used to simulate and analyze the impact of EV integration in a real grid with different EV penetration levels. For this, real distribution transformer data provided by Başkent Electric Distribution Co. is used. The proposed method produces more realistic results in comparison to the studies assuming random scenarios. © 2015 Chamber of Electrical Engineers of Turkey.Article Citation Count: 78Sensors in Assisted Living a Survey of Signal and Image Processing Methods(Ieee-inst Electrical Electronics Engineers inc, 2016) Erden, Fatih; Erden, Fatih; Velipasalar, Senem; Alkar, Ali Ziya; Cetin, A. Enis; Department of Electrical & Electronics Engineering[No Abstract Available]Conference Object Citation Count: 3Respiratory Rate Monitoring Using Infrared Sensors(Institute of Electrical and Electronics Engineers Inc., 2016) Erden,F.; Erden, Fatih; Cetin,A.E.; Department of Electrical & Electronics EngineeringRespiratory rate is an essential parameter in many practical applications such as patient and elderly people monitoring. In this paper, a novel contact-free system is introduced to detect the human breathing activity. The system, which consists of two pyro-electric infrared (PIR) sensors, is capable of estimating the respiratory rate and detecting the sleep apnea. Sensors' signals corresponding to the thoracic movements of a human being are sampled using a microprocessor and analyzed on a general-purpose computer. Sampled signals are processed using empirical mode decomposition (EMD) and a new average magnitude difference function (AMDF) is used to detect the periodicity and the period of the processed signals. The resulting period, by using the fact that breathing is almost a periodic activity, is monitored as the respiratory rate. The new AMDF provides a way to fuse the data from the multiple sensors and generate a more reliable estimation of the respiratory rate. © 2015 Chamber of Electrical Engineers of Turkey.Conference Object Citation Count: 15Examination of Ev-Grid Integration Using Real Driving and Transformer Loading Data(Ieee, 2015) Erden, Fatih; Erden, Fatih; Kisacikoglu, Mithat C.; Gurec, Ozan H.; Department of Electrical & Electronics EngineeringThe growing environmental concerns and the increase in oil prices will lead to the proliferation of electric vehicles (EVs) in the near future. The increase in the number of EVs, while providing green and inexpensive solutions to transportation needs, may cause constraints on the operation of the utility grid that should be investigated. In this paper, the real user driving information is collected from individual data tracking devices of passenger vehicle owners instead of assuming randomly distributed trip characteristics. The collected trip data are first analyzed to generate a statistical model of the trip characteristics in terms of home arrival times and state of charge (SOC) levels. The resulting model is then used to simulate and analyze the impact of EV integration in a real grid with different EV penetration levels. For this, real distribution transformer data provided by Baskent Electric Distribution Co. is used. The proposed method produces more realistic results in comparison to the studies assuming random scenarios.
